Intelligent Systems in Digital Transformation. Theory and Applications 9783031165979, 9783031165986

Table of contents :
Preface
Contents
Literature Review on Digital Transformation
A State of the Art Literature Review on Digital Transformation
1 Introduction
2 Literature Review
3 Digital Transformation by Sectors
3.1 Digital Transformation in Manufacturing Industry
3.2 Digital Transformation in Service Industry
4 Conclusion
References
Similarities and Differences Between Digital Transformation Maturity Models: A Literature Review
1 Introduction
2 Literature Review
2.1 The Digital Transformation and the Digital Economy
2.2 Maturity Models (MM)
2.3 Digital Transformation Maturity Models (DTMM)
3 Methodology
4 Results and Discussion
5 Conclusions
References
Safety and Security
Intelligent Digital Transformation in Modern Socio-Technical Systems – A Sustainable Approach
1 Introduction
2 Intelligence and Digital Transformation—Basic Assumptions and the Literature Review
3 Safety and Security as a Core Aspects of Digital Transformation Process
4 Intelligent Digital Transformation in Socio-Technical Systems—An Idea and Case Studies
5 Sustainability Concept in Digital Transformation Processes
6 Conclusion
References
Intelligent Digital Transformation
Intelligent Digital Transformation Strategy Management: Development of a Measurement Framework
1 Introduction
2 Literature Review on Intelligent Digital Transformation Strategy Management
2.1 Background of the Study
2.2 Existing Studies on Intelligent Digital Transformation Strategy Management
3 The Proposed Measurement Framework for Intelligent Digital Transformation Strategy Management
3.1 Main Dimensions
3.2 Sub-dimensions
3.3 KPIs and Metrics
3.4 The Measurement Process
3.5 Pilot Study
4 Discussion and Conclusion
References
Automotive
Digital Transformation in Automotive Sector
1 Introduction
2 Literature Review
3 Digital Technologies Used in Automotive Industry
4 Spherical Fuzzy AHP and TOPSIS Methodology for Robot Technology Selection in Automotive Industry
4.1 Preliminaries
4.2 Steps of the Proposed Methodology
4.3 Application
4.4 Sensitivity Analysis
5 Conclusion
References
Energy
Digital Transformation and Prosumers Activities in the Energy Sector
1 Introduction
2 Literature Review
3 Digitization on the Prosumer’s Market
4 New Technologies in the Energy Sector
5 Digital Twins, Virtual Power Plants in the Energy Sector
6 Blockchain in the Energy Sector
7 Conclusions
References
Digital Transformation Success Factors Evaluation in Energy Industry
1 Introduction
2 Literature Survey
2.1 Digital Transformation
2.2 Digital Transformation in Various Industries
2.3 Digital Transformation in Energy Industry
2.4 FCM Studies in Energy Industry
3 Fuzzy Cognitive Maps
4 Digital Transformation Success Factors Evaluation for the Energy Industry
5 Discussions and Conclusions
References
Smart Manufacturing
Education as a Promoter of Digital Transformation in the Manufacturing Industry
1 Introduction
2 Literature Review
3 Promoting Digital Transformation in Manufacturing Industry - Necessary Education Aspects Concerning Digitalisation
4 Conclusions and Recommendations
References
Standardization in Smart Manufacturing: Evaluation from a Supply-Side Perspective
1 Introduction
2 Literature Review
3 Global Trends of Digital Manufacturing
4 Government Support for Digital Transformation
5 International Standardization Landscape in the Field of Digital Manufacturing
5.1 National Policy for Digitalization
5.2 International Bodies Related to Digital Manufacturing
6 Capacity of Smart Manufacturing Development and Standardization
7 Conclusion
References
White Goods
Redesign, Smart and Digital Enablement of Sales and Operations Planning Processes: A Study of White Goods Manufacturing
1 Introduction
2 Literature Review on Digital Transformation of S&OP Processes
3 Redesigning the S&OP Cycle with Digital Enablement
3.1 Product Review
3.2 Forecasting
3.3 The Consensus Process
3.4 Inventory Planning
3.5 Supply Planning
3.6 Demand and Supply Reconciliation
3.7 Distribution Planning
4 Conclusion
References
Health
Cybersecurity Framework Prioritization for Healthcare Organizations Using a Novel Interval-Valued Pythagorean Fuzzy CRITIC
1 Introduction
2 Literature Review
3 Methodology
3.1 Traditional CRITIC Methodology
3.2 Interval-Valued Pythagorean Fuzzy Operators
3.3 Proposed Interval-Valued Pythagorean Fuzzy CRITIC
4 Application
4.1 NIST Cybersecurity Framework
4.2 Numerical Solution: Reorganization of the NIST Cybersecurity Framework
4.3 Sensitivity Analysis
5 Conclusion
References
Multi-layered InterCriteria Analysis as a Digital Tool for Studying the Dependencies of Some Key Indicators of Mortality During the Pandemic in the European Union
1 Introduction
2 Literature Review
3 Preliminaries
3.1 Intuitionistic Fuzzy Pairs
3.2 Definition of Three-Dimensional Extended Multilayer Index Matrix (3-D EMLIM) and Some Basic Operations
4 Three-Dimensional ICrA of Intuitionistic Fuzzy Data
5 A Form of Three-Dimensional ICrA of Intuitionistic Fuzzy Data in Multilayer IMs
6 A Multilayer ICrA Approach to Number of Deaths for COVID-19 and Some Key Indicators of European Union Countries
7 A Juxtaposition of the Results of the ICrA Approach and Classical Correlation Analyzes
8 Conclusion
References
Development of Intelligent Healthcare Sytems Through Digital Transformation and Operations Research Modeling
1 Introduction
2 Literature Review
2.1 Motivation for Digital Transformation in Healthcare Services
2.2 The Advent of Digital Health
3 Practicality of Digital Health from a Modeling Perspective
3.1 Operational Efficiency in Healthcare Delivery
3.2 Preventive Healthcare
3.3 Public Health
4 Challenges Against Digital Health
5 Conclusion
References
Banking
Imperatives, Trends and Dynamics of Digital Transformation as Banks Adopt Technology and Intelligent Systems
1 Introduction
2 Imperatives for Digital Transformation Drive Banks to Adopt Technology and Intelligent Solutions
2.1 Geo-Political, Social, Economic, Regulatory Conditions Including Impacts of Prevailing Uncertainties and Health Crisis
2.2 Evolution of Customer Expectations and Demand for Digital Bank
2.3 Focus on Sustainable Development Across Industries and Globally
2.4 Value from Differentiated Digital Technology Leadership and Innovations
2.5 Industrial Convergence Towards Solution-Driven Ecosystems
2.6 Risks of not Transforming Including Threats from Financial Technology (Fintech) and Other Technology Organizations
3 Adoption of Technology and Intelligent Systems—Digital Transformation Trends Across Global Banks
3.1 Overcoming Financial Product Commoditization in the Face of Threat from Fintech and Technology Differentiations
3.2 Digitization of Banking to Improve Processes and Customer Outreach
3.3 Transformation and Innovations in Banks’ Internal Systems and Associated Infrastructure
3.4 Focus on Artificial Intelligence
3.5 New Banking Offerings and Partner Ecosystems
4 Digital Transformation Dynamics to Evolve for the Future
5 Conclusion
References
Tourism
Digital Transformation in Tourism: An Intelligent Information System Proposition for Hotel Organizations
1 Introduction
2 Trends in Digitalization
2.1 Digital Transformation and Smart Tourism
2.2 Elements of a Digital System in Tourism
2.3 Disruptive Role of Digital Transformation
3 An End-to-End Smart Management System Proposition for Hotels
3.1 Data Collection
3.2 Information System
3.3 Recommendation System Approach
4 Conclusion
References
Insurance and Finance
Digital Workplace Transformation and Innovation in the Financial Service Sector
1 Introduction
2 Theoretical Background
2.1 Digital Workplace Transformation
2.2 Digital Workplace Transformation vs. Digital Workplace Innovation
2.3 Digital Workplace Transformation in Financial Services
3 Methods
4 Data Analysis
5 Results
6 Discussion
7 Conclusion
References
Digital and Customizable Insurance: Empirical Findings and Validation of Behavioral Patterns, Influential Factors, and Decision-Making Framework of Baltic Insurance Consumers in Digital Platforms
1 Introduction
2 Research Methodology and Methods
3 Digitalized and Customizable Insurance Platforms and Processes: Analysis on Theoretical Foundation and Conceptual Modeling
3.1 Literature Review
3.2 Conceptual Modelling
4 Reflections of Digital Platforms in the Baltic Non-Life Insurance Market: A Case Study and Empirical Validation
4.1 Empirical Data Analysis and Modelling
5 Discussion and Limitations
6 Conclusions
References
Education
Digital Transformation in Higher Education: Intelligence in Systems and Business Models
1 Introduction
2 Literature Review: Understanding Digital Transformation, Its Importance, and Barriers to It
2.1 Understanding Digital Transformation
2.2 The Importance of Digital Transformation
2.3 Barriers to Digital Transformation in Higher Education
3 Planning Digital Transformation
3.1 Establishing a Realistic Diagnosis
3.2 Innovating the Mission for the New Digital Environment
3.3 Strengthening the Business Mentality of Higher Education Institutions
3.4 Becoming a Student-Centric Organization: Multimodality and Personalized Learning
3.5 Engaging the Main Stakeholders
3.6 Overcoming DT Barriers
3.7 Adopting Change Management Methodologies
4 Implementing Digital Transformation: A Roadmap
5 Conclusions
References
Digital Transformation in Education: Relevant Paradigms and Theories of Teaching and Learning in the Industry 4.0
1 Introduction
2 The Historical Background of Education Through the Industrial Revolutions
3 Industry 4.0, Education and Digital Transformation
4 Brief Review of Important Empirical Literature Review on Digital Transformation of Education
4.1 Underlying Theories and Principles of the Study
5 Conclusion and Policy Recommendation
6 Future Research
References
Digitalization Maturity Model Development for Higher Education
1 Introduction
2 Literature Review on Digitalization in Education
2.1 Definition of Education 4.0
2.2 Education 1.0 to Education 4.0
2.3 Digital Transformation on Education
2.4 Determination of Maturity Model
3 The Proposed Methodology
3.1 Step 1: Determination of the Main and Sub Criteria
3.2 Step 2: Prioritization of Criteria by AHP Method
3.3 Step 3: The Calculation of Digital Maturity Score for a Real Case
4 Conclusion
References
Smart Cities
Smart City and Smart Communities: Emerging Conditions for Digital Transformation
1 Introduction
1.1 Literature Review
1.2 Methods
1.3 Procedures
1.4 Data
2 Results and Discussion
2.1 General Description
2.2 Reactions of Residents of the Metropolis
2.3 Sentiment Analysis
2.4 Associations Network Analysis
3 Conclusion
References
Digital Transformation for Intelligent Road Condition Assessment
1 Introduction
1.1 Digital Transformation
1.2 Smart City
1.3 Intelligent Road Condition Assessment
2 Road Data Acquisition
2.1 Laser Scanning
2.2 Infrared Sensing
2.3 Multi-view Geometry
2.4 Shape (depth) from Focus
3 Road Defect Detection
3.1 Traditional 2D Image Processing-Based Approaches
3.2 Machine/Deep Learning-Based Approaches
3.3 3D Road Surface Modeling and Segmentation-Based Approaches
3.4 Hybrid Approaches
4 Future Insights
5 Summary
References
Transportation
Digital Maturity Assessment of Ship Management Companies Towards Organizational Intelligence: Blue Digital Focus
1 Introduction
2 Literature Review on Digital Maturity Assessment
3 Blue Digital Focus Framework
3.1 Framework
3.2 Strategy
3.3 Organization
3.4 Customer
3.5 Technology
3.6 Operations
3.7 Innovation
3.8 Process Improvement
4 Illustrative Analysis and Reporting
5 Conclusion and Discussion
References
Digital Transportation Maturity Measurement
1 Introduction
2 Maturity Models
3 Literature Review
4 Proposed Model
4.1 Sub-criteria of Material Flow
4.2 Sub-criteria of Business Culture
4.3 Sub-criteria of Organization and Strategy
4.4 Sub-criteria of Customer Satisfaction and Marketing
4.5 Sub-criteria of Smart Logistics
5 Research Methodology and Application
6 Conclusions and Future Research Directions
References
Transport Digitalization
1 Introduction
2 Literature Review
3 Analysis Methods
4 Results
4.1 Seaborne Trades
4.2 Road Transport
4.3 Rail Transport
4.4 Air Transport
5 Discussion
6 Conclusions
References
Future of Digital Transformation
Future of Digital Transformation
1 Introduction
1.1 Digital Transformation in Manufacturing Industry
1.2 Digital Transformation in Service Industry
2 Future Trends in Digital Transformation
3 Possible Digital Technologies in the Future
4 Conclusion
References

Citation preview

Lecture Notes in Networks and Systems 549

Cengiz Kahraman Elif Haktanır   Editors

Intelligent Systems in Digital Transformation Theory and Applications

Lecture Notes in Networks and Systems Volume 549

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Cengiz Kahraman · Elif Haktanır Editors

Intelligent Systems in Digital Transformation Theory and Applications

Editors Cengiz Kahraman Department of Industrial Engineering Istanbul Technical University Maçka, Turkey

Elif Haktanır Department of Industrial Engineering Bahcesehir University Besiktas, Istanbul, Turkey

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-16597-9 ISBN 978-3-031-16598-6 (eBook) https://doi.org/10.1007/978-3-031-16598-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Digital transformation (DT), which is a long-term investment has a vital role in the sustainability of organizations anymore. Nowadays, the need for digital transformation has become more necessary than ever, especially with the advent of COVID19 pandemic. While companies will undoubtedly face significant challenges in the digital transformation process, it has become an obligation for managers to accelerate the digital transformation of operations. Digital transformation has been needed in many areas such as digital manufacturing, digital economy, digital tourism, digital government, digital health, and digital education. Digitizing an industrial company is a challenging process, which involves rethinking established structures, processes, and steering mechanisms. This book is an excellent guide for managers who plan to start digital transformation processes in their systems. The book has content prepared to be a digital transformation guide for all sectors. All managers and engineers in the automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance and finance, education, smart cities, and transportation sectors can benefit from this book. The section for each sector has been prepared by expert authors who have done practical and theoretical studies in that field. The fact that each chapter is prepared to include figures and tables increases intelligibility and readability without boring the reader. Case studies successfully show how intelligent systems are applied in digital transformation. Intelligent digital transformation is a smart digital transformation, which is the integration of intelligent technologies to evolve a business to meet modern best practices and get ahead of the competition. Artificial intelligence (AI) collects data using AI techniques such as machine learning, fuzzy sets, and algorithms to advise data-driven decision-making. Implementing an intelligent digital transformation ultimately makes the company more productive, profitable, and centralized. This book contains 26 chapters of theoretical and practical applications under 15 main categories on intelligent digital transformations in a comprehensive way. First category involves two chapters on a literature review on digital transformation. The first chapter reviews the DT status of the business sectors automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance, education, v

vi

Preface

smart cities, and transportation. Tabular and graphical illustrations are presented for several perspectives. The second chapter reviews the available literature in the field of research on maturity models of DT. The chapter helps professionals from different areas of activity, specialists, and academics to understand the similarities and differences between the models and to assess the organization’s readiness and capacity to make significant changes, such as strategy, business model, products and services, and technology. The second category is on safety and security and involves one chapter highlighting the challenges of the modern world in the face of the new circumstances created by digital transformation. The third category on intelligent DT involves one chapter. It presents the issue of the location of intelligence in digital transformation processes against the background of the assumptions of safety and security of complex socio-technical systems. The fourth category starts with the DT in automotive industry. It focuses on the DT in automotive production processes in both academic literature and industrial applications and presents the development of automobile technology throughout the years. The fifth category on energy sector involves two chapters. The first chapter presents innovative solutions used in the power industry, consisting of the implementation of digitization and high-tech solutions. The second chapter evaluates the digital transformation success factors in energy industry. The success factors are obtained through a detailed literature review and experts’ opinions. Fuzzy cognitive map approach is employed to analyze the obtained digital transformation success factors. The sixth category on smart manufacturing involves two chapters. The first chapter concentrates on educational solutions to promote digital transformation in the manufacturing industry. Digital and additive manufacturing specifically are used as examples to educate new experts on the requirements of digital transformation in technology. The second chapter provides an assessment of smart manufacturing performance of countries by examining related policy for the uptake of digital technologies and its standardization in production industries. The seventh category on white goods involves one chapter. It discusses the concept of digital transformation related with sales and operations planning and how it can benefit the white goods sector. Having the network structure and inventory strategy as initial inputs, it re-evaluates the sales and operations planning cycle starting at demand planning by revisiting forecasting hierarchies. The eight category on health involves three chapters. The first chapter evaluates an internationally accepted cybersecurity framework by health experts, and the framework is prioritized for the use by healthcare organizations. The second chapter develops a model for successful optimization of multi-criteria systems embedded in intuitionistic fuzzy Intelligence healthcare expert systems by expanding three-dimensional intercriteria analysis. The third chapter addresses the motives and practices of digital health and elaborates on the contributions of digitalization in healthcare sector. The ninth category on banking involves one chapter which highlights the key imperatives for digital transformation in banking, enabling technologies, and presents a view of the transition to intelligent operating models of the future. Included are perspectives based on industry observations and experience across transformations of global banks. The chapter establishes the adoption of intelligent digital systems through an assessment of strategic fitment and

Preface

vii

digital technology maturity. The tenth category on tourism involves one chapter, which discusses digital transformation and the role of digital tools in the accommodation dimension of the tourism sector based on the digitalized tourism perspective called Tourism 4.0 and Smart Tourism, following the Industry 4.0 prospect. The eleventh category on insurance and finance involves two chapters. The first chapter focuses on the research question whether employees’ engagement, their well-being and support to the digital work expedites digital work environment transformation. The second chapter synthesizes and conceptualizes consumer decision-making and technology acceptance models and frameworks, applicable to digital insurance platforms management. The twelfth chapter on education involves three chapters. The first one provides a review and reflection of the emerging digital transformation trends and an illustration of good practices in higher education. The second chapter explores the paradigms and theories of education that will help the education sector to move in the right direction in Industry 4.0 and concludes that it is important to adopt a constructivism type of teaching to minimize the negative effects of technological advancement than steaking to a behaviorism type of teaching. The third chapter attempts to provide a measurement tool to determine the digitalization maturity level for universities. Its objectives are to discuss the relevant literature to reveal all criteria affecting the development of digitalization maturity in higher education, to figure out the importance of these criteria by the help of a traditional multi-criteria decision-making method, and to apply the proposed methodology on a university to validate how it is successfully implemented for determination of digitalization maturity level. The thirteenth category on smart cities involves two chapters The first chapter presents an algorithm for analyzing the communicative behavior of actors in cyberspace to determine the perception and track opinions and attitude changes of metropolitan residents in terms of digital transformation during pandemic. The second chapter presents the state-of-the-art intelligent road condition assessment systems, the existing challenges, and future development trends. The fourteenth category on transportation involves three chapters. The first chapter proposes an approach called Blue Digital Focus (BDF) to be useful both to ship management companies and maritime researchers interested in understanding the digital readiness level in practice. BDF has great potential to investigate the future needs of key maritime stakeholders responding to priorities of smart, green and sustainable transportation system. The second chapter presents a novel maturity model with the help of the literature and the experiences of experts. Within the scope of the proposed maturity model, five main criteria are proposed. In addition, the proposed model is solved by a multi-criteria decision-making approach called hesitant fuzzy analytic hierarchy process. The third chapter focuses on revolutionary changes in the development of the industry, which will result in structural changes in value added chains, shifts in the geography of the deployment of production facilities, the further spread of electronic document flow and payments, the growth in the use of electric cars and autonomous vehicles, robotization of warehouses and port infrastructure, the introduction of the Internet of Things in traffic control. The last category on future of DT involves one chapter, which discusses the current situation of digital transformation one by one on the basis of sectors such as automotive, energy, smart manufacturing,

viii

Preface

white goods, health, banking, tourism, insurance, digital education, smart cities, and transportation. The trends that await these sectors in the future are examined, and finally, future-oriented digital transformation expectations are given. We would like to thank anonymous reviewers and our publisher Springer Publishing Company, Series Editor, Prof. Janusz Kacprzyk, and Interdisciplinary and Applied Sciences and Engineering, and Editorial Director, Thomas Ditzinger, for their supportive, patient, and helpful roles during the preparation of this book. Cengiz Kahraman Elif Haktanır

Contents

Literature Review on Digital Transformation A State of the Art Literature Review on Digital Transformation . . . . . . . . Elif Haktanır, Cengiz Kahraman, Sezi Çevik Onar, Ba¸sar Öztay¸si, and Selçuk Çebi Similarities and Differences Between Digital Transformation Maturity Models: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paulo Carrijo, Bráulio Alturas, and Isabel Pedrosa

3

33

Safety and Security Intelligent Digital Transformation in Modern Socio-Technical Systems – A Sustainable Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adam Jabło´nski and Marek Jabło´nski

55

Intelligent Digital Transformation Intelligent Digital Transformation Strategy Management: Development of a Measurement Framework . . . . . . . . . . . . . . . . . . . . . . . . . Umut Sener, ¸ Ebru Gökalp, and P. Erhan Eren

77

Automotive Digital Transformation in Automotive Sector . . . . . . . . . . . . . . . . . . . . . . . . . Elif Haktanır, Cengiz Kahraman, Selçuk Çebi, ˙Irem Otay, and Eda Boltürk

97

Energy Digital Transformation and Prosumers Activities in the Energy Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Piotr F. Borowski

ix

x

Contents

Digital Transformation Success Factors Evaluation in Energy Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Burak Berkay Havle and Mehtap Dursun Smart Manufacturing Education as a Promoter of Digital Transformation in the Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Ari Pikkarainen and Maarit Tihinen Standardization in Smart Manufacturing: Evaluation from a Supply-Side Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Yulia Turovets and Konstantin Vishnevskiy White Goods Redesign, Smart and Digital Enablement of Sales and Operations Planning Processes: A Study of White Goods Manufacturing . . . . . . . . . . 221 Burak Kandemir, Eren Özceylan, and Mehmet Tanya¸s Health Cybersecurity Framework Prioritization for Healthcare Organizations Using a Novel Interval-Valued Pythagorean Fuzzy CRITIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Hatice Camgöz Akda˘g and Akın Menek¸se Multi-layered InterCriteria Analysis as a Digital Tool for Studying the Dependencies of Some Key Indicators of Mortality During the Pandemic in the European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Velichka Traneva and Stoyan Tranev Development of Intelligent Healthcare Sytems Through Digital Transformation and Operations Research Modeling . . . . . . . . . . . . . . . . . . 295 Gozdem Dural-Selcuk Banking Imperatives, Trends and Dynamics of Digital Transformation as Banks Adopt Technology and Intelligent Systems . . . . . . . . . . . . . . . . . . 323 Swayambhu Dutta, Himadri Sikhar Pramanik, Sayantan Datta, and Manish Kirtania Tourism Digital Transformation in Tourism: An Intelligent Information System Proposition for Hotel Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Tutku Tuncalı Yaman and Hülya Ba¸se˘gmez

Contents

xi

Insurance and Finance Digital Workplace Transformation and Innovation in the Financial Service Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Jasmina Selimovic, Amila Pilav-Velic, and Lamija Krndzija Digital and Customizable Insurance: Empirical Findings and Validation of Behavioral Patterns, Influential Factors, and Decision-Making Framework of Baltic Insurance Consumers in Digital Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Gedas Baranauskas Education Digital Transformation in Higher Education: Intelligence in Systems and Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Albert Rof, Andrea Bikfalvi, and Pilar Marques Digital Transformation in Education: Relevant Paradigms and Theories of Teaching and Learning in the Industry 4.0 . . . . . . . . . . . . 453 David Mhlanga Digitalization Maturity Model Development for Higher Education . . . . . 471 Nursel Buse Ulufer, ˙Ikra Tuba Dolgun, Sevval ¸ Birinci, Atalay I¸sık, Semiha Bal, Gül T. Temur, and Alper Camcı Smart Cities Smart City and Smart Communities: Emerging Conditions for Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Aleksey N. Raskhodchikov and Maria Pilgun Digital Transformation for Intelligent Road Condition Assessment . . . . . 511 Sicen Guo, Yue Bai, Mohammud Junaid Bocus, and Rui Fan Transportation Digital Maturity Assessment of Ship Management Companies Towards Organizational Intelligence: Blue Digital Focus . . . . . . . . . . . . . . 537 Kadir Cicek, Metin Celik, and S. M. Esad Demirci Digital Transportation Maturity Measurement . . . . . . . . . . . . . . . . . . . . . . . 561 Bilge Varol, Gulfem Er, and Gül Tekin Temur Transport Digitalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 G. V. Kuznetsova and G. V. Podbiralina

xii

Contents

Future of Digital Transformation Future of Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 Elif Haktanır, Cengiz Kahraman, Sükran ¸ Seker, ¸ and Onur Do˘gan

Literature Review on Digital Transformation

A State of the Art Literature Review on Digital Transformation Elif Haktanır, Cengiz Kahraman, Sezi Çevik Onar, Ba¸sar Öztay¸si, and Selçuk Çebi

Abstract Digital transformation (DT) is the integration of digital technology by transforming all manual and semi-manual processes of business into full digital processes. In this chapter we review the DT status of the business sectors automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance, education, smart cities, and transportation. Tabular and graphical illustrations are presented for several perspectives such as number of studies on DT, subject areas of DT publications, and document types of these publications. We conclude the chapter with future research suggestions and comments on the future of DT. Keywords Digital transformation · Digital twin · Industrial internet of things · Virtual and augmented reality · Cloud computing · Artificial intelligence · Blockchain

1 Introduction During the recent years with the advent of recent technologies not only our daily lives but also the businesses have changed dramatically. Especially after Covid-19 pandemics, this transformation has accelerated. The companies that were prepared and could adopt to this new normal create a competitive advantage over the others. Transforming company though digitalization become necessity for many business areas [1]. Digital transformation (DT) can be defined as the utilization of digital E. Haktanır (B) Department of Industrial Engineering, Bahcesehir University, 34349 Besiktas, Istanbul, Turkey e-mail: [email protected] C. Kahraman · S. Ç. Onar · B. Öztay¸si Department of Industrial Engineering, Istanbul Technical University, 34367 Besiktas, Istanbul, Turkey S. Çebi Department of Industrial Engineering, Yildiz Technical University, 34220 Esenler, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_1

3

4

E. Haktanır et al.

technologies for creating novel business procedures, culture, and consumer practices for adapting to the new business environment. DT is a joint effort that needs to involve not only by one department but the entire organization [2]. This holistic transformation creates major deviations in classical industry practices [3]. The main technologies that enhance DT can be listed as mobile technologies and apps, social media, Internet of Things (IoT), business analytics and applications, communication and data share environments, and collaboration apps [4]. Yet, DT is not only applying these new digital technologies, but also a change in the whole structure of the company towards digitalization [5]. DT has four different phases: transformation of the process, transformation of business model, transformation of domain, and transformation of culture [6]. Assessing the DT readiness level and defining the improvement areas is important for a smooth transformation. Li et al. [7] create a DT monitoring index that consists of “transformation stages”, “single-domain digitalization”, “integration and interconnection, and collaboration”, “interaction and mode innovation” as the main monitoring areas. Although DT become a necessity and firms not only want but have to transform digitally, there are many factors that affects the success of DT. Funding problems, the problems in the existing digital capabilities of the entity, problems in the human resources and the technical problems are among the main limitations of a successful DT [8]. Saraji et al. [9] focused on the DT of fintech firms. In this study, the challenges have been defined as “difficulty in coordination and collaboration” and “resistance to change”. Liu et al. [10] studied the association among adaptation capacity and DT performance and showed that this association varies in different industries with diverse technology levels. The term Industry 4.0, known as the Industrial Revolution, was first mentioned at the Hannover Fair in Germany in 2011. Industry 4.0 is a DT process that can be defined as the integration of industrial activities-informatics-technology that emerges by integrating the developments in the field of informatics with the technologies in the field of industry [11]. In order to maintain their competitive advantage in the globalizing market, companies have to keep up with the changes that emerged after the industrial revolutions, and they have to make new investments in their company structures. Today, within the scope of Industry 4.0, businesses are investing in areas such as the IoT, big data and analytics, artificial intelligence, cyber security, autonomous systems, cloud computing, and wired & wireless communication infrastructure because of DT. Figure 1 presents four major industrial revolutions throughout history. Industrial revolutions and their features can be summarized as follows [11–13]. ● Industry 1.0 Mechanization: The use of the steam engine in production is considered as the first industrial revolution. With the invention of the steam engine in the 1780s, it started to be used in the production area of machines working with water and steam power. Thus, it has been possible to produce large forces that cannot be produced with human power, and it has made products that are impossible to produce or take a long time to be produced easily.

A State of the Art Literature Review on Digital Transformation

5

Fig. 1 Industrial revolution

● Industry 2.0 Mass production: The use of electric motors in industry and mass production is considered as the second industrial revolution. After the 1870s, Henry Ford’s production line design and the use of electricity in mass production improved the logic of the production line. This resulted in a decrease in production times and an increase in production quantities. ● Industry 3.0 Automation: The introduction of automation in production is considered as the third industrial revolution. The automation system, which was created in the 1970s by utilizing different technologies such as mechanical, hydraulic, pneumatic, electrical, electronic, software, and computer brought a new dimension to the production. In this period, with the use of computercontrolled machines and programmable logic controller technology, the quality and the efficiency of the production have been increased. ● Industry 4.0 Smart Factories: Industry 4.0 is the production period in which all systems and machines used in production can talk to each other, and human intervention is minimized. Cyber-physical systems, the IoT, networks are shown as the basic pieces of equipment of Industry 4.0. The basic components of Industry 4.0 are defined as interoperability, virtualization, autonomous management, and real-time capability. A production process can be characterized by five basic functions including (I) the force required for the production, (II) the quantity of the production, (III) the direction of the force required for the production, (IV) communication and panning, (V) initial setup and maintenance. Before the industrial revolution, all these functions were implemented by human workers. By the first industrial revolution, the production force was provided by the steam engine instead of human force. The rest of the production functions were also performed by human workers. In the second industrial revolution, the transition to electric power was made instead of steam power, and the logic of mass production was developed. With the third industrial revolution, the direction and control of the force were transferred to computer

6

E. Haktanır et al.

numerically controlled systems, and production quantities and production quality were increased by automating the production systems. Hence, the direction, control, and handling activities of the residual force were transferred to mechanical systems. With the fourth industrial revolution, communication and production planning functions in the production working environment were transferred to cyber systems. Hence, through the communication network among the machines, it is aimed to pull the raw material from the warehouse, transport it to the workbench, process it on the bench, and deliver it to the customer. In 2021, European Commission announced Industry 5.0 which provides a vision of an industry that aims beyond efficiency and productivity [14]. While Industry 4.0 is technology-driven, Industry 5.0 is defined as value-driven that drives technological transformation with a particular purpose [15]. After the fourth industrial revolution, maintenance and the first installation of the production systems are the last activities that have been carried out by human workers. In the future, with the transfer of these tasks to humanoid robots, the fifth revolution in the industry (Industry 5.0) will be realized. Table 1 shows the changes in production systems caused by the industrial revolutions. The rest of the paper has been organized as follows: In Sect. 2 a brief literature review on the DT framework is given. In Sect. 3 a more detailed analysis based on the main sectors, (automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance, education, smart cities, and transportation) is provided. The last section concludes the chapter and gives further suggestions. Table 1 Improvements provided by industrial revolutions in production systems Components of production systems

Before industry revolution

Industry 1.0

Industry 2.0

Industry 3.0

Industry 4.0

Industry 5.0

Source of power/force

Human

Steam

Electrical

Electrical

Electrical

Electrical

Quantity of production

Handmade Custom-made Mass Mass Mass Mass production production production production

Motion-force control

Human

Human

Human

Machine

Machine

Machine

Communication Human and planning

Human

Human

Human

Cyber physical systems Networks

Cyber physical systems Networks

Initial installation and maintenance

Human

Human

Human

Human

Humanoid Robots

Human

A State of the Art Literature Review on Digital Transformation

7

2 Literature Review In this study we have focused on the DT literature. We have utilized Scopus database and analyzed the development of DT starting from 1990. We have checked four different keywords, namely DT, Industry 4.0, digital twin, and industrial IoT. These four keywords represent different aspects of DT. Figure 2 gives the number of studies that focus on these four DT areas. In order to see the total amount of studies, all of the keywords are combined with OR operator and the results are shown by Total time series in Fig. 1. The technological changes after 2010 has created an intense need for the firms to digitally transform. Similar to the industry, the academic studies on DT has increased after 2010. Especially after 2014, DT has become a hot topic for the academicians. Figure 1 shows the increase in the DT studies in literature after 2014. Yet the major acceleration has started after 2018 with the Covid-19 pandemics. Thus, we can conclude that the technological changes along with the disruptive events create an immense focus on DT. Figure 3 summarizes the fields that focus on DT based on Scopus database. DT by its nature has a strong connection with computer science and engineering fields. Consequently, the majority of the DT studies in the literature come from computer science and engineering. The surprising fact is that the third research area that investigates DT is social sciences. This shows that besides the technical aspects, the impact of DT on human life is also an interesting research area. The top 10 countries that publish studies on DT is given in Fig. 4. According to Fig. 4, the leading countries that focus on DT are United States and China. These two countries are followed by Germany, Russian Federation, United Kingdom, India, Italy, France, Japan, and Spain. In the literature, many authors show the association between DT and financial success. In the future, we will be able to see the impact of focusing on DT on the financial capability of these firms.

Fig. 2 The number of studies in literature that focus DT (based on Scopus database)

8

Fig. 3 The fields that focus on DT based on Scopus database

Fig. 4 Top ten countries that published studies on DT

E. Haktanır et al.

A State of the Art Literature Review on Digital Transformation

9

Fig. 5 Document types of published DT studies

Figure 5 presents the document types which contain DT and related keywords in the title, keywords or abstract. The majority of documents are articles (37,488) and conference papers (37,032). Documents in Book chapter (2856) type follows the others. Nearly 90% of all DT studies are published in journals and conferences. The most related journals can be listed as IEEE Access, Sustainability Switzerland, and IEEE Transactions on Industrial Informatics. Figure 6 presents the top ten journals which publish DT related studies. Conferences paper is another important document type for DT. The most related conferences can be listed as Proceedings of SPIE The International Society for

Fig. 6 Top 10 DT publishing journals

10

E. Haktanır et al.

Fig. 7 Top 10 DT publishing conferences

Optical Engineering, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, and ACM International Conference Proceeding Series. Top 10 most DT related conferences are presented in Fig. 7.

3 Digital Transformation by Sectors DT has affected many different sectors and has led to various radical changes. In this section, the impact of DT on eight different sectors (automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance, education, smart cities, and transportation) is discussed and supported by studies from the literature.

A State of the Art Literature Review on Digital Transformation

11

3.1 Digital Transformation in Manufacturing Industry 3.1.1. Automotive Mobility as a service (MaaS) is a type of service that, through a joint digital channel enables users to plan, book, and pay for multiple types of mobility services. Block chain technology can be used for elimination of excessive paperwork, constantly increasing transaction fees, lack of transparency, and cybersecurity risks in automotive sector. Predictive operations can diagnose the root causes of the problems which allow automotive companies to prevent errors in production processes and shortcomings in manufactured products. 3D printing is used in the automotive industry by carmakers to create automotive prototypes to check their form and fit. Transformation in Fig. 4 indicates that a sophisticated mechanism to collect, organize, and analyze its customer data and transform it into meaningful information which can be used to engage more effectively with consumers. Data privacy, cyber security and protection is a strong selling point for car buyers. The objective is providing a smooth communication in an easy way despite such protective features. Autonomous driving is a driverless car, incorporating vehicular automation, as a ground vehicle capable of sensing its environment and moving safely with little or no human input. An electric vehicle is a vehicle that uses one or more electric motors for propulsion. The objective is accelerating the world’s transition to sustainable energy with electric cars, solar and integrated renewable energy solutions. Top DT trends in the automotive industry are presented in Fig. 8.

Fig. 8 Top DT trends in automotive industry

12

E. Haktanır et al.

Scientific publications on DT in automotive industry appear increasingly day by day in the literature. Llopis-Albert et al. [16] presented an application of qualitative comparative analysis to DT in the automotive industry in Spain. The results of their work showed that investing in DT is crucial and will positively affect manufacturers’ profit growth, productivity, and competitive advantage. Also, it will allow the customers to reach higher quality services, and this will increase customer satisfaction. Keilbach et al. [17] investigated how automotive companies try to align their strategies with increasingly digitalized and software-based products. Their findings revealed how digitized products have changed the way of working within and between organizations and old-style mass production. Szalavetz [18] investigated how DT may assist factory economy digital entrepreneurs in their integration in the automotive global value chains depending on interviews with ten Hungarian digital automotive technology providers. Stein and Schmidt [19] designed a platform thinking based service engineering strategy for DT and presented a case study of an automotive logistics service platform. 3.1.2. Energy Digital twin technologies in the energy industry develop and maintain smart grids equipped with high-tech sensors and machine-learning models. Virtual power plants integrate the distributed energy resources such as wind farms, solar parks, and combined heat and power units as a whole. The RFID system is used in energy industry to mark network assets and track the use of materials. IoT uses sensors and communication technologies to sense and transmit real-time data in the energy industry to enable a change from a centralized to a distributed, smart, and integrated energy system. Blockchain records and facilitates transactions between generators and consumers of energy in the energy sector. Top DT trends in the energy industry are shown in Fig. 9. In the literature, energy industry’s DT is studied by many researchers. Maroufkhani [20] developed a rule-based model for increasing the energy efficiency of smart cities’ DT processes. Torkunova and Khabrieva [21] developed a model for assessing the impact of the DT of the energy sector economy on the hospitality industry. You and Yi [22] conducted an empirical study on mechanism of energy enterprises’ DT. Liu and Lu [23] conducted strategic analysis and development plan design of energy DT from a global perspective. Chebotareva [24] outlined the main stages of the DT of the energy sector of Russia since 2014. Dall’Ora et al. [25] aimed to support production engineers approaching DT by exemplifying its key elements on a real-life scenario. Wei et al. [26] constructed a classification system for energy data resources to provide a data foundation for the DT of the energy industry. Zhao et al. [27] examined the panorama of DT of power system of China. Berezin [28] proposed a method to choose the best ways to digitalize the engineering and energy infrastructure of housing and communal services. Klochkova et al. [29] analyzed the possible ways to develop electric power industry in Russia by considering the threats of electric power deficiency, environmental degradation, higher safety requirements, and the challenges of the digital era. Osmundsen [30] provided insight into which competences were identified as important for the DT at a firm in the Norwegian

A State of the Art Literature Review on Digital Transformation

13

Fig. 9 Top DT trends in energy industry

energy sector. Dang and Vartiainen [31] examined the process of implementing a DT strategy at a global Finland-based energy company. Shinkevich et al. [32] analyzed the technical readiness of energy systems to implement the ideas of DT. Mozokhin and Mozokhin [33] presented reasons for transition to digital energy and the development of intelligent electrical networks. Havle and Dursun [34] investigated the influences of DT and processes starting with the use of digital technologies in the energy sector. 3.1.3. Smart Manufacturing Smart sensors provide real-time and meaningful information to operators, technicians, and engineers to increase the productivity, efficiency, and flexibility of the manufacturing industry. Smart sensor, which transform the inputs from the physical environment into analyzable data, consists of three elements: the sensor, the microprocessor, and the communication unit. The data obtained by the sensor fusion created by combining the sensors are evaluated with algorithms, filters, and artificial intelligence in manufacturing industry. Big data takes manufacturing systems one step further, considering elements such as years of accumulated data, supply chain reports, inventory values, sales orders, and market trends. With Industry 4.0, it is ensured that all the information required for predictive maintenance is collected in a single point by integrating the service portals of the companies with the IT systems of the customers. Industrial IoT offers revolutionary insights in manufacturing and field operations, as well as accurate assessment of enterprise resources. By enabling manufacturers to access instant data, the Industrial IoT enables businesses to dramatically increase their productivity. With the rise of the industry cloud, core processes in manufacturing are moving to the cloud. These core processes include processing the flow of parts and materials entering the production line, systems used to run factory lines, and feeding the end product entering systems for delivery or sale to

14

E. Haktanır et al.

Fig. 10 Top DT trends in smart manufacturing industry [35]

customers. The process, which started with the replacement of traditional production methods with automation, leaves its place to a new era in which automation systems are shaped by AI. As automation systems have already started to replace human power, it is foreseen that almost all of the production industry systems will be controlled by machines together with AI technologies in the future. Advanced algorithms are used in the manufacturing industry for purposes such as obtaining the most suitable production program in just a few seconds, showing the production path to be followed, and revealing which machine, mold, and part the investment should be made in. Especially in some activities in the field of industry and scientific works, the execution of all works without the need for manpower can be realized with production automation. Figure 10 [35] presents the top DT trends in the smart manufacturing industry. DT of smart manufacturing has been analyzed by various studies in the literature. Xia et al. [36] proposed a data-driven approach to utilize DT methods to automate smart manufacturing systems. Mittal et al. [37] proposed a new smart manufacturing maturity model for small and medium-sized enterprises that supports them during the challenging DT journey and paradigm shift towards smart manufacturing and Industry 4.0. De Carolis et al. [38] presented models and tools that are usable together for assessing a manufacturing company’s ability to initiate the DT of its processes towards smart manufacturing. Wuest et. al. [39] explored the impacts of the rapid adoption of smart technologies with regard to the workforce’s role in smart manufacturing systems during and post-COVID-19. Ku et al. [40] aimed to develop a solution to support traditional industries to adopt smart manufacturing and empower DT. 3.1.4. White Goods The first step in creating 360° digitalized processes in the white goods industry is smart supply management. IoT based solutions find their place in the process, which starts with the supply of materials needed for production at the right time. Systems

A State of the Art Literature Review on Digital Transformation

15

that place orders at the right time to keep minimum stock by controlling stock, track average supply times, monitor the vehicles of materials transferred between warehouses, plan daily shipments, and provide mobile integration with drivers are of significant importance for increasing efficiency and reducing costs. White goods factories are getting smarter with the goal of using less resources and maximizing efficiency. It is aimed to reduce error rates and contribute to total quality with a databased and end-to-end traceable factory in each production line. For this reason, DT becomes a necessity in white goods production processes. Monitoring the production moment by moment with IoT-based solutions, making sense of the data, creating digital twins of the factories, digitizing the R&D and P&D stages not only reduces the error rates in the white goods factories, but also positively affects the profitability. Within the scope of Field Vehicle Route Management, it is aimed to increase operational efficiency and speed in the field with smart routing. Thanks to the analysis of instant data and historical reports with big data, improvement in business efficiency and operational costs is observed. Thanks to AI, the tools we use every day will be able to think like us and provide many opportunities that make our work easier. For this reason, some white goods produced and developed are now equipped with AI technology. Dishwashers developed by Candy Hoover became the step that started the AI era in the white goods industry. AXI is a dishwasher that recognizes sound and works individually. This tool, which can be controlled by Wi-Fi, can indicate how much is loaded, recognize the sound, can be controlled remotely, and notify with sound when the work is finished or there is a problem. In the following process, we came across washing machines using AI. These tools can independently regulate the washing power and detergent to be used according to the load weight and fabric type. In addition, it can automatically send a warning when the detergent is out of stock. By making use of these technologies, users can reduce their detergent and power consumption by approximately 30%. Top DT trends in the white goods industry are summarized in Fig. 11.

Fig. 11 Top DT trends in white goods industry

16

E. Haktanır et al.

Some of the DT studies in white goods industry are as follows. Valle et al. [41] presented a study on supporting supply chain digitization through lean startups with a case study from the household appliances industry. Yıldırım and Demirba˘g [42] explored the current Industry 4.0 practices of two Turkish white goods manufacturing companies through a case study. Türkyılmaz and Cebeci [43] investigated Industry 4.0 maturity levels of suppliers in white goods manufacturing sector. Aheleroff et al. [44] aimed to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. Hancıo˘glu [45] presented a study on DT and environmental management applications of approaches used for value creation in the white goods industry.

3.2 Digital Transformation in Service Industry 3.2.1. Health Thanks to Telemedicine, doctors and patients can communicate without being in the same physical environment. It is a great opportunity especially for very old or bedridden patients or patients living in rural areas with limited hospital access. In addition, the same patient groups can be monitored with this method, and the necessary drugs can be prescribed via telemedicine and viewed retrospectively if desired as e-prescriptions. With the help of big data used in health services, it is possible to predict how often and in which periods the patients will be hospitalized in the future by looking at their past data. Another benefit of using big data in healthcare is that it facilitates strategic planning. By analyzing the examination results of various patient groups, health administrators can find the factors that discourage people from starting treatment. By analyzing prescribed drugs and patient data with big data, mistakes such as wrong medication or wrong prescription can be minimized. IoT-enabled devices facilitate remote monitoring in the healthcare industry. In this way, the risk of sudden illness can be reduced by making predictions about the condition of the patients. The healthcare industry should embrace virtual reality to provide better experiences to their patients. For example, patients can now spend a 4.5-h chemotherapy session by reading, chatting, or watching TV. Especially in pediatric patient groups, for children who need procedures such as suturing or removing foreign objects in emergency services, children can be distracted during the process with a virtual reality headset so that they do not feel pain. In similar ways, VR technology can be used for pain reduction therapies. AI is used to identify patterns in cancer cells via digital pathology platforms. It is possible for pathologists to leverage AI-enabled image analysis to connect data points that support cancer diagnosis and treatment. Also, thanks to AI, a clinician can increase productivity by discovering interesting features on slides before reviewing data. The classical drug development process is manual and time consuming. With the use of AI, hundreds and thousands of molecules can be analyzed in a noticeably short time, which would take too long for humans to test manually. Using AI, experts search all molecules and compare them with other molecular parameters. The AI-enabled system will continue to learn

A State of the Art Literature Review on Digital Transformation

17

from the data generated and find one or more compounds best equipped to fight the disease. Similarly, vaccines can be successfully developed and evaluated with the help of AI. By using patients’ contact information, hospitals can save both time and decrease stress by informing patients about waiting time at the clinic or hospital with Amazon’s voice assistant Alexa. Again, with the help of Alexa, patients can receive instant notifications about their medication hours, preventing them from missing medication hours. Figure 12 illustrates top DT trends in health industry [46]. DT in the health sector has also attracted the attention of academics. Tortorella et al. [47] proposed a value stream-oriented approach on DT of health services. Iyamu et al. [48] presented a literature review on defining digital public health and the role of DT. Bauer and Brown [49] illustrated how the DT will affect oral health care.

Fig. 12 Top DT trends in health industry [46]

18

E. Haktanır et al.

Iljashenko et al. [50] developed a strategy for personnel key performance indicator establishment at health care organization DT. Mingolla et al. [51] assessed the digital health transformation with the SCIROCCO exchange tool in Puglia region. Herrmann et al. [52] presented an internet-based observational study on DT and disruption of the health care sector. Marques and Ferreira [53] explored the potential of existing digital solutions to improve the quality and safety of healthcare and analyzed the emerging trend of digital medicine. Kwon et al. [54] argued that AI can provide clinical-community linkage that enhances overall population health. Sullivan et al. [55] outlined a case study and detailed the lessons learnt to inform and give confidence to others contemplating digitization of public health systems in response to the COVID-19 pandemic. 3.2.2. Banking AML (anti-money laundering) serves as a platform and database independent solution developed for the diagnosis, analysis, and reporting of remarkable financial transactions. Thanks to chatbots, bank customers do not face waiting situations as in call center services. They can reach the banks 24/7 and get support for information and solutions. Algorithmic trading operations, also called “black box trading” or “automated trading/automatic buying/selling transactions”, provide the opportunity to instantly apply various trading strategies that investors have determined. With machine learning, it is possible to analyze the past online banking data of customers and recommend the most suitable campaigns and services for them. Robo advisors are defined as automated digital financial management tools to help investors manage their portfolios with moderate to minimal human involvement from the bank. Top trends of DT in banking industry are shown in Fig. 13 [56]. Some of the studies on banking industry’s DT are presented in the following. Sajic et al. [57] described methods for using digital electronic technologies for transformation of classical banks into modern digital banks. Shcherbina [58] identified the dominant trends in the transformation of the banking sector under the impact of financial technologies. Zamaslo et al. [59] assessed the banking digitalization influenced by economy digitalization, dynamically spread electronic payments, e-commerce, and innovative digital service technologies. Kazarenkova et al. [60] investigated the separate commercial banks in the conditions of digital economy demanding introduction of financial innovations and technologies in bank activity. Krasonikolakis et al. [61] developed a framework to understand DT by examining the development, deployment, and use of digital technologies in retail banking. Liu et al. [62] explored the development process of DT through an e-banking project based on the resource fit concept. Diener and Spacek [63] identified the main perceived obstacles to DT in both the private and commercial banking sectors from a managerial point of view. Zurdo et al. [64] analyzed how the new digital society in changing the relationship model between co-operative banks and their members and customers. 3.2.3. Tourism As in the other industries discussed, the DT effect is inevitable in the tourism sector. In some tourism centers, customers who check in or check out online or request

A State of the Art Literature Review on Digital Transformation

19

Fig. 13 Top DT trends in banking industry [56]

room service online are given priority, while in some businesses, simple tasks such as opening and closing the door lock can only be done online, thus preventing risks such as card loss. With IoT technology, hotel rooms have become smart. Services such as turning the light on and off with remote access and adjusting the air conditioner before entering the room have become quite common. With the use of beacons, hotel customers can receive notifications about instant events and services based on their location. Thanks to the voice assistant, the need for hotel information staff has decreased, and it has become possible for hotel customers to receive unlimited voice support in all languages 24/7. The tourism sector is one of the sectors that benefit greatly from data analysis. By analyzing the previous customer data, purchase histories and preferences of past customers together with big data, it increases the chance of making sales by suggesting the most suitable opportunities and campaigns in the future. With the virtual reality technology, hotel customers can visit and choose the room they will stay in online before they reach the hotel. Tourists who have physical access problems can visit the places they want with virtual reality. 3D travel stories allow its users to have interactive online experiences with local guides, built with 360-degree photo and video content. By using chatbots, booking processes can be completed, guest services can be accessed, and timely information can be obtained in a very short time. With RPA (Robotic Process Automation), booking and claims processes are completed quickly, reporting and auditing are done easily. Figure 14 shows the top DT trends in tourism industry [65]. There are various studies in the literature on DT in the tourism sector. Pumaleque et al. [66] presented a DT model for the development of tourism companies. Busulwa et al. [67] proposed a framework integrating DT and digital business management

20

E. Haktanır et al.

Fig. 14 Top DT trends in tourism industry [65]

competencies required by hospitality managers. Alrawadieh et al. [68] conducted a study on DT and revenue management with evidence from the hotel industry. Stavrianea and Kamenidou [69] offered insights into experiential values and online accommodation booking in the DT era of the tourism sector. Cuomo et al. [70] suggested how to apply “big social data” in the tourist experience co-design, providing an increased value for the visitors and a better decision making approach for managers. Fragniere et al. [71] analyzed the sociological obstacles to the adoption of blockchain technology in the tourism industry. Pencarelli [72] illustrated the main changes brought by the digital revolution 4.0 in industry, the web, and tourism. 3.2.4. Insurance Blockchain technology ensures that fraudulent transactions are prevented in the field of digital insurance, and that physical and digital assets are registered. With IoT systems, traditional insurance claims are changing, insurance companies accelerate their data-driven decision-making processes, and customers do not have to deal with long-term paperwork. Chatbots in the insurance industry analyze what customers say and guide them to fulfill their requests in a very short time. AI solutions, a unique blend of technologies such as machine learning and natural language processing, are constantly learning from the feedback from their users. The services these technologies provide are drastically changing insurance marketing and the insurance company-customer relationships. Figure 15 presents top DT trends in insurance industry. DT in the insurance industry has attracted several academics. Voronova et al. [73] studied the development of insurance company strategies based on the shift of the perception values of clients, employees, and owners of insurance companies in the era of DT. Eckert et al. [74] illustrated the opportunities to increase customer satisfaction for insurance companies with digital applications. Pillay and Njenga [75] showed how operational inefficiencies affect customers’ costs and how using digital innovation can reduce these expenses with a case study of an established

A State of the Art Literature Review on Digital Transformation

21

Fig. 15 Top DT trends in insurance industry

insurance company. Stronen [76] contributed to the literature with insight on understanding drivers for DT capabilities among large banks and insurance companies, from a managerial perspective. Fritzsche and Bohnert [77] showed the implications of bundled offerings for business development and competitive strategy in digital insurance. Padilla-Barreto et al. [78] presented some reflections on what the incorporation of big data analytics implies in an insurance company. Obukhova et al. [79] determined the main directions for the development of digital technologies in the Russian insurance market. 3.2.5. Education Remote proctoring allows an instructor to run the exam from a remote location while providing cheat-proof online assessments. With its innovative structure, video conferencing for online students includes features such as virtual classroom (web conference, webinar), video streaming, online storage, lesson management, question bank, test and exam system and reporting. The immersive nature of virtual reality (VR) and artificial reality (AR) enhances students’ learning experience, allowing them to absorb course materials in ways that have never been done before. To take full advantage of what VR and AR have to offer, students’ senses need to be stimulated so they are fully involved in the lessons through the auditory and visual components. Students and teachers can also use these technologies to rewind, pause and skip the content. Smart Classrooms are structured with video conferencing and live broadcasting technology and unites the instructor and the participants, who are physically located in different places, in an interactive environment. The device connected to a suitable part of the classroom can measure the air quality (temperature, humidity, gas) and trigger all other devices that affect the comfort of the classroom. These

22

E. Haktanır et al.

Fig. 16 Top DT trends in education industry [80]

devices can be such as air conditioners, heaters, and fans to change ventilation and ambient comfort, as well as automatic blinds and window systems. Announcements to be made to students are managed by a central software specially installed for this automation. The entrance and exit times of the teachers are tracked by the device, and students’ attendance information can be obtained by using this technology, if desired. Adaptive learning is a computer-based education method that organizes and plans the trainings in the most appropriate way, considering the different needs of each student. The computer presents the educational material in the most appropriate way to the learning needs of the learner. To reach the best methods, it monitors the behavior and experiences of the learner in their past education and gets to know the learner. Educational chatbots can improve communication, increase productivity, and minimize uncertainty from interactions. Thus, they can effortlessly engage in focused, results-oriented online conversations which is exactly what modern educational institutions need now. Figure 16 illustrates the top DT trends in education industry [80]. Some of the scientific studies on DT of education are as follows. Hashim et al. [81] developed a qualitative model to show how DT can be used to build competitive advantages for universities. Tolnaiova [82] focused on transformation of education and training system in the context of DT and communication technology in sociocultural perspective. Iivari et al. [83] examined the DT in the basic education of the young generation, the variety of digital divides emerging and reinforced, and the possible barriers reported along the way. Vaganova et al. [84] analyzed the experience of training students in the context of DT. Marcum [85] explored the changes in expectations that faculty and students have for gaining access to digital information, and how librarians are responding to those expectations. Alenezi [86] discussed the existing models for the incorporation of DT in higher education institutions and delineates the challenges faced by higher education institutions in pursuit of DT. Garcia-Penalvo [87] defined a reference framework for introducing eLearning practices in mainly face-to-face higher education institutions. Valdes et al. [88] evaluated the involvement of an institutional ecosystem in the DT at universities. Aditya et al.

A State of the Art Literature Review on Digital Transformation

23

[89] tested a theoretical framework to identify and prioritize barriers in the implementation of DT in higher education. Lis [90] outlined the main aspects of the relationship between universities and the business sector in the context of DT processes. Ari et al. [91] presented the problems related to inclusive education faced in the special education schools and rehabilitation centers and put forward suggestions for DT. Okunlaya et al. [92] developed an AI library services innovative conceptual framework for the DT of university education. Sadovets et al. [93] explored the way gamification transforms the informal learning space of the higher education institutions inspired by information technologies and the all-consuming digitalization of human society. 3.2.6. Smart Cities In smart cities, with the digital twins of all power lines, substations, traffic control networks, sewer systems, water networks, emergency services, Wi-Fi networks, highways, security systems and much more, all problems can be solved with a little effort of the main control center. Urban predictive operations convert the vast array of data that cities generate into information that various city departments can use to respond quickly and efficiently to emergencies. With an immersive city experience, viewers are pulled into another real or imagined city, and enabled to manipulate and interact with their environment. Intelligent Infrastructure, Intelligent Transportation and Communication Technologies are systems that transmit, analyze, measure, monitor the data collected by sensors used in the components, and create public value and intelligently respond to user demands and changes in the environment for improved performance and user experience. Digital infrastructures are systems that transmit, analyze, measure, monitor data collected by sensors and create public value and intelligently respond to user demands and changes in the environment for improved performance and user experience. The existence and positioning of physical life in the digital world creates digital identity. Digital identity can also be expressed as a set of assets that represent a company or person digitally. One of the application areas of AI is smart cities. In a city with autonomous vehicles and smart traffic lights, the traffic problem can be effectively improved by inter-vehicle communication and reinforcement learning. Examples of smart city applications of Industry 4.0 solutions are areas such as traffic, parking, lighting, waste management of municipalities. When you go to traffic in the morning, the data about the traffic density predicted to you in the maps section of your smartphone is the result of the applications made in this area. Since the sharing economy is all about how urban resources are shared, examining the smart city through the lens of the sharing economy can help governments better understand how it is expanding in terms of resource allocation. Figure 17 shows the top DT trends in smart cities [94]. Some academic problems related to smart cities are explained as follows. Salem [95] presented a study on how to build a smart city by overcoming the challenges of DT with case of “smart Dubai”. Anthony et al. [96] developed a qualitative research approach about DT with enterprise architecture for smarter cities. Masucci et al. [97] employed a social justice framing to examine youth perspectives on digital technologies and urban transformations of the smart city. Orlowski et al. [98] developed a rule-based model of negentropy for increasing the energy efficiency of the city’s DT

24

E. Haktanır et al.

Fig. 17 Top DT trends in smart cities industry [94]

processes into a smart city. Bakogiannis et al. [99] presented current trends in the evolving transformation of the smart city. Osman et al. [100] presented a data-driven decision-making method in smart cities with a DT case study. Hamalainen [101] presented a framework for a smart city design with a DT case of Helsinki smart city. Kharlamov et al. [102] investigated the public response to accelerating DT of smart city data sensing. 3.2.7. Transportation Drones facilitate same-day delivery to areas with difficult access and traffic congestion, providing great convenience in situations where traditional delivery options are insufficient. Employees’ use of digital wearables helps to monitor and control the delivery process with higher accuracy in the transportation industry and increases work efficiency by facilitating hands-free workflow for staff. AI plays an important role in analyzing both historical and real-time data in transportation. Blockchain provides information about the origin and location of goods, shipping conditions, expiration date, etc. It allows safe storage of operational data related to the On the other hand, it provides bulletproof security, making blockchain-based products virtually impenetrable. With the help of algorithms, machine learning can assist organizations in discovering repetitive patterns in their transportation networks. IoT has enabled organizations to have an enhanced communication with means of transportation including trains, trucks, airplanes, ships, etc. Thanks to cloud computing, operations such as stationary vehicle tracking, logistics area management, online ticket processing, and data recording without using any external storage unit can be done easily in transportation industry. Figure 18 illustrates the top DT trends in transportation industry. Some representative studies on transportation industry’s DT are as follows. Haftor and Climent [103] investigated CO2 reduction through DT in long-haul transportation. Zimnoch [104] highlighted the key trends in the transportation industry and the role of emerging technologies and DT in acceleration of value creation. Drozdov et al. [105] developed a criterion of a safe technological solution in the DT of a transportation complex. Suvorova et al. [106] discussed DT in management of

A State of the Art Literature Review on Digital Transformation

25

Fig. 18 Top DT trends in transportation industry

container-on-flatcar transportation. Nekrasov and Sinitsyna [107] considered an integrated approach to the management of the life cycle of the transport and logistics infrastructure based on DT and logistics engineering concepts. Naumova et al. [108] discussed DT in regional transportation and social infrastructure. Merkaš et al. [109] pointed out the significance of blockchain technology in the DT of logistics and transportation.

4 Conclusion As a result of the literature review, it has been seen that the interest in digital transformation (DT) has increased in both scientific studies and industrial applications, especially after 2014. DT has become a focus area for both academicians and the industry professionals. Especially after 2019 with the Covid-19 pandemic, DT has become a necessity, and the studies in this area have significantly accelerated. It has seen that Amazon, Google, Microsoft, Apple, IBM, and Deloitte are among the DT leaders in the world. DT takes place at different speeds on the basis of sectors and countries. DT investments generally require high level initial capital investments. Financial and infrastructural problems cause some companies, sectors, and countries to lag behind in DT. Companies that successfully implement DT will make great contributions both to themselves and to the economic development of the countries they are in, in the long run. Countries that can make the necessary infrastructure

26

E. Haktanır et al.

investments for DT, set national standards, and create security policies in this area will be successful. Important research areas of DT in the future will be digital twin, industrial IoT, virtual and augmented reality, cloud computing, artificial intelligence, and blockchain. If countries want to increase their competitiveness, they should intensify research and development studies especially in these areas. With the advent of new technologies, new platforms such as Metaverse will impact the future business life. Although it might not be seen very plausible today, there will be many disruptions for the ways we do the businesses.

References 1. Vial G (2019) Understanding digital transformation: a review and a research agenda. J Strat Inf Syst 28(2):118–144 2. Hess T, Matt C, Benlian A, Wiesböck F (2016) Options for formulating a digital transformation strategy. MIS Q Exec 15:103–119 3. Dehning B, Richardson VJ, Zmud RW (2003) The value relevance of announcements of transformational information technology investments. Manag Inf Syst Q 27(4):637–656 4. Wade M, Shan J (2020) Covid-19 has accelerated digital transformation, but may have made it harder not easier. MIS Q Exec 19(3):213–220 5. Kane G, Palmer D, Phillips AN (2015) Is your business ready for a digital future? MIT Sloan Manag Rev 56:37–44 6. Zimmermann A, Schmidt R, Sandkuhl K, Masuda Y (2021) Architecting intelligent digital systems and services. In: Zimmermann A, Howlett R, Jain L (eds) Human centred intelligent systems. smart innovation, systems and technologies, vol 189. Springer, Singapore 7. Li J, Dou K, Wen S, Li Q (2021) Monitoring index system for sectors’ digital transformation and its application in China. Electronics 10:1301 8. Chen CL, Lin YC, Chen WH, Chao CF, Pandia H (2021) Role of government to enhance digital transformation in small service business. Sustainability 13:1028 9. Saraji MK, Streimikiene D, Kyriakopoulos GL (2021) Fermatean fuzzy CRITIC-COPRAS method for evaluating the challenges to industry 4.0 adoption for a sustainable digital transformation. Sustainability 13(17):9577 10. Liu J, Yang W, Liu W (2021) Adaptive capacity configurations for the digital transformation: a fuzzy-set analysis of Chinese manufacturing firms. J Organ Chang Manag 34(6):1222–1241 11. Sarvari PA, Ustundag A, Cevikcan E, Kaya I, Cebi S (2018) Technology roadmap for industry 4.0. In: Industry 4.0: managing the digital transformation. Springer series in advanced manufacturing. Springer, Cham 12. Bartodziej CJ (2017) The concept Industry 4.0: an empirical analysis of technologies and applications in production logistics. Springer, Gabler 13. Propris DL, Bailey D (2020) Industry 4.0 and regional transformations. Routledge Taylor & Francis Group, New York 14. European Commission (2022) What is Industry 5.0? https://ec.europa.eu/info/research-andinnovation/research-area/industrial-research-and-innovation/industry-50_en 15. Xu X, Lu Y, Vogel-Heuser B, Wang L (2021) Industry 4.0 and Industry 5.0—Inception, conception and perception. J Manuf Syst 61:530–535 16. Llopis-Albert C, Rubio F, Valero F (2021) Impact of digital transformation on the automotive industry. Technol Forecast Soc Change 162:120343 17. Keilbach A, Karanasios S, Molla A (2020) Strategic Responses to digitised products: a case study of an automotive firm. In: Conference of the association-for-information-systems (AMCIS), p 19

A State of the Art Literature Review on Digital Transformation

27

18. Szalavetz A (2020) Digital transformation- enabling factory economy actors’ entrepreneurial integration in global value chains? Post-Communist Econ 32(6):771–792 19. Stein S, Schmidt M (2018) Designing a platform thinking based service engineering strategy for digital transformation: a practitioners case study of an automotive logistics service platform. In: IEEE international conference on engineering, technology and innovation (ICE/ITMC), pp 1–5 20. Maroufkhani P, Desouza KC, Perrons RK, Iranmanesh M (2022) Digital transformation in the resource and energy sectors: a systematic review. Resour Policy 76:102622 21. Torkunova J, Khabrieva M (2022) Impact analysis of digital transformation of the energy sector economy on hospitality industry. In: Lecture notes in civil engineering, vol 190, pp 105–112 22. You Y, Yi L (2021) A Corpus-based empirical study on energy enterprises digital transformation. Energy Rep 7:198–209 23. Liu P, Lu C (2021) Strategic analysis and development plan design on digital transformation in the energy industry: a global perspective. Int J Energy Res 45(14):19657–19670 24. Chebotareva G (2021) Digital transformation of the energy sector: a case of Russia. E3S Web Conf 250:01001 25. Dall’Ora N, Alamin K, Fraccaroli E, Poncino M, Quaglia D, Vinco S (2021) Digital transformation of a production line: network design, online data collection and energy monitoring. IEEE Trans Emerg Topics Comput 26. Wei Z, Cai J, Zheng H, Chen Z, Zhang Z, Liu R (2021) Research on the infrastructure for data resource classification in the energy industry under digital transformation. In: 2021 IEEE IAS industrial and commercial power system Asia, I and CPS Asia 2021, pp 545–550 27. Zhao Y, Xia S, Zhang J, Hu Y, Wu M (2021) Effect of the digital transformation of power system on renewable energy utilization in China. IEEE Access 9:96201–96209 28. Berezin A (2021) Digital transformation of city’s engineering and energy infrastructure systems. In: Proceedings of the 12th international conference on contemporary problems of architecture and construction, ICCPAC 2020, pp 299–303 29. Klochkova E, Sadovnikova N, Darda E, Samotsvetova A (2019) Digital transformation in the energy industry: trends and prospects. Adv Intell Syst Comput 726:288–299 30. Osmundsen KS (2020) Competences for digital transformation: insights from the Norwegian energy sector. In: Proceedings of the annual Hawaii international conference on system sciences 2020-January, pp 4326–4335 31. Dang D, Vartiainen T (2020) Changing patterns in the process of digital transformation initiative in established firms: the case of an energy sector company. In: Proceedings of the 24th Pacific Asia conference on information systems: information systems (IS) for the future, PACIS 2020 32. Shinkevich MV, Mashkin NA, Ishmuradova II, Kolosova VV, Popova OV (2020) Management of sustainable consumption of energy resources in the conditions of digital transformation of the industrial complex. Int J Energy Econ Policy 10(5):454–460 33. Mozokhin AE, Mozokhin AE (2020) Perspective development of energy systems in conditions of Russian economy digital transformation. Sci Tech J Inf Technol Mech Opt 20(1):82–93 34. Havle BB, Dursun M (2019) Digital transformation in energy industry: a literature review for future studies. In: Proceedings-2019 3rd international conference on data science and business analytics, pp 166–170 35. Spectral Engines (2018) Industry 4.0 and how smart sensors make the difference. https://www. spectralengines.com/articles/industry-4-0-and-how-smart-sensors-make-the-difference 36. Xia KS, Sacco C, Kirkpatrick M, Saidy C, Nguyen L, Kircaliali A, Harik R (2021) A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J Manuf Syst 58:210–230 37. Mittal S, Romero D, Wuest T (2018) Towards a smart manufacturing maturity model for SMEs ((SME)-E-3). In: Moon I et al (eds) APMS - production management for data-driven, intelligent, collaborative, and sustainable manufacturing, Seoul, Korea, Volume: IFIP, AICT 536, Part II. Springer, pp 155–163

28

E. Haktanır et al.

38. De Carolis A, Macchi M, Kulvatunyou B, Brundage MP, Terzi S (2017) Maturity models and tools for enabling smart manufacturing systems: comparison and reflections for future developments. In: Ríos J, Bernard A, Bouras A, Foufou S (eds) Product lifecycle management and the industry of the future, PLM 2017. IFIP advances in information and communication technology, vol 517. Springer, Cham 39. Wuest T, Romero D, Cavuoto LA, Megahed FM (2020) Empowering the workforce in postCOVID-19 smart manufacturing systems. Smart Sustain Manuf Syst 4(3):281–285 40. Ku CC, Chien CF, Ma KT (2020) Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. Comput Ind Eng 142:106297 41. Valle PD, Deschamps F, Pécora Júnior JE (2018) Supporting supply chain digitization through lean startups: a cases study from the household appliances industry. In: 2nd international symposium on supply chain 4.0: digital transformation in SME (Small and Medium Enterprises), ISSC4, São Paulo, Brazil, 3–4th September 2018, pp 99–103 42. Yıldırım N, Demirba˘g KS¸ (2020) From chaos to calm: Industry 4.0 practices of Turkish white goods companies. In: Durakbasa N, Gençyılmaz M (eds) Proceedings of the international symposium for production research 2019, ISPR 2019. Lecture notes in mechanical engineering. Springer, Cham 43. Türkyılmaz E, Cebeci U (2018) Industry 4.0 maturity levels of suppliers in white goods manufacturing sector. Int J Mech Eng Technol 9(10):964–969 44. Aheleroff S, Xu X, Lu Y, Aristizabal M, Velásquez JP, Joa B, Valencia Y (2020) IoT-enabled smart appliances under industry 4.0: a case study. Adv Eng Inform 43:101043 45. Hancıo˘glu Y (2020) Digital transformation and environmental management applications: approaches used for value creation in the white goods industry. In: Handbook of research on strategic fit and design in business ecosystems, pp 545–569 46. Takyar A (n.d.) The impact of digital transformation in healthcare. https://www.leewayhertz. com/digital-transformation-in-healthcare/#telemedicine 47. Tortorella GL, Fogliatto FS, Mendoza DT, Pepper M, Capurro D (2022) Digital transformation of health services: a value stream-oriented approach. Int J Prod Res 48. Iyamu I, Xu AXT, Gomez-Ramirez O, Ablona A, Chang HJ, Mckee G, Gilbert M (2021) Defining digital public health and the role of digitization, digitalization, and digital transformation: scoping review. JMIR Public Health Surveill 7(11):e30399 49. Bauer JC, Brown WT (2001) The digital transformation of oral health care - teledentistry and electronic commerce. J Am Dent Assoc 132(2):204–209 50. Iljashenko O, Bagaeva I, Levina A (2019) Strategy for establishment of personnel KPI at health care organization digital transformation. In: International scientific conference digital transformation on manufacturing, infrastructure and service, IOP conference series-materials science and engineering, vol 497, p 012029 51. Mingolla S, Pantzartzis E, Graps EA (2020) Assessing the digital health transformation with the SCIROCCO Exchange tool in Puglia Region. Eur J Public Health 30(5) 52. Herrmann M, Boehme P, Mondritzki T, Ehlers JP, Kavadias S, Truebel H (2018) Digital transformation and disruption of the health care sector: internet-based observational study. J Med Internet Res 20(3) 53. Marques ICP, Ferreira JJM (2020) Digital transformation in the area of health: systematic review of 45 years of evolution. Heal Technol 10(3):575–586 54. Kwon IWG, Kim SH, Martin D (2021) Integrating social determinants of health to precision medicine through digital transformation: an exploratory roadmap. Int J Environ Res Publ Health 18(9):5018 55. Sullivan C, Wong I, Adams E, Fahim M, Fraser J, Ranatunga G, Busato M, McNeil K (2021) Moving faster than the COVID-19 pandemic: the rapid, digital transformation of a public health system. Appl Clin Inform 12(2):229–236 56. Marous J (2018) 4 key strategies to create a future-proof digital bank. https://thefinancial brand.com/75610/digital-omnichannel-open-banking-platform-strategy-trends/ 57. Sajic M, Bundalo D, Bundalo Z, Pasalic D (2017) Digital technologies in transformation of classical retail bank into digital bank. In: 2017 25th telecommunication forum (Telfor), pp 542–545

A State of the Art Literature Review on Digital Transformation

29

58. Shcherbina TA (2018) Digital transformation of the banking sector. In: GCPMED 2018 international scientific conference global challenges and prospects of the modern economic development, vol 57, pp 987–989 59. Zamaslo O, Kovalenko V, Lozynska O (2021) Digital transformation level indicators of banks. Balt J Econ Stud 7(2):77–82 60. Kazarenkova N, Hova AO, Svetovtseva T, Aseev O (2019) Transformation of the Russian banking system in digital economy. In: Education excellence and innovation management through vision, pp 1456–1461 61. Krasonikolakis I, Tsarbopoulos M, Eng TY (2020) Are incumbent banks bygones in the face of digital transformation? J Gen Manag 46(1):60–69 62. Liu DY, Chen SW, Chou TC (2011) Resource fit in digital transformation: lessons learned from the CBC Bank global e-banking project. Manag Decis 49(9–10):1728–1742 63. Diener F, Spacek M (2021) Digital transformation in banking: a managerial perspective on barriers to change. Sustainability 13(4):2032 64. Zurdo RP, Torres YF, Fernandez MG (2018) Cooperative banking and digital transformation: towards a new relationship model with members and clients. Revesco-Revista De Estudios Cooperativos 129:161–182 65. Exponential Digital Solutions 10xDS (2020) A peek into digital trends for hospitality & tourism. https://10xds.com/blog/digital-trends-for-hospitality-tourism/ 66. Pumaleque AAP, Fernandez IR, Perez DDI, Bedrinana MAA (2021) Digital transformation model for the development of tourism companies. Empresa 3C:47–60 67. Busulwa R, Pickering M, Mao I (2022) Digital transformation and hospitality management competencies: toward an integrative framework. Int J Hosp Manag 102:103132 68. Alrawadieh Z, Alrawadieh Z, Cetin G (2021) Digital transformation and revenue management: evidence from the hotel industry. Tour Econ 27(2):328–345 69. Stavrianea A, Kamenidou I (2022) Complying with digital transformation in online booking through experiential values of generation Z. Eur J Tour Res 30:3003 70. Cuomo MT, Tortora D, Foroudi P, Giordano A, Festa G, Metallo G (2021) Digital transformation and tourist experience co-design: big social data for planning cultural tourism. Technol Forecast Soc Chang 162:120345 71. Fragniere E, Sahut JM, Hikkerova L, Schegg R, Schumacher M, Grezes S, Ramseyer R (2022) Blockchain technology in the tourism industry: new perspectives in Switzerland. J Innov Econ Manage 37:65–90 72. Pencarelli T (2020) The digital revolution in the travel and tourism industry. Inf Technol Tour 22(3):455–476 73. Voronova I, Shatrevich V, Freimane G (2018) The impact of digital transformation on development of Latvian insurance companies’ digitalization strategies and shift of perception values. In: Vision 2020: sustainable economic development and application of innovation management, pp 5058–5069 74. Eckert C, Neunsinger C, Osterrieder K (2022) Managing customer satisfaction: digital applications for insurance companies. Geneva papers on risk and insurance-issues and practice 75. Pillay CP, Njenga JK (2021) Opportunities for reducing expenses through digital innovation: the case of an insurance company. Afr J Inf Syst 13(1):99–112 76. Stronen F (2020) Digitalization and knowledge management in large Norwegian banks and insurance companies. In: Proceedings of the 21st European conference on knowledge management (ECKM 2020), pp 765–771 77. Fritzsche A, Bohnert A (2021) Implications of bundled offerings for business development and competitive strategy in digital insurance. The Geneva papers on risk and insurance - issues and practice 78. Padilla-Barreto AE, Guillen M, Bolance C (2017) Big-data analytics in insurance. Anales Del Instituto De Actuarios Espanoles 23:1–19 79. Obukhova AS, Kolmykova TS, Serebryakova NA, Goncharov AY, Astapenko EO (2022) Trends in digital transformation of the Russian insurance market. Imitation Market Model Digit Econ Game Theoret Approaches 368:46–53

30

E. Haktanır et al.

80. Takyar A (n.d.) Digital transformation in education industry. https://www.leewayhertz.com/ digital-transformation-in-education/ 81. Hashim MAM, Tlemsani I, Matthews R (2021) Higher education strategy in digital transformation. Educ Inf Technol 82. Tolnaiova SG (2020) Transformation of education and training system in the context of digital information and communication technology in sociocultural perspective and its axiological and ethical dimension. Eur J Trans Stud 8(2):89–105 83. Iivari N, Sharma S, Venta-Olkkonen L (2020) Digital transformation of everyday life - how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int J Inf Manage 55:102183 84. Vaganova OI, Kutepova LI, Smirnova ZV, Bulaeva MN, Bobylev EL (2021) Professional education in the digital transformation society. Eduweb-Revista De Tecnologia De Informacion Y Comunicacion En Educacion 15(3):215–224 85. Marcum D (2014) The digital transformation of information, education, and scholarship. Int J Humanit Arts Comput 8:1–11 86. Alenezi M (2021) Deep dive into digital transformation in higher education institution. Educ Sci 11(12):770 87. Garcia-Penalvo FJ (2021) Avoiding the dark side of digital transformation in teaching. An institutional reference framework for eLearning in higher education. Sustainability 13(4):2023 88. Valdes KN, Alpera SQY, Suarez LMC (2021) An institutional perspective for evaluating digital transformation in higher education: insights from the Chilean case. Sustainability 13(17):9850 89. Aditya BR, Ferdiana R, Kusumawardani SS (2021) Identifying and prioritizing barriers to digital transformation in higher education: a case study in Indonesia. Int J Innov Sci 90. Lis M (2021) Shaping relations between higher education institutions and the enterprise world in the age of digital transformation. Pol J Manage Stud 23(1):294–314 91. Ari R, Altinay Z, Altinay F, Dagli G, Ari E (2022) Sustainable management and policies: the roles of stakeholders in the practice of inclusive education in digital transformation. Electronics 11(4):585 92. Okunlaya RO, Abdullah NS, Alias RA (2022) Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education. Library Hi Tech 93. Sadovets O, Martynyuk O, Orlovska O, Lysak H, Korol S, Zembytska M (2022) Gamification in the informal learning space of higher education (in the context of the digital transformation of education). Postmodern Openings 13(1):330–350 94. Nazir S (2021) National digital transformation and smarter cities: eight forces that will shape the future. https://e.huawei.com/tr/eblog/industries/insights/2021/eight-forces-willshape-future 95. Salem F (2017) Building a smart city: overcoming the challenges of digital transformation the case of “Smart Dubai.” Actions Insights-Middle East North Africa 6:139–176 96. Anthony B, Petersen SA, Helfert M, Guo H (2021) Digital transformation with enterprise architecture for smarter cities: a qualitative research approach. Digit Policy Regul Gov 23(4):355–376 97. Masucci M, Pearsall H, Wiig A (2020) The smart city conundrum for social justice: youth perspectives on digital technologies and urban transformations. Ann Am Assoc Geogr 110(2):476–484 98. Orlowski C, Cofta P, Orlowski A (2022) The rule-based model of negentropy for increasing the energy efficiency of the city’s digital transformation processes into a smart city. Energies 15(4):1436 99. Bakogiannis E, Siti M, Kyriakidis C, Vassi A (2017) Using traditional and new digital technology tools to promote sustainable mobility: current trends in the evolving transformation of the smart city. In: Smart cities in the Mediterranean: coping with sustainability objectives in small and medium-sized cities and island communities, pp 113–133 100. Osman AMS, Elragal AA, Stahlbroest A (2022) Data-driven decisions in smart cities: a digital transformation case study. Appl Sci-Basel 12(3):1732

A State of the Art Literature Review on Digital Transformation

31

101. Hamalainen M (2020) A framework for a smart city design: digital transformation in the Helsinki smart city. In: Entrepreneurship and the community: a multidisciplinary perspective on creativity, social challenges, and business, pp 63–86 102. Kharlamov AA, Raskhodchikov AN, Pilgun M (2021) Smart city data sensing during covid19: public reaction to accelerating digital transformation. Sensors 21(12):3965 103. Haftor DM, Climent RC (2021) CO2 reduction through digital transformation in long-haul transportation: institutional entrepreneurship to unlock product-service system innovation. Ind Mark Manage 94:115–127 104. Zimnoch D (2021) Digital transformation of transportation in the age of Covid-19. Problemy Zarzadzania-Manage Iss 19(3):100–121 105. Drozdov NA, Kuzina EL, Vasilenko MA, Tagiltseva JA, Nadolinsky PV, Poltorak AV (2021) The safety of technological solutions in the digital transformation of the transportation complex. In: Proceedings of the 2021 IEEE international conference “quality management, transport and information security, information technologies”, T and QM and IS 2021, pp 15–19 106. Suvorova S, Naumova E, Scherbanyuk I, Nos V (2020) Digital transformation in management of container-on-flatcar transportation: evaluation of business effects. IOP Conf Ser Mater Sci Eng 918(1):012044 107. Nekrasov AG, Sinitsyna AS (2020) Digital transformation infrastructure and transportation logistics systems. IOP Conf Ser Mater Sci Eng 832(1):012052 108. Naumova E, Buniak V, Golubnichaya G, Volkova L, Vilken V (2020) Digital transformation in regional transportation and social infrastructure. E3S Web Conf 157:05002 109. Merkaš Z, Perkov D, Bonin V (2020) The significance of blockchain technology in digital transformation of logistics and transportation. Int J E-Serv Mobile Appl 12(1):1–20

Similarities and Differences Between Digital Transformation Maturity Models: A Literature Review Paulo Carrijo, Bráulio Alturas , and Isabel Pedrosa

Abstract This chapter has the main purpose of structuring and analyzing the available literature in the field of research on Maturity Models of Digital Transformation, both in the academic literature and in the publications of Consulting companies and Market studies. The research method was content analysis and it was possible to find 11 categories in which comparative analyzes were performed between DTMM The result of the comparison and analysis of the models helps professionals from different areas of activity, specialists, and academics to understand the similarities and differences between the models, and to assess the organization’s readiness and capacity to make significant changes, such as, strategy, business model, products and services, and technology. Keywords Digital transformation · Maturity model · Digitization · Industry 4.0 maturity models

1 Introduction In an increasingly digital scenario, in society and business, it is essential to think and rethink the use of digital technology to solve traditional and everyday problems. In this way, the Information Technology area must be structured to understand, in P. Carrijo · B. Alturas (B) Instituto Universitário de Lisboa (ISCTE-IUL) University Institute of Lisbon, ISTAR-Iscte (Information Sciences Technologies and Architecture Research Center), Lisbon, Portugal e-mail: [email protected] P. Carrijo e-mail: [email protected] I. Pedrosa Instituto Politécnico de Coimbra, Coimbra Business School, ISCAC, Coimbra, Portugal e-mail: [email protected] ISTAR-Iscte (Information Sciences Technologies and Architecture Research Center), Lisbon, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_2

33

34

P. Carrijo et al.

detail, the company’s value chain and, thus, to incorporate technological innovations, which will serve as foundations for the creation of competitive advantage. Information-based strategies are used to facilitate process changes to reduce costs, creating differentiation in the product or service, increase customer loyalty, and leveraging new business models to grow revenue growth over time [1]. With technological evolution and the increase in computing capacity, new disruptive technologies have gained more and more relevance. As an example, the IoT, the Internet of Things, can be mentioned, which promises to connect not only people but also objects of different natures to the world network, such as household appliances, vehicles, industrial machinery, and public lighting, among others. These changes are some examples of how Digital Transformation technologies and innovation have the potential to cause intense changes in the economy and society, including changes in procedures in different areas. Digital Transformation impacts and modifies the paradigm of technology use, in culture, government, environment, politics, economy, labor market, entrepreneurship, education, medicine, arts, religion, science, global communication, and international organizations [2]. Digital Transformation is a process that comprises several phases, leading the company to the ability to meet the demands of a digital world with excellence. This process, to be better understood, should be considered as a process of “maturity” [3]. There are many models developed to measure maturity, strategic alignment, continuous improvement, organizational agility and flexibility, enterprise architecture and knowledge management, and business development. These models are prescriptive in nature and were designed to assess the competence, capacity, and performance level of a selected domain based on a more or less broad set of criteria, that is, to assess the maturity of processes in organizations. There is an exponential increase in the number of models, but there are still many challenges, such as limited empirical studies on their validation and a limited extension of the actionable properties of these models in guiding their application. Due to the high complexity of the digital transformation process, companies have significantly increased the number of initiatives to improve their competitiveness in the digital world. Researchers and consulting companies have developed several Digital Transformation Maturity Models (DTMM), with the aim of helping companies diagnose their stage of maturity and guide the way they should move towards leveraging a higher level of digital maturity. As the nomenclature DTMM—Digital Transformation Maturity Models is not unanimous among consulting companies, nor in academia, in this work, Maturity Models, related to industry 4.0 [4] are also considered DTMMs. In the analysis of the current situation, academics and consulting companies identify three deficiencies: the first one is that the majority of DTMMs are not academic, and therefore, they lack methodological rigor. The second shortcoming is that most DTMMs have dimensions that have not been empirically tested, which means problems of methodological rigor and relevance of dimensions. Finally, most DTMM define a process of linear evolution on the way to the maturity of digital transformation, criticized by the authors for disregarding the specific characteristics of the industry and the organization [4].

Similarities and Differences Between Digital Transformation Maturity Models …

35

Many DTMMs were developed to assess the status of an organization’s digital transformation, and most of them were developed by companies in the field of specialized market research, with many similarities and many differences between them. Additionally, the DTMMs mention that managers and academics lack information to help them choose the DTMM that presents the best diagnosis to identify the organization’s level of maturity in the digital transformation process [3]. The main objective and contribution of this work is to Present a detailed and critical comparative analysis of Academic, Consulting Firms, and Market Research Firms DTMMs proposals in order to help entrepreneurs, managers, specialists, and academics understand the similarities and differences between DTMMs. This chapter is organized as follows: after Sect. 1 (Introduction), Sect. 2 presents the literature review, Sect. 3 presents the methodology, Sect. 4 presents the results and discussion, and finally Sect. 5 presents the conclusions of the study.

2 Literature Review In this section, the basic concepts of this chapter are presented. Initially, the framework is presented on the relevance of digital transformation and the digital economy. Next, the meaning of the maturity model, its origin, characteristics, and its applicability to different segments of the organization are exposed. Finally, the MMTDs developed by academia, consulting firms, and companies specializing in market research are presented.

2.1 The Digital Transformation and the Digital Economy Digital transformation (DT) is the result of digitisation and digitalisation of economies and societies. DT is an ongoing process. The introduction of digital technologies creates both new opportunities and new challenges. Consider the challenges posed by a process, digital transformation, which is a complex phenomenon of different development. These challenges are related to the following issues: ● which areas are most affected by the digital transformation. ● how the digital transformation affects the labor market, the training of future professionals, and social life in general. ● what are the ways to implement digital transformation for different industries. ● what steps need to be taken for the digital transformation of companies, production, ecosystem, and a particular industry as a whole. ● what changes in educational systems need to be made to adapt people and accelerate their inclusion in the processes of digital transformation. One of the key issues for the implementation of digital transformation is changes in the way of thinking and requirements for the competencies of workers in the industry. First of all, it is connected with people’s understanding of digital transformation processes and with their ability to use digital technologies effectively [5].

36

P. Carrijo et al.

Digital Transformation, which received an enormous contribution through the third industrial revolution, especially in terms of communications, the Internet, and ICT (Information and Communication Technologies), still presents some uncertainty in terms of its different phases, comprises two well-known concepts: transformation and digital [6]. By “transformation” it can be understood as a general process that starts from an initial situation and moves towards an altered situation, supposedly better. However, some authors recognize that the choice of the term “transformation” was not the best, due to the fact that the alterations and transformations underlying the concept usually take place continuously, without an end in mind. ‘Digital’ suggests that many of the changes in society, business, and industry will be driven by information technologies that allow real-time data processing and intelligently derive information to provide stakeholders with improved knowledge of your processes and products [6]. Industry 4.0 is still an emerging topic in literature and industrial applications. Thus, the number of scientific publications is still low compared to other mature topics in engineering. The concepts and technologies addressed are of great relevance for manufacturing industries and, in the medium and long-term, can significantly change the competition between companies and entire value chains. It is therefore imperative that companies be prepared for the great changes in business environments and have practical and robust tools for assessing maturity in the implementation of these concepts and technologies. The literature review shows that the implementation of Industry 4.0 in manufacturing companies requires a holistic view, not only focused on hardware and software improvements in the production environment, but also including a new strategic orientation, the development of new workforce competencies, the adaptation of business models, the development of new products and services with new functionalities and the implementation of enabling technologies [7]. The digital transition of the economy is transversal to all societies, irreversible, and is undergoing an accelerated implementation process, that is, it is global. Digitization is present in everyone’s daily lives, technology evolves at a fast pace and the impact it has on the economy and on the real-life of society are significant. The digital transformation of companies cannot be dissociated from investing in the training and empowerment of people with digital skills, in competitive infrastructure, in innovation and generation of a favorable ecosystem, or in the digitalization of public administration. It is necessary to work on the strategy and positioning towards the digital economy, in order to help prepare companies for the current technological disruptions and work with the public and private ecosystem to generate a favorable environment in the digital economy to take advantage of opportunities for modernization and competitiveness. Digital transformation and the digital economies are a reality and a necessity for a large number of companies, and there is still a lot of work to do for companies to benefit from this process.

Similarities and Differences Between Digital Transformation Maturity Models …

37

Companies’ digitization is essential to ensure their competitiveness in a globalized economy based on information and knowledge. As the Digital Transformation subject is a contemporary topic, many articles were identified and evaluated in the literature review, but only two articles carried out a systematic literature review on Digital Transformation: ● “The Shape of Digital Transformation: A systematic review of the literature” [8]. ● “Conceptualizing Digital Transformation in Business Organizations: A Systematic Review of Literature” [8]. A systematic review of the literature demonstrated the growth of digital transformation publications in the last five years [9]. The articles identified an evolution of the concept of digital transformation, previously defined as the development, adoption, and use of digital technologies, also treated as technological innovation, to a broader concept of the adoption of digital technologies, the transformation of processes and the business model so that the company can compete effectively in a digital world” [10]. In this evolution, adopting digital technology is no longer the final objective of the transformation process, becoming just one of the factors necessary for the transformation of the business model, which will allow the company to survive in a more digital business environment. The articles also found the evolution of the concept of digital transformation, of an implementation of digital technologies or the development of digital capabilities, for the business model or a remodeling of the existing business model, considering digital capabilities [11, 12].

2.2 Maturity Models (MM) Conceptually, a maturity model can be defined as a conceptual structure composed of parts and states that define the maturity or level of development of a particular study area of interest. Maturity models help identify and describe the processes that an organization must work on and develop to achieve the desired future scenario. The maturity models reflect aspects of reality to classify the capabilities of certain domains of interest that can be used for internal analysis, market analysis, competitor analysis, and comparisons with domain references (benchmark). These models generally include dimensions and levels [13]. Maturity models are of fundamental importance for understanding what needs to be improved in an organization, as they assist in the assessment of factors such as organizational culture, strategy, technological management, often ways of prioritizing improvement measures and monitoring progress. Thus, it is believed that the adoption of disruptive technologies in agribusiness, at the heart of the adoption of agribusiness 4.0, can be facilitated through the development of specific maturity models for this context [14]. The authors Proença and Borbinha [15] carried out a bibliographic survey, demonstrating, firstly, the evolution of the concept of “Maturity”, from 1993 to 2009, and

38

P. Carrijo et al.

found in their study that many authors, using the concepts of quality, continuous improvement and benchmarking, claim that the MM is an instrument used to assess qualitative and quantitative capabilities, through a series of sequential levels, which together form an anticipated or desired logical path from an initial state to a final state of maturity. In this way, the MM allows companies to compare their maturity levels with their competitors and with those that present the best market practices [16]. When consolidating the characteristics of several maturity models, [15] categorized the Maturity Models by a number of maturity levels (Level 1—Initial; Level 2—Managed; Level 3—Defined; Level 4—Quantitatively managed; Level 5—In optimization); discrete or continuous nature; quantitative or qualitative results; if they present a vision of continuous improvement; by applicability; model dispersion level; ease of use; simplicity of interpretation and consistency in terms of continuity between versions of the model [17]. State that Maturity Models have five characteristics: 1. Object of evaluation: refers to the objects to be evaluated in terms of maturity, such as technology, systems, people, project management. 2. Dimensions: refers to the specific capability areas that describe different aspects of the maturity of the assessed object such as “digital impact” and “digital readiness”. Dimensions must be exhaustive and straightforward. 3. Levels: refer to the maturity state of the evaluated object. 4. Maturity principles: refers to the type of continuous Model and Maturity, where the classification is measured by the average of individual levels of different dimensions, and to the type of “staged” Maturity Model, when all elements of the level must be carried out in order to move to the higher level. 5. Assessment: refers to the method used to assess maturity, which can be qualitative (interviews) or quantitative (questionnaires with Likert scales). In general, the MMs are considered an excellent tool for evaluating business strategy, models, and processes. Its organization and systemic structure allow executives, specialists, and academics to evaluate different aspects of the organization, defining, compared to the best market practices, the strengths, the level of maturity in relation to benchmarking, and competitors, enabling the development of action guides to reach the highest levels of market maturity [15].

2.3 Digital Transformation Maturity Models (DTMM) For many organizations, digital transformation is a strategic priority to renew their business and remain competitive. However, managers find it difficult to define and implement digital agendas because they are uncertain about the process, topics, and configuration. In order to provide an overview of the most important topics to the management, a literature review identified eighteen validated digital maturity models

Similarities and Differences Between Digital Transformation Maturity Models …

39

and frameworks that describe various dimensions or fields of action to consider for a digital transformation strategy [18]. According to the author [19], from VTT—Technical Research Center of Finland Ltd, MMTD helps to understand and structure the concept of digitization. In addition, it provides an estimate of the current capabilities and maturity of the organization and general directions towards the desired maturity level [19]. This process affects the IT, strategy, business model, products and services, internal and external processes, the organization, and the culture of a company [19]. Several authors corroborate this definition [3, 4, 10, 20–25]. During the implementation of a digital transformation strategy, managers must understand the concept of Digital Transformation and indicate possible areas of action [20]. DTMM must consider the differences of industries and their stages of development, so that its diagnosis and the classification of the maturity level are related to the organization’s reality [20]. Digital transformation has different effects in different industries, and those that are customer-oriented and B2C (business-toconsumer) are impacted by digitalization faster and much greater than those with B2B (business-to-business). DTMMs can be classified as [26]: a) descriptive: the current maturity level and objective of the company. b) prescriptive: guidelines for the company to reach the desired level of maturity. c) comparatives: comparison of the current level with the market benchmarking and the maturity levels of competitors. The DTMM cannot be generic, as the model is designed to guide managers through the analysis of company- and industry-specific dimensions and categories of digitization. In addition, it helps to define Digital Transformation guides, comparing benchmarking with other organizations and assessing DT level of maturity. Only specialization can make this process viable [19]. A descriptive-qualitative-comparative DTMM is, for the most part, composed of a database fed by a questionnaire in the format of a five-level “Likert” scale. Its dimensions and maturity levels are validated with specialists, executives, and managers [19]. In order to be able to offer a comparison of maturity levels with organizations of the same sectors, size and location, there must be a process of constant updating of the database, through a digital solution by the internet and a final report with the diagnosis of the company’s Digital Transformation [19]. When the model is owned by a consulting company or Market Research Company, road mapping, business modeling, training, benchmarking, process modeling, and definition of digitization prerequisites are offered, with the aim of helping them on the path of digitization [19]. In a quantitative descriptive model, its categories, dimensions and maturity levels are deliberated with the definition of categories, dimensions defined and validated by specialists, managers, and academics, and their maturity levels, calculated by statistical methods [20, 27]. The DTMM research, covering the publication period from 2010 to 2019, which will be the subject of Sect. 3, resulted in 8 academic publications, and 10 from Consulting and Market Research companies.

40

P. Carrijo et al.

3 Methodology The methodology used for the selection, categorization, comparison, and analysis of DTMMs is the content analysis methodology. Content analysis is a set of communication analysis techniques [28]. It can be a research technique that, through an objective, systematic and quantitative description of the manifest content of the communications, aims to interpret these same communications [29]. It encompasses initiatives to make explicit, systematize and express the content of messages, with the aim of making logical and justified deductions regarding the origin of messages [28]. It is configured as a set of communication analysis techniques that use systematic and objective procedures to describe the content of messages [28]. It consists of a few steps to carry out the content analysis, organized into three phases: Phase 1—Pre-analysis It is considered the phase of organization of the material to be analyzed in which, based on the defined objectives, the documents are read and selected. Finally, the categories that will base the comparison of the DTMMs and the final interpretation are defined, according to the following five steps: ● Step 1.1 Floating reading It refers to the survey and reading of articles referring to Digital Transformation, the Maturity Model, and DTMM. ● Step 1.2 Formulation of objectives: The objective defined for the content analysis of this work is to compare the selected DTMMs, identifying similarities and differences. ● Step 1.3 Choosing documents: At this stage, the criteria for selecting the documents to be analyzed are defined. ● Step 1.4 Definition of categories for comparison and analysis: In the last stage of phase 1, the categories to be used for comparison and analysis of the DTMMs are defined. Phase 2—Exploration of the material At this stage, academic DTMMs, from Consulting Firms and from Market Research Firms are categorized and compared through the frequencies of each category. ● Step 2.1 Categorization According to [29], categorization is the process by which raw data are systematically transformed and aggregated into units, which allows an accurate description of the relevant characteristics of the content. ● Step 2.2 Comparison of articles

Similarities and Differences Between Digital Transformation Maturity Models …

41

To compare DTMMs, quantitative analyzes will be used, using the frequency of the categories. Phase 3—Treatment of results, inference, and interpretation. The last phase consists of processing the results, inferring, and interpretation. At this stage, the condensation and highlighting of information for analysis occurs, culminating in inferential interpretations, it is the moment of intuition, reflection, and critical analysis [28]. And for the selection of DTMM, the following criteria and rules proposed by [29] were used: ● Exhaustiveness rule The research field must be justified, in the case of Digital Transformation, Maturity Model, and DTMM, and the database to be searched (Web of Science and Science Direct). ● Representativeness rule The selected documents must be a representative part, in this case, of the digital transformation universe, Maturity Model, and DTMM. ● Rule of Homogeneity The selected documents must be homogeneous, that is, they must comply with precise selection criteria. ● Relevance rule: The selected documents must be suitable, as a source of information, to support the concepts of the digital transformation and maturity model and for comparison with other DTMMs. The selection of documents must meet the rules of exhaustiveness, representativeness, homogeneity, and relevance [28]. Of the total of 10 DTMMs from consulting firms and market research firms selected by the authors [4]. Six DTMMs were discarded due to the lack of information necessary for their categorization and/or the impossibility of access. With the same criterion, academic articles were selected, out of a total of 8 DTMM, 1 DTMM was discarded, according to Tables 1 and 2. In the systematic review of the literature, several studies were identified in which a definition of the categories on which comparative analyzes between DTMM were performed [3, 4]. These authors proposed 17 categories for the comparative analysis between DTMM. These 17 categories were consolidated into 7 categories due to the similarity of the content between them and added to 4 categories defined according to the main factors in the implementation of the digital transformation process according to [19]. Total of 11 categories presented in Table 3.

42

P. Carrijo et al.

Table 1 Digital transformation maturity models of consulting firms and market research firms DTMM

N.º

Name

Reference

Selected “Consulting Companies”

1

Deloitte

Achieving Digital Maturity: MIT Sloan Management Review and Deloitte University Press (Kane et al. 2017)

2

PWC

Digital Business: Towards a Value-Centered Maturity Model. PwC Chair in Digital Economy at the Queensland University of Technology. Austrália (Shahiduzzaman et al. 2017a, 2017b)

3

Forrester

The Digital Maturity Model 4.0 Benchmarks: Digital Business Transformation Playbook, 2016 Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA 02,140 USA (Gill & VanBoskirk 2016)

4

VTT

VTT Report “Towards a new era in manufacturing” Final report of VTT’s For Industry spearhead programme (Paasi 2017)

5

Acatech

Industrie 4.0 maturity index. Managing the Digital Transformation of Companies (Acatech STUDY), Munich: Herbert Utz Verlag (Schuh et al. 2017)

6

Accenture

Accenture. European Financial Services Digital Readiness Report 2016 (Knickrehm et al. 2016)

7

Arthur D Little

“Digital Transformation: How to Become Digital Leader” Arthur D. Little Digital Transformation Study (Opitz et al. 2015)

8

IBM

Digital Transformation: Opportunities to create new business models, Strategy & Leadership (Berman & Bell 2011)

9

Microsoft

The Digital Transformation Report—Microsoft Company and Qvartz. Denmark (Moller & Galskov 2016)

Selected “Market Research Companies”

Not Selected “Consulting Companies and Market Research Companies”

(continued)

Similarities and Differences Between Digital Transformation Maturity Models …

43

Table 1 (continued) DTMM

N.º

Name

Reference

10

VDMA

Industrie 4.0 Readness—VDMA’s IMPULS-Stiftung. Aachen. Cologne (Lichtblau et al. 2015)

Table 2 Academic digital transformation maturity DTMM

N. º

Name

Reference

Selected

1

(Berghaus et al. 2015)

Stages in Digital Business Transformation: Results of an Empirical Maturity Study. MCIS-Mediterranean Conference on Information Systems. Proceedings. 22. Suiça

2

(De Carolis et al. 2017)

A maturity model for assessing the Digital Readyness of manufacturing companies. IFIP Advances in Information and Communication Technology. IFIP WG 5.7 International Conference, APMS

3

(Klötzer and Pflaum 2017)

Toward the development of a Maturity Model for Digitalization within the supply chain of the manufacturing industry. Proceedings of the 50th Hawaii International Conference on System Sciences

4

(Remane et al. 2017)

Digital Maturity in Traditional Industries: An Exploratory Analysis. 25th European Conference on Information Systems. (In Proceedings)

5

(Schumacher et al. 2016)

A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises

6

(Tonelli et al. 2016)

A Novel Methodology for Manufacturing Firms Value Modeling and Mapping to Improve Operational Performance in the Industry 4.0 era 49th CIRP Conference on Manufacturing Systems (CIRPCMS)

7

(Valdez-de-Leon 2016)

A Digital Maturity Model for Telecommunications Service Providers Technology Innovation Management Review (Volume 6, Issue 8)

8

(Lichtblau et al. 2015)

Industrie 4.0 Readiness. Impuls-Stiftung für den Maschinenbau, den Anlagenbau und die Informationstechnik, Online Publication

No selected

44

P. Carrijo et al.

Table 3 DTMM categories Content

Categories 1

Objective

Purpose of application of the DTMM

2

Dimensions

Dimensions: refer to the specific capability areas that describe different aspects of the maturity of the assessed object, for example, “digital impact” and “digital readiness”. Dimensions must be exhaustive and straightforward Number and focus of dimensions: refers to the number of dimensions of analysis and the areas of focus capabilities of the analysis

3

Maturity Levels

Clusters: refer to the maturity levels proposed by the DTMM The digital maturity assessment refers to the maturity levels of the model Evolution: refers to the process of linear or non-linear evolution between maturity levels Axes of analysis or direction: refer to the number of alternatives or directions that the company has to go through the maturity levels, which can be unidirectional or with more directions Determining the maturity level refers to the method for defining the maturity level, which can be qualitative (by interview), quantitative (questionnaire with a Likert scale, complex statistical techniques), or mixed

4

Industry Type

Industry Type: refers to the type of industry in which the DTMM can be applied Target Audience: Refers to the target audience that will gain value or benefits from your organization’s maturity analysis

5

Preparation and maintenance of the DTMM database

Result visualization: refers to the presentation models of dimensions and maturity levels, for example: graphs, tables, numerical scores, etc Assessment and data collection: refer to the model for capturing data through self-assessment, the use of an online or printed questionnaire and indicators of best practices Benchmarking and Gap Analysis: Refers to using industry-level benchmarks and presenting the difference analysis with reference to the benchmark (continued)

Similarities and Differences Between Digital Transformation Maturity Models …

45

Table 3 (continued) Content

Categories 1

Objective

Purpose of application of the DTMM Approach: refers to the method used to define maturity levels, which can be argumentative or empirical

6

Descriptive, prescriptive, and comparative

Type of MM: refers to descriptive models whose characteristic is to present only the organization’s digital maturity level; also, the prescriptive models, which, in addition to presenting the level of maturity, offer a roadmap of actions to advance between the different levels of maturity, for each of the dimensions analyzed

7

Methodological Rigor and Adaptive Potential

Methodological rigor: refers to the use of methodology to support the DTMM, allowing the use of its results for future research or generalizing its conclusions Adaptive potential: refers to the ability of the DTMM to adapt to the characteristics of the company and industry, and to update indicators with best practices

8

Technology versus Organizational Transformation

Technology and Organizational Transformation refers to the comparison of the importance level of the model for actions focused on technology and/or organizational transformation with the objective of DT process effectiveness

9

Organizational Areas and processes

The main organizational areas and processes refer to the comparison of the organizational areas and processes proposed by the authors to be addressed by the DTMM

10

People Preparation

People preparation: refers to the comparison of the main development factors of human resources proposed for the effectiveness of the DT process

11

Business Model and Organizational Strategy

The Business Model and Organizational Strategy refer to comparing the level of importance assigned to changing the organizational strategy and/or the business model

4 Results and Discussion The analysis of the results was obtained through the comparisons of Academic DTMMs, Consulting companies and Market Research companies based on the seven

46

P. Carrijo et al.

categories proposed by [3]. In addition, four more categories refer to the main critical factors for the implementation of the Digital Transformation process [19]. The analyzes were structured to compare the characteristics of the 11 selected DTMMs with 11 categories [30]. The use of 11 categories, in addition to meeting the categories of [3], aims to enable a comprehensive and detailed comparative analysis of DTMMs, from the perspective of the researcher, who wants to study and develop a DTMM, and from the perspective of the entrepreneur, who wants to use it as a tool to support him in defining a guide. and implement Digital Transformation. The first category refers to the objectives of the DTMMs. When comparing the objectives of Academic DTMMs, Consulting companies and Market Research companies, it appears that three are common to all DTMMs, namely: 1. Identify the organization’s level of digital maturity; 2. Compare your results with those presented by the researched market; 3. Present analysis and information that helps the entrepreneur to define his Digital Transformation guide. The second category refers to the dimensions of the DTMMs. Based on the comparison between Academic DTMMs, Consulting Firms, and Market Research Companies, it can be verified that of the 8 DTMMs of greater complexity, 5 DTMMs use axis dimensions; that is, they consider that the dimensions referring to organizational management are impacted by digital transformation and should be considered to assess the level of maturity in the DTMM. It is verified, when comparing the dimensions of DTMM of higher complexity with those of lower complexity, that the dimension “Organizational Strategy” is more referenced in models with higher complexity. The third category involves maturity levels. It can be concluded that organizational factors are preponderant, if compared to technological ones, in the evaluation of the organizations’ maturity levels. Furthermore, all DTMM authors point out technology, management, and human factors, business model, and strategy as one of the five most relevant factors for the digital transformation process. The Academic DTMMs adopt the highest amounts of maturity levels, if compared with the DTMMs of Consulting companies and Market Research companies. Another point of analysis about DTMMs is about the type of evolution of maturity levels, which can be linear and one-dimensional or nonlinear and multidimensional. In the fourth category, referring to the type of industry to be served by the DTMM, three different approaches are identified. In the first, the author specifies the type of industry to be served by the model; in the second, he develops a generic model to meet any type of organization, and finally, a generic model to meet the objectives of Industry 4.0. The results demonstrate that two DTMMs are generic, nine are specialists and, of these nine DTMMs, eight are targeted at Industry 4.0. Of the two generic DTMMs, one is academic and the other is from a consulting firm. The fifth category refers to the formation and maintenance of the DTMM database. Regarding data entry, ten DTMMs use online questionnaires in a “Likert” scale format. When analyzing the update and maintenance of the model and the database, only five DTMM update the database and review the dimensions and maturity levels. In the analysis of the formation of the database to define dimensions and maturity levels, it appears that all Academic DTMMs, from Consulting companies and Market

Similarities and Differences Between Digital Transformation Maturity Models …

47

Research companies, had their surveys, interviews, and case studies carried out in European and US. The sixth category refers to the classification of DTMMs into descriptive, prescriptive, and comparative. As for the descriptive models, only two descriptive DTMMs do not have a database that allows comparing the results with competitors and benchmarking. All other descriptive and comparative models have a historical database that allows you to compare the maturity level with benchmarking and competitors presented online. It appears that only two DTMMs have their models defined as prescriptive and comparative by their authors. Despite being prescriptive, none of them presents a Digital Transformation Guide as a result. The seventh category refers to the methodological rigor and adaptive potential of DTMMs. As a result of the methodological rigor analysis, all qualitative DTMMs lack information on the criteria for defining organizational dimensions and maturity levels. As for the adaptability of the DTMMs regarding the database and new versions, three DTMMs do not present information that indicates the process of updating the data in the database and the model itself. Regarding the adaptability of DTMMs for applications in other industries, it is verified that all models categorized as generic by their authors meet this requirement. The eighth category refers to the comparison of technological and organizational factors. It appears that 100% of the authors report greater relevance of organizational factors compared to technological factors in the digital transformation. In the comparative analysis of academic DTMMs, Consulting companies, and market study companies, regarding dimensions, not only the presence of organizational factors are observed, but also the greater weight attributed to these factors in the composition of DTMMs regarding technology. And it turns out that all DTMMs have several dimensions with organizational factors much larger than the technological ones. As for the ninth category, organizational areas are one of the factors for comparing the DTMMs that allow us to understand the authors’ view regarding the extent of the impact of digital transformation within the organization. It appears that all authors refer to organizational areas as a basic factor in calculating and evaluating the level of digital maturity of the organization involved in a process of digital transformation. Five DTMMs define that all areas must be analyzed generically, without their digital maturity levels being calculated individually. Two academic DTMMs define that all organizational areas must be analyzed individually and their digital maturity levels calculated. Due to the different languages and criteria used by the authors of the DTMMs, they can treat organizational areas as processes. Regarding the tenth category, to analyze the preparation of human resources for the Digital Transformation process, the dimensions ‘Digital Talent’, ‘Leadership’ and “Culture” should be considered. It appears that the DTMMs of consulting companies and market research companies point to the factors ‘Digital Talent’, ‘Leadership’, ‘Culture’ with less frequency in organizational dimensions and maturity levels than academic DTMMs. The last category refers to the comparison of DTMMs by the factors “Business model” and “Organizational strategy”. Based on the data presented, it can be inferred that the authors of the Academic DTMMs, from Consulting companies and Market

48

P. Carrijo et al.

Research companies studied, consider the two dimensions, “Business Model” and “Organizational Strategy”, as the relevant factors for assess and calculate the level of digital maturity of an organization. In addition, planning and adapting these factors to the reality of the digital world will help entrepreneurs, managers, and experts to define a more effective Digital Transformation guide, which can help the company to leverage higher levels of digital maturity. Still, taking advantage of the benefits of a more digital world to achieve your goals. The strategy dimension represents the extent to which the organization has developed and implemented a strategic plan to achieve its goals and objectives [31]. The strategy dimension includes three indicators: strategic focus, strategic alignment, and strategic adaptability [32]. This dimension is built on the premise that the digital strategy and the organizational strategy should be aligned and adaptable to support the achievement of measurable goals and outcomes related to quality and safety [33–35].

5 Conclusions The present study had as its starting objective ‘To present a detailed and critical comparative analysis of academic DTMM, consulting companies and Market Research companies to help entrepreneurs, managers, specialists and academics understand the similarities and differences between them’. This objective was fully achieved with the presentation of the comparative and critical analysis of the DTMMs, as the results of the analysis should help entrepreneurs, digital transformation specialists, and academics to: – Understand and structure the concept of digitization. – Understand the impact and assess the organization’s readiness and ability to change strategy, business model, technology, products and services, internal and external processes, organizational structure, and company culture. – Guide managers in the analysis of dimensions and categories of digitization specific to the company and industry to which it belongs. – Provide general instructions with a guide to the digital transformation towards the desired maturity level. – Analyze the similarities and differences of the Digital Transformation Maturity Models studied and presented in this work, help companies from different sectors identify at what level of digital maturity the business is, and will help in the evaluation of the technologies used, to observe the organizational culture, thinking about the market vision, and being attentive to the customer experience. The great value of models based on levels or stages of maturity lies in their ability to provide organizations with guidelines to consistently develop their processes, which implies that these will be documented, measured, controlled, and continually improved over the course of time.

Similarities and Differences Between Digital Transformation Maturity Models …

49

The results presented must be analyzed with restrictions, recognizing their limitations. The first involves the reduced amount of information from DTMMs from consulting firms and market research firms. As scientific rigor is not required for these documents, much information is not presented, which limits the analysis of comparison categories. The main impact of the lack of this information is at the level of comparative analysis of the business models and the proposed digital strategies. Another limitation refers to the categorization of the DTMMs: categorizations and consolidations were made in the axis dimensions, organizational dimensions and the factors of maturity levels. The analyzed texts are written in English, originating in different countries, types of industries and markets. Categorizations were made by interpreting the text of the DTMMs by the author of this work. As the language of Digital Transformation and the DTMM is not standardized, different interpretations can be given to the analyzed texts, which can imply interpretation bias. The massification of Internet access and the use of smartphones has been causing one of the biggest and fastest transformations in human history. Never has human society undergone so many transformations in such a short space of time, as in the period since the turn of the millennium. The way people work, communicate, inform, and entertain has changed radically in recent years, largely due to the entry of digital into the various components of our lives. And not to mention the countless changes, possibilities, and impacts resulting from Covid-19. In this context, Digital Transformation is one of the main topics on the agenda of companies, in the most diverse sectors and geographies. In the relationship with consumers, companies are using digital technology to better understand their customers and thus provide products and services that are more personalized and customized to the needs and preferences of each. Digital is also being used to expand the channels available to consumers, such as e-commerce (Electronic Commerce) and e-care (automated health care) solutions, which allow customers to purchase products and services, as well as manage their relationship with companies, anytime and from anywhere. Digital technology is currently also the main driver of innovation and transformation of the way companies operate. The digitization and automation of processes guarantee strong gains in efficiency, allowing employees to dedicate themselves to functions with greater added value. The dematerialization of documents is an example of how digital allows cost reduction with strong environmental benefits. In addition, the emergence of digital tools allows for greater agility in the way people work. In many companies, there are no more fixed jobs, with employees having the freedom to sit next to colleagues with whom they are collaborating on a given project, and there are more and more mobility solutions where work from home is allowed for several days. per week. Companies also play a key role in ensuring that the benefits of digital technology reach the greatest number of people and have a positive impact across society. Another conclusion of this chapter, after the research and studies carried out, is the need for companies to create a culture for Digital Transformation. Taking into account the conceptual model, there is an urgent need to better understand that a

50

P. Carrijo et al.

digital culture is a consequence of a new organizational structure, faced with risks, investments, and new digital business models. In this sense, the search for digital transformation has been an alternative to overcome the lack of understanding on the subject and the future risks for the business. Therefore, it is essential to have a renewed business agenda with a broader perspective, which combines new processes, management, effective technological conditions, and decision levels committed to the new business environment. The success of digital transformations depends on several variables, and the speed of digital transformation can vary depending on the company and according to how close a sector is to its inflection point. The need to invest in resources to capture value from existing business models or to develop new ones increases with the transformation of the sector. It is worth mentioning that companies that want to be successful in their Digital Transformation and want to reach higher levels of digital maturity in the global context need to be attentive to specific actions that are directly related to the dimensions investigated in this work, and to the dimensions suggested in the item “Proposal for future work” of this work. Strategy: Important and essential to execute digital initiatives inside or outside the company: deciding where to execute the initiatives according to the strategy, leveraging existing strengths in units within the company (e.g., channels and customer base) or launching initiatives “from the zero”, which have fewer synergies, separately. Skills: It is recommended to develop new skills in the company: choose a digitalization model that allows digital skills to mature, establish policies, tools, and new ways of working with the goal of achieving an organization in which digital and the rest of the business are indistinguishable. Organization: In organizations, ensuring accountability for the transformation: defining the structure and governance to follow up on the transformation (e.g., a centralized “transformation office”, governance forums), minimizing management fragmentation and aiming for greater accountability on the part of the organization’s top management. Search, hire, and have digitally savvy leaders in the organization directly involved in the transformation, supporting the mindset of challenging the status quo and driving change. Broadly, build digital competencies in the organization, preparing the employees for the future, in addition to digitizing tools and work processes of daily use and expanding decision-making based on data and analytics. Culture: Work and promote new ways of working that foster greater autonomy, continuous learning, and open work environments. Often communicate the purpose of the transformation, using both digital and traditional means. In short, the success of a company’s Digital Transformation will be decisive for its permanence in the current and future market. The detailed and critical comparative analysis of Academic MMTDs, Consultancies, and Market Research Companies resulted in the clarification of the current situation of the development of MMTDs. The analysis and comparison of the objectives, target audience, dimensions of the axes, organizational dimensions, and maturity levels of the MMTD, contributed to those interested in the topic:

Similarities and Differences Between Digital Transformation Maturity Models …

51

● Identify the degree of digital maturity. ● Assessing whether the organization is taking advantage of technologies to improve different productive, organizational, and work aspects, among others. ● Understanding the challenges of digitization. It is believed that this work has great potential for several future works, as it deals precisely with a relevant and extremely current theme. Thus, one possibility and suggestion for further research are to carry out surveys and studies of other dimensions for a more consistent Digital Transformation, such as: i) ii) iii) iv)

Digital Technologies and Processes. Analytical and Predictive Capacity (Predictive Analytics); Relationship with Customers. Network relationships (Suppliers, Startups, Governments, Universities, Investment Funds, among others); v) Risks and investments.

References 1. Soares TC (2013) Estrutura e Processos Organizacionais [Organi-zational Structure and Processes]. UnisulVirtual, Palhoça 2. Baptista GL, Figueiredo JS (2017) Impacto da transformação digital nas organizações: um estudo sobre diferentes aborda-gens de condução do processo de transformação [Impact of digital transformation on organizations: a study on different approaches to driving the transformation process]. Anais do SeTII, November, pp 118–125 3. Chanias S, Hess T (2016) How digital are we? Maturity models for the assessment of a company’s status in the digital transformation. Manage Rep 2(16):1–14 4. Remane G, Hanelt A, Wiesboeck F, Kolbe L (2017) Digital maturity in traditional industries an exploratory analysis. In: 25th European conference on information systems (ECIS), June, pp 1–16 5. Morze N, Strutynska O (2021) Digital transformation in society: key aspects for model development. Icon-MaSted 2021 J Phys Conf Ser 1946:1–13 6. Gray J, Rumpe B (2017) Models for the digital transformation. Softw Syst Model 16(2):307– 308 7. Santos R, Martinho J (2020) An Industry 4.0 maturity model pre-proposal. J Manuf Technol Manage 31(5):1023–1043 8. Morakanyane R, Grace A, O’Reilly P (2017) Conceptualizing digital transformation in business organizations: a systematic review of literature. In: 30th BLED conference: digital transformation - from connecting things to transforming our lives, December, pp 427–444 9. Mahraz M, Benabbou L, Berrado A (2019) A systematic literature review of digital transformation. In: Proceedings of the international conference on industrial engineering and operations management, October, pp 917–931 10. Kane G, Palmer D, Phillips AN, Kiron D, Buckley N (2017) Achieving digital maturity: adapting your company to a changing world. MIT Sloan Manage Rev 59180 (2017) 11. Sandberg J, Mathiassen L, Napier N (2014) Digital options theory for IT capability investment. J Assoc Inf Syst 15(7):422–453 12. Rothmann W, Koch J (2014) Creativity in strategic lock-ins: the newspaper industry and the digital revolution. Technol Forecast Soc Change 83(1):66–83

52

P. Carrijo et al.

13. Donovan PO, Sullivan DTJO, Bruton K (2016) IAMM: a maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities. Int J Progn Health Manage 7(32):1–11 14. Mendes J, Careta C, Gerolamo M (2021) In search of maturity models in agritechs. AGRITECH-V-2021 IOP Conf Ser Earth Environ Sci 839:1–10 15. Proença D, Borbinha J (2016) Maturity models for information systems - a state of the art. Procedia Comput Sci 100:1042–1049 16. Kohlegger M, Maier R, Thalmann S (2009) Understanding maturity models results of a structured content analysis. In: Proceedings of I-KNOW 2009 and I-SEMANTICS 2009, Graz, Austria, 2–4 September, pp 51–61 17. Raber D, Winter R, Wortmann F (2012) Using quantitative analyses to construct a capability maturity model for Business Intelligence. In: Proceedings of the annual Hawaii international conference on system sciences, pp 4219–4228 18. Bumann J, Peter M (2019) Action fields of digital transformation - a review and comparative analysis of digital transformation maturity models and frameworks. Digital-isierung Und Andere Innovationsformen Im Management, March, 40 19. Paasi J (2017) Towards a new era in manufacturing - final report of VTT’s for industry spearhead programme. VTT technology, 288 20. Berghaus S, Back A, Kaltenrieder B (2015) Digital transfor-mation report 2015. Bestandteil Der Swiss Digital Transformation Initiative 21. Shahiduzzaman M, Kowalkiewicz M, Barett R, McNaughton M (2017) Digital business towards a value-centric maturity model - part A. PWC report chair in digital economy, pp 1–26 22. Schumacher A, Erol S, Sihn W (2016) A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52:161–166 23. Carolis A, Macchi M, Negri E, Terzi S (2017) A maturity model for assessing the digital readiness of manufacturing companies. In: IFIP advances in information and communication technology, vol 513, pp 13–20 24. Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 WG, April 25. Wendler R (2012) The maturity of maturity model research: a systematic mapping study. Inf Softw Technol 54(12):1317–1339 26. Becker J, Knackstedt R, Pöppelbuß J (2009) Developing maturity models for IT management - a procedure model and its application. Bus Inf Syst Eng 1(3):213–222 27. Lahrmann G, Marx F, Mettler T, Winter R, Wortmann F (2011) Inductive design of maturity models: applying the Rasch algorithm for design science research. In: Lecture notes in computer science, vol 6629, pp 176–191 28. Bardin L (2016) Análise de Conteúdo [Content analysis]. São Pau-lo: Edições 70 29. Berelson B (1971) Content analysis in communications research. Edited by Hafner Public Co Macmillan 30. Carrijo P, Alturas B, Pedrosa I (2021) Análise de modelos de maturidade de Transformação Digital [Analysis of digital transformation maturity models]. In: CISTI 2021 - 16th Iberian conference on information systems and technologies, Chaves, Portugal, pp 1–6 31. Carvalho JV, Rocha A, Abreu A (2019) Maturity assessment methodology for HISMM hospital information system maturity model. J Med Syst 43(2):35 32. Duncan R, Eden R, Woods L, Wong I, Sullivan C (2022) Synthesizing dimensions of digital maturity in hospitals: systematic review. J Med Internet Res 24(3):32994 33. Kulju S, Morrish W, King L, Bender J, Gunnar W (2022) Patient misidentification events in the veterans health administration: a comprehensive review in the context of high-reliability health care. J Patient Saf 18(1):290–296 34. Randall KH, Slovensky D, Weech-Maldonado R, Patrician PA, Sharek PJ (2019) Self-reported adherence to high re-liability practices among participants in the children’s hospitals’ solutions for patient safety collaborative. Jt Comm J Qual Patient Saf 45(3):164–169 35. Sullivan JL, Rivard PE, Shin MH, Rosen AK (2016) Applying the high-reliability health care maturity model to assess hospital performance: a VA case study. Jt Comm J Qual Patient Saf 42(9):389–399

Safety and Security

Intelligent Digital Transformation in Modern Socio-Technical Systems – A Sustainable Approach Adam Jabłonski ´ and Marek Jabłonski ´

Abstract Intelligent digital transformation is one of the core challenges for the development of global industry and business. This concept should be considered not only in technical terms, but also humanistic terms. Both of these perspectives allow for a better understanding of the transformation taking place not only in the sphere of individual technologies or spheres of life, but in particular in the new shape and functionality of the ecosystems of business, industry and the real world. All links in human activity through digitalisation reduce the gap between market participants and increase the possibility of creating innovative solutions for the sake of progress. The challenges of the modern world in the face of the new circumstances created by digital transformation were highlighted. Keywords Intelligent digital transformation · Sustainability · Inequality · Safety · Security · Technical solutions

1 Introduction The context of the development of the knowledge-based economy creates a need to explain and understand the role of intelligence in the processes of digital transformation of enterprises. It is also important to clarify its embedment in the design and functioning of enterprises operating in the network economy. Taking the specificity of socio-technical ecosystems in the environment of which modern organisations operate into account is vitally important in terms of business technology. These ecosystems are created spontaneously through the dynamics of digitalisation development and building network relationships between organisations. The processes of shaping these relationships are often stimulated by the functionalities of modern A. Jabło´nski (B) Janowskiego 13/7, 41-200, Sosnowiec, Poland e-mail: [email protected] M. Jabło´nski Zawidzkiej 54, 41-300, Dabrowa Gornicza, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_3

55

56

A. Jabło´nski and M. Jabło´nski

digital platforms. This creates places for the production of intelligence centres as local or global hubs that stimulate business activity in digital networks. The dispersion and independence of companies operating in different sectors of the economy to date is turning into the centralisation of their data, rules of conduct and habits imposed by the capabilities of systems supporting their operation. Intelligence in this area becomes not only the domain of the organisational ecosystem, but a distinguishing feature of individual network participants. As a consequence, intelligent digital transformation is dealt with, which, in the context of the challenges and threats posed by market instability nowadays, is an opportunity for organisations to transform difficulties into strengths. Those organisations that try to digitally transform their processes and business models, even in cooperation with partners throughout the supply chain, have the chance to implement actions that help them to adapt to new business conditions. The novelty of the content presented in this chapter is the presentation of the issue of the location of intelligence in digital transformation processes against the background of the assumptions of safety and security of complex socio-technical systems. In addition, the concept of sustainability and its attributes were highlighted in the context of its integration into the intelligent digital transformation process. Sustainability and intelligence together can contribute more to achieving the effective and efficient digital transformation of organisations and entire socio-technical systems oriented around the value chain. The purpose of this chapter is to present an original approach to understanding intelligent digital transformation based on sustainability and taking process safety and data protection management mechanisms into account. Such a combination of factors helps to create the conditions for the creation of intelligence in the digital transformation process, both in the field of control of technical systems and in building relationships between network participants. This chapter is devoted to clarifying the complex issues of the place, role and scope of the potential applications of intelligent digital transformation. The aim and thematic scope of this chapter is presented in the introduction. Section 2 is devoted to clarifying the definition of intelligence and digital transformation by means of a literature review. The determinants responsible for the intelligent digital transformation were identified. Section 3 highlights safety and security as the core aspects of the digital transformation process. The aim was to make readers aware of the importance of process safety and the protection of data sources and digital media in the digital transformation process, and to ensure the continuity of socio-technical systems. Section 4 is dedicated to intelligent digital transformation in socio-technical systems—the concept and case studies. The aim was to explain, based on specific examples, the complexity and possibilities, as well as the need to build intelligent solutions in the technical and business spheres. Section 5 refers to the concept of sustainability in digital transformation processes and demonstrates the role of environmental, economic and social factors in creating intelligence centred around digital transformation processes. Section 6 contains a set of final conclusions with a set of recommendations for the creators of smart solutions in digital transformation.

Intelligent Digital Transformation in Modern Socio-Technical Systems …

57

2 Intelligence and Digital Transformation—Basic Assumptions and the Literature Review The issues of intelligence and digital transformation are two factors that should be considered together. It is difficult to imagine a well-run process of digital transformation without doing so intelligently. But what does ‘intelligently’ mean? Such a question arises immediately. Intelligence means the ability to understand, learn and use one’s knowledge and skills in different situations. Such a situation is found in digital transformation, the difficulty of which in the practical implementation phase lies in the limits of the application of the most innovative technologies, which should not be a collection of individual digital elements but rather a coherent relational digital ecosystem. The literature review focused on clarifying the definition of the concept of intelligence, in particular against the background of digital transformation processes. In general, the following types of intelligence that are relevant and play a role in digital transformation processes were isolated from conceptual typologies, namely: Intelligence, social intelligence, technical intelligence, digital intelligence, and artificial intelligence. Intelligence essentially refers to the characteristics of a particular human being—it is the general ability to reason, the ability to solve problems and learn. It integrates cognitive functions such as perception, attention, memory, language, and planning. Intelligence is the ability to perceive, analyse and adapt to changes in the environment. It is the ability to understand, learn and use one’s knowledge and skills in different situations [9]. Intelligence is the ability to learn from experience, as well as the ability to adapt, shape and choose a living environment [37]. In social terms, intelligence is the ability to understand humans, and more broadly, human interactions [5]. In social sciences, it can be seen as the ability to solve social inequalities [22]. In technical terms, technical intelligence is a collection of analytical information on scientific or technological activities, development or organisations likely to affect the short or long-term competitive situation of the company [3]. The first approach to technical intelligence was based on the weak links—if there were links at all— between corporate strategy and technology. The second generation is characterised by a higher degree of synchronisation of corporate strategy and technology. The advanced approach is characterised by the highest levels of synergy between R&D activities and the overall corporate strategy [21]. In the context of digitalisation, digital intelligence includes both the ability to work in the digital environment and the skills for specific actions in this environment. The concept of digital literacy can be defined as the ability required by everyone to be able to live, learn and work in a digital society [33]. Digital intelligence as competency can be characterised by three dimensions, namely a cognitive dimension that enables the design of digital technology learning strategies, a behavioural dimension that is appropriate for technology together with the use of technology, and an affective dimension involving being agile and having faith in one’s own effectiveness. Digital intelligence fosters digital creativity in digital transformation processes [6]. Digital intelligence methods and tools include, in particular, Artistic Intelligence, Computer Graphics and Animation,

58

A. Jabło´nski and M. Jabło´nski

Image Processing, Cryptography, Computer Network Security, Modelling and Simulation, Information Retrieval, Information Filtering, Multimedia, Computer Architecture Design, Computer Vision and Robotics, Parallel and Distributed Computing, Operating Systems, Information Systems, Mobile Computing, Natural Language Processing, Data Mining, Machine Learning, Expert Systems, and Geographical Information Systems [39]. As a consequence of technological development, the concept of artificial intelligence is crucial. Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving [18]. The perspective of the development of the artificial intelligence concept is dynamic. It is now considered to have great cognitive and creative potential. From a narrow perspective, this intelligence is defined as being smaller than that of a human brain. By 2040, it is predicted that this intelligence, called Artificial General Intelligence, will already be equal to that of a human being, while by 2060 it will have significantly surpassed human intelligence [35]. The discussion and bibliographic analysis showed that intelligently, above all, means holistically at a technical level, but also a social level. People, technologies and the system working environment should make the designed system intelligent during the design, construction and usage stages. The means of digital transformation are numerous, but successes are achieved by such solutions that, in addition to technical aspects, fulfil social expectations and do not cause conflict but rather symbiosis at every stage of the life cycle. Digital transformation solutions must be characterised by a high level of safety and security. They must be safe in terms of a process; that is to say, they must ensure reliable functioning but also be protected against the hostile actions of third parties. Safety and security must protect users and other stakeholders from the negative effects of human errors in the management and control system, as well as the lack of appropriate supervision over the operation of digital solutions. In this area, in particular, this intelligence should be revealed. Similarly, there is a need for transformation processes to proceed in a prudent and rational manner in line with the concept of sustainability. Balancing is a manifestation of intelligence because it is based on the conscious positive and negative consequences of action ensuring that the expectations of various stakeholder groups are fulfilled. Intelligence in the digital transformation process is an aspect which requires analysis and results-based practical implications focused on achieving high efficiency and effectiveness of the digital economy solutions which have been designed and implemented. It is widely believed that intelligent digital transformation is digital transformation driven by artificial intelligence, which in a sense is crucial but not mandatory. The idea is based on the centralisation of data, the intelligent use of data and, in particular, learning through experience, including machine learning—learning by people and machines. Changes at the level of the environment as well as within economic and technical systems are conducive to this process through intelligent digital transformations that focus on exploiting the potential of the Internet of Things (IoT) and the accompanying software and equipment. In this context, there is a need for universal change management oriented towards the

Intelligent Digital Transformation in Modern Socio-Technical Systems …

59

intelligent application of modern technologies with the use of humanism, reasonable thinking and wisdom to the forefront.

3 Safety and Security as a Core Aspects of Digital Transformation Process The concepts of safety and security are crucial to achieving the objectives of digital transformation in the context of an intelligent approach to the conduct of these processes. At the same time, they require a clear distinction in terms of various sectors of the economy and industry. The concept of safety is broader and refers to process safety, covering risks resulting not only from digitalisation but more generally from technical, operational and organisational processes. Security, on the other hand, relates only to such threats that arise directly from action in the field of the digital economy and where there is a risk of data loss, unauthorised access to data resources and any other type of critical action by third parties. It addresses issues such as interruption, i.e. partial or complete destruction of access to technical system information or the inability to utilise it; interception, i.e. unauthorised access to resources; modification, i.e. an unauthorised person gaining access to resources and making changes thereto; counterfeiting, which is an attack consisting of the counterfeiting of transmitted data; as well as the possibilities for uncontrolled use of all intra-network services and resources by third parties, the ability of third parties to manipulate the flow of data between subsystems, partners, the ability of third parties to intercept confidential data, etc. While the issue of security is related to real-time data transmission, safety, in addition to all the risks arising from the functioning of the digital economy and the implementation of digitally-threatened processes, as previously mentioned, is broader and concerns all risks related to operational, maintenance, organisation and management processes that could lead to disastrous consequences. The concept of safety is related to the functioning of companies that meet the definition of a high reliability organisation, i.e. organisations in which the probability of a disaster is very low but the consequences of the disaster result in extremely high human losses and significant financial damage. Risks arising from the area of security may also be a source of non-protection of the expected level of safety. As such, security is included in safety. The issue of safety must be clearly separated from the concept of security. The application of both terms requires some precision. These terms cannot be used interchangeably. The definitions above make it possible to understand the clear differences between these two concepts. Safety refers to the state of being away from hazards caused randomly by natural forces or human errors. The source of a hazard is formed by natural forces and/or human errors. Security refers to the state of being away from hazards caused by deliberate human intention to cause harm. The source of a hazard is deliberately posed by humans [28]. The key to distinguishing between the two

60

A. Jabło´nski and M. Jabło´nski

approaches is the balance point, which in the case of safety focuses on negative actions related to situations of random human error, while security is fundamentally oriented towards eliminating negative and deliberate human actions against a resource. In each of these areas, damage and the risk thereof are at the centre of the analysis. Risk mitigation as a core aspect of eliminating the negative impact of the resulting risks in both areas is of the same nature. The same is true of the understanding of risk control measures. Thus, the result of the overall safety or security effect is a reflection of the same control mechanisms, while the essence of understanding these concepts is different. Understanding this confusing and semantically complex conceptual node allows for the correct assessment of the situation based on specific examples. It should be noted that setting a safety threshold results directly from the arbitrarily established level of acceptable risk [14]. In the same sense, such an approach is appropriate both in the area of safety and security. In recent years, as digitalisation and the widespread use of such solutions have increased, complex ecosystems have emerged which, unlike conventional systems, form holistic sociotechnical systems. If they are built from digital components and create a uniform, coherent system, the relationship between the operator, the technical creation and the working environment can generate different types of hazards and risks, which should be managed effectively and efficiently. In technical systems based on the rail transport sector, safety means freedom from unacceptable risk of harm [10]. As regards security, on the other hand, it will ensure that no intentional action by third parties will lead to such a dysfunction of the digital system that will cause damage. Thus, as indicated above, it is necessary to seek risk-mitigating solutions so that such damage within the meaning of safety and security does not occur. Appropriate management mechanisms are used for this. Referring to the experience of the rail transport sector, which is a good industrial representation in the context of the topic addressed, safety is ensured through a system paradigm, which requires a holistic view of the relationship between dynamic transport processes. All processes should be defined and precisely described. For each process, the potential risks which are to be linked to the effects should be identified, thus defining the risk. Each risk should be mitigated by appropriate risk management measures relevant to the situation. Staff competence in safety management mechanisms should be maintained [24]. In the context of digital transformation processes, in particular, it concerns the protection of systems built on digital platforms. Therefore, the concept of security expands to include the prefix ‘cyber- ’, creating the word cybersecurity. As regards cybersecurity, one should ensure that both IT and OT systems use the same type of mechanisms to enhance the security of network and information systems. These include organisational and procedural safeguards, including information security management systems and systems for granting and withdrawing access rights, systems and solutions to ensure the continuity of IT and OT systems, including backup systems, hardware and software redundancy, and the protection of data centres against power loss or fire; technical security measures including authentication systems, malware protection and data processing and transmission control systems; and physical security features, including remotely supervised locks, video surveillance systems and other systems supporting physical protection [16]. In terms of the human factor and

Intelligent Digital Transformation in Modern Socio-Technical Systems …

61

human error, digital security will be focused on the mitigation of risk associated with, inter alia, events such as malware, (distributed) denial of service ((D)DoS), unauthorised access and theft, and software manipulation. Such events reflect the risks of operation in the digital environment. A review of the definition of cybersecurity was developed by D. Craigen, N. Diakun-Thibault, and R. Purse (Table 1). Despite the relatively broad coverage of the definitions of cybersecurity, it mainly addresses the issue of ensuring the operation of critical infrastructure and other relevant infrastructure against attacks by hostile parties aimed at destroying or causing Table 1 Review of definitions of cybersecurity Definition of Cybersecurity

Author

1

“Cybersecurity consists largely of defensive methods used to detect and thwart would-be intruders”

Kemmerer [25]

2

“Cybersecurity entails the safeguarding of computer networks and the information they contain from penetration and from malicious damage or disruption”

Lewis [26]

3

“Cybersecurity involves reducing the risk of malicious attack to software, computers and networks. This includes tools used to detect break-ins, stop viruses, block malicious access, enforce authentication, enable encrypted communications, and on and on”

Amoroso [2]

4

“Cybersecurity is the collection of tools, policies, ITU [23] security concepts, security safeguards, guidelines, risk management approaches, actions, training, best practices, assurance and technologies that can be used to protect the cyber environment and organisation and the user’s assets”

5

“The ability to protect or defend the use of cyberspace from cyber-attacks”

CNSS [11]

6

“The body of technologies, processes, practices and response and mitigation measures designed to protect networks, computers, programmes and data from attack, damage or unauthorised access so as to ensure confidentiality, integrity and availability”

Public Safety Canada [32]

7

“The art of ensuring the existence and continuity of the information society of a nation, guaranteeing and protecting, in cyberspace, its information, assets and critical infrastructure”

Canongia and Mandarino [7]

8

“The state of being protected against the criminal or Oxford University Press unauthorised use of electronic data, or the measures taken to achieve this”

9

“The activity or process, ability or capability, or state whereby information and communications systems and the information contained therein are protected from and/or defended against damage, unauthorised use or modification, or exploitation”

No.

DHS [13]

(continued)

62

A. Jabło´nski and M. Jabło´nski

Table 1 (continued) No.

Definition of Cybersecurity

Author

10

“Cybersecurity is the organisation and collection of resources, processes, and structures used to protect cyberspace and cyberspace-enabled systems from occurrences that misalign de jure from de facto property rights”

Craigen et al. [12]

Source Own study based on [12]

cybersecurity safety

security

Fig. 1 Location of the concept of cybersecurity against the background of safety and security ontology Source Own study

permanent or temporary damage to the loss of part or functionality. Figure 1 presents the place and role of cybersecurity against the background of ontologically separated concepts of safety and security. The original approach presented points to the ontological separation of the concepts of safety and security, but in the context of cybersecurity, a common part appears which particularly concerns systems composed of or constituting critical infrastructure. The common part requires management and supervision such that hazards and risks arising from dynamic digitalisation-based operational processes are protected against hostile attacks. For example, this applies to areas such as rail and air transport.

4 Intelligent Digital Transformation in Socio-Technical Systems—An Idea and Case Studies Digital transformation processes are now a key challenge for all areas of human activity, not only in business terms but in particular in social terms. Digitalisation offers opportunities for dynamic progress in many areas of life. Intelligent solutions are, in a certain sense, embedded in the idea of digitalisation. Traditional processes are either partly or even fully transferred to the virtual world and are, in a way, a representation of reality, which is strongly related to the widespread access and use of mobile devices. Digital transformation is characterised by planned changes built on the foundation of advanced technologies [1]. These technologies stimulate the ability to transform systems. The technological space is expanding significantly as

Intelligent Digital Transformation in Modern Socio-Technical Systems …

63

part of the digital transformation of knowledge absorptive services, creating links between these technologies and industrial clusters [42]. Intelligent digital transformation needs to be assessed in many respects. These include cognitive areas such as creating new products and services, increasing the comfort and quality of life of people, acquiring new impressions, and minimising prices in the area of consumer service. In the context of the activity of businesses undergoing digital transformation, increased productivity, effectiveness and efficiency, new ways of creating value and expansion into new markets are expected. In terms of people, this influences the creation of the new models of work such as remote work, changes in other forms of engagement in work processes, sharing limited resources, and increased mobility in terms of exchange of work, knowledge and experience. In the field of social issues, it includes improving the processes of government and local government administration, and improving the quality and organisation of processes in the area of public services. Intelligent digital transformation must therefore include a broad view of interdependent socio-technical systems, where complexity determines the conditions of life and the creation of new forms of life, work and economic activity. It also changes operational processes in technical systems, which broaden the modern dimension of existence of people who enter into interfaces with these technical systems. Sectors which suitably illustrate the need to design intelligent solutions in the area of digital transformation processes are those in which leading organisations are High Reliability Organisations, i.e. those where the probability of adverse catastrophic events are very unlikely, but if they occur, they have extremely negative consequences. This includes the rail transport sector. The example of the rail transport sector shows the importance of digital transformation at the level of the entire socio-technological system. The specificity of this sector shows that if digital transformation processes are initiated, they change the entire business ecosystem as a consequence. Based on advanced control and communication technologies, the rail system undergoes digital transformation by changing its individual components, which need to be considered as a whole because of the many complex interfaces between its components. It can therefore be concluded that once the process of digital transformation has begun, over time it shapes a uniform system of links that form a single system. In terms of rail transport, safety is a core factor which digitalisation activities need to focus on. Figure 2 shows the combination of components which form a configuration that shapes the railway system, aimed at ensuring the safe operation of digital railway systems. The system of relationships presented covers four core areas built with individual components. In this system, innovation, modern solutions and digital technologies are diffused to build and improve an effective and efficient control system. For this purpose, data sharing, real time monitoring, train operation control and the intelligent dispatching and usage of digital technology of train safety are used [41]. Digitalisation in this area is complete. All systems of action must be based on the assumptions of the digital economy. If progress in the digitalisation of such systems is considered, it must come from the design of complex socio-technical systems. The second part of the scheme presented, which includes the design of digital systems in terms of complexity, may

64

A. Jabło´nski and M. Jabło´nski

include the following steps (Colclough), starting with the “start right” phase, which involves the use of digital solutions at the earliest possible design stage for the faster and better preparation of the design work and the necessary data. The next step is “smart design”, which includes digital simulations of potential results of the design process. It allows for the identification and mitigation of the risk of errors and their elimination. In the next step, called “build safe”, actions are based on the assumption that construction works are often dangerous and that digital technologies can identify risks in advance. The digital transformation process is changing the way construction works are carried out in the investment cycle through the widespread use of digital sensors and management support based on monitoring progress with digital solutions. The subsequent step, called “smooth operator”, includes the use of digital simulations and the overlapping of video content with real information, which also provides a visual and analytical tool to enhance safety. An important stage in the design of rail systems is to “manage assets”. These include solutions such as remote resource detection methods, for example advanced track measurements, sensors, the capture of digital data from satellites and the creation of smarter resources that provide information themselves, as well as 3D printing. The last issue, called “behaviour change”, includes aspects of the safe use of technologies, processes and people together to provide protection against the loss and misuse of a complex system. Such an approach stimulates the design of railway systems which are subject to digital support. Another area of rail transport is harnessing the potential of digital transformation regarding the issue of safety critical communications—another part presented in Fig. 2. This part of the rail system is a priority, especially in terms of the safety of railway traffic in the context of intelligent digital transformation processes. The scope of communication in rail systems includes defining its structure, establishing responsibilities, setting parameters for digital protocols, confirming understanding of information and developing staff communication skills. Communication in the event of dynamic interactions between railway system users must be continuously improved. The intelligence of digital transformation processes in communication systems should be manifested by a conscious distribution of the knowledge and wisdom of designers, users and other stakeholders, which is crucial to achieving the objectives set. All these areas should be supported by effective and efficient process management in complex socio-technical systems based on digital technologies, which is specific to the rail transport sector. Digital transformation processes should primarily aim to improve railway traffic safety. Otherwise, changing them radically from analogue to digital systems is not justified. In this context, the aim is to oversee the entire digital system and not just some of its components. Therefore, digitalisation in this case must be economically and technically justified. This confirms the assumption that intelligent digital transformation represents a radical reconfiguration of business models, systems, processes, and in particular the organisational and technical culture of the organisation to implement change and create new value for key stakeholders. Maritime ports are an interesting area of the economy at the forefront of conducting intelligent digital transformation processes.

Intelligent Digital Transformation in Modern Socio-Technical Systems …

65

Intelligent digital transformation model based on a case study of the rail transport sector Recommendations for digital solutions — progress Smart operator

Manage assets

Safe operation of digital rail systems

Behaviour change

Communication structure

Responsibility

Digital Protocols

Real time monitoring

Confirming understanding

Data sharing, fusion

Communication skills

Improving communication systems

Train operator control and intelligent dispatching

Build safe

Safety critical communications

Smart designs

Usage of digital technology for train safety Control technology

Innovation and modern solutions and digital technologies

Start right

Process management in complex digital-based sociotechnical systems

Fig. 2 A model of intelligent digital transformation based on a case study of the rail transport sector Source Own study

The experience of European ports shows that they are undergoing dynamic processes of intelligent digital transformation involving the use of artificial intelligence, the Internet of Things, Big Data, the Digital Twin solution, and digital train navigation systems. They also support port operations by means of modern digital communication systems. They use modern platforms to ensure that the rail traffic management system at a port does not consist of separate devices, but rather constitutes a comprehensive single integrated digital system. Such an approach seems reasonable, since in a digital environment where all the necessary data is available in a very short time, it can be converted into information and thus data in a specific context. This helps to improve the speed of decision-making and the timely forecasting of rail traffic flows to and within the port. Such solutions include systems such as real-time location systems (RTLS); Port Information and Control Systems (APICS); Dispo web applications for train tracking—Antwerp Port Information and Control Assistant (APICA) for port event management; autonomous drones and smart cameras for infrastructure inspection; Digital Twins; digital 3D maps with real-time information; EDI/EDIFACT information exchange standards; intelligent port administration systems, e.g. Bulkchain, which ensures that administrative processes run faster and more efficiently; intelligent container flow management systems, e.g. Certified

66

A. Jabło´nski and M. Jabło´nski

Pick up (CPu), which offer a digital, secure and integrated container dispensing solution; intelligent systems that aim, among others, to simplify communication between different players in the logistics chain, e.g. the Barge Traffic System (BTS), the Rail Traffic System (RTS) and Terminal Planner; and smart e-administration, e.g. e-VD20 within EMCS. Furthermore, in terms of solutions supporting the transmission of information on incidents occurring at seaports, gate operators confirming traffic flows at ports play a crucial role. RFID systems are commonly used to identify objects by means of radio waves. In addition, crucial solutions which support port transport operations include GPS/DGPS systems (they enable position detection and tracking moving objects such as containers, vehicles and equipment), RTLS (realtime location systems that allow for the identification and constant tracking of the location of marked objects), OCR (optical character recognition systems that allow for the automatic recognition of alphanumeric patterns and handwritten characters in scanned documents or images), WSN (wireless sensor networks that describe a large-scale system consisting of interconnected wireless sensors), EDI (channels allowing for cheaper and flexible communication, which builds the basis for paperless communication and more efficient integration of different stakeholders in many ports), SBAS and EGNOS (optical systems, in particular laser and radar systems, which sometimes connect for higher reliability, developed to complement the existing GPS). The description of the solutions presented confirms that seaports use intelligent digital transformation processes to a large extent. Digital transformation therefore requires fundamental changes in the organisation, including their organisational structures, processes, strategies and organisational culture [38]. The implementation of intelligent digital transformation should ultimately lead to a significantly higher level of productivity and profitability for the organisation, and business processes in this context will be centralised. Commercial enterprises in particular are trying to transform their businesses from traditional, product/machine-oriented businesses to digital businesses. It is new business concepts that are becoming dependent on emerging new technologies [20]. The Internet of Things (IoT) is vitally important. The medical sector is also an interesting example of the use of digital transformation processes, for example in the field of telemedicine and anaesthesiology, solutions such as the use of artificial intelligence to build chatbots that answer patients’ questions during the perioperative period, the creation of tools to facilitate the assessment and observation of patients with chronic pain, Big Data management in intensive care units to create automated operating algorithms, the use of virtual reality and augmented reality for training specialists and residents, and ensuring patient education on cooperation during illness, as well as 3D printing of medical devices [27], etc. An important role that needs to be clearly highlighted is the ability to intelligently conduct digital transformation. This intelligence should be based on the following assumption. The essence is not that a digitalisation-oriented enterprise will apply as many digital solutions and technologies as possible, as this will always be insufficient and competitors with larger budgets may apply more. In turn, full automation consisting of replacing people with machines can still be extremely expensive at this stage of the development of economies. Instead, companies should digitalise value

Intelligent Digital Transformation in Modern Socio-Technical Systems …

67

chains as much as possible so that users—consumers, employees and partners— interact as much as possible with new digital technologies. To this end, developing the smart digital culture of digital transformation focused on continuous learning, designing new solutions, stimulating continuous change and dynamic replacement of digital archaic solutions is recommended. The greatest power of intelligence revealed in the processes of digital transformation is people and their ability to adopt new, innovative solutions and to renounce existing practices. Not being afraid of the risk of change and abandoning the comfort of the lack of need for continuous learning means that this intelligent digital transformation is built in people and not in the multitude of digital solutions used.

5 Sustainability Concept in Digital Transformation Processes The concept of sustainability is now widely described and referred to in many areas of human and business activity. Digital transformation should be subject to sustainability processes so that progressive digitalisation processes can deliver positive effects rather than generate adverse phenomena such as digital exclusion, social exclusion, digital poverty or social inequalities [4] in terms of accessibility to different values, products, and services. Sustainability strategy is based on the preservation of synergy, symbiosis and symmetry in the creation of the modern world in ethical, ecological and economic terms. Modern technologies offer a great opportunity for the development of human civilisation in many areas of human life, professional activity and the creation of innovations, but they also create new forms of functioning that threaten the current status quo. The paths to this progress should be sustainable in the sense that social relations, the environmental impact and the laws of the economy will be preserved and the development of new solutions will have a positive influence on social processes, creating a new reality. New concepts such as the sharing economy, the circular economy and Big Data allow for the creation of a completely different view of economic, industrial and social processes. Inequalities, limited access to and shortages of some goods can have an adverse impact on the functioning of societies, but also business, environmental and economic order. Risks are therefore emerging which should be mitigated through an appropriate and balanced approach to decisionmaking in the areas of socio-technical and business order which stimulates progress in the area of new technologies and business models, which describe the new ways of creating and delivering products and services. The chapter will explain the concept of sustainability in the context of intelligent digital transformation processes in light of modern technical, economic and social conditions. The concept of sustainability is a response to the growing instability of the modern world. Inequalities and shortages compared to consumption and surpluses of goods in the age of information societies are drivers of social and political conflicts. In the basic sense, the concept of sustainability is associated directly with the assumptions

68

A. Jabło´nski and M. Jabło´nski

of sustainability development, which is not unambiguous in the context of contemporary conditionalities and a broader view [34]. In critical terms, this concept is not unambiguous and involves a great deal of areas of interest. However, the fact that it concerns socio-ecological processes is undisputed [40]. The starting point for sustainability is the concept of the Triple Bottom Line, which underlies the idea of sustainable development to ensure equivalence between economics, the environment and society [15]. In this context, the digital economy, together with its dynamics of development, which is conducive to economic, environmental and social changes, forces the need to look at these issues by searching for a certain balance in this area as well. Access to information and technology determines progress and improves the quality of life in society. Equality in terms of access to information, knowledge, and technology and the protection of societies from information exclusion is a core aspect of digital transformation at both global and local levels. The main challenge for humanity in the twenty-first century is knowledge and progressive innovation, which should serve to eliminate conflicts and crises and achieve sustainable progress [29]. Digital transformation as a long-term process should be subject to these assumptions. Sustainability and digital transformation profoundly transform industrial societies and drive them towards progress [30]. This progress should be balanced so that no part of society is excluded from access to knowledge, information, education, science and work for reasons of poverty and other non-economic factors. In the context of the climate protection strategy, the convergence of digitalisation and sustainable development provides new tools that enable a sustainable impact on the planet’s natural capital. Digital technologies enhance the economy of joint action and the usefulness of private value for public goods [19]. Intelligent digital transformation can be considered to meet its underlying assumptions if it is sustainable. The experience of modern biotechnology is a good example of the combined implementation of sustainability and digital transformation. Digital technologies support biotechnology in promoting sustainable land and supporting digital agricultural processes, which generates significant progress. Digital transformation based on intelligence and knowledge can cover issues such as laboratory digitalisation and experimentation, high-performance computing and Big Data analytics, in particular the combination of phenomics and genomics, which can strengthen conventional plant breeding activities. Another area for biotechnology will be the application of management techniques for the development of farms, plantations, forests and landscape by means of smart, large-scale, real-time monitoring technologies. Another element of such a digital transformation to improve the climate is the use of digital technologies for monitoring underground conditions such as nutrients and water flow dynamics [36]. The conceptualisation and operationalisation of the digital transformation for sustainable development will offer many more opportunities than before the digital era. The use of the Internet of Things (IoT), Big Data, Artificial Intelligence, and Cloud Technologies provides opportunities to influence and respond more quickly to adverse phenomena through access to Big Data and processing capabilities to reach critical conclusions. This will allow decision-makers to take difficult decisions on climate protection and environmental improvement. The spectrum of design, implementation and inference in digital transformation processes to meet

Intelligent Digital Transformation in Modern Socio-Technical Systems …

69

sustainability assumptions is extremely broad, but the core of these actions is always to improve the quality of life of people organised in specific societies and to protect the climate. Similarly, in the chemical industry, sustainability should be taken into account in the development of digitalisation. A shift towards a sustainable future requires a radical change in the design of non-toxic chemicals. Digitalisation creates new opportunities for achieving this objective. The leading ideas, but also solutions, for sustainability through digital transformation in the chemical sector will include Safe & Sustainable by Design, Circular Supply Chains, Responsible Consumption, Block Chain, AI Agents, Datascience/ML tools, and others [17]. These solutions support the concept of High Reliability Organisations, which are chemical manufacturers who have responsibility and reason embedded in their intelligent DNA. Safety and sustainability are complementary in this context. In the context of economic circumstances, innovative business models used by businesses in the digital age foster socialisation and improved access to services, provided that there is appropriate digital infrastructure and users use mobile devices widely. This way, products and services and goods can be available to everyone. It is therefore important to pay attention to and eliminate all types of digital exclusion, social exclusion due to the lack of access to technology, digital poverty due to the lack of up-to-date software or modern devices or social inequalities. This also applies to the lack of access to various values, products, or services due to having limited or no access to the technologies and equipment concerned. Therefore, sustainability is crucial in the context of digital transformation processes. It must not be overlooked in the design of tasks for digital transformation, and must be a fundamental perspective for the evaluation of these processes. Therefore, it is worth discussing responsible technologies and their responsible implementation in order to avoid inequalities and shortages that always lead to crises and conflicts. Based on the above analyses and discussion, Fig. 3 presents the author’s original method of addressing the mechanisms for balancing digital transformation processes. Shortages and inequalities undermine the sustainability of digital transformation processes at the level of specific communities. Therefore, balancing forces derived from the components of the Triple Bottom Line concept that stabilise this system should be used. The mechanism presented works in such a way that if inequalities Balancing forces

Shortages

Digital transformation

Triple Bottom Line

Inequalities

Economics Ecology Ethics

Fig. 3 A mechanism for balancing digital transformation processes Source Own study

70

A. Jabło´nski and M. Jabło´nski

and shortages are eliminated, the digital transformation process will be implemented without any restrictions. In practice, such a model will be difficult to achieve, but nonetheless is worth striving for. The concept of sustainability in this sense requires a broad and holistic view. Understanding the place and role of sustainability in the digital transformation process is fundamental to its effectiveness and efficiency.

6 Conclusion The reflections, analyses and examples presented in the chapter confirm that intelligence is fundamental to digital transformation. The concept of intelligent digital transformation based on modern technologies, and in particular the concepts of Artificial Intelligence, the Internet of Things (IoT), Big Data and Computing Clouds, shapes the modern conditions for the transformation of digital economies and industry. The dynamics of change processes in digitalisation cover not only individual companies but entire socio-technical ecosystems. If they are not clearly defined as in the rail transport sector in relation to rail systems, they are shaped by a network effect in the form of business-to-business relationships, often in the form of industry clusters. When analysing digital transformation processes in the context of systems theory, attention should be paid to the place and role of safety and security issues as issues that ensure the ability of digital systems to operate at the expected level of reliability. Both process safety and data protection are essential to their performance so as to achieve high levels of business continuity indicators of digital systems. Digital transformation programme designers must have these issues in mind so that change processes are effectively and efficiently implemented. A sustainability approach plays an important role in intelligent digital transformation. In theoretical and practical terms, it means that intelligent digital transformation processes are reinforced by a positive social factor, which increases the level of public acceptance of the proposed solutions, while accelerating progress. This approach to understanding and implementing intelligent digital transformation determines a coherent and logical proposal, which should be considered in the context of intentions related to the process. Technological change, including the economy of technological change caused by globalisation, the development of information and communication technologies, rather than balancing growth and progress factors, often increases inequalities. Therefore, intelligent digital transformation processes should take into account the need to mitigate the risk of such adverse side effects. Inequalities arise as a result of changes in market, political, social and financial imbalances. Therefore, efforts should be made to ensure that digital transformation takes into account the moral factors underlying economics, politics and social engineering. It is not possible to think of intelligent digital transformation only from a technological point of view; it is not sufficient to make these actions improve the quality of life of societies and contribute to prosperity. All examples of solutions and ways of smart digitalisation presented in the chapter should be subject to extensive analysis taking into account the concept of sustainability, which is a panacea to

Intelligent Digital Transformation in Modern Socio-Technical Systems …

71

ensure that these processes, in addition to real technological coverage, are reinforced by the implementation of strategies to neutralise the emergence of inequalities in access to technology, inequalities in opportunities and, consequently, inequalities in the performance of people and organisations. Then intelligent digital transformation should in particular mitigate the imbalances between maximising corporate profits and macroeconomic and microeconomic stability and more sustainable growth. This should be the essence of sustainable and intelligent digital transformation. Based on the studies and analyses, several core conclusions were formulated as a summary of their results: 1. Intelligence is a driver of dynamic digital transformation processes and determines the ability and speed of the adaptation of organisations to implemented changes. 2. Intelligent digital transformation is based on knowledge, solutions and technologies that are used to ensure the ability to understand, learn and implement knowledge and skills in converting analogue solutions to digital ones. 3. In the case of digital transformation, it is necessary to ensure the security of technical systems and protection against cyber-attacks. The fundamental premise is that the system after change, i.e. digital transformation, is at least as secure as before the change. 4. Intelligent digital transformation should be based on sustainability. In this way, the so-called “extended intelligence” that takes social, technical and environmental issues into account should be created. It is also important to bear in mind the fundamental issues for the effectiveness of intelligent digital transformation, which include the issues of inequality and shortages. 5. The case studies discussed in this chapter confirm that the digital business ecosystem, as a complex socio-technical system, is a condition for achieving an effective digital transformation. Social factors, understood as taking the role of the individual in the social system into account against the background of technological changes, determine the ability to transform that system without the negative phenomenon of digital exclusion. If the solution is to be intelligent, it must take the technological advancement of civilisation into account, provided that it is beneficial to all the participants in society. As far as recommendations for future studies related to these issues are concerned, it is appropriate to distinguish such issues that should be studied and are of interest to the authors of this chapter. These include, but are not limited to, assessing the effectiveness and efficiency of the application of the concept of sustainability in the process of shaping and conducting intelligent digital transformation processes, analysing the features of intelligence and its impact on the process safety of technical systems, and assessing the complexity of socio-technical systems during and after their transformation into digital ecosystems.

72

A. Jabło´nski and M. Jabło´nski

References 1. AlNuaimi BK, Kumar Singh S, Ren S, Budhwar P, Vorobyev D (2022) Mastering digital transformation: the nexus between leadership, agility, and digital strategy. J Bus Res 145:636– 648 2. Amoroso E (2006) Cyber Security. Silicon Press, Summit 3. Ashton WB, Klavans RA (eds) (1997) Keeping Abreast of Science and Technology: Technical Intelligence for Business. Battelle Press, Columbus 4. Atkinson AB (2015) Inequality. What can be done?. Harvard College 5. Belton D, Ebbert AM, Infurna FJ (2016) Social intelligence. In: Farazmand A (ed) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi. org/10.1007/978-3-319-31816-5_2393-1 6. Boughzala I, Garmaki M, Chourabi O (2020) Understanding how digital intelligence contributes to digital creativity and digital transformation: a systematic literature review. In: Proceedings of the 53rd Hawaii international conference on system sciences 7. Canongia C, Mandarino R (2014) Cybersecurity: the new challenge of the information society. In: Crisis management: concepts, methodologies, tools and applications, pp 60–80. IGI Global, Hershey 8. Colclough J (2022) Digital solutions for railway safety. AECOM. https://aecom.com/withoutlimits/article/digital-solutions-railway-safety/. Accessed 03 Mar 2022 9. Colom R, Young RE, Karama S, Haier R (2010) Human intelligence and brain networks. Dialogues Clin Neurosci 12(4):489–501 10. Commission Implementing Regulation (EU) No 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and repealing Regulation (EC) No 352/2009 11. Committee on National Security Systems (CNSS) (2010) National Information Assurance (IA) Glossary, CNSS, Instruction No. 4009 12. Craigen D, Diakun-Thibault N, Purse R (2014) Defining cybersecurity, defining cybersecurity. Technol Innov Manage Rev 4(10):13–21 13. Department of Homeland Security (DHS) (2014) A Glossary of Common Cybersecurity Terminology. National Initiative for Cybersecurity Careers and Studies, Department of Homeland Security. http://niccs.us-cert.gov/glossary#letter_c. Accessed 03 Mar 2022 14. Elagin YP (1996) The concept of “safety.” At Energ 80:389–393 15. Elkington J (1997) Cannibals with Forks: The Triple Bottom Line of 21st Century Business Capstone. Capstone, Oxford 16. European Commission (2021) Transport cybersecurity toolkit 17. Fantke P et al (2021) Transition to sustainable chemistry through digitalisation. Chem 7(11):2866–2882 18. Frankenfield J (2021) Artificial Intelligence (AI). https://www.investopedia.com/terms/a/artifi cial-intelligence-ai.asp. Accessed 09 Apr 2022 19. George G, Schillebeeckx SJD (2022) Digital transformation, sustainability, and purpose in the multinational enterprise. J World Bus 57(3):101326 20. Ghosh S, Hughes M, Hodgkinson I, Hughes P (2021) Digital transformation of industrial businesses: a dynamic capability approach. Technovation 102414 21. Gonçalves LR, Carvalho de Almeid F (2019) How technology intelligence is applied in different contexts? Int J Innov 7(1) 22. Holden LR, Hart SA (2021) Intelligence can be used to make a more equitable society but only when properly defined and applied. J Intell 9(4):57 23. International Telecommunication Union (ITU) (2009) Overview of Cybersecurity. Recommendation ITU-T X.1205, International Telecommunication Union (ITU), Geneva 24. Jabło´nski A, Jabło´nski M (2022) Digital Safety in Railway Transport - Aspects of Management and Technology. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/ 10.1007/978-3-030-96133-6 25. Kemmerer RA (2003) Cybersecurity. In: Proceedings of the 25th international conference on Software engineering, pp 705–715. IEEE Computer Society

Intelligent Digital Transformation in Modern Socio-Technical Systems …

73

26. Lewis JA (2006) Cybersecurity and Critical Infrastructure Protection. Center for Strategic and International Studies, Washington 27. Méndez Arias E, Serrano Afonso A (2022) Telemedicine and digital transformation in anesthesiology. Revista Española de Anestesiología y Reanimación 69(1):1–3 28. Nas S (2015) The definitions of safety and security. J ETA Maritime Sci 3(2):53–54 29. Pater LR, Cristea SL (2016) Systemic definitions of sustainability, durability and longevity. Procedia Soc Behav Sci 221:362–371 30. Pauliuk S, Koslowski M, Madhu K, Schulte S, Kilchert S (2022) Co-design of digital transformation and sustainable development strategies - What socio-metabolic and industrial ecology research can contribute. J Clean Prod 343:130997 31. Powered by Oxford Lexico. Oxford Online Dictionary. http://www.oxforddictionaries.com/def inition/english/Cybersecurity. Accessed 03 Mar 2022 32. Public Safety Canada (2014) Terminology Bulletin 281: Emergency Management Vocabulary. Translation Bureau, Government of Canada, Ottawa 33. Rahman T, Amalia A, Aziz Z (2020) From digital literacy to digital intelligence a comparative study of digital literacy frameworks, advances in social science, education and humanities research. In: Proceedings of the 4th International Conference on Sustainable Innovation 2020– Social, Humanity, and Education (ICoSIHESS), vol 518 34. Ruggerio CA (2021) Sustainability and sustainable development: a review of principles and definitions. Sci Total Environ 786:147481 35. Saracco R (2018) Computers keep getting better … than us. IEEE Future Directions. In: Mohammed Z (ed) Artificial Intelligence Definition, Ethics and Standards, Electronics and Communications: Law, Standards and Practice, 18ELEC07I. The British University in Egypt 36. Sheng Goh C, Ahl A, Thye Woo W (2021) Sustainable transformation of land-based economic development in the era of digital revolution. Trends Biotechnol Sci Soc 39(1):1–4 37. Sternberg RJ (2012) Intelligence. Dialogues Clin Neurosci 14(1):19–27 38. Tuukkanen V, Wolgsjö E, Rusu L (2022) Cultural values in digital transformation in a small company. Procedia Comput Sci 196:3–12 39. Universitas Muhammadiyah Pontianak. http://openjurnal.unmuhpnk.ac.id/index.php/diligent. Accessed 09 Apr 2022 40. Virtanen PK, Siragusa L, Guttorm H (2020) Introduction: toward more inclusive definitions of sustainability. Curr Opin Environ Sustain 43:77–82 41. Wang J (2018) Safety theory and control technology of high-speed train operation. Elsevier 42. Yu H, Fletcher M, Buck T (2022) Managing digital transformation during reinternationalization: trajectories and implications for performance. J Int Manag 28(4):100947

Intelligent Digital Transformation

Intelligent Digital Transformation Strategy Management: Development of a Measurement Framework Umut Sener, ¸ Ebru Gökalp, and P. Erhan Eren

Abstract Intelligent Digital transformation (DX) targets the implementation of interconnecting, smart, and self-controlled business processes by utilizing various technologies such as the Internet of Things (IoT), cloud computing, and data analytics. Organizations have been trying to reshape their business processes and transform them into a smart environment to have competitive advantages in the market. The literature review reveals a fundamental need for a measurement framework for intelligent DX strategy management to assist companies in measuring their current capabilities and guiding them to improve their existing situation in a standardized, objective, and more intelligent way. To address this research gap, this chapter proposes a measurement framework that aims to enable organizations to evaluate readiness and their current DX strategy capabilities. The framework consists of dimensions, corresponding sub-dimensions, and metrics to guide the organizations toward intelligent DX strategy management. The main contributions of the study are as follows: establishing a common base for performing an assessment for intelligent DX strategy management, benchmarking their capabilities with other organizations, and providing a roadmap towards achieving a higher capability level to maximize the economic benefits of DX. The measurement process is shown in order to demonstrate the applicability of the proposed measurement framework. Keywords Digital transformation · Intelligent systems · Strategy management

U. Sener ¸ (B) · P. Erhan Eren Graduate School of Informatics, Middle East Technical University, Ankara, Turkey e-mail: [email protected] P. Erhan Eren e-mail: [email protected] E. Gökalp The Department of Computer Engineering, Hacettepe University, Ankara, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_4

77

78

U. Sener ¸ et al.

1 Introduction Intelligent Digital Transformation (DX) has been accelerated since the first introduction of the terms. It provides the implementation of interconnecting, smart, and self-controlled business processes by utilizing various technologies such as interconnected, integrated, intelligent, and self-adaptive components containing CyberPhysical System (CPS) and Internet of Things (IoT). These applications offer companies many advantages such as operational excellence, resource productivity, meeting personalized customer requests, having self-adaptive and intelligent decision-making processes, improved work-life balance, etc. According to Mc Kinsey’s latest survey [1], 94% of respondents of the survey conducted among more than 400 firms reported that DX implementations support them to keep their businesses alive during the crisis, while 56% of them indicated that the DX technologies were critical for their quick response during the pandemic period. Although industry leaders are conscious of the benefits of Industry 4.0 applications, they need a guideline in constructing effective strategy and governance related to intelligent transformation activities [43]. Organizations try to reshape their business processes in line with the recent development to stay competitive and survive in the marketplace,however, they suffer from a lack of guidance. Most organizations have difficulty assessing the variety of developments and recent technological advancements as well as developing their corporate strategies. DX is mainly about rationalizing a strategy and creating innovative business models [44]. Accordingly, strategy management of intelligent DX needs to be investigated. It is essential to clearly define the methodology of implementation guidelines for DX. Formalization of a digital strategy is the starting point of leading digital transition throughout the organization, and ultimately achieving operational excellence and increased profits. Developing a digital roadmap includes strategic choices about organizational goals, preferences, and the order of actions in a stepwise procedure [36]. This approach enables organizations to assess their current capabilities and which competencies they require for a successful DX [12–14]. Organizations have been trying to evaluate their DX strategy skills and current state of competencies for intelligent transformation by holistically considering all aspects, including processes, technology, and humans [15]. This evaluation produces valuable information about which area should be improved and what powerful assets are likely to rest on. Hence, organizations can construct a roadmap that consists of explicitly defined steps over long- or short-term time horizons and develop a strategic guideline specifically on digital investments with the highest return of investment (ROI) rate for companies to survive in the competitive market [8]. The DX strategy differs from the Information Technologies (IT) strategy. It is business-oriented and covers the conversion issues regarding products, processes, and other organizational dynamics resulting from employing innovative IT tools [24], while IT strategy only targets the upcoming use of technological assets within an organization and does not certainly consider the incorporation of IT infrastructure. Therefore, as indicated in the literature [33, 41], there is a need for a strategy

Intelligent Digital Transformation Strategy Management …

79

management framework that considers the organizational readiness for intelligent DX before starting the transition as well as assesses all aspects affecting the DX of an organization holistically. Correspondingly, this research aims to develop a measurement framework for DX strategy management. The proposed framework consists of dimensions, sub-dimensions, and corresponding measurement indicators, KPIs, and metrics. The remainder of the study is formed as follows: a literature review on intelligent DX strategy management is given in Sect. 2. Then, the proposed measurement framework for intelligent DX strategy management is presented in Sect. 3, followed by a pilot study and conclusion.

2 Literature Review on Intelligent Digital Transformation Strategy Management This section presents the literature review on intelligent DX strategy management. First, the background of the study covering the explanation of intelligent DX in the context of Industry 4.0 is given and followed by measurement frameworks. Then, existing studies on intelligent DX strategy management are analyzed.

2.1 Background of the Study With the latest technological advancements, organizations have entered the era of digitization to transform into an intelligent environment. Companies exploit the benefits of “digital twin” technologies by employing virtualization and simulation technologies and other advancements such as cloud computing, edge computing, etc. Hence, they can have a virtual factory where business processes are mapped into a digital setting by creating virtual representations of their physical constituents and value network stakeholders. This contributes to increased productivity and reduced cost of operational expenses by providing real-time data and value-added insights about all steps of primary and supporting activities throughout the process workflow. Since cutting edge technology-oriented processes are complicated and generate a high volume of data, having an intelligent decision turned out to be more complex for executive managers [35]. With the advancement of controlling technologies, sensing devices, and intelligent proficiencies, decision-making processes in a DX project become more effective [34]. Gathering essential data and retrieving knowledge mechanically without human intervention for accurate decisions just in time become essential for organizations [6]. These concerns can be handled by several leading technologies of intelligent DX, such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing [2, 21, 27], in addition to industrial robotics and blockchain.

80

U. Sener ¸ et al.

Intelligent DX has been accelerating after introducing the Industrial Revolution “Industry 4.0”. Since this transformation is still in the initial phase, organizations have been trying different ways to transform their current status assessment into a smart environment efficiently. Maturity models (MM) provide a structural framework to help organizations evaluate their current capabilities and construct a roadmap for a successful intelligent DX [39]. There are many studies from different domains such as [3, 7, 10, 11, 16, 17, 23, 28, 37] that employ MMs for studying maturity of Industry 4.0 applications. However, there is no MM that explicitly targets strategy management in the context of intelligent DX. Only a few studies [9, 12, 24] investigate the DX strategy, but they do not cover a measurement framework for the strategy management and a demonstration of the proposed framework applicability.

2.2 Existing Studies on Intelligent Digital Transformation Strategy Management As a result of conducting the literature review of existing studies on intelligent DX, the findings are summarized in Table 1. Table 1 indicates whether the study covers a measurement framework developed for DX strategy. The literature findings show that only a few studies [4, 5, 30, 37] cover both conceptual model and demonstration of a measurement framework for DX strategy management. However, they do not specifically target strategy management of DX,instead, they propose a readiness and maturity model for adopting DX technologies in general. Furthermore, some of the studies cover domain-specific MMs, such as logistics, manufacturing enterprises, pharmaceutical manufacturing, and oil & gas sector, etc., which are not applicable to other domains. Lastly, most of these studies, except the studies of [12, 24], evaluate IT readiness as a strategy assessment [41]. In conclusion, the findings indicate a need for a strategy measurement framework for intelligent DX strategy management, covering both a conceptual model and a demonstration of a measurement framework and providing an evaluation of DX readiness as a strategy assessment and current DX strategy capabilities. To satisfy this research gap, this study aims to develop a DX strategy management framework covering dimensions, corresponding sub-dimensions, and metrics to guide organizations toward intelligent DX strategy management. The proposed measurement framework for intelligent DX strategy management is explained in the following section.

Intelligent Digital Transformation Strategy Management …

81

Table 1 Existing studies on intelligent DX strategy measurement Reference

The study

[24]

Conceptual model (Yes/No)

Measures, KPIs, metrics (Yes/No)

SM (Yes/No)

Digital Transformation Yes Strategies

No

Yes

[9]

Strategic guidance Yes towards Industry 4.0–a three-stage process model

No

No

[37]

A Maturity Model for Yes Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises

Yes

No

[10]

A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

Yes

Yes

No

[12]

DX success under Industry 4.0: a strategic guideline for manufacturing SMEs

Yes

No

Yes

[4]

Industry 4.0 for Yes pharmaceutical manufacturing: Preparing for the smart factories of the future

No

No

[5]

Measuring the fourth Yes industrial revolution through the Industry 4.0 lens: The relevance of resources, capabilities and the value chain

Yes

No

[30]

An ‘End to End’ Yes Methodological Framework to Assist SMEs in the Industry 4.0 Journey from a Sectoral Perspective—an Empirical Study in the Oil and Gas Sector

Yes

No

Note SM is abbreviated for Strategy Management, and it questions whether the study focuses explicitly on strategy management or not

82

U. Sener ¸ et al.

3 The Proposed Measurement Framework for Intelligent Digital Transformation Strategy Management After reviewing the literature, it is aimed to develop a measurement framework by applying the Wideband Delphi approach, which consists of anonymous expert judgments and unrestricted group discussion [19]. First, a moderator was assigned to the group meeting of three experts, handing out a discussion form covering all issues related to the proposed model structure. Then, experts anonymously made judgments, shared their feedback, and discussed all conflicts in subsequent meetings until they reached over 86% consensus. After several revised versions were created, the final version was approved by all experts, and the proposed measurement framework was constructed. According to the COBIT framework [18], strategy is defined as the components to build and sustain a governance system: processes, organizational structures, policies and procedures, information flows, culture and behaviors, skills, and infrastructure. These components were termed enablers in COBIT. Correspondingly, the strategy for intelligent DX should be developed by considering all aspects of strategy components such as vision, mission, organizational objectives, achievement guidelines, KPIs, strategy planning, and management approaches (i.e., SWOT analysis). These strategy components significantly affect DX [9, 24, 41]. Accordingly, the proposed measurement framework is established as hierarchically formed consisting of three dimensions, which are further divided into sub-dimensions, and formalized KPIs and metrics.

3.1 Main Dimensions As seen in Fig. 1, the proposed measurement framework consists of three interconnected dimensions as follows: 1. Rationalization of a digital strategy: this dimension evaluates the development and implementation status of an intelligent DX strategy, as well as strategic planning capabilities. Fig. 1 The main dimensions of the proposed measurement framework for intelligent DX strategy management

Intelligent Digital Transformation Strategy Management …

83

2. Organizational alignment: This dimension evaluates the organizational assets and readiness for intelligent DX, as well as the operationalization level of the defined strategy via the system of indicators. 3. Financial Aspects: It covers the financial aspects (e.g., investments, funding, etc.) of DX in people, processes, and technology. For DX’s success, organizations first start with rationalizing a strategy. The organization’s DX strategy significantly affects its alignment and financial settings. On the other hand, financial aspects may restrict the planned activities for DX. These restrictions may force organizations to update their DX strategy by considering financial constraints such as limited budget allocation for the whole transformation period.

3.2 Sub-dimensions The framework has a hierarchical form that covers several sub-dimensions under the main dimensions, as seen in Fig. 2.

Framework

Dimensions D1. Rationalization of a DX strategy

The Intelligent DX Strategy Management Measurement Framework

Sub-dimensions SD1.1. Development and implementation status of the strategy SD1.2. Strategy planning tools SD2.1. Management Skills

D2. Organizational alignment

SD2.2. Employee Adaptability SD2.3. Communication Structure

SD2.4. Competitors

SD3.1. Funding and Investment Activities D3. Financial Aspects

SD3.2. Resource Allocation

Fig. 2 The proposed measurement framework for intelligent DX strategy management

84

U. Sener ¸ et al.

3.2.1 SD1.1. Development and Implementation Status of the Strategy This sub-dimension assesses the development and implementation status of an intelligent DX strategy. Corporate strategy should form a roadmap that consists of explicitly defined steps over long- and short-term time horizons [36]. In the phase of strategy development, all aspects of processes, technology, and humans should be considered in a holistic way to achieve organizational goals. Workshops with all involved stakeholders would significantly contribute to the DX strategy development phase. DX strategy can be explicitly documented and updated to adapt to the changing circumstances and stay competitive in the marketplace. 3.2.2 SD1.2. Strategy Planning Tools This sub-dimension evaluates the strategic planning capabilities of organizations. Strategic planning and management tools such as KPIs, metrics, and SWOT analysis significantly affect the organization’s success in implementing Industry 4.0 applications [37, 41]. Balanced Scorecard, Theory of Change (TOC) model, Hoshin Planning, VRIO, PESTLE Analysis, and Porter’s Five Forces are some examples of strategic planning and management tools. Organizations can choose at least one of these tools to assess their current performance and competitors’ market status. 3.2.3 SD2.1. Management Skills Top management support is a crucial factor for a successful DX [7, 41]. Managers’ commitment and support for intelligent DX are crucial for assigning essential resources for the conversion. Strategic decisions such as investing in cutting-edge technologies (e.g., artificial intelligence, cloud computing, blockchain, quantum computing, and 5G), hiring highly-skilled employees, and purchasing new IT-related equipment (i.e., network servers) are made by top managers. If they are knowledgeable about the benefits of intelligent DX, they will be more willing to transition toward an intelligent environment. From the perspective of SMEs, the achievement of intelligent DX relies on the managerial skills of the managers. Since SMEs have very limited monetary or non-monetary capabilities, managers in SMEs should have strategic vision, and know-how to apply the most appropriate transformation strategies, assign organizational assets, improve Human Resource (HR) skillsets, and make use of outer support (e.g., tax incentives, government policies on university-industry collaboration, etc.) [12, 26]. Consequently, since the management skill set is one of the most critical dimensions in successful DX Strategy management, it is stated in the proposed measurement framework. 3.2.4 SD2.2. Employee Adaptability Intelligent DX aims to build smart, interconnected, and self-adaptive systems by implementing many technologies such as IoT, cloud computing, wireless access network, etc. Moving from traditional systems to cyber-physical systems is mainly managed by organizations’ human assets [29]. Therefore, employee willingness to adapt to this change is seen as a very substantial factor for the success of DX, and is significantly related to organizational strategy [41]. Organization strategy can stimulate employees to have essential skills to transition from an on premise system

Intelligent Digital Transformation Strategy Management …

85

to an intelligent environment. According to Acatech Maturity Index [36], a motivational goal system can be developed to measure not only the performance of employees but also the achievement of the teams they belong to. This system will stimulate collaboration among both specialists of the same domain and organizational subdivisions for a shared organizational objective. Furthermore, a competencies pool consisting of capabilities of existing HR can be created, and further enlarged by providing additional training sessions, and motivational improvement openings. Lastly, the intelligent DX in the context of Industry 4.0 brings new job definitions with enriched technical expertise for using new technologies such as data analytics [31], mobile computing, IoT, blockchain, quantum computing, research and development, and other customer-oriented services to support innovation management of the organization. 3.2.5 SD2.3. Communication Structure Since the communication elements in organizations significantly affect the overall productivity of business operations, the corporate strategy should form compulsory procedures to support and sustain efficient communication within and outside. A well-organized and computerized communication structure will enable employees and business partners to interchange the information needed smoothly and improve cooperation in the business environment [2, 36, 42]. Semi-structured or structured information should be traceable by having the following characteristics of such a system: visible, well-documented, automatically tagged according to the context, available in real-time, and merged with the corresponding enterprise application [36]. 3.2.6 SD2.4. Competitors Organizations need to frequently analyze the status of their competitors in the value network of the market. It is essential to assess the competitor’s capabilities to place themselves in the market. SWOT analysis can be utilized to see the strengths and weaknesses of the competitors concerning the industry and identify possible opportunities and threats to project the future. Organizations should be able to evaluate their competencies in terms of technical and non-technical capabilities and adjust them according to fluctuating environment situations [20, 36]. 3.2.7 SD3.1. Funding and Investment Activities This sub-dimension evaluates the funding and investment activities of organizations. The financial aspect is found to be a significant factor for DX [12, 24, 38, 41]. This aspect can be both a driver and inhibitor for transitioning toward an intelligent environment. For instance, limited financial resources for fundamental activities of an organization may decrease the awareness of the necessity and immediateness of a change. Therefore, organizations should understand the potential opportunities and benefits of DX and discover their suitable choices in time [24]. DX initiates the tactical funding in HR, processes, and IT resources [40]. Organizations have been trying to implement IT investments with the highest ROI as digitization projects to

86

U. Sener ¸ et al.

accelerate intelligent DX. The main area of these investments can be listed as cuttingedge technologies such as IoT devices, sensors, wide-broad network access (i.e., 5G) equipment, computerized systems and applications package such as Manufacturing Execution Systems (MES), HR (e.g., the training program for new skills acquisition, and getting consultancy from domain experts, etc.); sustaining organizational revolution [31]. 3.2.8 SD3.2. Resource Allocation Since the stages of the transformation into an intelligent digital environment are vigorous and extremely resource-oriented, demanding continuous monetary funds and HR assignation, even large-sized enterprises may have difficulty in funding and sustaining the alteration [32]. Especially SMEs should consider the challenges in acquiring essential properties throughout the transformation phases [12, 22, 25].

3.3 KPIs and Metrics As the further steps, KPIs and metrics are defined for each sub-dimension of the proposed measurement framework to be able to measure the strategy management capabilities of an organization in the context of intelligent DX. Table 2 shows each sub-dimension and corresponding KPIs. It is formed as assessment questions to measure organizations’ DX strategy management capability. Table 2 KPIs and metrics of the proposed measurement framework #SD

KPIs and measurement metrics

SD1.1

To what extent do you conduct workshops with stakeholders for a DX strategy development? To what extent do you have documents that explicitly state and explain your DX strategy? To what extent do you have a roadmap of activities for intelligent DX in your organization? How frequently does your organization update the DX strategy documents?

SD1.2

How do DX objectives support the enterprise strategy? Does your organization employ strategic planning and management tools (e.g., SWOT analysis, Balanced Scorecard, Theory of Change (TOC) model, Hoshin Planning, VRIO, PESTLE Analysis, Porter’s Five Forces, etc.)? Do you have any pre-defined metrics, KPIs, or indicators for strategy management for each tactical objective?

SD2.1

What is the satisfaction level of a top manager with a DX strategy in your organization? (continued)

Intelligent Digital Transformation Strategy Management …

87

Table 2 (continued) #SD

KPIs and measurement metrics To what extent does your manager perceive the benefits of moving from on premise systems to a smart environment? To what extent does your manager support investing in cutting-edge technologies? To what extent does your manager support hiring talented employees in technology domains (e.g., data scientists, cloud computing experts, 5G specialists, etc.) when needed?

SD2.2

What is the satisfaction level of employees with a DX strategy in your organization? What is the level of employee willingness for a change in the context of intelligent DX? Are the technical skills of employees sufficient to handle DX? Does your company need to hire highly skilled personnel for the specific purpose of the transformation period? Is your organization’s motivational goal system sufficient to encourage employees to work in more productive and goal-oriented ways?

SD2.3

Is the knowledge management system effective to exchange ideas, information, and insight with your colleagues or other employees of the organization? Is semi-structured or structured information in the knowledge management system traceable, well-documented, automatically tagged according to the context, available in real-time, and merged with the corresponding enterprise application?

SD2.4

To what extent do you evaluate competitors’ performance via benchmarking tools? To what extent does your organization update its competencies according to the competitors’ status in the market?

SD3.1

To what extent does your organization allocate tactical funding in HR, processes, and IT resources? What is the level of investments in digitization projects to accelerate intelligent DX? To what extent does your organization spend on cutting-edge technologies (e.g., IoT devices, sensors, 5G equipment, WLAN, etc.)? To what extent does your organization spend on computerized systems and application packages such as manufacturing execution systems (MES)? To what extent does your organization allocate funding for the development of its human assets (e.g., a training program for new skills acquisition, getting consultancy from domain experts, etc.)?

SD3.2

To what extent do you access essential materials and resources for intelligent DX? To what extent does your organization have difficulty in funding essential materials and resources? To what extent does your organization have difficulty in covering the expenditure regarding HR assignation?

3.4 The Measurement Process The measurement process of the Intelligent DX Strategy Management Framework is summarized as depicted in Fig. 3.

88

U. Sener ¸ et al.

Fig. 3 The steps of the measurement framework assessment

Table 3 Exemplary measurement metric of the sub-dimension SD1 # of Question 4

How frequently does your organization update the digital strategy documents?

1

2

3

4

5

▢

▢

▢

▢

▢

1. Far below standards, 5. Far above standards

As the first step, an interview environment is set via online meeting platform, and the list of the questions given in Table 2 is shared with the participants of the measurement process. Consequently, the intelligent DX strategy management of the selected company is evaluated based on the responses of the questions, which are converted from linguistic judgement to the Likert Scale. The judgement is rated from 1: “not distinct” to 5: “very distinct” scale. An example measurement of the metric named SD1.1.4 (the fourth metric of the “development and implementation status of the strategy” sub-dimension of SD1) is given in Table 3. First, the representative of the selected company provides required information for evaluating the answers of the corresponding questions. Then the participants identify the achievement rate of the corresponding metric. Participants consists of a top manager of the selected company and three experts for evaluating the answers. The achievement rates of the metrics are identified based on a shared consensus on the scaling of the metrics. Secondly, the overall score of the sub-dimensions and dimensions are calculated by computing the average value of the corresponding scores. The overall scores of three dimensions and eight sub-dimensions of the assessment framework are calculated. At the final stage, the assessment report of the intelligent DX strategy management is generated based on the score of each item.

3.5 Pilot Study The pilot study of the proposed measurement framework for strategy management is given in this section. A company from the energy sector is selected for the pilot study. This company has dedicated branches for intelligent DX and data analytics. Over four years, they have announced their intelligent DX strategy as refined with the “connected” terms. Accordingly, a digital strategy that will create the wheels of a whole as connected data, connected departments and connected platforms is adopted by the company. The questions of the measurement framework stated in Table 2 are

Intelligent Digital Transformation Strategy Management …

89

shared with the managers of Data Analytics Center in the company. This department mainly focuses on digital transformation in energy sector by strengthening their major capabilities and decision making skills through data analytics in the context of Industry 4.0. A meeting was set via online meeting tool in May 2022. Three experts attended sessions to assist evaluating the answers of questions by employing Likert Scale which converts linguistics judgement into numerical values. In this study, the participants of the sessions have reached a shared consensus on rating of the metrics. According to the interview results with the company manager, the strategy management of intelligent DX is assessed via metrics defined in Table 2, and overall results are depicted in Fig. 4 and Fig. 5, respectively. According to the results, the scores of D1, D2, and D3 are found as 3.8, 3.6, and 3.5, respectively, as depicted in Fig. 4. Although the gap between the scores of the main dimensions is not very distinctive, Rationalization of a Digital Strategy is found as the most significant dimension for intelligent DX strategy management for the company, while Financial Aspects has the lowest score among the main dimensions of the proposed model. That means the company has already built an intelligent DX strategy and tried to employ some strategic planning tools for further improvement, Fig. 4 Overall scores of the dimensions of the proposed measurement framework

Fig. 5 Radar chart of sub-dimensions’ overall scores of the proposed measurement framework

90

U. Sener ¸ et al.

but the financial constraints restrict the planned activities for DX to some extent. However, Organizational Alignment of the company is found to be slightly better than the Financial Aspects of the organization. Despite limited resource allocation for intelligent DX and the operationalization of the defined strategy, the company’s organizational readiness for intelligent DX seems to be noticeably successful. In this case, it can be said that the company can update its intelligent DX strategy management by considering its financial aspects, such as resource allocation. As for the sub-dimension of the proposed model, the radar chart for the overall scores of sub-dimensions is illustrated as in Fig. 5. According to the results, Management Skills, Strategy Planning Tools, Employee Adaptability and Funding and Investment Activities have the highest score on the overall assessment. As the results indicate several findings: ● The managers highly perceive the advantages of moving from on-premises systems to a smart environment, and they are very eager to invest in cutting edge technologies and hire talented employees in technology domains (e.g., data scientists, cloud computing experts, 5G specialists, etc.) when needed. ● Although the intelligent DX strategy of the company fully supports the enterprise strategy, and the company employs some strategic planning and management tools (e.g., SWOT analysis, Balanced Scorecard, Theory of Change (TOC) model, Hoshin Planning, VRIO, PESTLE Analysis, Porter’s Five Forces, etc.); the organization needs to extend its pre-defined metrics, KPIs, or indicators for strategy management for each tactical strategy. This will support the transparent management system and increase the visibility of the progress in every phase of intelligent digital transformation. ● The company prioritizes its human assets and supports them by initiating funding and investment activities. On the other hand, the score of the Communication Structure, Competitors, and Resource Allocation dimensions are found as the lowest dimensions among the groups as seen in Fig. 5. These results indicate several findings as follows: ● The company’s communication structure should be improved by transforming into a well-organized and computerized communication system, resulting in more productive cooperation among in-house personnel and the business partners. ● The competitors should be analyzed more systematically via several tools (i.e., SWOT analysis, benchmarking) by considering their technical and non-technical competencies. The company should update its competencies regularly and adjust its capabilities according to fluctuating market situations. ● It is observed that the company faces some challenges in obtaining essential properties for intelligent DX. That means, if the company focuses on solving problems related to resource allocation, this will boost the performance of Financial Aspects of the strategy management, and affect the overall performance of other dimensions such as the Rationalization of a Digital Strategy and Organizational Alignment.

Intelligent Digital Transformation Strategy Management …

91

4 Discussion and Conclusion This study aims to construct a measurement framework for strategy management of intelligent DX. Since the transformation into DX applications is still in a very early phase, there is an essential need to assist organizations in transitioning to a smart environment. Therefore, this study presents a strategy management framework that enables organizations to develop a roadmap for intelligent transformation. The model is constructed by applying the Wideband Delphi approach. It consists of three main dimensions: rationalization of a digital strategy, organizational alignment, and financial aspects. Dimensions are further divided into eight sub-dimensions: development and implementation status of the strategy, strategy planning tools, management skills, employee adaptability, communication structure, competitors, funding and investment activities, and resource allocation. Furthermore, a measurement process is also given to show the applicability of the proposed measurement framework. The main contribution of the study is that organizations can identify their current competencies and readiness for intelligent DX. They can evaluate their DX strategy skillsets by considering all aspects, including processes, technology, and humans. The proposed measurement framework generates valuable insights about which capabilities of organizations should be enhanced and which powerful assets the company should build on. Furthermore, this framework encourages managers or decisionmakers to form a step-wise action plan and exploit the opportunities of intelligent transformation by evaluating their strategy effectiveness for intelligent DX. As part of future work, it is planned to conduct case studies in different sectors and organizations to prove the model’s generalizability.

References 1. Agrawal M, Dutta S, Kelly R, Millan I (2021) COVID-19: an inflection point for Industry 4.0 2. Ahmed S, Miskon S (2020) IoT driven resiliency with artificial intelligence, machine learning and analytics for digital transformation. In: 2020 international conference on decision aid sciences and application, DASA 2020, pp 1205–1208 3. Akdil KY, Ustundag A, Cevikcan E (2018) Maturity and readiness model for industry 4.0 strategy. In: Industry 4.0: managing the digital transformation. Springer, Cham, pp 61–94. 4. Arden NS, Fisher AC, Tyner K, Yu LX, Lee SL, Kopcha M (2021) Industry 4.0 for pharmaceutical manufacturing: preparing for the smart factories of the future. Int J Pharm 602:120554 5. Castelo-Branco I, Oliveira T, Simões-Coelho P, Portugal J, Filipe I (2022) Measuring the fourth industrial revolution through the Industry 4.0 lens: the relevance of resources, capabilities and the value chain. Comput Ind 138:103639 6. Cavalcante IM, Frazzon EM, Forcellini FA, Ivanov D (2019) A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int J Inf Manage Pergamon 49:86–97 7. Clarke P, Yilmaz M (2017) Software process improvement and capability determination conference 2016, Computer standards & interfaces, vol 54. Springer, Cham, pp 117–118 8. Digital Density Index (2015) Guiding digital transformation, Accenture strategy

92

U. Sener ¸ et al.

9. Erol S, Schumacher A, Sihn W (2016) Strategic guidance towards Industry 4.0 – a three-stage process model, Coma, pp 495–501 10. Facchini F, Ole J, Ranieri L, Urbinati A (2020) a maturity model for logistics 4.0: an empirical analysis and a roadmap for future research 11. Ganzarain J, Errasti N (2016) Three stage maturity model in SME’s toward industry 4.0. J Ind Eng Manage 9(5):1119 12. Ghobakhloo M, Iranmanesh M (2021) Digital transformation success under Industry 4.0: a strategic guideline for manufacturing SMEs. J Manuf Technol Manag 32(8):1533–1556 13. Gökalp E, Martinez V (2021) Digital transformation capability maturity model enabling the assessment of industrial manufacturers. Comput Ind 132:103522. https://doi.org/10.1016/j.com pind.2021.103522 14. Gökalp E, Martinez V (2021b) Digital transformation maturity assessment: development of the digital transformation capability maturity model. Int J Prod Res 1–21. https://doi.org/10. 1080/00207543.2021.1991020 15. Gökalp E, Sener ¸ U, Eren PE (2017) Development of an assessment model for industry 4.0: Industry 4.0-MM. In: International conference on software process improvement and capability determination, pp 128–142. Springer, Cham. https://doi.org/10.1007/978-3-319-67383-7_10 16. Gökalp MO, Gökalp E, Kayabay K, Koçyi˘git A, Eren PE (2021) Data-driven manufacturing: an assessment model for data science maturity. J Manuf Syst 60:527–546. https://doi.org/10. 1016/j.jmsy.2021.07.011 17. Gökalp MO, Kayabay K, Gökalp E, Koçyi˘git A, Eren PE (2021) Assessment of process capabilities in transition to a data-driven organisation: a multidisciplinary approach. IET Softw 15(6):376–390. https://doi.org/10.1049/sfw2.12033 18. ISACA (2018) COBIT 2019 framework: governance and management objectives, COBIT 2019 framework. https://www.isaca.org/resources/cobit 19. Jones C (2007) Estimating software cost, Mc-Graw Hi 20. Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the Industrie 4.0 WG. https://doi.org/10.13140/RG. 2.1.1205.8966 21. Kayabay K, Gökalp MO, Gökalp E, Erhan Eren P, Koçyi˘git A (2022) Data science roadmapping: an architectural framework for facilitating transformation towards a data-driven organization. Technol Forecast Soc Change 174:121264. https://doi.org/10.1016/j.techfore.2021.121264 22. Kumar R, Singh RK, Dwivedi YK (2020) Application of Industry 4.0 technologies in Indian SMEs for sustainable growth: analysis of challenges. J Clean Prod 275:124063 23. Lin T, Wang KJ, Sheng ML (2020) To assess smart manufacturing readiness by maturity model: a case study on Taiwan enterprises. Int J Comput Integr Manuf 33(1):102–115 24. Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5):339– 343 25. Mittal S, Khan MA, Purohit JK, Menon K, Romero D, Wuest T (2020) A smart manufacturing adoption framework for SMEs. Int J Prod Res 58(5):1555–1573 26. Moeuf A, Lamouri S, Pellerin R, Tamayo-Giraldo S, Tobon-Valencia E, Eburdy R (2020) Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs. Int J Prod Res 58(5):1384–1400 27. Naseeb C (2020) AI and ML-driving and exponentiating sustainable and quantifiable digital transformation. In: Proceedings - 2020 IEEE 44th annual computers, software, and applications conference, COMPSAC 2020, pp 316–321 28. Neff AA, Hamel F, Herz TP, Uebernickel F, Brenner W, Vom Brocke J (2014) Developing a maturity model for service systems in heavy equipment manufacturing enterprises. Inf Manage 51(7):895–911 29. Palazzeschi L, Ornella B, Annamaria DF (2018) Re-thinking innovation in organizations in the industry 4.0 scenario: New challenges in a primary prevention perspective. Front Psychol 9:30 30. Perea Muñoz L, Pan Nogueras ML, Suarez Anzorena D (2022) An ‘end to end’ methodological framework to assist SMEs in the Industry 4.0 journey from a sectoral perspective - an empirical

Intelligent Digital Transformation Strategy Management …

31. 32.

33. 34. 35.

36. 37. 38. 39.

40. 41. 42.

43.

44.

93

study in the oil and gas sector. Lecture notes in mechanical engineering. Springer, Cham, pp 705–712 PWC (2016) 2016 Global Industry 4.0 Survey, Industry 4.0: building the digital enterprise Raj A, Dwivedi G, Sharma A, de Sousa Jabbour ABL, Rajak S (2020) Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. Int J Prod Econ 224:107546 Rajnai Z, Kocsis I (2018) Assessing industry 4.0 readiness of enterprises. In: 2018 IEEE 16th world symposium on applied machine intelligence and informatics (SAMI). IEEE, pp 225–230 Riahi Sfar A, Natalizio E, Challal Y, Chtourou Z (2018) A roadmap for security challenges in the Internet of Things. Digit Commun Netw 4(2):118–137 Ruchi S, Srinath P (2018) Big Data Platform for Enterprise project management digitization using Machine learning. In: Proceedings of the 2nd international conference on electronics, communication and aerospace technology, ICECA 2018, Institute of Electrical and Electronics Engineers Inc., pp 1479–1484 Schuh G, Anderl R, Gausemeier J, ten Hompel M, Wahlster W (2017) Industry 4.0 maturity index, assembly, vol 61 Schumacher A, Erol S, Sihn W (2016a) A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52:161–166 Schumacher A, Erol S, Sihn W (2016b) A maturity model for assessing and maturity of manufacturing enterprises. Procedia CIRP 52:161–166 Sener ¸ U, Gökalp E, Eren PE (2019) Towards a maturity model for industry 4.0: a systematic literature review and a model proposal. In: Gülseçen S, Ayvaz Reis Z, Gezer M, Erol Ç (eds.) Industry 4.0 From The Management Information Systems Perspectives, (Issue December, pp. 291–303). Peter Lang D. https://doi.org/10.3726/b15120 Solis B (2017) The six stages of digital transformation maturity. Altimeter Group. https://www. cognizant.com/whitepapers/the-six-stages-of-digital-transformation-maturity.pdf Sony M, Naik S (2020) Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature review. Benchmark Int J 27(7):2213–2232 Stich V, Gudergan G, Zeller V (2018) Need and solution to transform the manufacturing industry in the age of Industry 4.0 – a capability maturity index approach. In: 19th IFIP WG 5.5 working conference on virtual enterprises, PRO-VE 2018, Cardiff, UK, vol 534, pp 33–42 World Economic Forum (2022) The global smart industry readiness index initiative : manufacturing transformation insights report 2022. https://www3.weforum.org/docs/WEF_The_Glo bal_Smart_Industry_Readiness_Index_Initiative_2022.pdf Zimmermann A, Schmidt R, Sandkuhl K, Jugel D, Schweda C, Bogner J (2021) Architecting digital products and services. In: Intelligent systems reference library, vol 188. Springer, Cham, pp 181–197

Automotive

Digital Transformation in Automotive Sector ˙ Elif Haktanır, Cengiz Kahraman, Selçuk Çebi, Irem Otay, and Eda Boltürk

Abstract The automotive industry is one of the industries that adapts the fastest to digital transformation (DT) in the world because of its standard production process and mass production. It is the sector that needs digitalization the most in order to gain a competitive advantage and increase its production quality. This chapter focuses on the DT in automotive production processes in both academic literature and industrial applications. It presents the development of automobile technology throughout the years, the type of robots used in automobile production, and an assessment of alternative robot technologies for digital production processes with a sensitivity analysis. The chapter is concluded by future research directions and suggestions. Keywords Digital transformation · Automotive industry · Multi-criteria decision making · Spherical fuzzy sets · AHP · TOPSIS · Cobots

E. Haktanır (B) Department of Industrial Engineering, Bahcesehir University, 34349, Besiktas Istanbul, Turkey e-mail: [email protected] C. Kahraman Department of Industrial Engineering, Istanbul Technical University, 34367, Besiktas Istanbul, Turkey S. Çebi Department of Industrial Engineering, Yildiz Technical University, 34220, Esenler Istanbul, Turkey ˙I. Otay Department of Industrial Engineering, Istanbul Bilgi University, 34060, Eyüpsultan ˙Istanbul, Turkey E. Boltürk Graduate School, Istanbul Technical University, 34367, Besiktas Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_5

97

98

E. Haktanır et al.

1 Introduction The invention of wheel is traced back to the late Neolithic age, is one of the most important inventions of all the times. Wheel cart with two wheels is pulled by animals such as horses, buffalo, and oxen, was designed for transportation [1]. The word automobile traced back from the ancient Greek word “αÙτ´oς” (self) and the Latin word “mobilis” (movable). The word of car is believed to be coming from the Latin word carrus (wheeled vehicle), or the English word carre (cart) [1]. • Before 1900 The first automobile called as ‘Motorwagen’ created by German inventor Karl Benz in 1886. Benz received a grant of a patent for his car in the same year. Production started in 1888 right after Bertha Benz, the wife of Karl Benz, had a long trip (almost 104 km) in 1888 [2]. After several improvements, four wheels, a fuel tank, and rear brakes were added to the model. However, the Americans dominated the industry in the beginning of twentieth century [3]. • 1900s During the first years of the century, automobiles were expensive and costly to the public. The main problem of producing a car was how to produce in large quantities with a low unit cost. For that reason, many companies operating in Europe and America worked hard to reduce the production costs [3]. In 1902, Ransom Olds at his factory focused on production in large-scale and production-line manufacturing technique based on assembly line concept pioneered by England in 1802 [1]. 1908 was the first time when a car was aggressively marketed to regular families by Ford Motor Company. By expanding assembly line technique, the company launched its well-known model called the Model T [4]. The Model T has become the first car to be mass-produced on the assembly line [3]. With the reduction in costs, the price of the car decreased from $950 to $290 in 1926; so that, a regular assembly line worker could afford to purchase the model with four months’ pay [1]. From 1908 to 1927, Ford produced and sold around 15 million cars and became a legend in the history [4, 5]. In 1908, William C. Durant founded a motor giant, the largest privately manufacturing enterprise in the world and potential major competitor in the U.S. named as “General Motors” by combining Buick, Oakland, and Oldsmobile companies [3, 6]. In 1920s, The Chrysler Corporation and many other companies started production of automobiles. By the 1920s, a very well-known concept “Big Three” emerged from Ford, General Motors, and Chrysler [5]. • 1930s By 1929, the Great Depression causing the stock market crash happened in the U.S. and spread to all over the world. At that time period, automobile companies were producing and selling more than 5 million cars per year. Since the Great Depression hit the car industry very hard, many companies were either shrink or closed;

Digital Transformation in Automotive Sector

99

hence, many workers were laid off. Accordingly, the United Auto Workers Union was founded in 1935 [3]. In 1936, General Motors had 43% of the U.S. market while Ford (22%) had been in the third rank behind Chrysler having 25% of the industry [5]. • 1940s World War II (WWII) yielded the automobile industry to get out of the Great Depression. During WWII, the government shut down many automobile factories and converted the current stocks to be used for the army related services. Since new vehicle production stopped, most of the companies worked on the design and production of the defense vehicles and war materials which caused enormous technological advancements [1, 3, 5]. • 1950–1970s After the end of WWII, construction of highways being one of the largest government expenditures, enhanced dramatically in the 1950s. At the same time period, through innovation and new technological improvements, there had been changes in design and mechanical features of the cars. The American society started purchasing more cars than before. Hence, cars have played an important role for the American’s daily lives [3]. Post WWII led the rapid modernization and advancements in the automobile industry. For instance, the industry has put more emphasis on safety of the passengers. In 1964, seat belts which later have become a standard equipment on all the vehicles, were introduced. Besides these, producing large and luxurious cars with powerful motors, and comfortable seats were among other concerns of the industry [3]. From 1950 to 1970s, some new properties integrated into cars were as follows: Fiberglass bodies, higher compression ratio fuels, aesthetical properties, safety and environmental regulations, speed limits, heating and ventilation equipment [2]. However, owning and keeping large and luxurious cars with large gasoline or petrol consumption was quite costly for regular person [1]. On the other hand, in 1966 the electronic fuel injection system was proposed which can be considered as a milestone in terms of improvement in effectiveness and efficiency of the engines [4]. In the 1970s, a major oil crisis emerged which forced automobile companies to make more fuel-efficient cars [3]. At the same time period, Japanese cars have been very popular because of producing small and relatively fuel-efficient cars [7]. • 1980–1990s In 1980, approximately 87% of American households had one or more vehicles. Since then, Americans have been auto dependent in their daily lives [5]. After 1980s, globalization has affected the automobile industry worldwide. The increasing demand for vehicles have met with the production in countries as China or India with the low cost of skilled and experienced workers. As it was stated in the study of Sturgeon et al. [8], around 80% of world automobile production came out from seven countries in 1975, while in the beginning of 2000s 11 countries led the same production percentage [3, 8].

100

E. Haktanır et al.

In 1990s, especially Sport Utility Vehicles shortly called as SUV became popular. At that time period, since gas prices were stable and environmental issues were not seen as important as today, the car users were used to be more comfortable about using large size cars. In the late 1990s, the first hybrid technology-based cars being less dependent on gasoline and petrol, and more environmentally friendly were introduced to the industry. Specifically, Honda and Toyota automobile companies are popular for their award-winning hybrid vehicles [4]. • 2000s There has been a noteworthy growth in the global competition. During the first couple of years of the millennium, SUV type of cars have still preferred by the consumers. In 2008, there was an economic downturn which resulted in consumers tighten their belts. At the same time, fuel prices climbed up. Statistics showed that many consumers sold their large vehicles and bought small and more efficient ones in 2008. That was the time period when hybrid cars have become popular, and people were directed to more energy saving cars and technologies. When the recession was over, the popularity of hybrid cars and fuel efficiency still remained popular [3]. The automobiles have developed over the years by the help of Artificial Intelligence (AI) and smart technologies such as automated braking systems, collision sensors, and self-driving and parking capabilities; hence, smart, efficient, and more environmentally friendly vehicles have produced. Those innovative advancements are milestones in the automobile manufacturing history [4]. • 2010–2020s In the 2010s, the drivers had more options on vehicle types and luxurious features than before. Moreover, fuel-efficient, and self-directed cars with internet connected services were taking attentions of the consumers. In 2016, there have been many research on the autonomous vehicles as well [3]. For that reason, technology development companies have supported the auto manufacturers to deliver the new generation of a car with electronic functionalities [7]. Through digital simulation concept, an early prototype of the vehicle could be designed which caused reduction in time-to-market and decreasing research costs of the vehicle. New materials including carbon fiber material have started to be used for the early designs of light weight automobiles. In Fourth Industrial Revolution shortly called as Industry 4.0, human and robot collaborated systems provided the high precision and technological improvement with humans’ cognitive skills and creativity [9]. By the time when we came to 2019, the Covid-19 outbreak occurred which has affected many industries in a negative way across the world. Only in Germany, the COVID-19 crisis caused around 95% of automobile related companies to be downsized and employees to be temporarily laid off and they were supported from government with a substantial amount of pay. Nevertheless, new structure of e-commerce

Digital Transformation in Automotive Sector

101

Fig. 1 Historical background of automotive industry

activities, contactless test-drives, and car home deliveries have taken attentions of the consumers and during the pandemics the automobile companies have concentrated on more resilient supply chains [10]. Figure 1 presents the historical background of automotive industry. • 2020s Since innovative and smart manufacturing technologies have been introducing, we observe constant development in the cars. Tesla can be considered as having an essential affect in the automobile industry by setting a new set of standards for electrical vehicles [7]. As the Internet on Things (IoT), robotic systems, and other new technologies have come front, the way the vehicles are designed, manufactured, distributed, and even used will change sharply [11]. With the development of technology around the world, companies needed to keep up with this development in order to survive in a more competitive way. Industry 4.0 have offered some technologies such as AI, big data, cloud computing, robotics, industrial IoT, automation, simulation, augmented reality, and data visualization. These technologies brought many advantages to the automotive industry. Drones are started to be used in transportation and logistics which caused a reduce in delivery time. Data analytics and AI are applied on assembly lines and increased the overall quality and efficiency. 5G wireless communication added flexibility and aided connection to more automation applications. The machinery monitoring started

102

E. Haktanır et al.

to be done in real time. Consequently, Industry 4.0 has brought efficiency, agility, competitiveness, and sustainability to the automotive industry. It is thought that the use of the innovations brought by Industry 4.0 will become more and more widespread in the automotive sector as the benefits are seen and the obstacles are overcome. These new digital technologies in the automotive industry brought some new decision-making problems to the real-life applications. Decision making problems in such applications compel decision makers to make accurate and precise evaluations by considering uncertainties and lack of information. Zadeh [12] introduced the fuzzy set theory in 1965, to deal with uncertainty and subjectivity in human perceptions and thoughts. Since then, many extensions of the ordinary fuzzy sets have been introduced. As one of the latest extensions of fuzzy sets, spherical fuzzy sets (SFSs) have the advantages of better representation of uncertainties by considering membership, non-membership, and hesitancy degrees in which the squared sum of these degrees are restricted by one [13]. Since SFSs define a fuzzy number on a spherical surface by covering a larger domain, they are integrated with almost all multicriteria decision making (MCDM) techniques. Some of these extensions are, spherical fuzzy (SF) AHP [14, 15], SF AHP & ARAS [16], SF AHP & WASPAS [17], SF AHP & DEA [18], SF SWARA & MAIRCA [19], interval valued SF TOPSIS [20, 21], SF CODAS [22, 23], SF EDAS [24], SF WASPAS [25, 26], SF MULTIMOORA [27], SF PROMETHEE [28], and SF VIKOR [29]. This chapter aims to contribute to digital transformation (DT) in automotive industry by using a hybrid SF AHP & TOPSIS methodology. The model’s purpose is to select the best Cobot (Collaborative Robots), which are the robots operating safely and independently without humans. To the best knowledge of the authors, it is the first study focusing on the selection of the best Cobot technology by proposing a SF MCDM methodology. The remaining of the chapter is designed as follows. Section 2 presents a literature review on digital transformation of automotive industry. Section 3 gives insights on the digital technologies used in automobile sector. Section 4 provides preliminaries on SFSs and explains the steps of the proposed SF AHP & TOPSIS methodology. In the same chapter, the proposed SF methodology is implemented, and the results of the sensitivity analysis are also displayed. Section 5 concludes and summarizes the paper and gives directions for further research studies.

2 Literature Review DT in automotive sector has been studied by many researchers in the literature. Nayal et al. [30] analyzed data collected from 361 automotive firms in India to evaluate sustainable supply chain performances in the DT era. Giacosa et al. [31] explored the impact of customer agility and digitalization in restructuring an automotive company and its daily operations. Kossukhina et al. [32] proposed a mathematical model to

Digital Transformation in Automotive Sector

103

choose a DT model based on smart manufacturing concept by considering practical examples of smart factory in Russian automotive market. Aydın et al. [33] evaluated suppliers in DT perspective and presented a hydrogen storage systems example for automobiles. Gunduz et al. [34] evaluated companies’ maturity of DT and sustainable practices in the supply chain functions with a case study in the automotive manufacturing industry. Kulkarni and Kulkarni [35] discussed how AI and robotics will transform the mobility sector and the use of AI and machine learning in driverless cars. Balashova and Maiorova [36] analyzed creation of digital value chain of an industrial enterprise of the automotive industry by determining its key factors of DT. Bosler et al. [37] explained multi-sided changes required for the development and management of digital innovations based on multiple exploratory case studies including the automotive industry in Germany. Stroganov et al. [38] presented the development of digital quality management system concept in the automotive industry. Riasanow et al. [39] analyzed similarities of DT in five platform ecosystems including automotive industry. Li et al. [40] presented a research on DT maturity evaluation of automotive enterprises. Mokudai et al. [41] explored how Japanese automotive manufacturers, that has production systems with lean principle, address DT. Sommer et al. [42] deduced an index of digital maturity theoretically to measure digitalization to examine 167 global automotive companies. Brewis and Strønen [43] explored the innovation capabilities of firms require for DT based on four companies in automotive industry and the fast-moving consumer goods industry. Bosler [44] addressed the challenges occurred due to the introduction of digital services and the firms’ corresponding responses to them based on a multiple-case study of four incumbent automobile manufacturers. Venâncio et al. [45] developed a DT framework for adequacy of maintenance systems for Industry 4.0 and presented a multinational industrial entity belonging to the automotive sector case study to test and validate this framework. Paolucci et al. [46] investigated the interplay between DT and governance mechanisms in supply chains by evidence from the Italian automotive industry. Llopis-Albert et al. [47] analyzed the future impact of DT on business performance models and the different actors’ satisfaction by considering a wide range of aspects and actors derived from the DT process in the automotive industry. Dacal-Nieto et al. [48] presented a specific case of an automotive factory DT with a roadmap, an architecture, and analyses of quality, reliability, and predictive maintenance in the paint shop. Szalavetz [49] investigated how DT assists factory economy digital entrepreneurs in their integration in the automotive global value chains depending on interviews with ten Hungarian digital automotive technology providers. Abdelghafar and El-Sharief [50] presented concepts of Industry 4.0 and IoT by highlighting retailing process of the automotive industry. Keilbach et al. [51] examined how an automotive firm aligns its strategy to an increasingly digitized and software-based product. Gerster and Dremel [52] showed how a German premium car manufacturer increased agility in sourcing and contracting of IT services for an autonomous driving development platform. Papanagnou [53] applied the digital twin concept to an assembly production plan found in the automotive industry. Bolton et al. [54] focused on the security aspects of DT impacting productivity and innovation within a global automotive enterprise. Bondar et al. [55] provided an approach for agile

104

E. Haktanır et al.

DT of enterprise architecture models in engineering collaboration in the automotive industry. Tekin et al. [56] investigated the impact of Industry 4.0 and the DT on the automotive industry. Beckers et al. [57] provided an approach for 3D measurement data management in process chains in the automotive industry to meet technological needs of Industry 4.0. Verevka et al. [58] studied the digital technology impact on the product and regional structure of the global automotive market. Szalavetz [59] investigated the differences in the application and impact of digital technologies between manufacturing subsidiaries and lead companies of global automotive value chains. Rodrigues et al. [60] classified DT projects in the IT department of an automotive industry. Cay et al. [61] developed a decision model that can be applied for any sector which demands to conduct customer-driven innovation through DT and conducted a case study in automotive industry to clarify the strategic decision of digitalization investments of the companies. Kuntonbutr et al. [62] examined the effect of foreign ownership and capital on DT and innovation in automotive business performance. Buchmüller et al. [63] provided insights about the contributions and challenges of integrating approaches from gender studies into the field of automotive engineering in order to support interdisciplinary dialogues that foster a socially fair and inclusive DT. Ebrahimi et al. [64] proposed a new way for the determination of a roadmap for transformation in the automotive industry. Cherviakova and Cherviakova [65] proposed key steps of transformation strategy for automotive companies based on AI. Bondar et al. [66] applied a framework with the collaborative engineering services for the global automotive supply chain to demonstrate its importance in the agile digital transform. Piccinini et al. [67] investigated specific managerial challenges associated with the impact of DT in the physical product-manufacturing automotive industry. A literature review on DT in automotive industry based on Scopus database gives a list of 134 publications. Figure 2 shows the distribution of these publications with respect to years.

Fig. 2 Distribution of DT in automotive publications with respect to years

Digital Transformation in Automotive Sector

105

Fig. 3 Document type distributions of DT in automotive sector publications

After the first study on DT in automotive sector was published in 2015, the highest publication rate was attained in 2021 with 35 studies. As given in Fig. 3, most of the DT in automotive industry studies are in conference papers form which is followed by articles, conference reviews, book chapters and book, respectively. DT in automotive has been applied to many subject areas. Figure 4 shows the frequencies of these publications. Computer science, engineering, and business, management and accounting are the most frequently applied subjects, respectively.

3 Digital Technologies Used in Automotive Industry The conventional automobile manufacturing process begins with the formation of the chassis by combining raw materials in the form of steel profiles. Then, the body is formed by combining sheet metal and aluminum parts with various welding processes. The third step in the manufacturing process is painting, which aims to protect the body from corrosion and give it its final appearance. The next step is assembly. At this stage, the vehicle’s mechanical and electronic equipment, steering wheel, vehicle windows, seats, interior upholstery, and accessories are mounted on the vehicle. Finally, tires are attached to the vehicle and consumable necessaries such as fuel, antifreeze, and oil are put into the vehicle and the vehicle is made ready for operation. The last step is the controls. In this step, the engine, brake system, lighting are controlled, and the tires are balanced. After this step, the car is ready for the test drive [68, 69]. The automotive production process is really hard to manage since a vehicle is obtained by combining thousands of parts [70]. The main difficulties encountered in the production process are high working times, improving

106

E. Haktanır et al.

Fig. 4 Subject area distributions of DT in automotive sector publications

cycle time, ensuring resource efficiency and cyber security, making the agility planning required for new models (hybrid, electric, autonomous, flying vehicles), and employees compatible with current technology [71]. Computer Integrated Manufacturing (CIM), Computer-Aided Design (CAD), Group Technology (GT), Flexible Manufacturing Systems (FMS), and robots are the leading technologies used in conventional automotive production [68]. As in other industries, automobile production processes and the technologies used in these processes have been recently digitalized in order to increase the efficiency of the production process, reduce costs, and improve quality. Within the scope of digitalization of welding, it is transformed from a human-oriented process to a process that reads data from laser welders. Leveraging this digitized data allows them to visualize resource issues in real-time. Another digitalization is experienced in the determination of the vehicle to be taken into the assembly process. Automobile companies can offer multiple products and different versions for each product since the assembly line is not modeled for a single product. For instance, a sports vehicle can be assembled after a commercial vehicle on the production line where the hybrid production model is preferred. Or, after a passenger vehicle, the sun-roofed model of the same vehicle can be put on the assembly line. With the digitalization used here, more efficient production scheduling can be done by using software tools that work with real-time

Digital Transformation in Automotive Sector

107

data. Similarly, real-time data-based error analysis and process control are performed to prevent production errors in the paintworks. Moreover, with augmented reality, the infrastructure of digital assembly processes is created that shows operators which parts and where will be mounted [71]. Raw materials turn into a technological product with the contributions of workers and robots working on the automobile production line. In particular, robots sometimes work together with humans and sometimes work autonomously in this process to increase the speed, quality, efficiency of production and reduce production costs. In automobile production, industrial robot technology is generally used in dangerous and difficult production stages such as assembly, painting, welding, and quality control. Placing the car chassis on the carrier, welding and bolting the floor sheet, holding the heavy welding device, casting, laser application, painting/coating, lifting parts, transporting, and finally especially during assembly (installation of the engine and transmission on the chassis, installation of the suspension, gas tank, rear axle, front axle, gearbox, steering box, wheel drum, and braking system) take part in the many stages [72]. An industrial robot is defined as a device that is automatically controlled, a reprogrammable, multi-purpose, fixed, or mobile manipulator with three or more programmable axes, used in industrial applications according to ISO (International Organization for Standardization) 8373 standard. Industrial robots can be classified in various ways according to their forms, application areas, and industry branches. Today, industrial robots have gained the ability to see and make decisions within the scope of DT. The image taken with the help of a camera integrated into the robot is analyzed with the image processing algorithm, allowing robots to perform tasks that require visual perception such as identifying the faulty product and sorting products, as well as requiring hand-eye coordination, with high precision. Various types of industrial robots are used in practice as follows [73]. Cartesian Robots: Cartesian robots given in Fig. 5 are one of the most widely used types of robots for industrial applications. These types of robots which are often used for CNC machines and 3D printing, move linearly (in and out, up and down, and side to side) in a cartesian coordinate system (X, Y, and Z). Selective Compliance Assembly Robot Arm (SCARA): SCARA robots which are used for bio-med applications, assembly, and palletizing also provide rotational motion in addition to cartesian robots (Figs. 6 and 7). Articulated Robots: Articulated robots, often used in tasks such as assembly, arc welding, material handling, machine feeding and packaging, and whose mechanical movement and configuration closely resemble a human arm, are mounted on a base with a twist joint (Fig. 8). Cylindrical Robots: Cylindrical robots often used in tight workspaces for simple assembly, machine feeding or coating applications have a cylindrical shaped working plane achieved with a rotating shaft and an extendable arm that moves in a vertical and sliding motion (Fig. 9).

108

E. Haktanır et al.

Fig. 5 Cartesian robot

Fig. 6 Selective compliance assembly robot arm (SCARA)

Delta Robots: Delta robots typically used in the food, pharmaceutical and electronics industries, have three arms attached to a single base mounted above the work area. Delta robots operate in a dome shape and can move at high speeds and precisely because each joint of the end effector is directly controlled by three arms (Fig. 10). Polar Robots: Polar robots or spherical robots, commonly used for die casting, injection molding, welding and material handling, have an arm with two rotating joints and a linear joint connected to a base with a rotating joint. The axes of the robot work together to form a polar coordinate, which allows the robot to have a spherical working space (Fig. 11). Collaborative Robots (Cobots): Collaborative robots or Cobots which are often used in areas such as picking and placing, palletizing, quality inspection, and machine maintenance, are robots that can interact both directly and safely with humans in

Digital Transformation in Automotive Sector

109

Fig. 7 Articulated robot

Fig. 8 Cylindrical robot

a shared workspace. ISO 10218 defines four main types of collaborative robots which are Safety Monitored Stop, Speed and Separation, Power and Force Limiting, and Hand Guiding. Safety Monitored Stop Cobot is a type of Cobot that includes various safety sensors to detect whether a worker enters into the robot’s working space. Contradict to the Safety Monitored Stop Collaborative robots, the Speed and Separation Cobots are designed for applications that frequently interact with human workers. Both Safety Monitored Stop Cobots and Speed and Separation Cobots often utilize vision systems to monitor the Cobot’s workspace. Power and Force Limiting Cobots using the sensitive collision monitors are designed to allow for direct interaction with human workers without including additional safety barriers, vision systems, or external scanners. Hand Guiding Cobots is another type of Cobots

110

Fig. 9 Delta robot

Fig. 10 Polar robot

Fig. 11 Collaborative robot

E. Haktanır et al.

Digital Transformation in Automotive Sector

111

that can be programmed quick and easily by allowing operators to program new paths and positions by a hand-operated device.

4 Spherical Fuzzy AHP and TOPSIS Methodology for Robot Technology Selection in Automotive Industry 4.1 Preliminaries Gündo˘gdu and Kahraman [13] proposed SFSs as an extension of intuitionistic fuzzy sets and neutrosophic sets. In SFSs, while the squared sum of membership, nonmembership, and hesitancy parameters can be between 0 and 1, each of them can be defined between 0 and 1, independently. In the following, some definitions of SFSs are presented. Definition 1. SFS A˜ S of the universe of discourse U is given by } { ( )| A˜ S = u, μ A˜ S (u), ν A˜ S (u), π A˜ S (u) |u ∈ U

(1)

where μ A˜ S : U → [0, 1], ν A˜ S (u) : U → [0, 1], π A˜ S : U → [0, 1] and 0 ≤ μ2A˜ (u) + ν 2A˜ (u) + π A2˜ (u) ≤ 1 S

S

∀u ∈ U

S

(2)

For each u, the numbers μ A˜ S (u), ν A˜ S (u) and π A˜ S (u) are the degree of membership, non-membership, and hesitancy of u to A˜ S , respectively. Definition 2. The basic arithmetic operations for SFSs are defined as follows where λ > 0. ( )1/2 \ μ2A˜ + μ2B˜ − μ2A˜ μ2B˜ , v A˜ S v B˜ S , S S S S ) ) )1/2 ( A˜ S ⊕ B˜ S = (( 1 − μ2B˜ π A2˜ + 1 − μ2A˜ π B2˜ − π A2˜ π B2˜

(3)

( )1/2 / \ μ A˜ S μ B˜ S v 2A˜ + v 2B˜ − v 2A˜ v 2B˜ , S S S S ) ) )1/2 ( A˜ S ⊗ B˜ S = (( 1 − v 2B˜ π A2˜ + 1 − v 2A˜ π B2˜ − π A2˜ π B2˜

(4)

/

S

S

S

S

S

S

S

S

S

S

S

S

112

E. Haktanır et al.

( ( )λ _) 21 \ 1 − 1 − μ2A˜ , S ˜ λ · AS = (( )λ ( )λ _)1/2 λ 2 2 2 − 1 − μ A˜ − π A˜ v A˜ , 1 − μ A˜

(5)

( )λ _) 21 ( \ , μλA˜ , 1 − 1 − v 2A˜ S S λ A˜ S = (( )λ ( )λ _)1/2 2 2 2 1 − v A˜ − 1 − v A˜ − π A˜

(6)

/

S

S

S

S

/

S

S

S

Definition 3. Spherical Weighted ∑nArithmetic Mean (SWAM) with respect to, w = wi = 1, is defined as follows. (w1 , w2 ......., wn ); wi ∈ [0, 1]; i=1 ( ) SW AMW A˜ S1 , . . . . . . A˜ Sn = w1 A˜ S1 + w2 A˜ S2 + · · · + wn A˜ Sn ⎧ ⎫ ⎡ )wi ⎤1/2 ∏n ( ⎪ ⎪ 1 − i=1 1 − μ2A˜ ⎨ ⎬ Si ⎤ ⎡ 1/2 = ∏ ( ) ( ) w i wi ∏n ∏n n ⎪ ⎪ 2 ⎩ i=1 ⎭ 1 − μ2A˜ − π A2˜ v wA˜ i , − i=1 i=1 1 − μ A˜ Si

Si

Si

(7)

Si

4.2 Steps of the Proposed Methodology The steps of the proposed integrated SF AHP and TOPSIS methodology are summarized as follows [74, 75]. Step 1: Generate an MCDM a goal, a finite set of criteria ⎡ ) problem by representing ⎤ ( C j (X i ) = μi j , vi j , πi j , j = 1, 2, ..., n and sub-criteria, and an alternative set X = {x1 , x2 , ...xm }. Step 2: Collect pairwise comparison matrices based on the linguistic terms as in Table 1 with their corresponding SF numbers, from decision maker(s). Step 3: Check the consistency of the pairwise comparison matrices by applying the classical consistency analysis methods based on score indices [75]. Step 4: Calculate the weights of the criteria using SF AHP method. Step 4.1: Use the SWAM operator in Eq. (7) to obtain the criteria’s SF weights. Step 4.2: Defuzzify the weights using and normalize them by dividing each defuzzified weight by the sum of the defuzzified criteria weights. Step 5: Calculate the weights of sub-criteria through Step 4.1 and Step 4.2. Step 6: Evaluate the alternatives by SF TOPSIS method. Step 6.1: Ask decision maker(s) to fill out a SF decision matrix (or matrices) D = (C j (X i ))mxn using the linguistic scale.

Digital Transformation in Automotive Sector

113

Table 1 Linguistic scale with corresponding SF numbers Linguistic Terms for pairwise comparisons

(μ, v, π )

Linguistic Terms for decision matrix

(μ, v, π )

Absolutely High (AH)

(0.9, 0.1, 0)

Absolutely Poor (AP)

(0.1, 0.9, 0)

Very High (VH)

(0.8, 0.2, 0.1)

Very Poor (VP)

(0.2, 0.8, 0.1)

High (H)

(0.7, 0.3, 0.2)

Poor (P)

(0.3, 0.7, 0.2)

Medium High (MH)

(0.6, 0.4, 0.3)

Medium Poor (MP)

(0.4, 0.6, 0.3)

Approximately Equal (AE)

(0.5, 0.4, 0.4)

Neutral (N)

(0.5, 0.4, 0.4)

Medium Low (ML)

(0.4, 0.6, 0.3)

Medium Good (MG)

(0.6, 0.4, 0.3)

Low (L)

(0.3, 0.7, 0.2)

Good (G)

(0.7, 0.3, 0.2)

Very Low (VL)

(0.2, 0.8, 0.1)

Very Good (VG)

(0.8, 0.2, 0.1)

Absolutely Low (AL)

(0.1, 0.9, 0)

Absolutely Good (AG)

(0.9, 0.1, 0)

Equal (EE)

(0.5, 0.5, 0.5)

Step 6.2: Aggregate the judgments of the decision makers using the SWAM operator in Eq. ( ) (7) and obtain the aggregated SF decision matrix Dagg = (C j (X i ))mxn . ( w ) Step 6.3: Estimate the aggregated weighted SF decision matrix Dagg . ( ) w Dagg = (C j X iw )mxn

⎛ ( w w w) μ11 , v11 , π11 · · · ⎜ .. .. =⎝ . . ) ( w w w μm1 , vm1 , πm1 · · ·

( w w w) ⎞ μ1n , v1n , π1n ⎟ .. ⎠ . ( w w ) w μmn , vmn , πmn

(8)

w Step 6.4: Defuzzify the values in the Dagg matrix using Eq. (9).

_) _) ( ( πiwj 2 πiwj 2 ( ( )) Scor e C j X iw = 2μiwj − − viwj − 2 2

(9)

( ) ( ) Step 6.5: Estimate SF Positive X P I S and Negative Ideal Solutions X N I S . } ⌠ ( ( )) X P I S = C j , max < Scor e C j X iw > | j = 1, 2, . . . , n

(10)

} ⌠ ( ( w )) > | j = 1, 2, . . . , n = C j , min < Scor e C j X i

(11)

i

X

NIS

i

Step 6.6: Calculate the distances between each alternative and ideal solutions. (

d Xi , X

) PIS

/ =

( _) 1 ∑n (μ X i − μ X P I S )2 i=1 +(v X − v X P I S )2 + (π X − π X P I S )2 2n i i

(12)

114

E. Haktanır et al.

(

d Xi , X

NIS

)

/ =

( _) 1 ∑n (μ X i − μ X N I S )2 i=1 +(v X − v X N I S )2 + (π X − π X N I S )2 2n i i

(13)

Step 7: Finally, derive the closeness coefficient ratio (CC R(X i )) for each alternative and rank the alternatives based on the descending values. ) ∑ ( d Xi , X N I S ) ∑ ( ) CC R(X i ) = ∑ ( d Xi , X P I S + d Xi , X N I S

(14)

4.3 Application In this section, Collaborative Robots (Cobots) which are the robots that can operate safely alongside their human counterparts and work independently without humans, are evaluated by SF AHP and TOPSIS methodology. With the help of sensors, Cobots can interact to human contact and track the location of humans at the factory. Cobots automatically stop when someone gets too close to the production system. This provides a safe working environment for employees. Cobots are the robots that can learn through simulation and machine learning, and they don’t require to be supervised by highly skilled technicians. There are about 10 well-known companies which sell Cobots in the international markets [76]: FANUC, Yaskawa, ABB, KUKA, Universal Robots, Precise Automation, Techman Robot, AUBO Robotics, Rethink Robotics, and Doosan Robotics. In our study, among those 10 well-known companies, we selected the five alterative companies as Fanuc, Universal Robots, Rethink Robotics, KUKA, and ABB. Because of privacy policies and concerns, these alternatives are represented by symbols ranging from A1 to A5 by changing the given order. Through an extensive literature review, criteria and sub-criteria sets are determined as listed in Table 2. As seen in Table 2, there are five main criteria and a total of 21 sub-criteria evaluated in the study. Table 3 illustrates the pairwise comparisons of criteria collected from three experts by using the linguistic scale in Table 1. After linguistic terms are transformed to their corresponding SF numbers, the consistency of each pairwise comparison matrix has been measured and found to be 0.02, 0.09, and 0.08, respectively, which indicates that the evaluations are consistent. By following the proposed methodology, we computed the main and sub-criteria weights. Table 4 presents the weights of criteria and sub-criteria obtained via SF AHP. After the calculation of main and sub-criteria weights, linguistic decision matrices are collected from experts to be used in SF TOPSIS method. Table 5 displays the collected linguistic decision matrices.

Digital Transformation in Automotive Sector

115

Table 2 The list of criteria and sub-criteria C1: Control and feedback

C23: Accuracy

C2: Performance-related factors

C24: Repeatability

C3: Reliability

C25: Payload

C4: Safety

C31: Error rate

C5: Physical parameters

C32: Efficiency

C11: Vision capability or hand camera

C41: Collaborative standards

C12: Infrared sensor range

C42: Force sensing capability

C13: Number of input ports of controller

C43: Infrared sensor range

C14: Number of output ports of controller

C51: Number of arms

C15: Image recognition system

C52: Noise

C16: Ability to detect change in environment

C53: Camera maximum resolution

C21: Gantry reach

C54: Space requirements of cobot

C22: Cycle time

C55: Operating system

Table 3 Pairwise comparison matrix of criteria by three experts Criteria C1 E1 C1

C2 E2

E3

E1

C3 E2

E3

E1

C4 E2

EE EE EE ML ML ML MH H

C2

EE

EE

EE

C3

C5

E3

E1

H

MH MH H

VH

H

VH VH

EE

EE EE

C4

E2 H

E3 H

E1

E2

E3

H

VH

VH

AH

VH

AH

E

MH EE MH H

EE

EE

H

EE MH MH H

C5

EE

EE

EE

Table 4 The weights of criteria and sub-criteria Criteria

Weights

Sub-criteria

Weights

Sub-criteria

Weights

Sub-criteria

Weights

C1

0.244

C11

0.249

C22

0.129

C42

0.329

C2

0.310

C12

0.182

C23

0.332

C43

0.460

C3

0.174

C13

0.137

C24

0.115

C51

0.248

C4

0.163

C14

0.130

C25

0.177

C52

0.111

C5

0.108

C15

0.209

C31

0.536

C53

0.147

C16

0.092

C32

0.464

C54

0.189

C21

0.247

C41

0.211

C55

0.305

Once the judgments are aggregated utilizing SWAM operator in Eq. (7), the weighted aggregated SF decision matrix given in Table 6 is obtained by multiplying the sub-criteria weights with the evaluations in the aggregated SF decision matrix.

116

E. Haktanır et al.

Table 5 Decision matrices A1

A2

A3

A4

A5

E1

E2

E3

E1

E2

E3

E1

E2

E3

E1

E2

E3

E1

E2

E3

C11 AP

VP

P

P

MP

P

VP

AP

VP

P

P

MP

N

MP

N

C12 VP

P

VP

MP

P

VP

N

MP

MP

MG G

G

MP

N

VP

G

G

VG

P

P

P

MG G

G

P

P

P

C13 MG MG G C14 G

G

MG MG MG MG MP

N

N

G

MG MG AP

AP

AP

C15 N

N

N

AP

AP

VP

VG

VG

AG

VG

VG

VG

AG

VG

VG

C16 AG

AG

AG

VG

AG

G

N

MP

MP

MP

N

P

G

MG MG

C21 AP

VP

P

N

N

N

G

G

G

AG

AG

AG

VG

AG

AG

C22 MP

MP

MP

AG

VG

AG

G

G

G

P

P

N

AG

AG

VG

C23 VG

AG

AG

MG MG MG AG

AG

VG

AG

AG

AG

VP

VP

MP

C24 AG

VG

VG

VP

VP

AP

AP

VP

AP

AP

AP

AP

MG G

G

C25 P

VP

AP

N

N

N

AP

VP

AP

N

MP

MP

AP

AP

AP

C31 VG

AG

AG

G

G

VG

VG

VG

AG

AG

AG

AG

N

MP

N

C32 N

N

N

P

MP

VP

AG

AG

VG

N

MP

MP

AP

AP

AP

C41 VP

P

VP

AP

AP

VP

MG MG MG AP

AP

AP

MP

N

VP

C42 N

N

N

N

N

N

VP

AP

VP

AP

AP

AP

N

MP

N

C43 G

G

MG MP

P

MP

N

MP

MP

VP

VP

VP

G

MG MG

C51 AG

VG

VG

VG

AG

G

P

P

P

G

MG MG VP

VP

MP

C52 MP

MP

MP

VP

VP

P

AP

VP

AP

MP

N

P

AG

VG

AG

C53 AP

AP

P

AG

VG

AG

AG

AG

VG

VG

VG

VG

P

P

P

C54 MG MG G

AG

VG

AG

MP

N

N

VP

VP

VP

VG

AG

AG

C55 P

AP

AP

VP

MG MG MG N

N

MP

MG G

VP

AP

G

The SF values in Table 6, are defuzzified using the score function given in Eq. (9). Positive and negative ideal solutions are determined using Eq. (10) and Eq. (11), and they are presented in Table 7. Afterwards, the distances of each alternative to PIS and NIS with regard to subcriteria are computed employing Eqs. (12) and (13) as listed in Table 8.

(0.054,0.987,0.033)

(0.049,0.989,0.029)

(0.132,0.967,0.065)

(0.137,0.966,0.057)

(0.121,0.954,0.109)

(0.191,0.95,0)

(0.06,0.983,0.037)

(0.083,0.98,0.067)

(0.367,0.811,0.032)

(0.211,0.935,0.024)

(0.053,0.987,0.034)

(0.351,0.828,0.03)

(0.152,0.929,0.137)

(0.043,0.991,0.026)

(0.124,0.952,0.112)

(0.209,0.92,0.087)

(0.184,0.951,0.021)

(0.046,0.994,0.037)

(0.025,0.997,0.016)

(0.103,0.979,0.051)

C11

C12

C13

C14

C15

C16

C21

C22

C23

C24

C25

C31

C32

C41

C42

C43

C51

C52

C53

C54

A1

(0.174,0.957,0.012)

(0.154,0.967,0.01)

(0.027,0.997,0.016)

(0.167,0.957,0.032)

(0.107,0.965,0.084)

(0.124,0.952,0.112)

(0.027,0.995,0.011)

(0.088,0.972,0.063)

(0.267,0.882,0.074)

(0.125,0.951,0.114)

(0.033,0.994,0.016)

(0.212,0.91,0.122)

(0.241,0.919,0.016)

(0.148,0.932,0.133)

(0.153,0.964,0.03)

(0.033,0.993,0.014)

(0.119,0.971,0.069)

(0.162,0.956,0.045)

(0.069,0.984,0.052)

(0.083,0.976,0.061)

A2

Table 6 Weighted aggregated SF decision matrix

(0.07,0.985,0.062)

(0.15,0.968,0.012)

(0.015,0.998,0.006)

(0.05,0.99,0.035)

(0.128,0.951,0.11)

(0.042,0.99,0.021)

(0.124,0.969,0.072)

(0.331,0.847,0.027)

(0.331,0.841,0.039)

(0.031,0.993,0.012)

(0.025,0.995,0.01)

(0.371,0.808,0.03)

(0.163,0.953,0.056)

(0.224,0.912,0.077)

(0.07,0.985,0.06)

(0.248,0.91,0.03)

(0.088,0.976,0.077)

(0.056,0.988,0.039)

(0.099,0.971,0.085)

(0.045,0.988,0.022)

A3

(0.029,0.995,0.015)

(0.127,0.975,0.021)

(0.046,0.993,0.038)

(0.12,0.973,0.057)

(0.055,0.983,0.028)

(0.023,0.994,0)

(0.019,0.996,0)

(0.133,0.947,0.114)

(0.379,0.807,0)

(0.11,0.964,0.094)

(0.019,0.996,0)

(0.397,0.788,0)

(0.08,0.978,0.067)

(0.346,0.838,0)

(0.063,0.988,0.052)

(0.225,0.921,0.037)

(0.13,0.968,0.062)

(0.139,0.964,0.06)

(0.16,0.953,0.069)

(0.086,0.975,0.064)

A4

(0.168,0.959,0.015)

(0.039,0.994,0.027)

(0.134,0.975,0.009)

(0.048,0.991,0.035)

(0.199,0.925,0.094)

(0.118,0.957,0.105)

(0.073,0.982,0.061)

(0.028,0.992,0)

(0.155,0.927,0.137)

(0.024,0.994,0)

(0.144,0.962,0.062)

(0.095,0.967,0.069)

(0.236,0.921,0.019)

(0.319,0.856,0.028)

(0.11,0.977,0.052)

(0.251,0.908,0.028)

(0.018,0.997,0)

(0.056,0.988,0.039)

(0.083,0.978,0.069)

(0.125,0.952,0.111)

A5

Digital Transformation in Automotive Sector 117

118

E. Haktanır et al.

Table 7 SF PIS and NIS values SF PIS

SF NIS

C11

(0.125,0.952,0.111)

(0.045,0.988,0.022)

C12

(0.16,0.953,0.069)

(0.049,0.989,0.029)

C13

(0.162,0.956,0.045)

(0.056,0.988,0.039)

C14

(0.137,0.966,0.057)

(0.018,0.997,0)

C15

(0.251,0.908,0.028)

(0.033,0.993,0.014)

C16

(0.191,0.95,0)

(0.063,0.988,0.052)

C21

(0.346,0.838,0)

(0.06,0.983,0.037)

C22

(0.241,0.919,0.016)

(0.083,0.98,0.067)

C23

(0.397,0.788,0)

(0.095,0.967,0.069)

C24

(0.211,0.935,0.024)

(0.019,0.996,0)

C25

(0.125,0.951,0.114)

(0.024,0.994,0)

C31

(0.379,0.807,0)

(0.155,0.927,0.137)

C32

(0.331,0.847,0.027)

(0.028,0.992,0)

C41

(0.124,0.969,0.072)

(0.019,0.996,0)

C42

(0.124,0.952,0.112)

(0.023,0.994,0)

C43

(0.209,0.92,0.087)

(0.055,0.983,0.028)

C51

(0.184,0.951,0.021)

(0.048,0.991,0.035)

C52

(0.134,0.975,0.009)

(0.015,0.998,0.006)

C53

(0.154,0.967,0.01)

(0.025,0.997,0.016)

C54

(0.174,0.957,0.012)

(0.029,0.995,0.015)

From Table 8, the total distances to PIS and NIS values for each alternative and CCR value of each alternative are computed as given in Table 9. Accordingly, the rank of the alternative Cobots are obtained as A3 > A4 > A1 > A5 > A2.

4.4 Sensitivity Analysis In sensitivity section, we examined the effects of the changes in the experts’ weights on the decisions. The weight of each expert have been changed in the range of 0 and 1. The weight is increased by “0.1” each time while the others’ weights are kept equal by meeting the condition that the sum of the weights is “1”. In this way, 30 different cases have been analyzed. The obtained results are presented in Fig. 12. The sensitivity analysis demonstrates that there is no difference in the ranking of Cobot alternatives for changing weights of Expert 1. However, for higher weights of Expert 2 such as 0.9 or more, A2 and A5 are replaced while the rankings of the others are same. For Expert 3, the weights of 0.7 and more, the order of A3 and A4 are changed while there is no difference for the others.

Digital Transformation in Automotive Sector

119

Table 8 Distances between alternatives and PIS and NIS d(X i , X PIS )

d(X i , X NIS )

A1

A2

A3

A4

A5

A1

A2

A3

A4

A5

C11

0.012

0.005

0.016

0.004

0.000

C12

0.015

0.010

0.004

0.000

0.007

C11

0.000

0.003

0.000

0.004

0.016

C12

0.000

0.001

0.006

0.015

C13

0.001

0.000

0.012

0.001

0.003

0.012

C13

0.007

0.012

0.000

0.008

0.000

C14

0.000

0.000

0.003

C15

0.026

0.055

0.000

0.000

0.018

C14

0.018

0.016

0.011

0.017

0.000

0.001

0.000

C15

0.018

0.000

0.054

0.043

C16

0.000

0.003

0.055

0.020

0.021

0.010

C16

0.021

0.009

0.000

0.000

0.002

C21

0.104

C22

0.031

0.066

0.026

0.000

0.002

C21

0.000

0.019

0.034

0.104

0.083

0.000

0.009

0.032

0.000

C22

0.000

0.031

0.007

0.000

C23

0.029

0.002

0.064

0.002

0.000

0.128

C23

0.100

0.020

0.103

0.128

0.000

C24

0.000

0.035

0.038

0.041

0.007

C24

0.041

0.000

0.000

0.000

0.021

C25

0.013

0.000

0.021

0.001

0.025

C25

0.002

0.025

0.000

0.017

0.000

C31

0.002

0.024

0.005

0.000

0.084

C31

0.060

0.019

0.048

0.084

0.000

C32

0.051

0.076

0.000

0.057

0.113

C32

0.038

0.008

0.113

0.026

0.000

C41

0.009

0.014

0.000

0.017

0.003

C41

0.001

0.000

0.017

0.000

0.007

C42

0.000

0.000

0.017

0.025

0.000

C42

0.025

0.025

0.001

0.000

0.021

C43

0.000

0.012

0.008

0.031

0.000

C43

0.031

0.006

0.013

0.000

0.028

C51

0.000

0.000

0.020

0.006

0.020

C51

0.020

0.015

0.000

0.006

0.000

C52

0.009

0.012

0.015

0.009

0.000

C52

0.002

0.000

0.000

0.002

0.015

C53

0.017

0.000

0.000

0.001

0.014

C53

0.000

0.017

0.017

0.011

0.000

C54

0.007

0.000

0.014

0.022

0.000

C54

0.007

0.022

0.004

0.000

0.021

C55

0.011

0.016

0.000

0.003

0.000

C55

0.000

0.000

0.000

0.009

0.016

Table 9 Total distances for each alternative and closeness coefficient values A1

A2

A3

A4

A5

0.086

0.097

0.074

0.080

0.103

0.097

0.077

0.101

0.106

0.087

CCR

0.5285

0.4444

0.5773

0.5690

0.4583

Rank

3

5

1

2

4

) ∑ ( d Xi , X P I S ) ∑ ( d Xi , X N I S

120

E. Haktanır et al.

Fig. 12 Results of sensitivity analysis

5 Conclusion The digital transformation in the automotive industry is developing with a great momentum from year to year. In parallel to this development, it has seen that academic studies in this field have increased exponentially after 2017 and 2019. In particular, computer sciences and engineering fields are the leading research subjects that employs DT. It has observed that DT is widely applied especially in robot technologies used in automotive manufacturing. Therefore, in this study a MCDM model was applied under uncertainty for the selection of the most suitable robot types that can work in cooperation with humans for the digital process. By representing the uncertainty with SFSs, the most suitable robot selection has been carried out successfully. The validity of the robot rankings obtained from the sensitivity analysis was analyzed and it has observed that the ranking results were quite robust with respect to possible changes in experts’ weights. For future research, we suggest that other technologies such as laser application and painting/coating used in the automotive industry to be handled with multiple criteria analysis under other types of fuzzy sets’ extensions.

Digital Transformation in Automotive Sector

121

References 1. National Council of Educational Research and Training. (n.d.). History and evolution of automobiles. https://ncert.nic.in/vocational/pdf/ivas101.pdf 2. Technofunc (2012) History of automotive industry, 30 June 2012. https://www.technofunc. com/index.php/domain-knowledge/automotive-industry/item/history-of-automotive-industry 3. Miller-Wilson K (n.d.) History of cars and the automobile industry. https://cars.lovetoknow. com/History_of_the_Automobile_Industry 4. Brooks B (2019) The biggest milestones in the history of automotive manufacturing, 3 December 2019. https://www.qad.com/blog/2019/12/the-biggest-milestones-in-the-historyof-automotive-manufacturing 5. History.com Editors (2018) Automobile history, 21 August 2018. https://www.history.com/top ics/inventions/automobiles 6. Britannica (n.d.) Ford and the assembly line. https://www.britannica.com/technology/automo tive-industry/Ford-and-the-assembly-line 7. NES Fircroft (2020) A brief history of American automotive manufacturing, 21 October 2020. https://www.nesfircroft.com/blog/2020/10/a-brief-history-of-american-automotive-man ufacturing 8. Sturgeon TJ, Memedovic O, Van Biesebroeck J, Gereffi G (2009) Globalisation of the automotive industry: main features and trends. Int J Technol Learn Innov Dev 2(1/2):7–24 9. Airskin (n.d.) The future of robotics in automotive manufacturing. https://www.airskin.io/en/ blog/future-automotive-manufacturing 10. McKinsey and Company (2020) Reimagining the auto industry’s future: It’s now or never, 27 October 2020. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/ reimagining-the-auto-industrys-future-its-now-or-never 11. Otto Motors (2017) Automotive manufacturing keeps up with future demand, 4 April 2017. https://ottomotors.com/blog/future-of-automotive-manufacturing 12. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353 13. Kutlu Gundogdu F, Kahraman C (2019) Spherical fuzzy sets and spherical fuzzy TOPSIS method. J Intell Fuzzy Syst 36(1):337–352 14. Arıöz Y, Yılmaz I, Yıldızba¸sı A, Öztürk C (2021) Big data-driven in COVID-19 pandemic management system: evaluation of barriers with spherical fuzzy AHP approach. Int Conf Intell Fuzzy Syst 308:811–818 15. Buyuk AM, Temur GT (2021) A framework for selection of the best food waste management alternative by a spherical fuzzy AHP based approach. In: Kahraman C, Cevik Onar S, Oztaysi B, Sari I, Cebi S, Tolga A (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. AISC, vol 1197, pp 151–159. Springer, Cham. https://doi.org/10.1007/978-3030-51156-2_19 16. Gocer F, Sener N (2022) Spherical fuzzy extension of AHP-ARAS methods integrated with modified k-means clustering for logistics hub location problem. Expert Syst 39(2):12886 17. Nguyen P, Tsai J, Dang T, Lin M, Pham H, Nguyen K (2021) A hybrid spherical fuzzy MCDM approach to prioritize governmental intervention strategies against the COVID-19 pandemic: a case study from Vietnam. Mathematics 9(20):2626 18. Yilmaz MK, Kusakci AO, Aksoy M, Hacioglu U (2022) The evaluation of operational efficiencies of Turkish airports: an integrated spherical fuzzy AHP/DEA approach. Appl Soft Comput 119:108620 19. Erdo˘gan M (2022) Assessing farmers’ perception to agriculture 4.0 technologies: a new interval-valued spherical fuzzy sets based approach. Int J Intell Syst 37(2):1751–1801 20. Farrokhizadeh E, Seyfi-Shishavan SA, Kutlu Gündo˘gdu F, Donyatalab Y, Kahraman C, Seifi SH (2021) A spherical fuzzy methodology integrating maximizing deviation and TOPSIS methods. Eng Appl Artif Intell 101:104212

122

E. Haktanır et al.

21. Kutlu Gündo˘gdu F, Kahraman C (2021) Optimal site selection of electric vehicle charging station by using spherical fuzzy TOPSIS method. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_8 22. Gundogdu FK, Kahraman C (2019) Extension of CODAS with spherical fuzzy sets. J Multiple Valued Logic Soft Comput 33:481–505 23. Kara¸san A, Boltürk E, Kutlu Gündo˘gdu F (2021) Assessment of livability indices of suburban places of Istanbul by using spherical fuzzy CODAS Method. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. STUDFUZZ, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_12 24. Gül S (2021) Spherical fuzzy version of EDAS and an application. Int J Adv Eng Pure Sci 33(3):376–389 25. Boltürk E, Kutlu Gündo˘gdu F (2021) Prioritizing manufacturing challenges of a contract manufacturing company for personal auto by using spherical WASPAS method. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. STUDFUZZ, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_11 26. Kutlu Gundogdu F, Kahraman C (2019) Extension of WASPAS with spherical fuzzy sets. Informatica 30(2):269–292 27. Aydın S, Kutlu Gündo˘gd F (2021) Interval-valued spherical fuzzy MULTIMOORA method and its application to Industry 4.0. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. STUDFUZZ, vol 392. Springer, Cham. https://doi.org/10.1007/ 978-3-030-45461-6_13 28. Sharaf IM (2021) Evaluating geothermal energy systems using spherical fuzzy PROMETHEE. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. STUDFUZZ, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_16 29. Sharaf IM (2021) Spherical fuzzy VIKOR with SWAM and SWGM operators for MCDM. In: Kahraman C, Kutlu Gündo˘gdu F (eds) Decision Making with Spherical Fuzzy Sets. STUDFUZZ, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_9 30. Nayal K, Raut RD, Yadav VS, Priyadarshinee P, Narkhede BE (2022) The impact of sustainable development strategy on sustainable supply chain firm performance in the digital transformation era. Bus Strateg Environ 31(3):845–859 31. Giacosa E, Culasso F, Crocco E (2022) Customer agility in the modern automotive sector: how lead management shapes agile digital companies. Technol Forecast Soc Chang 175:121362 32. Kossukhina MA, Shvetsova OA, Zaozerskaya NI (2022) Smart factory in the era of fourth industrial revolution. In: Radionov AA, Gasiyarov VR (eds) Proceedings of the 7th International Conference on Industrial Engineering (ICIE 2021). ICIE 2021. LNME, pp 98–107. Springer, Cham. https://doi.org/10.1007/978-3-030-85230-6_13 33. Aydın S, Aktas A, Kabak M (2022) Evaluation of suppliers in the perspective of digital transformation: a spherical fuzzy TOPSIS approach. In: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. LNNS, vol 308, pp 724–732. Springer, Cham. https:// doi.org/10.1007/978-3-030-85577-2_85 34. Gunduz MA, Demir S, Paksoy T (2021) Matching functions of supply chain management with smart and sustainable tools: a novel hybrid BWM-QFD based method. Comput Ind Eng 162:107676 35. Kulkarni A, Kulkarni P (2021) Transformation of mobility industry by advanced digital technologies. J Phys: Conf Ser 1964(4):042020 36. Balashova E, Maiorova K (2021) Transforming industrial value creation in aspect of sustainable technology digitalization. E3S Web Conf 258:12006 37. Bosler M, Burr W, Ihring L (2021) Digital innovation in incumbent firms: an exploratory analysis of value creation. Int J Innov Technol Manag 18(2):2040003 38. Stroganov VI, Guly VV, Kozlovsky VN, Panyukov DI, Blagoveshchensky DI (2021) Quality management in the automotive industry in the digital transformation era. In: 2021 Systems of Signals Generating and Processing in the Field of on Board Communications, Conference Proceedings, p 9416116

Digital Transformation in Automotive Sector

123

39. Riasanow T, Jäntgen L, Hermes S, Böhm M, Krcmar H (2021) Core, intertwined, and ecosystem-specific clusters in platform ecosystems: analyzing similarities in the digital transformation of the automotive, blockchain, financial, insurance and IIoT industry. Electron Mark 31(1):89–104 40. Li P, Xu B, Wang M (2021) Research on digital transformation maturity evaluation of automotive enterprises: Based on EWM, AHP, DEMATEL, etc. method. In: 2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021, pp 16–20 41. Mokudai T et al (2021) Digital technologies as lean augmentation: a preliminary study of Japanese automotive manufacturers. Int J Automot Technol Manage 21(3):228–249 42. Sommer S, Proff H, Proff H (2021) Digital transformation in the global automotive industry. Int J Automot Technol Manage 21(4):295–321 43. Brewis C, Strønen F (2021) Digital transformation in FMCG and automotive industries – emergence of digital innovation capabilities. In: Proceedings of the European Conference on Knowledge Management, ECKM, pp 104–111 44. Bosler M (2021) Incumbent firms offering digital services: insights and lessons learned from the automotive industry. Int J Automot Technol Manage 21(4):365–389 45. Venâncio ALAC, de Freitas Rocha Loures E, Deschamps F, Diogo RA, Lumikoski AF, dos Santos N (2021) Digital transformation framework for adequacy of maintenance systems for Industry 4.0. In: Rossit DA, Tohmé F, Mejía Delgadillo G (eds) Production Research. ICPRAmericas 2020. CCIS, vol 1407, pp 280–292. Springer, Cham. https://doi.org/10.1007/978-3030-76307-7_21 46. Paolucci E, Pessot E, Ricci R (2021) The interplay between digital transformation and governance mechanisms in supply chains: evidence from the Italian automotive industry. Int J Oper Prod Manag 41(7):1119–1144 47. Llopis-Albert C, Rubio F, Valero F (2021) Impact of digital transformation on the automotive industry. Technol Forecast Soc Chang 162:120343 48. Dacal-Nieto A, Areal JJ, García-Fernández M, Lluch M (2020) Use cases and success stories of a data analytics system in an automotive paint shop. In: Proceedings - 2020 8th International Symposium on Computing and Networking, CANDAR 2020, vol 09394369, pp 95–100 49. Szalavetz A (2020) Digital transformation–enabling factory economy actors’ entrepreneurial integration in global value chains? Post-Communist Econ 32(6):771–792 50. Abdelghafar NM, El-Sharief MA (2020) The use of Industry 4.0 concepts in the retailing process of the automotive industry. In: Proceedings of the international conference on industrial engineering and operations management, pp 755–761 51. Keilbach A, Karanasios S, Molla A (2020) Strategic responses to digitized products: a case study of an automotive firm. In: 26th Americas Conference on Information Systems, AMCIS 2020 52. Gerster D, Dremel C (2020) How agility can be increased in IT sourcing and contracting: learnings from an autonomous driving case. In: Oshri I, Kotlarsky J, Willcocks LP (eds) Digital Technologies for Global Sourcing of Services. Global Sourcing 2019. LNBIP, vol 410, pp 17–37. Springer, Cham. https://doi.org/10.1007/978-3-030-66834-1_2 53. Papanagnou CI (2020) A digital twin model for enhancing performance measurement in assembly lines. In: Farsi M, Daneshkhah A, Hosseinian-Far A, Jahankhani H (eds) Digital Twin Technologies and Smart Cities. ITTCC, pp 53–66. Springer, Cham. https://doi.org/10. 1007/978-3-030-18732-3_4 54. Bolton A, Goosen L, Kritzinger E (2020) Security aspects of an empirical study into the impact of digital transformation via unified communication and collaboration technologies on the productivity and innovation of a global automotive enterprise. In: Venter H, Loock M, Coetzee M, Eloff M, Eloff J (eds) Information and Cyber Security. ISSA 2019. CCIS, vol 1166, pp 99–113. Springer, Cham. https://doi.org/10.1007/978-3-030-43276-8_8 55. Bondar S, Beckers R, Hsu JC, Pfouga A, Stjepandic J (2020) Digital transformation in context of engineering collaboration. In: 26th international association for management of technology conference, IAMOT 2017, pp 1522–1535

124

E. Haktanır et al.

56. Tekin AT, Özkale L, Gültekin-Karaka¸s D (2020) The Turkish automotive industry in the era of digital technologies and autonomous cars. In: Durakbasa N, Gençyılmaz M (eds) Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. LNME, pp 319–327. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_28 57. Beckers R, Emmer C, Pfouga A, Glaesner KH, Stjepandic J (2020) Technology management on 3D measurement for industry 4.0. In: 26th international association for management of technology conference, IAMOT 2017, pp 1444–1455 58. Verevka TV, Gutman SS, Shmatko A (2019) Prospects for innovative development of world automotive market in digital economy. In: ACM international conference proceeding series, p 3373320 59. Szalavetz A (2019) Digitalization, automation and upgrading in global value chains - factory economy actors versus lead companies. Post-Communist Econ 31(5):646–670 60. Rodrigues CM, de Freitas ICH, Cestari JMAP, Deschamps F, de Freitas Rocha Loures E (2019) Classification of projects involving digital transformation in an automotive industry. In: IISE Annual Conference and Expo 2019, p 967187 61. Cay D, Goker N, Dursun M (2019) WSEAS Modelling R&D strategy to fulfil customer demands through digital transformation. Trans Bus Econ 16:525–531 62. Kuntonbutr C, Jaturat N, Nichs A (2019) The effect of foreign ownership and capital on digital transformation and innovation in automotive business performance. Int J Innov Creativity Change 8(8):14–29 63. Buchmüller S, Bath C, Henze R (2018) To whom does the driver’s seat belong in the future? A case of negotiation between gender studies and automotive engineering. In: ACM international conference proceeding series, pp 165–174 64. Ebrahimi M, Baboli A, Rother E (2018) A Roadmap for evolution of existing production system toward the factory of the future: a case study in automotive industry. In: 2018 IEEE international conference on technology management, operations and decisions, ICTMOD 2018, vol 8691276, pp 274–281 65. Cherviakova V, Cherviakova T (2018) Value opportunities for automotive manufacturers in conditions of digital transformation of the automotive industry. J Appl Econ Sci 13(8):2351– 2362 66. Bondar S, Hsu JC, Pfouga A, Stjepandi´c J (2017) Agile digitale transformation of enterprise architecture models in engineering collaboration. Procedia Manuf 11:1343–1350 67. Piccinini E, Hanelt A, Gregory RW, Kolbe LM (2015) Transforming industrial business: the impact of digital transformation on automotive organizations. In: 2015 international conference on information systems: exploring the information frontier, ICIS 2015 68. Motor Vehicle Manufacturers Association of the United States (1987) Making the Car. Motor Vehicle Manufacturers Association of the United States 69. Mortimer J (ed) (1987) Advanced Manufacturing in the Automotive Industry. Springer-Verlag New York, Inc., New York 70. Han YH, Zhou C, Bras B, McGinnis LF, Carmichael C, Newcomb PJ (2004) Paint line color change reduction in automobile assembly through simulation. In: Proceedings of the 2003 winter simulation conference, pp 1204–1209 71. General Electric (n.d.) Experience the future of automotive manufacturing. https://www.ge. com/digital/industry/automotive 72. Yüksel B (2013) Endüstriyel Robotların Yükseli¸si. Bilim ve Teknik, pp 54–58 73. Process Solutions Inc. (2018) What are the different types of industrial robots and their applications? 1 October 2018. https://processsolutions.com/what-are-the-different-types-of-indust rial-robots-and-their-applications 74. Kahraman C, Kutlu Gundogdu F, Onar SC, Oztaysi B (2019) Hospital location selection using spherical fuzzy TOPSIS. In: 2019 conference of the international fuzzy systems association and the European society for fuzzy logic and technology (EUSFLAT 2019), Atlantis Press

Digital Transformation in Automotive Sector

125

75. Kutlu Gündo˘gdu F, Kahraman C (2020) A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Comput 24(6):4607–4621 76. Emergen Research (2022) Top 10 companies in cobot market revolutionizing the future of workplace, 2 March 2022. https://www.emergenresearch.com/blog/top-10-companies-in-cobot-mar ket-revolutionizing-the-future-of-workplace

Energy

Digital Transformation and Prosumers Activities in the Energy Sector Piotr F. Borowski

Abstract Changes are taking place in the energy sector that can be divided into technological change and social change. The technological field includes digitization, which allows for the creation of new business models based on blockchain, distributed energy, and remote measurements. In addition, it influences the creation of a network of interconnected generating units while maintaining independence and a centralized structure. The social changes include, firstly: consumer activity in the energy market, which is possible thanks to technological changes, and secondly: moving towards reducing the negative impact on the environment. Both trends of these changes interpenetrate and allow for the emergence of energy-friendly for humans and the environment. Technological innovations such as remote meters, devices for remote control and monitoring, and digital twins, used in the energy sector allow for increasing the efficiency of the sector and its faster development towards zero emissions. Keywords Digitization · Blockchain · RES · Energy · Innovation · Prosumer

1 Introduction The fourth industrial revolution has been called, for several years, the Digital Economy or Industry 4.0. The digital economy influences the structural transformations in the energy sector. Technological development and, consequently, the development of digitization in the energy sector determine the socio-economic activities undertaken. To determine the level of digitization, it is worth setting indicators that will allow the assessment of the degree of that digitization [52]. This industrial revolution, like the previous ones, is characterized by the ability to transform economies and jobs, as well as influence changes in societies along with their activation on the market. This is done by conducting research and implementation works on the introduction of new and innovative technologies and processes, including those classified P. F. Borowski (B) Faculty of Business and International Relations, Vistula University, 02-787 Warsaw, Poland e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_6

129

130

P. F. Borowski

as broadly understood digital technologies. The novelty of the research described in this chapter is the linking of innovative technological solutions with the energy sector. The chapter presents innovative solutions used in the power industry, consisting mainly of the implementation of digitization and high-tech solutions. The discussed solutions allowed, among others to activate producers and consumers of energy at the level of micro-producers of green energy. The management of enterprises in the energy sector currently focuses on a few necessary actions, namely the digitization of the sector [44, 53] and its development towards zero-emission (decarbonization) and sustainable development [32]. The development of various digital technologies in the energy sector can provide increased energy demand and support the transformation toward clean energy, more sustainable, and renewable technologies [50]. Power systems around the world are undergoing unprecedented changes and decentralization and digitization trends are transforming the energy sector by integrating renewable resources to meet the ambitious decarbonization goals. Companies that have the ability to adapt to the requirements of the macro-environment, in particular legal (incl. law, resolution, directive), ecological (e.g. environmental protection, zeroemission), or technological (green innovation, digitization, etc.) will have internal potential to implement new innovative solutions operating on the market [7, 8]. Employees also play a key role in this process. Thanks to solutions based on digital transformation, enterprises can demonstrate an increasingly innovative approach, and their employees are characterized by entrepreneurship in their activities. In the new model of running a business, which is based on digital solutions, each employed person becomes an entrepreneur and at the same time becomes a potential creator of innovation. Thanks to its creative commitment, it influences the adaptation of the company’s operations to the environment and initiates close cooperation with the end-user, and thus can be considered an entrepreneur and innovator [33]. If an enterprise has the ability to react in advance (the so-called anticipatory adaptation [7, 8] and to use various flexible processes and integration with high-tech systems, it has the potential to create a digital enterprise. Such a company is based on virtual reality and has integrated solutions from the world of automation and robotization. The direction and measures taken in the management of enterprises in the broadly understood energy sector are shown in Fig. 1. The chapter structure consists of five main points. The beginning of the chapter starts with a literature review, which presents the most recent scientific studies related to digitization, technological innovations and the activity of prosumers. Then, the development of digitization on the prosumer market was discussed, which allowed for increased activity on the energy market and the development of distributed energy based on renewable sources. The next section of the chapter presents new, innovative technologies used in the energy sector. On the other hand, digital twins and virtual power plant solutions constitute the content of the penultimate point and the whole ends with considerations related to blockchain used in the energy sector.

Digital Transformation and Prosumers Activities …

Digital twins

Prosumers

131

Blockchain

Digitization on energy sector

Fig. 1 Prosumers and innovative technologies in the energy market

2 Literature Review Issues related to technological innovations and taking active measures by prosumers in the energy market are widely researched and discussed in the literature. Thanks to the digital possibilities of transaction settlement, it is possible for prosumers to take active steps in the energy market. The activity of prosumers decentralizes the market, makes it more attractive and influences the transition to energy production based on RES. In addition, the energy market implements digital solutions that ensure global lowering of energy costs and increasing the efficiency of the sector. The table below summarizes the latest research results that were published in international scientific journals in 2019–2022. A dozen or so of the most important articles with the greatest number of citations were selected for the compilation (Table 1). Table 1 . Title of the paper, name of the journal

Main issue

Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development, Technological Forecasting and Social Change

The impact of technological innovations on energy efficiency is analyzed. In addition, the aspect of structural transformation and its impact on the energy sector were analyzed. In both cases, a positive impact was found, while at the same time marking a negative impact of the shadow economy [19]

Digital transformation in the resource and energy sectors: A systematic review. Resources Policy

The digital transformation in the energy sector has brought significant benefits that can be presented as sustainable development and economic growth in many branches and service industries and sectors. In addition, digitization accelerated and improved energy efficiency and the ability to increase the value of enterprises thanks to the implemented innovations [36] (continued)

132

P. F. Borowski

(continued) Title of the paper, name of the journal

Main issue

Artificial Intelligence and emerging digital technologies in the energy sector. Applied Energy

Digitization is an increasingly important direction in the development of energy innovations. Among digital technologies, Artificial Intelligence is the most widely used in the energy sector. Artificial intelligence has the greatest impact on the performance of energy [35]

Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and sustainable energy reviews

Blockchains and distributed ledgers are widely used in companies in the energy sector, start-ups, technology developers and other branches of the economy. Blockchains provide transparent, tamper-resistant and secure systems. These new technologies can be used from peer-to-peer (P2P) connection of the energy system, commerce and Internet of Things (IoT) applications, enable the implementation of a decentralized market model, charging electric vehicles and are also used in e-mobility [2]

Integrating blockchain technology into the energy sector—from theory of blockchain to research and application of energy blockchain. Computer Science Review

The development of blockchain technology has contributed to the decentralization of the energy sector. Innovative blockchain solutions used in the energy sector focus their attention on the production and distribution of energy obtained from RES. These solutions activate the wider replacement of fossil energy with renewable energy [56]

Energy efficiency: The role of technological innovation and knowledge spillover. Technological Forecasting and Social Change

Technological innovations in the era of rising energy prices and paying attention to the environmental aspect have a positive impact because they reduce energy consumption and contribute to the reduction of carbon dioxide emissions without harm to global economic growth. Innovation is based on the exploitation of knowledge and research has demonstrated a positive key link between knowledge spread and energy efficiency performance. Thanks to the development of research and development abilities of enterprises, it will be possible to expand the infrastructure based on innovations [45]

Challenges in the decarbonization of the energy sector. Energy

Modern technologies reduce CO2 emissions, and this has a positive effect on climate change [38] (continued)

Digital Transformation and Prosumers Activities …

133

(continued) Title of the paper, name of the journal

Main issue

Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Energy Policy

The transformation of the energy sector towards a low carbon economy is leading to a new role for citizens who move from passive energy consumers to active players in the energy market. Renewable energy communities are emerging, energy civic communities are being formed, which together act as prosumers of energy produced from renewable sources [29]

People in transitions: Energy citizenship, prosumerism and social movements in Europe. Energy Research & Social Science

The paper discusses the issue of the active participation of prosumers in the energy market. It examines whether active energy citizens influence changes in the market, creating new production and consumption structures. The initiatives proposed by prosumers influence the decentralization and transformation of the sector toward renewable energy sources [18]

Smart hybrid microgrid for effective distributed renewable energy sharing of PV prosumers. Journal of Energy Storage

Prosumers using photovoltaics or wind energy must take into account the intermittent nature of renewable energy sources and the problem of power fluctuations. In addition, the legislative issues regarding RES for prosumers are important [4]

3 Digitization on the Prosumer’s Market Increasingly broader perspectives and opportunities related to easy access to renewable energy sources and the possibility of easy and quick connection of system elements according to the peer-to-peer principle will contribute to the emergence of a new model of market operation. The current model of one-way energy flow from a centralized producer to the end-user will be changed into a two-way model. The new bidirectional model will allow for energy transmission with branches between valuable networks and prosumers (producers and consumers), who can be dispersed in a virtually unlimited number [39]. The diagram of energy flow between producers and consumers is shown in Fig. 2. As shown in Fig. 2, between the producer and the prosumer, i.e. the producerconsumer, it is possible to carry out transactions in both directions. This two-way model activates market participants, they become active players who can buy and sell electricity. Prosumers are divided into two groups: off-grid and on-grid. The first group is characterized by the production of energy for their own use, while the second group consists of prosumers who produce energy, which they allocate for their own needs and discharge some of the energy to the grid and from there to other recipients [15, 31, 42]. Thanks to the production capacity of numerous micro-producers

134

P. F. Borowski

Fig. 2 One-way and multi-directional energy flow. Source: own adaptation of the Figure from the Energy Atlas 2018 [23] to the digital situation

of energy, society is activating. There will not only be passive energy consumers on the market but also active prosumers who produce energy from renewable energy sources, including home windmills, photovoltaic panels mounted on the roofs of houses or around buildings, and small water turbines on streams and rivers flowing near farms. In addition, more and more often the use of innovative energy storage devices (i.e. batteries). Public interest in the issues of innovative digital solutions on the energy market determines the demand, which in turn drives production, which in the long run leads to the dissemination of modern solutions and a drop in their prices. The sphere of energy and its production ceases to be only the domain of engineers and energetics and becomes the area of sociological works and various social discussions and public debates. In the process of shaping climate and energy strategies and taking appropriate development policies in the field of energy, an important role is played by the public opinion and an increasingly aware and active civic movement [30]. Progress in the implementation of new energy-saving technologies is expected primarily in the areas characterized by the highest energy and raw materials consumption. The diagram of the connection of consumers and prosumers

Digital Transformation and Prosumers Activities …

135

Fig. 3 Energy management system Source [1]

in the network system along with the direction of information flow is presented in Fig. 3. The development of civic energy (prosumer, energy clusters) will be aimed at activating the inhabitants of towns and villages, local governments, entrepreneurs, housing communities and cooperatives, or public benefit organizations. Thanks to appropriate regulations and support systems as well as digital technologies, citizens, local governments, companies, and social organizations will be able to actively participate in the production of renewable energy and its transmission. These groups will effectively manage the energy they use and even profit from it. Such participation of private persons, organizations, institutions, and enterprises from outside the energy sector in the production and management of energy is the direction of the development of community energy. The key forces driving the successful digitization of the energy sector is among others legislative and systemic support for prosumers and consumers operating in the energy market [46]. And in the future, the development of electromobility will also contribute to increasing the importance of the end-user and climate protection. Electromobility is also a part of the zero-emission economy. A necessary condition for electromobility to have a positive impact on the environment is obtaining electricity in a process that has no negative side effects. Such energy is called clean energy obtained mainly from renewable energy or other non-emission sources [12].

136

P. F. Borowski

4 New Technologies in the Energy Sector New generation technologies, such as wind and solar power plants, have reduced the marginal costs of electricity production basically to zero [13]. The forecasted further decline in prices and the development of energy storage technologies will allow individual consumers to gain partial energy independence and contribute to the decentralization of the energy sector. Along with the growing share of microgeneration, the specificity of the operation of the energy sector will change. Microgeneration is the production of electricity on a small scale, primarily using low-carbon or renewable energy technologies. Entities investing in microgeneration for their own use become prosumers—consumers and producers of electricity at the same time [16]. The low-emission energy transformation aims to introduce significant changes for the entire economy in order to achieve climate neutrality [30]. Microgeneration will enable independence from depleting natural resources and will contribute to reducing the negative impact on the environment through the use of hybrid systems [41]. In developed countries, for the first time, wide interest in renewable energy sources and microgeneration appeared with the outbreak of the oil crisis in the 1970s, which resulted in stagflation—a multi-year economic slowdown and soaring prices of goods and services. Since the beginning of the 21 st century, a significant increase in the use of renewable energy micro-installations has been observed, primarily in the European Union, the United States and China, although their development takes place in virtually all countries of the world. It is worth noting that the economic crisis that broke out in the United States at the end of 2008, and then spread to other developed economies, did not inhibit the development of microgeneration, on the contrary—in recent years, energy production has grown exponentially. Distributed energy sources are gaining importance in energy mixes [16]. The combination of wind and sun is one of the most advantageous hybrid systems when installed in the right locations. However, depending on the area in which the installation is located, other resources such as geothermal energy, biomass, water, tides, etc. can be added to the integrated solar and wind power plant. Hybrid energy systems have some fundamental advantages, especially as they are less costly in terms of energy production and carry a lower risk of power shortage compared to systems with only one energy source. Statistics on the use of renewable and sustainable energy sources indicate that solar energy is the most beneficial solution among renewable energy sources. This high popularity is due to the development of the equipment used, as well as the high availability of solar radiation around the world. An additional source of renewable energy was wind energy which had been used for a long time. Currently, the most popular method of using wind energy is the installation of wind turbines. Some highly developed countries have taken serious steps to meet their electricity needs from wind energy. An example is the energy sector in Germany. The government in Berlin intends to continue expanding the wind energy sector and in the coming years plans to build and commission from 1,000 to 1,500 new wind towers in the energy sector every year. This means that the planned new wind projects will be able to cover up to 2 percent of the country’s demand [61]. Another renewable method

Digital Transformation and Prosumers Activities …

137

of generating electricity, much less exploited than solar and wind energy, is the hydrokinetic energy of river streams. This indicates the use of kinetic energy available in the waters of flowing rivers or artificial waterways to generate electricity [59]. Hydropower is a source of clean energy, but the construction of this type of power plant depends on geographical factors. The countries with favorable conditions have the greatest potential, i.e. mountain countries with numerous rivers with large slopes and countries generally rich in rivers and water reservoirs. The International Energy Agency recommends that in many countries the production of hydropower should be increased and small hydropower plants expanded. Such solutions are also important in countries with a steppe climate because in addition to energy production, they affect small retention. A small-scale hydropower plant is one of the technological options for generating and delivering electricity to near-zero-emissions in rural networks and applications. Small hydroelectric power plants are, by design, structures that generate small amounts of energy for local needs. Both small hydropower projects and small hydropower plants contribute to the breakdown of the scenario model of electricity generation and influence the development of microgrids. The future of the energy market is tending towards an active consumer (prosumer). It is achieved by mobilizing consumers who, thanks to the growing awareness and implementation of technological and economic innovations on the market, as well as organizational and social innovations, become more active. The advantage of small power plants for the local community and the environment is that the water is not polluted and flows back into the river or reservoir, so there is no environmental degradation and the energy produced helps to electrify local settlements or farms (Borowski 2021b [9]). From the beginning of community formation, rivers have been the center of attention. Consequently, there are now many megacities, towns, and small communities that have rivers close to or in the middle of them. This means that these areas have an excellent source of energy ready to be used. For this transition to take place, turbines convert the kinetic energy of water into electricity via alternators connected to the turbine shaft [59]. The progressing digitization of the sector will affect the optimization of the power grid operation (smart grid, smart metering) and will increase the possibilities of active use of the resources connected to the distribution network. Digitization of various processes has a great future in the energy sector. An example is the functioning of the sector according to a nodal model and not according to price zones. Today’s price zones do not reflect the real costs of network operation, and the problem cannot be solved by the central planner, in this case by the network operator. In the nodal model operating in the energy market, energy costs depend on the distance between the place of its generation and the place to which it is delivered. If energy is transported over long distances, its cost is higher. If the transmission is for a shorter distance, the recipient bears lower costs. Therefore, each producer is paid by customers adequately to the price determined in a given node. Prices depend not only on the customer’s location but also on the current generation sources and the level of infrastructure development. Energy pricing performed in the node model ensures optimal management of the transmission and distribution sector, as well as situations related to network overload, which translates into stable network operation. The node price system is

138

P. F. Borowski

more complex and requires more advanced computational techniques due to taking into account the physical phenomena related to transmission, i.e. distance and transmission losses. Compared to the simplified zone model, the price calculation system in the nodal model is more complex and therefore requires the implementation of digital solutions. Activation of prosumers, their active activities increasing the number of transactions made is possible to settle thanks to digital technologies. Advanced technologies, blockchain, and the use of smart grids allow for unlimited trade in surplus energy between prosumers who have a surplus from distributed renewable energy sources and other market participants and trade-in peer-to-peer (P2P) energy. The increase in the number of prosumers underlines one of the newest trends in renewable energy. These emerging technologies can help to protect the environment, drive economic development and provide a wider choice of energy—stimulating even more competition and innovation in the energy sector (Office of Energy Efficiency & Renewable Energy 2017 [37]). Prosumer energy consists of three basic elements. First, this energy model assumes that the prosumer is an existing consumer who undertakes the production of electricity in order to meet some of his needs. Importantly, its purpose is not to generate income, but to diversify electricity sources and reduce its cost. A large group of prosumers acting together in the energy market can be a bargaining power. In this way, the associated prosumers jointly increase the amount of energy offered, thanks to which they can negotiate the best purchase/sale price, which becomes more attractive for them [40]. The prosumer is characterized by the awareness of responsibility for the natural environment and the transfer of the perspective so far focused only on consumption, on the form of energy production. Secondly, the pro-consumer energy sector supports the transformation of the energy system from sectoral to integrated energy. This means that the current model of purchasing a product in the form of electricity from sector suppliers is being replaced by a new type of energy economy that integrates the area of demand and supply [16]. Third, the prosumer energy model is directly related to the development of intelligent infrastructure for electricity management at all levels, providing prosumers with the technical capabilities to implement their activities. The new type of infrastructure primarily includes the implementation of smart grids and smart metering, as well as the digitization of the entire system. Smart Grid (SG) is a system that allows for a two-way flow of energy and information between users of energy technology. This two-way flow is the generation of energy and consumption to enable the use of energy and the return of energy to the grid, making it possible to share energy for sale with other users (Rathnayaka i in. 2011). Distributed energy based on many generating and storing points, also based on numerous devices producing energy from hydroelectric sources: windmills, photovoltaic panels, and small hydropower require advanced metering systems. Advanced measurement systems include smart metering, smart grid, and smart infrastructure capable of managing and controlling dispersed resources [51]. In this way, new technologies and prosumer energy are strongly interconnected and mutually stimulate their development, which reflects the prosumer value chain. Digitization is conducive to the convergence of the broadly

Digital Transformation and Prosumers Activities …

139

understood energy system, enabling simultaneous development in the area of electricity and heat generation, mobility, and communication. As a result, new fields of activity are created, there is a change in priorities and business models. Thanks to digitization, it becomes possible to expand offers related to energy and energy services, thanks to which comprehensive solutions and services can be offered in the area of electricity management. Active consumers not only expect efficiency gains in all areas: electricity, heating, mobility, and communication but also use digital offers and services (such as phone applications) along with the development of their own electricity production. In this way, passive energy consumers turn into active market participants who generate added value and achieve specific benefits. The latest technologies in the energy sector are based on digital solutions, which include, among others. digital twin and blockchain. The aforementioned solutions, thanks to their wide application in distributed energy, facilitate or even allow the implementation of a low-emission or even zero-emission economy. The wide application of the above-mentioned digital solutions also enables the activation of prosumers taking an active part in the energy mix.

5 Digital Twins, Virtual Power Plants in the Energy Sector Digital twins are the concept of creating a digital replica of a selected physical object. As a digital image is produced, it is called a digital twin of a physical object. This digital solution allows you to monitor, simulate and search for the optimal solution without the need to conduct research on a real object [26]. Digital twins allow you to combine engineering, operational, and information technologies to visualize your analysis. The digital twin has become an advantageous solution used in the power industry thanks to the possibility of real-time data processing and the possibility of continuous updating of variable states of the examined objects and processes. Solutions with the use of a digital twin are used to keep track of and identify problems by continuously visualizing changes over time. In the era of smart factories, processes using a digital twin will be gaining in popularity [11]. The model authentically simulates the natural user environment of the product/equipment/facility and all processes during its operation. Thanks to the simulation, you can monitor and analyze the operation of the device on an ongoing basis, as well as control how the equipment works, how it is maintained and secured by engineers. Digital-Twin solutions use elements of artificial intelligence, machine learning and advanced programming techniques. This allows for the analysis of data collected in production plants, which are then used to create simulation models. Digital twins can simulate any aspect of the work of a physical object or an entire process. Digital models allow, without the need to incur additional financial outlays, for continuous updating and information about the occurring situations that require corrections in the operation of a given device. In the power industry, they are used to optimize the operation and maintenance of the physical facility, systems, and production processes. This technology enables real-time remote monitoring, can save significant downtime and maintenance costs

140

P. F. Borowski

in the power industry. The digital twin will show the remaining service life of the components, and based on this, you can safely optimize your maintenance schedule and determine when to repair or replace components. The digital twin will allow power equipment operators to use real data to predict the actual remaining life of a machine—before it is in use. You can combine sensory data with design knowledge and engineering expertise in a single digital model. More and more energy companies are using this solution. The Reality Model is combined with other realworld data to update the design’s digital twin, enabling you to respond immediately to changes. The digital twin helps reduce costs and improve maintenance and operation processes. Many energy companies are exploring how to get the most out of digital twins. The digital twin also enables “what if” scenarios to predict possible outcomes. For example, what would happen if the production rate was increased from 100 to 110% when, for example, the price of electricity is at its peak? The technology also allows you to calculate the most optimized version regarding the work. As a result, companies can manage information more efficiently and have better access to it. Or you can benefit from an open, integrated, and connected structure [55]. Digital systems allow for the integration of various energy sources, thanks to which they will be more and more widely used in the process of managing energy distribution. Digitization will allow for remote monitoring and control of energy production and its market demand. Virtual Power Plant (VPP) connects many geographically dispersed generating units, both conventional and renewable, as well as energy storage facilities, recipients and consumers. The units included in the virtual power plant consist of various dispersed energy sources. Petroleum energy is based on energy from photovoltaic parks, wind farms, hydroelectric power plants, and cogeneration units (CHP), and is also based on flexible receivers, batteries and various types of energy storage systems. The most important aspect is the fact that the units connected to the virtual power plant system remain independent in operation and ownership. Although they are connected and communicate via a central control room, they remain fully autonomous. The connected units form an optimized ecosystem that allows you to dynamically plan and adjust production and intelligently trade in the energy market at the lowest operating costs. VPPs participate in wholesale energy markets to provide the flexibility necessary for a low-carbon energy system rich in renewable energy sources. The energy sector is facing an increasing implementation of digital solutions. If the energy industry does not adapt to the changes taking place in the environment, there will be interruptions in the supply of energy to its recipients, as has happened many times in many parts of the world [6]. Large-scale interruptions in energy supply occurred in 2001 and were called the Californian crisis,1 and the events of 2003: a gigantic blackout on the eastern coast of the USA and Canada or in Italy.2 Energy distribution systems require adaptation to maintain the security of uninterrupted energy supply and real-time balancing. A virtual power plant is 1

At its peak, the crisis affected 1.5 million end-users and lasted over a year. The largest power grid failure in North American history has occurred in the Northeast United States and the Southeast provinces of Canada. Electricity was interrupted in Ohio, New York, New England, Michigan, Ontario, Pennsylvania, northern New Jersey, and on the Canadian side in Quebec. In total, about 50 million people were affected by the power outage. As a result of the

2

Digital Transformation and Prosumers Activities …

141

Fig. 4 Schematic representation of the idea of a virtual power plant Source [3]

an IT system that prevents blackouts through short-term automated reductions in energy consumption performed on the part of its recipients. From the perspective of the Power System, the power of a virtual power plant complements the power of conventional power plants and an important instrument for transforming the energy market in terms of the so-called capacity market. A schematic presentation of the idea of a virtual power plant is presented in Fig. 4. A VPP operator, which may be an industrial customer, energy cluster, broker, network operator, a leading energy supplier, transforms dispersed energy resources into one power pool. VPP operators may, but do not have to be, owners of generation assets, but only manage data that is made available within the created virtual network or energy cluster. VPP, thanks to its structure, which allows for the collection, combination and aggregation of dispersed energy resources, supported by remote control technologies, functions as an integrated, uniform whole. The technology enables the operators of the Virtual Power Plant to implement dynamic business models tailored to their needs, thanks to the ease of use of such platforms as VPP on international markets, flexible adaptation to local mechanisms, users, and infrastructure, and the accident, over 100 power plants in the USA and Canada were shut down, including 22 nuclear power plants. Part of Switzerland and all of Italy (from the Alps to Sicily) were left without electricity. In total, the largest blackout in the history of Europe cut off approximately 57 million people from the power supply.

142

P. F. Borowski

ability to scale the system along with the development of the organization. Virtual Power Plant effectively supports the forecasting of energy production from renewable energy installations of discontinuous nature, dependent on the weather, such as wind and solar farms. Forecasts of energy production and prices, as well as historical and up-to-date information on production and loads, are controlled, integrated, and managed in real-time on a single platform that allows you to analyze potential revenue streams and provides a comprehensive tool supporting decision-making in the face of constantly changing demand and supply. The task of the virtual power plant is the integral operation of all components in such a way that it is possible to relieve the network through the intelligent distribution of power generated by individual units of the VPP during peak load periods. Virtual Power Plants (VPPs) will increasingly start to function as a network of decentralized medium-scale generation units. The evolution of energy markets and new concepts of energy services will require the combination of VPP technology with Blockchain technology - a platform that is the only scalable development model and becoming a new financial architecture for a decentralized energy system with rapidly increasing complexity. The technologies used to allow the Virtual Power Plant to manage an unlimited number of sources and receive installations, and at the same time ensure the level of cybersecurity required for elements of critical infrastructure in the power industry. In addition, the system provides information on meteorological conditions, current parameters, and technical limitations of the infrastructure, as well as on current commercial data and price signals [47]. New technologies based on digital solutions can be included in the key areas of innovation used in the energy sector. Innovative solutions will increase efficiency and productivity, but also contribute to increasing energy security, speed, and convenience of energy trading and settlement. In addition, digitization will reduce costs thanks to the use of artificial intelligence and the use of advanced sensors and meters for remote monitoring and billing processes [50]. A network of innovative, intelligent sensors installed throughout the energy infrastructure allows for better, more accurate monitoring and control of the reported and calculated energy demand and the effective management of its transmission to the appropriate points and its storage. The sensors working in the network have one more important function related to the maintenance of the appropriate technical level. They monitor the technical condition on an ongoing basis, e.g. the level of corrosion (Transformation 2050 n.d.). In distributed energy, thousands of producers, consumers, and prosumers actively participating in energy trading can function, conclude and settle transactions thanks to the use of AI algorithms [49]. The accelerated trend of development in the areas of information and communication technologies (ICT) and energy systems has led to the emergence of a new concept known as the internet of energy (IoE). Internet of Energy (IoE) is a technological term referring to the modernization and automation of electricity infrastructure for energy producers and producers. It means the use of advanced digital controllers, sensors, actuators and meters with the possibility of exchanging information via IT infrastructure. This allows for more efficient and clean energy production with as little waste as possible. The benefits of using the Internet of Energy include increased efficiency, significant cost savings and reduced energy

Digital Transformation and Prosumers Activities …

143

waste [43]. To strengthen the Internet of Prosumer Energy (IoE) efforts and strengthen the integration of energy-saving services, the two basic elements, namely: digitization and decentralization, are key factors in achieving a transactive Internet of Everything. The Internet of Energy (IoE), together with the integration of advanced information and communication technologies (ICT), has transformed traditional networks into intelligent systems. The Internet of Things, Machine Communication (M2M), Artificial Intelligence, Machine Learning, Digital Twin and more must find application in the energy system. The Internet of Things (IoT) drives the digitization of IoE transactions and blockchain facilitates the decentralization of transactions. Internet of Energy (IoE) is the implementation of innovations in the field of the Internet of Things to energy circulation systems in order to increase the efficiency of energy infrastructure. The Internet of Energy allows you to collect and sort data from individual gadgets on the edge of the grid throughout the system to share it with other network management participants. In addition, the development and success of the Internet of Energy will depend on how cloud-based systems are used for integration. From increased efficiency to saving money and less waste, the Internet of Everything has many benefits. The Internet of Energy helps countries manage their energy needs by enabling utilities to generate more electricity during peak hours and less when energy demand is low. The widespread adoption of this technology could prevent power cuts for countries in the future. However, the advantages of IoE technology are not limited to the big manufacturers. Solar companies, utilities, and electric vehicle drivers can benefit as well. Grid infrastructure can be upgraded with IoE technologies, producing and transmitting energy efficiently while facilitating the integration of renewable energy sources. Moreover, the Internet of Everything has cultivated the application of smart grid technologies to collect data all the way to the edge of the web. This data can then be used to assist utilities in decision-making on load balancing, forecasting, and other decisions.

6 Blockchain in the Energy Sector Blockchain is a decentralized database. The specific features of this database are that it exists in many identical copies for individual users and each copy contains a set of data in the form of interconnected blocks. Blockchain is therefore a shared, distributed and fault-tolerant database that maintains records in individual blocks. The blocks are linked together in the form of a chain, each block keeps the identifier of its predecessor. Each block contains from several to several thousand verified transactions (value depending on the solution used), a time stamp indicating the moment of its creation and a random number (nonce) for cryptographic operations performed to confirm the correctness of the block. The blockchain network consists of the so-called nodes that keep the blockchain distributed (peer-to-peer network). All nodes can access blocks but cannot control them completely. One of the most important advantages of this technology is consistency and transparency—the blocks

144

P. F. Borowski

are available to all users of the chain, but they cannot be removed or modified. The idea of the blockchain is shown in Fig. 5. Contrary to centralized software systems, where components are arranged around and connected to one central component, DLT components form a network of interconnected components without any central or control component. In DLT, each component connects directly to everyone else. The distributed ledger technology enables the expansion of a chronologically ordered list of cryptographically signed, immutable transaction records that are made available to all network participants [5]. Every day, members of the energy sector exchange hundreds of data, hundreds of files, conclude contracts and perform transactions. During these transactions, they use different applications on multiple devices and networks to access resources. Such fragmentation requires fragmented controls. Blockchain is distributed by nature, so blockchain-based security services can be implemented in this way. Decisions on subsequent blocks in the chain are made by decentralized consensus, majority, and inter-node agreement. Each transaction and block is signed in such a way that the execution of the transaction cannot be denied later (immutability). Each transaction is transparent to all participants of the chain [25]. Blockchain provides members of a given energy community with automatic access to information in real-time. Thanks to this, each participant, e.g. a commune that produced excess energy, can make it available to a commune that is struggling with its shortage. Even an individual user,

Fig. 5 Blockchain concept Source [25]

Digital Transformation and Prosumers Activities …

145

who owns a cell on the roof, can make excess energy available to a nearby block. Such smart contracts help in this case to maintain a balance between supply and demand. In real-time, the user would decide how much energy he wants to buy or share, or generate himself. In the future, all consumers, regardless of the size of their transactions, should be able to take full advantage of the market’s offer and participate under the same conditions as other participants [20]. Moreover, consumers should be able to independently control and manage their own energy consumption thanks to innovative technological solutions. Unlimited access to energy management will play a key role both among retail customers and enterprises [57, 58]. In the era of the creation of distributed energy based on numerous, often small or micro-producers of energy based on renewable sources, blockchain solutions will ensure liquidity on the market. Prosumers as active participants can trade even a small amount of energy, and blockchain enables the execution of all reported transactions [2, 21]. Blockchain technology offers new possibilities for combining energy flow, data flow, and business flow on the DLT (Distributed Ledger Technology) platform. DLT technology is considered one of the promising technologies that will change the future business and social behavior of consumers in several industry segments, including the energy sector [17]. Blockchain may change relations between entities from the energy sector guaranteeing security and opening the way for the sale of micro-amounts of energy between prosumers [60]. Blockchain in the field of energy enables, among others automation of transactions, i.e. automatic conclusion of contracts after prior fulfillment of certain conditions. In addition, thanks to the use of blockchain, it is possible to monitor energy consumption and production by individual prosumers [49]. In the blockchain system, an important issue is the minimum intervention of intermediaries (various companies and energy agencies). Consumers and prosumers carry out energy exchange transactions in a decentralized manner, i.e. directly using smart contracts designed for the specific needs of active market players [14]. Thanks to these solutions the cost of the transaction and the time for their realization is reduced. A networked dynamic power system using various elements of distributed generation and which is based on intelligent devices is shown in Fig. 6. Many consumers can introduce flexibility in their households with regard to energy and its use. This is especially true for users with inertial or delayed processes that are independent of the time of day/night, eg washing, cleaning, etc. [24, 34]. These customers can postpone or delay periods in which they will need high energy consumption for periods in which electricity will be cheap, without risking losses or disruptions to their operations or activities. Such an approach in the energy sector is called response to demand or demand-side management (DSM) [48], e.g. night tariff as part of constant optimization. Prosumers can use energy management systems that provide remote control capability. The systems can collect detailed real-time data for each piece of equipment and then generate tips on the most attractive energy-saving options (Veolia 2020 [54]). The benefits of using the DSM method can be presented in two aspects. The first aspect concerns consumers who can lower their electricity bills by adjusting the time and amount of energy used depending on the price at different times during the day. The second aspect concerns an energy system that

146

P. F. Borowski

Fig. 6 The power system of the future. Source Electric Power Research Institute 2015 [22]; Borowski 2020c [10]

will benefit from shifting energy consumption from peak to off-peak hours. It is clear that not all consumers will be able to change their electricity consumption behavior. Thanks to the growing awareness of consumers and the implementation of technological, economic, organizational, and social innovations, the future of the energy market is moving towards an active consumer (prosumer. Demand-side management also applies to large energy consumers, such as production plants. Demand management is therefore an integral part of the functioning of large enterprises. The purpose of standard demand management is to reduce energy purchasing costs and to optimize production processes. The recipient’s participation in the demand reduction programs can take place in two ways: first, reducing the demand that interferes with basic production processes, second, the reduction of the demand performed by devices unrelated to the company’s core process. Achieving these assumptions requires taking actions aimed at transitioning to more flexible processes implemented in the economy at the industrial level. One possible way to accomplish this transition is to implement a Demand Side Management (DSM) system.

7 Conclusions The world of business and the world of energy companies interpenetrate and complement each other based on innovative mega-processes. The use of digital solutions, the implementation of automation and robotization in an increasing number of processes, the use of artificial intelligence and machine learning contribute to the development of the energy sector. In order to assess the development of the energy sector and enterprises, it is worth analyzing the basic performance measures used in a given sector.

Digital Transformation and Prosumers Activities …

147

Digitization is already contributing to the safety, efficiency, availability, and sustainability of energy systems. Digitization allows for increased energy efficiency thanks to technologies collecting and analyzing data. This data is converted into useful information using data analysis technologies such as artificial intelligence algorithms and then transferred to devices that can affect physical changes for optimization. The entry into the energy market of smart grids, interconnected and interoperable energy trading and management systems, as well as the use of the potential of artificial intelligence may cause a change in the position of suppliers and recipients in the near future. Thanks to digitization, it is possible to significantly expand the offer of solutions for all electricity recipients—from large industrial plants to individual recipients. New technologies and new energy sources are changing the market. The number of data processing centers is systematically growing, as is the number of electric vehicles, which will significantly affect the energy market, causing an increase in energy demand. At the same time, the share of energy from renewable sources such as wind and sun or small hydropower is growing. As a result, energy supply markets become more volatile and energy suppliers will find it difficult to manage and balance the demand on the grid. For energy suppliers who use data in management, digitization will enable them to establish direct relationships with end-users, and they, in turn, can find intelligent ways to manage their energy resources. Technological innovation, cost reductions, new business models, and pro-development policies are accelerating the transformation of the traditional power grid into a decentralized grid where both energy and information flow in both directions. The results of the research are in line with the latest trends in the development of the energy sector and indicate the most important elements influencing the achievement of the goal and success, namely: digitization of the sector, decentralization and support for prosumer energy and the development of distributed energy. Future digital energy systems may be able to identify who needs energy and deliver it at the right time, in the right place and at the lowest cost (IEA report [28]). Calculations and analyses carried out in the energy sector show that thanks to digitization it is possible to save annually around 5% of the total annual costs related to energy production (Esi-africa 2020 [27]).

References 1. Ahmed SA, Huang Q, Amin W, Afzal M (2020) Cost-effective energy management system in prosumer based electricity market. In: 2020 International Conference on Smart Grids and Energy Systems (SGES), pp 181–185. https://doi.org/10.1109/SGES51519.2020.00039 2. Andoni M et al (2019) Blockchain technology in the energy sector: a systematic review of challenges and opportunities. Renew Sustain Energy Rev 100:143–174 3. Bai H, Miao S, Ran X, Ye C (2015) Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market. Energies 8(3):2268–2289. https://doi.org/ 10.3390/en803226

148

P. F. Borowski

4. Balakumar P, Vinopraba T, Sankar S, Santhoshkumar S, Chandrasekaran K (2022) Smart hybrid microgrid for effective distributed renewable energy sharing of PV prosumers. J Energy Storage 49:104033 5. Bekhta A (2021) Blockchain in the public sector: possibilities and challenges, Ewolucja elektronicznej administracji publicznej, E-Wydawnictwo. Prawnicza i Ekonomiczna Biblioteka Cyfrowa. Wydział Prawa, Administracji i Ekonomii Uniwersytetu Wrocławskiego, pp 189– 204. https://doi.org/10.34616/23.21.019 6. Borowski PF (2016) Development strategies for electric utilities. Acta Energetica 4:16–21 7. Borowski PF (2019) Adaptation strategy on regulated markets of power companies in Poland. Energy Environ 30:3–26. https://doi.org/10.1177/0958305X18787292 8. Borowski PF (2020a) New technologies and innovative solutions in the development strategies of energy enterprises. Hightech Innov J 1:39–58. [CrossRef] 9. Borowski PF (2020b) Nexus between water, energy, food and climate change as challenges facing the modern global. Eur Pol Econ AIMS Geosci 6:397–421 10. Borowski PF (2020c) Zonal and nodal models of energy market in European Union. Energies 13(16):4182. https://doi.org/10.3390/en13164182 11. Borowski PF (2021) Digitization, digital twins, blockchain, and industry 4.0 as elements of management process in enterprises in the energy sector. Energies 14(7):1885. https://doi.org/ 10.3390/en14071885 12. Borowski PF et al (2021) Problems of selected sectors of biofuels for transport in Poland. Processes 9:1573. https://doi.org/10.3390/pr9091573 13. Bronk L (2019) Mo˙zliwo´sci zwi˛ekszenia elastyczno´sci pracy Krajowego Systemu Elektroenergetycznego. Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gda´nskiej 63:99–102 14. Brown D, Hall S, Davis ME (2019) Prosumers in the post subsidy era: an exploration of new prosumer business models in the UK. Energy Policy 135:110984 15. Buchmann M (2017) Governance of data and information management in smart distribution grids: increase efficiency by balancing coordination and competition. Util Policy 44:63–72 ´ 16. Bukowski M, Pankowiec A, Szczerba P, Sniegocki A (2014) Przełomowa energetyka prosumencka, WISE, Warszawa. 17. Cali U, Lima C, Li X, Ogushi Y (2019) DLT/Blockchain in transactive energy use cases segmentation and standardization framework. In: IEEE PES Transactive Energy Systems Conference (TESC), 2019, pp 1–5. https://doi.org/10.1109/TESC.2019.8843372 18. Campos I, Marín-González E (2020) People in transitions: energy citizenship, prosumerism and social movements in Europe. Energy Res Soc Sci 69:101718 19. Chen M, Sinha A, Hu K, Shah MI (2021) Impact of technological innovation on energy efficiency in industry 4.0 era: moderation of shadow economy in sustainable development. Technol Forecast Soc Chang 164:120521 20. Clean Energy for All Europeans (2019) Publications Office of the European Union: Luxembourg 21. Di Silvestre ML et al (2020) Blockchain for power systems: current trends and future applications. Renew Sustain Energy Rev 119:109585 22. Electric Power Research Institute, EPRI Maps out Power System of the Future (2015). https://www.bpa.gov/news/newsroom/Pages/EPRI-maps-out-power-system-of-the-fut ure.aspx. Accessed 6 Aug 2020 23. Energy Atlas (2018) Green European Foundation: Brussels, Belgium, 2018 24. Gargiulo M, Vailancourt K, De Miglio R (2016) Documentation for the TIMES Model Part IV; International Energy Agency, pp 1–121. Paris, France 25. Grusznic JT (2020) Blockchain–5 faktów o jakich powiniene´s wiedzie´c, a boisz si˛e zapyta´c. https://aspolska.pl/blockchain-5-faktow-o-jakich-powinienes-wiedziec-a-boisz-sie-zapytac/ 26. Hou L, Wu S, Zhang GK, Tan Y, Wang X (2021) Literature review of digital twins applications in construction workforce safety. Appl Sci 11:339 27. https://www.esi-africa.com/smart-grids/how-digitalisation-is-reshaping-the-energy-sector/ 28. https://www.iea.org/reports/digitalisation-and-energy

Digital Transformation and Prosumers Activities …

149

29. Inês C, Guilherme PL, Esther MG, Swantje G, Stephen H, Lars H (2020) Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Energy Policy 138:111212 30. Jałowiec T, Wojtaszek H, Miciuła I (2022) Analysis of the potential management of the lowcarbon energy transformation by 2050. Energies 15(7):2351 31. Jarnut M, Benysek G, Kaniewski J (2015) Semi off-grid as the new mode of operation of prosumer owned microgrids. In: Proceedings of the 2015 IEEE Conference on Energy Conversion (CENCON), Johor Bahru, Malaysia, 19–21 October 2015, pp 48–53 32. Kueppers M et al (2021) Decarbonization pathways of worldwide energy systems–definition and modeling of archetypes. Appl Energy 285:116438 33. Łobejko S (2020) Digital transformation and innovativeness of enterprises. Optim Econ Stud 100(2):36–46 34. Loulou R, Goldstein G, Kanudia, A, Lehtila A, Remme U (2016) Documentation for the times model part I; international energy agency: Paris, France 35. Lyu W, Liu J (2021) Artificial Intelligence and emerging digital technologies in the energy sector. Appl Energy 303:117615 36. Maroufkhani P, Desouza KC, Perrons RK, Iranmanesh M (2022) Digital transformation in the resource and energy sectors: a systematic review. Resour Policy 76:102622 37. Office of Energy Efficiency & Renewable Energy (2017) Consumer vs Prosumer what’s Difference. https://www.energy.gov/eere/articles/consumer-vs-prosumer-whats-difference 38. Papadis E, Tsatsaronis G (2020) Challenges in the decarbonization of the energy sector. Energy 205:118025 39. Petropoulos G, Willems B (2020) Long-term transmission rights and dynamic efficiency. Energy Econ 104714 40. Rathnayaka AJ, Potdar V, Hussain OK, Dillon TS (2011) Identifying prosumer’s energy sharing behaviours for forming optimal prosumer-communities. In: 2011 International Conference on Cloud and Service Computing, 12–14 December 2011, pp 199–206. https://doi.org/10.1109/ CSC.2011.6138520 41. Sanchez-García J, Rodríguez-Aguilar R, Marmolejo-Saucedo JA (2022) Electric vehicles as distributed micro generation using smart grid for decision making: brief literature review. In: Vasant P, Zelinka I, Weber, GW (eds) Intelligent Computing & Optimization. ICO 2021. LNNS, vol 371, pp 981–991. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_93 42. Sergi B, Babcock M, Williams NJ, Thornburg J, Loew A, Ciez RE (2018) Institutional influence on power sector investments: a case study of on-and off-grid energy in Kenya and Tanzania. Energy Res Soc Sci 41:59–70 43. Shahinzadeh H, Moradi J, Gharehpetian GB, Nafisi H, Abedi M (2019) Internet of energy (IoE) in smart power systems. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pp. 627–636, February 2019. IEEE 44. Sheveleva A, Tyaglov S, Khaiter P (2021) Digital transformation strategies of oil and gas companies: preparing for the fourth industrial revolution. In: Konina N (eds) Digital Strategies in a Global Market, pp 157–171. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3030-58267-8_12 45. Sun H, Edziah BK, Kporsu AK, Sarkodie SA, Taghizadeh-Hesary F (2021) Energy efficiency: the role of technological innovation and knowledge spillover. Technol Forecast Soc Chang 167:120659 ´ 46. Swiatowiec-Szczepa´ nska J, St˛epie´n B (2022) Drivers of digitalization in the energy sector—the managerial perspective from the catching up economy. Energies 15(4):1437 47. Tauron 2021. https://media.tauron.pl/pr/711553/nowe-zrodla-w-wirtualnej-elektrowni-tau rona 48. Thiaux Y et al (2019) Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior. Appl Energy 253:113530 49. Transformacja 2050: The future of digitization in the energy sector. (In Polish). https://transf ormacja2050.pl/project/cyfryzacja-w-energetyce-analiza/. Accessed 29 Jan 2021

150

P. F. Borowski

50. Trzaska R, Sulich A, Organa M, Niemczyk J, Jasi´nski B (2021) Digitalization business strategies in energy sector: solving problems with uncertainty under industry 4.0 conditions. Energies 14(23):7997. https://doi.org/10.3390/en14237997 51. Uribe-Pérez N, Hernández L, De la Vega D, Angulo I (2016) State of the art and trends review of smart metering in electricity grids. Appl Sci 6(3):68. https://doi.org/10.3390/app6030068 52. Valeeva Y, Kalinina M, Sargu L, Kulachinskaya A, Ilyashenko S (2022) Energy sector enterprises in digitalization program: its implication for open innovation. J Open Innov Technol Mark Complex 8(2):81 53. Varela I (2018) Energy is essential, but utilities? Digitalization: what does it mean for the energy sector? In: Linnhoff-Popien C, Schneider R, Zaddach M (eds) Digital Marketplaces Unleashed, pp 829–838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49275-8_73 54. Veolia (2020). https://inzynieria.com/energetyka/wiadomosci/59698,czym-tak-wlasciwiejest-system-zarzadzania-energia-cztery-rzeczy-ktore-warto-wiedziec,jak-to-dziala 55. Walters AM (2019). https://inzynieria.com/energetyka/artykuly/57351,jak-cyfrowe-blizniakinapedzaja-innowacje-w-sektorze-energetycznym 56. Wang Q, Su M (2020) Integrating blockchain technology into the energy sector—from theory of blockchain to research and application of energy blockchain. Comput Sci Rev 37:100275 57. Wojdalski J, Krajnik M, Borowski PF, Dró˙zd˙z B, Kupczyk A (2021) Energy and water efficiency in the gelatine production plant. AIMS Geosci 6(4):491–503 58. Wojdalski J et al (2015) Energy efficiency of a confectionery plant—case study. J Food Eng 146:182–191 59. Xia T et al (2021) Techno-economic assessment of a grid-independent hybrid power plant for co-supplying a remote micro-community with electricity and hydrogen. Processes 9(8):1375. https://doi.org/10.3390/pr9081375 60. Zhang X, Xu Q, Lu J, Xu J (2021) Conceptual characteristics and analysis of typical application scenarios of energy blockchain. J Phys Conf Ser 1738:12113. IOP Publishing, Bristol, UK 61. Zimmermann A (2022) German green wind power electricity, Politico. https://www.politico. eu/article/germany-green-wind-power-electricity/

Digital Transformation Success Factors Evaluation in Energy Industry Burak Berkay Havle and Mehtap Dursun

Abstract Recently, developments in technology have caused a new wave that affects the whole world. The emerging technologies have initiated the digital transformation process. Companies have made efforts to meet the digital demands and gain a share in the digital market and ensure their sustainability in the digital ecosystem. Digital transformation has become a necessity rather than an optional process. Digital is the key driver in energy industry to increase workforce efficiency and productivity, to make faster and better decisions, and to reduce costs. This study aims to evaluate the digital transformation success factors in energy industry. The success factors are obtained through a detailed literature review and experts’ opinions. Three experts are determined based on their experiences, positions in the firm, knowledge about digital transformation and its applications in the energy industry and their academic backgrounds. Fuzzy cognitive map (FCM) approach is employed to analyze the obtained digital transformation success factors with the assumption of relationship between criteria. Keywords Decision making · Digital transformation · Energy industry · Fuzzy cognitive maps

1 Introduction Since its existence, humanity has experienced step by step improvements in the fields of agriculture, industry, education, information and informatics. These developments have accelerated with the contribution of technology. With the integration of processes into each other and the use of advanced business models, developments have become transformations. The transformations that take place today are called digital transformation. Digital transformation expresses the fastest and most effective process development and its biggest support is technological advances. B. B. Havle · M. Dursun (B) Decision Analysis Application and Research Center, Galatasaray University, Ortakoy, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_7

151

152

B. B. Havle and M. Dursun

Companies have started to improve their processes in order to stay in a competitive market, increase profitability rates and ensure sustainability by ensuring customer satisfaction, which has a significant impact on their performance. Companies have begun to integrate digital technologies into their structure to easily monitor all their processes and show their transparency to customers. Emerging digital technologies such as cloud technology, mobile phones, IoT, AI and its applications, wearable smart devices, mobile applications, social media platforms and other digital platforms initiated the digital transformation have caused a new wave that influence all over the world. These technologies and digital transformation process created new digital markets and digital ecosystems where the companies are forced to have to meet the digital demands and gain a market share to ensure their sustainability, competitiveness and profitability. This study aims to evaluate digital transformation success factors in energy industry. Digital transformation has affected many industrial areas such as education [64], banking [49], healthcare [61], aviation [12], travel and tourism [42], automotive [37], agriculture [22] and mining (Accenture 2016) including the energy industry [21, 23]. In recent years, it has become very important to meet the increasing energy demand due to the rapid increase of the global population and the developments in the industry. The effects of digital transformation can be seen clearly in energy industry. Nowadays, digital is the key driver in energy industry to increase workforce efficiency and productivity, to make faster and better decisions, and to reduce costs. Due to abovementioned advantages of digital transformation, companies are trying to start to shift their business models and strategies. Interactions with consumers, internetenabled solutions, “as-a-service” business models and new digital platforms have been adopted by the companies in energy industry. This study objects the evaluation of the digital transformation success factors in energy industry. The success factors are obtained through a detailed literature survey and experts’ opinions. Cognitive Maps (CM) [65] approach is utilized to analyze the obtained digital transformation success factors with the assumption of relationship between criteria. However, real-life decision problems are complex and contain uncertainty. To eliminate this ambiguity and uncertainty, the classical cognitive map approach is extended to fuzzy sets [69] and it is named as fuzzy cognitive map (FCM) [35] approach. FCM has a flexible structure and it provides to use various threshold values. Furthermore, it is possible to perform scenario analysis thanks to its dynamical behavior. The relationship between factors and the importance degree of these relations are determined by the DMs. To reach a robust decision, it is key to analyze the cause-and-effect relationships among variables under vague and uncertain environment. In the decision problem, a decrease or an increase in importance degree on a variable may result to a decrease or an increase on importance degrees of the other variables, which means that there are cause-and-effect relationships among pair of variables. The existence of the causal links between variables, the presence of uncertain data, and the need for utilizing fuzzy numbers or linguistic variables require to use FCM.

Digital Transformation Success Factors …

153

The originality of the study is based on its following properties: • The digital energy industry concept is introduced, • The digital transformation success factors are revealed, and the most significant factor is determined, • FCM is used in to manage causal links among pair of digital transformation success factors, • To the best of the authors knowledge, it is the first study focuses on the evaluation of the digital transformation success factors. The rest of this study incorporates the following sections: Sect. 2 gives an illustrative literature survey. FCM methodology is presented in Sect. 3. Applications are given in Sect. 4. Finally, conclusions and discussions are provided in the last section.

2 Literature Survey 2.1 Digital Transformation Digital Transformation is a transition process in which the companies and brands in the competitive environment are experiencing improvements by using both digital and social technologies in their organizational structures. Digital Transformation provides enrichment of customers and living environments, it helps to develop and structure business processes and business models, and it increases the competencies and capabilities of the company. Digital transformation, which has no clear expression, is expressed in various ways by different researchers. A few of these definitions are presented in Table 1. Companies object to form the working atmosphere of the future within the framework of competition and making moves in this direction should be more agile and consistent than their competitors with the dynamic factors experienced in the market. Under these circumstances, it is very important for companies to constantly work on the digitalization of business models and operations in the working environment and corporate digital transformation actions.

154

B. B. Havle and M. Dursun

Table 1 Various definitions of digital transformation Source

Brief explanation

Stolterman [60]

The change in life, created and developed in every subject in question is caused by the utilization of digital technologies

Bowersox et al. [6]

In today’s supply chain management, it is the formulating processes using digital technologies in all kinds of operational activities of businesses and redefining and defining steps

Martin [40]

Supporting the creation of new capacities and capabilities in processes where automation using information and communication technology is no longer important

Westerman et al. [67]

Digital transformation, defined as an improvement, is the development and further development of the technology through the design and use of technology

Fitzgerald et al. [27]

It is to define and develop the experiences of digital technologies, infrastructures such as social media and mobile devices, and to improve and facilitate the business models in the development processes

Mazzone [41]

Digital transformation, which is referred to as a digital evolutionary process, refers to the developments that aim at strategic and tactically permanent forward on the philosophy of business models and processes carried out in an institution

Solis et al. [58]

Digital is to make new and effective investments to use digital technologies, to improve these uses and to redefine business models in the processes that the customer has been in contact with at every point of his life

Bouée and Schaible [5] Digital transformation is defined as the fact that firms in all sectors are economically connected with a consistent network, and the developments that will be realized should be within this scope

2.2 Digital Transformation in Various Industries In this section, the results of the literature survey on which industries are influenced by digital transformation, what are the effects of this transformation, and which digital tools are utilize during digital transformation process are provided. The results of the literature survey are illustrated in Table 2.

Digital Transformation Success Factors …

155

Table 2 Digital transformation in different industries Source

Sector

Effects of digital transformation

Digital tools

Dongoski and Selck [22]

Agriculture

• Farm sustainability • Better, faster and cheaper delivery through digital technology • Delivering climate information through mobiles

• • • • •

Moray [42]; Tiven et al. Education [64]

• Competency • Computational Thinking • Self-Efficacy • Teaching Global

• AR & Digital Video • Design-based • Generic modelling language • Learning manager

Seino et al. [55]; Manufacturing Chryssolouris et al. [15] and Buˇcková [7]

• Early approval of fabricating processes • Transforming specialized and mechanical know-how into numerical data

• • • •

3D designing Static calculation Collecting big data Computer simulate

Cloud computing Breeding inf Big data analytics AI, IoT Machine learning

Taylor [61], Neittaanmäki and Galeieva [47]

Healthcare

• Justifiable info on symptoms/medical conditions • Urgent and emergency care • E-health and tele-health

• • • •

Big data and security Medical data Cloud platform Visual analytics

The World Economic Forum [63]

Aviation

• Updating Client Interaction Business frame and processes

• • • •

Big data Cloud computing Self-service systems Mobile application

Cheung et al. [14]; Gelter [28]; Mil [42]

Tourism

• Hologram guidance concept • Digital mobile behavior • Micro-moments in travel • Financial arranging and booking management

• • • • • •

Eye-tap technology 4D movie AI & E-agent VR-AR Robotization Autonomous travel counsellors

• Wealthy range of items and services • Fair cost with straight forwardness and comparability • Embracing the developing computerized ways

• • • • • • •

Peer to peer pay AR Mobile banking app APIs Smart phone Chatbots Digital wallet

Eistert [25]; Oracle [49] Banking

(continued)

156

B. B. Havle and M. Dursun

Table 2 (continued) Source

Sector

Effects of digital transformation

Digital tools

Cisco [18] and Riverbed Retail [54]

• Selecting suppliers associated with environmental • Manage risk by securing physical and digital operations

• Smart beacons • IoT and streamlining shopping • Facial recognition • AR and VR • Mobile application

Andrea [3]; Doyuran [23]; Deloitte [21]

Energy

• Increasing data-based decision-making mechanisms • Smart demand response • Integrate variable renewables

• IoT • Virtual interfaces with Cyber Physical Systems (CPS) • Advanced sensors • 5G • Big data • Software analysis

Jamadagni [33]

Communication • Voice and data integration • Reliable communication; less sensitivity to changes in environmental conditions

Automotive Cordence [20]; Eser [26]; Kuhnert et al. [37]

• Vehicle sales increase • Cost savings and revenue generation • Improved manufacturing

• • • •

Social medial Big data Telematics Speech to speech translation • User interfaces, • • • • • • • • • • •

AI & AR Cognitive systems Robotics 3D printings Cloud computing Mobile informatics Big data analytics Blockchain IoT, IoE Simulation CPS

2.3 Digital Transformation in Energy Industry The energy sector undoubtedly has the largest share among many industrial areas effected by digital transformation. The energy industry, which is the key point of all industrial revolutions so far, has faced with the new concept of Energy 4.0, with digital transformation. Energy 4.0 and digital transformation have led to new applications in this industrial field. The energy sector includes many categories such as oil, electricity, solar, wind, water, nuclear, biogas, geothermal, biomass. The interconnected and developing digital technologies used in the energy sector aim to create the energy of the future by revealing that energy is a brand-new value. The future energy includes three basic elements illustrated in Fig. 1.

Digital Transformation Success Factors …

157

Generation

Consumption

Grid

Fig. 1 Digital energy ecosystem [2]

• Digital centralized generation: The centralized generation within distributed energy sources has an important role in the rise of these sources. These energy sources provide the majority of the power supply. This process is based on fossil fuels and renewable resources, ensuring continuity and reliability. • Digital network: It indicates a relationship layer which produces and consumes energy and information and allows versatile flows. • Digital usage column: It covers the development of consumption models with distributed production and storage area. Digital transformation brings new and different challenges to the players in the energy industry and energy industry itself. These challenges are shown in Fig. 2 and 3, respectively. The traces of digital transformation adaptation are seen in renewable power divisions and creation of new clean energy. Especially vast investments are assigned to transportation infrastructure of cleaner energy. Digital transformation provides

Balancing the combination of energy source

Technology aggregation and capability

Power supply efficiency and performance

Challenges

Capturing population aging and awareness

Visibility of the asset rate

Definition of new sources of income

Fig. 2 New challenges for energy players [2]

158

B. B. Havle and M. Dursun

Challenges

Exploration of significant resources Production and generation Communication gaps Technology shortfalls The challenges of supply assurance, cost containment, and risk management Not equipped to handle demands of mobile sales and distribution Fail to provide the in-depth insight into individual Inability to access up-to-date documentation Struggling to obtain a 360-degree view of customers for customer analysis Complicated data generated by innovations to connect customer interactions across all channels Risk management Influenced call center operations by social engineering attacks

Fig. 3 New challenges for energy industry [2]

improved financial performance for energy players. Digital transformation provides, transparency, customer convenience, enabled interaction, security, collaboration and connectivity based on the new platform-based business models [57]. Energy companies use emerging digital technologies such as mobile platforms, IoT, cloud technology, digital platforms and reusable softwares to provide value for consumers, their partners, communities and themselves [57]. Some of the digital technologies used in energy industry are given in Fig. 4.

2.4 FCM Studies in Energy Industry FCM is a knowledge representation and system modeling methodology that expresses the causal relationship between system concepts. It is suitable for predictive modeling, and it is utilized in many sector such as logistics and supply chain management, construction, ceramics industry, agriculture, project management and so on. FCM is also employed in energy industry. Zare et al. [70] employed FCMs to determine the wind energy deployment scenarios in Iran. Chuang et al. [16] used FCM for finding interactions in residential expenses and energy consumption. Pereira et al. [52] integrated FCM and the system dynamics model to analyze energy change effects on the sustainability of small and medium sized organizations. Poczeta et al. [53] constructed a nested FCM-based structure for time series forecasting in the field

Digital Transformation Success Factors …

159

IoT Cloud technology

Robotics

Big data analytics

AI Digital technologies

Virtual reality

Blockchain

Augmented reality

Digital platforms Mobile devices

Fig. 4 Digital technologies used in energy industry

of appliances’ energy consumption prediction. Alipour et al. [1] utilized FCM to explore the interactions and joint effect of the effecting factors of the development of solar energy industry in Iran. Çoban et al. [19] employed FCM to compute the causal relationship among the cost of solar energy and its influencing factors. Olazabal et al. [48] used FCM to develop reasonable policy scenarios that support the carbonization of the urban energy system. Mpelogianni et al. [44] modeled the total energy behavior of an autonomous building for commercial or residential use via FCM. Ghaderi et al. [29] modeled profit maximization behavior in the electricity market employing FCMs. Amer et al. [2] used FCM to create scenarios for wind energy deployment.

3 Fuzzy Cognitive Maps In this study, Fuzzy Cognitive Maps (FCMs) are used after the determination of success factors. Success factors are determined through a detailed literature survey and Decision makers’ (DMs) opinions. FCM approach, which is used for different purposes in different fields in the literature, also has several advantages. Some of these advantages are as follows:

160

B. B. Havle and M. Dursun

• Conceptual values are used in the fuzzy cognitive mapping method. In fuzzy environment, it affects the degrees between these concepts, and in this method linguistic variables are quite easy to reach compared to reaching crisp values [4]. • FCMs feature the ability to model both positive and negative causal effects simultaneously [4]. • FCM has a flexible structure. It uses this flexibility feature to take advantage of many threshold functions that allow systemic movements to be achieved over long periods of time [8] (Tsadiras 2008). • It is possible to perform simulation experiments without the need for static analysis of FCMs [50]. FCM is a graphical method that can model the causal relationships among the factors of a system with dynamic behavior and used in the structuring and analysis of the problems [35]. The FCM involves directional arrows. These arrows represent variables and causal relationships among variables [39]. Let Ci represents the nodes, i = 1, 2, 3, ..., N . N refers to the total number of variables. Nodes are tied with bows of wi j weight). Causal relationships are changed in the range [−1, 1]. The mathematical equation employed in the FCM model is given in Eq. (1). ( Σn Ai(k+1) = f Ai(k) +

j=1, j/=i

A(k) j wi j

) (1)

Ai(k+1) represents the value of the factor at step (k + 1). Computational steps of the FCM given in Fig. 5 are as follows [59]. Step 1. Obtain the evaluation criteria: Evaluation criteria are obtained through the literature survey and DMs’ opinions. Step 2. Determine the linguistic scale: The linguistic scale given in Table 3 is used to evaluate the obtained evaluation criteria. Step 3. Obtain the judgments of the DMs for evaluation criteria: The relationships between the criteria and the importance degree of these relationships are determined using Table 4 by the DMs. Let n be the number of the DMs, therefore, n indicates the number of the evaluation matrix. Step 4. Aggregate the evaluation matrices: The matrices evaluated by DMs are aggregated into a fuzzy group decision matrix. Step 5. Defuzzify the fuzzy group decision matrix: Combined individual matrices construct fuzzy group decision matrix based on aggregation. Aggregated triangular ) fuzzy values are transformed into crisp numbers in this ( step. wi j = li j , m i j , u i j refers to aggregated fuzzy weights. This aggregation is done between C i and C j criteria. In this triangular of fuzzy numbers, u i j represents the upper value, m i j is the middle value, and li j represents the low value. The center

Digital Transformation Success Factors …

161

Determine the digital transformation success factors

Identify the linguistic scale

Obtain the judgments of the DMs for evaluation criteria (success factors)

Aggregate the evaluation matrices

Defuzzify the fuzzy group decision matrix and obtain the weighted matrix

Construct the FCM (Visualization)

Calculate the graph indices

Calculate the concepts’ values

Rank the criteria (success factors)

Fig. 5 Computational steps of FCM approach Table 3 Linguistic scale [11] Linguistic terms

TFNs

Positively very strong (PVS)

(1.00, 1.00, 0.75)

Positively strong (PS)

(1.00, 0.75, 0.50)

Positively medium (PM)

(0.75, 0.50, 0.25)

Positively weak (PW)

(0.50, 0.25, 0.00)

Zero (Z)

(0.25, 0.00, −0.25)

Negatively weak (NW)

(0.00, −0.25, −0.50)

Negatively medium (NM)

(−0.25, −0.50, −0.75)

Negatively strong (NS)

(−0.50, −0.75, −1.00)

Negatively very strong (NVS)

(−0.75, −1.00, −1.00)

162

B. B. Havle and M. Dursun

Table 4 Digital transformation success factors for energy industry Label

Concept

References

C1

Digital supply chain

Büyüközkan et al. [10]

C2

Digital road map

CGI [13]

C3

Digital capability

Jaffrey et al. [32]

C4

Education and skills development

Kung et al. [38]

C5

Cyber security

Havle and Ucler [30]

C6

Digital asset management

Keathley [34]

C7

Strategic vision

Wu et al. [68]

C8

Process optimization and excellence

Krakovics et al. [36]

C9

Digital marketing

Bulunmaz [9]

C 10

IT capability

Narayanan et al. [45]

C 11

Quality of accessibility

Sharda and Chattaerjee [56]

C 12

Reliability

Kung et al. [39]

C 13

Innovation

Taylor [62]

C 14

Digital technology

Nebeker et al. [46]

C 15

Integration and interoperability

Vernadat [66]

of gravity method (Chou and Chang [17]) is used to perform defuzzification process through Eq. (2). wi =

li + m i + u i 3

(2)

where wi illustrates defuzzified weight. Step 6. Construct the FCM: For the visualization of the FCM structure, weights of the connections between the links and the nodes in the structure are used. Step 7. Compute the graph indices: Types of the criteria in FCM can be divided into three parts as transmitter, receiver and ordinary variables, respectively [23]. Three values are used for these three types of criteria as out degree [od(υi )], in degree [id(υi )], and centrality ci or td(υi ), respectively [49]. The out-degree value, which shows the total power of connections (aik ) exiting the variable is obtained through the row sum of absolute values of a variable within the adjacency matrix gives. It is calculated through Eq. (3) [49]. od(υi ) =

ΣN k=1

a ik

(3)

The in-degree value calculated using Eq. (4) that shows the total power of connections (aki ) entering the variable is obtained through the column sum of perfect values

Digital Transformation Success Factors …

163

of a variable means [49]. id(υi ) =

ΣN k=1

aki

(4)

where N refers to the total number or variables. Centrality value indicates how associated the variable is to other factors and it is computed using Eq. (5) [49]. ci = td(υi ) = od(υi ) = id(υi )

(5)

Step 8. Calculate the concept values: Concept values are obtained using Eq. (1). Step 9. Rank the criteria.

4 Digital Transformation Success Factors Evaluation for the Energy Industry In this study, FCM approach is used to analyze digital transformation success factors for the energy industry. 3 DMs are determined based on their experiences, positions in the company that they work, knowledge levels about digital transformation and its applications in the energy industry and their academic backgrounds. Further information cannot be provided due to privacy concerns and confidentiality. Computational steps of the FCM approach given in section above are applied. Step 1. Determination of digital transformation success factors: Digital transformation success factors for energy industry shown in Table 4 are obtained through a literature survey and DMs’ opinions. • Digital Capability: It refers to the degree to which the culture, approaches and foundation of a system empower and back digital practices. • Digital Road Map: It refers to the way the organization is built from all aspects of the organization to create an integrated digital business that can create innovative ways to run, change and grow their business more effectively. • Digital Technology: It refers to advances are electronic devices, frameworks, gadgets and assets that create, store or handle information. • Education and Skill Development: It refers to the overall performance, skill, technical and theoretical knowledge as well as the efficiency of the staff working for the process that takes place within any organization.

164

B. B. Havle and M. Dursun

• Cyber Security: It refers to the administration of security dangers on account of tall level organizing among cleverly machines, items and frameworks. • Digital Assets Management: It refers to all components such as hardware, software, business models developed to perform basic operations such as the production, transmission of all assets in an organization. • Strategic Vision: It refers to the basis for a consistent and strong business model or planning of what an organization could be and what should happen in the future. • Process Optimization and Excellence: It refers to an effort to improve the product or service resulting processes within an organization, and a system that contributes to the further sustainability and improvement of holistic key performance criteria, as well as individuality, is an element of organizational leadership. • Digital Marketing: It refers to the marketing method that is carried out using digital channels and that performs all marketing applications in digital environment. • IT Capability: It refers to the hardware of both the equipment and the program of the organization and the ability to use it in data innovations. • Quality of Accessibility: It represents the quality of organization to access required digital platform and information while performing processes. • Reliability: It refers to the reliability of the organization for keeping the private and confidential information of the other firms or all customers. • Innovation: It refers to the criterion and execution of modern forms, items, administrations and strategies of conveyance which result in noteworthy changes in results, proficiency, viability or quality. • Digital Supply Chain: Digital supply chain refers to a network that aims to create smart and valueoriented new business and income by using technology as infrastructure. • Integration and Interoperability: Integration is the process of combining small components into a single holistic system, and interoperability represents the ability of one system to utilize and share information or functionality of another system, following common standards. Step 2. Identification of the linguistic scale: The linguistic scale given in Table 4 is selected to be used in the study. Step 3. Obtaining the judgments of the DMs to evaluate the criteria: The evaluations are provided in Tables 5, 6 and 7.

Z

PM

C 12

PM

PVS

Z

Z

PVS

Z

PVS

C 13

C 14

C 15

Z

PS

PS

PS

C 10

Z

C9

Z

PVS

Z

PM

PM

PVS

C 11

PS

Z

C7

Z

C6

C8

PVS

PM

C4

C5

PVS

C3

Z

PVS

Z

PW

C1

C2

C2

C1

DM3

NM

Z

PVS

Z

PM

PS

Z

Z

PS

PW

NM

PM

Z

PM

Z

C3

Z

Z

PVS

PVS

Z

PVS

Z

Z

PS

Z

NM

Z

PVS

PM

Z

C4

NM

Z

PVS

Z

PW

Z

Z

PW

PM

PS

Z

Z

PVS

PW

PW

C5

Table 5 Importance degree of relationships via DM1

NM

Z

PVS

Z

Z

PM

Z

Z

PS

Z

NW

PW

PVS

PM

PM

C6

Z

Z

PVS

PS

Z

PS

PS

PM

Z

Z

NM

Z

PVS

PVS

PW

C7

NM

Z

PVS

PW

Z

PM

Z

Z

PS

PS

PW

PM

PVS

PS

PS

C8

Z

Z

PVS

Z

PS

PW

Z

PS

PVS

Z

PW

PM

PVS

PS

PS

C9

Z

Z

PVS

PM

Z

Z

Z

PM

PS

Z

Z

PVS

PVS

PS

Z

C 10

PW

Z

PVS

Z

Z

Z

Z

PS

Z

PS

Z

Z

PVS

PW

Z

C 11

Z

Z

PVS

Z

Z

PVS

Z

PM

PM

Z

Z

PM

PVS

PM

PS

C 12

Z

Z

Z

Z

Z

PW

Z

Z

PVS

Z

Z

Z

PVS

PW

PW

C 13

PM

Z

PVS

Z

Z

PW

PS

PS

PVS

PM

Z

PW

PVS

PVS

PS

C 14

Z

Z

PVS

Z

Z

Z

Z

PVS

PS

PS

Z

Z

PVS

PVS

PS

C 15

Digital Transformation Success Factors … 165

Z

PS

C 12

PM

PVS

Z

Z

PVS

Z

PVS

C 13

C 14

C 15

Z

PVS

PS

PVS

C 10

Z

C9

Z

PVS

Z

PS

Z

PVS

C 11

PVS

Z

C7

Z

C6

C8

PVS

PS

C4

C5

PVS

C3

Z

PVS

Z

Z

C1

C2

C2

C1

DM3

Z

Z

PVS

Z

Z

PS

Z

Z

PVS

Z

NW

Z

Z

Z

Z

C3

Z

Z

PVS

PVS

Z

PVS

Z

Z

PVS

Z

Z

Z

PVS

PM

Z

C4

NW

Z

PVS

Z

Z

Z

Z

Z

PVS

PS

Z

Z

PVS

Z

PW

C5

Table 6 Importance degree of relationships via DM2

NW

Z

PVS

Z

Z

PS

Z

PS

PVS

Z

NM

Z

PVS

PW

PM

C6

Z

Z

PVS

PS

Z

PVS

PVS

PVS

Z

Z

NS

Z

PVS

PVS

Z

C7

PS

Z

PVS

PW

Z

PS

NM

Z

PVS

PVS

Z

PW

PVS

PS

PS

C8

Z

Z

PVS

Z

PVS

Z

Z

PVS

PVS

Z

Z

PM

PVS

PS

PS

C9

Z

Z

PVS

PS

Z

Z

Z

PVS

PVS

Z

Z

PVS

PVS

PS

Z

C 10

PM

Z

PVS

Z

Z

Z

Z

PVS

Z

PVS

Z

Z

PVS

Z

Z

C 11

Z

Z

PVS

Z

Z

PVS

Z

PVS

PVS

Z

Z

PW

PVS

PM

PS

C 12

Z

Z

Z

Z

Z

Z

Z

Z

PVS

Z

Z

Z

PVS

Z

Z

C 13

PVS

Z

PVS

Z

Z

Z

PVS

PVS

PVS

PS

Z

PW

PVS

PVS

PS

C 14

Z

Z

PVS

Z

PW

Z

Z

PVS

PVS

PVS

NM

Z

PVS

PVS

PS

C 15

166 B. B. Havle and M. Dursun

PVS

Z

PVS

Z

Z

Z

PM

PVS

PVS

PW

Z

PM

Z

Z

PS

PVS

PM

PVS

Z

PVS

C1

C2

C3

C4

C5

C6

C7

C8

C9

C 10

C 11

C 12

C 13

C 14

C 15

Z

PM

PS

Z

PVS

Z

PM

PVS

PVS

Z

C2

C1

DM3

NVS

Z

PVS

Z

PVS

PVS

Z

Z

PS

PS

NS

PVS

Z

PVS

Z

C3

Z

Z

PVS

PVS

Z

PVS

Z

Z

PM

Z

NM

Z

PVS

PS

Z

C4

NVS

Z

PVS

Z

PM

Z

Z

PM

PW

PS

Z

Z

PVS

PS

PW

C5

Table 7 Importance degree of relationships via DM3

NM

Z

PVS

Z

Z

PW

PW

NM

PM

Z

NW

PM

PVS

PVS

PM

C6

Z

Z

PVS

PS

Z

PM

PM

Z

Z

Z

NW

Z

PVS

PVS

PM

C7

NM

Z

PVS

PM

Z

PW

PW

Z

PS

PS

PM

PS

PVS

PVS

PS

C8

Z

Z

PVS

PW

PM

PM

Z

PM

PVS

Z

PM

PS

PVS

PVS

PVS

C9

Z

Z

PVS

PM

Z

Z

Z

Z

PM

Z

Z

PVS

PVS

PS

PW

C 10

Z

Z

PVS

Z

Z

Z

Z

PS

Z

PM

Z

Z

PVS

PS

Z

C 11

Z

Z

PVS

Z

Z

PVS

Z

Z

PW

Z

Z

PS

PVS

PM

PVS

C 12

Z

Z

Z

Z

Z

PM

Z

Z

PVS

Z

Z

Z

PVS

PM

PM

C 13

Z

Z

PVS

Z

Z

PM

PS

PM

PVS

PM

Z

PW

PVS

PVS

PVS

C 14

Z

Z

PVS

Z

Z

Z

Z

PVS

PM

PM

PM

Z

PVS

PVS

PS

C 15

Digital Transformation Success Factors … 167

0

0.625

C 12

0.5

0.923

0

0

0.923

0

0.923

C 13

C 14

C 15

0

0.798

0.798

0.798

C 10

0

C9

0

0.923

0

0.625

0.393

0.923

C 11

0.673

0

C7

0

C6

C8

0.923

0.5

C4

C5

0.923

C3

0

0.923

0

0.25

C1

C2

C2

C1

0

0

−0.393

0.923

0.923

0

0.923

0

0

0.673

0

0

0.923

0

0.393

0.798

0

0

0.798

0.352

0

−0.25

0.393

0.923

0.625

0

C4

−0.5

0

0.393

0

C3

−0.505

0

0.923

0

0.25

0

0

0.25

0.505

0.75

0

0

0.923

0.352

0.25

C5

−0.375

0

0.923

0

0

0.5

0.125

0.083

0.673

0

−0.375

0.25

0.923

0.505

0.5

C6

Table 8 The weight matrix based on aggregation and defuzzification

0

0

0.923

0.75

0

0.673

0.673

0.393

0

0

−0.5

0

0.923

0.923

0.25

C7

0.083

0

0.923

0.375

0

0

0

0.923

0.125

0.673

0.25

0

−0.103 0.5

0.673

0.923

0

0.25

0.625

0.923

0.798

0.798

C9

0

0.798

0.798

0.25

0.5

0.923

0.798

0.75

C8

0

0

0.923

0.625

0

0

0

0.393

0.673

0

0

0.923

0.923

0.75

0.125

C 10

0.25

0

0.923

0

0

0

0

0.798

0

0.673

0

0

0.923

0.352

0

C 11

0

0

0.923

0

0

0.923

0

0.393

0.505

0

0

0.5

0.923

0.5

0.798

C 12

0

0

0

0

0

0.25

0

0

0.923

0

0

0

0.923

0.25

0.25

C 13

0.393

0

0.923

0

0

0.25

0.798

0.673

0.923

0.625

0

0.25

0.923

0.923

0.798

C 14

0

0

0.923

0

0.125

0

0

0.923

0.673

0.673

0

0

0.923

0.923

0.75

C 15

168 B. B. Havle and M. Dursun

Digital Transformation Success Factors …

169

Step 4. Aggregation of the evaluation matrices through MATLAB Fuzzy Toolbox via MAX method. Step 5. Defuzzification and obtaining group decision matrix: Obtained group decision matrix via MAX aggregation method is defuzzified in MATLAB Fuzzy Toolbox through center of gravity method. Obtained weight matrix is given in Table 8. Step 6. Construction of the FCM: At this step, the FCM model of the digital transformation success factors for energy industry is visualized in Fig. 6 based on the weight matrix. Step 7. Calculation of graph indices: Graph indices are calculated as in Table 9. Step 8. Calculation of the concept values: The value of each concept is calculated as in Table 10.

Fig. 6 Constructed FCM model of the digital transformation success factors for energy industry

Table 9 Graph indices Label

Concepts

Outdegree

Indegree

Centrality

C1

Digital supply chain

6.19

7.34

13.53

C2

Digital road map

8.34

6.01

14.35

C3

Digital capability

12.92

4.94

17.87 10.00

C4

Education and skills development

4.76

5.24

C5

Cyber security

3.25

4.71

7.96

C6

Digital asset management

3.87

5.23

9.10

C7

Strategic vision

9.66

6.01

15.67

C8

Process optimization and excellence

4.58

6.80

11.38 (continued)

170

B. B. Havle and M. Dursun

Table 9 (continued) Label

Concepts

C9

Digital marketing

Outdegree 2.50

Indegree

Centrality

6.96

9.46 11.70

C 10

IT capability

6.36

5.34

C 11

Quality of accessibility

2.24

3.92

6.16

C 12

Reliability

3.42

5.47

8.89 15.52

C 13

Innovation

12.92

2.60

C 14

Digital technology

0.00

7.48

7.48

C 15

Integration and interoperability

2.92

5.91

8.83

Table 10 Concept values of success factors Label

Concepts

Concept value

C1

Digital supply chain

0.999745414

C2

Digital road map

0.999047270

*C 3

Digital capability

0.999781071

C4

Education and skills development

0.996641211

C5

Cyber security

0.990412196

C6

Digital asset management

0.990823884

C7

Strategic vision

0.997441243

C8

Process optimization and excellence

0.999467714

C9

Digital marketing

0.999631586

C 10

IT capability

0.998140627

C 11

Quality of accessibility

0.992335401

C 12

Reliability

0.998369283

C 13

Innovation

0.972078309

C 14

Digital technology

0.983838176

C 15

Integration and interoperability

0.998953071

Results shown in Table 10 reveal that the most important success factor is digital capability. Companies in energy industry should focus on their digital capabilities for their sustainability, profitability, and competitiveness in the digital ecosystem.

Digital Transformation Success Factors …

171

1

0.995

0.99

0.985

0.98

0.975

0.97

ap ility ent rity ent ion ce ing ility ility ility tion gy ility ain Ch d M pab pm ecu em Vis llen rket pab sib liab va nolo rab ply l Roa l Ca velo er S anag tegic Exce l Ma T ca cces Re Inno tech rope p u ita I l A De Cyb t M Stra and igita ita lS ita Inte of se D ita Dig Dig kills ty n Dig and As ali tio Dig l u a dS n a z n Q i it io a tim rat Dig on eg Op ati Int uc ss d e c E o Pr

Fig. 7 Importance degrees of the digital transformation success factors

5 Discussions and Conclusions Important technological developments in the world since the industrial revolution have made great positive contributions in terms of industrial efficiency. With the help of digital technologies, business processes and business models are tried to be restructured in order to bring the positive effect on efficiency to the desired level [31]. Today, many countries are trying to pay more attention to development goals, and they are trying to maintain this through digital platforms. At the end of the day, countries started to go beyond their borders in a collective union, independently and impartially. Sectoral developments and changes have begun to affect customers’ expectations. In recent years, developments in technology have caused a new wave that affects the whole world. The emerging technologies have initiated the digital transformation process. Increased internet access developed cloud technology, internet of things, artificial intelligence applications, increase use of smart mobile phones, the introduction of wearable smart devices and digital platforms such as social media and mobile applications have affected both people and companies. Companies have made efforts to meet the digital demands and gain a share in the digital market and ensure their sustainability in the digital ecosystem. Digital transformation has become a necessity rather than an optional process. Digital transformation is a process that

172

B. B. Havle and M. Dursun

must be managed properly. Otherwise, companies cannot be prevented from being subjected to the disruptive power of digital transformation. They need to know what their capabilities are and evaluate themselves in this regard in order to manage this transformation process correctly. In this study, digital transformation success factors in energy industry are analyzed using FCM approach. Results of the FCM approach, which is illustrated in Fig. 7, show that the most important success factor is digital capability for energy companies. Energy companies should focus on their capabilities to sustain successful digital transformation process. Successful digital transformation process provides, profitability, flexibility, sustainability, competitiveness in a digital ecosystem. Future researches will focus on the evaluation of the energy companies digital transformation capabilities. For this purpose, future study will be designed as a two phase study. The results of this research will be considered as the first phase, and by using the obtained FCM weights for digital transformation evaluation factors a multi-criteria decision framework will be developed for evaluating energy companies digital transformation capabilities. Acknowledgement This work is financially supported by Galatasaray University Research Fund Grant Number FOA-2022-1092.

References 1. Alipour M, Hafezi R, Papageorgiou E, Hafezi M, Alipour M (2019) Characteristics and scenarios of solar energy development in Iran: fuzzy cognitive map-based approach. Renew Sustain Energy Rev 116:109410 2. Amer M, Jetter A, Daim T (2011) Development of fuzzy cognitive map (FCM)-based scenarios for wind energy. Int J Energy Sector Manage 5(4):564–584 3. Andrea A, Edler T (2015) On global electricity usage of communication technology: trends to 2030. Challenges 6(1):117–157. https://doi.org/10.3390/challe6010117 4. Baykasoglu A, Golcuk I (2015) Development of a novel multiple-attribute decision making model via fuzzy cognitive maps and hierarchical fuzzy TOPSIS. Inf Sci 301:75–98 5. Bouee C, Schaible S (2015) Die Digitale Transformation der Industrie. Studie: Roland Berger und BDI 6. Bowersox DJ, Closs DJ, Drayer RW (2005) The digital transformation: technology and beyond. Supply Chain Manage Rev 9(1):22–29 7. Buˇckova M, Krajˇcoviˇc M, Jerman B (2017) Impact of digital factory tools on designing of warehouses. J Appl Eng Sci 15(2):173–180 8. Bueno S, Salmeron JL (2009) Benchmarking main activation functions in fuzzy cognitive maps2. Expert Syst Appl 36:5221–5229 9. Bulunmaz B (2016) Geli¸sen Teknolojiyle Birlikte De˘gi¸sen Pazarlama Yöntemleri ve Dijital Pazarlama. TRT Akademi 1(2):348–365. http://dergipark.org.tr/en/pub/trta/issue/ 23620/252060 10. Büyüközkan G, Göçer F (2018) Digital Supply Chain: literature review and a proposed framework for future research. Comput Ind 97:157–177 11. Büyüközkan G, Vardalo˘glu Z (2012) Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry. Expert Syst Appl 39(12):10438–10455

Digital Transformation Success Factors …

173

12. Büyüközkan G, Havle CA, Feyzio˘glu O (2020) A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. J Air Transp Manag 86:101817 13. CGI (2016) Digital strategy and roadmap. https://www.cginederland.nl/sites/default/files/files_ nl/brochures/cgi-nl_brochure_digital-strategy-and-roadmap.pdf 14. Cheung CW, Tsang IT, Wong KH (2017) Robot avatar: a virtual tourism robot for people with disabilities. Int J Comput Theory Eng Singapore 9(3):229–234 15. Chryssolouris G, Mavrikios D, Papakostas N, Mourtzis D, Michalos G, Georgoulias K (2009) Digital manufacturing: history, perspectives, and outlook. In: Proceedings of the institution of mechanical engineers 16. Chuang T, Xianzhong M, Jian C, Li K, Zheng Z, Yutong L, Guangwen H (2021) Study on the interactive relationship between urban residents’ expenditure and energy consumption of production sectors. Energy Policy 157:112502 17. Chou SW, Chang YC (2008) The implementation factors thatinfluence the ERP (Enterprise Resource Planning) Benefits. Decis Support Syst 46(1):149–157 18. Cisco (2017) Becoming the retailer of the future: your journey to digital transformation. https://cdn2.hubspot.net/hubfs/2295439/OneNeckSRWWebsiteResources/eBooks/ Industries_Retail_Ebook-AMR-CTE-EN.pdf 19. Coban V, Çevik Onar S (2018) Strategic analysis of solar energy pricing process with hesitant fuzzy cognitive map. Springer, pp 195–227 20. Cordence (2017) The digital odyssey of the automotive industry. http://www.cordenceworl dwide.com/Frontend/Documents/TheDigitalOdysseyoftheAutomotiveIndustry_V8.pdf 21. Deloitte (2018) Gelece˘gin enerji s¸irketi; Türkiye’de elektrik sektörünün gelece˘gi. https:// www2.deloitte.com/content/dam/Deloitte/tr/Documents/energyresources/Gelecegin-EnerjiSirketi.pdf 22. Dongoski R, Selck A (2017) Digital agriculture: helping to feed a growing World. Perform Ersnt Young 9(1):82–89. http://assets.fsnforum.fao.org.s3-eu-west-1.amazonaws.com/public/ discussions/contributions/digitalagriculture_helping%20to%20feed%20the%20world_0.pdf 23. Doyuran U (2018) Enerjide Dijital Dönü¸süm. https://www.dunyaenerji.org.tr/wpcontent/upl oads/2018/11/UDoyuranSunum.pdf 24. Eden C, Ackerman F, Cropper S (1992) The analysis of cause maps. J Manage Stud 29:309–323 25. Eistert T, Gordon F, Deighton J, Marcu S (2013) Banking in a digital World. https://www. atkearney.de/documents/856314/3998533/Banking+in+a+digital+world.pdf/44829263-4c004925-ae05-9fc37217d6bb 26. Eser E (2018) Gelece˘ge Hazırlanan Türkiye’deki Otomotiv Sirketleri ¸ Dijital Dönü¸sümün Potansiyelini Nasıl Kullanabilir. http://tehad.org/wp-content/uploads/2018/12/1545636773_IDC_ KocSistem_Otomotiv_Sektoru_Rapor_2018.pdf 27. Fitzgerald M, Kruschwitz N, Bonnet D, Welch M (2013) Embracing digital technology: a new strategic imperative. MIT Sloan management review, Research report 28. Gelter H (2017) Digital tourism - an analysis of digital trends in tourism and customer digital mobile behaviour for the Visit Arctic Europe Project. http://www.lme.fi/media/vae-outcomes/ rd-results/report-visit-arctic-europe-mission-3-summary-2.pdf 29. Ghaderi SF, Azadeh A, Nokhandan BP, Fathi E (2012) Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map. Expert Syst Appl 39:4635–4646 30. Havle CA, Üçler Ç (2018) Enablers for Industry 4.0. In: 2018 2nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT), Ankara, pp 1–6 31. Hinings B, Gegenhuber T, Greenwood R (2018) Digital innovation and transformation: an institutional perspective. Inf Organ 28(1):52–61 32. Jaffrey A, Macneil A (2017) Organisational approaches to digital capability. http://addl.ulster. ac.uk/docs/connect_more.pdf 33. Jamadagni HS (2004) Digital communications system. https://nptel.ac.in/courses/Webcoursecontents/IISc-BANG/DataCommunication/LearningMaterial-DataCommunication.pdf

174

B. B. Havle and M. Dursun

34. Keathley E (2014) Digital asset management: content architectures, project management, and creating order out of media chaos 35. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75 36. Krakovics F, Leal JE, Mendes P Jr, Santos RL (2008) Defining and calibrating performance indicators of a 4PL in the chemical industry in Brazil. Int J Prod Econ 115(2):502–514 37. Kuhnert F, Stürmer C, Koster A (2018) Five trends transforming the Automotive Industry. https://eu-smartcities.eu/sites/default/files/2018-03/pwc-five-trendstransform ing-the-automotive-industry.compressed.pdf 38. Kung CY (2006) Using fuzzy sets and grey decision-making to construct the performance evaluation model of firm’s outsourcing management – a case study of avionics manufacturer in Taiwan. Qual Quant 40:577–593 39. Kwahk KY, Kim YG (1999) Supporting business process redesign using cognitive maps. Decis Support Syst 25(2):155–178 40. Martin A (2008) Digital literacy and the “digital society.” Digit Lit Concepts Policies Pract 30:151–176 41. Mazzone DM (2014) Digital or death: digital transformation — the only choice for business to survive smash and conquer, 1st edn. Smashbox Consulting Inc., Mississauga 42. Mil B, Dirican C (2018) Endüstri 4.0 Teknolojileri ve Turizme Etkileri. Jomat, Disiplinlerarası Akademik Turizm Dergisi 1(3):1–9 43. Moray, House school of education election briefings (2016) Digital education. https://www.ed. ac.uk/files/atoms/files/electionbriefing2-digital-education-29-03-16.pdf 44. Mpelogianni V, Marnetta P, Groumpos PP (2015) Fuzzy cognitive maps in the service of energy efficiency. IFAC-Papers OnLine 48(24):1–6 45. Narayanan S, Jayaraman V, Luo Y, Swaminathan JM (2011) The antecedents of process integration in BPO and its effect on firm performance. J Oper Manag 29:3–16 46. Nebeker C, Bartlett Ellis RJ, Torous J (2019) Development of a decision-making checklist tool to support technology selection in digital health research. Transl Behav Med 47. Neittaanmaki P, Galeieva E (2016) Digital healthcare platform. https://www.jyu.fi/it/fi/tut kimus/julkaisut/it-julkaisut/digital-healthcare-platform-verk.pdf 48. Olazabal M, Pascual U (2016) Use of fuzzy cognitive maps to study urban resilience and transformation. Environ Innov Soc Trans 18:18–40 49. Oracle (2018) Digital banking: evaluating paths for progressive transformation. http://www. oracle.com/us/industries/financial-services/fs-digital-banking-evaluatingwp-3044117.pdf 50. Ozesmi U, Ozesmi SL (2004) Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecol Model 43– 64 51. Papakostas GA, Koulouriotis DE, Tourassis VD (2012) Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems. Expert Syst Appl 39:10620–10629 52. Pereira IPC, Ferreira FAF, Pereira LF, Govindan K, Meidut˙e-Kavaliauskien˙e I, Correia RJC (2020) A fuzzy cognitive mapping-system dynamics approach to energy-change impacts on the sustainability of small and medium-sized enterprises. J Clean Prod 256:120154 53. Poczeta K, Papageorgiou EI, Gerogiannis VC (2020) Fuzzy cognitive maps optimization for decision making and prediction. Mathematics 8:2059 54. Riverbed (2018) The riverbed retail digital transformation survey. https://www.riverbed.com/ document/fpo/Solutions/riverbed-retaildigitaltransformation-survey-2018.pdf 55. Seino T, Ikeda Y, Kinoshita M, Suzuki T, Atsumi K (2001) The impact of “digital manufacturing” on technology management. In: PICMET 2001. Portland international conference on management of engineering and technology. Proceedings Vol. 1: book of summaries (IEEE Cat. No. 01CH37199), vol 1. IEEE, pp 31–32 56. Sharda K, Chatterjee L (2011) Configurations of outsourcing firms and organizational performance: a study of outsourcing industry in India. Strateg Outsourc Int J 4(2):152–178 57. Smart A (2017) The digital oil company: getting ahead of the energy transition, Accenture. https://www.accenture.com/us-en/_acnmedia/pdf-58/accenture-the-digital-oil-companygetting-ahead-of-the-energy-transition.pdf 58. Solis B, Lieb R, Szymanski J Group (2014) The 2014 state of digital transformation. Altimeter

Digital Transformation Success Factors …

175

59. Soner O, Asan U, Celik M (2015) Use of HFACS–FCM in fire prevention modelling on board ships. Saf Sci 77:25–41 60. Stolterman E, Fors A (2004) Information technology and the good life. In: Information systems research, pp 687–692 61. Taylor K (2015) Connected health, How digital technology is transforming health and social care. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/lifesciences-hea lth-care/deloitte-uk-connected-health.pdf 62. Taylor SP (2017) What is innovation? A study of the definitions, academic models and applicability of innovation to an example of social housing in England. Open J Soc Sci 5(11):128–146 63. The World Economic Form White Paper (2017) Digital transformation initiative - aviation, travel and tourism industry, January 2017. https://www.accenture.com/t20170116T084449__ w__/us-en/_acnmedia/Accenture/Conversion-Assets/WEF/PDF/Accenture-DTI-Aviation-Tra vel-and-Tourism-Industry-White-Paper.pdf 64. Tiven MB, Fuchs ER, Bazari A, MacQuarrie A (2018) Evaluating global digital education: student outcomes framework. http://www.oecd.org/pisa/Evaluating-Global-Digital-EducationStudent-Outcomes-Framework.pdf 65. Tolman EC (1948) Cognitive maps in rats and men. Psychol Rev 55:189–208 66. Vernadat FB (2009) Enterprise integration and interoperability. In: Springer handbook of automation. Springer, Heidelberg, pp 1529–1538 67. Westerman G, Calm.jane C, Bonnet D, Ferraris P, McAfee A (2011) Digital transformation: a roadmap for billion-dollar organizations, pp 1–68. MIT Sloan Management, MIT Center for Digital Business and Capgemini Consulting 68. Wu D, Greer MJ, Rosen DW, Schaefer D (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564–579 69. Zadeh LA (1965) Fuzzy sets. Inf Contr 8(3):338–353 70. Zare SG, Alipour M, Hafezi M, Stewart RA, Rahman A (2022) Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method. Energy 238:121673

Smart Manufacturing

Education as a Promoter of Digital Transformation in the Manufacturing Industry Ari Pikkarainen and Maarit Tihinen

Abstract Today, the manufacturing industry is moving towards smart manufacturing systems due to digitalisation. There are various technologies and working life skills recognised as important for the adoption of change in the manufacturing industry, such as digital solutions and platforms, artificial intelligence (AI), diagnostics and data analysis. Digital manufacturing systems are complex to design and operate, so in addition to technology-specific knowledge, industry highly values engineers’ broad vision, open minds, strong consciousness and ability to absorb new technologies. Thus, education plays a vital role in supporting and promoting change. It is key to increasing awareness and knowledge about digitalisation in the manufacturing industry. The implementation of digitalisation themes in education requires curriculum-level planning and a target technology that can be used to promote these themes. This chapter therefore concentrates on educational solutions to promote digital transformation in the manufacturing industry. Digital and additive manufacturing (AM) specifically are used as examples to educate new experts on the requirements of digital transformation in technology. Keywords Digital manufacturing · Additive manufacturing · Education · Curriculum

1 Introduction The development of technologies, as accelerated by digitalisation, and their increasingly diverse utilisation in everyday life has changed and is constantly changing A. Pikkarainen (B) · M. Tihinen Mechanical Engineering, Lapland University of Applied Sciences, Kemi, Finland e-mail: [email protected] M. Tihinen e-mail: [email protected] Business Economics, Master School, Responsibility in Business and Services, Lapland University of Applied Sciences, Rovaniemi, Finland © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_8

179

180

A. Pikkarainen and M. Tihinen

society, the operating environment and the way we work [3, 16, 32, 42]. Nowadays this change is discussed and understood as a concept of digital transformation. [32] defined digital transformation as a change to models of working, roles and business offerings occasioned by an organisation’s adoption of digital technologies or changes to its operating environment in processes and business domains, as well as at organisational and societal levels. In the manufacturing industry, digital transformation strategies represent a top priority around the world [14], as they enhance companies’ global competitiveness [1, 27, 32]. Digital manufacturing has thus become a common global trend, with computer-integrated manufacturing systems and processes able to take advantage of the latest set of emerging technologies. In this way, product development time has been reduced and market demands have been met more quickly [30]. To support the manufacturing industry in embracing digital transformation, governments have established various research and technology agendas, such as Industrie 4.0 (I4.0) in Germany or the Smart Manufacturing Leadership Coalition [13, 17, 18, 42] in the United States. Digital transformation concerns not only software, data or technology but also people, culture and ways of working, as well as changes in company business models. As such, attention must be paid to the potential of education to promote and facilitate these changes. Accordingly, digital transformation in education requires a strategic approach from educational institutions. [3] recommend that administrators or program experts in education systems be prepared for and be able to manage this change. For example, they may provide a vision to create and guide an effective learning environment to be supported by technologically appropriate content and infrastructure. The global manufacturing trade is facing a transformation due to several factors related to digitalisation, and companies must be aware of the possibilities provided by different digital solutions. One key issue in this is the education and training of the workforce to meet the challenges set by digital transformation [46]. When discussing the educational needs for the future, anticipating required knowledge and skills is important. For example, according to a study done for the Finnish technology sector with over 300 companies [40], 130,000 new experts are required for different technology-related areas in the next 10 years. The study emphasises the importance of digitalisation as a cross-sectional factor when managing a range of skills and competences. Topics such as artificial intelligence (AI, data management, software engineering, robotics and cybersecurity are among the required skills for the future. When investigating the technology, process and manufacturing industries in detail and the skills required for the year 2035, the study also highlights the key role of digitalisation. Working life skills from robotics technology, digital solutions and platforms, remote and virtual services, AI and diagnostics are considered important skills for the future. In addition, nowadays generic skills such as creativity, problem solving, the ability to learn, analytical thinking and innovation are largely connected to digital skills [9, 11, 34]. Thus, when looking at the typical vocational areas of technology, such as mechanical, electrical and information and communications technology engineering, there is a growing need for multiprofessional competence, creative use of digital technology, automation, additive manufacturing (AM,

Education as a Promoter of Digital Transformation …

181

understanding of the environmental impact on digital technologies, the Internet of Things (IoT, and virtual and augmented reality (VR/AR application skills [9, 12]. Digital transformation in the manufacturing industry provides new opportunities but also challenges for both industry [10] and education [29, 43], as there are various skills to be taught or trained, the environment supported by digital elements is more complicated than in traditional manufacturing. The objective of this chapter is to highlight the educational solutions when promoting digital transformation in the manufacturing industry, and to provide examples and solutions for arranging digital transformation education. The workforce is proven to be a critical component of digital business transformation in digital manufacturing [28], so educational approaches are needed. The current literature presents mainly different digital transformation elements, but it lacks a connection to education. Therefore, this chapter presents a novel view of the educational factors of digital transformation. As AM represents one of the most promising and innovative technologies in the manufacturing industry today [29], it and digital manufacturing are used as examples to educate new experts based on the requirements of digital transformation in the technology context. The remainder of this chapter is organised as follows. The next section features a literature review of studies related to digital transformation, digital manufacturing and AM. After that, the chapter’s main findings are presented. Finally, the main conclusions and recommendations from the findings are highlighted, as well as future research possibilities.

2 Literature Review Today, digital manufacturing is a common global trend consisting of a set of promising technologies [10] that reduce product development time and cost, increase product quality and customisation possibilities, and speed up responses to market requirements [22, 30]. In smart manufacturing, a production system integrates multiple subsystems through an interconnected network to enable data exchange [22, 41]. The integration can include several advanced technologies [10] such as AR, AI, big data, cloud computing, cyber physical systems the IoT, robotic technologies and VR. The National Institute of Standards and Technology [31] defines smart manufacturing systems as “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs”. Affected by digital transformation, the manufacturing industry is heading towards smart manufacturing systems. In practice, digital manufacturing technologies integrate systems and processes across all fields of production to create an integrated approach to manufacturing (often called a digital factory) from design, to production, to end-product maintenance. Thus, digital manufacturing enables real-time data analytics to optimise the entire manufacturing process. However, digital manufacturing is more than just technology. Several authors compare the impact of the

182

A. Pikkarainen and M. Tihinen

ongoing digital transformation with the fourth Industrial Revolution [1, 8, 13, 14, 25, 26, 36, 42]. In addition, [22] introduce how this change is building smart manufacturing ecosystems in which various dimensions – 1) product design and development, 2) production systems and 3) business processes—feature in the new innovative and layered cycle of manufacturing. Historically, these separate dimensions have been managed in separate silos. Now, digitalisation has inserted a digital world between the physical world and engineers, so interaction between engineers and objects has changed from direct to indirect. Business models in manufacturing companies need to be transferred accordingly [10]. These changes, and the challenges they raise, in manufacturing processes and environments must be taken into consideration in education. For example, [45] define the concept of a digital triplet, which consists of an intelligent activity world in addition to the cyber and physical worlds. Digital transformation is also an inevitable part of education at all levels and in all sectors [27]. European and international policies, practices and standards have been developed and set to support the digital transformation in education, such as the International Society for Technology Education and the OECD Center for Educational Research and Innovation. Nowadays students use various technologies in their daily lives, including learning environments, and most of them have born into the digital world [3]. Especially in higher education, digital tools and learning spaces promote new kinds of knowledge generation and connect broader sets of actors outside the traditional education environment [6]. The recognition of the importance of digital skills, digital transformation affected by training and education institutions is increasingly growing. Several recent publications discuss the significant role of digital transformation in education [3, 4, 23, 24, 38]. However, the literature shows that there are still problems when adopting digital skills in education. [38] state that empowering students, stakeholders and other participants in educational settings requires active participation and the creation of new learning and research environments. They emphasise a virtual learning environment that is not constrained by space or time. [23] introduce the main digital transformation challenges in the United Arab Emirates higher education, such as a lack of holistic vision, digital transformation competency, personnel competency and IT skills, and data structure, processing and reporting. In addition, [4] state that both teachers and students still use a limited number of digital technologies from a learning perspective in German higher education. Furthermore, regarding online materials, [24] point out that students like to use technology, but online resources must be accessible, easy to use and support learner needs. Digital manufacturing systems are complex to design and operate, so industry highly values engineers’ broad visions, open minds, strong consciousness and ability to absorb new technologies and diverse knowledge [21]. The complexity of products originates from the fact that they can involve several sub-systems, multiple engineering domains, and multiple variants and system architectures, for example. Therefore, teaching methods in engineering education need to reform with actionoriented learning approaches like learning factories [44] to provide an environment

Education as a Promoter of Digital Transformation …

183

for developing and learning integrated skills, as required by industry. Learning factories demonstrate a digital manufacturing enterprise, created by advanced manufacturing techniques with the IoT, which is not only interconnected but also communicates, analyses and uses information to further drive intelligent actions in the physical world [35]. A minimum requirement for a learning factory is that it consists of learning parts (elements of education, like courses) and factory parts (elements of the production environment). In fact, the learning factory can cover a wide variety of different topics, such as automation, lean management, energy efficiency and logistics [21]. For example, [30] presents the concept of Teaching Factory 4.0 in which engineering students implement product development processes by utilising digital tools alongside traditional manufacturing. Furthermore, [43] discuss how manufacturing CPSs can be used to create a diverse educational environment to implement digital manufacturing themes (a so-called digital learning factory). AM (more generally known as 3D printing) has been recognised as one of the emerging technologies in the fourth digital revolution. The generalisation of technology (at the professional and consumer levels) has created new possibilities in the manufacturing sector. These possibilities enable new kinds of freedom, such as in product design and development. One key issue with AM is referring to it as a direct digital manufacturing method that enables the production of end-use parts without the need for production planning or preparation. The term digital manufacturing is considered manufacturing in which digital tools are used to manage the product lifecycle or to implement digital solutions in manufacturing [7, 39]. Digital elements of AM can include parametric computer modelling in 3D (CAD), digital product records with traceable datasets and digital on-demand manufacturing (e.g. digital spare parts) [15]. The use of computation in different phases of AM product design processes (e.g. in topology optimisation or flow simulation) makes AM one method in the digital manufacturing area. In some cases, AM is also referred to as smart AM, in which machine learning and algorithms are used for a data-driven approach to material and product design [5]. This has brought a whole new perspective to traditional product manufacturing, where lead times can be shortened in low-volume production or storage costs can be omitted through digital spare part manufacturing [19]. Regarding the product or part structure, through the application of computation, lighter and less expensive structures can be reached [2]. This leads to economic and sustainable advances in product manufacturing.

184

A. Pikkarainen and M. Tihinen

3 Promoting Digital Transformation in Manufacturing Industry - Necessary Education Aspects Concerning Digitalisation [33] present the concept of technical pedagogy, wherein the identification of various factors is necessary to arrange technical education. The model emphasises the importance of curriculum development, learning environments, technical upbringing and digitalisation elements (e.g. IoT and I4.0) when teaching a technical subject like AM. When teaching digital transformation topics, it is important to connect to context and surroundings based on the concept of technical pedagogy. This means that digitalisation needs to be learnt through suitable technology in a real-life learning environment. Figure 1 presents a process model for implementing digital transformation elements in technical education. As seen in Fig. 1, the core of the model is formed by the arrangement of an educational process where anticipation presents the origin of the planning. In this stage, the required competences (general and professional), work-life requirements and experience with educating technical subjects are taken into consideration [33]. The digital transformation point of view then moves to the anticipation stage, where the worklife requirements are mapped through discussions with a partner or by conducting a questionnaire. Digital transformation elements must be included in the mapping of the requirements to receive the required educational learning outcomes. Through this model, the education can be arranged at the curriculum level, whereby the learning outcomes are integrated into the education at the course level. The implementation of education requires a functional learning environment in which the digital transformation elements are used. Learning environments enable students to learn technical phenomena through practical learning and support their self-awareness of learning [20]. In the background of the process are generic skills, digital skills and equipment knowhow, which form the substrate for planning education. Generic skills include creativity, problem solving, the ability to learn, analytical thinking and innovation skills. These competences are important parts of technical education, and especially topics related to digital transformation [12, 30]. Digital skills are competences related

Fig. 1 Process of the education of technical subject from digital transformation point-of-view

Education as a Promoter of Digital Transformation …

185

to digitalisation, such as cloud computing, big data, digital twins, AR/VR solutions and simulation. Equipment knowhow forms the practical part of education that can be implemented in the learning environment. This work is done in close collaboration with industrial partners. This emphasises the importance of the worklife connection in technical education. The selected technology (e.g. AM) works as the substrate for implementing these elements in technical education in a real-life learning environment. Concerning the educational requirements of digital transformation, one key issue is to include the required competences, which show the digital expertise required for future experts. Table 1 presents the required digital skills based on the literature review and the derived learning outcomes, together with application examples that can be used when arranging education. As seen in Table 1, the topics related to digitalisation present the typical areas in which expertise is required. One key factor in arranging education is creating desired learning outcomes that can be used in course-level planning when creating a curriculum. Table 1 also presents examples of learning outcomes derived from the digitalisation skill topics. These help identify the desired educational outcomes, or the skills and knowhow that the students achieve when the training is complete (e.g. finishing a course with a passing grade). In addition, Table 1 also features examples of how the implementation can be done. Table 1 Educational examples related to digitalisation topics Skill topic

Learning outcome (The student…)

Application example in education

Artificial intelligence (AI)

Conceives the possibilities to use AI

Machine learning applications in industrial applications

Data management

Can use Product Data Using PDM software in Management (PDM) system in product development process digital product development

Software engineering

Can perform programming tasks related to manufacturing systems

Performing programming tasks related to cyber-physical (CP) manufacturing systems

Robotics

Can identify the application possibilities of robotics

Usage of educational robotic environment

Cybersecurity

Understands the meaning of cybersecurity in industrial applications

Cybersecurity issues in IoT applications

Digital solutions and platforms Understands the usage of digital solutions and systems in engineering

Usage of digital twins or Product- Lifecycle Management (PLM) systems

Remote and virtual services

Can operate manufacturing system with digital twin

Working with cyber-physical (CP) manufacturing systems

Diagnostics

Is able to perform diagnostics tasks with digital information

Analyzing operational data from cyber-physical (CP) manufacturing systems

186

A. Pikkarainen and M. Tihinen

Fig. 2 AM education model with digitalisation elements

One key issue, as presented in Fig. 2 is to connect digitalisation themes to certain technology to form an educational model. In this research, AM is used as an example. As seen in Fig. 2, AM forms the core technology which is used as a platform for digitalisation education. It points out the necessary elements to be taken into consideration when implementing the education of digitalisation and can be used as a tool to plan it. Smart solutions refer to the digital solutions used in the manufacturing process. The upper section of the model presents the digitalisation elements that can be found in AM. Parametric computer modelling presents the required 3D modelling of a product so users can create dimension-driven 3D models. By changing the dimensional parameters, CAD software modifies the corresponding part through computation and algorithms. Digital product record refers to the digital lifecycle of a product in which the product data is stored (e.g. in cloud services through a product lifecycle management (PLM) or product data management system (PDM). Digital on-demand manufacturing refers to the utilisation of digitally stored product data in manufacturing (e.g. digital spare parts). AM enables on-demand manufacturing so the product can be directly manufactured from product data (3D model) without specific production planning or storing physical spare parts. Simulation refers to the software used in AM for product structure (e.g. topology optimisation) of product function (e.g. flow simulation), which is performed through software computation and algorithms. To implement the education of these digitalisation elements, educational elements are needed, as presented in the lower section of the model in Fig. 2. CAD design and PDM/PLM system education aim to teach the required fundamentals of the required principles. In CAD design, the user must connect traditional product design principles to the CAD software. In PDM/PLM systems, the user must learn the principles of product data management and product lifecycle management. Equipment education refers to learning practical equipment use (e.g. 3D printers). Software education refers to learning software usage (CAD, PDM/PLM systems), not just the principles of these.

Education as a Promoter of Digital Transformation …

187

4 Conclusions and Recommendations Nowadays, digital manufacturing systems are extraordinarily complex. To reiterate, there is a digital layer, or virtual world, between the physical world and engineers. Thus, interaction between engineers and objects has changed from direct to indirect [22], creating difficulties in understanding relationships between the digital and real world. This chapter gave educational examples related to digitalisation topics, and introduced the process of the education of technical subjects from the digital transformation point of view. According to the concept of technical pedagogy [33], various factors are identified while arranging technical education. In digital manufacturing, generic skills, digital skills and equipment knowhow build a base and viewpoints that need to be taken into consideration when learning new technologies. This chapter presented a novel process model for arranging technical education that incorporates these factors. The implementation of education requires learning environments in which the combination of these skills and knowhow can be exercised. As [20] point out, learning environments enable students to learn technical phenomena through practical learning and support their self-awareness of learning. In addition, [37] prefer real-world rather than abstract challenges. Practical learning in the learning environment requires firm planning of the required skill topics and desired learning outcomes. This chapter presented a table of skill topics related to digitalisation and the learning outcomes derived from these. In addition, this table offered examples of how the skill topics can be learnt in physical environments or applications. One important issue when implementing the elements of digital transformation in education is to connect the digitalisation themes to a certain technology. Themes such as AI, data management and computing (software) are large concepts which require technological implementation at the practical level. Accordingly, this chapter presented a model of how AM can be used as a substrate technology when promoting digital transformation themes in education. The digitalisation elements of the selected technology must be mapped to plan the educational implementation, which itself requires curriculum-level work in which the digitalisation themes are derived into learning objectives to be used at the course level. This includes not only technological competences but also general skills such as creativity, analytical thinking and innovation capabilities, which are important factors in educating future experts. For future research purposes, technical subjects other than AM can be implemented in technical education via the model presented in this chapter. The model can also be adapted to other manufacturing technologies to promote digital transformation. This requires mapping the necessary digitalisation themes related to the selected technology, as presented in Table 1. Another important issue in introducing the model is testing it with target student groups and collecting feedback. This enables further development of the model, especially when using it with different manufacturing technologies and related digitalisation skills. In addition, one important research topic for the future is to look at how Education 4.0 characteristics (e.g. teacher’s role, source of content, classroom activities, student behaviour, teaching

188

A. Pikkarainen and M. Tihinen

technologies) can be better integrated into course implementation and curriculum development. This chapter presented an approach for developing education based on digitalisation elements in higher education and gave educational examples related to technical subjects from the digital transformation point of view.

References 1. Ailisto H, Komi M, Parviainen P, Tanner H, Tuikka T, Valtanen K (2016) The industrial internet in Finland: on route to success? In: Tihinen M, Kääriäinen J (eds) VTT Technology, p 278, VTT, Espoo, Finland. ISBN 978-951-38-8484-0 2. Alzyod H, Ficzere P (2021) Potential applications of additive manufacturing technologies in the vehicle industry. Des Mach Struct 11(2):5–13 3. Balyer A, Öz Ö (2018) Academicians’ views on digital transformation in education. Int Online J Educ Teach 5:809–830 4. Bond M, Marín VI, Dolch C, Bedenlier S, Zawacki-Richter O (2018) Digital transformation in German higher education: student and teacher perceptions and usage of digital media. Int J Educ Technol High Educ 15(1):1–20 5. Brown KA, Gu GX (2021) Dimensions of smart additive manufacturing. Adv Intell Syst 3(12):2100240 6. Bygstad B, Øvrelid E, Ludvigsen S, DÆhlen M (2022) From dual digitalization to digital learning space: exploring the digital transformation of higher education. Comput Educ 182:104463. https://doi.org/10.1016/j.compedu.2022.104463 7. Da Silva EHDR, Angelis J, De Lima EP (2019) In pursuit of digital manufacturing. Procedia Manuf 28:63–69 8. Degryse C (2016) Digitalisation of the economy and its impact on labour markets. ETUI Research Paper-Working Paper 2016.02; European Trade Union Institute (ETUI), Brussels, Belgium. https://doi.org/10.2139/ssrn.2730550 9. Dolgopolovas V, Dagien˙e V (2021) Computational thinking: enhancing STEAM and engineering education, from theory to practice. Comput Appl Eng Educ 29(1):5–11 10. Favoretto C et al (2021) Digital transformation of business model in manufacturing companies: challenges and research agenda. J Bus & Ind Market 37(4):748–767 11. Finnish National Agency for Education (2022a) National Forum for Skills Anticipation–Deck of skills cards by sector group 12. Finnish National Agency for Education (2022b) National Forum for Skills Anticipation–Deck of skills cards by vocational field. 13. Gerrikagoitia JK, Unamuno G, Urkia E, Serna A (2019) Digital manufacturing platforms in the industry 4.0 from private and public perspectives. Appl Sci 9:2934 14. Ghobakhloo M, Iranmanesh M (2021) Digital transformation success under industry 4.0: a strategic guideline for manufacturing SMEs. J Manuf Technol Manag 32(8):1533–1556 15. Gibson I, Rosen D, Stucker B, Khorasani M (2021) Additive Manufacturing Technologies. 3rd ed., vol. 17. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-030-56127-7 16. Hai TN, Van QN, Thi Tuyet MN (2021) Digital transformation: opportunities and challenges for leaders in the emerging countries in response to Covid-19 pandemic. Emerg Sci J 5:21–36 17. Kagermann H, Wahlster W, Helbig J (2013) Securing the future of German manufacturing industry: recommendations for implementing the strategic initiative INDUSTRIE 4.0; Final Report of the Industrie 4.0 Working Group. Forschungsunion, acatech: Germany. https://en.acatech.de/publication/recommendations-for-implementing-thestrategic-initiative-industrie-4-0-final-report-of-the-industrie-4-0-working-group/. Accessed 24 Feb 2022

Education as a Promoter of Digital Transformation …

189

18. Kang HS et al (2016) Smart manufacturing: past research, present findings, and future directions. Int J Precis Eng Manuf Green Technol 3:111–128 19. Kretzschmar N, Chekurov S, Salmi M, Tuomi J (2018) Evaluating the readiness level of additively manufactured digital spare parts: an industrial perspective. Appl Sci 8(10):1837. https:// doi.org/10.3390/app8101837 20. Kumpulainen K, Krokfors L., Lipponen L, Tissari V, Hilppö J, Rajla A (2010) Learning bridges. Toward participatory learning environments. CICERO Learning, University of Helsinki 21. Li F, Yang J, Wang J, Li S, Zheng L (2019) Integration of digitization trends in learning factories. Procedia Manuf 31:343–348 22. Lu Y, Morris KC, Frechette S (2015) Standards landscape and directions for smart manufacturing systems. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp 998–1005. IEEE 23. Marks A, Maytha AA, Attasi R, Elkishk AA, Rezgui Y (2021) Digital transformation in higher education: maturity and challenges post COVID-19. In: Proceedings of the International Conference on Information Technology & Systems 2021, Península de Santa Elena, Ecuador, pp 53–70, 4–6 February 2021. https://doi.org/10.1007/978-3-030-68285-9_6 24. McGuinness N, Vlachopoulos D (2019) Student experiences of using online material to support success in a-level economics. Int J Emerg Technol Learn 14:80–109 25. Michelsen K-E (2020) Industry 4.0 in Retrospect and in Context. Technical, Economic and Societal Effects of Manufacturing 4.0, pp 1–14. Palgrave Macmillan, Cham, Switzerland 26. Moghaddam M, Cadavid MN, Kenley CR, Deshmukh AV (2018) Reference architectures for smart manufacturing: a critical review. J Manuf Syst 49:215–225 27. Moraes EB et al (2022) Integration of industry 4.0 technologies with education 4.0: advantages for improvements in learning. Interactive Technology and Smart Education (ahead-of-print) 28. Motyl B, Baronio G, Uberti S, Speranza D, Filippi S (2017) How will change the future engineers’ skills in the Industry 4.0 framework? A questionnaire survey. Procedia Manuf 11:1501–1509 29. Motyl B, Filippi S (2021) Trends in engineering education for additive manufacturing in the industry 4.0 era: a systematic literature review. Int J Interact Des Manuf (IJIDeM) 15(1):103– 106 30. Mourtzis D (2018) Development of skills and competences in manufacturing towards education 4.0: a teaching factory approach. In: Ni J, Majstorovic V, Djurdjanovic D (eds) Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing, pp 194–210. AMP 2018. LNME. Springer, Cham. https://doi.org/10.1007/978-3-319-89563-5_15 31. NIST (2017) Smart Manufacturing Operations Planning and Control Program. Gaithersburg: National Institute of Standards and Technology. https://www.nist.gov/programs-projects/ smart-manufacturing-operations-planning-and-control-program. Accessed 18 Mar 2021 32. Parviainen P, Tihinen M, Kääriäinen J, Teppola S (2017) Tackling the digitalization challenge: how to benefit from digitalization in practice. Int J Inf Syst Proj Manag 5:63–77 33. Pikkarainen A, Piili H (2020) Implementing 3D printing education through technical pedagogy and curriculum development. Int J Eng Pedagog 10(6):95–119 34. Prezioso G, Ceci F, Za S (2021) Is this what you want? Looking for the appropriate digital skills set. In: Metallo C, Ferrara M, Lazazzara A, Za S (eds) Digital Transformation and Human Behavior. Lecture Notes in Information Systems and Organisation, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-030-47539-0_6 35. Promyoo R, Alai S, El-Mounayri H (2019) Innovative digital manufacturing curriculum for industry 4.0. Procedia Manuf 34:1043–1050 36. Rodriguez-Espindola O, Chowdhury S, Dey PK, Albores P, Emrouznejad A (2022) Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technol Forecast Soc Change 178:121562 37. Schenk B, Dolata M (2020) Facilitating digital transformation through education: a case study in the public administration. In: Proceedings of the 53rd Hawaii International Conference on System Sciences, Maui, HI, USA, 7–10 January 2020

190

A. Pikkarainen and M. Tihinen

38. Schmid, JT, Tang M (2020) Digitalization in education: challenges, trends and transformative potential. In: Harwardt M, Niermann PJ, Schmutte A, Steuernagel A (eds) Führen und Managen in der digitalen Transformation, pp 287–312. Springer Gabler, Wiesbaden. https://doi.org/10. 1007/978-3-658-28670-5_16 39. Skobelev PO, Borovik SY (2017) On the way from industry 4.0 to industry 5.0: from digital manufacturing to digital society. Int Sci J Ind 4.0 (6):307–311 40. Technology Industries of Finland (2022) Skills Pulse 2021. https://osaamispulssi.fi/wp-con tent/uploads/Osaamispulssin-tiedotustilaisuus-21.9.2021.pdf. Accessed 28 Mar 2022 41. Terry S et al (2020) The influence of smart manufacturing towards energy conservation: a review. Technologies 8:31 42. Thoben KD, Wiesner S, Wuest T (2017) Industrie 4.0 and smart manufacturing-a review of research issues and application examples. Int J Autom Technol 11:4–16 43. Tihinen M, Pikkarainen A, Joutsenvaara J (2021) Digital manufacturing challenges education– SmartLab concept as a concrete example in tackling these challenges. Futur Internet 13(8):192 44. Tisch M, Hertle C, Abele E, Metternich J, Tenberg R (2016) Learning factory design: a competency-oriented approach integrating three design levels. Int J Comput Integr Manuf 29(12):1355–1375 45. Umeda Y, Hongo Y, Goto J, Kondoh S (2022) Digital triplet and its implementation on learning factory. IFAC PapersOnLine 55–2:1–6 46. World economic forum (2022) The Global Smart Industry Readiness Index Initiative: manufacturing transformation insights report 2022. https://www3.weforum.org/docs/WEF_The_Glo bal_Smart_Industry_Readiness_Index_Initiative_2022.pdf. Accessed 28 Mar 2022

Standardization in Smart Manufacturing: Evaluation from a Supply-Side Perspective Yulia Turovets and Konstantin Vishnevskiy

Abstract Digital technologies significantly disrupt traditional industries and enable gains in efficiency for almost every firm at different stages of the supply chain. Smart manufacturing, also called digital manufacturing, Industry 4.0, is a complex concept, which integrates the pull of production and information technologies to operate on the production floor and drive product development in a virtual environment [1, 2]. In traditional industries, these changes often enhance the introduction of new or an improvement of existing products [3]. Raising the complexity of technologies and its interplay generate a variety of standardization alternatives that formalize the results of innovative activities [4]. This chapter provides an assessment of smart manufacturing performance of countries by examining related policy for the uptake of digital technologies and its standardization in production industries. Our research relies on the concept of innovation policy and theories of endogenous growth, which considers standards development a demand-side instrument for technology adoption [5–7]. By comparing cases of the leading industrial countries (China, Germany, Japan, the Republic of Korea, and the USA), we introduce three models of standardization in smart manufacturing, which are supported by the analysis of high-tech exports. The results could be useful for policymakers as well as businesses in the calibration of digitalization strategies. Keywords Assessment of digital manufacturing · Digital technologies adoption · Smart manufacturing standardization · Industrial internet of things · Artificial intelligence

Y. Turovets (B) · K. Vishnevskiy Digital Economics Center, Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, 20, Myasnitskaya Street, Moscow 101000, Russia e-mail: [email protected] K. Vishnevskiy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_9

191

192

Y. Turovets and K. Vishnevskiy

1 Introduction The comprehensive intellectualization through ICT has become a key trend of modern industry. It gains greater attention from an industrial and innovation policy perspective, as it stands for the drastic rearrangement of production and business processes [8, 9]. There are several terms related to digital manufacturing in the existing literature, such as smart manufacturing, cyber-physical systems, smart/digital/virtual factories, Industry 4.0, and some others. It integrates a set of production and information technologies that helps one operate on the production floor and drives product development in a virtual environment by using an extensive set of production data in the most effective way. Smart manufacturing’s core includes cyber-physical systems (CPS), cloud computing, AI, the Internet of Things (IoT), as wells as digital modeling, 3D-printing, and virtual reality [1, 2]. Digital technologies have particular characteristics in comparison with other domains: 1) They are often embedded in hardware being intangible by nature. This is also the case for high-value machinery and equipment [10]. 2) Modern information systems are designed with high switching costs (this means a low level of interoperability between different systems). So, their implementation for the customer leads to quite high expenses for integration, learning, and adoption. 3) Network effects generate gains for users’ according to their number [11]. Production absorbs almost all digital technologies, which transform the sector via the massive hybridization of assets (digital + physical), servitization, and new business models. New solutions transform not only production operations, but also affect the industrial organization. Automated production systems in a factory should be compatible with existing systems, which in turn needs managerial realignment and an update of the technology architecture [12, 13]. So, changes in all areas are significant. In production, these new issues at the intersection of cyber-physical world due to digitalization are at times hardly evaluated. There are numerous efforts to measure the effects of the adoption and use of digital technologies, or gains in the scale of the whole national economy. However, these approaches do not provide a practical instrument to manage the transformation process as a single technological and economic cycle—from the development to use of digital products. That is why, with respect to the previous analyses, our research closes this gap by offering a flexible and simple framework. The main purpose of the study is to examine the relationship between policy for smart manufacturing technology adoption, standardization activities, and its conditions, which are represented by relevant innovative products, such as 3D-printing, CAD/CAM solutions, and artificial intelligence. In other words, we unify the supply and demand sides under a single methodological umbrella, inspired by the innovation policy theories and concepts of endogenous growth. In doing so, we use a set

Standardization in Smart Manufacturing …

193

of qualitative and quantitative methods such as desk-research, case study, document examination, and statistical analysis. This chapter contributes to previous empirical findings from many perspectives. First, it focuses on the relationship of different policy tools for digital technology adoption, which induce innovation activities within firms. Secondly, it analyses existing models of standardization of digital technologies in production industries based on several features (national standardization model, industrial and digitalization related strategies, initiatives in digital manufacturing and standardization, the existence of reference architecture, and international cooperation in the field). Thirdly, it assesses the effects of digital technology adoption, its standardization, and country performance. This chapter is organized as follows. The second section gives a short literature review on smart manufacturing, its standardization, and its evaluation. In the third section, we analyze major technological trends, which outline manufacturing industries in the last decade. The fourth section describes government support for digital transformation of industries. The fifth section represents the standardization landscape in digital manufacturing, both national and international. The sixth paragraph includes a proposed assessment approach to measuring the ability to procure smart manufacturing adoption and standardization. Finally, we highlight the main findings of three main models describing the standardization process in the selected countries and its outcomes for government and business.

2 Literature Review There is a growing interest in smart manufacturing as a novel tool for studying innovation both from a business and academic perspective [14]. A large part of theoretical approaches describe technological trends [3, 15], design principles [1, 16], and the effects of its implication [2, 17]. Other papers address mainly technological issues induced by information technology integration in production systems [18]. The number of studies focused on the evaluation of digital manufacturing and its particular issues concerning standardization is still rather limited. Standardization is considered one of the major triggers of new technology adoption, as well as a tool to improve the social effects of new technology solutions [4, 19]. Standards development is consensus-based, open, and transparent and facilitates agreement between stakeholders on technical specifications and its implementation [20, 21]. In general, standards could promote the innovation process by: 1) coordinating all interested parties; 2) making an agreed upon solution scalable; and 3) providing its confirmation. This leads to the harmonization of technology landscape at the international level. Standards also facilitate knowledge transfer, technology dissemination, and the promotion of innovations for users as well as the developers of solutions [20]. The latest research in the area makes effort to quantify different implications of standardization within the innovation discussion. To examine the impact of some

194

Y. Turovets and K. Vishnevskiy

novel innovation instruments, such as industrial IoT testbeds, on standards development, the authors rely on a qualitative approach and case studies in order to evaluate this effect [22]. Most of the studies use patent statistics on a firm level, panel data, and apply econometric models (logit, probit, ANOVA, linear probability etc.). Researchers assume standards are a driver for different aspects of the innovation process, especially as a tool to decrease economic uncertainty. According to [23], standards define the choice between the development of new products and using existing ones. Firms use standards as “insurance” against the risky innovation process to create new solutions. An original theoretical model reveals the relationships between incremental innovation and radical innovation at a firm. The paper by [24] provides similar results. It concludes that firms’ characteristics affect the choice of standardization strategy based on firm-level data extracted from the Community Innovation Survey of the EU. Namely, firms with technological uncertainty prefer standardization to other alternatives. Standards also stimulate enabling technologies and complementary innovations, and help to reduce uncertainty [25]. Another strand of the current literature focuses on spillover effects. Some papers, for example [26], evaluate knowledge spillover in patented technologies by using patent citations. SEP or the patented technology standard as a particular type of standard often has few precursors and a weak scientific core. The government’s role in standardization has attracted the attention of many researchers but it is still difficult issue to measure. The study of [27] shows that firms’ standardization with the government’s support increases the quality of an innovative product in terms of patents at a regional level. Certainly, there is a relationship with industrial development in various forms. For instance, in the case of the hightech industry of China, coupling and the coordinated development of technological innovation and standardization influence industrial performance [28]. In spite of the growing interest in the area, there is a lack of studies on manufacturing that consider the assessment of standardization activities across the supply– demand cycle on a macrolevel. Our research fills this gap through a proposed concept of linking standardization with the innovative performance of the sector that develops smart manufacturing solutions.

3 Global Trends of Digital Manufacturing Despite the fact that the development of digital production began more than 10 years ago when the Industry 4.0 concept emerged in Germany, the shift from traditional to digital manufacturing is still in progress and now it is transforming into Industry 5.0 [26]. The decrease in technology costs over the course of the previous decade provided a significant incentive for the broad dissemination of digital technologies. The price of sensors (which are one of the main components of IoT systems) fell from $0.82 in 2010 to $0.38 in 2020 [29]. Industrial robots’ costs have also decreased in the same period and are expected to reach $10,900 dollars in 2025 (from $31,800 dollars)

Standardization in Smart Manufacturing …

195

[30]. Reduced data storage costs are turning into a significant driver for a number of cross-cutting digital technologies such as big data, AI, and others [31]. New digital technologies extend businesses’ opportunities to optimize numerous processes and improve decision-making [32]. IoT and cloud computing optimize data collection and storage, while machine learning (AI) allows one to deeply process data to build relevant models. AI is useful in R&D activities, on the production floor, in transportation and logistics, customer and human relations, and all management processes. Big data and AI technologies help with the operation of the digital twins of products, processes, and other assets in a single virtual environment. Predictive analytics is aimed at building algorithms describing product and service use to automate production and delivery (e.g., the private labelling model, when a contractor makes a product and delivers it directly to the consumer) [33, 34]. The IoT applications induce service model development, since they assess various parameters of product usage and effects during a product life-cycle. Blockchain allows for decentralizing data collection, transfer, and storage, thus making transactions more secure for all partners and customers. Virtual and augmented reality technologies help train personnel, manage industrial equipment, and so on [35, 36]. Digitalization helps solve structural problems and enhance productivity with routine automation, customization, and localization. Product personalization is the first and most obvious gain. This is relevant not only for B2B, but for B2C markets such as pharmaceuticals, clothing, and electronic devices [34]. New markets and niches are another implication related to digitalization. New business models are customer-centric, flexible, platform-based and, in some cases, “fabless” [32, 37]. Several most popular business-models are: • Digital platforms, which make the direct interaction of sellers, buyers, suppliers, and other partners possible. They allow one to cut transaction costs and offer wider opportunities for the shared consumption of products and services [38]. They are search, operation, service, and product platforms. • “As a service”: service business models based on using resources instead of owning them (such as software-as-a-service, infrastructure-as-a-service, robotsas-a-service, etc.). They promote the personalization of goods and services, since the client has the opportunity to choose exactly what they need, in the amount they need, to achieve the result they need [38, 39]. Despite numerous companies’ success in automating production processes and applying distributed management and control systems, most of them still do not sufficiently employ the potential of big data analytics and AI-based decision-making algorithms. This is particularly relevant for companies owning significant material assets. AI needs additional computing infrastructure, the employment of highly skilled personnel (data scientists, data engineers, AI architects, etc.), and investments in training [40]. There are imbalances in digitalization across and within production sectors. Currently, companies use more actively traditional digital solutions. According to a survey of EU countries, cloud services are the most widely used technology, though with deep differentiation across countries in almost all technologies (the highest

196

Y. Turovets and K. Vishnevskiy

share of firms that use this technology—83%). ERP systems are in second place in business (Netherlands—65%, Finland—63%). Industrial robotics demonstrate a slightly lower level of adoption with the share near 40% of companies in the leading countries. Even greater distance across countries is observed in IoT among the three leaders (Czech Republic, Finland, Austria—46%, 43%, 41%). To date, AI and big data analysis have the lowest levels of adoption (in the leading countries near 20– 22% of firms), which demonstrates the rather slow dissemination of a new generation of technologies. Even in Germany—the acknowledged manufacturing leader—these indicators are on an average level with the predominance of ERP systems (Table 1). Table 1 Comparison of the adoption of key digital technologies in the EU Cloud services

ERP systems*

Industrial robotics

IoT

Big data analysis

AI

UK

…

35

7

…

18

3

Finland

83

63

33

43

18

10

Sweden

75

51

27

25

12

8

Norway

69

50

22

16

13

7

Denmark

66

62

38

22

21

13

Ireland

60

44

3

…

22

22

Italy

59

45

17

24

5

8

Estonia

55

29

10

15

5

3

Netherlands

48

65

24

19

22

5

Malta

47

39

22

21

22

17

Austria

39

62

23

41

7

7

Croatia

39

29

15

20

10

4

Slovenia

38

43

22

17

4

3

Germany

31

50

16

…

11

8

Lithuania

29

52

10

18

6

9

Czech Republic

27

48

17

46

8

6

Cyprus

27

27

10

20

1

2

Luxembourg

25

62

27

…

11

9

France

25

62

21

10

15

5

Spain

24

51

18

15

4

7

Slovakia

24

38

19

18

3

7

Montenegro

23

…

5

…

17

19

Portugal

23

41

18

13

6

7

Hungary

23

20

13

12

7

4

Poland

23

32

14

15

6

3

Latvia

19

32

9

23

6

2 (continued)

Standardization in Smart Manufacturing …

197

Table 1 (continued) Cloud services

ERP systems*

Industrial robotics

IoT

Big data analysis

AI

Serbia

17

30

8

…

2

1

Romania

16

21

6

5

3

6

Turkey

14

26

9

…

8

9

Bulgaria

9

25

13

10

4

4

Bosnia and Herzegovina

5

23

10

…

4

5

* Data as of the end of 2019, other indicators are dated with 2020 Source based on Eurostat data. One of the key roles in the dissemination of digital technologies at enterprises belongs to government support, the details of which are discussed in the next section

AI is still at the very beginning of its dissemination in production sectors. According to PwC, only 9% of the industrial companies in Germany—a global production hub—use AI [41]. The results of the first AI survey presented by the European Commission demonstrated a somewhat greater level of adoption: across the European countries manufacturing firms are among the most active users of AI with 37% of respondents using at least one technology [42].

4 Government Support for Digital Transformation Government policy is one of the major driving forces of manufacturing digitalization. Without its active participation neither successful development of technologies, nor their commercialization can be realized. Most developed countries adopted national strategies and programs to promote sectoral digitalization already in 2013–2015. Their goals and objectives are predominantly similar, but approaches to achieve them differ significantly. Some of the initiatives are part of a wider agenda: e.g., in an international (EU) or science, technology, and innovation (STI) area. For example, Germany’s Digital Strategy 2025 and relevant strategies of the other EU member states are aligned with the common Digital Agenda for Europe (2010) [43]. In 2021, the European Commission unveiled a set of new goals under the umbrella “Europe Digital Decade”, which includes multi-country projects and even closer conjunction with the members’ priorities. For a detailed analysis, we selected five countries in order to assess policy priorities and tools: China, Germany, Japan, South Korea, and the USA (Table 2). This selection considers two main aspects: 1) the general position of the country in manufacturing (source of information—the Competitive Industrial Performance Index 2020 calculated by the UNIDO1 ); 1

It includes a range of indicators that form their industrial performance, including manufacturing value-added, export, shares of high-tech and medium-tech industries, and some others.

198

Y. Turovets and K. Vishnevskiy

Table 2 The top five countries in production and digital performance Rank

Country

Competitive Industrial Performance Index 2020

World Digital Competitiveness Ranking 2021 Knowledge factor

Technology factor

Future Readiness factor

Overall ranking 79.3 (18)

1

Germany

0.46

75.9 (14)*

62.4 (31)

72.9 (18)

2

China

0.39

82.5 (6)

69.2 (20)

74.7 (17)

84.4 (15)

3

USA

0.35

85.6 (3)

87.5 (4)

1 (100)

1 (100)

4

Japan

0.35

64.8 (25)

63.2 (30)

64.2 (27)

73.0 (28)

5

South Korea

0.35

75.9 (15)

77.9 (13)

88.8 (5)

89.7 (12)

* The rank of a country is in the brackets Source [44, 45]

2) evolving background for digital transformation based on the the World Digital Competitiveness Ranking 2021. It should be noticed that it is impossible to modernize traditional sectors without high-speed and reliable ICT infrastructure. Such infrastructure activities in most selected countries are accomplished in previous periods (2010–2020) and now such a basic core enables a wide adoption of a new generation of digital technologies. Industrial upgrading is a common feature of most strategies. Digital technologies serve as a tool for increasing productivity, employment, and innovation through a mainly broad public–private partnership. In this view, the U.S. and China approaches stand aside. In the US, there is no single national strategy for comparison with another countries. However, there is an array of initiatives developed by particular state departments or responsible presidential bodies. China in its comprehensive initiative (“Made in China 2025”, “Internet plus”) focuses on a whole range of measures to enhance innovation in sectors followed by the development of national standards for digital technology use. The Internet Plus program aims at building information infrastructure across the country with mobile Internet technologies, cloud computing, big data, and the Internet of Things to modernize the industrial base across sectors. In doing so, the government is updating the legal framework and regulatory rules and adopting several laws on cybersecurity [46, 47]. In 2017–2022, many countries adopted artificial intelligence (AI) strategies which constitute the main trend in the global digital agenda (e.g., the German National Artificial Intelligence Strategy, National AI Strategy, Korean New Deal 2.0) [48, 49]. National governments intend to promote a favorable institutional framework for AI at all stages, from development to use and to attract the best talent from all over the world. Particular attention is paid to monitoring and assessing policy implementation. For example, Germany has annually published the Digital Economy Index since 2013, which describes the level of digitalization in the overall economy and specific

Standardization in Smart Manufacturing …

199

sectors [50]. At the same time, there is a certain imbalance between program objectives: the digitalization of public administration is given a somewhat higher priority, while promoting the application of digital solutions by the public and supporting the digitalization of businesses remains insufficient. Governments promote digitalization using two basic approaches: by supporting the ICT sector and other industries that use digital solutions [51]. Traditional financial (subsidies, grants, R&D tax, preferential taxation, public procurement of digital products and services, venture capital) and non-financial tools (information support, standards and guidance, legislation) are applied jointly, but financial tools dominate in most governments’ portfolios. A significant proportion of national programs are focused on supporting small and medium enterprises (SME) and start-ups. Over the last decade, networks of advanced manufacturing institutes are one of the popular instruments. This is the case for the USA (industrial innovation institutions), the UK (High Value Manufacturing Catapult centers), where they serve to boost industrial competitiveness in a concrete area (for example, 3D printing). Similar initiatives exist in Japan (Smart Manufacturing—Smart Monozukuri), South Korea (manufacturing innovation centers 3.0), and platforms to boost digitalization are operating in Germany (Platform Industrie 4.0) [52–54]. They also play a role in testing infrastructure for new solutions. Testing infrastructure is one of the ways to bridge supply and demand (for example, a “living lab” for driverless cars in Germany, testbeds for blockchain technologies in the Republic of Korea, etc.). Taking into consideration low adoption level, state support should cover all stages, from development to promotion and scaling digital technologies in sectors. German initiatives are a good example of systemic demand-side approach. Under the umbrella of “Mittelstand digitalization” several programs are realized. They provide software usability and modernization, e-standards implementation, and overall support. For this purpose, the Competence Centers have been created, which assist in personnel training, information on different cases of technology adoption gathering and dissemination, new solution testing, and, therefore, accumulation of all practical knowledge for digital transformation. For example, an AI competence center proposes a range of materials and communication platforms in order to clarify AI advantages [55]. An important issue in this discussion is that such support is available at the local level, closer to enterprises and their needs. The centers in the network are located in different regions and have a particular specialization [56]. Selected examples, shown in Table 3, illustrate the scope and purpose of new policy instruments in manufacturing. Most of them cover standardization issues. In the last several years, the focus of the policy has continuously shifted toward the promotion of digitalization at firms. Policy stimulating demand for digital technologies is more difficult to manage and assess. To implement longer-term initiatives, special industry digitalization funds have been established in different countries (e.g., a foundation for investing in equity or quasi-equity capital of ICT-companies, subject to private participation). Regulation plays a very particular role in smart manufacturing. It includes the development of a legal base of various digital technologies’ implementation and use, establishing “regulatory sandboxes” (e.g., for unmanned aerial vehicles), or

200

Y. Turovets and K. Vishnevskiy

managing human–machine interactions [62, 63]. Most of them assume the launch of pilot projects (such as using blockchain in public administration in the Republic of Korea, the launch of 5G networks in a number of Korean cities, and others). However, a large part of regulatory aspects considers standardization challenges at different levels—national/regional industry consortia (for example, the Industry 4.0 technology standardization roadmap in Germany), or international organizations. Standardization can offer a number of benefits and opportunities for digital technologies. One of the most important advantages lies in scaling up products on exporting markets. The next section discusses the standardization of digital manufacturing in detail using the same sample of countries. Table 3 Summary on the selected policy instruments to promote digital transformation in manufacturing Group of mechanisms

Examples and description Type of instruments

Platforms for standard promotion

Germany: An industrial consortium, the Plattform Industrie 4.0, serves as a platform for the development and promotion of standards in the field of smart (digital) production, research and training, and the development of regulatory conditions for the implementation of digital solutions [57]

Testbeds

Germany: The Digital Highway Test Bed is a joint initiative of the federal government and automakers to test automated vehicles. The state provides financial support for pre-competitive and interdisciplinary research in automated and connected driving. This includes the modernization of regulatory standards as well as the development of infrastructure (broadband access and other) [53]

Financial

+

Non-financial

Standardization

+

+

+

(continued)

Standardization in Smart Manufacturing …

201

Table 3 (continued) Group of mechanisms

Technology transfer centers and networks

Examples and description Type of instruments Financial

Non-financial

Standardization

South Korea: Several special platforms for testing blockchain technologies (Blockchain playgrounds), a research center (Blockchain Research Center), and a cloud platform “Blockchain as a Service” (BaaS) were established. Testing platforms represent an experimental space for the development of advanced solutions in blockchain and talented personnel training [54]

+

+

China: By 2023, 20 regional zones will be created to introduce priority technologies in industries. Such regional hubs promote cooperation between development firms and local administrations and support piloting innovative support tools [58]

+

+

South Korea: Within the framework of the Korea Technology Finance Corporation (KOTEC), a service to select appropriate technologies have been created — KTMS (Kibo Technology Matching System). The search is carried out in four stages and includes a technology assessment, the selection of the desired technology, communication with the parties, and support in negotiations and contract conclusion [59]

+

(continued)

202

Y. Turovets and K. Vishnevskiy

Table 3 (continued) Group of mechanisms

Technology entrepreneurship infrastructure

Examples and description Type of instruments Financial

Non-financial

Standardization

Germany: A network of + competence centers supports SMEs in the implementation of digital technologies by conducting technical expertise, creating demonstration factories for familiarization with technologies and providing other information support under the initiative Mittelstand-Digital [60]

+

+

China: Special clusters of + technological zones and clusters. The Technology Park in Beijing (2018–2023) is one of the recent examples. It will host about 400 companies developing AI solutions. Participants can receive support for big data analysis, deployment of deep learning and cloud computing systems, as well as supercomputers and 5G communications [49]

+

+

China: The government also supports the development of AI infrastructure, including computing power and smart computing centers as well as data centers. These activities assume cooperation with large national firms [61]

+

Source [49, 53, 54, 57–61]

+

Standardization in Smart Manufacturing …

203

5 International Standardization Landscape in the Field of Digital Manufacturing Connectivity and interoperability have become a central issue on the production digitalization agenda. Since smart manufacturing addresses both digital and physical standards, it integrates the features of both. The ICT industry standards are elaborated upon predominantly within industry consortia as a result of market competition between several specifications and introduced mostly by private companies. They are called de facto standards [4]. In machinery, for instance, firms often participate and promote their position in formal standard-developing organizations (SDO) or standard-setting organizations (SSO) [51]. Hence, they are de jure standards. Lately, due to technology convergence, consortia and alliances have become important forms of cooperation. In some areas of the information industry, they become rather useful. With an accelerated path of digitalization, consortium-driven activities should complement, not compete with, the formal standardization process, since they introduce their standards to formal organizations [21]. Standardization has several functions. Firstly, it plays a coordination role by gathering all stakeholders. It contributes to partnerships and alliances at the national and international levels, which are often initiated by the government authorities. Secondly, it reduces risks of new technology usage for customers. Thirdly, standards adoption may induce innovation [4]. Standards facilitate firms’ innovation capacity by creating a favorable regulative environment in which to introduce new products. They also facilitate communication across stakeholders, foster knowledge transfer, and further technology adoption [4]. Digital technologies require interoperability of parts and systems on a large scale taking into account sectoral specific requirements. This in turn suggests harmonization in technical specifications elaborated upon by private participants.

5.1 National Policy for Digitalization Standardization is as an important policy tool to tackle with economic challenges. The level of government participation is the main criteria to differentiate national approaches, along with this, some features could be similar across models. Standardization is also a way to strengthen the market power of big technological leaders, especially in digital technologies. We distinguished between three main models of the standardization manufacturing landscape as a part of industrial strategies (see Fig. 1): 1) market-centered (the USA, the Republic of Korea) 2) private-public partnership (Germany, Japan) 3) government-centered (China).

204

Y. Turovets and K. Vishnevskiy

Fig. 1 Standardization in the digital manufacturing landscape in selected countries Source authors’ compilation

In general, standards are an effective mechanism to promote high-value manufacturing exports (Germany) and keep competitiveness in the global value chains (GVC) (South Korea) [64]. GVC domination is also a main concern for the USA, which is supported by the leading position in the R&D. China describes this in terms of structural change, since IT should favor innovation of its national economy and technological upgrading. Germany’s example shows that traditionally German business has strong positions in standards development, but the government plays a coordination role at the international level [53]. A private-public partnership model supposes cooperation managed by government authorities, industrial associations and its members, as well as large research organizations. Compared with the USA, Germany places a substantial emphasis upon technological aspects [65]. There are three main initiatives dealing with standardization in manufacturing in Germany: Plattform Industrie 4.0, Standardization Council Industrie 4.0 (SCI 4.0), and Labs Network Industrie 4.0 (LNI 4.0). Being private-public partnerships, all three entities are managed by the Federal Ministry of Economics and Energy (BMWi) and the Federal Ministry of Education and Research (BMBF) and were created by important industrial associations with the participation of national

Standardization in Smart Manufacturing …

205

standardization bodies—the DIN and DKE. While LNI 4.0 aimed to test new technologies’ business models and application scenarios and SCI 4.0 is focused on standardization itself, Plattform Industrie 4.0 is the main operating entity in this system. Such a structure enables communication within the smart manufacturing community, where the government performs a coordinating role and aligns parties’ interests [67]. South Korea has a two-fold mission: to provide an enabling environment for national firms and systems compatibility and to secure South Korea’s position in the organization standards setting. Korea Manufacturing Innovation 3.0 is a part of the comprehensive Korean Creative Economy (CEI) strategy. According to it, 17 innovation centers that cover a range of digital technologies were created across the country. These centers are managed by large Korean corporations such as Samsung and Hyundai and are based on the specialization of a particular region (“Smart machinery” led by Doosan, “Shipbuilding/machinery”, “Textile/electronics” by Hyundai Heavy Industries, “Smart Factory” by Samsung, automobiles by Hyundai and Kia Motors) [68]. Each has roadmaps for technology deployment and standardization. Japan has both state-led (Industrial Value Chains Initiative, “Connected Industries”, the Robot Revolution Initiative of Japan) and business-led (IoT Acceleration Consortium) initiatives in smart manufacturing, which are in general coordinated by the government. The Industrial Value Chain Initiative (IVI) was introduced in 2015 as a cooperation platform of national manufacturing firms, coordinated by the Ministry of Economy, Trade and Industry (METI) and Japan’s Association of Mechanical Engineering (JSME). It accumulates joint use cases and scenarios in smart manufacturing. The idea of the initiative lies in the flexibility of standards with a set of models that connect different sectors [69]. There is a particular effort to promote standards for robotic systems across different sectors within the country and globally, since robotics is one of the five priority areas for the national economy in the long-run. International standards play a crucial role for production optimization, therefore national standards should be set up outside the country based on R&D achievements [70]. In the USA, standardization has a strong commercial motivation, where a large part of it is developed in consortia and alliances. Established in 2014, the Industrial Internet Consortium (IIC) is the most influential player, both in the USA and globally. It became a world community and provided its own reference architecture for the Internet of things (IoT) in different industries, including manufacturing. The concept of the IIC is based on a broader view of digitalization through the connection of manufacturing with other sectors such as healthcare, energy, transport, and public services. One of the major tasks of the consortium is to boost innovation activities by providing testbeds and regulation framework [71]. The transmission of R&D results to business is the main role of the state [72]. But even in the USA with its private sector dominance, the government provides several initiatives referring to digital manufacturing and standardization activities within them. The Manufacturing USA initiative (now called “America Makes”) was founded in 2011 and in 2014 the Advanced Manufacturing Partnership (AMP) was created to provide for the establishment of 14 manufacturing institutes across the country. One of them is the Digital Manufacturing and Design Innovation Institute

206

Y. Turovets and K. Vishnevskiy

(DMDII), created in 2014. The DMDII is mostly funded by the US Department of Defense and coordinated by the National Institute of Standards and Technology. Collaboration in R&D and commercialization within institutes contribute to the unification of requirements [71]. For China, standards play even more significant role in terms of upgrading an industry’s technologies with the government playing a leading role. It is based on strong government participation that raises funds and coordinates different projects. According to the strategy “Made in China 2025”, there is a strong effort to build one’s own approach to digitalization by examining global best practices [71]. This also corresponds to the statements of a new “Chinese Standards 2035” strategy with a strong focus on R&D. Taking into account the increasingly important role of standards, countries tend to develop their own national standards in order to secure their own technological base. The government extensively participates in international standards development activities, namely in 5G technologies [73] and expanding bilateral cooperation between Germany and the USA. To understand which approach is most effective, one should look at the dissemination of national standardization practices abroad. To date, there are two leading approaches in the standardization of smart manufacturing—Germany in Industry 4.0 and the USA in IoT. In order to promote their architecture models, it should be noted that they have the most intense cooperation network with economic and trade partners based on bilateral relations. The RAMI 4.0 could reveal new market opportunities related to digitalization, while the IIRA concerns business systems in the domain of connected devices [74]. Germany collaborates with China (within a joint Sino-German Commission on Standardization Cooperation), Japan in alignment of the Plattform I4.0 and Japanese Robot Revolution Initiative (Unified Reference Model), the IVRA), and with Korea via the Smart Factory Web, which is a joint Korean-Germany initiative supported by government and others [75–77]. The US network of partners is also large and comprises the US-Japanese cooperation between the IVI and the IIC (the Liaison Working Group), US-Korean cooperation (the Smart Factory Web as a test-bed of the Industrial Internet Consortium), and some others. Both approaches and the synchronization of RAMI and IIRA shape the global digital agenda in production industry.

5.2 International Bodies Related to Digital Manufacturing Digital (smart) manufacturing has become an essential part of the international standardization agenda. By 2022, the digitalization agenda within the ISO and IEC had a range of initiatives and policy issues. The international landscape encompasses separate initiatives as well as joint activities (see Fig. 2). Altogether, the aforementioned entities shape the digital ecosystem in production to enable systems’ interoperability, cybersecurity, and connected work. They concern not only standardization aspects, but also contribute to a more broad and

Standardization in Smart Manufacturing …

207

Fig. 2 International digital manufacturing landscape Source authors’ compilation

systematic discussion among national parties. In line with this, the ISO and IEC organized two strategic bodies—ISO/SMCC and IEC/SyC respectively, which integrate representatives from all technical committees related to production and information technologies [78]. In addition to joint initiatives, there are several technical committees within the ISO and the IEC that address particular issues such as the ISO TC 184 (product data, compatibility, integration, architecture for industrial automation) and the IEC TC 65 (industrial process control and automation, product data and processes integration) (see Table 4). Along with international organizations, a myriad of industrial consortia and alliances have gained momentum and become essential participants in the standards development process. Amongst them, there are MTConnect, OPC Foundation, and MESA. Developed by these alliances, standards are also adopted by international standardization bodies like the ISO and IEC. Some of them provide a free access to their standards and technical information. For example, the OPC Foundation now is an industrial standards organization that operates its own certification and testing program. The ITU and IEEE are important entities in information technologies that are also concern manufacturing aspects. Such great attention is paid to production industries, corporate and government activism originates from the vast efficiency gains at almost all stages of value chain. The importance of standardization is well illustrated by the example of the most popular area nowadays—artificial intelligence. The USA plays a significant role in standards development and promotion at international standards organizations. For example, at the Institute of Electrical and Electronics Engineers (IEEE) Standards Association, 67% of all board members that adopted standards were from the USA. The central role in this belongs to the NIST, which cooperates with all state institutes, acts as a consolidated agent of the corporate sector, and is supported by generous financial investments from government for managerial and technological functions [80].

208

Y. Turovets and K. Vishnevskiy

Table 4 Main international standardization bodies in digital manufacturing Name of the committee

Short description

ISO/SMCC (Smart Manufacturing Coordinating Committee)

The ISO/SMCC was formed in 2016 and included representatives of relevant technical committees from the 21 ISO, IEC, and ITU. Germany leads the committee and plays a coordinating role. The ISO/SMCC was established for the next two years and is a successor of the ISO Strategy Group “Industrie 4.0—Smart Manufacturing” which disbanded in 2016. Along with members of the ISO technical committees, it also comprises representatives from the IEC and the ITU. Designed to synchronize technical specifications, the ISO/SMCC publishes recommendations on information technology implementation. The ISO/SMCC parties have national representatives that should consolidate a vision on a country’s level and monitor international activities

IEC/SyC (IEC Systems Committee) Smart Manufacturing)

Similar to the ISO, the IEC/SyC Smart Manufacturing however focuses not only on coordination, but also addresses gaps in cooperation among standards developing organizations (SDO) and different consortia and fora. The IEC/SyC is the successor of the IEC Standardization Evaluation Group Smart Manufacturing (IEC/SEG 7) from 2017, aimed at formulating of a joint smart manufacturing concept

ISO/SMCC—IEC/SEG 7 Task Force Smart Manufacturing Standards Map

Close to the latter, a joint working group between the ISO/SMCC and IEC/SEG 7—ISO/SMCC—IEC/SEG 7 Task Force Smart Manufacturing Standards Mapfocuses on the development and streamlining of a set of smart manufacturing standards The IEC/SEG 7 is responsible for standards and the systematization of projects provided by the IEC, the ISO, and other international institutions, the interaction of related entities in the scope of the ISO, the IEC/ISO joint working groups, and other participants, as well as the study of gaps and shortcomings in smart manufacturing standardization, the elaboration of roadmaps and future projects. The IEC/SEG 7 provides for the integration and coordination of interested parties. A particular feature of the SyC is its horizontal character and high level of IEC member participation

ISO/IEC Joint Technical Committee (JTC) 1

The ISO/IEC JTC 1 is a large platform for discussion in information technology agenda that includes 22 subcommittees on the Internet of things, artificial intelligence, cloud computing, etc. (continued)

Standardization in Smart Manufacturing …

209

Table 4 (continued) Name of the committee

Short description

ISO/IEC Joint Working Group 21 (ISO/IEC JWG 21) “Smart Manufacturing Reference Model (s)” within the IEC TC 65

The ISO/IEC JWG 21 created in 2017 is a standardization entity that currently headed by Germany and Japan. Its main goal is to synchronize current reference models and new architectures development. The main aim is to build a basic framework in smart manufacturing taking into account world best practices

Source [79]

China also tends to increase the quantity of nationally originated inventions and its implementation of standards and their promotion on an international scale. To achieve this, there are efforts to create a network to support standardization, including research institutes, certification centers, and specially designated platforms [63]. The standardization of AI in production industries, however, has several challenges to consider, like data security, diverse suppliers, legacy equipment, difficulties with integration, and some others. All of them require the participation of a great number of business players. A variety of solutions from different firms needs additional efforts and financial resources for integration and adaptation into an industrial infrastructure. The world’s leading manufacturing associations such as VDMA are working in this field.

6 Capacity of Smart Manufacturing Development and Standardization Usually the term “innovation” presupposes R&D and its further commercialization. However, the innovation process is far more complicated with standards being a mechanism to formalize technological requirements. Standards represent a platform to work together on a consolidated technical base and develop new products. Often it defines the commercial prospects of a product itself. Without standards, now it is impossible to establish a strong ecosystem, which includes suppliers, technologies, consumers, state and relevant modes of communication between them [80]. Thus, innovation outcomes could be one of the ways to measure the performance of standardization activities. In the discussion of digital manufacturing, one should consider the development and adoption processes together. Standards themselves enable a wide adoption of advanced technologies, affecting both the developer and user of products, which are based on a set of technologies and their conjunction [19, 22]. From a demand point of view, standards and technical requirements simplify the process of integration and interoperability across systems, formalizing it and making it transparent for all participants (suppliers, intermediaries, consultants, users, etc.). From the supply side

210

Y. Turovets and K. Vishnevskiy

and business point of view, it is one of the major tools of market power and technical dominance for those seeking to disseminate innovations, the result of which is high levels of sales on the global market. Therefore, looking at trade statistics is one way to measure the success of standardization of a particular country. However, there are several difficulties with such an approach. Due to the specific, usually crosscutting nature of digital technologies, they are often integrated to some extent in almost all production process and objects. This presence is difficult to detect in certain products. On the other hand, for the large part of products related to digital technologies, international statistics is nascent. These issues very often hamper its measurement for some technologies like AI. In doing so, we use proxy indicators that represent the positions of a country in global trade with products of smart manufacturing. We assume that wide standardization activities and government support for manufacturing upgrading should result in leading positions of the sector that developed such solutions and exported them. Certainly, such a comparison is rather rough, but it has some advantages over widely used instruments such as patent statistics, ad-hoc surveys, or expert methods. The latter are often limited due to their subjectivity and rather narrow look at a particular topic. As we mentioned earlier, technologies for smart manufacturing have started to be disseminated over the last ten years and now several represent mature markets with leading suppliers. That is why its measurement becomes justified and timely. To identify the appropriate goods, we use the approach of Foster-McGregor and Nomaler [81]. It matches technologies that enable digital manufacturing with the nomenclatures for products according to the Harmonized System (HS) that embody these technologies [81]. The analysis assumes that each digital (intangible) technology should be materialized in a physical form. This technique focuses on three main comparatively mature technologies: systems for digital modeling and design (CAD/CAM), additive manufacturing, and industrial robotics. Table 5 shows detailed six-digit products classes. We exclude AI technology from the analysis due to the controversial issue of AI product classification. Based on the proposed classes, we collected export statistics from the UN Comtrade database for 2021 for all countries with data in for a particular HS Code. Next, we ranked countries by the trade value in US dollars of exported products by each HS Code and systemized the ranking of the studied countries. This helped us to reveal some patterns of countries’ specialization in digital manufacturing. After ranking we focused on the studied five countries (China, the USA, Germany, Japan, and South Korea), as we expect that these countries should demonstrate the highest exports in those technological areas (see Table 6). Our results show that Germany and China hold the most stable positions in all three technological areas: in 17 out of the 18 product classes identified by the sixdigit codes, they occupy the first five positions. This is the best result among the studied countries due to the overall stable status of the country in machinery production. Japan shows confident place in the industrial robotics together with Germany, which are one of the most technologically advanced equipment. It shows one of the highest values in some types of 3D printing, as well as in some numerically controlled equipment (horizontal and other lathes). The USA is also among the

Standardization in Smart Manufacturing …

211

Table 5 Correspondence of smart manufacturing with HS products classes Technology

HS codes

HS name

Industrial robots

847,950

Industrial robots, not elsewhere specified or included

Additive manufacturing (3D printing)

847,710 847,720 847,730 847,740 847,751 847,759 847,790

Injection-moulding machines, Extruders Blow moulding machines Vacuum moulding machines and other thermoforming machines Other machinery for moulding or otherwise forming: For moulding or retreading pneumatic tyres or for moulding or otherwise forming inner tubes Other machinery for moulding or otherwise forming.—Other Parts

CAD/CAM techniques

845,811 845,819 845,921 845,931 845,951 845,961 846,011 846,021 846,031 846,221 846,231 846,241

Horizontal lathes: Numerically controlled Other lathes: Numerically controlled Other drilling machines: Numerically controlled Other boring-milling machines: Numerically controlled Milling machines, knee-type: Numerically controlled Other milling machines: Numerically controlled Flat-surface grinding machines, in which the positioning in any one axis can be set up to an accuracy of at least 0.01 mm: Numerically controlled Other grinding machines, in which the positioning in any one axis can be set up to an accuracy of at least 0.01 mm: Numerically controlled Sharpening (tool or cutter grinding) machines: Numerically controlled Bending, folding, straightening or flattening machines (including presses): Numerically controlled Shearing machines (including presses), other than combined punching and shearing machines: Numerically controlled Punching or notching machines (including presses), including combined punching and shearing machines: Numerically controlled

Source [81]

212

Y. Turovets and K. Vishnevskiy

Table 6 Ranking of the countries by the value of exported products related to smart manufacturing technologies HS code China Germany Japan USA

Industrial Additive manufacturing CAD/CAM techniques robots 847950 847710 847720 847730 847740 847751 847759 847790 845811 845819 845921 845931 845951 845961 846031 846221 846231 846241 4 1 2 2 1 1 1 2 4 1 2 8 2 4 4 4 1 5 2 3 1 1 2 3 3 1 2 3 3 1 8 1 1 2 2 1 1 2 4 4 6 8 5 8 1 6 4 6 7 6 2 6 10 2 5 7 7 5 5 4 3 5 5 6 9 4 8 7 8 5 7

The colored cells mean that a particular country occupies the top-5 position by a particular product. Source: Authors’ calculations based on the [87].

leaders, but follows Japan. It takes between fifth and eight places in most of products. Not surprisingly, Germany leads (in first and second places) in almost all types of numerically controlled machines, which are working based on the CAD/CAM systems. Information about South Korea is absent in the database, that is why we do not consider it in the analysis. Thus, our hypothesis is fully confirmed, and these results may indicate that countries’ performance in smart manufacturing technologies has links with their activity in technological development and standardization. Of course, this depends upon other factors like current manufacturing competitiveness, companies’ incentives to develop and introduce new products, government support, available financial resources, and international cooperation in the field of smart manufacturing. Such a situation makes entry onto the related market rather complicated. Simply participating in standardization in not enough. Firms should systemically expand their R&D activities and innovation capacity, introduce new solutions, and train staff. It also depends upon a range of suppliers that provide for the integration of their technologies in a complex manufacturing product with a common technological base. Nevertheless, the results support the proposal that in order to digitalize manufacturing, it is important to provide a technological base for it with formalized standards. In its turn, the development of standards accelerates the adoption of digital technologies by firms, mitigates risks, and disseminates scalable solutions. As digitalization in production industries has a disruptive nature, it is reasonable to look at it as a systemic phenomenon with several groups of factors like techno-economic (technological core for different products) and strategical (government support and standardization) elements. The former sheds light on the supply side, while the latter describes the links between developers and potential users of technologies. Existing models of digital technology standardization in production industries reflect the rather high participation of the state in this process, which induces innovation activities within firms. As a result, companies promote their solutions within and outside the country and those who do this contribute to technology adoption in their own country.

Standardization in Smart Manufacturing …

213

7 Conclusion Digital transformation in manufacturing provides significant gains in efficiency, productivity, and increases in value added. It also drives innovative activities, giving additional resources and tools for this. There are several approaches that governments undertake to encourage a digital shift in manufacturing. The leading countries on global manufacturing markets—China, Germany, Japan, the Republic of Korea, and the United States—provide active policies for promoting the adoption of smart manufacturing technologies, supporting participation in the development of standards and their global propagation. The current wave of smart manufacturing aims at the integration of solutions from different suppliers by overcoming variation in technical characteristics and maintenance requirements. Due to large investments and complex, lasting changes, governments actively support the development and adoption of a new generation of digital technologies. The strategies have many similar characteristics, but the major one is the fact that most governments support platforms and cooperation between participants for joint the development of standards that promote national technology advantages. Particular attention to standardization may foster technological upgrading in different production sectors. For manufacturing, a consensus on technical specifications obtains even more attention due to the need for the compatibility of complex systems and elements within them. Digital (smart) manufacturing initiatives rely on a public-private partnership, where on the one hand, a business suggests technical specifications (models, architectures); on the other hand, the government promotes them within the economy with trading partners and at the international level. This process is multifaceted and requires participation In international global bodies, as well as domestic research and technical expertise capacities. From such a comprehensive view, there are two leaders in standardization in terms of digital manufacturing—Germany and China. Our empirical results rely on the assumption that leading countries in smart manufacturing and its standardization also have strong positions in digital technology development. To validate this hypothesis, we use the approach of FosterMcGregor and Nomaler and analyze three main mature technologies and relevant related products according to the HS Codes—systems for digital modeling and design (CAD/CAM), additive manufacturing, and industrial robotics. Germany has the most stable positions in all three technological areas and occupies first two positions in 17 out of 18 products, as well as China. Japan is also among the leaders and dominates industrial robotics, but also demonstrates some of the highest values in certain types of 3D-printing and numerically controlled equipment. Nevertheless, all countries support the idea that in order to achieve a high level of adoption a country needs first a strong proper supply of these technologies, which is confirmed by the value of the exported goods and, thus, its relevance on the global market.

214

Y. Turovets and K. Vishnevskiy

To sum up, these results may indicate that countries’ standardization performance in digital manufacturing technologies is tied to active standards development. The leading countries in standardization are indeed the leaders in global markets, but, certainly, there is a range of other factors to consider such as countries’ current manufacturing competitiveness, companies’ incentives to develop and introduce new products, and government support. Germany is a salient example: in spite of its strong positions in Industrie 4.0, it leads only in a narrow set of intelligent machinery types. A possible explanation lies in the fact that the country trades not only in final products (equipment and machines with embodies digital technologies), but also in technologies in the form of licensees and standards. Our findings make a step toward the assessment of smart manufacturing through a lens of complex innovation activities that include government policy, standardization, and trade statistics. The results could be useful in a view of the national competitiveness of manufacturing and the elaboration of strategies on a corporate level. In order to reveal stronger links between standards and manufacturing performance with modern statistical tools, there is a need for further research. It could be realized at the firm level with data that integrate patent and export statistics, as well as production firms’ particular characteristics. Acknowledgements The chapter was prepared within the framework of the Basic Research Program of the National Research University Higher School of Economics.

References 1. Ghobakhloo M (2018) The future of manufacturing industry: a strategic roadmap toward industry 4.0. Manuf Technol Manag 29(6):910–936 2. Strange R, Zucchella A (2017) Industry 4.0, global value chains and international business. Multinatl Bus Rev 25(3):174–184 3. Kagermann H, Anderl R, Gausemeier J, Schuh G, Wahlster W (2016) Industrie 4.0 in a global context: strategies for cooperating with international partners (acatech STUDY). Herbert Utz Verlag, Munich, p 70 4. Blind K, Mangelsdorf A (2016) Motives to standardize: empirical evidence from Germany. Technovation 48–49:13–24 5. Borrás S, Edquist C (2013) The choice of innovation policy instruments. Technol Forecast Soc Chang 80(8):1513–1522 6. Edler J, Fagerberg J (2017) Innovation policy: what, why, and how. Oxf Rev Econ Policy 33(1):2–23 7. Blind K (2016) The impact oftandardizationn and standards on innovation. In: Edler J, Cunningham P, Gök A, Shapira P (eds) Handbook of Innovation Policy Impact. Edward Elgar Publishing, Cheltenham, UK. Northampton, MA, USA, pp 423–449 8. European Commission (2018) Re-Finding Industry Report from the High-Level Strategy Group on Industrial Technologies. https://ec.europa.eu/research/industrial_technologies/pdf/ re_finding_industry_022018.pdf. Accessed 01 Apr 2022 9. OECD (2017) The Next Production Revolution. Implications for Governments and Business. Paris: OECD Publishing. https://espas.secure.europarl.europa.eu/orbis/sites/default/files/gen erated/document/en/9217031e.pdf. Accessed 01 Apr 2022

Standardization in Smart Manufacturing …

215

10. Foucart F, Li QC (2021) The role of technology standards in product innovation: theory and evidence from UK manufacturing firms. Res Policy 50(20). https://doi.org/10.1016/j.respol. 2020.104157 11. Riillo CAF, Allamano-Kessler R, Asnafi N, Fomin VV, Van De Kaa G (2022) Technological uncertainty and standardization strategies: a coopetition framework. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3145240 12. Ananyin VI, Zimin KV, Lugachev MI, Gimranov RD, Skripkin KG (2018) Digital organization: transformation into the new reality. Bus Inform 2(44):45–54 13. Park H, Kim H, Joo H, Song J (2016) Recent advancements in the internet-of-things related standards: a oneM2M perspective. ICT Express 2(3):126–129 14. Akatkin YM, Karpov OE, Konyavskiy VA, Yasinovskaya ED (2017) Digital economy: conceptual architecture of a digital economic sector ecosystem. Bus Inform 4(42):17–28 15. Esmaeilian B, Behdad S, Wang B (2016) The evolution and future of manufacturing: a review. J Manuf Syst 39:79–100 16. Gilchrist A (2016) Industry 4.0. Apress, Berkeley. https://doi.org/10.1007/978-1-4842-2047-4. Accessed 01 Apr 2022 17. Szalavetz A (2019) Industry 4.0 and capability development in manufacturing subsidiaries. Technol Forecast Soc Change 14I:384–395. Elsevier 18. Choi S, Jung C, Kulvatunyou B, Morris KC (2016) An analysis of technologies and standards for designing smart manufacturing systems. J Res Nat Inst Stand Technol 121:422–433 19. Wakke P, Blind K, De Vries H (2015) Driving factors for service providers to participate in standardization: insights from the Netherlands. Ind Innov 4(22):299–320 20. Narayanana VK, Chen T (2012) Research on technology standards: accomplishment and challenges. Res Policy 41:1375–1406 21. Baron J, Ménière Y, Pohlmann T (2014) Standards, consortia, and innovation. Int J Ind Organ 36:22–35 22. Koch C, Blind K (2021) Towards agile standardization: testbeds in support of standardization for the IIoT. IEEE Trans Eng Manag 68(1):59–74 23. Wen W, Forman C, Jarvenpaa SL (2022) The effects of technology standards on complementor innovations: evidence from the IETF. Res Policy 51(6). https://doi.org/10.1016/j.respol.2022. 104518 24. Lee P-C (2021) Investigating the knowledge spillover and externality of technology standards based on patent data. IEEE Trans Eng Manag 68(4):1027–1041. https://doi.org/10.1109/TEM. 2019.2911636 25. Hu Y, Liu D (2022) Government as a non-financial participant in innovation: how standardization led by government promotes regional innovation performance in China. https://doi.org/ 10.1016/j.technovation.2022.102524 26. Yang J, Shao L, Jin X, Zhou L (2022) Impact of coupling of technological innovation andtandardizationn on industrial development. Int J Emerg Mark. https://doi.org/10.1108/IJOEM-052021-0758 27. Foster C, Heeks R (2013) Innovation and scaling of ICT for the bottom-of-the-pyramid. J Inf Technol 28(4):296–315 28. Kim D-H, Leeb H, Kwak J (2017) Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: an investigation of the M2M/IoT patent network. Res Policy 46:1234–1254 29. Lan F, Hou L (2021) Dynamic capability reconstruction of digital transformation for emerging market enterprises: learning from Chinese experience. Int J Technol Policy Manag 21(3):231– 252 30. Ministry of Science and ICT (2019) National Strategy for Artificial Intelligence. https:// www.msit.go.kr/bbs/view.do?sCode=eng&mId=10&mPid=9&bbsSeqNo=46&nttSeqNo=9. Accessed 01 Apr 2022 31. Federal Ministry for Economic Affairs and Energy (2019a). Die Wirtschaft bei der Digitalisierung beobachten. https://www.bmwi.de/Redaktion/DE/Artikel/Digitale-Welt/monitoringwirtschaft-digital.html. Accessed 01 Apr 2022

216

Y. Turovets and K. Vishnevskiy

32. Diab WW et al (2022) Industrial IoT artificial intelligence framework. Industry IoT Consortium. https://www.iiconsortium.org/pdf/Industrial-AI-Framework-Final-2022-02-21.pdf. Accessed 01 Apr 2022 33. ISO (2021) White Paper on Smart Manufacturing. https://www.iso.org/files/live/sites/isoorg/ files/store/en/PUB100459.pdf. Accessed 01 Apr 2022 34. Kohtamäki M, Parida V, Oghazi P, Gebauer H, Baines T (2019) Digital servitization business models in ecosystems: a theory of the firm. J Bus Res 104:380–392 35. Cenamor J, Parida V, Wincent J (2019) How entrepreneurial SMEs compete through digital platforms: the roles of digital platform capability, network capability and ambidexterity. J Bus Res 100:196–206 36. Athanasopoulou A, de Reuver M, Nikou S, Bouwman H (2019) What technology enabled services impact business models in the automotive industry? An exploratory study. Futures 109:73–83 37. Ipsos (2020) European enterprise survey on the use of technologies based on artificial intelligence. https://www.ipsos.com/en/european-enterprises-and-ai-technologies. Accessed 17 May 2022 38. IMD (2021) World Digital Competitiveness Ranking 2021, IMD World Competitiveness Center. https://www.imd.org/centers/world-competitiveness-center/rankings/world-dig ital-competitiveness/. Accessed 01 Apr 2022 39. UNIDO (2021) CIP 2021. https://stat.unido.org/database/CIP%202021. Accessed 01 Apr 2022 40. Ertz M, Leblanc-Proulx S, Sarigöllü E, Morin V (2019) Made to break? A taxonomy of business models on product lifetime extension. J Clean Prod 234:867–880 41. Dalenogare LS, Benitez GB, Ayala NF, Frank AG (2018) The expected contribution of Industry 4.0 technologies for industrial performance. Int J Prod Econ 204:383–394 42. PwC (2020) Digital Factories 2020. https://www.pwc.de/de/digitale-transformation/digital-fac tories-2020-shaping-the-future-of-manufacturing.pdf. Accessed 17 May 2022 43. Park H, Choi SO (2019) Digital innovation adoption and its economic impact focused on path analysis at national level. J Open Innov Technol Mark Complex 5(3):1–21 44. JETRO: Manufacturing Government Initiatives. https://www.jetro.go.jp/en/invest/attractive_s ectors/manufacturing/government_initiatives.html. Accessed 01 Apr 2022 45. GAO (2019) Advanced Manufacturing: Innovation Institutes Have Demonstrated Initial Accomplishments, but Challenges Remain in Measuring Performance and Ensuring Sustainability. https://www.gao.gov/assets/700/699310.pdf. Accessed 01 Apr 2022 46. Digital in NRW (2020) Künstliche Intelligenz im Mittelstand Potenziale und Anwendungsbeispiele. https://www.mittelstand-digital.de/MD/Redaktion/DE/Publikationen/zentrum-dor tmund-ki-im-mittelstand.pdf?__blob=publicationFile&v=2. (in German). Accessed 17 May 2022 47. BMWi (2019b) SME’s Digital: Strategies for the digital transformation. https://www.bmwi.de/ Redaktion/EN/Publikationen/Mittelstand/smes-digital-strategies-for-digital-transformation. pdf?__blob=publicationFile&v=4. Accessed 01 Apr 2022 48. Federal Ministry for Economic Affairs and Energy (2020) Mittelstand-Digital. https://www. mittelstand-digital.de/MD/Navigation/DE/Home/home.html. Accessed 17 May 2022 49. The State Council for the People’s Republic of China (2017) National New Generation AI Plan. OECD AI Policy Observatory. Accessed 01 Apr 2022 50. NTIA (2018) The President’s National Spectrum Strategy Will Give America a Boost in 5G. https://www.ntia.doc.gov/blog/2018/president-s-national-spectrum-strategy-will-giveamerica-boost-5g. Accessed 17 May 2022 51. Shin D-H, Kim H, Hwang J (2015) Standardization revisited: a critical literature review on standards and innovation. Comput Stand Interfaces 38:52–57 52. Federal Ministry for Economic Affairs and Energy (2019c) Plattform Industrie 4.0: Digital Transformation “Made in Germany”. https://www.plattform-i40.de/PI40/Redaktion/EN/Dow nloads/Publikation/plattform-flyer-en.pdf?__blob=publicationFile&v=6. Accessed 17 May 2022

Standardization in Smart Manufacturing …

217

53. Federal Ministry of Transport and Digital Infrastructure (2017) Digital Test Beds. https:// www.bmvi.de/EN/Topics/Digital-Matters/Digital-Test-Beds/digital-test-beds.html. Accessed 01 Apr 2022 54. OpenGov Asia (2018) South Korea pilots six public projects to harness blockchain technology. https://www.opengovasia.com/south-korea-pilots-six-public-projects-to-harnessblockchain-technology/. Accessed 01 Apr 2022 55. Federal Ministry for Economic Affairs and Energy (2016) Digital Strategy 2025. Federal Ministry for Economic Affairs and Energy (BMWi). https://www.de.digital/DIGITAL/Red aktion/EN/Publikation/digital-strategy-2025.pdf?__blob=publicationFile&v=8. Accessed 17 May 2022 56. Mattauch W (2017) Digitising European Industries—Member States Profile: Germany. https:// ec.europa.eu/futurium/en/system/files/ged/de_country_analysis.pdf. Accessed 01 Apr 2022 57. Federal Ministry for Economic Affairs and Energy (2019c) Plattform Industrie 4.0: Digital Transformation “Made in Germany”. https://www.plattform-i40.de/PI40/Redaktion/EN/Dow nloads/Publikation/plattform-flyer-en.pdf?__blob=publicationFile&v=6. Accessed 01 Apr 2022 58. Roberts H, Cowls J, Morley J et al (2021) The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. AI Soc 36:59–77. https://doi.org/10.1007/s00146020-00992-2 59. Korea Technology Finance Corporation (2019) 2018 Annual Report. https://www.kibo.or.kr: 444/src/down_file_ks.asp?filename_01=158&filename_02=15&filename_03=40455&filena me=a_0. Accessed 01 Apr 2022 60. Federal Ministry for Economic Affairs and Energy (2020) Mittelstand-Digital. https://www. mittelstand-digital.de/MD/Navigation/DE/Home/home.html. Accessed 01 Apr 2022 61. Feifei F (2021) Green data Centers in focus. The State Council. The People’s Republic of China. http://english.www.gov.cn/statecouncil/ministries/202112/09/content_WS61b13eda c6d09c94e48a1f81.html. Accessed 17 May 2022 62. Shivakumar S (2022) Securing Global Standards for Innovation and Growth. Center for Strategic & International Studies. https://www.csis.org/analysis/securing-global-standardsinnovation-and-growth. https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/ 220127_Shivakumar_Securing_Global_Standards.pdf?RxVe0c96Njjy24Af_R8kcoDkW8M hSYsi. Accessed 01 Apr 2022 63. Federal Ministry of Economic Affairs and Energy (2019) The Standardization Roadmap of Predictive Maintenance for Sino-German Industrie 4.0/ Intelligent Manufacturing. https:// www.dke.de/resource/blob/1711308/ad04db2c91a6749c86e7311c1a294644/the-standardisat ion-roadmap-of-predictive-maintenance-for-sino-german-industrie-4-0-data.pdf. https://csiswebsite-prod.s3.amazonaws.com/s3fs-public/publication/220127_Shivakumar_Securing_G lobal_Standards.pdf?RxVe0c96Njjy24Af_R8kcoDkW8MhSYsi. Accessed 01 Apr 2022 64. Federal Ministry for Economic Affairs and Energy (2016) Digital Strategy 2025. Federal Ministry for Economic Affairs and Energy (BMWi). https://www.de.digital/DIGITAL/Redakt ion/EN/Publikation/digital-strategy-2025.pdf?__blob=publicationFile&v=8. Accessed 01 Apr 2022 65. Mattauch W (2017) Digitising European Industries—Member States Profile: Germany. https:// ec.europa.eu/futurium/en/system/files/ged/de_country_analysis.pdf. Accessed 17 May 2022 66. ITIF (2018) Why Manufacturing Digitalization Matters and How Countries Are Supporting It. http://www2.itif.org/2018-manufacturing-digitalization.pdf. Accessed 17 May 2022 67. DIN (2018) The Reference Architectural Model RAMI 4.0 and the Standardization Council as an element of success for Industry 4.0. https://www.din.de/blob/271306/340011c12b8592d f728bee3815ef6ec2/06-smart-manufacturing-jens-gayko-data.pdf. Accessed 17 May 2022 68. MSIT (2018) Smart Korea Creative Economy. https://msit.go.kr/SYNAP/skin/doc.html?fn= 8a5c9cc0262b0da349d6d641227368a7&rs=/SYNAP/sn3hcv/result/201811/#. Accessed 20 June 2022 69. IVI (2016) The Industrial Value Chain Initiative. A Japanese contribution to Smart Manufacturing. https://iv-i.org/en/docs/doc_160428_hannover.pdf. Accessed 01 Apr 2022

218

Y. Turovets and K. Vishnevskiy

70. METI (2017) Connected Industries Tokyo Initiative 2017. http://www.meti.go.jp/english/press/ 2017/pdf/1002_004b.pdf. Accessed 01 Apr 2022 71. Acatech (2017) Industrie 4.0 in a Global Context. Strategies for Cooperating with International Partners. http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/ Acatech/root/de/Publikationen/Projektberichte/acatech_eng_STUDIE_Industrie40_global_ Web.pdf. Accessed 01 Apr 2022 72. The White House (2018) Strategy for American Leadership In Advanced Manufacturing. https://www.whitehouse.gov/wp-content/uploads/2018/10/Advanced-ManufacturingStrategic-Plan-2018.pdf. Accessed 01 Apr 2022 73. SESEC (2018) Chinese Standards 2035, the standardization strategy research is kicked off. http://www.sesec.eu/24-05-2018-chinese-standards-2035-the-standardization-strategy-res earch-is-kicked-off/. Accessed 20 June 2022 74. IIC (2017) Architecture Alignment and Interoperability An Industrial Internet Consortium and Plattform Industrie 4.0 Joint Whitepaper. https://www.iiconsortium.org/pdf/JTG2_Whit epaper_final_20171205.pdf. Accessed 01 Apr 2022 75. Plattform Industrie 4.0 (2018) Fortschrittsbericht 2018. Industrie 4.0 anwenden. Wegweisend. Praxisnah. Vernetzt. https://www.plattform-i40.de/I40/Redaktion/DE/Downloads/Publikation/ hm-2018-fortschrittsbericht.pdf?__blob=publicationFile&v=5. Accessed 20 June 2022 76. RRI (2017) What’s RRI/WG1?—toward horizontal dynamic manufacturing. https://www.jmf rri.gr.jp/content/files/Open/2017/20170315_RRI_forCeBIT/RRI_for_CeBIT_IoT_panel.pdf. Accessed 01 Apr 2022 77. IVI (2018) The Allianz Industrie 4.0 Baden-Württemberg and the Industrial Value Chain Initiative Sign MoU. https://iv-i.org/wp/wp-content/uploads/2018/03/Allianz-I4.0-IVI-MOU-pressrelease.pdf. Accessed 20 June 2022 78. DIN (2018) The Reference Architectural Model RAMI 4.0 and the Standardization Council as an element of success for Industry 4.0. https://www.din.de/blob/271306/340011c12b8592d f728bee3815ef6ec2/06-smart-manufacturing-jens-gayko-data.pdf. Accessed 01 Apr 2022 79. DIN (2017). German Standardization Roadmap Industrie 4.0 Version 3. https://www.din. de/blob/65354/57218767bd6da1927b181b9f2a0d5b39/roadmap-i4-0-e-data.pdf. Accessed 01 Apr 2022 80. Shivakumar S (2022) Securing Global Standards for Innovation and Growth. Center for Strategic & International Studies. https://www.csis.org/analysis/securing-global-standards-inn ovation-and-growth. Accessed 01 June 2022 81. UNIDO (2019) Measuring the Creation and Adoption of New Technologies Using Trade and Patent Data. Inclusive and Sustainable Industrial Development Working Paper Series, WP 11, 2019. https://www.unido.org/api/opentext/documents/download/16411459/unido-file-164 11459. Accessed 01 Apr 2022 82. UN Comtrade (2022) UN Comtrade Database. https://comtrade.un.org/data/. Accessed 20 June 2022

White Goods

Redesign, Smart and Digital Enablement of Sales and Operations Planning Processes: A Study of White Goods Manufacturing Burak Kandemir , Eren Özceylan , and Mehmet Tanya¸s

Abstract Traditional sales & operations planning (S&OP) processes have lack of speed, accuracy, and availability in today’s digital age and global epidemic environment. In addition, major domestic appliance industry faces the challenge of increasing complexity in channel, customization, product, facility location, component and supplier dimensions, which brings the necessity of effective and timely planning processes. Redesigning and digital enablement of S&OP processes can provide realtime analytical capabilities in the supply chain, thereby helping stakeholders to focus on the most pressing issues. While increasing complexity brings the availability of big data, it has become another challenge for the industry to collect, interpret and use this data in advanced analytics techniques. Manufacturers and distributors in the white goods industry apply various data governance approaches in line with their supply chain capabilities and structure. This chapter discusses the concept of digital transformation related with S&OP and how it can benefit the white goods sector. Having the network structure and inventory strategy as initial inputs, we re-evaluate the S&OP cycle starting at demand planning by revisiting forecasting hierarchies, horizon and frequency and applying artificial intelligence (AI) based algorithms, supply planning process with real time data, and scenario generation with the financial impacts of each alternative. Keywords White good · Digital transformation · S&OP · Industry 4.0

B. Kandemir KoçDigital Solutions Company, 34700 Istanbul, Turkey e-mail: [email protected] E. Özceylan (B) Department of Industrial Engineering, Gaziantep University, 27100 Gaziantep, Turkey e-mail: [email protected] M. Tanya¸s Department of International Trade and Logistics Management, Maltepe University, 34857 ˙Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_10

221

222

B. Kandemir et al.

1 Introduction Sales and operations planning (S&OP) is the key business process to balance customer demand with supply capabilities. The general objective of S&OP is matching demand and supply in the medium term, by providing an instrument for the vertical alignment of business strategy and operational planning, and for the horizontal alignment of demand and supply plans [1]. As businesses worldwide seek to improve the agility and resiliency of supply chains (due to COVID-19), the time has come to ditch manual processes wherever possible. One of those processes may be digital enablement of S&OP process [2]. Digital S&OP provides real-time analytical capabilities to help identify issues in all stages of the sales and operations planning process, thereby helping stakeholders to focus on the most pressing issues. There are several inherent problems with “traditional S&OP”, many of which expose businesses as they try to navigate the turmoil caused by the pandemic. It comes down to the lack of speed, accuracy, and availability [3]. The necessary information to be collected can be analyzed more effectively and brought back to the S&OP process with significantly reduced timing to address both market drivers and reduce the bullwhip effect of demand and supply gyrations. This has been especially evident in the global downturn during 2008–2009 (today the epidemic), and even now as economies and consumers are recovering [4]. As all sectors will need, the white goods manufacturing sector has to adapt to digital S&OP processes. The white goods sector is one of the pioneer fields in Turkey and Turkish economy. Turkey is Europe’s top white goods manufacturer. Domestic sales in Turkey’s home appliances sector rose 9% year-over-year in 2021, according to sector association TÜRKBESD (White Goods Manufacturers’ Association of Turkey) data, reflecting the demand amid the coronavirus outbreak [5]. Production was up 17%, while exports surged 18% compared to a year ago, the data showed, despite challenges such as a spike in raw material prices and supply chain disruptions. Turkish home appliances sector, which exported 26 million products in 2021, has ranked first in Europe and the annual export volume has exceeded $4.5 billion. According to the reports of TÜRKBESD, the industry aims to invest around $480 million in 2022 for additional production facilities, capacity increase, as well as technology innovation and energy efficiency. At the same time, four sectors in Turkey have considerable strength in digital transformation, namely automotive, machinery, white appliances and chemicals [6]. In the light of the above information, the objectives of this study are to conduct an exploratory research on digital transformation and how they relate to the S&OP process in white goods manufacturers, to introduce redesigning the S&OP cycle with digital enablement and to focus on product review, demand forecasting, consensus process, inventory planning, supply planning, demand & supply reconciliation, distribution planning in terms of digitalization. To the best knowledge of the authors, this chapter is one of the rare studies in literature that differs from other studies in that it focuses on the white goods sector.

Redesign, Smart and Digital Enablement of Sales and Operations …

223

In the following section, digital transformation with relevant literature is presented. The third section describes the digital S&OP cycle in white goods including forecasting, distribution planning and etc. The chapter ends with the conclusions set out in the last section.

2 Literature Review on Digital Transformation of S&OP Processes Digital transformation has become a strong customer value proposition tool for many executives. Although digital technologies can enhance business efficiency, using digital technologies for that purpose doesn’t transform a company into a digital business. Ross et al. [7] have identified five digital building blocks (see Fig. 1) that develop the new capabilities companies will need to succeed in the future. These building blocks establish a foundation on which to rapidly develop and scale digital offerings that customer’s value. Three of the five digital building blocks are technology platforms: an operational backbone, a digital platform, and an external developer platform. The other two building blocks are organizational capabilities: shared insights about what customer’s

Accountability Framework

External Developer Platform

Shared Customer Insights Digital Platform

Operational Backbone

Fig. 1 Five building blocks of digital transformation [7]

224

B. Kandemir et al.

value, and an accountability framework that coordinates the efforts of autonomous teams. Operational backbones consist of technological capabilities such as enterprise systems, which in turn enable business capabilities through processes implemented in, and supported by, these systems [8]. The digital platform is a repository of business, technology, and data components facilitating rapid innovation and enhancement of digital offerings for customers. The external developer platform relates to the ecosystem and external partners and their components. While the accountability framework portrays the dispersion of obligations between people, shared customer insights are organizational understanding about customer experiences throughout all the aspects of the buyer’s new journey [9]. The literature on digital transformation of S&OP processes applied industry is very limited. Although there have been a lot of researches (e.g. [10] and [11]) that focus on digital supply chain management, theoretical and practical applications have not yet been sufficiently reflected on the digital S&OP studies. As one of the recent studies, Choudhury et al. [12] aimed at identifying and analyzing numerous critical success factors that may improve the efficiency of a digital supply chain. They tested the success factors on S&OP strategies, strategic sourcing techniques, smart manufacturing processes and warehouse management. Their analysis confirmed that the S&OP strategies enhanced the success of a digital supply chain. Dos Santos et al. [13] proposed a digital twin (DT) focused on S&OP activities (physical and human). The DT was composed of a discrete event simulation model, an artificial intelligence model, and a decision dashboard that provides a user-friendly interface for the decision-maker. For an excellent review of methodological and case-study based papers in usage of information technology and business analytics within S&OP, the reader is referred to Nicolas et al. [14].

3 Redesigning the S&OP Cycle with Digital Enablement Sales and operations planning is the backbone of all supply chain planning functions in demand-driven systems as it is a cohesive, iterative and collaborative set of processes. It aims to establish a holistic view of all supply activities to be in line with demand with contribution from all related internal stakeholders enlightened by the information from external ones as well. Bridging the gaps between corporate and supply chain related strategies with operations, majority of tactical plans are generated, outcomes are evaluated, and a systematic approach of proactive actions are taken. Eventually, it is not a narrow bridge. The planning horizons and granularities differ with respect planning objectives, sometimes causing the relatively short-term planning named differently such as sales and operations execution or supply and demand synchronizing. No matter how close to operations or strategy within the process hierarchy, the aim does not differ -establishing plans and executing actions of them in order to meet customer expectations in line with predefined policies and minimum cost.

Redesign, Smart and Digital Enablement of Sales and Operations …

225

Chain stores

Dealers

.

.

.

.

Distributors

. .

Online sales Production

Distribution Centers

Mobile Apps

Fig. 2 Distribution network in white goods industry

White goods industry is no different at all, besides, it is one of the sectors that an effective sales and operations planning brings visibility and enables optimum plans to be generated especially considering the dense complexity it has in many dimensions. The manufacturing companies are widely multinational, serving different kinds of product offerings as well as distribution networks depending on and high dominated by the requests of the market country. A typical white goods distribution network involves different distribution channels such as chain stores, (exclusive) dealers, local distributors, online sales channels both corporate and marketplaces, and even the mobile applications that white goods can be purchased by the end consumers (Fig. 2). The characteristics of the sales channels affect the physical distribution too, mostly forming a multi-echelon supply chain structure with need of differentiated service and inventory policies. The complexity is not limited to channel-based requirements but high number of customizations of the product as well. The wide range of technical specifications, market-based regulations and cosmetics related to cultural preferences brings a high number of products to be in the portfolio, adding the production and material complexity increase. In order to compete in the challenging environment, white goods manufacturers evaluate some key enablers in terms of both managerial and technological aspects, and that is one of the most obvious reasons that a digital transformation in sales and operations planning of white goods is seen almost as an obligation. In this chapter, we will match all sales and operations planning functions in white goods with the digital transformation building blocks stated above and address all related technological, process-wise and organizational areas. The challenges and enablers in white goods industry supply chains are given in Table 1.

226

B. Kandemir et al.

Table 1 Challenges and enablers in white goods industry supply chains Challenge

Key enablers Managerial

Technological

• Defining competition differentiators • Defining service strategies

• Advanced forecasting techniques • Optimized inventory levels

Increasing complexity in terms • Revisiting planning of network, manufacturing, hierarchies • Modularity/standardization component and product options portfolio • Evaluating production strategy

• Enabling modular and multi-echelon supply chain replenishment structure

Differentiated channel-based requirements

3.1 Product Review The initial step in the S&OP cycle is the product review, where the portfolio is evaluated and updated information of nee product introduction (NPI) and end of life (EOL) is shared with time plans. Customers always look for innovative products such as low energy consumption, reduced working sounds as well as cosmetic appearance of white goods. However, continuous innovation brings additional complexity and challenges in terms of managing the supply chain and therefore, product review process are the initial enabler of an effective S&OP cycle. Information about the new products such as their technical, planning and distribution features are usually stored in the operational backbone. The manufacturing company, however, can have this data on a digital platform as well. In white goods industry, product transitions can occur from several reasons, from customer expectations to customization, from energy consumption to regulations, and the information about predecessor-successor SKU match is the baseline of all planning activities since historic consumption of an item is the major input of forecasting and inventory planning. This information is also crucial for executional processes of warehousing and shipment loading, depending on the rules the company has, such as first-in-firstout (FIFO) policies. In addition to the NPI and product transition match information, the operational backbone also provides the planned launch dates of the new product and the planned end of life date of the old one. This information will also enable differentiated inventory policies to be generated based on the lifecycle stage of the product. External stakeholders such as end customers, distributors, dealers, even the logistics service providers can provide feedback about the new product releases and/or transitions. From packaging dimensions to quality related features, these feedbacks can turn into a major input for the manufacturer company’s innovation policy and therefore the external developer platform should provide the ability to collect, transfer, and sometimes analyze this information. Most companies lack the traceability of product performance when new products are concerned. The threshold questions about the performance -estimated sales

Redesign, Smart and Digital Enablement of Sales and Operations …

227

volume, market penetration, and cost advantages are naturally asked during the innovation process, however the complexity brought to supply chain is often disregarded. There should be a clear mechanism for both planned vs. actual product performance, relating it to all supply chain costs, not only the easily visible ones. The same visibility and performance evaluation should also exist for the plan adherence of new product introductions [15].

3.2 Forecasting Material flows in demand-driven supply chains are usually triggered by demand forecasts. Hence, forecasting keeps its importance despite many mathematical algorithms being in use, and evolution of machine learning analytics took the performance of forecasting success to higher levels. Except the product review process, the S&OP cycle of white goods industry starts with forecasting as well. Forecasting process uses historical sales data, product transition information, product hierarchies, prices, inventory levels of finished goods from corporate databases namely ERP systems. Also, corporate promotion information can be taken from these systems in case they exist. Having these as input, forecasting algorithms are usually generated and run on the digital platform itself. Considering the widespread range of white goods, the algorithms are usually run on a higher product group level and a breakdown algorithm or rule set are defined on the digital platform too. Process wise, the initial step of forecasting is determining the hierarchical level of product group, channel and geography that forecasting to be run, followed by generating the breakdown algorithm which gives the best output for operational level. It should also be noticed that the granularity level of forecasting may (and probably should) change for different time horizons. While the S&OP cycle should ideally be set for 18 months horizon in order to support decision making for evaluating investment alternatives and construct a robust procurement plan for materials with longer lead times, the planning hierarchies can be set for higher levels of hierarchies for mid and long terms horizons. A similar change is also valid for timing buckets of forecasting. Production schedules and short-term replenishment plans are generated on weekly basis for SKU and customer or distribution center combination, while mid- and long-term plans usually have a monthly frequency. Once the levels of forecasting algorithms and input originating from internal and historic data are set, our next step appears as determining external data and features to be used in the model. The external developer platform is used for collecting pointof-sales (POS) data, real-time demand signals, and inventory data from different channels mentioned in the distribution network. Hence, the external data is not limited to distributor or channel-specific real-time data but includes many features in the outer ecosystem circle as well. Currency rates, custom tariffs and regulation related data, interest rates, distribution network costs, customer trust indexes, and many socio-economic features are used in forecasting algorithms. This process is usually

228

B. Kandemir et al.

Fig. 3 Impact of external data usage for forecasting performance

held by a feature engineering step before taken into machine learning algorithms and selection of features are done based on their impact on the performance of forecasting process. An example of how external data affect forecasting performance is given Fig. 3. The figure is based on an actual project conducted in one of the major white goods manufacturers in Europe and demonstrates the demand forecast without and with use of external data. The weekly weighted forecast accuracy generated without use of external data is 76.4% while it increases up to 89.2% when external data is used. All the processes mentioned in the digital enablement of forecasting process require an accountability framework on the organizational perspective. Accountability usually involves multiple functions each of whom to carry out one or more steps of the process. Depending on the size of the organization, a cross-functional team can also be formed. The steps of processes based on data handling, clearing, integration of external data, ownership of forecasting process on different product groups and channels, tracking forecast errors and bias and informing relevant stakeholders of the performance. Considering the added value to the customers and what they would be willing to pay for, one can easily name the fundamental supply chain performance indicators namely increased service level and inventory turns. In fact, the S&OP process itself with all sub-processes enable the two mentioned metrics. White goods supply chains are also complicated enough to easily have a created chaos, blurry rule sets, and a continuous firefighting mode which can eventually create a disturbance on customer’s side. Starting with the forecasting process, the S&OP cycle also aims to overcome this disturbance by clear definition and infrastructure of all planning processes.

Redesign, Smart and Digital Enablement of Sales and Operations …

229

3.3 The Consensus Process Once the company obtains the statistical demand forecast, the next step in the S&OP cycle is enriching this outcome with views of different functions such as field sales, marketing, and product management and reaches an unconstrained consensus forecast. Especially when white goods industry is concerned, consensus process plays a vital role due to high number of product transitions and general and/or channel-specific campaigns and promotions. Sales budgets, inventory figures and back orders are executional data and taken from the ERP systems for the consensus process. Another executional data is the campaign and promotion information and usually stored in CRM systems which are also a part of the operational backbone. Promotion input can be taken in the forecasting algorithm as well as mentioned above or taken into account in the consensus process. Sales force insights can be taken from the sales effectiveness systems or can manually be uploaded to the digital platform itself. The digital platform for white goods consensus process should have a high integration capability due to the density of data to be transferred, and the relevant stakeholders mentioned above should be able to input their own data whether manually, from the systems they use for their function or by file uploads. White goods manufacturing usually requires a performance analysis of several forecasts which are part of the consensus process, such as the accuracy of not only the consensus forecast but sales teams’ forecast as well, and the root causes of forecast errors and deviations. This analysis enables to understand the performance of multi-echelon structures and identify whether the rationing game reason of the bullwhip effect exists or not [16]. So, the digital platform is seen as the major analysis tool in addition to its algorithmic capabilities. Many white goods distribution channels have a campaign and promotion policy for them as well, which makes the external development platform to obtain such kind of data from the relevant parties for it to be taken as an input. One of the blurriest areas of S&OP process in white goods industry is the accountability framework. Having the statistical forecast and field and marketing insights, the demand planning team should reach an unconstrained consensus forecast but there is a risk of domination of conflicts during the review meetings. Therefore, orchestration of the process plays a vital role and usually conducted by demand planners who have supply chain point of view and has the most ability to be neutral. But the most important point to construct an effective consensus process is the company culture itself which should coach all relevant parties to the global best for the company. Generating the consensus forecast of the white goods manufacturer enables the company to track the channel, customer, and product specific demand, clarify the demand priorities of demand, and consecutively shorten the planning cycles. Firm and mutually agreed demand prioritization and shortened planning cycle eventually create competitive advantage and define an area customers will look for.

230

B. Kandemir et al.

3.4 Inventory Planning In white goods industry, effective inventory management is a crucial area of focus. The industry itself has a complex supply chain network and inventory management focus should cover the hard production point of view as well as fundamental aspects of retail sector. The major aim of inventory management is to enable all defined supply chain strategies with minimum operational cost and working capital. Being a major tool of strategic supply chain management, optimized inventory levels bring an invaluable tool for competitive advantage. Determining the decoupling points, and appropriate inventory policies and parameters are the first steps through effective inventory management. White goods industry usually consists of multi-echelon structures and forms a periodic inventory control system and therefore, well-known (s, S) replenishment policy is widely in use. Safety stocks and order-up-levels are calculated by historic sales fluctuation and lead times which are mainly obtained from operational backbone systems, where forecasts can be sourced from operational backbone or the digital platform. A sample of the outputs for (s, S) policy parameters is shown in Fig. 4. Data include forecasts on weekly basis for a major domestic appliance with seasonal demand pattern, calculated safety stock (s) for 0.95 service level and 5 weeks lead time, and the order-up-to level (S) which is obtained by sum of safety stock and total demand during replenishment cycle time (lead time (LT) + order frequency (OF)); formula of which is given in Eq. (1): S=s+

∑

F

L T +O F

Fig. 4 Projection of forecast, safety stock and order-up-to levels for a seasonal product

(1)

Redesign, Smart and Digital Enablement of Sales and Operations …

231

Fig. 5 Outputs of item clustering for service level differentiation

Prior to obtaining optimized safety stocks levels, a product and customer segmentation is advantageous to differentiate service levels. Item clustering has been an area of widespread research, from multi-objective optimization to analytic hierarchy process and advanced statistical tools [17]. Two main dimensions of clustering are volume and variability, however there are methods taking product specs and characteristics into account as well. Once the clusters have been defined, differentiated service levels can be assigned to each cluster based on company strategies. This process is often done on the digital platform where the abovementioned calculation steps are also carried out, however the parameters can also be a hard input at the operational backbone. Figure 5 shows the outputs of our item clustering algorithm done in the digital platform for the same product mentioned above. Our algorithm takes volume as an input for ABC classification where 80% of the volume is obtained by 27.3% of the SKUs, and coefficient of variance for XYZ classification for variation. As a prerequisite of effective and seamless planning, multi-echelon structures also take into account the channel inventory levels. In terms of different corporate structures existence such as distributors and chain markets, channel inventory can be obtained by the external platform. On the accountability area, planning, production and sales functions have the shared responsibility based on their functional interest. A tracking mechanism of inventory turns, and aging should have the firmness and visibility as well as an updating mechanism for proactive planning, not only a reactive performance reporting. As mentioned, an effective inventory management process brings an invaluable tool for strategy alignment. This includes the cost optimization on differentiated service levels, where can be a major input for order management principles. Customers know in advance their on-spot orders will be treated differently in terms of prioritization or cost. An open and firm communication can be formed where the possible outcomes of the alternatives are set and known before execution takes place.

232

B. Kandemir et al.

It also enables both white goods manufacturers and channel partners to take relevant actions in case of obsolete and aged inventory occurrence.

3.5 Supply Planning Having the unconstrained demand and inventory policies on hand, the next step of the S&O cycle is planning the supply in order to meet them. Supply planning covers the rough-cut capacity planning and material availability. Demand is cross-checked with the production and distribution capacities as well as material availability and best-fit plans are generated to meet the demand in lowest cost possible. Material and capacity check should be based on not only the demand, but the inventory position. Inventory position forms the baseline of total net requirement where forecasted demand, safety stocks and backorders form the gross requirement and netted with on-hand and transit inventory. While demand netting process is done periodically, it also involves a multi-period horizon for projection and middle and long-term plans. After netting is done, the availability check should be conducted with bill-of-material (BOM) information as well. An example of demand netting process is given Fig. 6. To obtain the rough-cut capacity plans, one should have the production capacity information, routes, material inventory levels, work-in-process (WIP) inventory levels, bill-of-material (BOM) information and procurement lead times, all which are obtained the operational backbone. Before having the availability and capacity check with respect to demand, the white goods manufacturers may need the supplier capacity and lead times with an end-to-end supply chain point of view, and this

Production & procurement lead times

Agreed service levels

Safety stock

Inventory position

Demand forecast

Transit inventory

+ Backorders

On-hand inventory

Net requirement

Fig. 6 Demand netting process flow in white goods industry

Redesign, Smart and Digital Enablement of Sales and Operations …

233

information can be gathered via the external digital platform that enables supplier integration.

3.6 Demand and Supply Reconciliation In white goods industry, it is very rare to have a balanced demand and supply, especially when deep-dived into granular levels of product, customer, and material. Even if a balanced plan is obtained for short horizon, a reconciliation mechanism is almost a necessity for the future planning buckets. Some of the white goods such as freezers, dryers, air conditioners have a higher seasonality factor the others, and capacity planning faces several challenges when these groups are concerned. Supply and demand reconciliation process aims to develop a recommended plan with alternatives and financial impacts of each alternative. When white goods industry is concerned, scenario generation for the alternative plans is another vital process since the possible scenarios would have a great impact on the supply chain performance. A pre-season stock build might be an option in order to meet high seasonal demand, but it will yield to inefficiency for the working capital. Besides, increasing complexity in terms of product portfolio and supply chain network will bring uncertainty and risk of obsolete inventory. Capacity increase by increasing number of working shifts would be another option as an example, but it will also require a good workforce planning and communication. It is a hard task to identify the financial impacts of the generated scenarios and should be conducted with great care. Companies would want not only the alternative scenarios but also a simulation of system behavior in terms of service levels and inventory turned with all financial implications. The reconciliation process will therefor require a high volume of data to be used. Differentiated demand types such as firm sales orders, forecasts, safety stocks, production schedule for short term and master plans for future, backorders, and related financial information such as sales price and costs are taken from the operational backbone. External data can be an input for the scenario generation process. Although demand netting takes into account the pipeline and transit inventory, the phase lag between netting and reconciliation process might require the updated data ad this data can be obtained from the external development platform. The digital platform carries out the scenario generation task, and these scenarios can be based on optimization such as profit maximization, cost minimization or a multi-objective model and simulation as well. However, the scenarios can also be built manually in white goods industry and digital platform can be used for evaluation purpose. The accountability framework should avoid cross-functional and crossdepartment conflicts with the holistic view just as in the consensus process but this time more among various customers and channels. A well-established segmentation strategy and demand prioritization infrastructure helps the reconciliation in this manner. Based on company’s prioritized objectives such as profitability, volume, cost,

234

B. Kandemir et al.

market or product entry the prioritization often changes over time and the process should have clearly defined rules in order not to create ongoing dominance of specific stakeholders. The added value of the reconciliation sub-process lies in the reliable, mutually agreed, accurate order confirmation and allocation information availability to the customers.

3.7 Distribution Planning Though not a main process of the S&OP cycle, distribution planning is the touch point of tactical level of integrated planning and execution, and therefore should definitely be aligned with the agreed plans. With use of highly complex data, distribution planning is also a major area of digital transformation. Sales orders, forecasts, safety stocks, backorders, inventory levels and production schedules are taken as input from the operational backbone in the distribution planning. Inventory in-transit and real-time shipment tracking is the input of external developer platforms, enabling pipeline information while generating distribution plans. As mentioned as a major area of digital transformation, digital platforms carry multiple and complex tasks. First of all, distribution planning algorithms should be in line with the allocation process and outcomes of the S&OP cycle. Distribution requirements are obtained by the transformation of planning parameters into physical units such as volume and weight. Majority of white good products are volumeconstrained in the loading and shipment processes, and the shipments are affected by the loading and routing plans. Loading and route optimization do not indicate only the unit shipment cost reduction as mostly seen in milk run logistics but also short-term replenishment of white goods especially to the regional distribution centers. The accountability framework of distribution planning enables order management and logistics functions of white goods manufacturers to work closely with planning departments. These functions are also in a close coordination with third party logistics companies such as transporters, outsourced warehousing companies and customs offices in terms of international business. In addition to the order confirmation and allocation information which are outcomes of the reconciliation process, customers will now be able to see the executed shipments prior to loading and will have the visibility of real-time tracking both of which will enable reduction of order-to-delivery lead times. Matching digital transformation building blocks with S&OP processes is given in Table 2.

• • • • • • •

• • • •

• • • •

Demand forecasting

Consensus process

Inventory planning

• NPI information

Digital platform

Lead times Historic sales Forecasts Seasonality

Sales budget Corporate promotions Sales force insight Backorders • Item clustering • Forecast • Differentiated decoupling points • Differentiated inventory management policies

• Forecast outputs • Hosting inputs from various stakeholders • Root cause investigation

Historic sales • Forecasting algorithms Product transitions • Breakdown algorithms Product hierarchies Finished goods inventory Corporate promotions Calendar features List prices

• NPI information • Predecessor-successor match • Launch dates • End-of-lie dates

Product review

Operational backbone

• Multi-echelon and/or channel inventory

• Channel promotions

• POS data and real-time demand signals • Eco-system related external data • Channel or customer inventory

• Feedback on NPI and dates from stakeholders and customers

External developer platform

Table 2 Matching digital transformation building blocks with S&OP processes

• Inventory strategy based on segmentation • Proactive planning for obsolete inventory and stock-outs

• Planning priorities • Shortened planning cycles

• Increased service level • Faster inventory turns

• Innovation and supply chain alignment

Shared customer insights

(continued)

• Inventory accountability

• Orchestration of consensus process • Backorder management

• Data handling • Integration of external data • Ownership of forecasting process • Bias and error tracking • Feedback loops

• Tracking NPI and date adherence

Accountability framework

Redesign, Smart and Digital Enablement of Sales and Operations … 235

• Allocation information • Distribution requirements • Route optimization • Loading optimization • Real-time production tracking

• • • • •

Distribution planning

Sales orders Forecast Safety stock Production schedule On-hand inventory

• Netted demand with type • Scenario generation, visibility optimization and/or simulation • Back orders • Available supply information • Financial data (costs, prices, profitability)

Demand & Supply reconciliation

• Optimized production plans

Digital platform

• Production capacity information • Routes • Material and WIP inventory • Procurement lead times • BOM

Operational backbone

Supply planning

Table 2 (continued)

• Real-time shipment tracking • Transit inventory

• Eco-system data affecting scenarios

• Supplier integration • Supplier capacity information

External developer platform

• Coordination of planning, production and logistics

Accountability framework

• Advance shipment information • Reliable and real-time order-status tracking • Reduces order-to-delivery lead time

• Replenishment process accountability • Integration and coordination with external stakeholders (e.g. 3PL)

• Shared and mutually • Avoidance of agreed allocation cross-functional and • Reliable and accurate cross-department order confirmation based conflicts on plans

•

Shared customer insights

236 B. Kandemir et al.

Redesign, Smart and Digital Enablement of Sales and Operations …

237

4 Conclusion Dynamics of evolution from S&OP data analysis could provide useful suggestions and advices to companies that intend to undertake the challenge of improving their S&OP process, by explaining how they could act on S&OP dimensions over time in a transition. Thus, a logical roadmap for digital transformation is required to mitigate and manage uncertainty, complexity, and risk within your S&OP process. White good manufacturing industry that has to develop direct-to-consumer channels due to the shift to online sales has to redesign their S&OP process and transform it to a digital form. The aim of this chapter is to introduce the digital transformation roadmap and match the digital transformation building blocks with S&OP processes in white good manufacturing processes. The research findings indicate the importance of integration of external data into S&OP and applications of big data in S&OP. In the period after this chapter, the authors are planning to digitize the processes of a white goods manufacturer in Turkey and bring a real application to the literature. Further research should also investigate the different decision levels of S&OP in terms of digitalization.

References 1. Tuomikangas N, Kaipia R (2014) A coordination framework for sales and operations planning (S&OP): synthesis from the literature. Int J Prod Econ 154:243–262 2. Holmstrom J, Holweg M, Lawson B, Pil FK, Wagner SM (2019) The digitalization of operations and supply chain management: theoretical and methodological implications. J Oper Manag 65:728–734 3. Jonsson P, Kaipia R, Barratt M (2021) Guest editorial: the future of S&OP dynamic complexity, ecosystems and resilience. Int J Phys Distrib Logist Manag 51(6):553–565 4. Schlegel GL, Murray P (2010) Next generation of S&OP: scenario planning with predictive analytics & digital modeling. J Bus Forecast 29(3):20–23 5. Düzgün B, Bayındır R, Aydınalp Köksal M (2021) Estimation of large household appliances stock in the residential sector and forecasting of stock electricity consumption: ex-post and ex-ante analyses. Gazi Univ J Sci Part C Des Technol 9(2):182–199 6. Erdil E: The strategic studies for digital transformation in Turkey. https://open.metu.edu.tr/bit stream/handle/11511/92047/tekpol-newsletter-07-2021b.pdf. Accessed 31 Mar 2022 7. Ross JW, Mocker M, Beath CM (2018) Five building blocks of digital transformation. In: MIT Sloan Center for Information Systems Research 8. Winkler TJ, Kettunen P (2018) Five principles of industrialized transformation for successfully building an operational backbone. MIS Q Exec 17(2):123–140 9. Hajishirzi R, Costa CJ (2021) Artificial Intelligence as the core technology for the digital transformation process. In: 16th Iberian Conference on Information Systems and Technologies, pp 1–6 10. Awawdeh H, Abulaila H, Alshanty A, Alzoubi A (2022) Digital entrepreneurship and its impact on digital supply chains: the mediating role of business intelligence applications. Int J Data Netw Sci 6(1):233–242 11. Rasool F, Greco M, Grimaldi M (2022) Digital supply chain performance metrics: a literature review. Meas Bus Excell 26(1):23–38 12. Choudhury A, Behl A, Sheorey PA, Pal A (2021) Digital supply chain to unlock new agility: a TISM approach. Benchmarking Int J 28(6):2075–2109

238

B. Kandemir et al.

13. Dos Santos CH, Gabriel GT, Do Amaral JVS (2021) Decision-making in a fast fashion company in the Industry 4.0 era: a digital Twin proposal to support operational planning. Int J Adv Manuf Technol 116:1653–1666 14. Nicolas FNP, Thomé AMT, Hellingrath B (2021) Usage of information technology and business analytics within sales and operations planning: a systematic literature review. Braz J Oper Prod Manag 18(3):e20211152 15. Lee H (2002) Aligning supply chain strategies with product uncertainties. Calif Manag Rev 44:105–119 16. Lee H, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558 17. Rossetti MD, Achlerkar AV (2011) Evaluation of segmentation techniques for inventory management in large scale multi-item inventory systems. Int J Logist Syst Manag 8(4):403–424

Health

Cybersecurity Framework Prioritization for Healthcare Organizations Using a Novel Interval-Valued Pythagorean Fuzzy CRITIC Hatice Camgöz Akda˘g and Akın Menek¸se

Abstract Cybersecurity is the discipline of defending systems, networks, and programs against digital assaults intended to gain access to, alter, or delete sensitive data, or disrupt regular business activities. With the transformation of digitalization, information on the internet becomes vulnerable to cyber attacks, and healthcare organizations have a critical importance in this regard. Digital healthcare technology is widely used across the world, and the security of healthcare data and equipment is a growing problem since medical equipment has been exposed to new cybersecurity risks as its access to current computer networks has increased. However, the cybersecurity frameworks offered provide a generic framework for all organizations, and prioritizing the categories within the framework for the healthcare organization is critical in terms of developing an effective security policy. In this study, an internationally accepted cybersecurity framework is evaluated by health experts, and the framework is prioritized for the use by healthcare organizations. Since such a task is carried out on linguistic expressions and experts may be uncertain about some of the categories, there is a need for a model that both converts linguistic expressions into numerical measurable form while comprehensively addressing the vagueness. For this purpose, a novel interval-valued Pythagorean fuzzy CRiteria Importance Through Intercriteria Correlation (CRITIC) method has been developed for ranking the categories within each domain of the National Institute for Standards and Technology (NIST) cybersecurity framework for the use of healthcare organizations. A sensitivity analysis, theoretical and practical consequences, and future research recommendations are also provided within the study.

H. Camgöz Akda˘g (B) · A. Menek¸se Istanbul Technical University, 34467 Istanbul, Turkey e-mail: [email protected] A. Menek¸se e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_11

241

242

H. Camgöz Akda˘g and A. Menek¸se

1 Introduction Digital transformation is undergoing a period of robust development, playing a crucial role in the growth of public and commercial businesses, and is at the forefront of discussions due to the recent surge in digital technology use [17]. On the other hand, the digital transition has confronted organizations and businesses with several cybersecurity issues, e.g., cyberattacks that pose the greatest dangers to computers and communication networks by destroying equipment and stealing important data [1]. With the increasing number of cyber risks, cybersecurity policies and frameworks to strengthen organizations’ resilience to cyber risks have become part of the agendas of almost every organization, including healthcare organizations. Healthcare is a universal need that impacts every member of society. Healthcare organizations are charged with the responsibility of gathering and preserving extremely sensitive and secret data while also being compelled to share it with medical personnel, patients, and other organizations. As a result of the development of new technology and digital possibilities, healthcare is continuously developing toward digitization. That represents an excellent opportunity, but it also exposes healthcare organizations to a variety of digital risks that might result in an attacker jeopardizing the security of medical procedures and, perhaps, patient safety. Thus, the importance of cyber risk and cybersecurity concerns has also grown in the healthcare industry, just as it has in other areas. Technological cybersecurity remedies are utilized to safeguard the privacy, security, and accessibility of data and information systems in healthcare organizations. With the growing value of sensitive health information and the expanding accessibility to digitalized personal health data, cybersecurity concerns in the health industry rise globally [27]. Due to a variety of malicious assaults on hospitals and other critical components of the healthcare infrastructure in recent years, healthcare cybersecurity has turned into a significant topic [22]. After the rapid hit of the COVID-19 pandemic, healthcare cybersecurity has grown more critical with the boosted use of digital technology in healthcare services worldwide, while also encouraging hackers to attack healthcare organizations in larger numbers [26]. Moreover, these kinds of attacks put patients’ identities and finances at risk, slow down hospital operations and put patients’ health and well-being at risk. In this setting, organizations need a framework to better comprehend, manage, and mitigate their cybersecurity risk, as well as safeguard their networks and data. The National Institute of Standards and Technology (NIST) has released a framework to help firms in critical infrastructure sectors decrease the risk of cybersecurity breaches [31]. NIST aims to aid cybersecurity risk management inside enterprises and has quickly gained widespread acceptance. It establishes a standard form of language for communicating cybersecurity risks, is aimed at global adoption by public and private sector enterprises, and handles cybersecurity risks cost-effectively [36]. NIST has an inclusive and adaptable structure and helps organizations in managing cybersecurity risks. It has an organized and planned nature, which makes it simpler for organizations to apply it at the enterprise level than other standards. It also gives a

Cybersecurity Framework Prioritization for Healthcare Organizations ...

243

high-level picture of how corporations should handle cybersecurity risk management, leaving implementation specifics to individual organizations [13]. This standardized style of NIST may also be regarded as more user-pleasing and simplified, which is particularly important for upper-level executives [30]. This framework separates all cyber-risk-related tasks into five domains, which are: identify, protect, detect, respond, and recover. Based on these five domains, security issues can be organized in a way that makes it easier to put controls in place. Since every organization has unique cybersecurity risks, each one is expected to draw up a cybersecurity roadmap according to its own priorities. As a basis for such a study, the NIST cybersecurity framework is an internationally accepted and reliable framework used in many areas of the world. Individualization of this framework for a particular organization can be accomplished by prioritizing the categories inside each domain of the NIST framework. Prioritizing the categories in the framework based on how important they are to the organization helps create a good cybersecurity policy and manage the resources that will be used to fight cybersecurity risks in an efficient and cost-effective way. CRITIC is a technique for calculating the weights of criteria in an objective manner [9]. Weights are calculated based on the contrast intensity of each criterion and the conflict between them. Criteria may be considered as sources of information, and their significance weights may represent the quantity of information contained in each. In this context, the CRITIC method is a good way to figure out how to put categories in the NIST framework in order of importance. On the other hand, the evaluation of the categories here can usually be done through linguistic expressions. The NIST framework’s categories are usually qualitative, and experts may be uncertain about their comparability and can only assess them in a verbal manner. At this stage, fuzzy systems offer appropriate ways of converting expert language judgments to numerical values and dealing with uncertainty. Pythagorean fuzzy sets are one of the extensions of ordinary fuzzy sets that provide a large area for experts to model membership and non-membership degrees. In classic CRITIC approaches, neglecting to account for the problem’s uncertainty may have an impact on the final rankings. Furthermore, existing fuzzy CRITIC approaches (e.g., intuitionistic and Pythagorean fuzzy CRITIC) may discard some information while modeling the fuzziness because there may be issues where more space is required to model the uncertainty or the decision-maker is unsure of the membership and non-membership degrees. In such situations, interval-type parameters might be utilized instead of a single point. Interval-valued Pythagorean fuzzy sets, have a greater capability for fuzziness modeling since their domain has an interval type of character rather than a single point. Cybersecurity in the health field is unique due to the type of information at risk and the consequences for patient safety. A wide range of security vulnerabilities, including human mistakes, hostile or criminal acts, and system failures, may occur in the healthcare industry. Although there is an increased risk of cyberattacks on patient care and healthcare delivery, the health industry has fallen behind other businesses in terms of cybersecurity. Moreover, there is no sufficient cybersecurity framework that takes the health sector’s particular requirements into account. Although the

244

H. Camgöz Akda˘g and A. Menek¸se

healthcare industry has security issues regarding patient information, cybersecurity in the healthcare sector has received little attention so far [7]. There are currently no healthcare-specific cybersecurity guidelines in place, and none are implemented on a regular or consistent basis. In this context, it is clear that there is a need for a prioritized cybersecurity framework for the health sector, and NIST is a framework that is internationally accepted and can be revised on a sector-based basis. The major objective of this study is to fill the gaps mentioned above and to provide a new method for prioritizing the NIST framework for the healthcare industry. This study’s originality stems from the presentation of CRITIC in an IVPF setting and the prioritization of the NIST cybersecurity framework for the healthcare business. The rest of the chapter is organized as follows: The literature review is provided in Sect. 2. The methodology is given in Sect. 3. In this context, first of all, the CRITIC method, which is the backbone of the methodology, is introduced, and then the basic operations of the interval-valued Pythagorean fuzzy sets, which undertake the fuzziness modeling of the presented methodology, are presented. Finally, the interval-valued Pythagorean fuzzy CRITIC, which is recommended to be used within the scope of prioritization of the NIST framework, is established. In Sect. 4, the application is presented as follows: First, explanations of the NIST cybersecurity framework’s domains and categories are provided. Then the numerical solution, which rearranges the NIST framework for the healthcare organizations, is presented. Finally, a sensitivity analysis is given. Section 5 summarizes the chapter and provides theoretical and practical implications as well as limitations and future research avenues.

2 Literature Review Cybersecurity has been the subject of a variety of studies, and this section covers some of the most relevant cybersecurity studies centered on the healthcare industry, as well as state-of-the-art cybersecurity-related MCDM works applied to a variety of industries. It is intended to shed light on the literature to emphasize the study’s objective and motivation, both in the thematic and methodological sense. The primary research focusing on cybersecurity for healthcare organizations are as follows: Gupta et al. [16] proposed a hierarchical deep learning strategy for multicloud healthcare system cybersecurity, and Nunes et al. [25] conducted an analysis of attitudes and practices about cybersecurity in healthcare organizations. Zaki et al. [37] proposed a framework for cybersecurity in the healthcare industry based on next generation firewall. On the other hand, Ali and Alyounis [4] analyzed cybersecurity in the healthcare system and determined ways to improve healthcare infrastructure cybersecurity. Moreover, Rachh [29] examined the opportunities and limitations within the digital healthcare market. The prominent MCDM Studies within the scope of cybersecurity can be summarized as follows: Moreira et al. [23] investigated how cybersecurity controls for NIST’s critical infrastructure will interact with the banking industry using an MCDM method. On the other hand, Ning et al. [24] proposed an assessment system based

Cybersecurity Framework Prioritization for Healthcare Organizations ...

245

on NIST that aims to select suitable cryptographic ciphers. The authors used the CRITIC and TOPSIS methodologies in their work. Alghassab [3], on the other hand, presented a study for industrial control systems and established that the hesitant fuzzy AHP and TOPSIS are practical methodologies for predicting cybersecurity evaluations by analyzing the various features and their effects on cybersecurity systems, respectively. Alenezi [2] presented an approach for choosing security protections for personal computing on a daily basis, and demonstrated how the use of MCDM methodologies may aid in addressing this challenge through a variety of situations. Gourisetti et al. [14] discussed the use of several rankweighting algorithms to improve cybersecurity vulnerability analysis. The authors illustrated the distinctions between multiple methodologies for a blockchain cybersecurity architecture. Kumar et al. [21] developed a triangular fuzzy AHP TOPSIS model for cybersecurity professionals and assessed the numerous elements that contribute to healthcare cyberattacks. Based on the BWM approach, Tusher et al. [34] presented a framework for analyzing cybersecurity risk in the context of autonomous shipping. Torbacki [33] employs a combination of DEMATEL ANP and PROMETHEE II techniques to rank the suggested groupings of measurements, dimensions, and criteria for use in enterprises to ensure cybersecurity in Industry 4.0 and to facilitate the adoption of sustainable production fundamentals. Moreover, Erdogan et al. [10] developed a framework based on hesitant fuzzy sets for evaluating alternatives and criteria to select the most appropriate cybersecurity system, and Bhol et al. [6] contrasted the AHP and Electre III approaches for evaluating cybersecurity metrics used to determine an institution’s cybersecurity trustworthiness.

3 Methodology In this section, the methodology is presented as follows: In Sect. 3.1, the steps of classical CRITIC, which is the backbone of the proposed methodology, are given. In Sect. 3.2, definition and necessary mathematical operations of intervalvalued Pythagorean fuzzy sets are presented. Section 3.3, establishes the proposed methodology.

3.1 Traditional CRITIC Methodology The steps of the classical CRITIC method are as follows: The decision-makers evaluate the alternatives with respect to the criteria, and the decision matrix M is obtained as given in Eq. 1.

246

H. Camgöz Akda˘g and A. Menek¸se

⎡

Mm×n

r11 ⎢ r21 =⎢ ⎣ ... rm1

r12 r22 ... rm2

⎤ ... r1n ... r2n ⎥ ⎥ ⎦ ... ... rmn

(1)

where i = 1, 2, ..., m are the number of alternatives and j = 1, 2, ..., n are the number of criteria, and ri j is the evaluation of ith alternative with respect to jth criterion. The decision matrix is normalized by utilizing Eqs. 2 and 3 for positive and negative attributes respectively. xi j =

ri j − ri− , i = 1, 2, ..., m; j = 1, 2, ..., n ri+ − ri−

(2)

xi j =

ri j − ri+ , i = 1, 2, ..., m; j = 1, 2, ..., n ri− − ri+

(3)

where xi j is the normalized value of ri j and ri+ = max(r1 , r2 , ..., rm ) and ri− = min(r1 , r2 , ..., rm ). The correlation coefficient ρ jk between each attribute pair is calculated by utilizing Eq. 4. ∑m (xi j − x¯ j )(xik − x¯ k ) ρ jk = /∑ i=1 (4) ∑m m ¯ j )2 i=1 (xik − x¯ k )2 i=1 (x i j − x where x¯ j and x¯ k are the mean values of jth and kth attributes, and x¯ j is obtained by utilizing Eq. 5. x¯ k is also obtained in the same way. n 1∑ xi j , i = 1, 2, ..., m x¯ j = n j=1

The standard deviation σ j of each criterion is calculated as given in Eq. 6. ⎡ | n | 1 ∑ (xi j − x¯ j )2 ), i = 1, 2, ..., m σj = | n − 1 j=1

(5)

(6)

The C index of each criterion is calculated as given in Eq. 7. Cj = σj

n ∑ (1 − ρ jk ), j = 1, 2, ..., n k=1

Criterion weights w j are obtained as given in Eq. 8.

(7)

Cybersecurity Framework Prioritization for Healthcare Organizations ...

Cj w j = ∑n j=1

247

(8)

Cj

3.2 Interval-Valued Pythagorean Fuzzy Operators Definition and basic operations of interval-valued Pythagorean fuzzy sets are as follows: A Pythagorean fuzzy set A˜S of the universe of discourse ∪ is given in Eq. 9. A˜P = {u, μ A˜P (u), ν A˜P (u), | u ∈ ∪} where μ A˜S (u) : ∪ → [0, 1],

(9)

ν A˜S (u) : ∪ → [0, 1], and

0 ≤ μ2A˜ (u) + ν 2A˜ (u) ≤ 1 | ∀u ∈ ∪ P

P

(10)

where μ A˜S (u) and ν A˜S (u) are the degrees of membership and non-membership to A˜P . ~P of the universe of discourse U is An interval-valued Pythagoren fuzzy set A given in Eq. 11. { } ~P = u, ([μ L~ (u), μU~ (u)], [ν L~ (u), ν U˜ (u)] | u ∈ U (11) A AP AP AP A P

where 0 ≤ μ LA~P (u) ≤ μUA~P (u) ≤ 1; 0 ≤ ν AL~P (u) ≤ ν UA~P (u) ≤ 1 where μ LA~ (u), ν AL~ (u) are the lower degrees of membership and non-membership P P degrees, and μUA~ (u), ν UA~ (u) are the upper degrees of membership and nonP P membership degrees. The hesitancy degree of an interval-valued Pythagorean fuzzy set is calculated as in Eqs. 12 and 13. (π AU~P (u))2 = (μ LA~P (u))2 + (ν AL~P (u))2

(12)

L (u))2 = (μUA~P (u))2 + (ν UA~P (u))2 (π A~ P

(13)

For convenience, an interval-valued Pythagorean fuzzy set, ~P = ([μ L~ (u), μU~ (u)], [ν L~ (u), ν U~ (u)]) A AP AP AP AP

248

H. Camgöz Akda˘g and A. Menek¸se

is denoted by ~ α = [a, b], [c, d] where a and c stand for the lower degrees of membership and non-membership degrees, and b and d stand for the upper degrees of membership and non-membership degrees respectively; [a, b] ⊂ [0.1] and [c, d] ⊂ [0.1]. ~ Mw for interval-valued Pythagorean fuzzy numbers Geometric mean operator G are defined as follows [12]: Let α~j = be a collection of interval-valued Pythagorean fuzzy weighted geometric mean with respect to w j = (w1 , w2 , ..., wn ), w j ∈ [0.1] and ∑n w = 1. j j=1 w

n ~ G Mw (α~1 , α~2 , ..., α~n ) = α~1 w1 ⊗ α~2 w2 ⊗ .... ⊗ α~n = n n n {∏ ∏ ∏ w w b j j ], [(1 − (1 − c2j )w j )1/2 , [ aj j,

j=1

j=1

j=1

(1 −

n ∏

(14)

}

(1 − d 2j )w j )1/2 ]

j=1

Defuzzification operator i.e. score function S(~ α ) of interval-valued Pythagorean fuzzy numbers [11] are defined as follows: S(~ α) =

a 2 + b2 − c2 − d 2 2

(15)

The normalized Euclidean distance D(α~1 , α~2 ) between two interval-valued Pythagorean fuzzy numbers (α~1 , α~2 ) [20] is given in Eq. 16. D(α~1 , α~2 ) =

√ √ 2 √ [ ((a1 − a2 )2 + (b1 − b2 )2 ) + ((c1 − c2 )2 + (d1 − d2 )2 )] 4 (16)

3.3 Proposed Interval-Valued Pythagorean Fuzzy CRITIC The decision experts evaluate the alternatives with respect to the criteria by utilizing the linguistic terms that is given in Table 1. The linguistic evaluations of each decision-maker are transformed to intervalvalued Pythagorean fuzzy numbers in a matrix form, and all matrices are aggregated to obtain one unique collective matrix by utilizing the interval-valued Pythagorean fuzzy weighted geometric mean operator that is given in Eq. 14. This matrix can be named as average solution matrix AS M. The structure of the AS M is given in Eq. 17.

Cybersecurity Framework Prioritization for Healthcare Organizations ...

249

Table 1 Linguistic terms and corresponding interval valued Pythagorean fuzzy numbers [18] Linguistic term Abbreviation ([μ L , μU ], [(ν L ), (νU )] CLI VLI LI MLI HI VHI CHI

Certainly low importance Very low importance Low importance Medium level importance High importance Very high importance Certainly high importance

([0.10,0.30],[0.70,0.90]) ([0.20,0.40],[0.60,0.80]) ([0.30,0.50],[0.50,0.70]) ([0.40,0.60],[0.40,0.60]) ([0.50,0.70],[0.30,0.50]) ([0.60,0.80],[0.20,0.40]) ([0.70,0.90],[0.10,0.30])

⎤ ([a11 , b11 ], [c11 , d11 ]) ... ([a1n , b1n ], [c1n , d1n ]]) ⎢ ([a21 , b21 ], [c21 , d21 ]) ... ([a2n , b2n ], [c2n , d2n ]) ⎥ ⎥ ~ AS M = (C j (Ai ))mxn = ⎢ ⎦ ⎣ ... ... ... ([am1 , bm1 ], [cm1 , dm1 ]) ... ([amn , bmn ], [cmn , dmn ]) (17) where i = 1, 2, ..., m are the number of alternatives and j = 1, 2, ..., n are the number of criteria, and ri j is the evaluation of ith alternative with respect to jth criterion. The decision matrix is normalized by utilizing Eqs. 2 and 3 for positive and negative attributes, respectively. In the numerator and denominator of Eqs. 2 and 3, the normalized Euclidean distance between two interval-valued Pythagorean fuzzy numbers that is given in Eq. 16 is utilized. The correlation coefficient and standard deviation of each criteria is obtained by utilizing Eqs. 4 and 5. The C index and criterion weights w j are calculated as in Eqs. 7 and 8. ⎡

4 Application In this section, the application of the proposed methodology is presented as follows: In Sect. 4.1, the architecture of the NIST cybersecurity framework is given and the meanings of domains and categories are explained. In Sect. 4.2, a numerical solution for the proposed interval-valued Pythagorean fuzzy CRITIC is provided, and the NIST framework is reorganized for a healthcare organization. In Sect. 4.3, analyses the stability of the results is analysed through a sensitivity analysis.

250

H. Camgöz Akda˘g and A. Menek¸se

Fig. 1 NIST cybersecurity framework

4.1 NIST Cybersecurity Framework The architecture (Fig. 1) of the NIST cybersecurity framework and the explanations of each domain and category are given below. Identify Domain This module fosters organizational awareness of security hazards to handle them effectively. It aims to include processes, employees, resources, and assets. The identify domain includes asset management, corporate environment, governance, risk assessment, and risk management strategy categories. Asset Management Asset management takes into account cross-domain elements of security, such as the physical location and digital links that connect the hospital’s many systems. The availability of resources and bed occupancy are critical issues to manage in the day-

Cybersecurity Framework Prioritization for Healthcare Organizations ...

251

to-day operations of a hospital. This is especially crucial in emergency scenarios and events such as natural disasters, terrorist attacks, and other dangers [32]. Business Environment Cybersecurity roles and responsibilities, as well as risk management actions, are based on a deep understanding of the institution’s purpose, goals, key stakeholders, and activities. Governance Governance is the key mechanism by which hospitals are accountable for upholding high standards of health care, continuously improving the quality of their services, and developing and sustaining an atmosphere conducive to clinical excellence [28]. Risk Assessment The methods of risk identification, risk analysis, and risk evaluation are all part of risk assessment. The purpose of risk identification is to identify unknown factors and their range of possible consequences for achieving goals. Uncertainties may affect one or more of the organization’s goals in a variety of ways. These consequences might be tangible or intangible, and they can set off a chain reaction [15]. Risk Management Strategy Establishing and using the institution’s goals, restrictions, risk tolerances, and expectations to assist in institutional risk decision-making. Protect This function is responsible for establishing and executing the safeguards required to guarantee the continued operation of services. It aims to minimize cybersecurity incidents. The protect domain includes access control, awareness and training, data security, information protection processes, maintenance and protective technology categories. Access Control It is necessary to restrict access to physical and logical resources, as well as related facilities, to only authorized people, business processes, and devices. This restriction is in accordance with the evaluated risk of unauthorized access to approved events and operations. Awareness and Training Information security awareness training is offered to the organization’s people and partners, and they are appropriately prepared to fulfill their cybersecurity-related tasks and obligations in accordance with the institution’s objectives, procedures, and policies. Data Security Data security is critical when exchanging information via a public network, because outsiders may abuse or change the information. The regular transmission of data

252

H. Camgöz Akda˘g and A. Menek¸se

across a wireless channel may be disrupted by a variety of assaults. Healthcare data security is a critical concern due to the risk of active and passive threats [19]. Information Protection Processes Establishment and employment of security policies, protocols, and practices to oversee the security of data systems and applications. Maintanance Maintenance is the process of performing maintenance and adjustments in accordance with established rules and procedures. Detect This role is responsible for formulating the procedures required to detect the presence of a cybersecurity incident. This domain includes the categories of anomalies and events, security continuous monitoring, and detection processes. Anamolies and Events The detection of anomalous activity and the comprehension of the possible consequences of occurrences. Security Continuous Monitoring The purpose of security continuous monitoring is to constantly monitor the security of an institution’s networks, information, and systems and to adapt to changing circumstances by accepting, avoiding, transferring, or reducing risk [5]. Detection Processes Conducting processes to offer knowledge of unusual cybersecurity activities. Respond Identifying and executing suitable steps in response to the detection of a cybersecurity event. The respond domain includes response planning, communication, analysis, mitigation, and improvements categories. Response Planning Providing a prompt reaction to suspected cybersecurity issues, response protocols, and procedures are implemented and maintained. Communications Managing communications with stakeholders, law enforcement, and other external parties as needed during and after an incident is essential. Analysis Investigation is carried out to enable effective response and assist recovery actions, which may include forensic analysis and identifying the impact of events on a community or organization.

Cybersecurity Framework Prioritization for Healthcare Organizations ...

253

Mitigation Carrying out activities to avoid the spread of an occurrence, reduce its consequences, and settle the situation. Improvements Implementation of improvements by taking into account lessons learned from current and prior detection and response actions. Recover This role is responsible for carrying out operations and maintaining resilience plans to restore any service that has been rendered inoperable as a result of a cybersecurity event. The categories of this domain are recovery planning, improvements, and communication. Recovery Planning The formulation, implementation, and management of methods for successfully restoring and recovering any compromised capabilities or services as a consequence of a cyber-attack are all part of recovery planning. Implementation and maintenance of recovery protocols and procedures to guarantee that systems or assets that have been compromised by cybersecurity events are restored as soon as possible [8]. Improvements Improvements are being implemented as a result of experiences gained and evaluations of current strategies [8]. Communications In hospitals, communication between various systems is established via the use of wired and wireless connections [35], and during and after a cybersecurity event, local and global information are synchronized [8].

4.2 Numerical Solution: Reorganization of the NIST Cybersecurity Framework In this subsection, the problem of reorganizing the NIST cybersecurity framework for the health sector is discussed through the proposed interval-valued Pythagorean fuzzy CRITIC, and a numerical solution is presented. Within the context of NIST, four distinct healthcare organizations are examined, and each of these organizations is assessed independently with respect to five domains. The research includes three separate expert groups having equal weights. The evaluation of each expert group is presented as a separate case.

254

H. Camgöz Akda˘g and A. Menek¸se

Table 2 Linguistic evaluations of healthcare organizations (A, B, C and D) for the “Identify” domain Category A B C D Case Case 1

Case 2

Case 3

Asset management Business environment Governance Risk assessment Risk management strategy Asset management Business environment Governance Risk assessment Risk management strategy Asset management Business environment Governance Risk assessment Risk management strategy

MLI MLI MLI LI MLI

LI CLI VHI CLI VHI

VLI LI VLI CHI HI

CLI VHI LI VHI VHI

VLI CHI CHI CLI MLI

CHI VLI MLI LI VHI

CLI VLI CLI MLI CHI

VLI MLI CHI LI VHI

CHI VLI MLI VLI VHI

LI LI VLI CLI LI

CLI LI VHI CHI CHI

VLI CHI CHI CLI LI

Numerical Solution for the “Identify” Domain Four different healthcare organizations are evaluated with respect to five categories in the “Identify” domain of NIST cybersecurity framework as in Table 2. The judgments of three expert groups are denoted in this table as case 1, case 2, and case 3. In the “Asset Management” category, for example, organization “A” is rated as MLI in the first case, VLI in the second, and CHI in the third. Table 3 shows the aggregated and quantified forms of the linguistic expressions filled by three different expert groups. First, each linguistic expression is converted to its corresponding interval-valued Pythagorean fuzzy numbers, and then the fuzzy numbers from all three cases are aggregated by utilizing the geometrice aggregation operator defined for interval-valued Pythagorean fuzzy sets as given in Eq. 14. The standard deviation, C indices, criterion weights, and ranking of categories for the “Identify” domain of the NIST cybersecuity framework are shown in Table 4. Since “Asset Management” is the most crucial category, healthcare organizations may concentrate their fundamental cybersecurity activities around it. The second important category is obtained as “Risk Management Strategy”, and the third one is “Governance”, and so on. Numerical Solution for the “Protect” Domain Linguistic evaluations of three expert groups with respect to categories in the “Protect” domain are given in Table 5.

Cybersecurity Framework Prioritization for Healthcare Organizations ... Table 3 Average solution for the “Identify” domain Category A B Asset management

([0.38, 0.60], [0.44, 0.64]) Business ([0.38, 0.60], environment [0.44, 0.64]) Governance ([0.48, 0.69], [0.34, 0.53]) Risk assessment ([0.18, 0.39], [0.61, 0.82]) Risk management ([0.46, 0.66], strategy [0.55, 0.55])

[0.40, 0.61], [0.42, 0.62]) ([0.18, 0.39], [0.61, 0.82]) ([0.35, 0.58], [0.44, 0.65]) ([0.14, 0.36], [0.65, 0.86]) ([0.48, 0.68], [0.34, 0.54])

255

C

D

([0.13, 0.33], [0.67, 0.87]) ([0.26, 0.46], [0.54, 0.74]) ([0.23, 0.46], [0.57, 0.78]) ([0.58, 0.79], [0.25, 0.44]) ([0.63, 0.83] [0.19, 0.38])

([0.16, 0.36], [0.64, 0.84]) ([0.55, 0.76], [0.27, 0.46]) ([0.53, 0.74], [0.31, 0.50]) ([0.26, 0.49], [0.53, 0.75]) ([0.48, 0.68], [0.34, 0.54])

Table 4 Weights and importance orders of the categories of the “Identify” domain Category Standard C index Weight Rank deviation Asset management Business environment Governance Risk assessment Risk management strategy

0.501

2.791

0.266

1

0.426

1.633

0.156

5

0.446 0.430 0.420

2.005 1.918 2.155

0.191 0.183 0.205

3 4 2

Average solution for the “Protect” domain is obtained as in Table 6. The standard deviation, C indices, category weights, and ranking of categories for the “Protect” domain are evaluated as shown in Table 7. For the “Protect” domain of NIST, “Information Protection Processe” category has the highest weight the it can be considered as the most critical category. Numerical solution for the “Detect” domain Linguistic evaluations of the categories in the “Detect” domain are obtained as in Table 8. Linguistic evaluations obtained in Table 8 are converted to interval-valued Pythagorean fuzzy numbers and aggregated as in Table 9. The standard deviation, C indices, category weights, and ranking of categories for the “Detect” domain of are shown in Table 10. “Improvements” is the most crucial category with the highest weight, healthcare organizations should pay attention to allocate resources to this category in their cybersecurity planning.

256

H. Camgöz Akda˘g and A. Menek¸se

Table 5 Linguistic evaluations of healthcare organizations (A, B, C and D) for the “Protect” domain Case

Category

A

B

C

D

Case 1

Access control Awareness and training Data security Information protection processes Maintenance Protective technology Access control Awareness and training Data security Information protection processes Maintenance Protective technology Access control Awareness and training Data security Information protection processes Maintenance Protective technology

CLI CHI CHI CHI

LI VHI HI LI

CHI VLI LI VLI

CHI VLI VHI HI

LI LI CHI LI HI MLI

VLI VHI CHI VLI LI CLI

VHI HI LI VLI VHI CLI

LI CLI CLI MLI CHI HI

VLI MLI VHI CHI CHI MLI

LI CHI CHI CLI VLI CLI

CHI LI MLI CLI VHI LI

VLI CLI LI LI LI HI

CLI LI

LI VHI

VHI CLI

MLI LI

Case 2

Case 3

Table 6 Average solution for the “Protect” domain Category A B Access control Awareness and training Data security Information protection processes Maintenance Protective technology

C

D

([0.35, 0.60], [0.46, 0.69]) ([0.53, 0.74], [0.31, 0.50]) ([0.63, 0.83], [0.19, 0.38]) ([0.48, 0.69], [0.34, 0.53])

([0.53, 0.74], [0.31, 0.50]) ([0.23, 0.46], [0.57, 0.78]) ([0.31, 0.52], [0.49, 0.70]) ([0.14, 0.36], [0.65, 0.86])

([0.44, 0.65], [0.38, 0.58]) ([0.16, 0.36], [0.64, 0.84]) ([0.48, 0.68], [0.34, 0.54]) ([0.18, 0.39], [0.61, 0.82])

([0.28, 0.51], [0.53, 0.74]) ([0.29, 0.49], [0.51, 0.71]) ([0.50, 0.71], [0.33, 0.52]) ([0.50, 0.70], [0.30, 0.50])

([0.18, 0.39], [0.61, 0.82]) ([0.33, 0.53], [0.47, 0.67])

([0.26, 0.46], [0.54, 0.74]) ([0.63, 0.83], [0.17, 0.37])

([0.63, 0.83], [0.17, 0.37]) ([0.25, 0.47], [0.54, 0.76])

([0.29, 0.49], [0.51, 0.71]) ([0.14, 0.36], [0.65, 0.86])

Cybersecurity Framework Prioritization for Healthcare Organizations ...

257

Table 7 Weights and importance orders of the categories of the “Protect” domain Category Standard C index Weight Rank deviation Access control Awareness and training Data security Information protection processes Maintenance Protective technology

0.424 0.412

.430 2.083

0.165 0.143

3 6

0.425 0.506

2.233 2.986

0.150 0.192

5 1

0.418 0.413

2.791 2.347

0.188 0.161

2 4

Table 8 Linguistic evaluations of healthcare organizations (A, B, C and D) for the “Detect” domain Case

Category

A

B

C

D

Case 1

Anamolies and events Security continuous monitoring Detection processes Anamolies and events Security continuous monitoring Detection processes Anamolies and events Security continuous monitoring Detection processes

CLI

VLI

VHI

VLI

LI

CLI

CLI

VLI

VHI

VLI

MLI

MLI

CLI

VLI

CHI

MLI

HI

LI

CLI

CLI

HI

HI

MLI

CHI

CLI

CLI

VHI

VLI

LI

VLI

MLI

CLI

HI

CLI

VHI

CHI

Case 2

Case 3

258

H. Camgöz Akda˘g and A. Menek¸se

Table 9 Average solution for the “Detect” domain Category A B Anamolies and events Security continuous monitoring Detection processes

C

D

([0.10, 0.30], [0.70, 0.90]) ([0.36, 0.56], [0.45, 0.65])

([0.16, 0.36], [0.64, 0.84]) ([0.18, 0.39], [0.61, 0.82])

([0.63, 0.83], [0.17, 0.37]) ([0.16, 0.38], [0.63, 0.85])

([0.25, 0.46], [0.55, 0.75]) ([0.13, 0.33], [0.67, 0.87])

([0.53, 0.73], [0.27, 0.47])

([0.22, 0.44], [0.58, 0.79])

([0.46, 0.66], [0.35, 0.55])

([0.58, 0.79], [0.25, 0.44])

Table 10 Weights and importance orders of the categories of the “Detect” domain Category Standard C index Weight Rank deviation Recovery planning Improvements Communications

0.427

1.023

0.346

2

0.413 0.451

1.060 0.872

0.359 0.295

1 3

Table 11 Linguistic evaluations of healthcare organizations (A, B, C and D) for the “Respond” domain Category A B C D Case Case 1

Case 2

Case 3

Response planning Communications Analysis Mitigation Improvements Response planning Communications Analysis Mitigation Improvements Response planning Communications Analysis Mitigation Improvements

CLI HI VHI CHI CHI HI VHI CHI LI VHI MLI HI LI CHI CHI

VLI CHI CHI VLI VHI MLI CHI CHI CLI CHI CLI VHI MLI VHI CHI

CHI LI HI CLI VHI CHI VHI VHI CLI VHI VHI VHI VHI LI VHI

MLI MLI LI VLI VHI LI VLI VHI LI VHI LI VLI VHI CLI CHI

Numerical solution for the “Respond” domain Linguistic evaluations of the categories in the “Respond” domain is given in Table 11. Average solution for the “Respond” domain is obtained as in Table 12.

Cybersecurity Framework Prioritization for Healthcare Organizations ... Table 12 Average solution for the “Respond” domain Category A B Response planning Communications Analysis Mitigation Improvements

([0.13, 0.33], [0.67, 0.75]) ([0.53, 0.73], [0.27, 0.47]) ([0.50, 0.71], [0.33, 0.52]) ([0.52, 0.73], [0.32, 0.51]) ([0.66, 0.86], [0.14, 0.34])

([0.23, 0.43], [0.57, 0.82]) ([0.66, 0.86], [0.14, 0.34]) ([0.58, 0.78], [0.26, 0.44]) ([0.23, 0.46], [0.56, 0.78]) ([0.67, 0.87], [0.14, 0.33])

259

C

D

([0.59, 0.73], [0.65, 0.39]) ([0.49, 0.60], [0.45, 0.68]) ([0.57, 0.77], [0.24, 0.43]) ([0.15, 0.36], [0.65, 0.86]) ([0.60, 0.80], [0.20, 0.40])

([0.36, 0.56], [0.44, 0.67]) ([0.25, 0.45], [0.55, 0.76]) ([0.49, 0.69], [0.33, 0.53]) ([0.18, 0.39], [0.61, 0.82]) ([0.63, 0.83], [0.17, 0.37])

Table 13 Weights and importance orders of the categories of the “Respond” domain Category Standard C index Weight Rank deviation Response planning Communications Analysis Mitigation Improvements

0.421 0.425 0.454 0.426 0.472

2.51 1.213 1.517 1.683 1.874

0.292 0.137 0.171 0.190 0.211

1 5 4 3 2

The standard deviation, C indices, category weights, and ranking of categories for the “Respond” domain of the NIST cybersecuity framework are shown in Table 13. Since “Response Planning” is the most crucial category, healthcare organizations may concentrate their fundamental cybersecurity activities around it. The second critical category is “Improvement” and the third one is “mitigation”, and so on. Numerical solution for the “Recover” domain Linguistic evaluations of the categories in the “Recover” domain is given in Table 14. Average solution for the “Recover” domain is evaluated as in Table 15. The standard deviation, C indices, category weights, and ranking of categories for the “Recover” domain of the NIST cybersecuity framework are shown in Table 16. The most critical category for the “Recover” domain is identified as “Communications”. In this context, resource planning for cybersecurity research might be directed in this direction.

260

H. Camgöz Akda˘g and A. Menek¸se

Table 14 Linguistic evaluations of healthcare organizations (A, B, C and D) for the “Recover” domain Category A B C D Case Case 1

Case 2

Case 3

Recovery planning Improvements Communcations Recovery planning Improvements Communcations Recovery planning Improvements Communcations

VLI VLI HI VLI HI CHI VLI LI VLI

Table 15 Average solution for the “Recover” domain Category A B Recovery planning Improvements Communications

([0.25, 0.46], [0.54, 0.75]) ([0.32, 0.53], [0.48, 0.69]) ([0.41, 0.63], [0.41, 0.62])

([0.39, 0.59], [0.42, 0.62]) ([0.40, 0.60], [0.41, 0.61]) ([0.40, 0.62], [0.41, 0.61])

HI LI VLI HI MLI HI LI HI VHI

MLI HI MLI MLI VHI LI VHI HI LI

VHI VHI VLI LI MLI CHI VLI VHI CHI

C

D

([0.56, 0.76], [0.26, 0.45]) ([0.53, 0.73], [0.27, 0.47]) ([0.33, 0.53], [0.47, 0.67])

([0.31, 0.52], [0.49, 0.70]) ([0.52, 0.72], [0.29, 0.49]) ([0.48, 0.71], [0.36, 0.56])

Table 16 Weights and importance orders of the categories of the “Recover” domain Category Standard C index Weight Rank deviation Recovery planning Improvements Communications

0.419

0.847

0.291

2

0.471 0.429

0.733 1.334

0.251 0.458

3 1

4.3 Sensitivity Analysis A sensitivity analysis for various weights of expert groups is presented here. Scenario 1 represents the most possible scenario (present scenario). The other nine scenarios are obtained based on the possible changes in the expert group weights. Sensitivity Analysis for the “Identify” Domain The scenarios and corresponding obtained category weights are given in Fig. 2. The asset management category, for example, has a weight of 0.250 in the third scenario. According to sensitivity analysis results of the “Identify” domain, in all expert group weight distribution situations, the weight of the “Asset Management” category

Cybersecurity Framework Prioritization for Healthcare Organizations ...

261

Fig. 2 Sensitivity analysis for the “Identify” domain

Fig. 3 Sensitivity analysis for the “Protect” domain

seems to be the most essential category, whereas the weight of the “Business Environment” category appears to be the least important category in all circumstances. Overall, it can be observed that the relative significance of the various categories is typically maintained. According to these findings, it is possible to conclude that the categories in this domain are consistent under different expert group weight distribution scenarios. Sensitivity Analysis for the “Protect” Domain Figure 3 illustrates the ranking of the categories in the “Protect” domain in relation to various expert group weights. While “Information Protection Processes” is often regarded as the most essential category in these outcomes, it has been observed that “Maintenance” and “Access Control” emerge as the most significant categories in some scenarios. On the other

262

H. Camgöz Akda˘g and A. Menek¸se

Fig. 4 Sensitivity analysis for the “Detect” domain

hand, “Awareness and Training” is determined to be the least important in all situations except the sixth scenario. Although the rankings of this six-category domain may alter across various expert weighting scenarios, in general, it can be claimed that when considered collectively, they provide the user with an opinion on the order of rankings of the categories. Sensitivity Analysis for the “Detect” Domain The ranking of the categories for the “Detect” domain is shown in Fig. 4 in accordance with various expert group weight distribution scenarios. Except for two scenarios, it can be shown that the “Security Continuous Monitoring” category ranks highest, while “Detection Processes” ranks last. The results of the sensitivity analysis show that the category rankings have a consistent pattern. Sensitivity Analysis for the “Respond” Domain The “Respond” domain has five items, and the results of their sensitivity analysis are shown in Fig. 5. As a result, the best and worst categories are determined to be “Response Planning” and “Communications”, respectively. The rankings for the other categories are also consistent, which shows that the user provides important information about the “Identify” domain of the NIST cybersecurity framework. Sensitivity Analysis for the “Recover” Domain The findings of the sensitivity analysis for the “Recover” domain are shown in Fig. 6. Accordingly, the relative significance of the categories within this domain of the NIST framework is maintained throughout all situations. In all scenarios, “communications” is the most important, while “improvements” is the least important.

Cybersecurity Framework Prioritization for Healthcare Organizations ...

263

Fig. 5 Sensitivity analysis for the “Respond” domain

Fig. 6 Sensitivity analysis for the “Recover” domain

5 Conclusion Managing cybersecurity risk has become a vital step in many businesses, including healthcare organizations. Numerous sectors, including health, are now using cybersecurity frameworks in an attempt to strengthen their cyber resilience. These efforts may be prioritized in a variety of ways, based on the organizations’ goals. The purpose of this study is to integrate the many diverse viewpoints on health into a cybersecurity framework and to reorganize NIST’s globally offered general framework. This study accomplishes this goal via the use of the CRITIC method using interval-valued Pythagorean fuzzy sets by establishing a novel methodology. Within the study, four healthcare organizations are evaluated by three expert groups with respect to the categories of each domain of the NIST cybersecurity framework.

264

H. Camgöz Akda˘g and A. Menek¸se

The NIST framework is rearranged for the health sector as a consequence of the study, and the consistency of the results is verified through sensitivity analyses. The main findings of this study can be summarized as follows: • While the standard CRITIC approach is an effective methodology, it cannot address the ambiguity inherent in a problem. This work proposes a novel methodology for dealing with this ambiguity by extending the standard CRITIC technique with interval-valued Pythagorean fuzzy sets. For the first time in the literature, a new interval-valued Pythagorean fuzzy CRITIC method is developed in this work to combine the advantages of the CRITIC approach and the interval type of Pythagorean fuzzy sets. • Due to the introduced interval-valued character, Pythagorean fuzzy sets can now describe the above-mentioned fuzziness in a larger region. • The available cybersecurity frameworks provide a general framework for all types of organizations, and in order to establish an effective security plan for a healthcare organization, it is essential to prioritize the framework’s categories. In this study, health professionals assess a globally recognized NIST cybersecurity framework and prioritize its implementation for healthcare organizations. A sector-oriented NIST framework enables healthcare organizations to better prepare for unanticipated cyber risks and crises. The suggested framework may assist healthcare executives in developing cybersecurity-related strategic plans. • In today’s world, resources might be scarce in many disciplines, including healthcare. The framework proposed in this study contributes to the effective and economic use of resources by focusing on the most critical domains of the NIST framework. • By using the globally established NIST cybersecurity framework, the suggested approach enables cybersecurity planning activity to be carried out in a scientific, methodical, and measurable way. For the limitations and future research avenues, the following can be discussed: • It is suggested that additional fuzzy approaches such as spherical fuzzy sets, Neutrosophic sets, and picture fuzzy sets be used with the CRITIC method. • The NIST framework is rearranged for the health sector in this study using a newly developed methodology. Appraisal scores of categories are reached based on the views of three distinct expert groups in this case. In other studies, extending the number of expert groups may allow for a more comprehensive analysis. • Other areas, such as law, education, and engineering, may leverage the NIST framework to create industry-focused prioritized frameworks. The proposed cybersecurity framework is based on NIST principles, with a particular emphasis on those that pertain to healthcare organizations.

Cybersecurity Framework Prioritization for Healthcare Organizations ...

265

• Along with expert reviews, data analysis is critical for developing a cybersecurity architecture. Numerous approaches from machine learning theory might be applied and improved for this aim. Some researchers may utilize machine learning methods like logistic regression, linear discriminant analysis, and decision trees to create a model that uses actual data. This would be a fascinating and relevant problem for researchers who deal with cybersecurity frameworks.

References 1. Al Obaidan F, Saeed S (2021) Digital transformation and cybersecurity challenges: a study of malware detection using machine learning techniques. In: Handbook of research on advancing cybersecurity for digital transformation. IGI Global, pp 203–226 2. Alenezi M, Agrawal A, Kumar R, Khan RA (2020) Evaluating performance of web application security through a fuzzy based hybrid multi-criteria decision-making approach: Design tactics perspective. IEEE Access 8:25543–25556 3. Alghassab M (2021) Analyzing the impact of cybersecurity on monitoring and control systems in the energy sector. Energies 15(1):218 4. Ali KA, Alyounis S (2021) Cybersecurity in healthcare industry. In: 2021 International conference on information technology (ICIT). IEEE, pp 695–701 5. Alsadhan T, Park JS (2016) Security automation for information security continuous monitoring: research framework. In: 2016 IEEE world congress on services (SERVICES). IEEE, pp 130–131 6. Bhol SG, Mohanty J, Pattnaik PK (2020) Cyber security metrics evaluation using multi-criteria decision-making approach. In: Smart intelligent computing and applications. Springer, Heidelberg, pp 665–675 7. Burke W, Oseni T, Jolfaei A, Gondal I (2019) Cybersecurity indexes for ehealth. In: Proceedings of the Australasian computer science week multiconference, pp 1–8 8. Dempsey KL, Johnson LA, Scholl MA, Stine KM, Jones AC, Orebaugh A, Chawla NS, Johnston R, et al (2011) Information security continuous monitoring (iscm) for federal information systems and organizations 9. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res 22(7):763–770 10. Erdo˘gan M, Kara¸san A, Kaya ˙I, Budak A. Çolak M (2019) A fuzzy based mcdm methodology for risk evaluation of cyber security technologies. In: International conference on intelligent and fuzzy systems. Springer, Heidelberg, pp 1042–1049 11. Garg H (2017) A novel improved accuracy function for interval valued pythagorean fuzzy sets and its applications in the decision-making process. Int J Intell Syst 32(12):1247–1260 12. Garg H (2018) New exponential operational laws and their aggregation operators for intervalvalued pythagorean fuzzy multicriteria decision-making. Int J Intell Syst 33(3):653–683 13. Gordon LA, Loeb MP, Zhou L (2020) Integrating cost-benefit analysis into the nist cybersecurity framework via the gordon-loeb model. J Cybersecur 6(1):tyaa005 14. Gourisetti NG, Mylrea M, Patangia H (2019) Application of rank-weight methods to blockchain cybersecurity vulnerability assessment framework. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE, pp 0206–0213 15. Govender D (2019) The use of the risk management model iso 31000 by private security companies in South Africa. Secur J 32(3):218–235 16. Gupta L, Salman T, Ghubaish A, Unal D, Al-Ali AK, Jain R (2022) Cybersecurity of multi-cloud healthcare systems: a hierarchical deep learning approach. Appl Soft Comput 118:108439

266

H. Camgöz Akda˘g and A. Menek¸se

17. Hai TN, Van QN, Thi Tuyet M (2021) Digital transformation: opportunities and challenges for leaders in the emerging countries in response to covid-19 pandemic. Emerg Sci J 5:21–36 18. Haktanır E, Kahraman C (2019) A novel interval-valued pythagorean fuzzy qfd method and its application to solar photovoltaic technology development. Comput Ind Eng 132:361–372 19. Jabeen T, Ashraf H, Ullah A (2021) A survey on healthcare data security in wireless body area networks. J Ambient Intell Hum Comput 12(10):9841–9854 20. Karasan A, Ilbahar E, Kahraman C (2019) A novel pythagorean fuzzy ahp and its application to landfill site selection problem. Soft Comput 23(21):10953–10968 21. Kumar R, Pandey AK, Baz A, Alhakami H, Alhakami W, Agrawal A, Khan RA (2020) Fuzzybased symmetrical multi-criteria decision-making procedure for evaluating the impact of harmful factors of healthcare information security. Symmetry 12(4):664 22. Loi M, Christen M, Kleine N, Weber K (2019) Cybersecurity in health–disentangling value tensions. J Inf Commun Ethics Soc 23. Moreira FR, Da Silva Filho DA, Nze GDA, de Sousa Júnior RT, Nunes RR (2021) Evaluating the performance of nist’s framework cybersecurity controls through a constructivist multicriteria methodology. IEEE Access 9:129605–129618 24. Ning L, Ali Y, Ke H, Nazir S, Huanli Z (2020) A hybrid MCDM approach of selecting lightweight cryptographic cipher based on iso and nist lightweight cryptography security requirements for internet of health things. IEEE Access 8:220165–220187 25. Nunes P, Antunes M, Silva C (2021) Evaluating cybersecurity attitudes and behaviors in Portuguese healthcare institutions. Procedia Comput Sci 181:173–181 26. O’Brien N, Grass E, Martin G, Durkin M, Darzi A, Ghafur S (2021) Developing a globally applicable cybersecurity framework for healthcare: a delphi consensus study. BMJ Innov 7(1) 27. Offner K, Sitnikova E, Joiner K, MacIntyre C (2020) Towards understanding cybersecurity capability in Australian healthcare organisations: a systematic review of recent trends, threats and mitigation. Intell Natl Secur 35(4):556–585 28. Pragholapati A (2020) Governance in mental health nursing 29. Rachh A (2021) A study of future opportunities and challenges in digital healthcare sector: cyber security vs. crimes in digital healthcare sector. Asia Pac J Health Manag 16(3):7–15 30. Roy PP (2020) A high-level comparison between the nist cyber security framework and the iso 27001 information security standard. In: 2020 National conference on emerging trends on sustainable technology and engineering applications (NCETSTEA). IEEE, pp 1–3 31. Stine KM, Quill K, Witte GA, et al (2014) Framework for improving critical infrastructure cybersecurity 32. Stirano F, Lubrano F, Vitali G, Bertone F, Varavallo G, Petrucci P (2020) Cross-domain security asset management for healthcare. In: International workshop on cyber-physical security for critical infrastructures protection. Springer, Heidelberg, pp 139–154 33. Torbacki W (2021) A hybrid mcdm model combining danp and promethee ii methods for the assessment of cybersecurity in industry 4.0. Sustainability 13(16):8833 34. Tusher HM, Munim ZH, Notteboom TE, Kim TE, Nazir S (2022) Cyber security risk assessment in autonomous shipping. In: Maritime economics & logistics, pp 1–20 35. Wang J, Abid H, Lee S, Shu L, Xia F (2011) A secured health care application architecture for cyber-physical systems. arXiv preprint arXiv:1201.0213 36. Webb J, Hume D (2018) Campus IoT collaboration and governance using the nist cybersecurity framework. In: Living in the internet of things: cybersecurity of the IoT-2018. IET, pp 1–7 37. Zaki M, Sivakumar V, Shrivastava S, Gaurav K (2021) Cybersecurity framework for healthcare industry using NGFW. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV). IEEE, pp 196–200

Multi-layered InterCriteria Analysis as a Digital Tool for Studying the Dependencies of Some Key Indicators of Mortality During the Pandemic in the European Union Velichka Traneva and Stoyan Tranev

Abstract Intelligence healthcare expert systems (HCISs) assist decision-makers diagnose accurately and treat patients’ diseases. In a pandemic, they can also, by identifying the causes of higher mortality, predict a higher number of hospitalizations in intensive care units. The theory of intuitionistic fuzzy sets (IFSs), which is one of the first extensions of fuzzy sets of Zadeh, analyzes uncertain data. Uncertainty in data determines the need for the introduction of intuitionistic fuzzy HCISs (IFHCISs) for digital transformation of these processes which analyze uncertain data. The main determinant of the effectiveness of IFHCISs for diagnosing and treatment of diseases, prediction of ICU admission of patients during the pandemic and determining the main factors for higher mortality in pandemics is the optimal multicriteria system. An intuitionistic fuzzy interpretation of the classical rank correlation analyzes under the form of intercriteria analysis (ICrA) is proposed for optimizing each evaluation system of criteria in IFHCISs, based on the theories of index matrices and IFSs. The purpose of the chapter is to develop a model for successful optimization of multicriteria systems embedded in IFHCISs by expanding three-dimensional ICrA to multilayer ICrA (3-D MLICrA). The current pandemic situation raises problems about the dependence of pandemy deaths on certain factors. These problems have to process large data sets related to Covid’s mortality, some of which differ in the various reports. The effectiveness of 3-D MLICrA approach for optimizing the criteria of the IFHCIS is also demonstrated by an application on a dataset of total deaths attributed to COVID-19 and 15 key indicators of the European Union countries determining this mortality, provided by European Centre for DPC for 2021. Keywords Index matrix · Intercriteria analysis · Pandemic mortality V. Traneva (B) · S. Tranev “Prof. Asen Zlatarov” University, “Prof. Yakimov” Blvd, 8000 Bourgas, Bulgaria e-mail: [email protected] S. Tranev e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_12

267

268

V. Traneva and S. Tranev

1 Introduction The pandemic is a catalyst for digital transformation in all areas, including the health sector. A digital transformation (DT) is the usage of the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading of manual processes to automatic [69]. DT and information technology have an important role in transforming data into intelligence that can be used to improve patient care and healthcare services, and to provide quality treatment to patients [28, 67, 80]. HCISs are a set of different technologies and processes that assist healthcare organizations better access and analyze data to make smart and more informed decisions [43]. HCISs occupy a visible place in the process of digital transformation [34], improving efficiency of healthcare [73]. They ensure fast and accurate information for optimal decision making process [25] as they realize gathering data, analyzing the data, storing and spreading the analyzed data [84]. Computing in the cloud reduces healthcare integration costs and improves access to data from any location [60]. HCISs data, saved in the cloud will allow users to access information from any location. HCISs are important part of organizations because they help the users to make more intelligent decisions while keeping expenses [39]. Critical success factors for HCISs, which are generalized in [60], are support from top management [91, 115], project management [79, 115], clear business vision and process [79, 115], participation of end-users [79, 115], education and skills [79, 115], data quality and information accuracy [38, 79, 114, 115], resources, user-friendly system [79, 115] and organizational culture [79, 115]. A modern integrated HCIS enhances the actions of the supports the actions of the World Health Organization, in particular the Ministry of Health. It provides the accumulation and analysis of data of the Health Units, the hospital facilities, regional health inspectorates and the Ministry of Health. Successful HCIS is business-oriented and patient-focused [60]. Modern health organizations generate a huge amount of data (Big data) [29]. This data comes from different sources and in different formats and is challenging to process. For storage and processing of obscure and inaccurate information it is necessary to use the tools of index matrices (IMs) and IFSs, proposed by Atanassov in [1, 2]. Big data structure can be represented by IMs concept [16]. The key factor of the effectiveness of HCISs for diagnosing diseases or determining the main factors determining higher mortality during the pandemics is the optimal multicriteria evaluation system, data quality and information accuracy [38]. The study [42] presents the importance of the multicriteria decision analysis to solve the problems with ranking with its methodological structure in healthcare summarizing that the most commonly used methods are AHP [53] and fuzzy logic methods [56]. The InterCritera analysis (ICrA) was proposed by Atanassov et al. in the papers [11, 19] in order to digitize the process of optimizing evaluation systems by removing slower and more expensive criteria. It can detect hidden relationships between criteria using both clear and vague data. This new approach updates the classical rank correlation analyzes for data analysis [36] using the concepts of IFS and index matri-

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

269

ces (IMs). The concept of intuitionistic fuzzy intercriteria analysis (ICrA), which was originally developed in 2014 by Atanassov et al., is based on the mathematical theories of index matrices (IMs) and intuitionistic fuzzy sets (IFSs). The concept of index matrices (IMs) was introduced in 1987 in [2] to enable two matrices with different dimensions to be summed. The definitions of 3-dimensional intuitionistic fuzzy multilayered (3D-IFMLIM) and 3-dimensional intuitionistic fuzzy index matrices (3D-IFIM) were introduced respectively in [11] and [101]. They are a powerful digital tool for storing and processing both clear, fuzzy and intuitionistic fuzzy data and some of their applications in ICrA, transportation problem, OLAP-cube and Big data structure are presented in [16, 19, 101, 102]. The intuitionistic fuzzy sets (IFSs) [1], which are an extension of fuzzy sets (FSs) of Zadeh [118], are part of a computational intelligence toolbox [83]. ICrA is elaborated to discern possible relations between the pairs of criteria when multiple objects are considered [19] and in case of missing and incomplete data. The idea of ICrA is arisen from a problem in the petrochemical industry, where the approach has been applied to assist the evaluation of bulk and fraction properties of crude oils and thus to select potentially beneficial new crudes for processing in a refinery [96]. The original problem formulation of the oil industry, which led to the emergence of ICrA, states that the decision maker is aimed at optimizing the crude oil quality assessment system as an exception to its slower and more expensive criteria on the basis of these existing correlations. The chapter is a continuation of the papers [18, 19, 109, 113] and proposes for the first time a three-dimensional multilayer ICrA (3-D MLICrA) approach to criteria grouped by a given attribute, which is implemented through a specially developed software applications (see [54, 71]), freely available on a digital platform [122]. The evaluations of objects by criteria will be intuitionistic fuzzy pairs (IFPs) [12] or more general intuitionistic fuzzy data, saved in 3-D extended intuitionistic fuzzy multilayer index matrix (3-D EIFMLIM), which was defined in [101] in 2017. The global coronavirus pandemic has spread rapidly around the world. The recent events in Bulgaria related to the COVID-19 pandemic have posed many questions regarding the dependence of the lethal outcomes of COVID-19 on some factors such as population aging, social status, number of heart attacks and strokes, etc. This prompted us to apply the proposed MLICrA, based on intuitionistic fuzzy logic as a computational intelligence tool, over a unique database containing the number of deaths per 1,000,000 people in 2021 and some key indicators of European Union countries, characterizing the health and social status of the inhabitants, in order to establish useful relations between them. The founded dependencies between some basic indicators of the countries and the number of deaths caused by COVID-19 are performed in the chapter after the application of the proposed MLICrA and the classical rank correlation analyzes [36]. The proposed MLICrA in this chapter supports the process of digital transformation of data analysis and digitizes the decision-making on reducing the lethal outcomes of future pandemics in the EU by identifying key criteria affecting it such as population aging, social status, number of heart attacks and strokes, etc.

270

V. Traneva and S. Tranev

The rest of this chapter is organized as follows: Sect. 2 introduces literature review. Section 3 describes the definition and some basic operations over IFPs and 3-D extended multilayer IMs (EMLIMs). Section 4 introduces the three-dimensional ICrA. Section 5 proposes the 3-D MLICrA method extending 3-D ICrA. In Sect. 6, the 3-D MLICrA is applied to study the dependencies between the number of deaths per 1,000,000 people and 15 key indicators characterizing the health and social status of the inhabitants of the European countries [123, 124]. Finally, in Sect. 7, the detected correlation dependences on MLICrA are compared with those obtained by classical rank correlation analyzes. Section 8 points out the conclusion and emphasizes future directions for application of MLICRA for optimizing the evaluation system of IFHCISs, which will be used to determine the most important factors determining the high mortality rate during pandemics and on this basis to predict the workload of intensive care units as a result of an epidemic.

2 Literature Review Expert systems are smart programs that simulate the behavior of human experts from a specific field of knowledge [92]. In the literature, several methodologies are proposed for the development of these systems, some of them are classified as [40]: Knowledge Based (Case-Based, Rule-Based) [81], Intelligent Computing (Artificial Neural Sets, Fuzzy Systems, Intelligent Agents) [81], Statistical (Bayes’ Theorem) [93], and other emerging methodologies [93]. Fuzzy logic [118] uses membership and nonmembership functions for evaluations more complex logical propositions among uncertainty in real life. Intuitionistic fuzzy logic [1], as an extension of fuzzy logic, also has a degree of hesitancy and is more flexible in dealing with uncertain environments. The synergy between intuitionistic fuzzy logic and other methodologies has application in intelligent systems in general [75]. IFHCISs are of great importance for healthcare. A fuzzy expert system for prediction of chronic kidney disease is developed in [48] by identifying the diagnostic factors through a literature review and an expert survey. In work [53] is proposed a multi-layered fuzzy inference system to find the thyroid disease. A new fuzzy soft expert system to predict lung cancer disease by using criteria as weight loss, shortness of breath, chest pain, persistent cough, blood in sputum, and age of patients, is described in [59]. The prediction of prostate cancer is realized through fuzzy expert system. The used criteria are: age, prostate-specific antigen (PSA), prostate volume (PV) and Free PSA (FPSA) [68]. The research [77] is developed a fuzzy expert system for the diagnosis of heart disease for which the input variables as age, chest pain, electrocardiography, blood pressure systolic, diabetes, and cholesterol are interpreted with the help of fuzzy rules. In [90] is developed a fuzzy expert system for the prediction of ICU admission in COVID-19 patients using an indirect method, in which the rules are formed automatically using a clustering approach. The effectiveness of IFHCISs for diagnosing and treatment of diseases, prediction of ICU admission of patients during the pandemic and determining the main factors

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

271

for higher mortality in pandemics depends on the optimal multicriteria system. In the healthcare, the criteria for decision making are sometimes conflicting, raising the degree of uncertainty of the final answer [56]. Multicriteria Decision Support Methods (MCDA) have arisen in [70] to help the decision-making process and to guarantee a solution in accordance with the criteria. They have the aim to optimize health systems [99]. A systematic review of the main characteristics and methodological steps of MCDA are given in [42]. According [42], AHP [49] is the most used method in MCDA, following the fuzzy logic [117], then are EVIDEM [116], ANP [78], MACBETH [46], TOPSIS [66], VIKOR [65] and WHO-CHOICE [55]. The most cited paper [26] among MCDA presents a better allocation approach of public health resources. The concept of finding the relationships between the criteria has been extended to fuzzy observations in Chiang and Lin [35], Hong and Hwang [51], Liu and Kao [64]. In [104] was presented a retrospection analysis of ICrA. A fuzzy concept of correlation analysis has been first proposed by Gerstenkorn and Manko [44]. The variance and covariance in this correlation coefficient are constructed from the scalar product of the values of the membership function and the non-membership function of two intuitionistic fuzzy sets. Then, the correlation coefficient between the interval-valued intuitionistic fuzzy set is introduced by Bustince and Burillo in [32]. Hong and Hwang in [51] generalized the correlation coefficient of [44]. Using the statistical viewpoint, Hung and Wu [52], Hong and Hwang [51], Zeng and Li [119], and Mitchell [74] provided correlation coefficient for IFSs. Chen et al. [33] introduced the correlation of hesitant fuzzy set. A positive and negative type of a correlation coefficient is proposed in [120], whose calculations do not include the degree of hesitancy of IFSs. The calculations of intuitionistic fuzzy correlation analysis in [98] takes into account all degrees of IFSs. Liu et al. [64] constructed the correlation coefficient between IFSs based on the concept of the deviation of the IFSs. The algorithms for an optimal selection of service provider in uncertain conditions optimize the multicriteria system by eliminating slower or more expensive criteria that have been found to be strongly correlated to other criteria [57, 58, 105]. The digitization of the process for optimizing the evaluation system in conditions of vagueness requires the application of an intuitionistic fuzzy approach to correlation analysis. The concept of intuitionistic fuzzy ICrA, based on the apparatus of the IMs (see [2]) and IFPs (see [1, 12]), was introduced in [11, 19]. It calculating pairwise dependencies between each pair of evalution criteria. The method receives as input datasets of the evaluations of multiple objects against multiple criteria and returns as output a table of detected dependencies in the form of intuitionistic fuzzy pairs between each pair of criteria [21]. In the original problem formulation from an oil refinery that leads to the idea of ICrA, the measurement of oil quality is carried out through a criterion system in which some criteria are expensive and slow to measure. The goal of the decision maker is to optimize the evaluation system by eliminating slower and more expensive criteria without reducing the accuracy of measurement in a rapidly changing economic environment with unclear parameters on the basis

272

V. Traneva and S. Tranev

of these existing correlations [19, 94]. The complexity of the ICrA algorithm is O(m 2 n 2 ) [24]. The ICrA algorithm is easier and faster than other correlation analysis methods, because it is only based on comparisons “, =”, existing between the evaluations of the objects against the system of criteria, rather than on their numerical values. The method requires an m × n table with the measurements or evaluations of m objects against n criteria. As a result, it returns an n × n table with IFPs, defining the degrees of consonance between each pair of criteria, which gives it the name “intercriteria analysis”. The elaborated ICrA software [71] gives two n × n tables with the membership and the non-membership degrees of the respective IFPs. Later the ICrA concept is extended in a theoretical aspect to 2-dimensional interval-valued intuitionistic fuzzy and 3-dimensional intuitionistic fuzzy ICrA [18, 30, 109], with a software application being developed (see [54, 71, 72]), freely available from the website [122]. The ICrA approach is studied in a number of papers with applications in education [31, 47, 61], neural networks [94, 95], genetic algorithms and metaheuristics [41, 82, 87, 88], economic investigations [37, 110–113], medical and biotechnological processes [62, 86, 100], management of human resources [89], in shape patterns [13], etc.

3 Preliminaries 3.1 Intuitionistic Fuzzy Pairs The IFP is an object of the form a, b = μ( p), ν( p), where a, b ∈ [0, 1] and a + b ≤ 1, that is used as an evaluation of a proposition p [12]. μ( p) and ν( p) respectively determine the “truth degree” (degree of membership) and “falsity degree” (degree of non-membership). Let us have two IFPs x = a, b and y = c, d. Let us remind some basic operations and relations with two IFPs x = a, b and y = c, d following [3, 12, 97]: ¬x = b, a; x ∧1 y = min(a, c), max(b, d) x ∨1 y = max(a, c), min(b, d); x ∧2 y = x + y = a + c − a.c, b.d x@y =  a+c , b+d ; x ∨2 y = x.y = a.c, b + d − b.d 2 2 a≥c x ≥ y iff a ≥ c and b ≤ d; x ≥2 y iff b≤d x ≥ R y iff Ra,b ≤ Rc,d , x ≥ y iff where Ra,b = 0.5(2 − a − b)(1 − a).

(1)

(2)

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

273

3.2 Definition of Three-Dimensional Extended Multilayer Index Matrix (3-D EMLIM) and Some Basic Operations The concept of index matrices (IMs) is introduced by K. Atanasov in [2, 4] in 1984. Over the past 35 years, their main properties have been studied [5–7, 9, 10, 17, 101] as a tool for description of the transitions in generalized networks, of intuitionistic fuzzy relations and graphs. They have been used in intercriteria decision making [19, 20, 22, 23, 30, 109], in the number theory [76], for image recognition [63], in electronics [45, 50]. The research on them have been systematized and published in [11, 108]. In the theory of IMs, the following types of two-dimensional, three-dimensional and n-dimensional IMs are defined: IM with elements of real numbers, IM with elements {0,1}, IM with predicate elements, intuitionistic fuzzy IM, extended IM, temporal intuitionistic fuzzy IM, intuitionistic fuzzy IM, interval-valued intuitionistic fuzzy IM, IM with element matrix. Operations and relations are defined on twodimensional (2-D) and three-dimensional (3-D) IMs. Some of the operations are similar to those of ordinary matrices, but there are specific ones. This section defines three-dimensional extended multilayer index matrix (3-D EMLIM). This type of the IMs arises as a tool for modeling the data in Olap-cube and its operations [106]. Let fixed sets of some objects X and of indices I ∗ be given. A definition of a 3-D (r ) }] with P-levels (layers) of use of a dimension EMLIM A = [K , L , H, {a K ( p) ,L (q) ,Hg,c i,d j,b K , Q-levels (layers) of use of a dimension L and R-levels (layers) of use of a dimension H is the following [101, 108] : ⎧ (R) ⎫ L (Q) ... L (Q) ... L (Q) Hg ∈ H ⎪ ⎪ n 1 j ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ (P) ⎪ ⎪ (P) (Q) (R) . . . a (P) (Q) (R) . . . a (P) (Q) (R) K a ⎪ ⎪ 1 K 1 ,L 1 ,Hg K 1 ,L j ,Hg K 1 ,L n ,Hg ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ .. .. .. .. ⎨ ⎬ . . ... . ... . A= (3) ⎪ ⎪ (R) K i(P) a K (P) ,L (Q) ,Hg(R) . . . a K (P) ,L (Q) ,Hg(R) . . . a K (P) ,L (Q) ⎪ ⎪ n ,Hg 1 i i j i ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ .. .. .. .. ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ . . . . . . . . . . ⎪ ⎪ ⎪ ⎩ K (P) a (P) (Q) (R) . . . a (P) (Q) (R) . . . a (P) (Q) (R) ⎪ ⎭ m K m ,L ,Hg K m ,L ,Hg K m ,L n ,Hg 1

j

(P−1) (P−1) , K i,2 ,..., K = {K 1(P) , K 2(P) , . . . , K i(P) , . . . , K m(P) }, K i(P) = K i,1



(P−1) (P−1) (0) (0) (0) (for 1 ≤ i ≤ m), . . . , K u(1) = K u,1 , K u,2 , . . . , K u,U i.e. pK i,x , . . . , K i,I ( p) ( p−1) ( p−1) th layer of dimension K (1 ≤ p ≤ P) is performed by K u ∗ = K u ∗,1 , K u ∗,2 , . . . ,

( p−1) (Q) (Q) (Q) (Q) = {L (Q−1) , K u ∗,U∗ (for 1 ≤ p ≤ P); L = {L (Q) 1 , L 2 , . . . , L j , . . . , L n }, L j j,1 (Q−1) (Q−1) (Q−1) (0) (0) L j,2 , . . . , L j,y , . . . , L j,J } (for 1 ≤ j ≤ n), . . . , L (1) v = L v,1 , L v,2 , . . . ,

(q) (q−1) (q−1) (0) lv,V i.e. q-th layer of dimension Q is described by L v∗ = L v∗,1 , L v∗,2 , . . . ,

where

274 (q−1)

V. Traneva and S. Tranev



H = {H1(R) , H2(R) , . . . , Hg(R) , . . . , H (R) f },

Hg(R) = (R−1) (R−1) (R−1) (0) (R−1) , Hg,2 , . . . , Hg,z , . . . , Hg,G }(for 1 ≤ g ≤ f ), . . . , Hw(1) = Hw,1 , {Hg,1

(0) (0) , . . . , Hw,W i.e. r -th layer of dimension H is performed by Hw(r∗) = Hw(r∗,1−1) , Hw,2

−1) (for 1 ≤ r ≤ R) and (K , L , H ⊂ I ∗ ), and for 1 ≤ i ≤ Hw(r∗,2−1) , . . . , Hw(r∗,W ∗ m, 1 ≤ j ≤ n, 1 ≤ g ≤ f, 1 ≤ p ≤ P, 1 ≤ q ≤ Q, 1 ≤ r ≤ R, 1 ≤ d ≤ I, 1 ≤ (r ) ∈ X . b ≤ J, 1 ≤ c ≤ G : a K ( p) ,L (q) ,Hg,c L v∗,V∗

(for 1 ≤ q ≤ Q);

i,d

j,b

If the matrix elements are IFPs, we denote it as 3-D IFEMLIM. Then the evaluation function V is defined that juxtaposes to this 3-D EMLIM A a new one with elements— IFPs. So, we have obtained a 3-D EMLIM with intuitionistic fuzzy evaluations under the form (r ) }] = [K , L , H, {μ ( p) (q) (r ) , ν ( p) (q) (r ) }] (4) V = [K , L , H, {V {a K ( p) ,L (q) ,Hg,c K L ,Hg,c K L ,Hg,c i,d

j,b

i,d

j,b

i,d

j,b

Projection. Let it be given 3-D MLEIM A = [K , L , H, {a K ( p) ,L (q) ,Hg(r ) }]. The defic id jb nition of the operation “projection” over A is the following according to [108]: pr(K (P) , p),(L (Q) , q),(Hg(R) , r) A i

j

(R) = [(K i(P) , p ), (L (Q) j , q ), (Hg , r ), {b K ( p) ,L (q) ,Hg(r ) }], id

( p)

jb

c

(q)

where for each K id ∈ (K i(P) , p-th layer), L jb ∈ (L (Q) j , q-layer ) and Hg(rc ) ∈ (Hg(R) , r-layer) b K ( p) ,L (q) ,Hg(r ) = a K ( p) ,L (q) ,Hg(r ) , id

jb

c

id

jb

c

K i(P) ⊂ K , 1 ≤ p ≤ P, L (Q) ⊂ L , 1 ≤ q ≤ Q and Hg(R) ⊂ H, 1 ≤ r ≤ R. j Let finite index sets be given whose members are also index sets: , . . . , K u(P) , . . . , K u(P) }, K ∗ ⊆ K and K ∗ = {K u(P) 1 x t P∗ = { p1 , . . . , px , . . . , pt }, where 1 ≤ px ≤ P for 1 ≤ x ≤ t, (Q) (Q) L ∗ ⊆ L and L ∗ = {L (Q) v1 , . . . , L v y , . . . , L vs },

Q ∗ = {q1 , . . . , q y , . . . , qs }, where 1 ≤ q y ≤ Q for 1 ≤ y ≤ s, H∗ ⊆ H and H∗ = {H1(R) , . . . , Hw(R) , . . . , Hw(R) }, z e R∗ = {r1 , . . . , r z , . . . , re }, where 1 ≤ r z ≤ R for 1 ≤ z ≤ e. Let |K ∗ | = |P∗ | = t, |L ∗ | = |Q ∗ | = s, |H∗ | = |R∗ | = e. Then

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

275

pr(K ∗ ,P∗ ),(L ∗ ,Q ∗ ),(H∗ ,R∗ ) A = [(K ∗ , P∗ ), (L ∗ , Q ∗ ), (H∗ , R∗ ), {b K ( p) ,L (q) ,Hg(r ) }], id

jb

( p)

c

(q)

where for each K id ∈ {K v(P) , px − layer} for 1 ≤ x ≤ t, L jb ∈ {L (Q) u y , q y − layer} x (r) (R) for 1 ≤ y ≤ s and Hgc ∈ {Hwz , r z − layer} for 1 ≤ z ≤ e, b K ( p) ,L (q) ,Hg(r ) = a K ( p) ,L (q) ,Hg(r ) . ( p) K i,0

id

(q) K , L j,0

jb

c

id

(r ) Hg,0

jb

∈ / ∈ / L and ∈ / H. Aggregation Operations over 3-D EMLIM. Let Let {◦, ∗} : X × X −→ X and ⎧ {“ + ", “ × ", “average", “max", “min"}, if A ∈ 3D − E M L I MR ⎪ ⎪ ⎪ ⎪ ⎨ (5) ◦ ∈ {“max", “min"}, if A ∈ 3D − E M L I M{0,1} ⎪ ⎪ ⎪ ⎪ ⎩ {“ ∧ ", “ ∨ "}, if A ∈ 3D − E M L I MP or A ∈ 3D − E M L I MI F P In the case of 3-D IFEMLIM, in these operations can participate a pair of operations (◦, ∗) ∈ {(min, max), (min, average), (min, min), (average, average), (max, min)(average, min)}, which elements are applied respectively on the first and second element of the IFP. The definition of the aggregation operation on the p-th layer of the dimension K of the matrix A, which type is 3-D EMLIM, has the following form [101]: (◦) − α(K ,K (P) , p -layer) -aggregation i

( p)

α(K ,K (P) , p -layer ,◦) (A, K i,0 ) i

⎧ ⎪ Hg(R) ∈ H ⎪ ⎪ ⎪ ⎪ K 1(P) ⎪ ⎪ ⎪ ⎪ .. ⎪ ⎪ ⎪ . ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ K iP ⎪ ⎪ ⎪ ⎨ .. . = ⎪ ⎪ ⎪ ⎪ ⎪ ( p) ⎪ ⎪ {K iP , p-layer }K i,0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ .. ⎪ ⎪ ⎪ . ⎪ ⎪ ⎩ K m(P)

L (Q) 1 a K (P) ,L (Q) ,Hg(R) 1 1 .. . ... .. . ◦ 1≤ρ ≤ p−1 ( p) (ρ) K u ∈ K i∗

... ··· .. . .. . .. .

a K u(ρ) ,L (Q) ,Hg(R) · · · 1

.. .

a K m(P) ,L (Q) ,Hg(R) 1

..

. ...

c

276

V. Traneva and S. Tranev

L (Q) j

... ··· .. .

a K (P) ,L (Q) ,Hg(R) 1 j .. .

···

◦

..

. ···

1≤ρ ≤ p−1 ( p) (ρ) K u ∈ K i∗

... ... .. .

a K u(ρ) ,L (Q) ,Hg(R) · · · j

.. .

..

a K m (P) ,L (Q) ,Hg(R) j

. ...

L (Q) n (R) a K (P) ,L (Q) n ,Hg 1 .. .

◦ 1≤ρ ≤ p−1 ( p) (ρ) K u ∈ K i∗

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

(R) ⎪ a K u(ρ) ,L (Q) ⎪ n ,Hg ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ .. ⎪ ⎪ . ⎪ ⎪ ⎪ ⎭ (Q) (R)

a K m(P) ,L n

,

(6)

,Hg

where K i(P) ⊂ K , 1 ≤ p ≤ P. ( p) , . . . , K v(P) , . . . , K v(P) }, V∗ = {K v10 , . . . , Let the index set K ∗ ⊆ K , K ∗ = {K v(P) 1 x t ( p) ( p) / K for 1 ≤ p ≤ P be given. . . . , K vx 0 , . . . , K vt 0 } ∈ In this case, the following aggregation operation has been defined: (◦) − α(K ,K ∗ ,p) -aggregation α(K ,K ∗ ,p,◦) (A, V∗ ) ( p) ( p) ( p) = α(K ,K ∗ ,p,◦) ( A, K v1 0 , . . . , K vx 0 , . . . , K vt 0 ) ( p) ( p) = α(K ,K v(P) ,◦) ((. . . α(K ,K v(P) ,◦) (A, K v1 0 ) . . .), K vt 0 ). t 1 An extended form of the last aggregation operations obtained as follows: (p ) , . . . , K v(P) , . . . , K v(P) }, V∗ = {K v101 , . . . , let index sets be given: K ∗ ⊆ K , K ∗ = {K v(P) 1 x t (p ) (p ) / K and P∗ = { p1 , . . . , px , . . . , pt } (1 ≤ px ≤ P for 1 ≤ x ≤ . . . K vx 0x , . . . , K vt 0t } ∈ t). Let |K ∗ | = |P∗ | = |V∗ | = t. Then (◦) − α(K ,K ∗ ,P∗ ) -aggregation [103] α(K ,K ∗ ,P∗ ,◦) (A, V∗ ) = α(K ,K ∗ ,P∗ ,◦) (A, K v(1p01 ) , . . . , K v(xp0x ) , . . . , K v(tp0t ) ) = α(K ,K v(P) ,◦) ((. . . α(K ,K v(P) ,◦) (A, K v(1p01 ) ) . . .), K v(tp0t ) ). t

1

Operations {(◦) − α(L ,L (Q) , q) , (◦) − α(H,Hg(R) , r) }-aggregation, j α(K ,K (P) , p ,L ,L (Q) , q ,◦) , α(K ,K (P) , p ,H,Hg(R) , r ,◦) i j i and α(L ,L (Q) , q ,H,Hg(R) , r ,◦) − aggregation are similarly defined. j

4 Three-Dimensional ICrA of Intuitionistic Fuzzy Data This section extends two-dimensional (2-D) ICrA, proposed in [30], to three-dimensional (3-D) over intuitionistic fuzzy data according to [109]. The method can be used for prediction. The modern business organizations store large volumes of 2-, 3-, ndimensional data, related to evaluation by criteria. Some of this data has arisen in an

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

277

unclear or blurred information environment. This necessitates the extension of ICrA to be applied to three-dimensional, three-dimensional multilayered and n-dimensional intuitionistic fuzzy data. In [109, 113] were defined three-dimensional (3-D ICrA) and n-dimensional intuitionistic fuzzy (n-D ICrA) ICrA in order to identify the possible correlation dependencies between pairs of evaluation criteria. Let a set of criteria C = {C1 , C2 , ..., Cm } at a moment h g ∈ H (1 ≤ g ≤ f ) be given, where the index set H can be interpreted as a time-scale. The objects O = {O1 , O2 , ..., On } are assessed by criteria from the set C at a moment h g ∈ H for 1 ≤ g ≤ f. A 3-D IM ⎫ ⎧ O1 ··· Oi ··· Oj ··· On ⎪ ⎪ ⎪ ⎪ hg ∈ H ⎪ ⎪ ⎪ ⎪ C a · · · a · · · a · · · a ⎪ ⎪ 1 C ,O ,h C ,O ,h C ,O ,h C ,O ,h 1 1 g 1 i g 1 j g 1 n g ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ . . . . . . . . ⎪ ⎪ . . . . . . . . ⎪ ⎪ . . . . . . . . ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ Ck aCk ,O1 ,h g · · · aCk ,Oi ,h g · · · aCk ,O j ,h g · · · aCk ,On ,h g ⎬ A= , .. .. .. .. .. .. .. .. ⎪ ⎪ . . . . . . . . ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Cl aCl ,O1 ,h g · · · aCl ,Oi ,h g · · · aCl ,O j ,h g · · · aCl ,On ,h g ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ . . . . . . . . ⎪ ⎪ . . . . . . . . ⎪ ⎪ . . . ⎪ ⎪ . . . . . ⎪ ⎪ ⎭ ⎩ Cm aCm ,O1 ,h g · · · aCm ,Oi ,h g · · · aCm ,O j ,h g · · · aCm ,On ,h g is created, where for every p, q (1 ≤ p ≤ m , 1 ≤ q ≤ n): (1) C p is an evaluation criterion, (2) Oq is an evaluated object, (3) aC p ,Oq ,h g is a variable, formula or aC p ,Oq ,h g = αC p ,Oq ,h g , βC p ,Oq ,h g  is an IFP, that is comparable about relation R with the other a-objects, so that for each i, j, k, g: R(aCk ,Oi ,h g , aCk ,O j ,h g ) is defined. Let R be the opposite relation of R in the sense that if R is contented, then R is not contented and vice versa. μ The, we define Sk,l,g as the number of cases in which

αCk ,Oi ,h g , βCk ,Oi ,h g  ≤ αCk ,O j ,h g , βCk ,O j ,h g  and αCl ,Oi ,h g , βCl ,Oi ,h g  ≤ αCl ,O j ,h g , βCl ,O j ,h g , or αCk ,Oi ,h g , βCk ,Oi ,h g  ≥ αCk ,O j ,h g , βCk ,O j ,h g  and αCl ,Oi ,h g , βCl ,Oi ,h g  ≥ αCl ,O j ,h g , βCl ,O j ,h g  are simultaneously satisfied at a moment h g (1 ≤ g ≤ f ). ν is calculated as the number of cases in which Then, Sk,l,g αCk ,Oi ,h g , βCk ,Oi ,h g  ≥ αCk ,O j ,h g , βCk ,O j ,h g 

278

V. Traneva and S. Tranev

and αCl ,Oi ,h g , βCl ,Oi ,h g  ≤ αCl ,O j ,h g , βCl ,O j ,h g , or αCk ,Oi ,h g , βCk ,Oi ,h g  ≤ αCk ,O j ,h g , βCk ,O j ,h g  and αCl ,Oi ,h g , βCl ,Oi ,h g  ≥ αCl ,O j ,h g , βCl ,O j ,h g  are simultaneously satisfied. Therefore, μ

ν ≤ Sk,l,g + Sk,l,g

n(n − 1) 2

for each g(1 ≤ g ≤ f.) Now, for every k, l, g, such that 1 ≤ k < l ≤ m, n ≥ 2 and g is fixed, we define μ ν Sk,l Sk,l μCk ,Cl = 2 , νCk ,Cl = 2 . n(n − 1) n(n − 1) Hence,

μ

μCk ,Cl ,h g + νCk ,Cl ,h g = 2

Sk,l,g n(n − 1)

+2

ν Sk,l,g

n(n − 1)

≤ 1.

The pair μCk ,Cl ,h g , νCk ,Cl ,h g  is an IFP. The degrees of dependencies between criteria C1 , . . . , Cm are obtained as elements of the IM R as follows: ⎫ ⎧ C1 ··· Cm hg ∈ H ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ C1 μC1 ,C1 ,h g , νC1 ,C1 ,h g  · · · μC1 ,Cm ,h g , νC1 ,Cm ,h g  ⎬ R= .. .. .. .. ⎪ ⎪ . . . . ⎪ ⎪ ⎪ ⎪ ⎭ ⎩ Cm μCm ,C1 ,h g , νCm ,C1 ,h g  · · · μCm ,Cm ,h g , νCm ,Cm ,h g  / H and {◦, ∗} : X × X −→ X and Let h 0 ∈ ◦, ∗ ∈ {min, max, max, min, average, average}. Then, the aggregation operation α(H,◦) (R, h 0 ) is executed over the IM R as follows [107]: (◦) − α H -aggregation

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

⎧ ⎪ ⎪ ki ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ k ⎪ ⎪ 1 ⎪ ⎪ ⎪ ⎨ α(H,◦) (R, h 0 ) = k2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ... ⎪ ⎪ ⎪ ⎪ k ⎪ ⎪ ⎩ m

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ k1 ⎪ ⎪ ⎪ ⎪ ⎨ = k2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ... ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ km ⎪ ⎪ ⎩

h0 ◦

◦ ◦

1≤g≤ f

◦ 1≤g≤ f

ak1 ,k1 ,h g ak2 ,l1 ,h g .. . akm ,k1 ,h g

ak1 ,k1 ,h g

1≤g≤ f

◦ 1≤g≤ f

◦

ak2 ,k2 ,h g | ki ∈ K .. . akm ,km ,h g

1≤g≤ f

k1

1≤g≤ f

279

k2 ◦ 1≤g≤ f

◦

1≤g≤ f

◦ 1≤g≤ f

...

ak1 ,k2 ,h g . . . ak2 ,l2 ,h g . . . .. .

..

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

km ◦ 1≤g≤ f

◦

1≤g≤ f

.

akm ,k2 ,h g . . .

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

◦ 1≤g≤ f

ak1 ,km ,h g ak2 ,km ,h g

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ akm ,km ,h g ⎪ ⎪ ⎪ ⎭ .. .

If the pair ◦, ∗ = min, max is used in this aggregation operation, the pessimistic assessments of the intercriteria dependencies is obtained. With pair ◦, ∗ = max, min, then the optimistic estimates are obtained. With pair ◦, ∗ = average, average, we get the average score of the ICrA coefficients. The decision maker has a choice of which of the three scenarios to choose. The output of the ICrA algorithm is easier to interpret if presented in two matrices Aμ and Aν than with one A∗ with IFPs. The first IFPs components of A∗ are elements of the matrix Aμ , while the second IFPs components of A∗ are elements of Aν . The automation of ICrA calculations is carried out through the developed software application, described in [54, 71, 72]. The evaluation system of criteria can be optimized by eliminating slower or more expensive evaluation criteria in case strong dependencies are found with other faster or cheaper criteria using the following scale [15] (see Table 1):

280

V. Traneva and S. Tranev

Table 1 Correlation type scale. Membership degree of correlation [0, 95; 1] [0, 85; 0, 95] [0, 75; 0, 85] [0, 67; 0, 75) [0, 57; 0, 67) [0, 43; 0, 57) [0, 33; 0, 43) [0, 25; 0, 33) [0, 15; 0, 25) [0, 15; 0, 05) [0, 05; 0]

Type of correlation Strong positive consonance Positive consonance Weak positive consonance Weak dissonance Dissonance Strong dissonance Dissonance Weak dissonance Weak negative consonance Negative consonance Strong negative consonance

5 A Form of Three-Dimensional ICrA of Intuitionistic Fuzzy Data in Multilayer IMs This section extends three-dimensional intercriteria analysis from [109], performed in previous Sect. 4, so that it can be applied to intuitionistic fuzzy data, stored in 3-D (Q) EMLIM. Let us be given the set of objects O = {O1(Q) , . . . , O (Q) j , . . . , On }, which are valued by criteria from the set C = {C1(P) , . . . , Ci(P) , . . . , Cm(P) } at a moment Hg(R) ∈ H for 1 ≤ g ≤ f , where H = {H1(R) , H2(R) , . . . , Hg(R) , . . . , H (R) f } is a fixed (R) scale and Hg is its element. The index set H can be interpreted as a time-scale and its element Hg(R) —as a time-moment. Different attributes along each dimension of this type of IMs are often organized in hierarchical structures that determine the different levels. Let us define a 3-D EMLIM A with P-levels (layers) of use of a dimension C (a criteria), Q-levels(layers) of use of a dimension O (an object) and R-levels(layers) of use (r ) }] with of a dimension H (a time scale or a fixed scale) A = [C, O, H, {aC ( p) ,O (q) ,Hg,c i,d j,b ∗ a structure as 3-D EMLIM (3) and (C, O, H ⊂ I ), and for 1 ≤ i ≤ m, 1 ≤ j ≤ n, 1 ≤ g ≤ f, 1 ≤ p ≤ P, 1 ≤ q ≤ Q, 1 ≤ r ≤ R, 1 ≤ d ≤ I, 1 ≤ b ≤ J, 1 ≤ (r ) ∈ X , where for every p, q, r (1 ≤ p ≤ P , 1 ≤ q ≤ Q, 1 ≤ c ≤ G : aC ( p) ,O (q) ,Hg,c i,d j,b r ≤ R): (1) (2) (3) (4)

( p)

Ci,d is a p-level of an evaluation criterion, (q) O j,b is a q-level of an appraised object, (r ) Hg,c is a r-level of a time-moment or an element of another fixed scale, (q) (q) (r ) = α ( p) (r ) , β ( p) (r )  is an IFP, that is comparable about aC ( p) ,O (q) ,Hg,c Ci,d ,O j,b ,Hg,c Ci,d ,O j,b ,Hg,c i,d j,b relation R with the other a-objects, so that for each i, d, p, j, b, q, g, c, r for (q) (r ) , a ( p) (r ) ) is which the above limitations have been met: R(aC ( p) ,O (q) ,Hg,c C ,O ,Hg,c i,d

j,b

i,d

j,b

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

281

defined. Let R be the opposite relation of R in the sense that if R is satisfied, then R is not satisfied and vice versa. Let us fix subcategories of the three dimensions “criterion”, “object” and “time”, (r ) located respectively on the their p-th, q-th and r -th layers. For each index Hg,c μ (1 ≤ g ≤ f ) let with Sk,l, p,g,r we denote the number of cases in which αC ( p) ,O (q)

(r ) j1 ,b j ,Hg,c 1

k,dk

, βC ( p) ,O (q)

and αC ( p) ,O (q)

(r ) j1 ,b j ,Hg,c 1

l,dl

or αC ( p) ,O (q)

(r ) j1 ,b j ,Hg,c 1

k,dk

(r ) j1 ,b j ,Hg,c 1

(r ) , β ,Hg,c Ck,d ,O j ( p) k

 ≤ αC ( p) ,O (q)

j2 ,b j 2

k,dk

(r ) j1 ,b j ,Hg,c 1

(r )  ,Hg,c

(r )  ,Hg,c

(r ) , β ,Hg,c Ck,d ,O j

(r )  ,Hg,c

(r ) , β ,Hg,c Cl,d ,O j

(r )  ,Hg,c

( p) l

(q) 2 ,b j2

( p) k

 ≥ αC ( p) ,O (q)

(q) 2 ,b j2

( p) l

j2 ,b j 2

l,dl

(q) 2 ,b j2

(r ) , β ,Hg,c Cl,d ,O j

j2 ,b j 2

l,dl

 ≥ αC ( p) ,O (q)

, βC ( p) ,O (q) l,dl

j2 ,b j 2

k,dk

(r ) j1 ,b j ,Hg,c 1

l,dl

k,dk

(r ) j1 ,b j ,Hg,c 1

 ≤ αC ( p) ,O (q)

, βC ( p) ,O (q)

, βC ( p) ,O (q)

and αC ( p) ,O (q) l,dl

(r ) j1 ,b j ,Hg,c 1

k,dk

(q) 2 ,b j2

are simultaneously satisfied for each two objects O j1 ,b j1 and O j2 ,b j2 of the q-th layer of the dimension O. ν Sk,l, p,g,r

is defined as the number of cases in which αC ( p) ,O (q)

(r ) j1 ,b j ,Hg,c 1

k,dk

, βC ( p) ,O (q)

and αC ( p) ,O (q) l,dl

(r ) j1 ,b j ,Hg,c 1

or αC ( p) ,O (q) k,dk

(r ) j1 ,b j ,Hg,c 1

l,dl

k,dk

(r ) j1 ,b j ,Hg,c 1

 ≥ αC ( p) ,O (q)

(r ) j1 ,b j ,Hg,c 1

(r ) , β ,Hg,c Ck,d ,O j ( p) k

(q) 2 ,b j2

(r )  ,Hg,c

 ≤ αC ( p) ,O (q)

(r ) , β ,Hg,c Cl,d ,O j

(r )  ,Hg,c

 ≤ αC ( p) ,O (q)

(r ) , β ,Hg,c Ck,d ,O j

(r )  ,Hg,c

 ≥ αC ( p) ,O (q)

(r ) , β ,Hg,c Cl,d ,O j

(r )  ,Hg,c

(r ) j1 ,b j ,Hg,c 1

, βC ( p) ,O (q) l,dl

j2 ,b j 2

k,dk

, βC ( p) ,O (q)

, βC ( p) ,O (q)

and αC ( p) ,O (q) l,dl

(r ) j1 ,b j ,Hg,c 1

k,dk

(r ) j1 ,b j ,Hg,c 1

( p) l

j2 ,b j 2

l,dl

j2 ,b j 2

k,dk

j2 ,b j 2

l,dl

( p) k

( p) l

(q) 2 ,b j2

(q) 2 ,b j2

(q) 2 ,b j2

are simultaneously satisfied for each two objects O j1 ,b j1 and O j2 ,b j2 of the q-th layer of the dimension O. We can see, that μ

ν Sk,l, p,g,r + Sk,l, p,g,r ≤

n(n − 1) 2

for each g, so that 1 ≤ g ≤ f. Now, for every k, l from the p-th layer of the dimension C, g from the r -th layer of the dimension H , such that 1 ≤ k < l ≤ m, n ≥ 2 and g is fixed, we define μ ν Sk,l, p,g,r  Sk,l, p,g,r  (r ) = 2 (r ) = 2 μC ( p) ,C ( p) ,Hg,c and νC ( p) ,C ( p) ,Hg,c . n(n−1) n(n−1) k,dk

l,dl

k,dk

l,dl

( p) (r ) + ν ( p) (r ) = 2 Hence, μC ( p) ,C ( p) ,Hg,c C ,C ,Hg,c k,dk

l,dl

k,dk

l,dl

μ

Sk,l, p,g,r  n(n−1)

+2

ν Sk,l, p,g,r  n(n−1)

≤ 1.

282

V. Traneva and S. Tranev

( p) (r ) , ν ( p) (r )  is an IFP. Therefore, μC ( p) ,C ( p) ,Hg,c Ck,d ,Cl,d ,Hg,c k,dk l,dl k l Now, the IM R is calculated as follows

⎧ ⎫ ( p) ( p) (r ) ⎪ ⎪ C1,d1 ··· Cm,dm Hg,c ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ( p) ⎪ ⎪ ( p) ( p) ( p) (r ) , ν ( p) (r )  · · · μ ( p) (r ) , ν ( p) (r )  ⎬ ⎨ C1,d1 μC ( p) ,C ( p) ,Hg,c C ,C ,H C ,C ,H C ,C ,H g,c g,c g,c 1,d1 1,d1 1,d1 1,d1 1,d1 m,dm 1,d1 m,dm .. .. .. ... ⎪ ⎪ . ⎪ ⎪ . . ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ( p) ⎩C ( p) ( p) ( p) ( p) ( p) (r ) , ν ( p) (r )  · · · μ ( p) (r ) , ν ( p) (r )  ⎭ μ m,dm C ,C ,Hg,c C ,C ,Hg,c C ,C ,Hg,c C ,C ,Hg,c m,dm

1,d1

m,dm

1,d1

m,dm

m,dm

m,dm

m,dm

that determines the degrees of dependencies between each pair of criteria ( p) ( p) C p = {C1,d1 , . . . , Cm,dm }. Then, let us use one of the forms of IMs aggregation operation to the IM (r ) ∗ (r ) }] (C, H ⊂ I ) and let H / H. R = [C p , C p , H, {rC ( p) ,O (q) ,Hg,c g,0 ∈ i,d j,b Let be given the operations {◦, ∗} : X × X −→ X and (◦, ∗) are applied respectively on the first and second element of IFP (element of R), where ◦, ∗ ∈ {min, max, max, min, average, average}. We will use an aggregation operation by a ). dimension H [108]: α(H,Hg(R) , r-layer)◦,∗ (R, Hg(r) 0 By using of the pair ◦, ∗ = min, max in the aggregation operation, then we obtain a pessimistic forecast of ICrA correlation coefficient values. With pair ◦, ∗ = max, min, then optimistic evaluations are acquired and with pair ◦, ∗ = average, average—we obtain the averaged estimate of the ICrA coefficients. ( p) ( p) Let us be given an IFP γ , δ. We call that criteria Ck and Cl are in • (γ , δ)-positive consonance, if μC ( p) ,C ( p) > γ and νC ( p) ,C ( p) < δ; k l k l • (γ , δ)-negative consonance, if μC ( p) ,C ( p) < γ and νC ( p) ,C ( p) > δ; k l k l • (γ , δ)-dissonance, otherwise. The pair is most often used for practical research is γ , δ = 0.85, 0.15. To automate the calculations from ICrA, the software application [54, 72] is applied. At last step of the algorithm, the strongest correlation dependencies are found using the following scale [15].

6 A Multilayer ICrA Approach to Number of Deaths for COVID-19 and Some Key Indicators of European Union Countries The high mortality rate caused by the coronavirus pandemic raises many questions about whether there is a link between it and some key indicators of the European Union. Numerous reports of mortality during pandemic generate a large amount of unclear or incomplete data. The classical correlation analysis cannot handle data processing in uncertain conditions. Intuitionistic fuzzy logic is a part of computer

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

283

intelligence. The proposed MLICrA in the previous Sect. 5, as well as the software for its implementation support the process of digital data transformation and support decision-making on reducing the lethal outcomes of COVID-19 in the EU by identifying key criteria affecting it such as population aging, social status, number of heart attacks and strokes, etc. In this section, we will apply MLICrA over a unique database containing the number of deaths per 1,000,000 people for 2021 and 15 key indicators of European Union countries [123, 124] in order to establish the dependencies on some key factors characterizing the health and social status of the inhabitants of the respective European countries. The key factors for the countries are: • C1 (total_deaths_per_million)—total deaths attributed to COVID-19 per 1,000,000 people; • C2 (reproduction_rate)—real-time estimate of the effective reproduction rate (R) of COVID-19; • C3 (stringency_index)—Government response stringency index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response); • C4 (population_density)—number of people divided by land area, measured in square kilometers, most recent year available; • C5 (median_age)—median age of the population; • C6 (aged_65_older)—share of the population that is 65 years and older, most recent year available; • C7 (aged_70_older)—share of the population that is 70 years and older; • C8 (gdp_per_capital)—Gross domestic product at purchasing power parity, most recent year available; • C9 (extreme_poverty)—share of the population living in extreme poverty; • C10 (cardiovasc_death_rate)—death rate from cardiovascular disease (annual number of deaths per 100,000 people); • C11 (diabetes_prevalence)—diabetes prevalence (of population aged 20 to 79); (1) (smokers)—share of people who smoke, most recent year available; • C12 0 (female_smokers)—share of women who smoke, most recent year available; • C12,1 0 (male_smokers)—share of men who smoke, most recent year available; • C12,2 • C13 (hospital_beds_per_thousand)—hospital beds per 1,000 people; • C14 (life_expectancy)—life expectancy at birth in 2019; • C15 (human_development_index)—summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The initial data are presented in a 3-D EMLIM A[C, O, H ] with a structure as presented in (3). Its elements are the values of the countries of the European union O = {O1 , O2 , . . . , O27 } by the evalution criteria from the set C = {C1 , C2 , . . . , C15 } (the (1) has two levels 1 and 0) in a time-moment h g ∈ H (for g = 1, ..., 10— criterion C12 from January to December 2021). The results from ICrA software [54] are presented under the form of IM in μ - ν view. μ-data, which consists the membership degree of the intuitionistic fuzzy

284

V. Traneva and S. Tranev

Fig. 1 Membership degrees of the Intercriteria IFPs

Fig. 2 The IF interpretational triangle with ICrA IFPs

correlations, is saved in the upper triangular part of the result matrix. ν-data, which consists the non-membership degree of the intuitionistic fuzzy correlations is saved in the lower triangular part of the result matrix (see Fig. 1). The data of the matrix are colored in the greyscale such that the highest values are colored in the darkest shade of grey. Then the results, obtained by the ICrA software, are portrayed on the intuitionistic fuzzy (IF) interpretational triangle in Fig. 2. The dependencies between each pair of criteria are performed in Table 2: The strongest correlation pairs are  found by calculating the distances of each pair from the pair 1, 0 [97] da,b = (1 − a)2 + b2 , which are given in Table 3 with the order of the pairs.

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ... Table 2 Intercriteria pairs Strength of the correlation

Pairs of criteria

Strong positive consonance [0, 95; 1] Positive consonance [0, 85; 0, 95) Weak positive consonance [0, 75; 0, 85) Weak dissonance [0, 67; 0, 75) Dissonance [0, 57; 0, 67), strong dissonance [0, 43; 0, 57), dissonance [0, 33; 0, 43) Weak dissonance [0, 25; 0, 33)

Weak negative consonance [0, 15; 0, 25) Negative consonance [0, 05; 0, 15) Strong negative consonance [0; 0, 05]

Table 3 The strongest correlation pairs No dCi ,C j Criteria 1. 2. 3. 4.

0.17 0.23 0.25 0.33

285

C8 − C15 C6 − C7 C5 − C7 C5 − C6

– 8-15 5-6, 5-7, 6-7 1-3, 1-14, 2-3, 3-14, 8-14, 10-{12, 2}0 , 10-13, {12, 1}0 -{12, 2}0 , {12, 1}0 -13, {12, 2}0 -13, 14-15 Other couples 1-3, 1-5, 1-8, 1-{12, 2}0 , 2-9, 2-11, 2-{12, 1}0 , 3-10, 4-5, 4-7, 6-9, 7-10, 7-{12, 1}0 , 7-{12, 2}0 , 8-11, 9-{12, 2}0 , 10-11, 11-{12, 2}0 8-10, 8-{12, 2}0 , 10-14, 10-15 – –

μCi ,C j

νCi ,C j

0.88 0.83 0.82 0.77

0.12 0.16 0.18 0.23

The resulting ICrA correlations between the pairs of criteria support healthcare decision-makers on the causes of higher pandemic mortality. The conclusions are the following: • The criteria C8 “Gross domestic product at purchasing power parity” and C15 “summary measure of average achievement in key dimensions of human development”, and C6 “share of the population that is 65 years and older” are in the strongest dependence. A dissonance relation is found between the criteria pairs C7 “share of the population that is 70 years and older and C5 “median age of the population”. • The indicator C1 “total deaths attributed to COVID-19 per 1,000,000 people” is in strong dissonance with C3 “Government response stringency index” and with C14 “life expectancy at birth in 2019”. The criterion C2 “real-time estimate of the effective reproduction rate (R) of COVID-19” is in a strong dissonance with C3 “Government response stringency index”; C3 “Government response stringency index” and C8 “Gross domestic product” are related with C14 “life expectancy at birth”. The criterion C14 “life expectancy at birth” are related with C15 “summary measure of average achievement in key dimensions of human development”.

286

V. Traneva and S. Tranev

Open dependencies can help the governments take effective measures to curb the spread of COVID-19. 3-D MLICrA can be applied on the same criteria, but instead (1) (1) 0 0 (C12,1 and C12,2 ) to use the first level of C12 . of the zero level of the criterion C12

7 A Juxtaposition of the Results of the ICrA Approach and Classical Correlation Analyzes This section demonstrates that the four statistical analyzes ICrA, PCA, SCA and KCA complement each other, but only ICrA can be applied to vague and incomplete data. Let us compare the results from the classical statistical rank correlation [36] analyzes with these from ICrA after their applying to the real dataset of total deaths attributed to COVID-19 per 1,000,000 people from January to December 2021 with confidence interval 95%. Table 4 describes the strongest correlations between the criteria obtained after the application of ICrA, PCA, SCA and KCA. Some differences are observed between these four methods of correlation analysis: the criterion C3 “Government response stringency index” is in a strong dissonance with C14 “Life expectancy” (μ(C3 , C14 ), ν(C3 , C14 ) = 0, 68; 0, 32) according to the ICrA; C3 is in the strongest correlation relation 0,53 with the criterion C2 “Real-time estimate

Table 4 The strongest correlations between the intercriteria IFPs according ICrA, PCA, SCA and KCA Criterion ICrA PCA KCA SCA C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0 C12,1 0 C12,2 C1 3 C1 4 C1 5

C14 , C2 C3 , C4 C14 , C15 C8 , C14 C7 , C6 C7 , C5 C6 , C5 C15 , C14 C5 , C7 C13 C4 0 C12,2 0 ,C C12,1 10 C14 , C2 C15 C8 , C14

C14 , C2 C3 C2 C14 C6 , C7 C7 C7 , C5 C15 C10 , C5 C13 C4 0 C12,2 0 C10 , C12,1 C14 , C2 C15 C14 , C8

C14 C1 , C3 C2 , C1 , C14 C8 C7 C7 C6 C15 C5 C13 C4 0 C12,2 0 C12,1 C14 C15 C8 , C14

C2 , C14 C1 , C3 C2 , C1 , C14 C8 , C14 C7 C7 C6 C15 C5 C13 C4 0 C12,2 0 C12,1 C2 , C14 C15 C8

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

287

of the effective reproduction rate (R) of COVID-19”, and the correlation coefficient between C6 and C12 is equal to 0.14; 0.45 and 0.35 respectively according to the method PCA, SCA and KCA. The last difference between the criteria correlations are due to high non-membership degree π of the IFPs μ(C3 , C14 ), ν(C3 , C14 ) (ν(C3 , C14 ) = 0, 32). Therefore no comparative distinction between the results, obtained from the ICrA and the other classic correlation analyzes. The divergences between the criteria correlations are due to high hesitancy degree ν of the IFPs.

8 Conclusion Digital transformation, IFHCISs and healthcare multicriteria evaluation system can be used together in a synergistic way to improve the quality of service, reduce costs and better control risks in the healthcare sector [121] in conditions of pandemic and uncertainty. The current chapter is developed a model for successful optimization of multicriteria systems embedded in IFHCISs by expanding three-dimensional ICrA to multilayer ICrA (3-D MLICrA). The outlined MLICrA approach for digitization of the processing of obscure data as part of the process of digital transformation of existing methods for statistical analysis has the following advantages: – The defined algorithms, assisting the decision-making process, can be applied to both the crisp parameters and the fuzzy or intuitionistic fuzzy ones; – The defined algorithms can be expanded to recapture information to other types of multi-dimensional data cubes [10]. The proposed MLICrA in this chapter, based on intuitionistic fuzzy logic as a computational intelligence tool, supports the process of digital transformation of data analysis and decision-making on reducing the lethal outcomes of the pandemic in the EU by identifying key criteria affecting it such as population aging, social status, number of heart attacks and strokes, etc. The demonstrated 3-D MLICrA approach established the relations and dependencies between total deaths attributed to COVID19 per 1,000,000 people and some key indicators of EU countries determining this mortality, provided by European Centre for Disease Prevention and Control [123, 124], for the data for the period from January to December, 2021. In future, the outlined approach for MLICrA can be applied to other types of fuzzy or intuitionistic fuzzy multi-dimensional data [10, 109], assisting the decisionmaking process. The MLICrA can be digitized and integrated as part of each IFHCISs for better decisions. In future pandemics, the proposed approach can be used to identify the key factors that cause higher mortality and higher hospitalizations. Acknowledgement This work is supported by the project of Asen Zlatarov University under Ref. No. NIX-440/2020 “Index matrices as a tool for knowledge extraction”.

288

V. Traneva and S. Tranev

References 1. Atanassov K (1983) Intuitionistic fuzzy sets. In: Proceedings of VII ITKR’s session, Sofia 2. Atanassov K (1987) Generalized index matrices. Comptes rendus de l’Academie Bulgare des Sciences 40(11):15–18 3. Atanassov K (2012) On intuitionistic fuzzy sets theory. STUDFUZZ, vol 283. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-29127-2 4. Atanassov K (1984) Conditions in generalized nets. In: Proceedings of the XIII spring conference of the Union of Bulgarian Mathematicians, Sunny Beach, April 1984, pp 219–226 5. Atanassov K (2010) On index matrices. Part 1: standard cases. Adv Stud Contemp Math 20(2):291–302 6. Atanassov K (2010) On index matrices, Part 2: intuitionistic fuzzy case. Proc Jangjeon Math Soc 13(2):121–126 7. Atanassov K (2013) On index matrices, Part 3: on the hierarchical operation over index matrices. Adv Stud Contemp Math 23(2):225–231 8. Atanassov K (2017) Intuitionistic fuzzy logics. Studies in fuzziness and soft computing, vol 351. Springer. https://doi.org/10.1007/978-3-319-48953-7 9. Atanassov K (2014) On index matrices. Part 5: 3-dimensional index matrices. Adv Stud Contemp Math 24(4):423–432 10. Atanassov K (2018) n-Dimensional extended index matrices Part 1. Adv Stud Contemp Math 28(2):245–259 11. Atanassov K (2014) Index matrices: towards an augmented matrix calculus. Studies in computational intelligence, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-109459 12. Atanassov K, Szmidt E, Kacprzyk J (2013) On intuitionistic fuzzy pairs. Notes Intuitionistic Fuzzy Sets 19(3):1–13 13. Atanassov K (2018) Intercriteria analysis over patterns. In: Sgurev V, Piuri V, Jotsov V (eds) Learning systems: from theory to practice. Studies in computational intelligence, vol 756. Springer, Cham 14. Atanassov K, Atanassova V, Chountas P (2019) Intercriteria analysis and arithmetic functions. In: Intuitionistic fuzziness and other intelligent theories and their applications. Studies in computational intelligence, vol 757 15. Atanassov K, Atanassova V, Gluhchev G (2015) InterCriteria analysis: ideas and problems. Notes Intuitionistic Fuzzy Sets 21(1):81–88 16. Atanassov K, Bureva V (2022) Index matrix representation of Big data structures. Comptes rendus de l’Acad’emie bulgare des Sciences 75(5) 17. Atanassov K, Sotirova E, Bureva V (2013) On index matrices. Part 4: new operations over index matrices. Adv Stud Contemp Math 23(3):547–552 18. Atanassov K, Marinov P, Atanassova V (2019) InterCriteria analysis with Interval-valued Intuitionistic fuzzy evaluations. In: Cuzzocrea A, Greco S, Larsen H, Saccá D, Andreasen T, Christiansen H (eds) Flexible query answering systems, FQAS 2019. Lecture notes in computer science, vol 11529. Springer, Cham, pp 329–338 19. Atanassov K, Mavrov D, Atanassova V (2014) Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues Intuitionistic Fuzzy Sets Generalized Nets 11:1–8 20. Atanassova V, Doukovska L, Mavrov D, Atanassov K (2015) InterCriteria decision making approach to EU member states competitiveness analysis: temporal and threshold analysis. In: Angelov P, Atanassov K, Doukovska L, Hadjiski M, Jotsov V, Kacprzyk J, Kasabov N, Sotirov S, Szmidt E, Zadrozny S (eds) Intelligent systems 2014. Springer, Cham, pp 95–106 21. Atanassova V, Doukovska L (2019) Business dynamism and innovation capability in the European Union Member States in 2018 through the prism of InterCriteria analysis. In: Cuzzocrea A, Greco S, Larsen H, Saccà D, Andreasen T, Christiansen H (eds) Flexible query answering systems, FQAS 2019. Lecture notes in computer science, vol 11529. Springer, Cham, pp 339–349

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

289

22. Atanassova V, Doukovska L, Atanassov K, Mavrov D (2014) InterCriteria decision making approach to EU member states competitive analysis. In: Proceedings of 4th international symposium on business modeling and software design, Luxembourg, Grand Duchy of Luxembourg, pp 289–294 23. Atanassova V, Mavrov D, Doukovska L, Atanassov K (2014) Discussion on the threshold values in the InterCriteria Decision Making approach. Notes Intuitionistic Fuzzy Sets 20(2):94– 99 24. Atanassova V, Roeva O (2018) Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process. Notes Intuitionistic Fuzzy Sets 24(3):53–63. https://doi.org/10.7546/nifs.2018.24.3.53-63 25. Bach MP, Zoroja J, Celjo A (2017) An extension of the technology acceptance model for business intelligence systems: project management maturity perspective. Int J Inf Syst Proj Manag 5:5–21 26. Baltussen R, Niessen L (2006) Priority setting of health interventions: the need for multicriteria decision analysis. Cost Eff Resour Allocation 4(1):14 27. Basaran MA, Simonetti B, D’Ambra L (2016) Fuzzy correlation and fuzzy non-linear regression analysis. In: Kahraman C, Kabak Ö (eds) Fuzzy statistical decision-making. Studies in fuzziness and soft computing, vol 343. Springer, Cham 28. Behkami NA, Daim TU (2012) Research forecasting for health information technology (HIT), using technology intelligence. Technol Forecast Soc Chang 79:498–508 29. Brooks P, El-Gayar O, Sarnikar S (2015) A framework for developing a domain specific business intelligence maturity model: application to healthcare. Int J Inf Manag 35:337–345 30. Bureva V, Sotirova E, Atanassova V, Angelova N, Atanassov K (2018) Intercriteria analysis over intuitionistic fuzzy data. In: Lirkov I, Margenov S (eds) Large-scale scientific computing, LSSC 2017. Lecture notes in computer science, vol 10665. Springer, Cham, pp 333–340. https://doi.org/10.1007/978-3-319-73441-5_35 31. Bureva V, Sotirova E, Sotirov S, Mavrov D (2018) Application of the InterCriteria decision making method to Bulgarian universities ranking. Notes Intuitionistic Fuzzy Sets 21(2):111– 117 32. Bustince H, Burillo P (1995) Correlation of interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst 74:237–244 33. Chen N, Xu ZS, Xia M (2013) Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis. Appl Math Model 37:2197–2211 34. Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36:1165–1188 35. Chiang DA, Lin NP (1999) Correlation of fuzzy sets. Fuzzy Sets Syst 102:221–226 36. Doane D, Seward L (2016) Applied statistics in business and economics. McGraw-Hill Education, New York 37. Doukovska L, Atanassova, V (2019) InterCriteria analysis of the most problematic factors for doing business in the European union, 2017-2018. In: Cuzzocrea A, Greco S, Larsen H, Saccá D, Andreasen T, Christiansen H (eds) Flexible query answering systems, FQAS 2019. Lecture notes in computer science, vol 11529. Springer, Cham, pp 353–360 38. Dawson L, van Belle J-P (2013) Critical success factors for business intelligence in the South African financial services sector. S Afr J Inf Manag 15:1–12 39. Dutta A, Heda S (2000) Information systems architecture to support managed care business processes. Decis Support Syst 30:217–225 40. Fale M, Abdulsalam Y (2022) Dr. Flynxz - a First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis. J King Saud Univ Comput Inf Sci 34(4):1138– 1149 41. Fidanova S, Roeva O (2018) Comparison of different metaheuristic algorithms based on intercriteria analysis. Comput Appl Math 340:615–628 42. Frazao T, Camilo D, Cabral E et al (2018) Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Med Inform Decis Mak 18:90. https://doi.org/10.1186/s12911-018-0663-1

290

V. Traneva and S. Tranev

43. Gaardboe R, Sandalgaard N, Nyvang T (2017) An assessment of business intelligence in public hospitals. Int J Inf Syst Proj Manag 5:5–18 44. Gerstenkorn T, Manko J (1991) Correlation of intuitionistic fuzzy sets. Fuzzy Sets Syst 44:39– 43 45. Gochev V, Hinov V, Gocheva P (2019) Mathematical modelling of basic electronic components with index matrices. In: Proceedings of the 33rd international conference on information technologies (InfoTech-2019), Bulgaria, pp 1–4, 19–20 September 2019 46. Gómez A, Carnero MC (2016) Decision Support System for maintenance policy optimization in medicinal gases subsystems. IFAC-PapersOnLine 49(28):268–73 47. Çuvalcıo˘glu G, Bureva V, Michalíková A (2019) Intercriteria analysis applied to university ranking system of Turkey. Notes Intuitionistic Fuzzy Sets 25(4):90–97 48. Hamedan F, Orooji A, Sanadgol H, Sheikhtaheri A (2020) Clinical decision support system to predict chronic kidney disease: a fuzzy expert system approach. Int J Med Inform 104–134 49. Hillerman T, Souza JCF, Reis ACB, Carvalho RN (2017) Applying clustering and AHP methods for evaluating suspect healthcare claims. J Comput Sci 19:97–111 50. Hinov N, Gocheva P, Gochev V (2019) Mathematical modelling of electronic circuits with index matrices. In: Proceedings of the 33rd international conference on information technologies (InfoTech-2019), Bulgaria, 19–20 September 2019 51. Hong D, Hwang SY (1995) Correlation of intuitionistic fuzzy sets in probability spaces. Fuzzy Sets Syst 75:77–81 52. Hung WL, Wu JW (2002) Correlation of intuitionistic fuzzy sets by centroid method. Inf Sci 144:219–225 53. Hussain M, Malik M, Al Neyadi HS (2016) AHP framework to assist lean deployment in Abu Dhabi public healthcare delivery system. Bus Process Manag J 22(3):546–65. https://doi.org/ 10.1108/BPMJ-08-2014-0074 54. Ikonomov N, Vassilev P, Roeva O (2018) ICrAData - software for intercriteria analysis. Int J Bioautom 22(1):1–10. https://doi.org/10.7546/ijba.2018.22.1.1-10 55. Jehu-appiah C, Baltussen R, Acquah C, Aikins M, Amah S, Bosu WK, Koolman X, Lauer J, Osei D, Adjei S (2008) Balancing equity and efficiency in health priorities in Ghana: the use of multicriteria decision analysis. Int Soc Pharmacoecon Outcomes Res (ISPOR) 11(7):1081–7 56. Kahraman C, Onar SC, Oztaysi B (2015) Fuzzy multicriteria decision-making: a literature review. Int J Comput Intell Syst 8(4):637–666. https://doi.org/10.1080/18756891.2015. 1046325 57. Kahraman C, Onar SC, Öztay¸si B (2022) A novel spherical fuzzy CRITIC method and its application to prioritization of supplier selection criteria. JIFS 42(1):29–36 58. Kahraman C, Öztaysi B, Çevik Onar S (2020) An integrated intuitionistic fuzzy AHP and TOPSIS approach to evaluation of outsource manufacturers. J Intell Syst 29(1):283–297 59. Khalil AM, Li SG, Lin Y, Li HX, Ma SG (2020) A new expert system in prediction of lung cancer disease based on fuzzy soft sets. Soft Comput 1–29 60. Kitsios F, Kapetaneas N (2022) Digital transformation in healthcare 4.0: critical factors for business intelligence systems. Information 13:247–261 61. Krawczak M, Bureva V, Sotirova E, Szmidt E (2016) Application of the intercriteria decision making method to universities ranking. In: Atanassov KT et al (eds) Novel developments in uncertainty representation and processing. AISC, vol 401. Springer, Cham, pp 365–372. https://doi.org/10.1007/978-3-319-26211-6-31 62. Krumova S, Todinova S, Mavrov D, Marinov P, Atanassova V, Atanassov K, Taneva S (2017) Intercriteria analysis of calorimetric data of blood serum proteome. Biochem Biophys Acta 1861(2):409–417. https://doi.org/10.1016/j.bbagen.2016.10.012 63. Leyendekkers J, Shannon A, Rybak J (2007) Pattern recognition: modular rings & integer structure. RafflesKvB Monograph, North Sydney 9 64. Liu B, Shen Y, Mu L, Chen X, Chen L (2016) A new correlation measure of the intuitionistic fuzzy sets. J Intell Fuzzy Syst 30(2):1019–1028 65. Liu H-C, Wu J, Li P (2013) Assessment of health-care waste disposal methods using a VIKORbased fuzzy multi-criteria decision making method. Waste Manag 33:2744–2751

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

291

66. Lu C, You J-X, Liu H-C, Li P (2016) Health-care waste treatment technology selection using the interval 2-tuple induced TOPSIS method. Int J Environ Res Public Health 13(6):562 67. Lopes J, Braga J, Santos MF (2021) Adaptive Business Intelligence platform and its contribution as a support in the evolution of Hospital 4.0. Procedia Comput Sci 184:905–910 68. Mahanta J, Panda S (2020) Fuzzy expert system for prediction of prostate cancer. New Math Nat Comput 16(01):163–76 69. Malik H, Chaudhary G, Srivastava S (2022) Digital transformation through advances in artificial intelligence and machine learning. J Intell Fuzzy Syst 42(2):615–622. https://doi.org/ 10.3233/JIFS-189787 70. Marsh K, Goetghebeur M, Thokala PBR (2017) Multi-criteria decision analysis to support healthcare decisions. Springer, Cham 71. Mavrov D (2015) Software for InterCriteria Analysis: implementation of the main algorithm. Notes Intuitionistic Fuzzy Sets 21(2):77–86 72. Mavrov D, Radeva I, Atanassov K, Doukovska L, Kalaykov I (2015) Intercriteria software design: graphic interpretation within the intuitionistic fuzzy triangle. In: Proceedings of the fifth international symposium on business modeling and software design. Springer, Cham, pp 279–283 73. Mettler T, Vimarlund V (2009) Understanding business intelligence in the context of healthcare. Health Inform J 15(15):254–264 74. Mitchell HB (2004) A correlation coefficient for intuitionistic fuzzy sets. Int J Intell Syst 19:483–490 75. Nasmul S, Hojjat A (2013) Computational intelligence: synergies of fuzzy logic. Neural networks and evolutionary computing. Wiley, Hoboken 76. Nagell T (1950) Introduction to number theory. Wiley, New York 77. Naseer I, Khan BS, Saqib S, Tahir SN, Tariq S, Akhter MS (2020) Diagnosis heart disease using Mamdani fuzzy inference expert system. EAI Endorsed Trans Scal Inf Syst 7(26) 78. Nilashi M, Ahmadi H, Ahani A, Ravangard R (2016) Technological forecasting & social change determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technol Forecast Soc Chang 111:244–64 79. Olszak CM, Ziemba E (2012) Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of upper Silesia. Interdiscip J Inf Knowl Manag Poland 7:129–150 80. Pai FY, Huang KI (2011) Applying the technology acceptance model to the introduction of healthcare information systems. Technol Forecast Soc Chang 78:650–660 81. Pandey B, Mishra RB (2009) Knowledge and intelligent computing system in medicine. Comput Biol Med 39(3):215–230 82. Pencheva T, Angelova M (2017) InterCriteria analysis of simple genetic algorithms performance. In: Georgiev K, Todorov M, Georgiev I (eds) Advanced computing in industrial mathematics, SCI, vol 681. Springer, Cham, pp 147–159. https://doi.org/10.1007/978-3-31949544-613 83. Peters JF (2009) Fuzzy sets, near sets, and rough sets for your computational intelligence toolbox. In: Hassanien AE, Abraham A, Herrera F (eds) Foundations of computational intelligence. Studies in computational intelligence, vol 202. Springer, Heidelberg 84. Ratia M, Myllärniemi J, Helander N (2018) The new era of business intelligence: Big Data potential in the private health care value creation. Meditari Account Res 26(26):531–546 85. Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66 86. Roeva O, Vassilev P, Angelova M, Pencheva T (2015) InterCriteria analysis of parameters relations in fermentation processes models. In: Néñez M, Nguyen N, Camacho D, Trawi´nski B (eds) ICCCI 2015. LNCS (LNAI), vol 9330. Springer, Cham, pp 171–181. https://doi.org/ 10.1007/978-3-319-24306-1_17 87. Roeva O, Fidanova S, Paprzycki M (2016): InterCriteria analysis of ACO and GA hybrid algorithms. In: Fidanova S (ed) Recent advances in computational optimization. Studies in computational intelligence, vol 610. Springer, Cham, pp 107–126

292

V. Traneva and S. Tranev

88. Roeva O, Ikonomov N, Vassilev P (2019) Discovering knowledge from predominantly repetitive data by intercriteria analysis. In: Fidanova S (ed) Recent advances in computational optimization. Studies in computational intelligence, vol 795. Springer, Cham, pp 213–233 89. Rusev G, Bureva V (2019) InterCriteria analysis applied to human resources in science and technology. NIFS 25(2):67–76 90. Sadat Asl AA et al (2022) Fuzzy expert systems for prediction of ICU admission in patients with COVID-19. Intell Decis Technol 16:159–168 91. Sadeghi A (2018) Success factors of high-tech SMEs in Iran: a fuzzy MCDM approach. J High Technol Manag Res 29(29):71–87 92. Schakoff R (2009) Intelligent systems: principles, paradigms and pragmatics. Jones & Bartlett Publishers 93. Shu-Hsien L (2005) Expert system methodologies and applications - a decade review from 1995 to 2004. Exp Syst Appl 28:93–103 94. Sotirov S, Atanassova V, Sotirova E et al (2017) Application of the intuitionistic fuzzy intercriteria analysis method with triples to a neural network preprocessing procedure. Comput Intell Neurosci. https://doi.org/10.1155/2017/2157852 95. Sotirov S, Sotirova E, Atanassova V, Atanassov K et al (2018) A hybrid approach for modular neural network design using intercriteria analysis and intuitionistic fuzzy logic. Complexity 2018. https://doi.org/10.1155/2018/3927951 96. Stratiev D, Sotirov S, Shishkova I, Nedelchev A, Sharafutdinov I, Vely A, Mitkova M, Yordanov D, Sotirova E, Atanassova V et al (2016) Investigation of relationships between bulk properties and fraction properties of crude oils by application of the intercriteria analysis. Pet Sci Technol 34:1113–1120 97. Szmidt E, Kasprzyk J (2009) Amount of information and its reliability in the ranking of Atanassov intuitionistic fuzzy alternatives. In: Rakus-Andersson E, Yager R, Ichalkaranje N, Jain LC (eds) Recent advances in decision making, SCI 222. Springer, Heidelberg, pp 7–19 98. Szmidt E, Kacprzyk J, Bujnowski P (2012) Correlation between intuitionistic fuzzy sets: some conceptual and numerical extensions. In: WCCI 2012 IEEE world congress on computational intelligence, Brisbane, QLD, pp 1–7 99. Thokala P, Devlin N, Marsh K, Baltussen R, Boysen M, Kalo Z, et al (2016) Multiple criteria decision analysis for health care decision making - an introduction: report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value Health 19(1):1–13. https://doi.org/10. 1016/j.jval.2015.12.003 100. Todinova S, Mavrov D, Krumova S, Marinov P, Atanassova V, Atanassov K, Taneva S (2016) Blood plasma thermograms dataset analysis by means of InterCriteria and correlation analyses for the case of colorectal cancer. Int J Bioautom 20(1):115–124 101. Traneva V (2017) On 3-dimensional multilayer matrices and operations with them. Ann Sect Inform Union Sci Bulgaria 8(2015–2016):23–36 102. Traneva V (2016) One application of the index matrices for a solution of a transportation problem. Adv Stud Contemp Math 26:703–715 103. Traneva V (2017) On 3-dimensional multilayer matrices and operations with them. Ann Inform Sect Union Sci Bulgaria (2015–2016) 8:23–36. (in Bulgarian) 104. Traneva V, Tranev S (2020) Retrospection of intercriteria analysis and positioning in relation to some classical statistical methods. Ann Assen Zlatarov Univ XLIX(2) (2020) 105. Traneva V, Tranev S (2021) Intuitionistic fuzzy index-matrix selection for the outsourcing providers at a refinery. In: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU (eds) Intelligent and fuzzy techniques for emerging conditions and digital transformation, INFUS 2021. Lecture notes in networks and systems, vol 308. Springer, Cham, pp 119–128. https://doi.org/10.1007/978-3-030-85577-2_14 106. Traneva V, Bureva V, Sotirova E, Atanassov K (2017) Index matrices and OLAP-cube. Part 1: application of the index matrices to presentation of operations in OLAP-cube. Adv Stud Contemp Math 27(2):253–278 107. Traneva V, Sotirova E, Bureva V, Atanassov K (2015) Aggregation operations over 3dimensional extended index matrices. Adv Stud Contemp Math 25(3):407–416

Multi-layered Intuitionistic Fuzzy Intercriteria Analysis ...

293

108. Traneva V, Tranev S (2017) Index matrices as a tool for managerial decision making. Publ. House of the Union of Scientists, Bulgaria. (in Bulgarian) 109. Traneva V, Tranev S, Szmidt E, Atanassov K (2017) Three dimensional intercriteria analysis over intuitionistic fuzzy data. In: Kacprzyk J, Szmidt E, Zadro˙zny S, Atanassov K, Krawczak M (eds) Advances in fuzzy logic and technology, IWIFSGN, EUSFLAT. Advances in intelligent systems and computing, vol 643. Springer, Cham, pp 442–449 110. Traneva V, Tranev S (2020) Intuitionistic fuzzy intercriteria approach to the assessment in a fast food restaurant. In: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga A, Sari I (eds) Intelligent and fuzzy techniques in big data analytics and decision making, INFUS 2019. Advances in intelligent systems and computing, vol 1029. Springer, Cham, pp 589–597 111. Traneva V, Tranev S (2021) Optimization of an oil refinery valuation system through the intuitionistic fuzzy InterCriteria analysis approach. In: Kahraman C, Cevik Onar S, Oztaysi B, Sari I, Cebi S, Tolga A (eds) Intelligent and fuzzy techniques: smart and innovative solutions, INFUS 2020. Advances in intelligent systems and computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_181 112. Traneva V, Tranev S (2018) InterCriteria analysis of the human factor assessment in a mobile company. In: Georgiev I, Kostadinov H, Lilkova E (eds) Advanced computing in industrial mathematics. BGSIAM 2018. Studies in computational intelligence, vol 961. Springer, Cham, pp 381–392. https://doi.org/10.1007/978-3-030-71616-5_34 113. Traneva V, Tranev S (2020) A multidimensional intuitionistic fuzzy InterCriteria analysis in the restaurant. J Intell Fuzzy Systems/ifs189079 39(5):6059–6071. https://doi.org/10.3233/ JIFS-189079 114. Yeoh W, Popovic A (2016) Extending the understanding of critical success factors for implementing business intelligence systems. J Assoc Inf Sci Technol 67:134–147 115. Villamarín-García JM (2020) Contributions from organisational collaboration to business intelligence solutions success. Int J Bus Inf Syst 33:103–131 116. Wagner M, Khoury H, Willet J, Rindress D, Goetghebeur M (2016) Can the EVIDEM framework tackle issues raised by evaluating treatments for rare diseases: analysis of issues and policies, and context-specific adaptation. Pharmacoeconomics 34(3):285–301 117. Wang LE, Liu HC, Quan MY (2016) Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Comput Ind Eng 102:175–85 118. Zadeh L (1965) Fuzzy Sets. Inf Control 8(3):338–353 119. Zeng W, Li H (2002) Correlation coefficient of intuitionistic fuzzy sets. J Ind Eng Int 3(5):33– 40 120. Zhang Q, Jiang S (2009) Statistical correlation of intuitionistic fuzzy sets. In: Proceedings of the eighth international conference on machine learning and cybernetics, Baoding, pp 817–821 121. Zheng G, Zhang C, Li L (2014) Bringing business intelligence to health information technology curriculum. J Inf Syst Educ 25:317–326 122. InterCriteria Research Portal. http://intercriteria.net/software/. Accessed 21 Jan 2022 123. https://github.com/owid/covid-19-data/tree/master/public/data. Accessed 5 Mar 2022 124. https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases. Accessed 5 Mar 2022

Development of Intelligent Healthcare Sytems Through Digital Transformation and Operations Research Modeling Gozdem Dural-Selcuk

Abstract Healthcare service providers face with an ever increasing demand as an inevitable consequence of changing epidemiological and demographic dynamics. Increasing longevity is accompanied by rising morbidity levels such that people are living longer but unhealthier. Healthcare service providers try to keep up with the current trend against the backdrop of limited resources. They also encounter new challenges that stem from altering lifestyles of patients. Patients very often opt for ondemand healthcare services. As an answer to this multifaceted problem, healthcare service providers are in search of contemporary ways that are more efficient and effective. That being the case, common practices cover digitalization of services, creation of new channels to get in touch with patients and integration of healthcare services in an innovative way so that extra room could be spared for increasing demand. Behind the scenes, there is a data deluge in innovative healthcare services by the virtue of digitalization. This fact provokes data driven models that would enable healthcare practitioners to make informed decisions, both at systems and patient level. This book chapter addresses the motives and practices of digital health and elaborates on the contributions of digitalization in healthcare sector from a modeler’s perspective. Keywords Digital health · Operations research · Intelligent systems · Healthcare services

1 Introduction Governments worldwide confront soaring healthcare expenditures in the new millennium. Advances in healthcare services have resulted in increasing longevity with higher levels of morbidity due to non-communicable diseases [95]. This fact has a reflection in population structure, such that the proportion of elderlies with longterm conditions is getting higher than ever before. Those people are the ones who G. Dural-Selcuk (B) Social Sciences University of Ankara, Ankara, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_13

295

296

G. Dural-Selcuk

are in need of advance healthcare support. It hereby creates a dilemma that institutions invest in healthcare technology for better life conditions, however increasing longevity and morbidity result in higher healthcare expenditures which, in return, constitutes a great burden on healthcare services. As a result, sustainability of healthcare services becomes a matter of concern especially in countries with relatively older populations [19, 23, 66]. The point is, the rise in healthcare expenditures due to increasing morbidity is just the tip of the iceberg. There are other determinants such as advances in technology and high-cost treatments that feed in healthcare expenditures [82]. Another important aspect is the fact that expected life standards of nations are changing proportional to their income levels, indicating that a necessity of a higher income country is perceived as luxurious in lower income countries [6]. That is, healthcare standards in high income countries are re-defined towards more demanding systems as their level of welfare is increasing. The geographical discrepancy in health outcomes is once again highlighted by a recent study telling that the countries with lower socio-demographic index still suffer from the burden of infectious diseases [73]. A controversial finding, on the other hand, addresses that infectious diseases may have a mid to long-term effect on burden of communicable diseases even in countries with high socio-demographic index [18]. These contradicting outcomes inform that increasing demand for healthcare services is a permanent matter of fact regardless of the development level of a country. At this point, there occurs an inevitable need for smarter ways to provide decent and equitable healthcare services worldwide. World Health Organization (WHO) promotes digital health as means for Sustainable Development Goals (SDG) [98]: With the recognition that information and communications technologies present new opportunities and challenges for the achievement of all 17 Sustainable Development Goals, there is a growing consensus in the global health community that the strategic and innovative use of digital and cutting-edge information and communications technologies will be an essential enabling factor towards ensuring that 1 billion more people benefit from universal health coverage, that 1 billion more people are better protected from health emergencies, and that 1 billion more people enjoy better health and well-being (WHO’s triple billion targets included in its Thirteenth General Programme of Work, 2019-2023) [99].

WHO advises to utilize digital technologies for scaling up healthcare services towards a more efficient and sustainable future. By this means, their ultimate goal is to make quality health services attainable for all, including low and middle-income countries. This book chapter is envisaged to give information about digital health, its state-ofthe art applications and how it feeds into intelligent health systems through operations research modeling. Most studies in literature handles these concepts separately and overlooks the harmony among them. However, intelligent health systems can only be accomplished through the use of digital technologies in combination with analytical modeling approaches. The novelty of this chapter lies in the fact that it combines a wide range of information regarding digital health, its application areas and modeling approaches in one single study. By doing so, it aims to enable operations research modelers to get an idea about where their modeling attempts stand in the big picture

Development of Intelligent Healthcare Sytems ...

297

of intelligent health systems and how digital technologies contribute to their studies in that journey. The organization of the rest of this chapter is given as follows. Section 2 gives background information about motives for digital transformation in healthcare services and state-of-the art practices of digital health. Section 3 informs about the practicality of digital health from a modeling perspective and discusses a wide range of operations research modeling studies in the application areas of operational efficiency, preventive healthcare and public health. Section 4 is dedicated to challenges encountered in the journey of digitalization in healthcare. Finally, the chapter concludes with a summary and future directions for the development of digital health.

2 Literature Review This section gives background information about digital health in two ways. In the first part, main motivation factors for digitalization in helathcare are addressed, whereas in the second part state-of-the art digital health applications are introduced. This section does not provide readers with a complete and through review of the relevant literature, but it is rather designed to inform readers about the topic and warm up them for the upcoming sections.

2.1 Motivation for Digital Transformation in Healthcare Services The Deloitte Center for Health Solutions has conducted a survey with technology executives and leaders [25]. The outcomes reveal that the key reason why professionals want to invest in digital transformation in healthcare is to change the way they interact with patients. Better patient satisfaction, engagement and higher quality of care is listed among the ultimate goals. Most organizations evaluate themselves as on the midway in this digitalization journey and declare that there is much more room for improvement. They have also stated that before COVID, they have made piecemeal investments such as electronic health records, patient mobile apps, but did not change their business models. The COVID outbreak deemed as pandemic by WHO [100] has accelerated the digital transformation process [88, 89, 104]. It has instigated patient’s privatization of convenience and access to care. Patients recently embrace on-demand healthcare services, that is, they prefer to reach the service when and where they want it. Thus, service providers are now in need of comprehensive systems that would help them change the way they operate. In this direction, experts seek for digital tools that could enable their desire of novel and agile care models. This is the point where business intelligence and analytics come to scene given that digitalization aids unified data collection. On the contrary to fragmented and

298

G. Dural-Selcuk

not interoperability systems, a holistic approach to patient journey in the system has become a crucial phenomenon. A recent literature review study [70] lists “integrated management of information technology in health” among the most frequently studied categories in the field. As a consequence of this development in integrated services, contact point with patients is no more restricted to physical hospitals, but it is before, in and after hospital through alternative channels of interaction methods (e.g. mHealth [39], remote monitoring [9] etc.). In other words, digital transformation in healthcare results in new value propositions for end users, i.e. patients and customers and offers new ways of healthcare delivery [40]. New business models in healthcare services provide different stakeholders of the system, e.g. patients, clinicians, caregivers, etc., with an easy to access and improved care through a potential chance of personalization. The contribution of new business models in healthcare is depicted two-fold. Firstly it does have a patientcentric approach [40]. Secondly, new care models are expected to grant operational efficiency for different service providers [59]. We hereby articulate some particular benefits of digital transformation in healthcare from three different angles: patient’s, clinician’s and healthcare institution’s side.

Patient’s Side • More patients opt for on-demand services. Thanks to new digital channels, they can reach out the healthcare services when and where they are available. • With digital tools, patients are kept more engaged in the treatment process. • Patients are kept informed about their progress. • There is a chance of real-time alerting of patients through digital tools, so that, rate of medication errors are minimized.

Clinician’s Side • Clinicians get access to unified and reliable data about patients. • Clinicians get an option to monitor a patient’s vital indicators online. • Clinicians have a chance to get supported by artificial intelligence (AI) tools during their decision making process. • The use of AI together with unified and timely data sources would lower down the rate of error in clinical decision making process.

Healthcare Institution’s Side • Institutions change the current patient flow towards a cost efficient system. It may be disrupting at the beginning, however, it is supposed to be rewarding at the end.

Development of Intelligent Healthcare Sytems ...

299

• Thanks to the fact that there is access to unified data, institutions can adopt systems approach which would enable them to streamline their operations in a holistic way. There is particularly a higher potential for accurate estimates regarding staffing and resource management. • Through digital channels, institutions create alternative paths of patient flow, which in return, expands their service capacity without any physical investment. • Healthcare institutions get the chance to quit one-fits-all policies and switch to a patient-centric approach. National Healhcare Service (NHS) in the United Kingdom summarizes the benefits of digital transformation as [74]: • • • •

Improved clinical outcomes, patient safety, quality of care, Improved patient experience, Financial savings, Consistency in services via reduced number of errors and variation.

All in all, digital technologies equip healthcare service providers with an insightful way of operations and resource management, hence, journey to digital health is supposed to answer the major concerns of sustainability in healtcare against increasing demand. Higher efficiency levels in new care models are not only supposed to create extra room for rising demand but they are also considered as an alternative way of making healthcare services attainable for those who are deemed as disadvantaged and/or underrepresented.

2.2 The Advent of Digital Health This section is designed to introduce some frequently used tools of digital health. The common point of all is the fact that they collect individual patient level data that is pooled into a unified system depending on data governance rules. Hence, individual level data piles up and constructs a holistic database for wider populations. By this means, digitalization in health accelerates patient-centric approach in tandem with a chance of improvement in population-wide strategies. We herewith introduce and exemplify digital health technologies with a categorization of Electronic Health Records, IoT Devices, Intelligent Systems, Telemedicine and Virtual Clinics respectively.

Electronic Health Records (EHR) Electronic Health Records contain demographic and medical data of a patient in an electronic environment. On-paper recording has come to an end by the introduction of EHR. It keeps track of patient medical information, such as diagnosis, symptoms, treatments, medication on a dynamic time-line. The main advantage of EHR

300

G. Dural-Selcuk

compared to on-paper records is that it enables data sharing between healthcare institutions as long as local data governance rules permit. Data sharing have multi-facet practical advantages. For instance, EHR enables clinicians to access prior information about the health status of an emergent case regardless of the location that particular case has occurred and this could help lower down the risk of mistreatment in urgent care. It may also be helpful for those elderlies, who may suffer from dementia, such that the management of their health conditions might successfully be outsourced to professionals. It is possible to increase the number of examples to showcase the patient level advantage of EHR. On the other hand, individual level records construct a pooled database which caters an online monitoring platform that decision makers could utilize for the development of population-wide strategies. To summarize, use of EHR is highly promoted for time saving purposes (e.g. reduced time of documentation) and better health outcomes (e.g. lower rate of medical errors) [13] despite some contradicting outcomes indicating that it may lead to poor doctor-patient relationship [20] and inefficiencies in emergency departments [72].

Internet of Things (IoT) Devices Some prevalent digital health technologies cover IoT (Internet of Things) devices. These devices collect data through sensors and actuators, then they process it and deliver insights to either patients and/or clinicians via mobile, web applications or dashboards. In EHR, data is collected on discrete and inaccurate time intervals, on the contrary, IoT devices implement continuous flow of data. IoT devices that are used in hospitals facilitate online performance monitoring at operational level. It has become a common practice to use dashboards in emergency and surgical departments to keep track of key performance indicators (KPI). These dashboards tell the current status of KPIs and give alerts when an over-threshold deviation occurs such as a patient with a very long waiting time in an emergency department or a surgery duration longer than anticipated. This online monitoring system in a way forces for higher operational efficiency. Besides IoT devices that are used in hospital, the field is dominated mainly by personal wearable devices and mobile applications that are named as mHealth or eHealth. However, it is quite challenging when it comes to data collection and processing through personal devices. The point is that data collected within a hospital is governed by national regulations, nevertheless data collection through wearable personal devices highly relies on personal consent. There is a high potential to collect tremendous amount of patient level data through wearable IoT devices, which in return makes it arduous to govern and process. Interested reader is referred to [91] and [54] for the taxonomy of IoT technology in healthcare; [78] and [27] for the recent review of current and potential application areas of IoT.

Development of Intelligent Healthcare Sytems ...

301

Intelligent Systems There has been a data deluge as a consequence of the emergence of digital technologies. It has invigorated the field of intelligent systems through which one can conduct retrospective analysis and also gather insights for the future. Intelligent systems have found a fertile ground in healthcare sector as well. The areas where data mining and intelligence tools are used in healthcare can be listed but not limited to: • • • • •

Diagnosis Image processing Insight gathering from EHR and preventive care Automation of repetitive processes Notification of doctors and patients

Interested reader may refer to the review article for specific machine learning methodologies and their corresponding use cases in the healthcare literature [45]. The use case areas listed above are deemed as intelligent assistive technologies that are supposed to reinforce healthcare practices, yet, whether these technologies can replace professionals sometime in future is a major field of debate.

Telehealth Telehealth is an alternative channel to deliver and access healthcare services through telecommunication technologies. Telehealth provides patients with an easy access to healthcare services in situations where they are in a geographically disadvantaged region. Another prominent asset of this care delivery method is that people access care at the safety of their homes. Thus, it is not surprising that the use of telehealth has increased by 38 times compared to its usage rate in pre-COVID period [8]. The increased uptake level of telehealth is expected to be permanent to a certain extent such that 40% of telehealth users state that they will continue using it after COVID too [8]. It seems that telehealth care delivery system has the wind at its back. In that sense, it has even been advised to update education curriculum accordingly [31]. The penetration of telehealth has not been equal among specialties, yet, its use has been mostly prevalent in psychiatry and substance-use treatment. Howbeit it is possible to encounter various fields that would exploit telehealth to deliver care for those patients who are in need of a solid healthcare support. Example specialty cases can be listed but not limited to pediatric care [21], emergency care [31, 87], post-acute care [55], long-term conditions [24, 29], cancer [30, 97] and palliative care [53], etc.

Virtual Clinics The use of remote clinics in community healthcare dates back to 1990s [57], where the elderly Americans living in rural regions were screened to detect those ones who

302

G. Dural-Selcuk

would be under nutritional risk. In that way, the regional discrepancy in terms of health resources was intended to be eliminated in rural geography. In such healthcare models, remote clinics work in coordination with a centralized health care system and only used for first time screening and/or routine follow-up screening of patients and they manage the referral of patients who are in need of an upgraded care or intervention to the agents in upper levels of healthcare systems. The technical and academic community both refer remote clinics and virtual clinics interchangeably. The main characteristic of them is that they are not led by consultants. They are managed either by technicians and/or specially trained healthcare providers. They may but usually do not cover face-to-face appointments with the patients. In some cases, remote/virtual clinics collect patient information for a remote review later by a consultant specialist. The idea is to increase capacity for outpatient appointments with specialists while avoiding the high costs associated with setting up and running a full consultant led clinic. Tuberculosis [63], renal diseases [69] and glaucoma [58, 103] are listed among the practices of virtual clinics.

3 Practicality of Digital Health from a Modeling Perspective Healthcare problems are deemed to be complex in nature [14]. Definition of a problem situation in healthcare context requires an amalgamation of exceptional amount of dynamics, of which most is subject to change with time. In particular, treatment regimens are changing with advances in health and technology, whereas the population and epidemiological dynamics are also volatile and uncertain. In the meantime, micro-level aspects like patient disease progression are also dynamic and stochastic. Being that the case, policy makers struggle to develop effective strategies to tackle with evolving needs of the public under such unstable conditions. Moreover, policies that are implemented in a piece-meal fashion either fall behind the needs or trigger unanticipated spill-over effects within the healthcare delivery system. Recent developments in digital health permit astounding amount of data flow and accumulation that drag the attentions on health analytics [45]. Digital technologies have penetrated into healthcare sector through EHRs, IoT devices, personal wearable devices, smart phones and medical images collected within hospital or virtual clinics. Medical data is pooled and stored in centers as data governance rules apply, then it is fed into intelligence tools where it is processed online by cloud computing systems. The ultimate aim is to serve higher quality of care against increasing demand in the backdrop of limited resources. In addition to cloud computing systems, operations research discipline also pays a great effort on healthcare problems where they develop data-driven models to aid informed decisions under stochastic conditions by offering optimum use of resources [14]. The big picture that shows data flow and data processing in healthcare is illustrated with an overlook in Fig. 1.

Development of Intelligent Healthcare Sytems ...

303

Fig. 1 An overlook on data flow and processing in healthcare (Solid lines represent information flow, dash lines represent involvement of different stakeholders.) Source: Own elaboration

In the following part of this section, we articulate some specific operations reseacrh modeling studies and discuss their contributions in healthcare service delivery from three main perspectives: operational efficiency, preventive healthcare and public health respectively. The aforementioned areas of contribution are not mutually exclusive, they are rather overlapping and complementary. Hence, it could be possible that a study mentioned in one area can possibly be acknowledged in others, but, hereby counted in the area where its contribution is found to be dominant.

3.1 Operational Efficiency in Healthcare Delivery The overarching aim of this section is to inform the reader about modeling studies on operations management in healthcare services. We will present two main approaches: mathematical models focusing on capacity allocation decisions and systems level studies that are designed to evaluate the performance of particular innovations in healthcare delivery.

3.1.1

Capacity Allocation Problems

There is a frequent and crucial issue of congestion in healthcare services which results in high patient turn-down rates. This phenomena can be defined as the case of bed shortages that causes patients who are in need of care cannot get what they need. This is an inevitable outcome of the imbalance between supply and demand. By

304

G. Dural-Selcuk

Fig. 2 Hierarchy of resource allocation decisions in a healthcare delivery system Source: Own elaboration

this means, hospital management teams have the cumbersome duty of optimizing hospital resource utilization such that they minimize turn-down rates. The most prominent and the most expensive hospital resources are deemed to be hospital beds and surgical rooms. The challenge here is the allocation of these resources among specialties, which is named as “patient case mix problem”. It decides on the optimum allocation of beds and surgical room capacities among specialties given the demand structure. Hospital resource allocation is done in a hierarchical way: strategic, tactical and operational. Strategic level decisions cover capacity expansion and resource allocation (e.g. bed, ward, staff) among specialties at higher level (i.e. patient case mix). Tactical level decisions consists of master surgery schedules and allocation of operating room time slots to specialties. Operational level decisions, on the other hand, include daily operating room plans, surgery scheduling and staffing. The hierarchy of resource allocation decisions in a healthcare delivery system is illustrated in Fig. 2. Capacity allocation is a multi-dimensional problem. The decisions of scheduling and staffing should be done by taking into account downstream resources such as beds in wards, capacity in intensive care units (ICU) and recovery rooms where patients are awaited shortly after surgery. There are also stochastic components in the problem context, i.e. surgery duration, length of stay (LoS), number and content of emergency cases. It is quite challenging to pool all listed components within a single optimization problem, however, there is a wide range of mathematical modeling studies that include a subset of them. Some of them is summarized in Table 1 classified in terms of their level of decision (strategic, tactical, operational), downstream resources considered (ward, ICU, recovery), whether it is having a stochastic component or not. Mathematical models mentioned in Table 1, except few of them [7, 65, 67, 71, 85], do not use real hospital data for their numerical experiments, highlighting the need for digital health initiatives that generate quality data to be fed into such data-driven models for better decision making.

Development of Intelligent Healthcare Sytems ... Table 1 Resource allocation decisions: example modeling studies Article Level of decision Downstream resource [1] [94] [7] [2] [36] [17] [65] [35] [5] [38] [37] [71] [85] [106] [67] [96]

3.1.2

Strategic Strategic Tactical Strategic Tactical, Operational Tactical, Operational Strategic, Tactical, Operational Tactical Operational Strategic, Tactical Strategic Tactical Tactical, Operational Tactical, Operational Tactical, Operational Operational

305

Stochastic/Deterministic

ICU, Ward ICU, Ward Ward ICU, Ward Recovery room Ward Ward

Deterministic Deterministic Stochastic Stochastic Deterministic Deterministic Stochastic

Ward Ward ICU, Ward Ward ICU, Ward Ward ICU, Ward ICU, Ward Ward

Stochastic Stochastic Stochastic Stochastic Stochastic Stochastic Stochastic Stochastic Stochastic

Systems Approach

Another data intensive modeling approach commonly used in healthcare services is system simulation. Modeling techniques include system dynamics, discrete event simulation (DES) and agent-based modeling. System dynamics is mainly exploited for macro-level systems analysis through feedback mechanisms within or across organizational boundaries in order to evaluate the resulting system behavior. It does have a holistic modeling perspective that could be used in the evaluation of longterm strategic decisions. However, it does not capture individual-level patient flow. Agent-based models, on the contrary, focus on individual agents’ behavior within a closed-system, but the boundaries of system gets limited as a sacrifice. DES models, on the other hand, are used in cases where the problem structure is very complex and stochastic in nature so that it is not possible to solve it optimally. In such cases, DES models provide decision makers with a stochastic representation of real life problems with relatively lower number of limiting assumptions compared to mathematical models. These three modeling approaches help a healthcare decision maker evaluate patient flows under different system settings or system boundaries, e.g. a regional hospital, healthcare delivery system within a city/region, national healthcare system, etc. Interested reader could be referred to relevant literature for a through discussion of these simulation methodologies on their similarities and differences [10, 22, 77]. For a better understanding, we consider empirical system dynamics models that are used for the evaluation of healthcare systems. Some common application areas

306

G. Dural-Selcuk

Table 2 Data-driven system dynamics studies on healthcare delivery services Article Are population System Is a new patient Is sustainability dynamics boundaries pathway discussed? included? proposed? [102]

No

[61]

No

[11]

No

[49]

Yes

[92]

No

[68]

No

[64]

Yes

[80]

No

[16]

Yes

[32]

Yes

National healthcare system A regional hospital A regional healthcare system A regional hospital/Specific specialities A regional hospital A regional hospital National healthcare system National healthcare system National healthcare system A regional hospital

Yes

No

No

No

No

No

No

No

Yes

No

Yes

No

No

Yes

No

No

No

Yes

Yes

Yes

Adopted from [32]

have been disease screening strategies [44, 93], infectious disease modeling [3, 4] evaluation of long-term policies in regional or national care systems. We focus on the last one as a show case and elaborate on examples of data-driven system dynamics models that are designed for performance evaluation of an innovative healthcare service delivery alternative. We summarize the studies in Table 2 according to four criteria: whether it captures population dynamics, system boundaries (e.g. regional hospital, national health system, etc.), whether model introduces a new patient care pathway, whether the study does have a word on sustainability. As it is seen in Table 2, each study defines a different system within a different context,however,therearecertaincommonalitiesintheirconcernsandhighlights.Theyare conducted with a quest of better healthcare delivery despite increasing demand under tightbudgets.Theyagreethatcapacityexpansionwithinahealthcaresystemaccommodatesatemporaryrelief,onthecontrary,itisadvisedtoplayonflowrates(i.e.admission rates, discharge rates) such that patient throughput could be increased. Another focal point they declare is the fact that new policies that concentrate on a particular patient group (i.e. multi-morbidity patients, a certain specialty, a dedicated age-group) could inaugurate an overemphasis on the needs of that particular patient group which may unexpectedly result in additional demand [32].

Development of Intelligent Healthcare Sytems ...

307

3.2 Preventive Healthcare Managing health conditions of patients with chronic illnesses requires periodical visits to specialty consultants accompanied by recurring medical tests. A patient with a chronic illness have a health journey that goes through different risk stages that are characterized by different health conditions and treatment options. Despite the fact that it is a patient specific journey, the visit periods of patients are mostly determined by national and/or global health protocols according to a patient’s risk group for ease of application. However, the risk stage of a patient changes upon a random noise factor which is a function of both natural progress of the disease and the frequency of control and/or treatment that a patient receives as well as patientspecific vital determinants, life style and habits. One-fits-all protocols carry the risk of overlooking the health status of the patients. It is possible to come up with a riska-verse protocol proposal with a tighter control on patients, yet, it would generate hard-to-manage economic and operational burdens on healthcare systems. Hence, the management of chronic conditions at the backdrop of resource limitations and demand uncertainty is a challenging task such that operational research approaches could add great value to the field. Disease progression, by nature, is a gradual progress on a continuum starting from a healthy individual at one end and terminating by death at the other end. This continuum is frequently modeled by discrete disease stages (i.e. risk levels) where each stage is treated as a state in a Markov decision process. An individual or a population group graduates into an upper level of disease stage by state-specific probabilities. Figure 3 displays a basic disease progression process with three possible stages where each circle denotes a different disease stage. Transitions among stages is assumed to be reciprocal with transition probabilities of ri j (i = 0, 1, 2, 3 | i = 0 stands for patient at risk; j = 1, 2, 3, 4 | j = 4 stands for death and i, j = 1, 2, 3 denotes risk stages from 1 to 3.). This modeling approach enables researchers to develop models for policy makers and practitioners on optimal disease screening [33, 44, 46, 75, 81]; arranging person-

Fig. 3 A representative figure for disease progression with three stages Source: Own elaboration

308

G. Dural-Selcuk

alized medication doses [51], cost-effective resource allocation in community care [26, 60], cost-effective intervention policies [42, 43] and optimal treatment/control intervals between consecutive visits of chronic conditions [26, 47, 60]. The studies in literature mainly considers health outcomes (i.e. morbidity and/or mortality measures) as a reward in exchange for screening/treatment costs. By doing so, they propose patient-specific proactive policies instead of one-fits-all policies that are applied in conventional healthcare delivery settings. In addition to proactive screening and treatment policies mentioned, virtual clinics (see Sect. 2.2) are also deemed as an effective tool in preventive health care management. Hull et al. [50] present an impact evaluation study of a local project on renal services where electronic health records of patients in primary care is shared with the consultants in the secondary care for their virtual review of the health conditions of patients. They have addressed that this system established between primary and secondary care have kept outpatients away from hospitals if not necessarily needed and have supported preventive healthcare management incentive through providing timely consultant advice both to the patient herself and the healthcare provider in the primary care [50]. Another study [69] again highlights the importance of early diagnosis in renal diseases and promotes virtual clinics as the efficient and cost-effective way of patient screening for better health outcomes. A similar study is conducted for eye care services and it has been revealed that how specialist supervision to community care for patient triage is important for patient safety given that some high risk cases could be missed in the community care, losing the chance of an early diagnosis and treatment [103]. They also state that community care workers tend to be more conservative which would boost the demand, and virtual supervision of the specialist would provide a balancing feedback by deciding who really needs an enhanced care.

3.3 Public Health Public health is defined by Winslow in 1920 as “the science and art of preventing disease, prolonging life and promoting health through the organized efforts and informed choices of society, organizations, public and private, communities and individuals” [101]. In the light of this definition, it can be stated that the data-driven modeling efforts mentioned in the preceding subsections (see Sects. 3.1 and 3.2) contribute to public health improvement in such a way that they aid decision makers reach out for efficient and sustainable healthcare delivery systems in tandem with improved health outcomes through preventive policies. Chén and Roberts (2021) highlight the prominence of digital tools on public health improvement via integration of individual level enhancements [15]. They approach contribution of digital health advances to public health improvement from a temporal perspective: short-term, mid-term and long-term outlooks. We consider aforementioned models within mid- and long-term categories and we herein after would like to attract reader’s attention to short-term applications that correspond to the control of unforeseen outbreaks of infectious diseases. We address digital

Development of Intelligent Healthcare Sytems ...

309

healthcare applications and data driven models that are employed as emergency actions in pandemic control, especially in the cases where medical treatments and/or vaccination is not possible yet. In such cases, abrupt reaction of healthcare authorities is to intervene with disease transmission, lower down spread rate and postpone peak number of infections if possible with an ultimate aim of preventing healhcare delivery systems from a potential collapse down to offbeat demand.

Contact Tracing via mHealth Infectious diseases spread through physical contact. Influenza and COVID-19, for example, are transmitted through respiration and the reproduction rate is directly proportional to the contact rate. In the recent COVID-19 pandemic, mHealth has been used for contact tracing purposes. Contact tracing mobile applications keep track of individuals’ movements, contact history, chronic conditions if there is any and their exposure to virus. By doing so, healthcare authorities get the chance to get in contact with positive, exposed and/or high risk individuals so that they can impose selfquarantine policy to intervene with the transmission rate. Another key contribution of mHealth is that they collect geographical data, thus, healthcare authorities keep an eye on geographical risk maps in a timely fashion. That is, they are alerted if there is a problem raised by the adequacy of resources so that they can evaluate the options for demand diversion between territories. There are successful showcases that used contact tracing method via mHealth mobile apps in pandemic control. For more information and examples, the reader can be referred to [48, 62, 86].

Evaluation of Non-pharmaceutical Interventions In an outbreak of a brand-new infectious disease with no options of vaccination and medical treatment, emergent reaction of authorities is to impose non-pharmaceutical interventions such as lock-down, school closures, social distancing, etc. The question is the effectiveness of these interventions. It is a dynamic and an uncertain process, hence, simulation tools have been used to evaluate the effectiveness of non-pharmaceutical interventions. These models inform decision makers about what intervention to apply, when and how long to apply them [4]. A list of nonpharmaceutical interventions, possible modeling approaches and performance measures are summarized in Table 3. Majority of the studies in this field adopt a compartmental SEIR model, in which each individual starts his/her journey in one of the possible states (i.e. S for susceptible, E for exposed, I for infected and R for recovered) and moves between states based on a series of differential equations. A basic representation of SEIR modeling approach is illustrated in Fig. 4 and the differential equations representing the movement of individuals among states are given in Eqs. 1–4.

310

G. Dural-Selcuk

Table 3 Aspects of modeling for non-pharmaceutical interventions in pandemic control Non-pharmaceutical Modeling approaches Performance Measures interventions Social distancing Remote working School closure Self-quarantine

Agent-based modeling Microsimulation Discrete event simulation System dynamics

Travel restrictions

Number of deaths Number of infections Number of people hospitalized Quality Adjusted Life Years (QUALY) Disability Adjusted Life Years (DALY)

Lock-down

Fig. 4 A representation of compartmental SEIR model

Equation 1 tells the occasion when a susceptible individual is exposed to virus by contacting an injected individual. Equation 2 denotes that some of the people who were exposed to virus gets infected, while some of those who had got the infection recovers, Eqs. 3 and 4. d S(t) = −α.S(t).I (t) (1) dt d E(t) = α.S(t).I (t) − E(t).β dt

(2)

d I (t) = E(t).β − I (t).γ dt

(3)

d R(t) = I (t).γ dt

(4)

It is quite common to see the extensions of SEIR model where deaths, hospitalizations and symptomatic/asymptomatic differentiation are integrated [28, 41, 84] as well as the geo-spatial models where population is disaggregated according to regions and concurrent SEIR models are employed for each region [4]. At this point, it is important to highlight that such models are very data-intensive. One needs to use geographical and demographical data as well as the rates that represent the dynamics of the disease under consideration.The challenge here is the fact that these parameters and rates are not stable throughout the time-line of a pandemic. Current studies in the literature are kind of static applications and do not cover an online decison support tool that is fed with real-time data. However, in the cases of emergency, policy makers are desperately in need for real-time support tools. By courtesy of the penetration of digital tools, it could be possible to feed such models with reliable and timely data.

Development of Intelligent Healthcare Sytems ...

311

There have been studies that utilize SEIR infectious disease modeling in order to test the effectiveness of digital contact tracing tools [34, 56, 76, 90, 105].The findings are quite promising such that digital contact tracing tools help pandemic containment with even less number of school suspensions [90] and quarantines [105]. Many countries, during COVID pandemic, have already consulted with digital contact tracing applications in order to keep track of infectious and under-risk people who had a close contact with an infected individual. Those digital applications have provided governmental and healthcare agencies with geographical risk maps. If the data gathered via tracing apps is fed into a dis-aggregated SEIR model, then it could be possible to construct an on-line decision support tool, where decision makers can test the policies in action and evaluate the potential outcomes with the use of real-time data. In this respect, we would suggest to generate ensemble systems that works with combined data sources of digital contact tracing applications along with estimations done by SEIR models. The challenge is the public’s readiness for such mobile applications and their adoption level in an emergency case, because the success of these ensemble systems would solely depend on the availability of accurate data.

4 Challenges Against Digital Health Most healthcare institutions appraise themselves in the mid-way of digital transformation process [25]. It is highlighted that there is much more to get through in defiance of fundamental challenges that can be listed as privacy and security, organizational resistance, individual resistance and resource scarcity. Below, we briefly comment on those challenges and discuss potential answers for the concerns raised under each one.

Privacy and Security Privacy and security is listed number one concern against digital health where institutions require to integrate and manage fragmented patient level data [15]. Data flow occurs from various channels and data governance among interrelated stakeholders of the healthcare eco-system is found to be hard. The idea of data sharing across layers and different stakeholders within the system raises ambiguity about data security and integrity. There is this need for reliable data governance rules so that data security, integrity, interoperability are assured. Even data governance regulations and security protocols are not found to be enough to eliminate the risk of cyber attacks. At that point block chain technology is advised as a remedy for cyber security flaw [83]. Thanks to its unique characteristics such as digital signature schemes, consensus mechanism and chain of hashing it is claimed to present potentialities such that it integrates, stores and shares data in a secure way [52]. Applications of block chain in healthcare could be listed as:

312

• • • • •

G. Dural-Selcuk

Data sharing among different stakeholders Data management Health insurance claim statement Patient digital identity EHRs

It is obvious that the invasion of digital technologies in healthcare is undeniable. Unless healthcare institutions and regulatory parties invest in security assurence for medical data integration and sharing, security concern will remain as a great obstacle against the evolution of digital health.

Organizational Resistence Adopting digital technologies breeds new care pathways in healthcare service delivery. This is perceived as a kind of disruptive innovation and is possibly confronted with organizational resistance. In such cases, it is hardly seen whether the new investment has a break-even point or when. Given that the outcomes are blur, institutions instinctively manifest resistance against change. One main underlying reason would be the need for new skills and talent sets, which current employees are lacking of and reluctant to learn. Another concealed determinant of organizational resistance could be the absence of performance measurement tools for the new technology-oriented business model [25]. Organizations ought to develop appropriate strategies to overcome these two issues such that they opt for employee training and empowerment as a remedy for the former one and develop easy to assess KPIs and performance metrics to highlight the organizational progress in mid-way for the latter one. As a more general approach, it would make sense to invest in changing cultural mind-set in favor of innovative applications and embrace the employees who are more prone to this ideology.

Individual Resistence In parallel to security concerns and organizational resistance, there also exists individual resistance against digital technologies. It is partly because people hesitate about confidentiality and partly down to their low level of digital literacy. Younger generations are born into digital technologies and it should not be a concern to drag them into digital world. Nevertheless, heavy users of healthcare systems are not the youngsters. In that sense, it is crucial to engage the attention of elderly patients, who are mainly deemed as the high resistant category [15]. It has been acknowledged that computer literacy, information literacy and internet use are found to be correlated to the use of digital health apps [79]. In line with this finding, there is a noticeable need for improving digital health literacy before introducing digital health technologies. In other words, before launching a digital tool, it is advised to check the readiness of target users and get insights about their needs and preferences.

Development of Intelligent Healthcare Sytems ...

313

Resource Scarcity Last but not the least, resource scarcity is also mentioned among the challenges against digitalization in healthcare [12, 25]. Talent and skills scarcity is found to be paramount as well as limited budgets. Despite the fact that digital health has gained momentum especially after COVID, it is yet at its dawn. Scarcity of resources is a multi-dimensional problem such that healthcare institutions will raise their need for skills and talent, then it will probably have a reflection in education curriculum with a time lag, meaning that it will take time for knowledge accumulation and penetration throughout healhcare institutions and countries in the wider system in return. In a similar fashion, decision makers will behave cautious about new investments where they can barely see the reward, however, their attitude will change as they get positive feedbacks from the system. In that sense, it will take time to convince decision makers to invest in the fruitful world of digital health at the beginning. To summarize, there are various obstacles against development of digital health and it definitely requires time and effort to hurdle them.

5 Conclusion This book chapter gives information about digitalization in healthcare services and its reflection in modeling practices. First, key drivers of the adoption of digital tools in healthcare are introduced from different stakeholders’ perspectives, i.e. patient, clinician, healthcare institution. Then, commonly used digital technologies in this sector are mentioned (i.e.EHR, IoT, intelligent systems, telehealth, virtual clinics). It has been highlighted that the use of digital technologies in healthcare service delivery enables collection of tremendous level of patient level data that are integrated in regional and national databases. This advance database acquiry creates a great potential for data-driven modeling studies. Contemporary modeling studies have been discussed to showcase the effort of modelers to aid decision makers in their sought of efficient, sustainable healthcare delivery systems aligned with the evolving needs of population. Although it does not cover a comprehensive review of the relevant literature, it presents readers an initiative to get a step within the world of operations research techniques applied in healthcare services and its evolution in parallel to the penetration of digital tools in the sector. We have particularly addressed mathematical models, simulation studies employed in the application areas of operational efficiency in healthcare delivery, preventive healthcare and public health. Although all of the modeling studies mentioned are motivated upon a real life problem situation, not all of them are empirically tested. That is, all models have in common that they are very data intensive, nevertheless it is not always possible to have an accurate data source. Researchers opt for synthetic data to test the validity of their models in such cases and it raises a question mark in the minds of decision makers. However, encouraging the use of real-life data sources in modeling studies would stimulate successful collaborations between researchers

314

G. Dural-Selcuk

and practitioners. It would also increase the appetite of practitioners for modeling studies if on-line decision support tools that current literature is found to be scarce are offered. The literature is mostly dominated by modeling studies that offer a best solution for a snap-shot problem situation or a decision policy over a defined period of time under a predetermined set of assumptions. In either case, decision maker is provided with a static solution or tool to apply. On the contrary, policy makers invest in collecting live data through digital technologies and their desire is being equipped with on-line support tools, where they can manipulate live data sources and take timely actions. The development of operations research modeling studies is advised to evolve in that line, such that models are embedded in ensemble systems that are more interactive with live data sources and decision makers being involved in the process. We should also note that such ensemble models would require data accuracy and computationally efficient algorithms. Towards the end of the chapter, we also addressed the challenges against digitalization in healthcare, which are listed as privacy and security concerns, organizational and individual resistance and resource scarcity. Most of the challenges listed here could be common for any innovation that occurs in a system, however, when it comes to sharing personal data about their own health conditions, people are supposed to behave even more sensitive about privacy and security and they hesitate to adopt new technologies. In this respect, authorities should pay utmost effort to increase patient literacy about digital technologies and manage the perceptions of potential users such that they are convinced about the benefits and safety of digital applications. In short, we advise that related authorities ought to plan their investment budgets covering the expenditures for patient relationship management that is mostly overlooked. As a matter of fact, the success of all modeling studies disclosed in the preceding sections rely on the quality of data used. There is much more room to improve in digital health despite of the challenges at individual and organizational levels. The journey seems to be rough but very promising indeed. From a modeler’s perspective, it is envisioned that the future will bring data-driven, computationally efficient model ensembles that would provide healthcare practitioners with online support.

References 1. Adan I, Vissers J (2002) Patient mix optimisation in hospital admission planning: a case study. Int J Oper Prod Manag 2. Adan I, Bekkers J, Dellaert N, Vissers J, Yu X (2009) Patient mix optimisation and stochastic resource requirements: a case study in cardiothoracic surgery planning. Health Care Manag Sci 12(2):129–141 3. Araz OM (2013) Integrating complex system dynamics of pandemic influenza with a multicriteria decision making model for evaluating public health strategies. J Syst Sci Syst Eng 22(3):319–339 4. Araz OM, Lant T, Fowler JW, Jehn M (2013) Simulation modeling for pandemic decision making: a case study with bi-criteria analysis on school closures. Decis Supp Syst 55(2):564– 575

Development of Intelligent Healthcare Sytems ...

315

5. Astaraky D, Patrick J (2015) A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling. Eur J Oper Res 245(1):309–319 6. Baltagi BH, Lagravinese R, Moscone F, Tosetti E (2017) Health care expenditure and income: a global perspective. Health Econ 26(7):863–874 7. Beliën J, Demeulemeester E (2007) Building cyclic master surgery schedules with leveled resulting bed occupancy. Eur J Oper Res 176(2):1185–1204 8. Bestsennyy O, Gilbert G, Harris A, Rost J (2021) Telehealth: a quarter-trillion-dollar postcovid-19 reality. McKinsey & Company 9. Binci D, Palozzi G, Scafarto F (2021) Toward digital transformation in healthcare: a framework for remote monitoring adoption. TQM J 10. Brailsford S, Hilton N (2001) A comparison of discrete event simulation and system dynamics for modelling health care systems 11. Brailsford SC, Lattimer VA, Tarnaras P, Turnbull J (2004) Emergency and on-demand health care: modelling a large complex system. J Oper Res Soc 55(1):34–42 12. Burton-Jones A, Akhlaghpour S, Ayre S, Barde P, Staib A, Sullivan C (2020) Changing the conversation on evaluating digital transformation in healthcare: insights from an institutional analysis. Inf Organ 30(1):100255 13. Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A, Ricciardi W, Specchia ML (2016) The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Public Health 26(1):60–64 14. Capan M, Khojandi A, Denton BT, Williams KD, Ayer T, Chhatwal J, Kurt M, Lobo JM, Roberts MS, Zaric G et al (2017) From data to improved decisions: operations research in healthcare delivery. Med Decis Mak 37(8):849–859 15. Chén OY, Roberts B (2021) Personalized healthcare and public health in the digital age. Front Dig Health 3:26 16. Chong KC, Fung H, Yam CHK, Chau PYK, Chow TY, Zee BCY, Wong ELY, Wang MH, Yeoh EK (2021) Long-term effectiveness of elderly health care voucher scheme strategies: a system dynamics simulation analysis. BMC Public Health 21(1):1–12 17. Chow VS, Puterman ML, Salehirad N, Huang W, Atkins D (2011) Reducing surgical ward congestion through improved surgical scheduling and uncapacitated simulation. Prod Oper Manag 20(3):418–430 18. Coates MM, Kintu A, Gupta N, Wroe EB, Adler AJ, Kwan GF, Park PH, Rajbhandari R, Byrne AL, Casey DC et al (2020) Burden of non-communicable diseases from infectious causes in 2017: a modelling study. Lancet Glob Health 8(12):e1489–e1498 19. Colombier C (2018) Healthcare expenditure and fiscal sustainability: evidence from Switzerland. Public Sect Econ 42(3):279–301 20. Córdova González GA (2022) Electronic health records: its effects on the doctor-patient relationship and the role of the computer in the clinical setting. Health Technol 1–7 21. Curfman A, McSwain SD, Chuo J, Olson CA, Simpson K (2021) An economic framework to measure value of pediatric telehealth. Telemed e-Health 27(12):1440–1442 22. Dangerfield B (2016) System dynamics applications to European healthcare issues. In: Operational research for emergency planning in healthcare, vol 2. Springer, Heidelberg, pp 296–315 23. Danovi A, Olgiati S, D’Amico A (2021) Living longer with disability: economic implications for healthcare sustainability. Sustainability 13(8):4467 24. De San MK, Smith J, Lewin G (2013) Telehealth remote monitoring for community-dwelling older adults with chronic obstructive pulmonary disease. Telemed e-Health 19(9):652–657 25. Deloitte (2021) Digital transformation from a buzzword to an imperative for health systems. https://www2.deloitte.com/us/en/insights/industry/health-care/digital-transformation-inhealthcare.html. Accessed 16 Mar 2022 26. Deo S, Iravani S, Jiang T, Smilowitz K, Samuelson S (2013) Improving health outcomes through better capacity allocation in a community-based chronic care model. Oper Res 61(6):1277–1294 27. Dhanvijay MM, Patil SC (2019) Internet of things: a survey of enabling technologies in healthcare and its applications. Comput Netw 153:113–131

316

G. Dural-Selcuk

28. Di Domenico L, Pullano G, Pullano G, Hens N, Colizza V (2020) Expected impact of school closure and telework to mitigate covid-19 epidemic in france. EPIcx Lab 15 29. Dinesen B, Nonnecke B, Lindeman D, Toft E, Kidholm K, Jethwani K, Young HM, Spindler H, Oestergaard CU, Southard JA et al (2016) Personalized telehealth in the future: a global research agenda. J Med Internet Res 18(3):e5257 30. Doorenbos AZ, Demiris G, Towle C, Kundu A, Revels L, Colven R, Norris TE, Buchwald D (2011) Developing the native people for cancer control telehealth network. Telemed e-Health 17(1):30–34 31. DuBose-Morris R, McSwain SD, McElligott JT, King KL, Ziniel S, Harvey J (2021) Building telehealth teams of the future through interprofessional curriculum development: a five-year mixed methodology study. J Interprofessional Care 1–9 32. Dural Selcuk G, Vasilakis C (2021) Evaluating the sustainability of complex health system transformation in the context of population ageing: an empirical system dynamics study. J Oper Res Soc 1–17 33. Erenay FS, Alagoz O, Said A (2014) Optimizing colonoscopy screening for colorectal cancer prevention and surveillance. Manuf Serv Oper Manag 16(3):381–400 34. Esposito D, Dipierro G, Sonnessa A, Santoro S, Pascazio S, Pluchinotta I (2021) Data-driven epidemic intelligence strategies based on digital proximity tracing technologies in the fight against covid-19 in cities. Sustainability 13(2):644 35. van Essen JT, Bosch JM, Hans EW, van Houdenhoven M, Hurink JL (2014) Reducing the number of required beds by rearranging the or-schedule. OR Spect 36(3):585–605 36. Fei H, Meskens N, Chu C (2010) A planning and scheduling problem for an operating theatre using an open scheduling strategy. Comput Ind Eng 58(2):221–230 37. Freeman N, Zhao M, Melouk S (2018) An iterative approach for case mix planning under uncertainty. Omega 76:160–173 38. Fügener A (2015) An integrated strategic and tactical master surgery scheduling approach with stochastic resource demand. J Bus Logist 36(4):374–387 39. Galetsi P, Katsaliaki K, Kumar S (2022) Assessing technology innovation of mobile health apps for medical care providers. IEEE Trans Eng Manag 40. Ghosh K, Dohan MS, Veldandi H, Garfield M (2022) Digital transformation in healthcare: Insights on value creation. J Comput Inf Syst 1–11 41. Grimm V, Mengel F, Schmidt M (2021) Extensions of the seir model for the analysis of tailored social distancing and tracing approaches to cope with covid-19. Sci Rep 11(1):1–16 42. Gulliford MC, Bhattarai N, Charlton J, Rudisill C (2014) Cost-effectiveness of a universal strategy of brief dietary intervention for primary prevention in primary care: population-based cohort study and markov model. Cost Effect Res Alloc 12(1):1–9 43. Gulliford MC, Charlton J, Bhattarai N, Charlton C, Rudisill C (2014) Impact and costeffectiveness of a universal strategy to promote physical activity in primary care: populationbased cohort study and markov model. Eur J Health Econ 15(4):341–351 44. Güne¸s ED, Örmeci EL, Kunduzcu D (2015) Preventing and diagnosing colorectal cancer with a limited colonoscopy resource. Prod Oper Manag 24(1):1–20 45. Harerimana G, Jang B, Kim JW, Park HK (2018) Health big data analytics: a technology survey. IEEE Access 6:65661–65678 46. Harper PR, Jones S (2005) Mathematical models for the early detection and treatment of colorectal cancer. Health Care Manag Sci 8(2):101–109 47. Helm JE, Lavieri MS, Van Oyen MP, Stein JD, Musch DC (2015) Dynamic forecasting and control algorithms of glaucoma progression for clinician decision support. Oper Res 63(5):979–999 48. Hernández-Quevedo C, Scarpetti G, Webb E, Shuftan N, Williams GA, Birk HO, Jervelund SS, Krasnik A, Vrangbæk K (2020) Effective contact tracing and the role of apps. TEN 26(2):40 49. Homer J, Hirsch G, Minniti M, Pierson M (2004) Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. Syst Dyn Rev J Syst Dyn Soc 20(3):199–222

Development of Intelligent Healthcare Sytems ...

317

50. Hull S, Rajabzadeh V, Thomas N, Hoong S, Dreyer G, Rainey H, Ashman N (2020) Do virtual renal clinics improve access to kidney care? a preliminary impact evaluation of a virtual clinic in east London. BMC Nephrol 21(1):1–9 51. Ibrahim R, Kucukyazici B, Verter V, Gendreau M, Blostein M (2016) Designing personalized treatment: an application to anticoagulation therapy. Prod Oper Manag 25(5):902–918 52. Idrees SM, Agarwal P, Alam MA (2021) Blockchain for healthcare systems: challenges, privacy, and securing of data. CRC Press, Boca Raton 53. Johnston B, Kidd L, Wengstrom Y, Kearney N (2012) An evaluation of the use of telehealth within palliative care settings across Scotland. Palliative Med 26(2):152–161 54. Kashani MH, Madanipour M, Nikravan M, Asghari P, Mahdipour E (2021) A systematic review of IoT in healthcare: applications, techniques, and trends. J Netw Comput Appl 192:103164 55. Kim TJ, Arrieta MI, Eastburn SL, Icenogle ML, Slagle M, Nuriddin AH, Brantley KM, Foreman RD, Buckner AV (2013) Post-disaster gulf coast recovery using telehealth. Telemed e-HEALTH 19(3):200–210 56. Kinyili M, Munyakazi JB, Mukhtar AY (2022) Mathematical modeling and impact analysis of the use of Covid alert SA app. AIMS Public Health 9(1):106 57. Klein GL, Kita K, Fish J, Sinkus B, Jensen GL (1997) Nutrition and health for older persons in rural America: a managed care model. J Am Diet Assoc 97(8):885–888 58. Kotecha A, Baldwin A, Brookes J, Foster PJ (2015) Experiences with developing and implementing a virtual clinic for glaucoma care in an NHS setting. Clin Ophthalmol (Auckland, NZ) 9:1915 59. Kraus S, Schiavone F, Pluzhnikova A, Invernizzi AC (2021) Digital transformation in healthcare: analyzing the current state-of-research. J Bus Res 123:557–567 60. Kucukyazici B, Verter V, Mayo NE (2011) An analytical framework for designing communitybased care for chronic diseases. Prod Oper Manag 20(3):474–488 61. Lane DC, Monefeldt C, Rosenhead JV (2000) Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department. J Oper Res Soc 51(5):518–531 62. Lewis D et al (2021) Contact-tracing apps help reduce Covid infections, data suggest. Nature 591(7848):18–19 63. Long R, Heffernan C, Gao Z, Egedahl ML, Talbot J (2015) Do “virtual” and “outpatient” public health tuberculosis clinics perform equally well? a program-wide evaluation in alberta, canada. PLoS One 10(12):e0144784 64. Lyons GJ, Duggan J (2015) System dynamics modelling to support policy analysis for sustainable health care. J Simul 9(2):129–139 65. Ma G, Demeulemeester E (2013) A multilevel integrative approach to hospital case mix and capacity planning. Comput Oper Res 40(9):2198–2207 66. Maarse H, Jeurissen P, Ruwaard D (2013) Concerns over the financial sustainability of the Dutch healthcare system. CESifo DICE Rep 11(1):32–36 67. Makboul S, Kharraja S, Abbassi A, Alaoui AEH (2022) A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources. Health Care Manag Sci 25(1):63–88 68. Maliapen M, Dangerfield BC (2010) A system dynamics-based simulation study for managing clinical governance and pathways in a hospital. J Oper Res Soc 61(2):255–264 69. Mark D, Fitzmaurice G, Haughey K, O’donnell M, Harty J, (2011) Assessment of the quality of care and financial impact of a virtual renal clinic compared with the traditional outpatient service model. Int J Clin Pract 65(10):1100–1107 70. Marques IC, Ferreira JJ (2020) Digital transformation in the area of health: systematic review of 45 years of evolution. Health Technol 10(3):575–586 71. M’Hallah R, Visintin F (2019) A stochastic model for scheduling elective surgeries in a cyclic master surgical schedule. Comput Ind Eng 129:156–168 72. Mullins A, O’Donnell R, Mousa M, Rankin D, Ben-Meir M, Boyd-Skinner C, Skouteris H (2020) Health outcomes and healthcare efficiencies associated with the use of electronic health records in hospital emergency departments: a systematic review. J Med Syst 44(12):1–25

318

G. Dural-Selcuk

73. Murray CJ, Abbafati C, Abbas KM, Abbasi M, Abbasi-Kangevari M, Abd-Allah F, Abdollahi M, Abedi P, Abedi A, Abolhassani H et al (2020) Five insights from the global burden of disease study 2019. Lancet 396(10258):1135–1159 74. NHSProviders (2020) A new era of digital leadership-why digital transformation matters. https://nhsproviders.org/a-new-era-of-digital-leadership/why-digital-transformationmatters. Accessed 16 Mar 2022 75. Özekici S, Pliska SR (1991) Optimal scheduling of inspections: a delayed markov model with false positives and negatives. Oper Res 39(2):261–273 76. Pechlivanoglou T, Li J, Sun J, Heidari F, Papagelis M (2022) Epidemic spreading in trajectory networks. Big Data Res 27:100275 77. Pitt M, Monks T, Crowe S, Vasilakis C (2016) Systems modelling and simulation in health service design, delivery and decision making. BMJ Qual Saf 25(1):38–45 78. Pradhan B, Bhattacharyya S, Pal K (2021) Iot-based applications in healthcare devices. J Healthcare Eng 79. Rachmani E, Mubarokah K, Anjani S, Handayani S, Setyowati M, Yanuar AMF, et al. (2021) Digital transformation in healthcare: are patients ready to adopt ehealth in primary health care. In: 2021 international seminar on application for technology of information and communication (iSemantic). IEEE, pp 273–279 80. Rashwan W, Abo-Hamad W, Arisha A (2015) A system dynamics view of the acute bed blockage problem in the irish healthcare system. Eur J Oper Res 247(1):276–293 81. Rauner MS, Gutjahr WJ, Heidenberger K, Wagner J, Pasia J (2010) Dynamic policy modeling for chronic diseases: metaheuristic-based identification of pareto-optimal screening strategies. Oper Res 58(5):1269–1286 82. Santana IR, Aragón MJ, Rice N, Mason AR (2020) Trends in and drivers of healthcare expenditure in the English NHS: a retrospective analysis. Health Econ Rev 10(1):1–11 83. Sarkar A, Maitra T, Neogy S (2021) Blockchain in healthcare system: security issues, attacks and challenges. In: Blockchain technology: applications and challenges. Springer, Heidelberg, pp 113–133 84. Saunders-Hastings P, Quinn Hayes B, Smith? R, Krewski D, (2017) Modelling communitycontrol strategies to protect hospital resources during an influenza pandemic in Ottawa, Canada. PloS one 12(6):e0179315 85. Schneider AT, van Essen JT, Carlier M, Hans EW (2020) Scheduling surgery groups considering multiple downstream resources. Eur J Oper Res 282(2):741–752 86. Shahroz M, Ahmad F, Younis MS, Ahmad N, Boulos MNK, Vinuesa R, Qadir J (2021) Covid19 digital contact tracing applications and techniques: a review post initial deployments. Transp Eng 5:100072 87. Sikka N, Gross H, Joshi AU, Shaheen E, Baker MJ, Ash A, Hollander JE, Cheung DS, Chiu AR, Wessel CB et al (2021) Defining emergency telehealth. J Telemed Telecare 27(8):527–530 88. Soto-Acosta P (2020) Covid-19 pandemic: shifting digital transformation to a high-speed gear. Inf Syst Manag 37(4):260–266 89. Steinhauser S (2021) Covid-19 as a driver for digital transformation in healthcare. In: Digitalization in healthcare. Springer, Heidelberg, pp 93–102 90. Sun HC, Liu XF, Du ZW, Xu XK, Wu Y (2021) Mitigating covid-19 transmission in schools with digital contact tracing. IEEE Trans Comput Social Syst 8(6):1302–1310 91. Sworna NS, Islam AM, Shatabda S, Islam S (2021) Towards development of IoT-ml driven healthcare systems: a survey. J Netw Comput Appl 196:103244 92. Taylor K, Dangerfield B (2005) Modelling the feedback effects of reconfiguring health services. J Oper Res Soci 56(6):659–675 93. Teng Y, Kong N, Tu W (2015) Optimizing strategies for population-based chlamydia infection screening among young women: an age-structured system dynamics approach. BMC Public Health 15(1):1–11 94. Vissers JM, Adan IJ, Bekkers JA (2005) Patient mix optimization in tactical cardiothoracic surgery planning: a case study. IMA J Manag Math 16(3):281–304

Development of Intelligent Healthcare Sytems ...

319

95. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A et al (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 396(10258):1204–1222 96. Wang JJ, Dai Z, Chang AC, Shi JJ (2022) Surgical scheduling by fuzzy model considering inpatient beds shortage under uncertain surgery durations. Ann Oper Res 1–43 97. Weinerman BH, Barnett J, Loyola M, den Duyf J, Robertson S, Ashworth V, Apland L, Kazanjian A (2012) Telehealth-a change in a practice model in oncology. Telemed e-Health 18(5):391–393 98. WHO (2018) World health statistics 2018: monitoring health for the SDGs, sustainable development goals. World Health Organization 99. WHO (2021) Global strategy on digital health 2020–2025 100. WHO (2021) Listings of who’s response to covid-19. https://www.who.int/news/item/29-062020-covidtimeline. Accessed 16 Mar 2022 101. Winslow CE (1920) The untilled fields of public health. Science 51(1306):23–33 102. Wolstenholme E (1999) A patient flow perspective of UK health services: exploring the case for new “intermediate care” initiatives. Syst Dyn Rev J Syst Dyn Soc 15(3):253–271 103. Wright HR, Diamond JP (2015) Service innovation in glaucoma management: using a webbased electronic patient record to facilitate virtual specialist supervision of a shared care glaucoma programme. Brit J Ophthalmol 99(3):313–317 104. Yap KYL, Liu J, Franchi T, Agha RA (2021) The launch of the international journal of digital health: ensuring digital transformation in healthcare beyond covid-19. Int J Dig Health 1(1) 105. Yu Z, Zhu X, Liu X, Wei T, Yuan HY, Xu Y, Zhu R, He H, Wang H, Wong MS et al (2021) Reopening international borders without quarantine: contact tracing integrated policy against covid-19. Int J Environ Res Public Health 18(14):7494 106. Zhang J, Dridi M, El Moudni A (2021) A two-phase optimization model combining Markov decision process and stochastic programming for advance surgery scheduling. Comput Ind Eng 160:107548

Banking

Imperatives, Trends and Dynamics of Digital Transformation as Banks Adopt Technology and Intelligent Systems Swayambhu Dutta, Himadri Sikhar Pramanik, Sayantan Datta, and Manish Kirtania Abstract The substitution of physical interactions with digital channels in services industries has resulted in a rapid evolution of processes—to simplify and enrich the customer journey with new functional, experiential, and intelligent capabilities. In Banking, this transition to digital processes faces a variety of deployment considerations due to issues of trust, transparency, nature of interactions, compliance, and technological adaptation of customers. This chapter highlights the key imperatives for digital transformation in banking, enabling technologies, and presents a view of the transition to intelligent operating models of the future. Included are perspectives based on industry observations and experience across transformations of global banks. The chapter establishes the adoption of intelligent digital systems through an assessment of strategic fitment and digital technology maturity. While there can be no single way for this transformation, this chapter establishes the underlying technology infrastructure and their related manifestations in customer engagements and internal operations through several use-cases and potential future scenarios. Keywords Banking · Digital transformation · Drivers · Intelligent systems · Emergent technology · Artificial intelligence · Cloud · Open banking · Ecosystems · Future of banks

1 Introduction The objective of this chapter is to provide an exploratory assessment of digital transformations in banks. We explore how transformations enable shifts towards intelligent operating models. This is achieved through a study of transformation imperatives, enabling technologies and assessment of future scenarios to understand role of intelligent systems towards the future of banking. The chapter utilizes extant literature, grounded industry observations to bring out the market drivers for transformation, the structural elements for organizational S. Dutta (B) · H. S. Pramanik · S. Datta · M. Kirtania Tata Consultancy Services, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_14

323

324

S. Dutta et al.

consideration in digital transformation plans. Banking specific narratives from global industry observations provide guidelines for technological interventions. Through assessment of industry instances and trends, the chapter highlights key technology enablers for digital banking transformation. Based on present trends and views from experts we include potential response from banks in relation to the future scenarios. The future banks are influenced by a variety of factors ranging from technological innovations, customer expectations and ecosystem engagements. Observations on evolution of novel intelligent operating models as a derivative of the transformation drivers, changing market paradigms alongside responses from banks and industry will help both academia and industry. The chapter is segmented into three key sections. We initiate with a focus on the imperatives of digital transformation in banking, arising out of the evolving technology landscape, customer expectations and other relevant considerations. These imperatives range from effects of the pandemic to geo-political regulations, innovations, and sustainability among others. This clearly outlines the need to understand digital transformation in perspective of integrated variables and relevant ecosystems while banks adopt technology and intelligent systems. Moreover, this provides contextual perspectives for digital transformations. In the next section of this chapter, we discuss the trends of present and emerging technologies on intelligent banking operations and the transformations. Based on global observations, we indicate how banks exploit technology for differentiated offerings, proliferation of customer channels, transformation of internal systems and ecosystem collaborations. We conclude the chapter with the dynamics of digital transformation from perspective of an organization. Diffusion of innovation, technology organization and inter-organizational systems interact alongside digital maturity and strategic fitment. We conclude with a portfolio view of intelligent initiatives to better understand the dynamics of transformation. The concluding section highlights likely future transformation manifestations and intelligent operating models that will drive global agenda of change.

2 Imperatives for Digital Transformation Drive Banks to Adopt Technology and Intelligent Solutions Banks have undergone both incremental and disruptive changes affecting both internal and external stakeholders and operating paradigms. Digital banking transformation is driven by both demand and supply factors [1]. Evolving customer and other stakeholder expectations constitute the demand side, with growing availability of new digital channels and consumer services from technology enabled organizations. On the supply side, new banking applications and services are designed utilizing intelligent business and technological innovations to address the changing customer needs. Digital transformation alters the ways by which banks interact with customers and stakeholders [2]. Increasingly investments in technology and transformations are

Imperatives, Trends and Dynamics of Digital Transformation …

325

mediated via strategic focus involving wider participation across the organizations also including wider eco-system considerations. The transformation may be viewed as a complex process, consciously under-taken by banks in response to their ecosystem and operational imperatives—manifested through multiple technology and business initiatives, to capitalize differential benefits [3]. The transformation trajectory in banks is mediated by key variables such as strategic focus, industry influences, organizational ability to leverage technology alongside proliferations in digital and intelligent capabilities. This may vary based on the size of banks, nature of businesses, operating markets, technology exposure, maturity, and over time. This is operationally manifested by transformation focus which establishes deployment targets based on business impact of the initiatives. In most global observations banks aspire for system intelligence and effectiveness targeted at customer/stakeholder experiences, process improvement and business value generation. While there are multiple variables that influence banks’ transformation, we view following as key imperatives discussed through 1.1 to 1.6.

2.1 Geo-Political, Social, Economic, Regulatory Conditions Including Impacts of Prevailing Uncertainties and Health Crisis Banks play a key role in orchestrating and funding technology adoption across industries—while promoting future innovations. As per OECD, the rise of the different types of risks, need for public and institutional safety of assets and need for monitoring disruptions will mandate regulatory compliances across geographies. To optimally respond and navigate eventualities such as BREXIT, proliferation of cryptocurrency, GDPR in Europe among others, banks will need to be agile [4] enabling with technology. Low interest rates and high inflation across markets will influence primary revenue streams and risk models of banks. The banks are also expected to play a key role in shaping the economic recovery post the global pandemic. As industries try to navigate supply chain challenges and accelerate adoption of technologies and practices for sustainable growth, banks will need to prioritize their lending and services portfolio with enhanced features for optimal economic impact [5]. Many banks have aligned intelligent offerings with the policies and programs of their respective national governments for support to citizens during the pandemic induced economic disruptions, including moratoriums on loan repayments, financial aid for economically disadvantaged population and priority loans for small businesses and entrepreneurs. These engagements activated through agile, intelligent, and automated processes provided banks with valuable experience in deploying product changes rapidly.

326

S. Dutta et al.

2.2 Evolution of Customer Expectations and Demand for Digital Bank There is a shift in how customers (individual and institution) hope to interact with banks. Banks acknowledge a sense of keeping pace with customers by prioritizing investments in digital technologies and associated transformations. The growing preference for technology-enabled experiences almost puts banks into a sense of urgency to adopt digital innovations and transform [6]. Benefits attributable to such transformative initiatives serve as key motivators for global banks. For instance, banks adopt online and mobile app as key channels for services that previously required physical interactions, like meetings with advisors in branches for loan discussions or receiving financial advisory, handing over or signing paper-based documents for compliance, among others. App based self-service and video banking services, with digital signature option and digitized documents deployed along with artificial intelligence capabilities are now utilized as alternatives for interactions in branches. With simplified intuitive processes and AI enabled chatbots, intelligent natural language processing capabilities- banks personalize customer experiences and focus on preference and convenience. Digital transformation in banks is aligned with the changing demographics and growing proficiency of digital engagement among customers. While young adults were the initial targets of digital banking, it is now increasingly focused on all age groups. Senior citizens are specific target customer segments, with simplified and AI assisted processes to help them receive banking and allied services, in response to the growing ‘silver economy’. The increased digital adoption for a wide variety of consumer and citizen services despite the growing median age of the population globally is a key indicator of the business potential of digital banking development [7] among aging population globally. Concurrently, cross-industrial influences from the benchmarks of technology led transformations are evident in a banks’ peer-ecosystem responses. Availability of services from consumer technology organizations, manifested by but not limited to mobile applications, online commerce, intelligent wearables, IoT and social media channels, develop expectations of similar seamless customer experience in banking. Banks adopt certain elements of these technology benchmarks, including low-touch services, transparency, low latency processing, and automated offers or recommendations, with necessary customizations for security and compliance, to capitalize on opportunities and differentiate.

2.3 Focus on Sustainable Development Across Industries and Globally Globally, banks play a significant role in achievement of sustainable development goals (17 UN SDGs). Economic role, alongside ability to fund other industries render banks as a significant intermediary [8] for driving sustainable initiatives. Banks and

Imperatives, Trends and Dynamics of Digital Transformation …

327

financial services promote sustainable behaviors among industries, organizations, governments, and individuals and even trigger optimal social changes [9]. Governments globally advocate to prevent climate change, with targets for carbon footprint reduction. Banks play a key role through prioritized lending for green initiatives. It is observed globally, banks address sustainability through intelligent offerings, drives financial inclusion, sustainable financing, social banking, and impactful investing [10]. Banks are focused on development, diversity and inclusion, customer centricity, innovation, environment friendly practices while promoting industrial compliance in environmental, social and governance (ESG) [11]. Digital infrastructure to intelligently assess ESG footprint of customers, support inclusivity through self-service and automation, provide discounts in loan interest rates and processing fees are key focus areas for transformation. Sustainability advisory and training for business customer leaders are used as enablers for promoting adoption of sustainable financing products and encourage green initiatives.

2.4 Value from Differentiated Digital Technology Leadership and Innovations Banks strive for differentiated technology leadership, intelligent solutions to attract customers, demonstrate capabilities and build trust with investor communities. Public narratives from global banks demonstrate how they are improving customer experiences, by investing to improve payments, cyber-security [12], and other personalization options in accounts, loan, and investment products. Banking systems are transforming into intelligent systems, utilizing analytics, AI, Cloud and other technologies to address the unique needs of customers. Banks explore and experiment with various technologies like data analytics and Artificial Intelligence (AI) to provide insights on financial management and budgeting, and address queries or highlight recommendations through chatbots and robo-advisors. Major banks have invested in innovation centers, which model themselves in line with the technology benchmark organizations to explore innovative use cases and experiment on transformation possibilities, growing banks’ intellectual property and expanding technology partnerships. Beyond focus on customers, banks focus on process and business model transformations.

2.5 Industrial Convergence Towards Solution-Driven Ecosystems Increasingly industries are giving way to eco-systems—where cross industry participants come together for seamless value delivery. Value-chains are converging across different industries towards better experience and solutions. This will demand banks to be able to operate across disparate entities with seamless transactions, security,

328

S. Dutta et al.

and agility [13]. As a prime intermediary, the transformation agenda of banks, will factor the future of other industries. Banks also explore ideas from financial technology companies or fintechs, through collaborations, investments, or acquisitions to target innovative service options, intelligent technology solutions and untapped business opportunities. The choice of ecosystem partners is often determined by the adjacency in service and complementary capabilities that when integrated with banking offerings, develop a new value proposition for customers and organization. Some key instances of such ecosystem developments include—bank accounts with investment options and financial advisory, e-commerce with integrated payments and loyalty offers, small business banking with other software and marketing services, among others. We observe, banks typically explore two key models— (i) owning the platform for others to integrate their offerings or (ii) embedding banking services in third party owned applications through application programming interfaces (APIs), leading to unique and intelligent integrative value propositions.

2.6 Risks of not Transforming Including Threats from Financial Technology (Fintech) and Other Technology Organizations Global banks realize the threat from innovative and newer financial technology and other large technology organizations. They face risk in failure to respond in a timely manner to changing customer preferences, product obsolescence and new technology developments. This will result in depletion of market share. Customers are likely to switch to providers with better, easier, and more convenient, or specialized solutions. We observe multiple specialized solutions from technology organizations, which target unmet customer requirements or disrupt the value proposition of current financial products. At present, many fintechs globally are prime change agents, instrumental in transforming conventional banking [14]. In response, banks are investing in-house or working in close collaboration with technology providers, and other fintechs, to develop specialized industry and transformative solutions. The imperatives of transformation experienced by banks are explained in Fig. 1. It indicates an integrative view of the imperatives and their qualitative associations creating an inter-connected system that drives and accelerates digital transformation initiatives in banks. The diagram indicates push and pull variables of significance for digital transformation of global banks. • Push signifies imperatives that directly impact digital transformation. • Pull signifies imperatives influencing outcomes—thereby serve as pull-motives or purpose for banks to engage in digital transformations The push–pull imperatives show the directions or conceptualized associations the imperatives have with respect to the digital transformation in banks. These variables need to be viewed in a collective manner as integral part of an interacting eco-system

Imperatives, Trends and Dynamics of Digital Transformation …

329

Fig. 1 Push-Pull Imperatives for digital transformation of banks, Source Realized by author

building up the context in which banks operate today. They are collectively impacting choices for investments, technology adoption, focus on intelligent solutions and transformations.

3 Adoption of Technology and Intelligent Systems—Digital Transformation Trends Across Global Banks Transformation of banking processes are directed towards realizing intelligent systems focused on simplifying and personalizing customer experience, improved operational efficiency and value realizations through innovative digital technology solutions. Simultaneously, banks focus to improve workplace experiences for associates by providing enhanced digital capabilities, convenience, collaboration, ease, and agility. Increasingly there is a realization that banking infrastructure and applications need to be infused with intelligent digital tools to support both employees and customers, while ensuring a cyber-secure environment. Given commoditized traditional products, many banks are forced to compete primarily on pricing and scale. However, some are striving to differentiate with functional and experiential capabilities as well, driven by digital technologies and collaborations. Following are the key evolving trends we observe across global banks. These include the transformation focus enabled by digital technology, innovations, and intelligent systems. These are discussed through 2.1 to 2.5.

330

S. Dutta et al.

3.1 Overcoming Financial Product Commoditization in the Face of Threat from Fintech and Technology Differentiations During early 2010s banks could differentiate based on their branch, ATM networks and digital channels. However, these are not enough to improve customer experience today. Digital only neo-banks compete and differentiate over conventional banks with trillion USD asset size in some instances. Fintechs are providing similar, and in some cases enhanced features and experiences in their mobile apps vis-à-vis traditional banks. Some of the intelligent features in a mobile app, which are now increasingly commoditized include features like—multiple accounts management across different banks; real time money transfer with third party apps; check deposit and bill payments; enhanced security solutions and integration of credit cards and other financial products. While banks rethink their position in the digital society, they find two key dimensions of innovation—(i) newer financial services/products and (ii) technologies to create intelligent solutions [15]. The financial services innovation reimagines the delivery for traditional banking products, like deposits, payments, credit offerings and investment management. While technologies, like Cloud, AI/Machine Learning, Blockchain, Mobile, IoT and APIs, help in improving employee productivity and reducing friction in customer experience towards intelligent solutions. Additionally, banks explore the business potential of services beyond banking, enabled by the technologies and intelligent convergence of value chains across lines of business and in some cases beyond industry. In multiple global instances services are being explored beyond the banking industry by integrating with insurance, healthcare and even retail industry collaborators. Both technology platform providers and Fintechs challenge the traditional leadership role of banks in adopting new technologies. For example, a fintech firm transformed the small business lending market by introducing automated low latency loan processing for working capital credit. Independent digital wallets dominate the payment application market in several countries, despite the presence of large banks. As banks explore their options in addressing these market opportunities either inhouse or through collaborations, the banking market undergoes rapid transformations and roll-out of innovative solutions.

3.2 Digitization of Banking to Improve Processes and Customer Outreach The financial services innovation reimagines the service delivery for traditional banking products, and this relates to improvement of internal process and ensures effectiveness of operations. The key elements of digital banking (Fig. 2) are well established. Banks often establish a modular application infrastructure comprising

Imperatives, Trends and Dynamics of Digital Transformation …

331

core banking software and add-on software for different functionalities. The bank’s internal operations run on these systems, while customers access the products using a multitude of digital channels, with user experience applications running on servers and virtual machines. Banks need to identify key components where the maximum value is created, as they are likely to face competition from emergent players in those components [16]. We observe across global banks very high focus on customer channels, outreach, and experience. Multiple intelligence solutions are conceptualized. Online (laptop/desktop) or Mobile banking channels are augmented with Artificial Intelligence (AI) enabled chatbots with text/voice mode of communication. Chatbots are getting increasingly intelligent, analyzing customer data from back-end data storage and digital channel interaction history to suggest advisory for any query handling or financial plans. Such robotic interfaces in engaging customers and even employees are increasingly becoming intelligent. A variety of digital channels for customer engagement are proliferating as demonstrated in (Fig. 3). Further, banks are exploring video banking options to provide the human touch and support customers during complex processes where drop-out rates are high. Services like opening new accounts, mortgage and other loan applications and applying for cards or overdrafts are key focus areas for video channel banking. Video banking becomes more feasible due to higher network bandwidth with 5G networks. It can help in other intelligent digital channel proliferations as well—like wearables and Internet of Things (IoT) enabled devices. With physical distancing due to the pandemic and rising customer demand to receive all banking services over digital channels, contactless payments volumes accelerated since 2020 [17]. Smartwatches and contactless payment cards constitute majority of these transactions today. Retail stores explore ways to make the physical buying experience even

Fig. 2 Key elements of digital banking, Source Realized by author

332

S. Dutta et al.

more seamless with checkout-less buying experiences, enabled by IoT solutions. Major retail stores in the USA and Europe now deploy self-service checkouts in select locations, in collaboration with banks and FinTech firms. Banks are also supporting transport companies to enable IoT based intelligent payment experiences focused on invisible transactions. Another key element of digital customer experiences is the integration of banking information through APIs with ecosystem partners. This integration with retail, transportation, and other service industries can enable contextual and intelligent banking services, where customers can use 3rd party mobile/wearable apps to complete the banking transaction required for payment. With growth in open banking practices, banks are partnering with fintechs to integrate the financial services of individual customers in a single app. Banks initially utilized open banking with payments applications, expanded to account aggregation and now use it for selling third party fintech or ecommerce products to customers. In addition, banks are also partnering with cross-industry institutions for providing banking services beyond the regular banking channels. For example, employees and gig workers can now access their finances within the applications of some organizations. APIs are also used to transform customer KYC and compliance processes, by sharing customer information between banks. These data sharing networks are often developed in collaboration with government or regulatory bodies to ensure compliance with data privacy regulations and necessary security. Emergent channels of banking also include connected devices in customer ecosystems like smart homes, connected cars and intelligent voice enabled transactions. Banks explore use of voice biometrics, location, and ownership data of connected devices for seamless authentication for transactions. These new age channels will focus on experience with augmented and virtual reality

Fig. 3 Digital banking channels proliferate with focus on seamless customer experience, Source Realized by author

Imperatives, Trends and Dynamics of Digital Transformation …

333

(AR/VR) wearable devices, where customers can manage their financial products with more intuitive intelligent applications. Proliferation of new channels also present opportunities and challenges to banks—as fintechs and other technology companies try to gain market share with applications focused on those channels. Banks focus to develop integrated customer experience across these channels, while utilizing unique capabilities of these channels.

3.3 Transformation and Innovations in Banks’ Internal Systems and Associated Infrastructure Banks need a scalable infrastructure to support the integrated customer experience across channels, while maintaining regular banking operations continuously. Cloud acts as a key enabler to build the core systems supporting the intelligent digital transformation for the future. Most banks initiated their cloud technology journeys with private and hybrid cloud. It is a balance in migrating some applications to public cloud while keeping data and operation sensitive applications on private cloud servers. Simultaneously, public cloud infrastructure (Fig. 4) and data network connectivity becomes increasingly robust. Some banks leverage the scalability and cost effectiveness of public cloud for critical applications like core banking and storing consumer data. This shift enables a new generation of banking applications which are increasingly cloud native. Initial software applications used for migration to cloud were modified, from those running on servers in premises. Cloud native banking applications, on the other hand, are rapidly expanding in functionalities, adaptable to increasing loads and are easy to plug and play—reducing friction and time required in implementation and upgradations. The concerns of security on public cloud addressed by data encryption and storage segmentation, virtual firewalls, and continuous cybersecurity monitoring and other intelligent solutions. Yet another intelligent innovation that we observe is that many banks invested in microservices architecture (Fig. 5) to create a modular and scalable system on the cloud. This results in specific banking services like account management and transactions to be virtually integrated—with core applications, customer data, storage and processing requirements, and user experience integrated into one virtual module. New enhancements to applications become easier and integration with other applications can happen over a virtual controller or through APIs. Additionally, the virtual application modules can be cloud native and hence better utilize the storage facilities available over virtually connected networks. The scalability and flexibility provided by such systems enable agile software development practices. Each system and its sub-modules can be separately improved, based on operational experience and feedback. Time required to launch new versions of applications can be scaled down significantly. Independent teams can work on different modules in continuous iterations, compete and learn from successful practices of other teams resulting in rapidly improving processes, application software and enhanced product

334

S. Dutta et al.

Fig. 4 Public cloud infrastructure—key elements, Source Realized by author

portfolio leading to agility of digital transformation. Ease of integration due to use of internal APIs and automation in test processes help in zero defect implementations. Technology transformations becomes easier to realize. One of the key drivers of this continuous iterative development is data analysis— from both internal systems and external feedback systems. For agile development, this data collection and analysis needs to be continuous and automated. Robotic process automation (RPA) and artificial intelligence (AI) leveraging machine learning algorithms will be key in collecting and analyzing data to identify current challenges and improvement opportunities. The bi-directional data flow between the modular platforms is critical for enabling self-service with automation and straight through processing solutions.

Fig. 5 Banking microservices architecture—key modules with data transfer across platforms, Source Realized by author

Imperatives, Trends and Dynamics of Digital Transformation …

335

3.4 Focus on Artificial Intelligence Intelligent digital systems that can transform the customer experience with personalization and new product development will focus heavily on AI. Banks are progressively transforming applications to self-service for customers, with AI based assistants to provide any-time access and reduce the costs of service delivery. Many banks also introduced AI in internal systems, reducing human effort exponentially and analyzing increasing volumes of customer data to develop new products as per evolving needs. For instance, AI is increasingly used to streamline standardized but effort-intensive operations like, risk monitoring, legal document scanning, compliance, and reporting. Bank employees will be increasingly supported by AI powered digital assistants, that can take care of standardized activities like responding to standard e-mails or supporting compliance reporting, so that they can focus on more valuable activities like improved customer interactions and activities supporting revenue growth or process optimizations. It is also used to support customers through chat-bots and financial assistants helping to manage their financial journeys through real-time product advisories, customizable savings plans and investment portfolio management with robo-advisors. Increasingly robo-advisors have demonstrated advanced capabilities in natural language processing. Moreover, with launch of new sustainable investment products, use of AI is increasingly explored to continuously monitor carbon footprint of bank asset portfolios and suggest opportunities to business customers to utilize discounted financing for supporting ESG initiatives. Nevertheless, the adoption of AI and machine learning comes with multiple considerations like explainable algorithms, dealing with bias, trust among others.

3.5 New Banking Offerings and Partner Ecosystems Innovation in customer channels and transformation in internal systems accelerate the development of new offerings. Singular products such as deposits, payments, loans, and investment services are being customized and integrated as personalized financial service, with data analytics and AI support to align with individual financial aspirations. For seamless online buying experience, businesses are looking for customer digital authentication services. As part of open banking practices, banks provide services with real time analytics, API, and encryption for in-app transactions. Biometric identifications are enabled for multi-factor authentication—for both consumers and institutional customers. Financial transactions are enabled in real time for both domestic and cross border transactions, utilizing Cloud and blockchain technologies. Concurrently, banks explore strong business opportunities in sustainability focused services, in line with the sustainability goals in their country of operations. Loans and loyalty programs align with sustainable projects and investments. AI, Big Data and IoT technologies are utilized to continuously monitor carbon footprint

336

S. Dutta et al.

of projects and industries—linked to interest rate and other financial discounts for realizations of sustainability goals. Transformations are not limited to in-house initiatives but extend to ecosystem partners. Banks are partnering with investment management companies to enable customers save more using stocks, bonds, and other securities—integrating bank accounts with securities trading facilities in single app interfaces. Banks are also introducing innovative investment options for their customers, like sustainable investments and cryptocurrencies, enabled by Blockchain technology. Cross-border transactions is a key use case of Blockchain based currency exchange platforms. Concurrently, Blockchain and digital currency has potential to enable smart contracts, programmable transactions, targeted loans, and trade finance applications. With proper utilization of technologies, the role of banks as a key intermediary expands in more such areas. Figure 6 shows an illustration of the plausible partners of a bank in an embedded ecosystem. Horizontal ecosystems can help banks realize new value by positioning partners for beyond banking services, addressing customer pain areas—for both individuals and industries. Banks in Europe have created consumer service channels in partnership with ecommerce vendors and fintechs across retail and travel domains. Banks also partner with other platform providers to embed their banking services—like payments and loans in third party applications. Several banks in the USA partnered with fintechs and ecommerce companies to support merchant financing. The ecosystem play of banks are at an emergent stage; and can expand rapidly with focus around customer touchpoints, customer life goals and purpose orientations including sustainable development. Partnership with fintechs and other technology companies often form the foundation for banks in their ecosystem development. Subsequently, they bring in cross industry partners for data sharing and other consumer or business services. One such expanding value chain is illustrated in Fig. 7—how these partnerships are expanding mortgage banking services (represented in dark colors) across the customer buying journey. Major banks have dedicated innovation centers and venture capital arms to

Fig. 6 Key partner ecosystems for embedded banking, Source Realized by author

Imperatives, Trends and Dynamics of Digital Transformation …

337

Fig. 7 New banking value chain in housing ecosystem, Source Realized by author

explore new fintechs and partner with them. They run hackathons and other competitions to select promising intelligent solutions and innovative providers. The banks are also acquiring fintechs to take ownership of these platforms for future horizontal consolidation and expansions. These emergent horizontal ecosystems may include age group-based banking (for example senior citizen banking, children, or young adult banking, and so on), Healthy Living, subscription-based business models, and many more. The ability of banks to identify these opportunities and experiment with new products, intelligent solutions will be key for differentiation securing future business growth.

4 Digital Transformation Dynamics to Evolve for the Future Prevailing literature explains technology adoption by institutions through theoretical models including Roger’s Diffusion of Innovation (DOI) [18]; Technology, Organization, and Environment (TOE) [19]and Iacovou’s Model (Inter-Organizational Systems—IOS) [20]. Primarily global banks and financial organizations embrace emerging digital technologies to transform. In such digital technology led transformations there are multiple relevant considerations for the banks and financial organizations. These include leadership vision, internal organization characteristics, external operating environment, perceived benefits, organizational readiness to transform, transformative external forces among others. The DOI theory at firm level discusses technology adoption and innovativeness related to independent variables as organization leaders, internal organizational structure, and external characteristics. The TOE framework identifies technological context, organizational context, and environmental context. Iacovou et al. (1995) analyzed Inter-Organizational Systems (IOSs) that influences institutions to embrace technology. The framework is based on factors like perceived benefits, organizational readiness, and external pressure. These considerations are relevant to banks and financial organizations embracing digital and emerging technologies for transformation. Most banks focus on digital business strategy to operationalize their goals for digital transformation. Digital business strategy is formulated at organizational level and executed by leveraging digital technology resources to create differential value.

338

S. Dutta et al.

The construct of digital business strategy is emergent across banks and financial institutions. Digital business strategies may be categorized by (1) the scope of digital business strategy, (2) the scale of digital business strategy, (3) the agility of digital business strategy in terms of execution and continuity, and (4) the sources of business value creation [21]. Digital business strategy may be considered as strategic response to the collective variables of an organizations’ ecosystem leading to the construct of digital strategic posture [22]. Organizational planning elevates technology as a core capability to differentiate vis-à-vis perspectives of technology as mere enabler. Global banks and financial organizations are at varied maturity levels in adopting digital technologies. The transformation is not limited to implementing emerging digital technologies but includes ability of banks to re-imagine possibilities. These may include extensions, interactions, convergence, modularization, and integration of prevalent business by exploiting digital technologies [23]. Banks and financial organizations globally have high dependence on technologies—both information and financial technologies. Associated technology adoption is accelerating given prevailing political, social, health disruptions along with competitive threats from fintechs and emerging technology organizations. Industry disruptors employ ‘combinatorial’ levers for multiple sources of value—cost, experience, and business convergence among others [24]. FinTech, emerging technology startups along with big technology players are unbundling financial products and services. This helps in assessing potential value points and seizing profitable business from conventional banks, while avoiding the regulatory entry barriers. Increased focus on intelligent automations, algorithmic decisions, hybrid or robotic interfaces, artificial intelligence, analytics, mobile, drive for self-service, impacts customers and employees. Technology introduces new transformation paradigms in human– machine, machine–machine workflows and experiences. Technology-enables integrations and overlap across industrial value-chains beyond banks to help launch innovative cross industry solutions to differentiate. Narratives across global banks on transformation readiness and intention to deploy emerging digital technologies reveal that banks clearly acknowledge technology as a strategic alternative. Technology helps banks to develop core-competency, service differentiation and derive competitive advantage. Digital transformation is a complex process. It raises several valid considerations on the changes that conventional banks and management practices need to implement. These considerations involve embracing of new innovative business models simultaneously changing and improving the existing models with the help of digital technologies. Transformation agenda primarily includes—focus on customer experience, effectiveness of business operations/processes and enablement of work with transformed and improved business models [25]. Transformative processes involve multiple organizational dimensions including strategic focus, competitiveness, business model, market expectations, innovation, entrepreneurship, business performance, and effectiveness among others.

Imperatives, Trends and Dynamics of Digital Transformation …

339

In multiple instances, the ability to transform optimally is moderated by the digital maturity of respective organizations. Banks and financial organizations with high digital maturity seamlessly exploit emerging technologies for transformation aligned with strong leadership focus and can demonstrate better performances [26]. The process of digital transformation is continuous as the proliferation of technology and innovations offer newer capabilities that banks need to explore and exploit over time. Core-capability to continuously explore and exploit emerging technologies and transform will impact differentiation and competitive advantage. Alongside maturity, transformations led by adoption of emerging technologies needs to establish overall strategic fitment in context to an organization. In other words, the digital transformation trajectory is contextual to every organization in establishing a strategic fitness. Therefore, it becomes imperative for banks to understand the nature of digital transformation that aligns with the overall organization direction and purpose. Digital transformation is a strategic option for banks and attainment of fitness may be empirically attained, through complex adaptive processes including experimentation, organizational changes, and eco-system responses. Prior research indicates application of complex adaptive systems that view organizations as evolving systems that formulate transformational strategies by classifying, selecting, adopting, and exploiting various combinations of technological capabilities [27]. We believe, the success of digital transformation of organizations and banks is bounded by two key dimensions. One is a lead dimension—maturity of digital initiatives towards embracing emerging technology and intelligent solutions for transformation. The other is strategic fitment of the digital transformation initiatives. Maturity of digital initiatives typically pertain to classification of initiatives from mere digital enhancements to product/service extensions with technology, to even value chain transformations with technology and associated leadership focus. Maturity relates to progressive levels of transformation with digital technology. Both maturity and fitment are calibrated on a continuum from low to high as indicated in Fig. 8.

Fig. 8 Portfolio of digital initiatives driving enterprise transformations, Source Realized by author

340

S. Dutta et al.

Bounded by these dimensions, banks and organizations adopt multiple transformation initiatives. Assessment of digital transformation initiatives of global banks and prevailing literature indicates that most of these initiatives classify into the following primary focus areas—(i) customer and stakeholder experiences, (ii) organizational operations and process effectiveness and (iii) enablement of work including business model transformations.1 The intensity and impact of these technology initiatives can be evaluated based on organization focus, investments and nature of business transformations it generates. A portfolio view on the dynamics of transformation is evident from Fig. 8. With similar visualization banks can clearly assess how a particular technology initiative compares on digital maturity and strategic fitment continuum. With evolving initiatives over time alongside re-calibration of maturity and strategic focus the portfolio plot will evolve. The spatial position of digital technology initiatives on the plot may also vary over time—enabling banks to understand the continuity. The focus intensity including investment and optimal business performance impact of each initiative is visualized by the size of the bubbles. The portfolio visualization helps to understand initiatives for digital transformation of a bank as an associated system, how initiatives vary in maturity, and strategic fitment. Evaluation of such portfolio plots over time will reveal the dynamics of digital transformation for banks. Orientation of technology initiatives across customers, work, internal processes, business model and overlaps may be indicated on the portfolio plot through numerical classifications. An assessment of orientations on the plot will indicate the nature of balance and focus. Longitudinal studies will indicate organizational orientations over time. Organization structural changes for technology and innovations is well demonstrated by most banks. Such structural alignments are to promote a culture of innovation within the institutions leveraging across functional teams. Global banks deploy well defined process to harvest innovative ideas from within the institutions and from extended eco-systems. Banks are forging significant collaborations and partnerships with technology providers. These collaborations include fintechs and established technology companies to find intelligent solutions and enable transformation aspirations. Increasingly technology considerations are more embedded into re-imagination of core line functions. Technology functions at banks are not standalone but are increasingly linked with key operations. This is well demonstrated by the design of leadership positions and organization structures. We observe digital transformation steered by senior leadership roles like—Chief Digital Officers, Chief

1

Stakeholder Experience includes understanding stakeholders, growth through enhancing experiences and touch points providing integrated experience across digital touch points. The stakeholders included from both internal and external to organizations. Institutions can also use digital technology to enhance and automate the operational processes. Westerman et al. divides the transformation of operational processes into three segments: process digitization, worker enablement and performance management. Business Model(s): At its simplest form, digitalization can enable globalization and access to new markets and create new businesses. This entails to providing something new and innovative to existing businesses, in the form of value-adding services or augmenting products or services with digital components.

Imperatives, Trends and Dynamics of Digital Transformation …

341

Data Officers beyond conventional CTO and CIO roles. There is increased collaboration across leaders including CMO, Security and Compliance leader to operationalize digital transformation of banks. Banks amplify digital transformation initiatives as key organizational messages and promote both internally and externally. The practice of amplifying technology led transformations are observed at varying maturities across global banks to build trust and capability reputations among key stakeholders. Amidst multiple digital transformation initiatives by banks, we observe some key future focus among banks. These transformative focus and trends across global banks are relevant today and are likely to be manifested in the future more evidently. We believe, these trending focus areas on the future of banking will be driving most transformation agenda and intelligent banking systems. Table 1 indicates the key scenario manifestations on the future of banks. We conclude this chapter with a view on how these future scenarios will bring about transformation, intelligent systems, and technology adoptions. It is likely banks may focus on multiple areas that will lead to varied impacts across banks’ functions and lines of business. Table 1 Future of bank—focus on transformation and intelligence, Source Realized by author Future of bank

Focus

Discussion

Influencing technology

Banks will resemble technology organizations. Banks will increase collaborations with fintechs and established technology partners while focusing on inhouse research and development

This is likely to impact banking operations across lines of business including retail, commercial, and corporate banking along with asset and wealth management This will impact across enhancing experience, processes, and transformation of business models

Banks will adopt new technologies at par with consumer software providers. A few examples are wearable banking, bank driven e-commerce, service economy, fraud analytics and focus on cyber-security. Banks will invest in core transformation in their major business lines—with scope for easy integration with technology collaborations. Likely to be more collaboration than competition—FinTech and with technology firms to become technology solution providers. Greater in-house research and development on emerging technologies and large-scale adoption of cloud technology

Machine to machine communication; IoT, Cognitive; Automation; Robotics; Wearable apps, Blockchain, Bank linked e-commerce portals; 5G; Artificial Intelligence; Cloud; Quantum Computing; AR and VR; Analytics

(continued)

342

S. Dutta et al.

Table 1 (continued) Future of bank

Focus

Discussion

Influencing technology

Bank branches will become virtual experience centers The future branches will become engagement zones on intelligent technology platforms. Some physical branches will be integrated with other entities

This is likely to impact retail and commercial banking This will impact on enhancing experience, processes, and transformation of business models in retail and commercial banking

Given post pandemic realities—Branches will be conceptualized as experience centers Many global banks view branches as a differentiator over FinTechs and technology providers. Branches provide relationship, customer visibility and trust. Technology driven experiences will be at the core of branch transformations Branches will be integrated with retail, airports, transformed with experience pods, cafes, intelligent ATMs

Omni-channel banking, Video banking, Human–machine interface technologies, Artificial Intelligence, Natural Language Processing, Chatbots, Metaverse, NFT NFC/iBeacon

(continued)

Imperatives, Trends and Dynamics of Digital Transformation …

343

Table 1 (continued) Future of bank

Focus

Discussion

Influencing technology

Smartphones and wearables will replace card and cash Increasingly transactions will become virtual, invisible, and intelligent without having to explicitly perform them

This is likely to impact banking operations across lines of business including retail, commercial, and corporate banking along with asset and wealth management This will impact enhancing experience, processes, and transformation of business models and value chain

Regulators in both developing and emerging economies promote use of digital transactions. Growing threat from technology firms and increasingly customers find it convenient to carry apps on smartphone than multiple credit and debit cards. Contactless payments and biometric technologies will help in secured transactions; focus on customer protection against SIM spoofing, biometric data protection and mobile theft risk. Focus on financial inclusion –People who do not have bank cards will have smartphones; opportunity to bring them in the financial system. Will lead to real time payments focus on P2P, P2B and B2B; simplified international remittances. Increase with emergence of digital currency—Fiat Currency vs. Central Bank Digital Currency and Cryptocurrencies

Remote commerce emulation payment (BLE, NFC, iBeacon), biometric authentication, Blockchain for international money transfer, digital wallet consumer hub, quantum computing, Artificial Intelligence, Analytics, Wearable integrations, Human and motion sensing technology, IoT, drone, 5G technology

(continued)

344

S. Dutta et al.

Table 1 (continued) Future of bank

Focus

Discussion

Influencing technology

Marketplace model for Banks Changing a bank will be as simple as changing the mobile operator for the customer. Banks will aggregate solutions to multiple customer needs. Analytics will be deployed to curate personalized experiences

This is likely to impact retail and commercial banking This will impact across enhancing experience, processes, and transformation of business models

Marketplace model will proliferate globally and will impact customer retention capacity. Currently, marketplace lender and price comparison websites are gaining popularity; Governments may support this trend through new legislations. Big technology players like Google, Apple, Baidu or Tencent has a low barrier to entry in this segment. Banks will need to focus on their service model—for personalized customer engagement Banks utilize analytics to improve loyalty management

Blockchain for online documentation, Open Banking APIs, BIAN standards, Bank interoperability, Analytics driven personalization will be high focus

Contextual and Embedded banking for Purpose-led Ecosystems Bank services will be embedded in 3rd party applications, integrating purposeful eco-systems to deliver intelligent solutions

This is likely to impact retail and commercial banking This will impact across enhancing experience, processes, and transformation of business models

Banks will provide personalized services to clients with full array of savings, investment, and risk mitigation products through technology platforms and necessary partnerships with asset management and insurance firms Banking products will be embedded in other applications; with seamless customer experiences in cross-industry ecosystems and value-stream overlaps

Open Banking APIs, Cloud 2.0, analytics driven personalization, digital personal financial advisor, Aggregator services, Banking on technology platform, Artificial Intelligence, Robotics

(continued)

Imperatives, Trends and Dynamics of Digital Transformation …

345

Table 1 (continued) Future of bank

Focus

Discussion

Influencing technology

Democratization of Wealth management and other value-add services. Banks will be obligated to offer more to remain competitive. They will need to capitalize on cross-selling opportunities

This is likely to impact retail, commercial banking along with asset and wealth management

Customers’ need for wealth products driven by low interest rate environment. Banks will find it hard to demonstrate value delivered to justify high advisor fees. So, they will look to expand passive investment and robo-advisor services. Specialized wealth management firms will find it hard to sustain due to competition from technology players. Banks will acquire technology firms to expand robo-advisory; may get integrated with retail banking and PFM Wealth advisors to be supported by automated trading and big data technologies

Technology focus—Robo-advisory, digital assistants for bank advisors, automated trading cross-asset class; Artificial Intelligence, Big Data Capabilities, Analytics

Autonomous and Automated Banking for Compliance and Operations All back-office functions will witness a job shift from humans to intelligent software systems. National regulations and policies with specific country focus will be managed through integrated software systems

This is likely to impact banking operations across lines of business. This will impact across enhancing experience, processes, and transformation of business models

Breadth and depth of banking regulations will expand making it imperative for banks to optimize operations, risk management and reporting Bias is a point of concern in compliance; regular operation errors need to be identified better by AI systems. Automation and robotics in regulation and risk reporting will be more evident. Disparate rules across countries, competitive currency devaluation and trade practices and socioeconomic uncertainties will hurt banks’ global ambitions; evident from consolidation in banking industry across US, Japan, India, and Brazil

Technology focus—Regulation technologies (RegTech), cognitive automation, mobile imaging & natural language processing, digital signature, blockchain, quantum computing, digital twin, cloud technology, analytics, machine learning and artificial intelligence, Technology focus on payment infrastructure

(continued)

346

S. Dutta et al.

Table 1 (continued) Future of bank

Focus

Discussion

Influencing technology

Banks will drive Sustainable Development Goals Given the intermediary role, banks and financial services can promote sustainable behaviors among industries, organizations, governments, and individuals, can even trigger optimal changes in society

This is likely to impact banking operations across commercial, and corporate banking along with asset and wealth management This will impact across processes, and transformation of business models

Globally, banks are focusing on national development, diversity, and inclusion goals, promoting industrial compliance aligned to environmental, social and governance (ESG) norms. Banks support the green power organizations, green production ventures and green consumption practices with focus on circular economy. Central banks play significant role to steer sustainability focus for respective geographies. Focus on reporting performance on sustainable developments relating to ESG

Green technology, 3D Printing, IoT, Focus on greener transactions, Renewable energy, electronic vehicles, drones, 5G, product provenance technology, circularity, RFID

Banks indicate intelligent technology innovation as a strategic priority. Institutions demonstrate strategic objectives and aspirations for becoming a digital bank by building capabilities in emerging digital technologies. There is focus on analytics, mobile technology, unified communication, cloud infrastructure, security, and control. There is an inclination to pilot with other emerging technologies in banking like blockchain, IoT, artificial intelligence, robotics, wearables, quantum computing, drones, 5G, metaverse, non-fungible tokens among others. Many banks indicate investments in dedicated digital innovation laboratories. Some banks indicate that attracting, retaining, and developing top digital technology talent is important for in-house capability including research and developments. Banks have established technology laboratories focused on customer experience, improving process effectiveness, and exploring solutions for service extensions. Focus on forward-looking /emergent technology—preempting ‘What is next?’ for technology is important for banks to remain competitive and to differentiate effectively.

Imperatives, Trends and Dynamics of Digital Transformation …

347

5 Conclusion Digital transformation is a pervasive change in banks impacting all components of internal systems and customer channels. Diffusion of technology innovations have accelerated, with big technology companies building the platforms and providing the seamless customer experiences globally through consumer application platforms and social media channels. Banks leverage these platforms to compete with fintechs in developing intelligent financial applications and products. Utilizing their greater understanding of regulations and financial health, aspire to establish a prime role in emerging scenario. Value realization is likely to happen by identifying customer pain points and developing intelligent solutions to address them with agility. In many instances, successful product development by fintechs often lead to increased collaborations with banks and other ecosystem partners for improved customer value delivery. Our chapter on Digital Banking explores the imperatives of digital transformation, present and emerging technologies influencing this transformation to evolve intelligent solutions and studies the dynamics towards evolving the bank of the future. The chapter highlights the multi-faceted aspects of technology adoption and the resultant transformations in customer interactions, new product/solution development and cross-industry ecosystems. Future transformation of banking systems will follow the broad templates established by present innovations in processes and channels, and will be influenced further by new business objectives, such as sustainability, convergence, and resilience. As new technologies mature, more innovative use cases will emerge in the strategic fitment continuum of banks, and they will need to experiment on and assess those deployments for business impact. Future success of banks will be determined by deployment agility of emergent technology use cases, alignment to customer requirements continuously and the scale of co-ordination for purpose-driven cross-industry ecosystems.

References 1. OECD (2020) Digital Disruption in Banking and its Impact on Competition. OECD, Paris 2. Kitsios F, Giatsidis I, Kamariotou M (2021) Digital transformation and strategy in the banking sector: evaluating the acceptance rate of E-services. J Open Innov Technol Mark Complex 7(204):1–14 3. Pani A, Pramanik H (2020) Digital transformation of organizations – defining an emergent construct. In: Sharma SK, Dwivedi YK, Metri B, Rana NP (eds) Re-imagining diffusion and adoption of information technology and systems: a continuing conversation. Springer, Heidelberg, pp 511–523 4. Caplain J, Fry F, Topping S (2017) Navigating the impact of geopolitical change on banking regulation (2017). https://assets.kpmg/content/dam/kpmg/xx/pdf/2017/12/banks-navigate-unc ertainty-with-geopolitical-change.pdf. Accessed 4 Apr 2022 5. Diener F, Špaˇcek M (2021) Digital transformation in banking: a managerial perspective on barriers to change. Sustainability 13, 2032. https://doi.org/10.3390/su13042032

348

S. Dutta et al.

6. Pramanik HS, Kirtania M, Pani AK (2019) Essence of digital transformation—manifestations at large financial institutions from North America. Fut Gener Comput Syst 95(4):323–343 7. Oget Q (2021) When economic promises shape innovation and networks: a structural analysis of technological innovation in the silver economy. J Innov Econ Manag 35:55–80 8. Tsindeliani IA, Proshunin MM, Sadovskaya TD, Popkova ZG, Davydova MA, Babayan OA (2021) Digital transformation of the banking system in the context of sustainable development. J Money Laund Control 25:165–180 9. Louche C, Busch T, Crifo P, Marcus A (2019) Financial markets and the transition to a lowcarbon economy: challenging the dominant logics. Organ Environ 32(1):3–17 10. Rizzi F, Pellegrini C, Battaglia M (2018) The structuring of social finance: emerging approaches for supporting environmentally and socially impactful projects. J Clean Prod 170(2018):805– 817 11. Bis.org (2021) The Green Swan Conference - Coordinating finance on climate. https://www. bis.org/events/green_swan_2021/overview.htm. Accessed 23 Feb 2022 12. Al-Bassam S, Al-Alawi AI (2019) The significance of cybersecurity system in helping managing risk in banking and financial secto. J Xidian Univ 14(7):1523–1536 13. Hegarty A (2021) How digital transformation can build more resilient businesses. https://www. weforum.org/agenda/2021/10/digital-transformation-business-resilience-cyber/. Accessed 15 Dec 2021 14. Feyen E, Frost J, Gambacorta L, Natarajan H, Saal M (2021) Fintech and the digital transformation of financial services: implications for market structure and public policy. The Bank for International Settlements and the World Bank Group 2021, Basel 15. Liu MEX (2021) Stay competitive in the digital age: the future of banks. IMF eLibrary. https://www.imf.org/en/Publications/WP/Issues/2021/02/19/Stay-Competitivein-the-Digital-Age-The-Future-of-Banks-50071. Accessed 16 Dec 2021 16. Bundi D, Ramirez G, Lettig S (2018) Modular business architecture as banking use case. Disruption Disciples Whitepaper #01 17. Bis.org (2021) Covid-19 accelerated the digitalisation of payments. https://www.bis.org/statis tics/payment_stats/commentary2112.pdf. Accessed 20 Mar 2022 18. Rogers EM (2033) Diffusion of innovations, 5th edn. Simon & Schuster, New York 19. Eveland J, Tornatzky L (1990) Technological innovation as a process. In: Tornatzky L, Fleischer M (eds) The processes of technological innovation. Lexington Books, pp 27–50 20. Iacovou CL, Benbasat I, Dexter AS (1995) Electronic data interchange and small organizations: adoption and impact of technology. MIS Q 19(4):465–485 21. Bharadwaj A, Sawy OAE, Pavlou PA, Venkatraman N (2013) Digital business strategy: toward a next generation of insights. MIS Q 37(2):471–482 22. Mithas S, Tafti A, Mitchell W (2013) How a firm’s competitive environment and digital strategic posture influence digital business strategy. MIS Q 37(2):511–536 23. Kane GC, Palmer D, Phillips AN, Kiron D, Buckley N (2015) Strategy, Not Technology, Drives Digital Transformation. https://sloanreview.mit.edu/projects/strategy-drives-digital-transform ation/. Accessed 1 Apr 2022 24. Cuesta C, Ruesta M, Tuesta DA, Urbiola P (2015) The digital transformation of the banking industry. J Chem Res 1–10 25. Bonnet D, Westerman G (2020) the new elements of digital transformation. https://sloanreview. mit.edu/article/the-new-elements-of-digital-transformation/. Accessed 15 Mar 2022 26. Westerman G, Bonnet D, McAfee A (2014) The nine elements of digital transformation. https://sloanreview.mit.edu/article/the-nine-elements-of-digital-transformation/. Accessed 27 Mar 2022 27. McCarthy IP (2003) Technology management – a complex adaptive systems approach. Int J Technol Manag 25(8):728–745

Tourism

Digital Transformation in Tourism: An Intelligent Information System Proposition for Hotel Organizations Tutku Tuncalı Yaman

and Hülya Ba¸se˘gmez

Abstract The digital transformation process in the tourism industry has been developing further in recent years. In addition to the active use of traditional information technologies, access to data has become relatively easy thanks to the use of smart technologies in this sector, and it has become even more important to interpret this data and improve visitor experiences in line with visitors’ expectations. In this study, which is based on the digitalized tourism perspective called Tourism 4.0 and Smart Tourism, following the Industry 4.0 prospect, digital transformation and the role of digital tools in the accommodation dimension of the tourism sector is discussed and it is aimed to propose an end-to-end smart management system. Thanks to this system which is composed of multiple data collection tools, a relational database for data storage, and interspersed information system modules including a recommendation system, the preferences of the users could be examined to achieve maximum customer satisfaction, and it could be possible to make suggestions for better spending their time in their current visits in line with their preferences. The uniqueness of the chapter is that it simulates the end-to-end digitalization process of accommodation businesses, which is an aspect that has not been discussed in the literature before. Keywords Tourism · Digital transformation · Information systems · Machine learning

1 Introduction There have been many different trends of digital transformation in different industries such as healthcare, finance, retail, media, and entertainment but the extent of digital transformation in the tourism industry has been completely turned on in recent T. Tuncalı Yaman (B) Department of Management Information Systems, Marmara University, Istanbul, Turkey e-mail: [email protected] H. Ba¸se˘gmez Department of Management Information Systems, Beykent University, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_15

351

352

T. T. Yaman and H. Ba¸se˘gmez

years. The transformative impacts of digital technologies are evident in almost all industries and business environments [17, 54]. In particular, information technology has transformed the tourism and hospitality landscape [79] by disrupting traditional operational techniques and giving rise to more sophisticated big-data-based operational models [9, 35]. From the business management perspective, technology and its implications are becoming ever important for the effective application of revenue management as well. In the literature, the extent of digital transformation in tourism is handled in four interrelated phases: predestination, booking, accommodation, and post-destination. On the other hand, gathered data sources differ according to the type of the source, namely, users, devices, and operations. Anticipated applied research topics in the area mostly concentrated on unique cases and they are limited by the type of the data source [46]. Another phenomenon, called Smart Tourism, is positioned as a perspective to enhance tourism development with the help of “smart” technologies such as the use of digital data collection units (sensors, wi-fi networks, social media) and transform the data into useful information with advanced analytical models (i.e., big data analytics). Since it can be seen as a disruptive action, implementation of smart practices requires strong organizational management and the effort of management teams [13]. Without a doubt, a successful operationalization of smart systems can stand on its three legs: Technology, Human, and Institution [23]. As far as the third leg is not well-handled in the literature probably the lack of eligible data. This chapter, it is aimed to cover the accommodation aspect of digital transformation in tourism and propose an end-to-end application of a smart visitor management system for the most important tourism institutions that as hotels. Since the purpose of the proposed system is obtaining maximum guest satisfaction within the visiting period, the required data sources are selected as real-time transactions and actions of visitors. It does not seem possible to obtain the data to be used as input in the implementation of the proposed system, since such a system does not exist. However, every stage of the proposed application within the framework of the designed plan are explained in detail and expected to be a guide for institutions that want to establish a smart system with similar structure after a digital transformation process is occurred in their own businesses. On the other hand, for the academicians working on the subject, considering this point of view, which has not been discussed in the literature before, will pave the way for possible applications to be realized with real data in the future. The model offers an end-to-end digitally transformed system and will embraces the hotel by digitally recording the in-home user experience at every single point. The following questions are answered in model design. • • • • •

What types of data are needed? From what sources data can be collected? How can this data be collected? How can the collected data be transferred to an information system? What kind of information systems can be established?

Digital Transformation in Tourism …

• • • •

353

What kind of reports can be created via the information system? What kind of analytical methods can be embedded in the information system? How can the inputs of the information system be processed and turned into action Who will use the information system?

The contribution of the study to the literature is that it offers a model that will digitally record the end-to-end customer experience for a hotel organization to the attention of both researchers in the field and industry professionals. Until now, the focus has been on the attitudes of the visitors before or after their visits as a customer retention activity in tourism. Here, the use of information systems and data analytics are discussed from a different perspective to improve customer experience and increase loyalty by benefiting from the blessings of digital transformation. The remaining part of the chapter is organized as follows. In section two, the contemporary trends in digitalization, basic terminology, and general information about the elements of digital systems and the literature on current applications are discussed. In section three, the proposed recommendation system for hotel organizations is detailed. The last section is the conclusion and discussion part of the study.

2 Trends in Digitalization In the digital age we live in, also called as Industry 4.0, digitalization alone is accepted as a trend that has the power to change our society and businesses. Many scholars compared the existing impacts of this era with industrial revolution [63]. Among the rising digital trends on a global scale,5G, IoT, CDP (customer data platforms) that can enable meaningful correlation of customer data, Software 2.0, hyper automation, hybrid cloud applications, cyber security, headless tech, democratization of AI, “Total Experience” perspective that combines user experience, customer experience and employee experience, and hybrid (multi-functional) devices are rising [22, 58]. Plus, overall spending on digital transformation technologies and services worldwide has an upward trend, especially after the Covid-19 pandemic. It is forecasted that the spending will be 2.1 trillion U.S. Dollars as of 2023 [84]. Like every industry, the tourism industry gets its share of these trends.

2.1 Digital Transformation and Smart Tourism When different periods of human history are examined, it is seen that technological transformations started with the industrial revolution, especially after the transition to settled life. Along with technological innovations, digital transformation

354

T. T. Yaman and H. Ba¸se˘gmez

has emerged in the production and service sector [19]. Economic development is a product of social change. Digital transformation is the newest manifestation of this social change [93]. Digital transformation can be defined in different ways. One of the most general definitions is the transformation of the business world by adopting digital technologies and industrial and social transformation [15, 91]. Also, [49] defined digital transformation as the integration of digital technologies into business processes. [34] defined digital transformation as the combination of more than one new digital technology to achieve superior performance and competitive advantage in business processes related to the activities of the enterprises. [75] stated that digital transformation is to create new business models, processes, and systems by using technology to provide more competitive advantage and to achieve higher efficiency. [68] defines digital transformation as a construct characterized using new digital technologies to achieve significant business developments. [74] used the expression of reorganizing technology and business models to reach the digitalized customer at the highest level of interaction for digital transformation. With Industry 4.0, every sector is developing and renewing itself over time. This development and renewal can also be seen as a challenge that all sectors have to keep up with the environment and society. It seems almost impossible for tourism institutions to remain indifferent to this change while other sectors keep up with the concept of change, which includes this development and renewal, to maintain their sustainability and respond to the needs of the society. In this case, organizations in the tourism sector need to be created in a dynamic structure that can adapt to rapid changes so that they can become the leader in their market and increase their chances of survival. Although, the concept of “Industry 4.0” has found its counterpart in the tourism sector as “Tourism 4.0”, the most common example to be given to the tourism sector and digitalization is the digital marketing activities made on both national and international platforms. Countries that want to be the leader in the tourism market by keeping the touristic attractiveness at the highest level and wanting to increase their share of income from the tourism sector or to maintain it in a sustainable way, focus on promotional activities, and this is achieved by reaching a much more intense tourist mass than predicted digitally by benefiting from the blessings of digital technology. Conducting promotional activities through digital channels (i.e., internet ads, social media) also allows users who view the promotions using these channels to receive positive or negative comments about that destination and collect feedback. The most fundamental description of on-site digital transformation can be done by accepting this as a process of converting information into a digital format. If we consider this process in terms of tourism management, a digitally transformed institution would be a natural candidate for a smart tourism destination. The concept of a smart tourism destination based on information and communication technologies can also play an important role in attracting large audiences for destinations that are thought to be very different in attractiveness, as it has the capacity to provide personalized and heterogeneous experiences to the visitor by obtaining real-time data [39].

Digital Transformation in Tourism …

355

The concept of smartness in tourism first started to be evaluated within the scope of destination dimension as “Smart Cities” and made rapid progress with technological innovations. Since tourism has spread over a very wide area, it has ceased to be evaluated within the scope of destination and city over time and has met the concept of “Smart Tourism”, which is a wider scope. The purpose of the concept of smart tourism is defined as the coverage of touristic activities informed and supported by smart technologies; facilitating new product/service innovation, improving information and communication opportunities, providing an effective tourist experience, and increasing appealability to all segments [24]. [18] offer a proposal for how “big social data” can be applied in the tourist experience co-design, providing increased value for tourists and a better decision-making approach for management.The smart perspective is supported using advanced technologies like artificial intelligence, IoT (Internet of Things), robots, blockchain, collection of semantically linked data, natural language processing, big data analytics, smart recommendation systems with machine learning algorithms, virtual reality, and cloud computing. In parallel, several core technologies such as information and communication technologies and management information systems would also be in use to establish effective and sustainable management of tourism organizations. [38] listed best-practices from the industry in the context of digital transformation. The use of digital technologies accumulated in mostly predestination, booking, and post-destination phases whereas we are seeing the use of especially robots in service in the accommodation phase. [8] drew attention to the use of machine learning techniques in the tourism sector instead of the traditional information technology methods used in the IT industry. Also, [69] and [10] provided contemporary and future trends. Here, the importance of creating a personalized visitor experience is particularly emphasized. [60] showed that trust is one of the most determining variables in the digital market and keywords such as satisfaction, loyalty, and service quality are closely related to this concept. According to Statista’s up-to-date report [83], 62.5% of the global population (4.95 billion) have an internet connection and among them, 96.2% have any kind of smartphone. Nevertheless, smart tourism institutions do not need “smart” tourists to establish this smart system thanks to smart data collection items that can be integrated into the system except for smartphones. Simply put, the room cards that hotels give to their customers at check-in can provide real-time tracking of visitors’ activities at all antenna-mounted locations as RFID tags. In terms of the use of information systems, it is underlined that institutions should establish systems based on relational databases developed, integrated with all subsystems, and associated with their own business strategies [72]. The success of the sophistication of these systems can be evaluated by the potential of real-time experience enrichment for customers and the success of the connected recommendation system algorithms. In near future, it is expected that the role of cloud technologies, Software 2.0, and cybersecurity will be emphasized in the context of the architecture of information and related recommendation systems. Thus, the inclusive total experience perspective will also be embraced.

356

T. T. Yaman and H. Ba¸se˘gmez

2.2 Elements of a Digital System in Tourism In this section, general information is given about data sources, information systems, recommendation systems, the internet of things and artificial intelligence applications along with machine learning, which we can count among the indispensable factors in digital transformation. In addition, the applications encountered in the tourism sector are explained in the context of each factor.

2.2.1

Data Sources

The data used in tourism studies are collected from different channels. This data can be obtained from users, devices, or transactions made, especially if this is big data. Big data is defined as the processable and meaningful form of high volume and variety of data such as social media shares, photos, blogs, videos, texts and recorded files [80]. The largest share among these data sources is the textual data and photographic data from user-generated content (UGC). In addition, device data such as mobile roaming data, Bluetooth data, RFID data, Wi-Fi data, meteorological data, and transaction data such as web search data, website visit data, online booking and purchase data are also used for tourism research [30, 50, 77, 90]. [46] composed the distribution of the big data sources in tourism. Accordingly, users have a share of 47%, devices 36% and operations 17%. More than 50% of user-sourced data is photo and text data. When device data is examined, it draws attention that 21% is GPS data, while web search data is represented by 11% in operation data. In the light of these figures, one can interpret that most of the research were realized with user-data. But the source of data is mainly openly shared irregular social media data of users. If this data is organized appropriately, studies can be carried out to draw meaningful results with different techniques such as sentiment analysis, statistical analysis, clustering and categorization, semantic summarization and advanced modeling. In the tourism literature, there are no studies in which various unstructured data are used together, except for “forecasting” studies using internet searches and website traffic data [61]. However, by accepting tourists as “digital travellers” various types of usable data emerge in the process from the planning of a tourist’s trip to the next stage. Without an integrated data management approach, and collection of on-site real-time usage data succesful information systems cannot be built [87]. That’s why institutions should embed smart data collection methods into their smart systems. The data collection point should be predefined and suitable recording applications should be planned. For instance, all possible activities that a visitor can perform in the hotel can be listed. System architecture can be prepared for how data will be collected for each activity (i.e., gym use, meal time preferences, places where he/she spends certain times of the day, room service orders, housekeeping requests, pool visit periods), and data can be collected real-time in a relational database for customers on an individual basis. Historical data from previous visits with all other available data from the environment (website traffic, internet searches, social media

Digital Transformation in Tourism …

357

entries with location information, meteorological data, etc.) also feeds the database and structured data can be used as an input for both information and reccomendation system.

2.2.2

Information System

The use of information technologies in the tourism sector has been one of the most researched subjects since the early 1980s [14]. With the use of the latest information technologies in the tourism sector, remarkable progress has been seen especially in the last 20 years. In addition, many studies have been carried out in this area in recent years [16, 45, 53, 79]. It is inevitable that the tourism sector will be affected by the innovations that have emerged because of technological advances. Since information is at the center of the travel industry, the use of information technologies in this sector is of great importance. In tourism activities, it is of great importance to collect, process and transmit the information needed for daily operations. Information technologies have a dramatic impact on the tourism industry as it has the effect of compelling us to reconsider the current functioning in the tourism industry. In the tourism sector, various information systems such as reservation systems, internet, satellite systems, electronic payment systems are used to maintain the current order in an effective and developing way. If we consider information systems in terms of the hotel industry, it is possible to express it as computer systems that provide information about the commercial operations in a hotel. Information systems play an important role in terms of facilitating planning, management, daily operations, and policy planning processes, increasing administrative efficiency, providing international visibility, and contributing to performance improvement [5]. By analyzing the data obtained by consumers using these systems, the footprint for the individual is revealed. At the same time, better and more practical results can be achieved with the use of information technologies in processes that turn into a complex working environment due to the scope of service.

2.2.3

Recommendation System

Recommendation systems can be defined as software used to create personalized recommendations based on users’ current and past behaviour and to predict user preferences and interests [51]. In the tourism industry, tourists use various recommendation systems to find new destinations for their holidays. However, the results obtained from such systems can sometimes be misleading and do not meet the expectations of the tourist. One of the main reasons for having a disappointing trip is ignoring user reviews. Also, tourists often must manually review user reviews to decide [52].

358

T. T. Yaman and H. Ba¸se˘gmez

[37] developed a recommendation system that creates personalized profiles containing users’ preferences and various lifestyles and is updated based on historical and current services. This system interprets different types of data from RFID and creates user profiles from the information obtained using this data. [1] developed a recommendation system that compares a tourist’s preferences with the characteristics of nearby attractions and returns them to the most similar point of interest. In this recommendation system, to calculate the similarity of a user’s preferences with the features of a place of interest, they calculated the maximum similarity of any of the user’s preference elements with all the features of that place. As a result, they calculated the maximum similarity ratio, which means the average of all user preference elements The formula is given below. In this equation, P is the set of user preferences and F is the set of interests. Sim(P, F) =

1  Max similarit y(Pi , F j ) Pi ∈P |P|

[66] stated that the integration of 5G technology in the field of tourism and combining it with Augmented Reality, AI, social media, and other applications, as well as making use of high speed, wide bandwidth, and low latency features, will accelerate the customer interaction. After the spread of 5G technology, it will significantly increase the acceleration and development of B2C communication, which will be enriched through recommendation systems.

2.2.4

Internet of Things

European Research Cluster on the Internet of Things (IERC) has defined the Internet of Things as “a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols, where physical and virtual ‘things’ have identities, physical properties, and use virtual entities and intelligent interfaces” [32]. Today, it speaks of a new evolution with powerful, wearable accessories in technological devices that have significantly changed people’s lifestyles and habits, from smartphones and tablets to ‘smart’ watches, bracelets, glasses, and lenses [62]. Although there is a limited number of studies that focuses on only IoT and tourism issues. In [65], it is mentioned that the IoT through devices equipped with sensors that can detect and connect physical and digital assets through appropriate technologies. [89] discussed the use of medical wearable technologies in the context of medical tourism.

2.2.5

Artificial Intelligence

Artificial intelligence (AI) makes our lives easier by allowing some tasks that a human to perform faster with more data. Therefore, AI including voice-over assistants,

Digital Transformation in Tourism …

359

language translations, recommendation systems, navigation, e-commerce, and chatbots seem to take place in our daily life. [94] gave an example of the creation of the best travel route recommendations for tourists with the help of an AI based application. One of the technologies that can be used most effectively in meeting the increasing trend and expectation of personalized service in tourism will undoubtedly be AI. A recent study of [26], based on establishing the evaluation index system of a smart hotels, puts forward new suggestions for intelligent hotel construction.

2.2.6

Machine Learning

Although it has a deep-rooted history in terms of the period in which it is handled and the methods it covers machine learning, which has become widespread in the last decades due to the development and accessibility of computer processor technology and the amount of data available today. It is not odd that one could see the term is repeatedly brought up in the context of “Big Data” [36]. Within the limits of our available knowledge, [73] proposed the term ‘Machine Learning’, now, practitioners using it refer to a set of analytical methods that allow machines -such as computersto learn from the data and improve the prediction accuracy and modelling capability. Plus, the analytical techniques under the umbrella of machine learning help us to build AI-driven applications. The field is now separated into three main branches namely Supervised, Unsupervised, and Reinforcement Learning [76]. Supervised learning covers techniques to form segments or predict a real value or make an accurate decision from a set of observed features. Lately, decision support systems from different domains are based on Supervised Learning models. Deep learning is accepted as an extension of research in artificial neural networks (ANNs) under the branch of supervised learning [25]. Unsupervised learning which includes techniques like clustering, density estimation, and network analysis, is in use to describe the structure of the data and to figure out its latent patterns. And reinforcement learning usually refers to techniques that are used in n sequential decision tasks to predict the best action [42]. In the literature on the tourism sector examples, one could see that they are parallel to the available data sources such as online reviews, online reservation sites, interviews, and surveys distributed online [27, 44, 59, 64, 67, 78]. [20, 31, 56], and [4] works could be given as examples of contemporary instances that deal with the technical side of the subject within the context of tourism research. There are several literature reviews combine papers with data mining and machine learning applications in niche fields of tourism research [40, 47, 48, 55, 57, 70, 81] and a few had an attempt to examine studies with a general perspective [43, 85]. Even though the real-world applications of machine learning in the hotel business are limited to a few case studies [7, 64] stated that many executives supported the implementation of machine learning applications in understanding hotel customers and increasing their satisfaction and loyalty.

360

T. T. Yaman and H. Ba¸se˘gmez

2.3 Disruptive Role of Digital Transformation The disruptive role of innovation was first held by [12]. Today, digital technology is no longer confined to the field of informatics, it is used in almost every part of the value chains of companies. That’s why most of the managers suppose a disruptive effect of digital transformation [21]. According to the results of KPMG’s research, the disruptive technologies that will enable the digital transformation of companies in the next three years, 17 percent of the participants answered internet of things, 13 percent artificial intelligence and 10 percent robotics. More than 750 global technology leaders participated in the research and underlined that today’s business models will be replaced by new generation initiatives that provide added value. The technology leaders of the future, who foresee this situation, aim to gain market share by entering different sectors quickly [41]. In the tourism industry, the most known examples of disruptive innovations are Uber and Airbnb [3]. Previously discussed main effects of digitally transformed organizations are a potential increase in unemployed staff due to lack of required digital employability skills and relatively higher cost of operation due to frequent changes in technology [2, 82]. These kinds of structural changes could trigger organizational resistance due to safety and security concerns of employees. It is advised that a strong leadership profile is required to encourage the whole organization for their adaptation. In addition, existing organizational dynamics and culture should be considered while designing the structure of the new system to be activated. A well-adapted system will lead cost saving, more efficient and productive operation, developed and innovative services and eventually higher customer satisfaction.

3 An End-to-End Smart Management System Proposition for Hotels In this section, an end-to-end, comprehensive, and successful digital transformation system will be proposed. Before talking about the design of the proposed system, it is necessary to define its scope and address its limitations. The tourism location discussed here is an exemplary hotel business. Visitors can come here for business or leisure. The fact that the point where the accommodation service is given was chosen as an example in the study is that dimension has not been addressed in the literature before, perhaps due to the lack of data sources. It is not possible to obtain data for this system, which is not available in existing commercial hotel businesses. On the other hand, businesses that have somehow entered the digitalization process and have implemented various applications in line with their own strategies do not share customer and corporate information with third parties due to their data privacy policies protected by the local laws. For the reasons mentioned above, the proposed model has been developed by designing an exemplary hotel business. It is important to note that, hotel organizations which are anticipated to establish similar

Digital Transformation in Tourism …

361

systems, should obtain clear consent from their visitors to process their data within the recommendation system. At the stage of designing an inclusive and end-to-end digital system, the system architecture must first be created and analyzed. This architecture should be in line with the business strategy as well as in line with the products and services offered by the hotel to its visitors. In the example discussed here, besides the accommodation service of the hotel business, all the services in its facility are listed. The reason for this is to identify the primary data sources and to create inputs for the information and recommendation system to be developed in the next stage. In addition, customer data is important in creating solutions for the users of these systems. It is supposed that the hotel has a restaurant, lobby, swimming pool, beach, fitness center, bistro, bar, spa, children’s play club, and a business center with meeting rooms.

3.1 Data Collection Each facility should be assigned as a separate data collection point and each visitor is tracked when they entered the facility. To manage the data collection structure the wristbands of visitors can be designed with RFID tags and an antenna can be placed in the entering area of the facility. With these tags, visitors’ real-time transactions at the facility can be recorded. The content of the activity at the visited facility can be tracked by payment information. For the free of use activities such as fitness center and pool usage, the information regarding the total time spent here can be gathered. An on-site evaluation system can be gamified with a smart application and visitors can rate the service with an application in exchange for a certain reward. If login option to this application can be integrated with visitor’s social media account, social media data can be associated with the usage data. The data of many in-room activities like the minibar and pay-tv usage are already collected by hotels. Additional modules can be integrated into the air-conditioning system to record some additional preferences. In Fig. 1, proposed data sources with collection methods with sources are illustrated. A relational database can be configured to store customer data and feed integrated information systems. Undoubtedly, the most basic data to be stored in the database is the demographic data of the customer. Whether such data is received at the reception during check-in or during reservation, it should be ensured that the information system used transfers this data in accordance with the customer database. Data enrichment can be achieved by associating the repetitive visits of the same customers with the record in the existing database. A suitable standardization in transferring the data to the database, especially for unstructured data types such as social media data, will facilitate the data analytics applications to be carried out in the next stages.

362

T. T. Yaman and H. Ba¸se˘gmez

Fig. 1 Data collection structure1

3.2 Information System Organizational information systems propose inclusive solutions with well-known major elements such as Enterprise Resource Planning Systems (ERP), Decision Support Systems (DSS) and Customer Relationship Management (CRM) systems. The structuring of these systems in an integrated manner, beyond the benefits they will offer individually, is vital in achieving organizational goals. In the most general sense, context of the integration could be associated with goals of greater efficiency, effectiveness, and competitiveness in organizations together with harmony across work and society [88]. One of the main purposes of ERP systems are supply and inventory management, order management, human resource management and procurement. On the other hand, CRM systems allow businesses to manage customer relations with interaction data. DSSs are in function to gathering and analyzing the data and synthesizing information to produce comprehensive reports. Here, it is advised to construct a system that is integrated for the same purpose: customer satisfaction. Considering the proposed digital structure here, every unit within the hotel should use an integrated information system covers ERP, CRM, and a DSS is united with a recommendation system (RS). At the design stage, it is important to plan a “paperless” operation in every department to realize an all-inclusive digital transformation. Data entry interfaces, which are among the data sources discussed above, are the components of this information system, where customers’ orders, especially at points such as restaurants, spa, bar, etc. are entered in an appropriate format. Sub-interfaces of

1

Illustrations are designed by the authors. Source of thumbnails: https://www.shutterstock.com/tr/ category/illustrations-clip-art.

Digital Transformation in Tourism …

363

each facility are elements of ERP system however the collected data will be accessible by CRM system to follow-up the customer-based activities. The four main legs of an information system are people, data, software, and hardware. Considering of these legs, the operation should be designed by creating certain procedures to fulfill the operational excellence. In the first step, a user-friendly software is developed or purchased. Appropriate hardware tools are provided in the areas where this software will be used. The personnel who will use it are trained on how to use the system in accordance with the determined procedure. System administrators are determined on a departmental basis, and system activity can be reported instantly. In both ERP and CRM systems, there should be tools to manage the operations in the areas of responsibility of these departments and they should be designed to receive reports at desired periods and scopes. It should be ensured that these designs are carried out in line with the needs of each field of activity and that they create outputs that will shed light on daily operational decisions as well as senior management decisions. Each system should be flexible enough to adapt to the new needs of the organization and should offer ease of use that will contribute to the efficient running of business. It should be ensured that all employees of the organization believe in the benefits and requirements of the structured information system. Otherwise, systems that will ensure error-free realization and follow-up of all operations, which are important sources of the database, will become unusable. At this point, the effort to create a digital culture supported by senior management is important. In Fig. 2, the design of the proposed information system environment is illustrated. In the next section, details of the recommendation system and its role in enhancing customer experience will be explained.

Fig. 2 Design of the information system

364

T. T. Yaman and H. Ba¸se˘gmez

3.3 Recommendation System Approach Recommendation systems are highly in use especially in e-commerce sites on the internet but also search engines use this tool to refine customized search results. With a holistic perspective, recommendation systems have an important role in decisionmaking, to help its users to maximize their utilities or minimize risks [11]. Behind these systems, usually an advanced data analytical tool or an algorithm is placed to return customized recommendations to its users [33]. In recent years, use of machine learning algorithms rather than a predefined, stable if-else like decision making statements. Machine learning approaches has been studied since the midtwentieth century in line with the emergence of the field of AI. Nowadays, the most known algorithms are linear and logistic regression, decision trees, k-means and knearest neighbor in clustering, Bayesian networks, random forests, dimensionality reduction and feature selection algorithms however, there is no clear categorization for algorithms that in use into recommendation systems, mainly because there are several approaches and the variations proposed in the literature [71]. The success of the machine learning algorithm integrated into recommendation systems varies depending on the purpose and the characteristics of the data. Among the frequently used ones, variations of regressions, decision trees and the other algorithms that can be discussed under the heading of classification can be counted. The selection of the most successful among them in practice is only possible by comparing their results. The feature of systems based on machine learning is that the model has the ability to update itself with new data. After choosing an approach suitable for the basic nature of the data, the model performance will increase with each new data feed and will start to give better results. In our proposed approach data sources are vary but the main purpose here is to develop a smart recommendation system for hotel visitors to enhance their experience on-site. The system will carry a customer classification model in accordance with the data and will contribute to the creation of personalized recommendations during the visit in accordance with the profiles created based on customers’ transactions as output. At this stage, the recommendation system integrated with CRM and DSS will basically generate profile information through these two systems, which will provide input for the planned customer communication. To compile and organize the data to be transferred to the machine learning system in accordance with the model structure, it will be beneficial to create a data mart that is updated periodically and located under the database. Data mart can also be fed by the information of system repots. For the segmentation creation function of the system, a model that gives appropriate results from the classification algorithms should be preferred. Specially to choose the appropriate model for the classification models, the data is separated as training data (approximately % 70–80 of data) and test data during the model development phase. Then model is predicted using the training data set and then its validity is tested with the test data set [6, 92]. One of the important indicators to monitor at this stage is the confusion matrix that describes the performance of a classification model. After creating the segment definition according to various

Digital Transformation in Tourism …

365

categorical features for each determined segment, it is checked with this indicator whether the assignments made with the test data are made to the appropriate segments [86]. Another tool in use is called Receiver Operating Characteristic (ROC) curve or Gini Index [28]. The area under the ROC curve is a measure of performance of supervised classification rules [29]. After the model performance is evaluated, the visitor segments formed are forwarded to the customer relations department for the development of activity suggestions. Suggestion baskets are defined by considering the length of stay at the hotel, available activities on-site, and the time periods they prefer, to be presented to customers who enjoy similar activities and show similar behavior patterns through DSS. This information is transferred to the CRM system, the most appropriate communication way with the customer is determined and the suggestion is made. In this way that is illustrated in Fig. 3, it is ensured that the experience of the customers is improved by spending their limited vacation time in the hotel with their favorite activities. For instance, if a person who is determined to spend most of his/her time at the pool and/or on the beach and stays with his/her child in the same room, availability of the children’s playground, information about the entertainment activities near the beach, and introduction of the foods that children will love from the restaurant menu, could be a recommendation system output of the system. The communication method preference can be asked the visitor, or these recommendations can be sent via the mobile application or message application on his/her phone. It would be a good idea to leave an “recommended activities message” in their room for review. It is advised that the overall success of the system should be assessed by exit surveys. Then, the features can be reviewed and enhanced by the gathered results.

Fig. 3 Recommendation system

366

T. T. Yaman and H. Ba¸se˘gmez

4 Conclusion The digitalization process, which started with the digitization of analog data in businesses and continued with the digitization of processes, has left its place to the digital transformation process in recent years. In addition to business models based on personalized product offering, digital transformation affects and transforms the entire operation of the business, organizational structure and even company culture. Digital transformation, which is a disruptive and complex process, needs an integrative overall strategy that covers all areas of the business that will be transformed. Considering that digitalization is making rapid progress, not only businesses but also humanity is transitioning to a new era, it can be considered as one of the important issues in terms of sustainability that the problems that will be brought by artificial intelligence applications and developing digital technologies are determined and eliminated or measures are taken. Today, satisfying customers and gaining their loyalty requires providing an excellent user experience. However, this is not as easy as it sounds, because with the digitalization that permeates every aspect of our lives, experience design becomes more difficult and consumers’ expectations from businesses are increasing day by day. Although the inclusion of the tourism sector in compliance with digitalization is an indicator of the level of development, the effects of technology on tourists, personnel, and businesses, which are seen as important resources in the sector, should not be ignored. Emphasizing the process of harmonizing the personnel with technology and providing the necessary trainings may increase the motivation and work performance of the personnel, and then enable the enterprise to obtain positive outputs. In addition, the communication of the personnel, who have prior knowledge about the technologies used in the business, with the tourist can become stronger and increase the preferability by creating customer satisfaction. In this study, the issue of digital transformation in the tourism sector, which is considered as one of the most important sectors to keep customer satisfaction high, is discussed. The main purpose of the study is to address the digital transformation process in tourism institutions and to introduce the state-of-art, as well as to propose a digital transformation model designed through an exemplary hotel business including an integrated information and recommendation system. The reason for choosing a hotel business example within the scope is that digital transformation has not been discussed with this dimension in the literature due to the accessibility of the relevant data and the absence of any pilot application. With this dimension, it is hoped that the study will guide both industry professionals and academics working in the field. Although it is discussed in the literature that it is not possible for the tourism sector to enter the age of full digitalization since it is positioned in the service sector, in this study, our proposed structure provides a perspective on how this is possible. In the proposition of the smart system exemplary hotel described with its facilities and first the data collection sources and locations were analyzed. Then the architecture of the integrated information system including ERP, CRM and DSS modules were introduced. Finally, the recommendation system detailed with its stance that

Digital Transformation in Tourism …

367

creates the real difference for the sector. The recommendation system works with a machine learning module that is fed by the data sources explained in the first stage of the smart system and performs a segmentation with a superior classification algorithm that depends on the activities performed by the visitors during their stay. The reason why the machine learning model was preferred instead of a predefined classification model is that it can update itself with new input and has the ability to improve its results. The visitor segments taken from the recommendation system are transferred to DSS to be matched with the appropriate alternatives among the alternative suggestions determined by the customer relations specialists, and DSS transfers the information regarding which suggestion in which time frame should be made which customer to the CRM system. The CRM system communicates suggestions to the visitor by using customer communication preferences or predetermined communication channels alternatives. The performance of the system is tracked by the results of an exit survey that takes place at the end of the visitors stay. Thus, considering hotel businesses, it will be possible to optimize and enrich the visitor experience in a limited time and to ensure loyalty in this context. The uniqueness of the chapter is that it stimulates the end-to-end digitalization process of a hotel organization and for future studies, it is suggested that an applied study with the detailed implementation of the models with real data, can lay out the efficiency of the digitally transformed structures in tourism organizations. Additionally, model suggestions of new perspectives with new technology trends in different tourism institutions can enhance our knowledge in the sectoral developments.

References 1. Abbasi-Moud Z, Vahdat-Nejad H, Sadri J (2021) Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Syst Appl 167:114324 2. Adeyinka-Ojo S (2018) A strategic framework for analysing employability skills deficits in rural hospitality and tourism destinations. Tour Manag Perspect 27:47–54 3. Adeyinka-Ojo S, Abdullah SK (2019) Disruptive digital innovation and sharing economy in hospitality and tourism destination. In: IOP conference series: materials science and engineering, vol 495, no 1. IOP Publishing p 012006 4. Afsahhosseini F, Al-Mulla Y (2020) Machine learning in tourism. In: Proceedings of the 3rd international conference on machine learning and machine intelligence, pp 53–57. https://doi. org/10.1145/3426826.3426837 5. Agag G, El-Masry AA (2016) Understanding the determinants of hotel booking intentions and moderating role of habit. Int J Hosp Manag 54:52–67 6. Aggarwal CC (2015) Data mining: the textbook, vol 1. Springer, New York 7. Alotaibi E (2020) Application of machine learning in the hotel industry: a critical review. J Assoc Arab Univ Tour Hosp 18(3):78–96 8. Bangare ML, Bangare PM, Ramirez-Asis E, Jamanca-Anaya R, Phoemchalard C, Bhat DAR (2022) Role of machine learning in improving tourism and education sector. Mater Today Proc 51:2457–2461 9. Benckendorff PJ, Xiang Z, Sheldon PJ (2019) Tourism information technology. Cabi. 10. Bilgili B, Koc E (2021) Digital transformation in tourism. In: Emerging transformations in tourism and hospitality. Routledge, pp 53–65

368

T. T. Yaman and H. Ba¸se˘gmez

11. Bouneffouf D, Bouzeghoub A, Gancarski A (2013) Risk-aware recommender systems. In: Lecture notes in computer science, vol 8226, Springer 12. Bower LJ, Christensen MC (1995) Disruptive technologies: catching the wave. Harward Bus Rev 73:43–53 13. Buhalis D (2020) Technology in tourism-from information communication technologies to eTourism and smart tourism towards ambient intelligence tourism: a perspective article. Tour Rev 75(1):267–272 14. Buhalis D, Law R (2008) Progress in information technology and tourism management: 20 years on and 10 years after the internet—the state of eTourism research. Tour Manag 29(4):609– 623 15. Büyükuslu AR (2018) Dijital dönü¸süm, Der yayınları, ˙Istanbul 16. Cai W, Richter S, McKenna B (2019) Progress on technology use in tourism. J Hosp Tour Technol 10:651–672 17. Chanias S, Myers MD, Hess T (2019) Digital transformation strategy making in pre-digital organizations: the case of a financial services provider. J Strateg Inf Syst 28(1):17–33 18. Cuomo MT, Tortora D, Foroudi P, Giordano A, Festa G, Metallo G (2021) Digital transformation and tourist experience co-design: big social data for planning cultural tourism. Technol Forecast Soc Chang 162:120345 19. Do˘gru NB, Meçik O (2018) Türkiye’de endüstri 4. 0’ın i¸sgücü piyasalarına etkileri: Firma beklentileri, Süleyman Demirel Üniversitesi ˙Iktisadi ve ˙Idari Bilimler Fakültesi Dergisi, 23, (Endüstri 4. 0 ve Örgütsel De˘gi¸sim Özel Sayısı), pp 1581–1606 20. Egger R (2022) Machine learning in tourism. Springer, Cham 21. Furr N, Shipilov A (2019) Digital doesn’t have to be disruptive. Harvard Bus Rev. https://hbr. org/2019/07/digital-doesnt-have-to-be-disruptive 22. Gartner (2022) Top Strategic Technology Trends for 2022. https://www.gartner.com/en/inf ormation-technology/insights/top-technology-trends/top-technology-trends-ebook. Accessed 21 June 2022 23. Gretzel U (2022) The Smart DMO: a new step in the digital transformation of destination management organizations. Eur J Tour Res 30:3002–3002 24. Gretzel U, Sigala M, Xiang Z, Koo C (2015) Smart tourism: foundations and developments. Electron Mark 3(25):179–188 25. Gupta M (2019) Deep learning. In: Essentials of business analytics,. Springer, Cham, pp 569– 595 26. Guo R, Wu Y (2021) Research on the Construction path of smart hotel in the era of artificial intelligence. In: 2021 international conference on intelligent computing, automation and applications (ICAA). IEEE, pp 199–202 27. Gustavo N, Mbunge E, Belo M, Fashoto SG, Pronto JM, Metfula AS, Chiremba TR (2022) Emphasizing the digital shift of hospitality towards hyper-personalization: application of machine learning clustering algorithms to analyze travelers. In: Optimizing digital solutions for hyper-personalization in tourism and hospitality, pp 1–19. IGI Global 28. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, Waltham 29. Hand DJ, Till RJ (2001) A simple generalization of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171–186 30. Hu YH, Chen YL, Chou HL (2017) Opinion mining from online hotel reviews: a text summarization approach. Inf Process Manag 53(2):436–449 31. Huang A, Chao Y, de la Mora Velasco E, Bilgihan A, Wei W (2021) When artificial intelligence meets the hospitality and tourism industry: an assessment framework to inform theory and management. J Hosp Tour Insights. https://doi.org/10.1108/JHTI-01-2021-0021 32. IERC (2020) Internet of Things – The Call of the Edge - Everything Intelligent Everywhere - IERC Cluster Book 2020. http://www.internet-of-things-research.eu/pdf/IoT_The_ Call_of_the_Edge-Everything_Intelligent_Everywhere_E9788770221955.pdf 33. Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inf J 16(3):261–273

Digital Transformation in Tourism …

369

34. Ismail MH, Khater M, Zaki M (2017) Digital business transformation and strategy: what do we know so far. Cambridge Serv Allia 10:1–35 35. Ivanov S (2019) Ultimate transformation: how will automation technologies disrupt the travel, tourism and hospitality industries? Zeitschrift für Tourismuswissenschaft 11(1):25–43 36. Kelm O, Gerl K, Meißner F (2020) Machine learning. In: Borucki I, Kleinen-von Königslöw K, Marschall S, Zerback T (eds) Handbuch Politische Kommunikation. Springer Fachmedien Wiesbaden, pp 1–9 37. Kim J, Song C, Kim T, Rim K, Lee J (2009) Secure and efficient recommendation service of RFID system using authenticated key management. In: Proceedings of the 4th international conference on ubiquitous information technologies & applications. IEEE, pp 1–5 38. Konstantinova S (2019) Digital transformation in tourism. Knowl-Int J 35(1):188–193 39. Koo C, Shin S, Gretzel U, Hunter WC, Chung N (2016) Conceptualization of smart tourism destination competitiveness. Asia Pac J Inf Syst 26(4):561–576 40. Koushik AN, Manoj M, Nezamuddin N (2020) Machine learning applications in activity-travel behaviour research: a review. Transp Rev 40(3):288–311 41. KPMG (2018) The Changing Landscape of Disruptive Technologies. https://assets.kpmg/ content/dam/kpmg/pl/pdf/2018/06/pl-The-Changing-Landscape-of-Disruptive-Technologies2018.pdf 42. Kumar S (2019) Machine learning (supervised). In: Essentials of business analytics. Springer, Cham, pp. 507–568 43. Küpeli TS, ¸ Ünlüönen K (2021) Veri Madencili˘gi ve Turizmde Veri Madencili˘gi Çalı¸smaları. J Tour Gastronomy Stud 9(1):275–298 44. Le TH, Arcodia C, Novais MA, Kralj A (2021) Proposing a systematic approach for integrating traditional research methods into machine learning in text analytics in tourism and hospitality. Curr Issue Tour 24(12):1640–1655 45. Leung D, Law R, Van Hoof H, Buhalis D (2013) Social media in tourism and hospitality: a literature review. J Travel Tour Mark 30(1–2):3–22 46. Li J, Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tour Manag 68:301–323 47. Li Q, Li S, Zhang S, Hu J, Hu J (2019) A review of text corpus-based tourism big data mining. Appl Sci 9(16):3300 48. Li X, Law R, Xie G, Wang S (2021) Review of tourism forecasting research with internet data. Tour Manag 83:104245 49. Liu DY, Chen SW, Chou TC (2011) Resource fit in digital transformation: lessons learned from the CBC Bank global e-banking project. Manag Decis 49:1728 50. Lu D, Wu R, Sang J (2017) Overlapped user-based comparative study on photo- sharing websites. Inf Sci 376:54–70 51. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32 52. Luberg A, Granitzer M, Wu H, Järv P, Tammet T (2012) Information retrieval and deduplication for tourism recommender sights planner. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, pp 1–11 53. Malcien˙e Z, Skauron˙e L (2019) Application of information systems in tourism and leisure sector. Int J Social Sci Human Invent 6(02):5341–5346 54. Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5):339– 343 55. Mbunge E, Muchemwa B (2022) Deep learning and machine learning techniques for analyzing travelers’ online reviews: a review. In: Optimizing digital solutions for hyper-personalization in tourism and hospitality. IGI Global, pp 20–39 56. Mich L (2020) Artificial intelligence and machine learning. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-03005324-6_25-1 57. Nagar R, Singh Y, Jaglan V (2021) A review on machine learning applications in medical tourism. In:2021 fourth international conference on computational intelligence and communication technologies (CCICT). IEEE, pp 208–215

370

T. T. Yaman and H. Ba¸se˘gmez

58. Newman D (2020) Top 10 digital transformation trends for 2021. Forbes. https://www.forbes. com/sites/danielnewman/2020/09/21/top-10-digital-transformation-trends-for-2021/?sh=42f e961fc6f4 59. Nilashi M, Bagherifard K, Rahmani M, Rafe V (2017) A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput Ind Eng 109:357–368 60. Palácios H, de Almeida MH, Sousa MJ (2021) A bibliometric analysis of trust in the field of hospitality and tourism. Int J Hosp Manag 95:102944 61. Pan B, Yang Y (2017) Forecasting destination weekly hotel occupancy with big data. J Travel Res 56(7):957–970 ˙ Dubilewska I (2019) Innovative tourist products during 62. Panfiluk E, Godlewska M, Heksel Z, the industrial revolution 4.0. Acad Manag 3(2):92–102 63. Parviainen P, Tihinen M, Kääriäinen J, Teppola S (2017) Tackling the digitalization challenge: how to benefit from digitalization in practice. Int J Inf Syst Proj Manag 5(1):63–77 64. Pelet JE, Lick E, Taieb B (2019) Internet of Things and artificial intelligence in the hotel industry: which opportunities and threats for sensory marketing? In: Martínez-López F, Gázquez-Abad J, Roggeveen A (eds) Advances in national brand and private label marketing. NB&PL 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi. org/10.1007/978-3-030-18911-2_20. 65. Pencarelli T (2020) The digital revolution in the travel and tourism industry. Inf Technol Tour 22(3):455–476 66. Peng R, Lou Y, Kadoch M, Cheriet M (2020) A human-guided machine learning approach for 5G smart tourism IoT. Electronics (Switzerland) 9(6):1–14 67. Phillips P, Zigan K, Silva MMS, Schegg R (2015) The interactive effects of online reviews on the determinants of Swiss hotel performance: a neural network analysis. Tour Manag 50:130–141 68. Piccinini E, Gregory RW, Kolbe LM (2015) Changes in the producer-consumer relationshiptowards digital transformation. Wirtschaftsinformatik Proceedings, 1634–1648. https://aisel. aisnet.org/wi2015/109 69. Pindžo R, Brjaktarovi´c L (2018) Digital transformation of tourism. In Tourism international scientific conference Vrnjaˇcka Banja-TISC, vol 3, no 1, pp 340–355 70. Pineda-Jaramillo JD (2019) A review of Machine Learning (ML) algorithms used for modeling travel mode choice. Dynamics 86(211):32–41 71. Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205–227 72. Pumaleque AAP, Fernandez IR, Perez DDI, Bedriñana MAA (2021) Digital transformation model for the development of tourism companies. 3c Empresa: investigación y pensamiento crítico (1), 47–61 73. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210–229 74. Schuchmann D, Seufert S (2015) Corporate learning in times of digital transformation: a conceptual framework and service portfolio for the learning function in banking organisations. Int J Corpor Learn (IJAC) 8(1):31–39 75. Schwertner K (2017) Digital transformation of business. Trakia J Sci 15(1):388–393 76. Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: from theory to algorithms. Cambridge University Press, Cambridge 77. Shi Y, Serdyukov P, Hanjalic A, Larson M (2011) Personalized landmark recommendation based on geotags from photo sharing sites. In: Proceedings of the 5th international AAAI conference on weblogs and social media, Barcelona, Spain 78. Shibata N, Shinoda H, Nanba H, Ishino A, Takezawa T (2020) Classification and visualization of travel blog entries based on types of tourism. In: Neidhardt J, Wörndl W (eds) Information and communication technologies in tourism. Springer, Cham 79. Sintala SK (2019) Information and communication technology in hotel industry [Master’s thesis, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http:// rave.ohiolink.edu/etdc/view?acc_num=kent1554496660762962

Digital Transformation in Tourism …

371

80. Snijders C, Matzat U (2012) ‘Big Data’: big gaps of knowledge in the field of internet. Int J Internet Sci 7:1–5 81. Solakis K, Katsoni V, Mahmoud AB, Grigoriou N (2022) "Factors affecting value co-creation through artificial intelligence in tourism: a general literature review. J Tour Fut. https://doi.org/ 10.1108/JTF-06-2021-0157 82. Sousa MJ, Rocha A (2018) Skills for disruptive digital business. J Bus Res 17 83. Statista (2022) Digital 2022. https://wearesocial.com/uk/blog/2022/01/digital-2022/ 84. Statista (2022). Spending on digital transformation technologies and services worldwide from 2017 to 2025. https://www.statista.com/statistics/870924/worldwide-digital-transformationmarket-size/ 85. Uzan SB, ¸ Özüça˘glıyan MÖ (2020) Turizm Sektöründe Veri Madencili˘gi. Türk Turizm Ara¸stırmaları Dergisi 4(4):3465–3485 86. Visa S, Ramsay B, Ralescu AL, Van Der Knaap E (2011) Confusion matrix-based feature selection. MAICS 710:120–127 87. Volo S (2020) Tourism statistics, indicators and big data: a perspective article. Tour Rev 75(1):304–309 88. Wainwright D, Waring T (2004) Three domains for implementing integrated information systems: redressing the balance between technology, strategic and organisational analysis. Int J Inf Manag 24(4):329–346 89. Wong BKM, Hazley SAS (2021) The future of health tourism in the industrial revolution 4.0 era. J Tour Fut 7(2):267–272 90. Xiang Z, Du Q, Ma Y, Fan W (2017) A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism. Tour Manag 58:51–65 91. Yankın FB (2019) Dijital dönü¸süm sürecinde çalı¸sma ya¸samı. Trakya Üniversitesi ˙Iktisadi ve ˙Idari Bilimler Fakültesi 7(2):1–38 92. Zaki MJ, Meira W Jr, Meira W (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, Cambridge 93. Zaoui F, Souissi N (2020) Roadmap for digital transformation: a literature review. Procedia Comput Sci 175:621–628 94. Zhang L, Sun Z (2019) The application of artificial intelligence technology in the tourism industry of Jinan. In: Journal of physics: conference series, vol 1302, no 3. IOP Publishing, p 032005

Insurance and Finance

Digital Workplace Transformation and Innovation in the Financial Service Sector Jasmina Selimovic, Amila Pilav-Velic, and Lamija Krndzija

Abstract Future digital work environment does not refer only to the use of digital tools in the workplace, but has a much deeper meaning in the context of workplace redesign. In this context, psychological needs of employees are particularly important, such as the need for autonomy in the completion of work tasks, competence, and connections among employees in the workplace. If employees have positive expectations that digital workplace will enable them to achieve better performances and that it will bring them more satisfaction and well-being, they will be motivated to support digital workplace transformation. This is why we focused our research on the research question whether employees’ engagement, their well-being and support to the digital work expedites digital work environment transformation. Our findings confirm that interpersonal connections among employees in the workplace have significant influence on their performances and well-being, which finally adds to their willingness to accept a digital workplace. This chapter will enable companies, and especially pre-digital organizations, to reconsider their approaches and strategies in the light of how to include their employees as much as possible in the process of digital transformation. Also, we provide adequate recommendations for prospective research. Keywords Digital workplace · Digital transformation · Financial service · Insurance · Well-being · Performance

1 Introduction Work environment nowadays is also becoming digital due to the ubiquitous digital transformation [54], and the use of digital tools in the workplace is becoming an everyday routine. Employees are expected to use digital technologies when J. Selimovic · A. Pilav-Velic (B) · L. Krndzija Izetbegovi´c 1, School of Economics and Business, University of Sarajevo, Trg Oslobodenja-Alija 71000 Sarajevo, Bosnia and Herzegovina e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_16

375

376

J. Selimovic et al.

completing their work tasks, which also implies that employees should have adequate digital skills [20, 44]. This means that digital workplaces require employees to continuously learn and acquire new digital skills and competencies in order to be able to react to the envisaged circumstances quickly and effectively. This often involves teamwork and cooperation in solving some complex problems and challenges [11]. Teamwork and interplay of people and machines contribute to greater creativity and innovativeness of employees (Vom [70]. Therefore, digital workplace is not just employees using and accepting digital tools, but increasing the performances and productivity on the individual level and the level of the whole organization [69]. In that sense, digital literacy as an extremely desirable skill brings along numerous advantages. However, digital workplace environment is a lot more than digital tools, digital skills, and literacy. Digital workplace implies a change and innovation of work activities and processes, and redesign of work environment and organizational culture [15, 57]. Thus, [40] confirmed that the dominant organizational culture determines the effects of remote working. Organizations more and more often turn to the culture which promotes digital innovativeness and increased involvement of employees. In the context of workplace digitalization, such organizational culture does not imply only organizational changes or changes in the management style, but also changes on the level of employees. Therefore, apart from the digital skills of employees and their acceptance of digital tools, their intention to use a digital workplace is of crucial importance for a successful transformation. Their positive expectations and perceptions of the future work environment define the sustainability of the digital workplace. Those expectations of employees, as well as their intention to continue using digital work environment also depends on how much and in what way that environment affects their personal well-being. The existing literature stresses that it is very important that psychological needs of employees, such as autonomy, competences, and connections, are met. This is also in line with the self-determination theory (SDT) which shows that psychological needs of employees affect their performances and well-being in the digital workplace, including a positive attitude towards digital transformation [17, 46]. The existing studies show that there is a significant gap in the research of employees’ expectations and their intentions to support digital workplace transformation. This is especially evident when it comes to the research of the ways as to how to encourage and motivate employees to accept digital workplace. Thus, this chapter is focused on the research question: How can employees’ expectations and their psychological needs affect the success of digital transformation of workplaces in the so-called pre-digital organizations? Our main objective is to examine whether employees’ well-being and their support to digitalization contributes to a faster digital workplace transformation. In this way, this chapter closes the existing gap in literature and shows that interpersonal relatedness in the digital workplace has a significant influence on the employees’ performances and their well-being.

Digital Workplace Transformation and Innovation …

377

Focusing on employees’ expectations, their behaviour, intentions, and psychological needs in the sense of their influence on the digital workplace transformation, our research contributes to the existing literature which deals with a micro perspective of digital transformation. We confirm the need to take a work-place perspective of digital transformation apart from macro-perspective which includes broad perspective and highlight transformation of products and processes. Hence, our research perspective reflects originality of the study whereby focus is on personal traits of employees instead of their digital literacy and skills or even technological aspects as main assumptions for successful digital workplace transformation. This chapter will enable pre-digital organizations to redefine their strategies in the context of an increased involvement of employees in the response to the challenges of digital transformation. Our research includes a sample of 161 employees in the financial institutions in Bosnia and Herzegovina. Our choice to focus on examining digital workplace transformation in the financial sector lies in the fact that digital transformation in the financial sector happened the fastest. One of the reasons for this faster digitalization could be the fact that this sector is mostly in the foreign ownership, so unlike in other sectors, the transfer of knowledge and technologies from the outside is more prominent. Our findings show that social relatedness of employees in the workplace positively affects their intentions to support digital transformation of work environment. The structure of the chapter encompasses an adequate theoretical base, research methods and results presentation. Finally, there is a discussion of the most prominent results, as well as the limitations of the study, as well as conclusions. The remainder of the chapter is organized as follows. The next two sections include theoretical background and research methods. Following that, data analysis and results are examined. Finally, two last sections offer discussion of main findings and concluding remarks.

2 Theoretical Background 2.1 Digital Workplace Transformation Companies nowadays mostly base their pathways to digitalization on digital workplace transformation [15, 21, 35]. Although digital strategies most often place their focus on the client and their positive experience, previous research shows that success and sustainability of a company’s digital transformation mostly depends on the work task and workplace digitalization [15, 39]. The significance and role of digital workplace transformation is particularly stressed in the context of digitalization of the so-called traditional industries and sectors such as retail and financial services. Digital workplace transformation does not solely imply the use of digital technologies while performing work tasks, but the creation of a more favourable work environment [76]. The latter is especially important from the perspective of employees’ satisfaction, expectations, and perception. It is particularly important for employees

378

J. Selimovic et al.

to be convinced in the advantages of digital transformation through the improvement of working conditions by digital technologies implementation. Their positive experience becomes key in the implementation of digital workplace transformation, and in the overall business. Thus, [46] argued that workplace transformation is determined by employee s individual perceptions. On the other hand, the use of digital technologies in the workplace will result in an improved exchange of information and knowledge among employees as well as in an increased productivity in the work task accomplishment and better performances. Most research shows that bigger flexibility, which is provided in the digital workplace where tasks can be handled regardless of the time and place, has a massive contribution to the employees’ increased productivity, and satisfaction as well [15, 45]. Teamwork is significantly facilitated especially when it comes to communication and cooperation among employees who work in different departments. All this, along with the feeling of independence and responsibility, has effect on a positive perception of employees, simultaneously instigating their creativity and innovativeness. Although it is often pointed out that implementation of digital technologies in the workplace can cause additional pressure for employees such as technostress, relevant literature suggests that technostress is in most cases related to the complexity of technology or with the abilities and skills of employees to use it [5, 53]. Technostress occurs precisely in places where there is no appropriate work environment and where workplaces are not adequately designed or better adjusted to the implementation of the newest technologies [21]. This implies that digital transformation, and digital workplace transformation, does not only refer to the use of digital technologies in carrying out work tasks and work routine, but should be followed by an adequate strategy and change of culture in an organization. Therefore, the important role of leadership is often stressed in digital transformation processes, along with clear communication with employees as to what digital workplace transformation implies. Hence, transparency of the sole process is of crucial importance. This is why the style of process management and performance should be open and with a clear vision, not based on the command-and-control principle [50]. This also means that organizational culture should encourage employees’ cooperation, teamwork, creativity, and innovativeness, primarily through the implementation of digital technologies. The above mentioned implies that prospective digital workplace is not just a matter of digital technology implementation nor of its acceptance by employees, but of deeper effects such as a change in the very nature of working activities. Digitalisation does not only change work but also  how employees interact and learn at work [66]. Repetitive work tasks are being increasingly substituted with non-routine, creative and analytically demanding tasks, and social interaction and collaboration are becoming a paradigm for digital workplace [22]. Thus, employees’ motivation and support to the process of digital workplace transformation define its success. Their proactivity and familiarity with the advantages that the sole process brings along, such as the increase of productivity, better performances, positive experience, and personal wellbeing, are of crucial importance for a successful redesign of work environment [4, 19, 27, 47, 48, 55]

Digital Workplace Transformation and Innovation …

379

In this context, apart from employees’ digital literacy and their willingness and ability to learn and accept digital technologies in the workplace, a significant role belongs to their psychological needs, such as the need for autonomy, ability, and relatedness to others [16, 17]. Hence, [46] stress that precisely these psychological needs determine employees’ attitude and perception in regard to digital workplace transformation. If employees’ sense that thanks to the implementation of technology they have enough autonomy, that they are connected to others and capable of completing their work tasks, their motivation for digital workplace redesign will become prominent. Yet, it can be noticed that in certain situations it is hard to address all psychological needs of employees. For example, one of the biggest advantages of remote work that is often mentioned is the autonomy in handling work tasks and its positive effect on the employees’ wellbeing. On the other hand, although remote work adds to employees’ increased productivity, it can simultaneously lead to their exhaustion and isolation, which also has negative effects on the wellbeing of employees. What also contributes to this are insufficient connectedness and lack of direct communication with colleagues. Meeting employees’ psychological needs in the process of creation of prospective work environment especially emerged during the COVID-19 pandemic and the” new normal”. Namely, in the conditions when communication is mostly established via digital tools, and when employees’ autonomy and productivity are significantly increased, work from home brings new challenges along. Recent research [71] implies that employees’ focus and their dedication to work are greatly disturbed by certain family happenings and issues. In this context, further research will be needed as to how work from home as work environment affects performances and employees’ wellbeing.

2.2 Digital Workplace Transformation vs. Digital Workplace Innovation In practice, digital transformation is most often related to digital innovation because essentially, it is not just about the use of digital technologies in the workplace, but about completely new ways of work, innovated processes, and work tasks. Apart from the increase of employees’ productivity due to their enhanced mobility and agility [12] and improved engagement and motivation [8], digital work environment also adds to a rise in creativity and employees’ innovativeness. The use of digital technologies and applications enables employees to carry out their work tasks more independently and more flexibly and thus create new ways of work [30]. Employees can communicate with buyers more directly and intensively, which greatly contributes to the innovativeness of products and services offered by the company and shortens the length of the innovation introduction on the market [74]. Digital workplace provides employees with the possibility to acquire information more quickly and more easily and to meet buyers’ requirements more quickly

380

J. Selimovic et al.

and with high-quality, thus creating their positive experience [19]. This implies that digitalization and innovativeness of the workplace also contributes to the company’s innovativeness, that is, the innovativeness of its products and services. Therefore, the relation between digital transformation and workplace innovation and the company’s success is often pointed out. However, one should not forget that digital workplace transformation implies digital workplace innovativeness, and this does not include only the acceptance of new digital tools but also a better design of digital work environment [46]. Creating an innovative and digital work environment also requires certain leadership skills as to how to design and manage digital work environment [7]. Therefore, changes in the organizational culture are expected to go along with workplace digitalization [57].) Namely, the ecosystem of a digital workplace does not reflect only innovative activities and innovative behaviour of employees but also an organizational culture which promotes digital innovativeness [60]. Organizations recognize that a more and more present and necessary digital business transformation greatly depends on the digitalization of workplaces [56]. Thus, one of the key prerequisites for digital innovativeness and workplace transformation is to define an adequate digital strategy and the pertaining goals. Practically, this means that companies should define and follow certain steps in their transition towards digital environment and consistently implement transitional plans (Haddud and McAllen., 2018). Eventually, this will indicate less challenges and risk in the context of a successful digital transformation and the creation of digital work environment with all its advantages. So, although adding additional and introducing new digital technologies can be seen as a trigger of digital workplace transformation and innovativeness, they are the result of adequate changes in the organizational culture and leadership. In the end, this means that workplace digitalization is different in different sectors, reflecting the characteristics of those sectors, along with the organizational culture, management style, but also organizational routines that are characteristic of certain companies. Therefore, the next section offers a review of digital workplace digitalization in the financial service sector.

2.3 Digital Workplace Transformation in Financial Services Starting from the previously analysed relationships between psychological needs of employees and their performances and wellbeing in the digital environment, but also from the connectedness of digital transformation and digital innovativeness in the workplace, the aim of this chapter is to point out to specificities of these relations in the financial sector, the sector being part of a small transitional economy. There is a global trend in the changes of the digital financial services that will continue in a sustainable manner and that acts as a catalyst for the future development [6].

Digital Workplace Transformation and Innovation …

381

Namely, apart from significant legal and regulatory limitations, an important progress has been made in the digitalization of financial services and the whole financial sector. Research shows that digital transformation is the most all-encompassing and most dynamic in the financial sector. In that sense, the role of users who have increasing demands as regard to the quality and speed of service and who thus often opt for online services and more and more popular mobile applications, especially in the conditions of the COVID-19 pandemic, is significant. COVID-19 accelerated remote work culture and forced financial service sector to embrace digital transformation and adapt to the new reality [43]. The pandemic has accelerated digital transformation of the financial sector, so that many fintech companies gain banking licenses [67]. Also, the expected return of investment in digital transformation in the financial sector is the biggest in the observed period (until 2023) and amounts to 19% [14]. On the other hand, digitalization, and automatization of processes as well as of financial products and services is best conducted in this sector primarily because companies from the financial service sector are most often owned by foreigners, so there is transmission of knowledge and technologies from the outside. Furthermore, financial sector in BiH is highly standardized and regulated by the existing legal framework which means that financial companies are faced with two great challenges: they manage digital transformation in extremely regulated environment while at the same time they are supposed to meet stakeholders’ requirements for greater transparency and trust [73]. Those requirements become the source of the sector’s innovativeness. Therefore, most financial companies invest in online banking, digital platforms, online applications for insurance, etc. Also, one should not forget that digital transformation and digital innovation are not done ad hoc but that they should be carefully planned and realized through a digital strategy that should principally reflect strategic choices. Thus, according to [3], digital strategies should mainly be focused on the client’s positive experience through faster, more secure, and highquality services, and through the creation of completely new products and services which most often includes digital innovations. In this sense, employees’ innovativeness, and their flexibility in the use of new technologies and digital tools greatly contribute to the innovation of services which they offer to their clients, including their adjustment to personal preferences and needs. This implies that psychological needs of employees, their performances, and expectations in relation to the wellbeing in traditional industries such as the financial sector, have a significant role in digital transformation of work environment. Those relationships are hypothesized in the following section.

3 Methods When it comes to the methodology, this research was based on the philosophy pf positivism and adopted a deductive approach. Primarily, secondary data was obtained through an extensive literature review on digital workplace transformation in the

382

J. Selimovic et al.

Fig. 1 Conceptual model Source Author’s work

financial sector, locally and globally. Afterwards, the research was completed through quantitative analysis to explore the research topic and answer the research questions. According to the above examined literature, a conceptual research model was suggested as shown in Fig. 1. Throughout the literature review it was noticed that autonomy is positively connected to employees’ performance. Meaning that that employees independency in making certain decisions and their autonomy in resolving working tasks influences their productivity, innovativeness but also enjoyment [22]. Accordingly, [25] discovered that with more autonomy work exhaustion was decreasing, while their work commitment was increasing. So, when managers empower and support employees’ autonomy, they are more motivated and creative in their work [23, 32]. Thus, it may be noticed that management practices which promote autonomy and freedom on the job lead to higher organizational and individual performances [37]. Hence, it is proposed: H1a. Autonomy positively influences performance expectations in the digital workplace. Furthermore, [2] showed that satisfaction of employees’ psychological needs at the workplace results in higher enjoyment. Likewise, [28] and [68] claim that employee autonomy and fulfilment of other basic psychological needs has a positive influence on work-related well-being. Precisely, [68] confirmed that frustration of their basic psychological needs, including autonomy, results in poorer well-being. Consequently, autonomy leads to employees’ satisfaction and well-being [38]. It is therefore posited: H1b. Autonomy positively influences well-being expectations in the digital workplace. Moreover, employees’ performance and well-being also depend on interpersonal relatedness. Relatedness of employees requires collaboration which adds to the exchange of ideas, information, and knowledge, which in turn results in enhanced performances and employees’ innovativeness [29, 36]. By overcoming barriers, employees perform their tasks more effectively and efficiently, and make the business more agile and competitive [34]. So, connectedness in digital workplace improves individual’s performance. Therefore, it is hypothesized: H2a. Relatedness positively influences performance expectations in the digital workplace.

Digital Workplace Transformation and Innovation …

383

Additionally, improved relatedness with others positively affects the well-being and satisfaction of employees. Employees who communicate and interact with others are usually more satisfied, as they have the feeling of belonging to a certain group of people, and they have a higher level of confidence [13]. The idea of workplace connectedness becomes especially important due to the more frequent use of social media that employees use in their private time. Therefore, it becomes especially important to fulfil the need for socialization at the workplace via similar platforms [49]. Connectedness and collaboration are usually outlined as the benefits of digital working environment. Specifically, technological solutions make information available, and facilitate collaboration and document sharing [29]. Accordingly, interpersonal relatedness in digital work environment has been shown to have a significant influence on employees’ well-being. Hence, it is proposed: H2b. Relatedness positively influences well-being expectations in the digital workplace. The existing literature mentions employee performance and well-being as the main determinants of workplace transformation [42, 62, 63, 72]. Particularly, if employees have expectations that digital environment will enable them to accomplish better performance, bigger satisfaction, and personal well-being more easily, they will be more motivated and willing to support digital transformation. Employees will feel more comfortable in using digital technology and be less resistant to accept future workplace [10]. Therefore, it is posited that: H3. Performance expectations positively influence employees’ intentions to actively support digital workplace transformation. H4. Well-being expectations positively influence employees’ intentions to actively support the digital workplace transformation. Furthermore, in order to test the conceptual model, primary data was obtained through the survey method, by using a questionnaire. The research sample consisted of 79 financial institutions, banks and insurance companies in Bosnia and Herzegovina. The questionnaire was distributed to the managers and employees of those financial institutions by using a mailing list-based sampling structure. As the number of financial institutions in B&H is rather low, the corresponding contacts were gathered manually. Accordingly, just the e-mail addresses from the sample units were necessary and a random sampling method was used. The content, meaning and length of each question of the survey was attentively analysed to assure their relevance to the research. The data were collected in the period from September until November 2020. The survey was completely anonymous, and the participation was noncompulsory. Its structure included a preface with a brief research summary and a definition of the term digital workplace transformation. Moreover, the survey contained six main sections questions reflecting the research variables. Additionally, some demographic questions regarding gender, age, years of experience, and organization type, were also included. The questions measuring autonomy and relatedness were adopted from [18] and [59]. Autonomy was defined as the degree to which an individual can use all digitally transformed tools at the workplace in a self-determined manner. The variable was

384

J. Selimovic et al.

measured through three questions, such as “I would like to be able to/ I can decide for myself to what extent I will use digital technologies in the workplace”. On the other hand, relatedness was defined as the extent to which a digitally transformed workplace is apt to result in an impression of connectedness among employees within an organization. Three indicators were used to measure relatedness, as for instance “People in a digital work environment are peer-friendly”. Moreover, performance was measured by using questions recommended by (Venkatesh, 2003). This variable was defined as the extent to which an employee anticipates a digital workplace would contribute to their productivity. Four indicators were used to measure performance, as for instance “I would find s digitally transformed workplace useful in my job”. Questions related to well-being were adopted from [1]. The definition of well-being encompasses the extent to which an employee considers engaging within a digital workplace as being enjoyable. Four indicators were used to measure well-being, as for instance “I would have fun using the digitally transformed workplace”. The intention to actively support digital workplace transformation was measured following (Venkatesh, 2003). This outcome variable encompasses the support of reciprocal change through, for example, the individual’s feedback. Five indicators were used to measure the intention, as for instance “I intend to actively participate in the process of change towards a digitally transformed workplace.”. Finally, a fivelevel Likert scale was used in the questionnaire, and when it comes to the quantitative analysis of the data, the confirmatory factor analysis (hereinafter: CFA) and the method of structural equation modeling (hereinafter: SEM) were used by following the six steps recommended by [31].

4 Data Analysis The analysis of the quantitative data, collected through the questionnaire, was conducted through several steps. Specifically, the structural equation modeling was undertaken in six stages, following [31], which are presented in Fig. 2. The first step will be to preliminary test and verify the collected against any existing outliers, missing data and to test for the data assumptions of the multivariate statistical analysis. Secondly, following [31] confirmatory factor analysis will be performed, with the aim to assess the reliability, discriminant validity and the convergence of the measurement models. In the end, structural equation modeling will be used to test the structural model and the hypotheses. Missing value analysis (hereinafter: MVA) was the first step of data examination, with the aim to classify the sample according to which the values of the collected data are missing. Accordingly, [31] state that less than 10% of missing data per individual observation can be ignored, unless the mentioned data are missing per specific sample. After the performed data analysis, it was noticed that no data is missing, and all observations were kept.

Digital Workplace Transformation and Innovation …

385

Fig. 2 Steps of SEM Source [31]

The outlier analysis was performed through a multivariate analysis of the multidimensional position of each variable relative to some common points [31]. Specifically, this was the Mahalanobis D2 method that measures the distance of each observation of multidimensional space in regard to the centre of the mean values of all observations by calculating the value of each observation regardless of the number of variables. Following [31] the threshold was set to 3.50. The results of the multivariate analysis showed no outliers, and all observations were retained for further analysis. Afterwards, the data was tested on assumptions of multivariate techniques. The first assumption of multivariate analysis is the normality of the data. Although the

386

J. Selimovic et al.

fulfilment of this criteria is not always necessary, better results will be achieved with normally distributed data [31]. At approximately normal distribution, the skewness should be close to 0 and the kurtosis close to 3. The results showed that the data were not completely normally distributed. Failure to meet this assumption would have a significant influence on the results of smaller samples, where the sample size is lower than 50 [31]. Nevertheless, [61] showed that in case of larger samples, variables with symmetry and roundness that deviate from the normal distribution do not influence the essential results of the research. Moreover, this research data will be analysed through the Maximum Likelihood (ML) method which is significantly robust to data deviations from the normality assumption in multivariate techniques, as for example, factor analysis and modeling of structural equations [26, 51]. Consequently, as this research will use a highly reliable technique as the SEM method, and as the sample size is equal to 161 observations, it is acceptable to disregard the fact that the normality assumption is not satisfied, and to use data without further transformation. The assumption of homogeneity of variances or homoscedasticity assumes same variances in different groups which are being compared. This assumption was tested through the Breusch-Pagan test whose null hypothesis states that homoscedasticity of data exists. For this purpose, regression analysis was performed based on the average value, i.e., with aggregated variables, where the individual IT skills of the employees were dependent, and all other independent variables. The results of the test (p = 0.076) showed that the null hypothesis cannot be rejected, which confirms the existence of homoscedastic data. In the end, collinearity was tested by calculating the variance inflation factor (VIF) for all predictor latent variables [31], and the value results were compared with the maximum threshold of 5 or 10. The results showed that there is no significant multicollinearity of data. All over, 161 responses were collected from the survey. When it comes to the demographics of the survey participants, there were 35% male and 65% female respondents. Furthermore, over 60% of the participants were older than 40, and most participants had a bachelor and master’s degree, 31.7% and 43.5%, respectively. Moreover, the structure of the sample according to their organization was analysed most of the respondents were coming from insurance companies (50,3%) and banks (41%), while the rest was coming from microcredit organizations, leasing, and financial agencies. Also, most of the respondents, 47%, have been with the institution for between one and six years, 43% have been working for more than six years, and the rest has just recently joined. As mentioned before, the data was analysed in line with the recommendations of [31]. In order to test the eligibility of the measurement model confirmatory factor analysis was used. During the first model testing, some variables showed an insufficient fit across all the measuring indicators. Accordingly, those variables (PE3, WB3 and SU1), were excluded from the model and CFA was repeated. Among all indices which were tested during the CFA, it is important to underline that the difference in the observed and estimated covariance matrices is the focal value in assessing the goodness of fit of any SEM model (Hair et. al, 2010). The chi-square statistics is a basis for most goodness of fit heuristics. In terms of SEM,

Digital Workplace Transformation and Innovation …

387

the chi-square (χ2 ) test is the only adequate statistical test of the matrices difference and is presented by the following mathematical equation: χ 2 = f [(overall sample si ze − 1)(obser ved sample covariance matri x −S E M estimated covariance matri x)] or: χ 2 = f [(N − 1)(S − k )] One specificity of SEM compared to other procedures is seen in terms of degrees of freedom (df). Contrary to other multivariate techniques, the df in SEM model are not derived from sample size, but from the size of the covariance matrix. The net number of df for SEM is: df =

1 [( p)( p + 1)] − k 2

where: p presents the total number of observed variables and k is the number of free parameters. Consequently, after the second iteration, the confirmatory factor analysis showed a good model fit. As the model eligibility was proofed, the next step of the analysis was to verify the reliability and validity of the model. The model reliability was measured using the factor of composite reliability (CR) whose value needs to be above 0.6. Moreover, [31] claim that the CR values above 0.7 are the ones which properly measure reliability. Regarding this criterion, it may be noticed in Table 1 that all values of CR are above 0.7, and consequently the reliability of the model can be confirmed. The validity of the measurement model was verified through the convergent and discriminant model validity. The factor loadings and average variance extracted (AVE) were used to proof the convergent validity. According to [31], an appropriate convergence is achieved if these two values are higher than 0.5. Furthermore, Table 1 Reliability and validity testing AU

CR

AVE

AU

0.862

0.686

0.828

RE

PE

WB

RE

0.882

0.716

0.228

PE

0.913

0.779

0.102

0.706

0.883

WB

0.931

0.817

0.186

0.786

0.863

0.904

SU

0.912

0.777

0.111

0.655

0.697

0.759

Source Author’s calculations

SU

0.846

0.881

388

J. Selimovic et al.

discriminant validity was tested through the comparison of the square root of AVE, which had to be higher, to the correlations of all variables. It may be noticed in Table 1 that the assumptions of convergent validity are satisfied by the measurement model since the factor loadings are above 0.5 and values of AVE are above 0.5. Moreover, as the square root of AVE values (bold values) are greater than correlations with other constructs presented below and to their left, the discriminant validity is also confirmed.

5 Results As the tested measurement model showed a proper fit, it was proceeded to the testing of the structural model and hypotheses by using SEM with the maximum likelihood estimation. The resulting SEM model with the connections and results of calculations between variables is presented in Fig. 3. The goodness of fit was also tested for the SEM model, and all indicators confirmed a good fit, including the normed χ2/df of 3.666, and the RMSEA with a marginal fit of 0.129. Accordingly, the results of hypotheses testing were presented in Table 2. According to the analysis results from Table 2, four hypotheses are supported. Relatedness is proofed to significantly influences performance (β = 0.803; t = 8.731; p < 0.01) and well-being (β = 0.941; t = 10.858; p < 0.01). With other words, through the enhancement of relatedness at a digital workplace, the performance and well-being of the employees at the workplace will increase.

Fig. 3 SEM model Source Lisrel output on SEM

Digital Workplace Transformation and Innovation …

389

Table 2 Hypotheses testing Dependent variable

Independent variable

Non-standardized rating parameter

Standardized rating parameter

t-value

Hypotheses

H1a

Performance

← Autonomy

−0.0619

−0.088

−1.458

Not significant

H1b

Well−being

← Autonomy

−0.034

−0.030

−0.671

Not significant

H2a

Performance

← Relatedness

1.179

0.803

8.731***

Significant

H2b

Well-being

← Relatedness

2.159

0.941

10.858***

Significant

H3

Intention

← Performance

0.172

0.247

2.629***

Significant

H4

Intention

← Well-being

0.247

0.555

5.772***

Significant

*** p < 0.01; ** p < 0.05; * p < 0.1

Source Author’s calculations

Furthermore, results from Table 2 also show that performance significantly increases intention (β = 0.247; t = 2.629; p < 0.01). Meaning that a better performance at a digital workplace, will significantly increase the intention of the employees to actively support digital workplace transformation. Finally, the analysis showed that well-being significantly influenced intention (β = 0.555; t = 5.772; p < 0.01). In other words, employees’ well-being at a digital workplace will increase their intention to actively support digital workplace transformation. Moreover, this research also included some control variables, particularly, education and job experience. It may be seen in Table 3 that these variables significantly influence intention. Specifically, this would mean that an employee with a higher level of completed education and with more job experience will have a stronger intention to actively support digital workplace transformation. Table 3 Control variables testing Dependent variable

Independent variable

Non-standardized rating parameter

Standardized rating parameter

t-value

Hypotheses

H1a

Performance

← Autonomy

−0.0618

−0.088

−1.461

Not significant

H1b

Well-being

← Autonomy

−0.0348

−0.032

−0.701

Not significant

H2a

Performance

← Relatedness

1.180

0.806

8.745***

Significant

H2b

Well-being

← Relatedness

2.155

0.942

10.844***

Significant

H3

Intention

← Performance

0.186

0.268

3.018***

Significant

H4

Intention

← Well-being

0.257

0.580

6.304***

Significant

Intention

← Education

0.136

0.116

2.194**

Significant

Intention

← Experience

0.295

0.235

4.344***

Significant

*** p < 0.01; ** p < 0.05; * p < 0.1

Source Author’s calculations

390

J. Selimovic et al.

6 Discussion This study confirms that connections among employees positively affect their performances and well-being expectations in the workplace. This practically means that those employees who feel connected to their colleagues in the workplace achieve better performances, as well as a higher level of personal satisfaction. These results are in line with the results of previous research in this area which shows that there is a positive effect of psychological needs, work performances of employees and their well-being in the workplace [2, 38, 46]. This interpersonal connection as one of the prominent psychological needs has a significant influence on employees’ performances and their well-being in digital work environment. This connection is precisely provided by the use of digital technologies and tools [15]. In this way employees can cooperate, share information, knowledge and ideas with buyers and other external partners and also colleagues, not just within the framework of one organization but also globally, and thus achieve greater productivity, creativity, and performance of employees [21, 52]. Similarly, [65] found that factors necessary for digital transformation of workplace include team communication and collaboration but also workplace relationships. Namely, that feeling of connection contributes to a greater self-confidence and satisfaction of employees. Our results show that those employees who have positive performances and wellbeing expectations are more motivated to support workplace digitalization. Also, [15] digital work environment contributes to the development of digital literacy and the ability of employees to manage data, present information adequately, solve problems and create new ways of completing work tasks. Similarly, both [75] and [33] in their research papers show that digital workplaces significantly impact work performances and learning processes of employees and thus enhance their competences. On the other hand, the existing literature suggests that positive attitude and expectations of employees regarding digital work environment positively affect their support to digital workplace transformation, leading to an increase in the satisfaction of employees and a decrease of the stress level in the workplace [4, 47]. Therefore, it can be stressed that those employees who have a positive perception of performances and well-being expectations in a digital work environment will have less defiance towards the acceptance of a future workplace. However, our study does not confirm that autonomy of employees in the workplace contributes to the level of performances and well-being expectations in a digital environment. Possible reason for this can lie in the very context of research, i.e., in the characteristics of the financial services sector as well as in financial institutions such as banks which have prominently stable functioning structures. Procedures in these institutions are liable to complex bureaucratic processes with very meticulous activities and tasks such as the procedure of loan approval, selling of insurance policies, etc. Similar results were shown in the study by [9] which showed that employees in this sector completed their tasks in precisely defined ways which did

Digital Workplace Transformation and Innovation …

391

not leave them with much autonomy in the workplace in the sense of independent decision-making as to how to perform a certain task. On the other hand, independence of employees often depends on their motivation or some personal traits. So, employees who are insufficiently motivated need more structured tasks or even help of their superiors in order to achieve certain performances [24, 41]. Taking into consideration the recent popularity of remote work due to the COVID-19 pandemic, it would be interesting to research the relationship that exists between remote work and psychological needs of employees, especially those needs which refer to social connectedness. In this context, there are studies which showed that remote work had a prominent dimension of employees’ individuality in the process of the completion of work tasks and in the achievement of performances [58, 64]. Therefore, one could conclude that the relationship between autonomy of employees in the workplace and their performances including well-being expectations is complex and could be under the influence of various factors. Future studies could give a certain contribution in the research of these factors. Also, the results that we have come up with should be analysed from the perspective of transitional economies. It is extremely important to interpret the presented results through a prism of local context and culture.

7 Conclusion Our findings show that digital workplace transformation does not solely refer to digital technology. Employees’ motivation and intention to support digital workplace transformation depends on the acceptance and positive attitude of employees in regard to the performances and well-being in the future work environment which mostly depends on the connection with others in the workplace. Our findings confirmed that those employees who have positive well-being expectations and positive performances are more willing to support workplace digitalization. In this context, interpersonal connection, and affiliation between them are crucial. This practically means that those employees who feel connected to their co-workers in the workplace achieve higher performances, as well as higher level of personal satisfaction. The study significantly contributes to the research of digital transformation of pre-digital organizations. Namely, in these traditional organizations, digital transformation is most often identified with the use of digital tools and with digital literacy of employees while their psychological needs and expectations are neglected. However, the results of our study precisely show that psychological needs of employees and their intentions to accept digital environment are key to the successful transformation of the pre-digital organizations. In that context, this chapter also offers certain practical implications. Those implications are particularly important for the process of workplace redesign. With the aim of improving the performances and well-being of their employees, companies should

392

J. Selimovic et al.

provide an adequate support regarding the psychological needs of employees. Results of our research confirm that apart from digital skills, companies need to encourage the involvement of employees, their mutual cooperation, connection, and teamwork in the completion of work tasks. Interpersonal connections greatly contribute to an increased satisfaction of employees which eventually brings along numerous advantages for the organization as well, such as productivity and revenue growth. However, this research also has certain limitations. Our focus was primarily on the employees’ autonomy, their interpersonal connections, their expectations regarding work performances and their well-being as independent variables. Our methodological framework does not include competence as a psychological need, digital skills or other factors that can impact employees’ intentions to use a digital workplace. Apart from that, the collected data refer only to the financial sector in Bosnia and Herzegovina, so the research results should be interpreted in that context. Taking into consideration the bank-centric model of the financial market in BiH, as well as the dynamic and development of the market in the sense of innovative and complex products, our assumption is that the hypotheses and explained relations could be defined differently. Therefore, the findings should not be generalized. We suggest that researchers validate our study through research of other sectors and other contexts. Also, future studies could include some other factors in the analysis, such as digital skills of employees and their personal innovativeness, which can direct employees’ intentions to support digital workplace transformation. There should be more studies with a focus on employees’ motivation, their psychological needs, and different aspects of self-determination regarding digital work environment. Apart from that, it would be very useful that prospective studies are also focused on the research of work tasks, management styles and work culture, all in relation with motivation and performances of employees. Also, one should not forget that this research was conducted during the COVID19 pandemic, in the specific conditions of a lockdown, when remote work was not an option but a usual method of work. Thus, we assume that these results could be useful for future research of the remote work practice. Apart from that, we encourage researchers to also research the effects of the COVID-19 and post-COVID environment on the psychological needs of employees, their attitude and motivation to support digital workplace transformation. It would be interesting to research how the pandemic has influenced these factors.

References 1. Agarwal R, Karahanna E (2000) Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q 24:665–694 2. Andreassen CS, Hetland J, Pallesen S (2010) The relationship between ‘workaholism’ basic needs satisfaction at work and personality. Eur J Pers Published Eur Assoc Pers Psychol 24(1):3–17 3. Andrus G, Kejriwal S, Wadhwani R (2016) Digital transformation in financial services. Deloitte University Press, Westlake

Digital Workplace Transformation and Innovation …

393

4. Attaran M, Attaran S, Kirkland D (2019) The need for digital workplace: increasing workforce productivity in the information age. Int J Enterp Inf Syst (IJEIS) 15(1):1–23 5. Ayyagari R, Grover V, Purvis R (2011) Technostress: technological antecedents and implications. MIS Q 35:831–858 6. Azeez NA, Akhtar SJ (2021) Digital financial literacy and its determinants: an empirical evidences from rural India South Asian. J Soc Stud Econ 11(2):8–22 7. Basso F (2016) The digital evolution of information management. http://www.gartner.com/ima gesrv/mediaproducts/pdf/Systemware/Systemware1-38P08XF.pdf 8. Bazigos M, Caruso E (2016) Why frontline workers are disengaged? McKinsey Q 9. Belias D, Koustelios A, Vairaktarakis G, Sdrolias L (2015) Organizational culture and job satisfaction of Greek banking institutions. Procedia Soc Behav Sci 175:314–323 10. Briggs C, MaKiCe K (2012) Digital fluency building success in the digital age. SociaLense 11. Brynjolfsson E, McAfee A (2014) The second machine age: Work progress and prosperity in a time of brilliant technologies. WW Norton & Company, New York 12. Buchanan J, Kelley B, Hatch A (2016) Digital workplace and culture: How digital technologies are changing the workforce and how enterprises can adapt and evolve. Deloitte 13. Bucher E, Fieseler C, Suphan A (2013) The stress potential of social media in the workplace. Inf Commun Soc 16(10):1639–1667 14. Chaudhury D (2019) The Global Digital Transformation Expenditure to Shoot up to $23tn by 2023. https://enterprisetalk.com/featured/the-global-digital-transformation-expenditure-toshoot-up-to-2-3tn-by-2023/ 15. Colbert A, Yee N, George G (2016) The digital workforce and the workplace of the future. Acad Manag J 59(3):731–739 16. Deci EL, Ryan RM (1985) Motivation and self-determination in human behavior. Plenum Publishing Co., New York 17. Deci EL, Olafsen AH, Ryan RM (2017) Self-determination theory in work organizations: the state of a science. Annu Rev Organ Psych Organ Behav 4:19–43 18. Deci EL, Ryan RM, Gagné M, Leone DR, Usunov J, Kornazheva BP (2001) Need satisfaction motivation and well-being in the work organizations of a former eastern bloc country: a crosscultural study of self-determination. Pers Soc Psychol Bull 27(8):930–942 19. Deloitte (2012) The digital workplace: Think share do transform your employee experience. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/be/Documents/technology/ The_digital_workplace_Deloitte.pdf 20. Derks D, Bakker AB, Peters P, van Wingerden P (2016) Work-related smartphone use work– family conflict and family role performance: the role of segmentation preference. Human Relat 69(5):1045–1068 21. Dery K, Sebastian IM, van der Meulen N (2017) The digital workplace is key to digital innovation. MIS Q Execut 16(2):135–152 22. Dittes S, Richter S, Richter A, Smolnik S (2019) Toward the workplace of the future: how organizations can facilitate digital work. Bus Horiz 62(5):649–661 23. Duda JL, Williams GC, Ntoumanis N, Daley A, Eves FF, Mutrie N, Jolly K (2014) Effects of a standard provision versus an autonomy supportive exercise referral programme on physical activity quality of life and well-being indicators: a cluster randomised controlled trial. Int J Behav Nutr Phys Act 11(1):1–15 24. Dysvik A, Kuvaas B (2011) Intrinsic motivation as a moderator on the relationship between perceived job autonomy and work performance. Eur J Work Organ Psychol 20(3):367–387 25. Fernet C, Austin S, Trépanier SG, Dussault M (2013) How do job characteristics contribute to burnout? exploring the distinct mediating roles of perceived autonomy competence and relatedness. Eur J Work Organ Psy 22(2):123–137 26. Fuller EL, Hemmerle WJ (1966) Robustness of the maximum-likelihood estimation procedure in factor analysis. Psychometrika 31(2):255–266 27. Gantz J, Boyd A, Dowling S (2009) Tackling information overload at the source. IDC White Papers

394

J. Selimovic et al.

28. Gillet N, Fouquereau E, Forest J, Brunault P, Colombat P (2012) The impact of organizational factors on psychological needs and their relations with well-being. J Bus Psychol 27(4):437–450 29. Gilson LL, Maynard MT, Jones Young NC, Vartiainen M, Hakonen M (2015) Virtual teams research: 10 years 10 themes and 10 opportunities. J Manag 41(5):1313–1337 30. Haddud A, McAllen D (2018) Digital workplace management: exploring aspects related to culture innovation and leadership. In: 2018 portland international conference on management of engineering and technology (PICMET), pp 1–6 31. Hair JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis. Pearson Prentice Hall, New Jersey 32. Hon AH (2012) Shaping environments conductive to creativity: the role of intrinsic motivation. Cornell Hosp Q 53(1):53–64 33. Hsi S (2007) Conceptualizing learning from the everyday activities of digital kids. Int J Sci Educ 29(12):1509–1529 34. Igloo Software (2017) Ro-WHY: The Business Value of a Digital Workplace. https://igloos oftware.lookbookhq.com/resourcespage?_ga=2.211811411.753277889.1664143241-236791 246.1664143241 35. Kaarst-Brown M, Quesenberry J, Niederman F, Weitzel T (2018) New approaches to optimizing the digital workplace. MIS Q Execut 17(4):ix-xxiii 36. Karoui M, Dudezert A, Leidner DE (2015) Strategies and symbolism in the adoption of organizational social networking systems. J Strateg Inf Syst 24(1):15–32 37. Khedhaouria A, Belbaly N, Benbya H (2014) Exploring the impact of organizational climate factors on organizational creativity within a centralized IS. Systemes d’information management 19(4):7–30 38. Kim S, Chen RP, Zhang K (2016) Anthropomorphized helpers undermine autonomy and enjoyment in computer games. J Cons Res 43(2):282–302 39. Köffer S (2015) Designing the digital workplace of the future–what scholars recommend to practitioners. In: ICIS, pp 1–21 40. Krajcsák Z, Kozák A (2022) The moderating role of remote work in the relationship between organizational culture and OCB: case studies from the financial sector. J Adv Manag Res 41. Kuvaas B (2006) Performance appraisal satisfaction and employee outcomes: mediating and moderating roles of work motivation. Int J Hum Res Manag 17(3):504–522 42. Maier C, Laumer S, Wirth J, Weitzel T (2019) Technostress and the hierarchical levels of personality: a two-wave study with multiple data samples. Eur J Inf Syst 28(5):496–522 43. Maiti M, Vukovi´c D, Mukherjee A, Paikarao PD, Yadav JK (2022) Advanced data integration in banking financial and insurance software in the age of COVID-19. Softw Pract Exp 52(4):887– 903 44. Margaryan A, Littlejohn A, Vojt G (2011) Are digital natives a myth or reality? university students’ use of digital technologies. Comput Educ 56(2):429–440 45. Mazmanian M, Orlikowski WJ, Yates J (2013) The autonomy paradox: the implications of mobile email devices for knowledge professionals. Organ Sci 24(5):1337–1357 46. Meske C, Junglas I (2021) Investigating the elicitation of employees’ support towards digital workplace transformation. Behav Inf Technol 40(11):1120–1136 47. Miller P (2012) The digital workplace: how technology is liberating work. Dog Ear Publishing, Indianapolis 48. Miller P, Marsh E (2016) The digital renaissance of work: delivering digital workplaces fit for the future. Routledge, Abingdon 49. Munene AG, Nyaribo YM (2013) Effect of social media pertication in the workplace on employee productivity. Int J Adv Manag Econ 2(2):141–150 50. Nguyen PT, Yandi A, Mahaputra MR (2020) Factors that influence employee performance: motivation leadership environment culture organization work achievement competence and compensation (a study of human resource management literature studies). Dinasti Int J Dig Bus Manag 1(4):645–662 51. Nwabueze S N, Meso P N, Mbarika V W, Kifle M, Okoli C, Chustz M (2009 January) The effects of culture of adoption of telemedicine in medically underserved communities. In: 2009 42nd hawaii international conference on system sciences, p 1–10

Digital Workplace Transformation and Innovation …

395

52. Oeij PR, Dhondt S, Rus D, Van Hootegem G (2019) The digital transformation requires workplace innovation: an introduction. Int J Technol Transf Commer 16(3):199–207 53. Ragu-Nathan TS, Tarafdar M, Ragu-Nathan BS, Tu Q (2008) The consequences of technostress for end users in organizations: conceptual development and empirical validation. Inf Syst Res 19(4):417–433 54. Richter A, Heinrich P, Stocker A, Schwabe G (2018) Digital work design. Bus Inf Syst Eng 60(3):259–264 55. Schillerwein S (2011) the digital workplace redefining productivity in the information age. Infocentric Research AG 56. Schmid A M, Recker J, Vom Brocke J (2017) The socio-technical dimension of inertia in digital transformations. In: Proceedings of the 50th Hawaii international conference on system sciences 57. Schwarzmüller T, Brosi P, Duman D, Welpe IM (2018) How does the digital transformation affect organizations? Key Themes Change Work Des Leadership Manag Rev 29(2):114–138 58. Soroui ST (2021) Understanding the drivers and implications of remote work from the local perspective: an exploratory study into the dis/reembedding dynamics. Technol Soc 64:101328 59. Spreitzer GM (1995) Psychological empowerment in the workplace: dimensions measurement and validation. Acad Manag J 38(5):1442–1465 60. Svahn F, Mathiassen L, Lindgren R (2017) Embracing digital innovation in incumbent firms: how volvo cars managed competing concerns. MIS Q 41(1):239–253 61. Tabachnik BG, Fidell LS (2012) Using multivariate statistics. Pearson, United States 62. Tarafdar MD, Arcy J, Turel O, Gupta A (2015) The dark side of information technology. MIT Sloan Manag Rev 56(2):61 63. Tarafdar M, Maier C, Laumer S, Weitzel T (2020) Explaining the link between technostress and technology addiction for social networking sites: a study of distraction as a coping behavior. Inf Syst J 30(1):96–124 64. Taskin L, Devos V (2005) Paradoxes from the individualization of human resource management: the case of telework. J Bus Ethics 62(1):13–24 65. Trenerry B, Chng S, Wang Y, Suhaila ZS, Lim SS, Lu HY, Oh PH (2021) Preparing workplaces for digital transformation: an integrative review and framework of multi-level factors. Front Psychol 12:822 66. Vallo Hult H, Byström K (2021) Challenges to learning and leading the digital workplace. Stud Contin Educ, 1–15 67. Van der Kroft J (2021) Collaboration at the core: evolving partnerships between banks and FinTechs. https://wwweycom/en_nl/banking-capital-markets-transformationgrowth/collaboration-at-the-core-evolving-partnerships-between-banks-and-fintechs 68. Vander Elst T, Van den Broeck A, De Witte H, De Cuyper N (2012) The mediating role of frustration of psychological needs in the relationship between job insecurity and work-related well-being. Work Stress 26(3):252–271. https://doi.org/10.1080/02678373.2012.703900 69. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478 70. Vom Brocke J, Maaß W, Buxmann P, Maedche A, Leimeister JM, Pecht G (2018) Future work and enterprise systems. Bus Inf Syst Eng 60(4):357–366 71. Wang B, Liu Y, Qian J, Parker SK (2021) Achieving effective remote working during the COVID-19 pandemic: a work design perspective. Appl Psychol 70(1):16–59 72. Wang W, Daneshvar Kakhki M, Uppala V (2017) The interaction effect of technostress and nontechnological stress on employees’ performance. In: 23rd Americas conference on information systems, pp 1–5 73. Werth O, Schwarzbach C, Cardona D R, Breitner M H, von der Schulenburg J M G (2020) Influencing factors for the digital transformation in the financial services sector. Zeitschrift für die gesamte Versicherungswissenschaft, pp 1–25

396

J. Selimovic et al.

74. White M (2012) Digital workplaces: vision and reality. Bus Inf Rev 29(4):205–214 75. Yee N (2014) The Proteus paradox: how online games and virtual worlds change us-and how they don’t. Yale University Press, New Haven 76. Zimmer MP, Baiyere A, Salmela H (2020) Digital workplace transformation: the importance of deinstitutionalising the taken for granted. In: ECIS

Digital and Customizable Insurance: Empirical Findings and Validation of Behavioral Patterns, Influential Factors, and Decision-Making Framework of Baltic Insurance Consumers in Digital Platforms Gedas Baranauskas Abstract The phenomenon of digitization, digitalization, and customization became an operational and service platform standard, widely spread within the previous decade in the financial sector, but seems to be not enough balanced in the insurance service field. Here these domains had latency and strong implications mostly at the strategical planning level and in primary activities of the insurance specific-value chain. Accordingly, a multidimensional and cross-border scientific analysis of the effects and outcomes of digitalization and customization domains in the insurance industry, platforms, and consumers behavior is required. This particular chapter aims to synthesize and conceptualize consumer decision-making and technology acceptance models and frameworks, applicable to digital insurance platforms management. The conducted empirical investigation and statistical analysis resulted in a reveal of behavioral patterns and influential factors of decision-making of Baltic insurance consumers in digital insurance platforms and validation of a conceptual digital insurance consumer decision-making process framework. Keywords Online customization · Digital platforms · Insurance digitalization · Omnichannel · Baltic

1 Introduction The ongoing global pandemic has underlined numerous potential practical improvement areas in the insurance field including a lack of product flexibility, timely connection, data harmonization with the casual needs of customers, personalized and easily accessible platforms [89, 91]. This practical situation refers to recent findings indicating that insurance purchasing is not a linear and homogenous value G. Baranauskas (B) Institute of Management and Political Science, Mykolas Romeris University, Vilnius, Lithuania e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_17

397

398

G. Baranauskas

chain anymore, therefore, a comprehensive omnichannel, digital and personalized approach is compulsory [3, 10]. In general, it is argued that in modern insurance organizations, instead of maintaining a primary focus on operational standardization as a data-based decision in risk management, product and system development should center on the development of hybrid service, customer-driven digital business models, where sustainable innovative products and integration options with InsurTech and technology companies are delivered [44, 47, 66, 67]. In a parallel way, the format of insurers’ communication and interaction touchpoints fundamentally changes and accordingly fosters the transition in insurance sales from the traditional multichannel approach to platform business models innovations, omnichannel oriented distribution processes, and digital service platforms, which brings additional challenges [19]. Both e-retailers and traditional business-to-customer (B2C) organizations, including insurers, encounter challenges on a smooth transition and levering the product output-based, process-based, experiential or systemic type of service innovations and changes related to the online customization, omnichannel and platform business model [27–29]. Organizations are demanded to switch their understanding of the competitive advantage and shift from the primary focus on a low price strategy and strong branding activities to a more efficient utilization of internal resources and supply chain, an increased availability of online customized products and customization options, in order to ensure an improved customer experience and acceptance of platforms [49, 83, 94, 97, 89]. On the other hand, fundamental ideas and features of online customization frameworks, which firstly were introduced by [36, 37], no longer reflect global trends of automation and modernization of customer service operations, digital distribution platforms, and diversified insurance consumers’ behavioral patterns and their requirement for a service design. In a parallel way, scientific research gaps are identified in the synergetic management of combined internal and external obstacles of the omnichannel distribution, holistic and consistent frameworks of analysis on digital consumer experience, optimal design and integration of hybrid touchpoints structure as well as managerial practice on the phenomenon of estrangement, personalizing, and de-objectification [11, 28, 82, 96]. Therefore, the conceptual modeling and empirical validation of new combined frameworks of online customization and integrated consumers decision-making process framework, including both features of Technology Acceptance, and Consumers Decision-Making, applied in the platform business model, are required and are unraveled throughout the whole content in the chapter. The selected research subject of the insurance consumer decision-making process in digital platforms also brings a scientific novelty and originality in a sense of extending insurance digitalization and customization related research domains with a multilayer conceptual framework, interdisciplinary research approach, and stands as an initial empirical investigation of the insurance platform business model in the Baltic region. This dedicated scientific investigation and the analysis on results also have a practical value by revealing a state of the art in the Baltic non-life insurance consumers’ attitude towards the digital insurance distribution process and factors in the decision-making of purchasing insurance products in a digital environment.

Digital and Customizable Insurance: Empirical Findings and Validation …

399

The structure of the chapter consists of four main parts. The chapter begins with the introduction of the selected research methodology and methods. An analysis of the theoretical foundation, including a literature review and conceptual modeling in Sect. 2, follows afterwards. Then a comprehensive reflection on digital platforms in the Baltic non-life insurance market and empirical data validation is elaborated in Sect. 3 by applying methods of descriptive case study and multilevel factors and path analysis. The chapter is closed with an identification of future research directions and a summary of key findings in the Conclusion section.

2 Research Methodology and Methods The methodological foundation of the research arises both in research traditions and methods, applied to the analysis of modern digital business platforms and an enduser behavior, and by following the recent experience of COVID-19 and its practical impact in this field. It is recognized that the multifold content and dynamic pace of the digital environment and modern financial services contradict current scientific research practices to investigate these domains by using static and isolated case studies, a separate descriptive statistical or a narrow thematic theoretical analysis. Referring to the COVID-19 situation, which has undoubtedly accelerated practical changes in digital insurance platforms and preferences of insurance end-users, a holistic combined theoretical analysis and empirical investigations are on-demand. Accordingly, the research methodology of triangulation was selected by following a descriptive embedded single-case design and combining a qualitative online survey with a non-parametric statistical and path (SEM) analysis on results. The particular research aims to reduce the current scientific research gap in the modern insurance research field by combining different research subjects of the insurance decisionmaking process and digital platform, modern research approaches to data collection, and multilevel statistical data analysis. An online survey with 32 questions and a simplified Fuzzy, 9 point Likert scalebased questionnaire was selected as the main data collection method due to both methodology and specifics of the research subject. Firstly, there are methodologically confirmed advantages in applying the rating scales method comparing to applying a combination of an unstructured questionnaire logic and a nominal scale [68]. Secondly, the conceptual framework of the insurance decision-making process in digital platforms compounds multiple factors, whose evaluation on influence requires an improved and combined judgment scale with a gradual (quantitative) membership and linguistic (qualitative) expressions [65]. Essential details about the questionnaire and survey are provided in Table 1. The content of the questionnaire refers to variables and the logical structure of the integrated research framework presented in Table 2 and compounds sections dedicated to the investigation of variables from socio-demographical, social platform, process, and personal levels.

400

G. Baranauskas

Table 1 The structured online survey and questionnaire Structure

Methodological foundation

Distribution

32 structured and close-end type of questions applied in the following proportions: 3 screening questions 29 questions oriented to the validation of the conceptual framework

A combined full-blown Likert scale and a Fuzzy set of 9 points in the following range of numerical values and linguistic equivalents: 1—Extremely low 5—Neutral 9—Extremely strong

The questionnaire was translated to English Created and distributed by using the Pollfish market research software (https:// www.pollfish.com) Survey period: 19 October 2021–09 November 2021 Target population—insurance consumers of three Baltic countries

Source Composed by the author

The analysis of the collected data for empirical validation of the conceptual framework was concluded by conducting a descriptive statistical, Exploratory Factor Analysis (EFA), Confirmatory Factors Analysis (CFA), Pearson correlation, and Logistic Regression analysis in the SPSS statistical software (version 26). Findings of EFA, CFA, and correlation analysis were further applied in the path analysis and resulted in a structural equation modeling, whose outcome was an updated theoretical process framework of the insurance purchase decision-making in digital platforms. It is expected that such combination of multilevel statistical analysis techniques and a visualization of results in logical data flow diagrams (DFDs) would simplify an interpretation of quantitative variables and ensure a comprehensive examination of the empirical dataset. The sample size of 384 respondents was determined by following methodological recommendations of 95% of the confidence level, 5% of the margin error level, and 50% of the variance of the target population [76]. Furthermore, the received number of respondents (390) follows the [59] recommendation for a number of variables (factors) in the questionnaire to be multiplied by 10 participants per factor [61].

3 Digitalized and Customizable Insurance Platforms and Processes: Analysis on Theoretical Foundation and Conceptual Modeling 3.1 Literature Review A rapid internet and technological development, especially within digital communication and distribution advancements, due to the COVID-19 pandemic have stipulated and resulted in changes for both practice and scientific researches of organizations and consumers, including a case of the service innovations in insurance industry

Digital and Customizable Insurance: Empirical Findings and Validation …

401

Table 2 Determinants of the conceptual modeling of the integrated research framework Level

Type

Variable

Source

Facilitating conditions

External

Price value Brand Terms & conditions Insurance digitalization

UTAUT model (2003) [78] [81] [40] [97] [53] [90]

Personal conditions

Internal

Effort expectancy

UTAUT2 model [86]

Perceived value

[71]

Personal innovativeness and technology readiness Insurance literacy Curiosity Optimism Loyalty Habit Perceived risks Perception of need Perception of affordability Experience

TRI scale (2000) [77] UTAUT2 model [85] TAM 3 model [84] [79] [80] [42] [91] [97] [69] [25] [56] [90] [4] Extended TPB model (2020) [62]

External

Task characteristics Technology characteristics

TTF model (1995) [52] [39]

Internal

Perceived behavioral control

DTPB model (1985) [5]

Perceived interactivity

[56]

Perceived enjoyment

TAM 3 model (2008) [32]

Insurance information quality Insurance service quality Self-service platform quality

Updated DeLone and McLean Information Systems success model (2003) E-S-QUAL model (2005) [39]

Platform (technological) conditions

External

(continued)

402

G. Baranauskas

Table 2 (continued) Level

Type

Variable

Source

Internal

Insurance information satisfaction Insurance service satisfaction Self-service platform satisfaction

DeLone and McLean Information Systems (IS) Success Model [16] Updated DeLone and McLean Information Systems success model [17]

Performance expectancy

UTAUT2 model (2012) [54]

Process conditions

Internal

Perceived ease of use of platform Perceived usefulness of insurance Attitude toward behavior Behavioral intention Actual insurance purchase/usage in platform Individual Impact and Benefits Organization Impact and Benefits

TRA model (1975, 1980) TAM model (1986, 1989) DeLone and McLean Information Systems (IS) Success Model (1992) Updated DeLone and McLean Information Systems (IS) Success Model (2003) TAM3 (2008) [80] [81] [5] [69] [25] [56] [52] 1[51]

Social Conditions

External

Social Media and network Word of Mouth Sustainability

UTAUT model (2003) [79] [80] [40] [5] [24]

Source Composed by the author by following [2, 14, 15, 23], [16, 17]; [26, 63, 64, 77, 85, 84, 80, 71, 80, 86, 39, 4, 5, 24, 25, 32, 38, 71, 52–54, 56, 62, 69, 90, 91, 97]

[29, 48]. From the organizational perspective, a significant growth of e-commerce and online shoppers numbers, an active application of integrated marketing communication (IMC) framework, and a practical enablement of Insurance 4.0, open insurance and Customer 2.0 business models have been noted [9, 31, 58, 73]. From the consumer’s perspective, an increase in integrated cross-channel customer journeys, new availabilities in product designing, and decision-making in digital insurance platforms have been observed [9]. Nevertheless, even multiple types of research from recent years, including [10, 18, 19, 21] and [20], confirmed the insurance sector and organizations currently maintaining a position in a digital transformation period

Digital and Customizable Insurance: Empirical Findings and Validation …

403

in the entire insurance value chain and being interrelated with emerging technologies, but the practical development and application level within insurance markets is different and directions of scientific investigations are scattered. Retrospectively, fundamental works within the insurance digitalization field were made by [47, 57, 75, 97], and later notorious development within the theoretical foundation was made by Eling and Lehmann [21], [18, 19, 74, 89–91] and [20]. This research direction also gained popularity in emerging insurance markets of Central and Eastern Europe, Baltic regions, where critical regional level analyses were made by [34, 41–43, 95, 96, 51, 54, 56, 74] and [34]. Above listed regional studies revealed that such modernization of insurance is determined not only by global insurance business tendencies, technological advancements, and organizations priorities for costcutting, but also by situational factors, such as the economic growth, an advancement of internet technologies and insurance literacy in these regions [45, 54, 95, 7, 8]. In general, despite a proliferation of the academic interest in recent years on digitalization and omnichannel effects and outcomes in the insurance field toward the behavior of insurance consumers’ decision-making and choice of a sales channel, the influence of cross-channel and platformification outcomes appears to be not fully analyzed. Here, prior scientific investigations in the insurance field had a focal point on reasons of channels selection, consumers segmentation, and separate customer journey stages. The investigation of IT technologies and architecture effects on the insurance organization modernization and digital transformations compounds a notorious part of overall field investigations by minimizing attention to development and support of personalized insurance experience, customizable products, and service platforms [18, 31, 35]. An intensive evolvement of the practical phenomenon of digital media and networking, digital branding, mobile-first platfrom design and simplified online customization-based financial services has also been noticed, however, in the insurance research field investigated only fragmentally. Finally, an integrated research framework, covering the logic and variables of insurance consumer decision-making process and technological acceptance of digital insurance platforms, are missing. Therefore, this particular research aims to contribute to the modern insurance research field by filling the research gap of digital behavioral patterns and platforms’ influence by conducting a multidimensional analysis on the insurance consumers’ decision-making process in digital insurance platforms.

3.2 Conceptual Modelling The presented conceptual interpretation refers to the holistic marketing concept and a process design evaluation based on the customer-centricity approach. The main theoretical contribution to the insurance research field is the presentation of digital value co-creation, experience, and behavior of insurance customers as context-dependent, systematic, and dynamic within all stages of the purchase process. Furthermore, the suggested decision-making process logic and content are expected to extend the

404

G. Baranauskas

current scientific and practical knowledge of non-life insurance purchases depending mostly on the rational monetary evaluation dimension and purchase stage. The proposed integrated research framework follows the process logic and determinants of HCDM of [88], IS theories, and TAMs, including variables from UTAUT2 and TTF models. In general, the modeled process logic refers to the traditional model of three stages, pre-purchase, purchase, and post-purchase, and has content determinants of latent and observed variables. Such theoretical foundation reflects in the past researches of [7, 8], which confirms the insurance-decision-making process as a continuous, but not a simultaneous sequence of three-stage processes, which is influenced by multiple interrelated psychometric-perception, behavioral, situational, and contextual factors within the same decision-making. Together the research follows on the emerging practical prominence of digital insurance platforms, which combines technological innovations and behavioral patterns of fully digital insurance customers, seeking for not a static platform framework and linear progression throughout different process stages, but iterative, personalized, and customized decision-making options instead. From the content perspective, the proposed conceptual research framework consists of synthesizing and modeling dependent and independent variables as per Table 2. Determinants of the conceptual modeling of the integrated research framework, presented in Table 2, refer both to the content of the applied questionnaire in the online survey and the previous research of financial services decision-making and technology acceptance. Methodology, the selected synthesis and the multi-model integration approach are confirmed as acceptable research methods to reduce content limitations of individual theoretical models as well as to increase the scope of analyzed subjects and a variety of findings, which led to a higher quality and consistency of the path (SEM) analysis in later data analysis [52]. In detail, the conceptual integrated research framework comprises 2 types of variables (internal and external), grouped into 4 analysis levels: . The system-level, where 2 sub-levels, social conditions and facilitating conditions, were evaluated. The research subject of the insurance purchase in the digital platform is evaluated by using holistic variables from the insurance market situation and social perspectives. The setup of variables within the Facilitating Conditions level refers to findings of the insurance decision-making as a combined process influenced by multiple internal and external factors, including personal conditions, monetary and general situation evaluation, trust, competence, and pricing policy of insurance service providers. Additionally, by reflecting on recent practical trends of the digital transformation and an emerging new target consumer audience in the insurance industry, their information-sharing habits and preferences for service providers, variables of Insurance digitalization, Sustainability, Social Media and network were included as well. . The platform level, where technological conditions were evaluated. The research subject is evaluated by using both internal and external variables from information, service, platform quality and satisfaction perspectives. It is expected

Digital and Customizable Insurance: Empirical Findings and Validation …

405

that the synthesis of the above-listed models, such a IS, TAMs, and operational Self-Service Technology (SST) features, into a unified conceptual research level would allow examining the validity of the traditional interpretation of the insurance decision-making from a new theoretical angle. Here, the insurance purchase process and the final decision are analyzed in terms of a digital platform quality level, technological platform characteristics, and user perception of perceived behavioral control, interactivity, and enjoyment levels due to using an insurance platform. . The process level, where the research subject was evaluated by applying the logic and synthesizing determinants of IS theories, the traditional three-stage purchase decision-making process model and scientific studies listed in Table 2. The theoretical contribution was made by a re-conceptualization of TAM model constructs of Perceived Ease Of Use (PEOU) and Perceived Usefulness (PU) and introducing extended constructs of Perceived Ease of Use of Platform (PEOUP) and Perceived Usefulness of Insurance (PUOI). Moreover, the theoretical extension was made towards TAM model constructs of Behavioral Intention to Use and Actual System Use, resulting in presentation of new Behavioral Intention and Actual Insurance Purchase and Usage in Platform constructs. In general, these conceptual interpretations adjust traditional TAMs models and their constructs to the context of the research subject and define logical process steps, which insurance consumers complete within the decision-making of the purchase insurance, by rationally evaluating information that they possess about the insurance, a purchase process, and a digital platform. The focal point on the TAM model is maintained due to a confirmation of this model’s acceptability not only within a narrow analysis of the computer technology and information system adoption but also in a wider analysis of an end-user behavior and a general acceptance of technologies and platforms within the financial services field [39, 60]. Another re-conceptualization was performed in the case of determinants in the post-purchase stage, where introduced constructs of Individual Impact and Benefits and Organization Impact and Benefits refer to Individual Impact, Organizational Impact, and Net Benefits as theoretical dimensions of both traditional and updated DeLone and McLean Information Systems (IS) Success Models [16, 17]. The latent variable Attitude Toward Behavior was included in findings of [25, 69], and [52] studies, where this variable defined a significant positive or negative mental state in the insurance decisionmaking process, which is a result of combined multiple emotional, contextual, situational factors, and a personal utilitarian approach toward insurance. . The personal level, where the research subject was evaluated from the perception and assessment perspective of individual insurance consumers by comprising constructs of IS theories, model and insurance field studies listed next to this level in Table 2, where the insurance decision-making process is defined as mostly driven by personal habits, cognitive biases, and emotional factors. The theoretical contribution was made by a re-conceptualization of extended TPB and TAM3 model constructs, Perceived Behavioral Control and Perceived Enjoyment, and a synthesis of main results of [5, 71], and [56] researches respectively. The final result of this theoretical re-conceptualization and synthesis was the introduction

406

G. Baranauskas

of the construct Perceived Value, which defines a combined personal assessment toward benefits and costs of digital insurance platforms, insurance processes, and products. Another scientific novelty is related to a construct of insurance literacy as a stand-alone variable and its empirical validation, which in previous research was analyzed only at the conceptual discussion level. The theoretical synthesis presented in Table 2 also refers to main findings in research of the previous decade on behavioral patterns of insurance customers. Such findings reveal that the interpretation of the modern insurance decision-making is still narrowed down and influenced by a misperception of the risk probability and the general insurance concept, and appears to be driven by the use of simplified heuristic decision rules, bias, and variables of the social domain. Therefore, the introduction of a digital insurance platform as a stand-alone research subject in combination with the research subject of the insurance decision-making process is oriented to the current research gap of the modern insurance domain, especially in the case of the developing insurance market, such as the Baltic region. It is expected that the presented integrated framework of the insurance decision-making process would contribute a standpoint for a further scientific discussion and practical application of combined determinants from UTAUT2, TTF, the updated IS success model of DeLone and McLean, and ES-QUAL models within the development of digital insurance purchase processes and platform design. The completed theoretical synthesis and conceptual modeling have a practical value by introducing a setup of empirically measurable factors and, therefore, expanding the traditional understanding of the insurance decision-making process as driven by risks, monetary and trust-enhancing factors. Moreover, the presented conceptual research framework stands for a holistic interpretation of the digital insurance purchase process and outlines key determinants of Baltic insurance consumers’ acceptance of using digital insurance platforms.

4 Reflections of Digital Platforms in the Baltic Non-Life Insurance Market: A Case Study and Empirical Validation Domains of online customization and omnichannel have become recognized as an effective practical set of tools and methods for organizations to drive and ensure a new value standard in the society, where the highest value for an end-user is created not due to delivering the final product and ensuring its physical possession, but due to fulfilling the need for self-expression, belongingness, and interpersonal relationships [82]. It is confirmed that the application of online customization and omnichannel approaches enables consumers’ movement across different distribution channels, access to personalized information, and product customization on end-user demand, which leads to a unique and complete experience [11] (Juaneda-[33]). Nevertheless, issues of blurred boundaries among distribution channels, their consumer profiles, loops, and nonlinearities in stages of the purchase process appear [11, 27]. Together

Digital and Customizable Insurance: Empirical Findings and Validation …

407

with a missing consistency of managing the full scope of information, funds, products required for such operational transition, and a low managerial knowledge of dealing with estrangement, personalizing, and de-objectification, these problems slow down the implementation of online customization and omnichannel oriented strategies [89, 28, 87, 96]. Moreover, in the case of insurance industry studies, it is noticed that the focal point is still maintained over technological level challenges of emerging technologies and big data application as well as managerial level challenges, such as automating subsequent processes in insurance administrations and establishing smart insurance contracts [18–20]. According to [18], development of holistic digital insurance platforms is recognized not as a current working reality for insurers, but as one of major practical opportunities and business directions, which also tend to lack conceptual and empirical scientific investigations.

4.1 Empirical Data Analysis and Modelling 4.1.1

Data Analysis: Descriptive Statistics

The analysis of socio-demographic features of survey respondents, including gender, age group, and residence country, was completed as the initial stage. This type of analysis is confirmed as a usable method to summarize the profile of the research sample and support the evaluation of factor and path analysis results in later stages (Koyuncu and Kılıç [45]). An overview of sociodemographic features is provided in Table 3.

Table 3 Socio-demographic features of the research sample

Variables

Data values

Absolute number

%

Gender

Female

175

45

Male

215

55

18–24

127

33

25–34

77

20

35–44

107

27

45–54

48

12

55–64

20

5

+65

11

3

Estonia

57

15

Latvia

165

43

Lithuania

168

42

Age group

Country

Source Composed by the author

408

G. Baranauskas

In general, 390 respondents accepted to participate and completed the survey in the Pollfish platform from 19 October 2021 to 09 November 2021. Methodologically, according to the Nunnally’s rule of thumb [59], the size of the research sample stands for 14 respondents per 1-factor evaluation, therefore, is recognized as valid and acceptable to use in a factor analysis. In general, a proportional allocation of numbers in gender and age group variables was ensured by onboarding 215 male (55% of all respondents) and 175 female (45% of all respondents) participants from all 3 countries and 6 age groups.

4.1.2

Data Analysis: Pre-factor Analysis

Before conducting EFA, CFA, and Path analysis/SEM, it is required to complete an analysis of statistical prerequisites, including examination and validation of a data set appropriation for a factor analysis, a presence of correlations among variables, a sampling adequacy, reliability and consistency of a questionnaire and a measurement scale. Therefore, a pre-factor analysis was completed and main results are presented in Table 4 as follows. The investigation of Cronbach α indicator resulted in the value of 0.892, which is above the preferred threshold value. Therefore, it confirms an internal consistency among questionnaire items and sets the scale at the good level. The reliability and acceptance of the test and the adequacy of sampling were confirmed by values of the Spearman-Brown coefficient (0.805) and the Kaiser–Meyer–Olkin (KMO) coefficient (0.892) accordingly. Finally, the Bartlett’s test of the sphericity χ2 indicator was performed and resulted in the value χ2 (390) = 1140.42, p < 0.05. The received value of χ2 is confirmed to be significant as well as the received value of p (0.000) is lower than the threshold significance level. These results illustrate both a sufficient significant correlation within the data set and appropriation for a factor analysis.

Table 4 Results of indexes in the pre-factor analysis

Indices

Result

Threshold

Cronbach α

0.892

Acceptable if > 0.70 Preferred if > 0.80

Spearman-Brown

0.805

Acceptable if 0.70–0.90

Kaiser–Meyer–Olkin (KMO)

0.892

Adequate if 0.80–1

Bartlett’s test of sphericity χ2

0.000

Acceptable if p < 0.05

Source Composed by the author by following [13] and using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

Digital and Customizable Insurance: Empirical Findings and Validation …

409

Table 5 Results of CFA indices. Indices

Result

Threshold

Standardized Root Mean Square Residual (SRMR)

0.049

Well-fitting if < 0.05

Root Mean Square Error of Approximation (RMSEA)

0.046

Good fit if < 0.06

Comparative Fit Index (CFI)

0.917

Good fit if > 0.09

Tucker-Lewis index (TLI-NNFI)

0.901

Satisfactory fit if 0.90–0.95

Source Composed by the author by following [30] and using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

4.1.3

Data Analysis: EFA and CFA Principal Procedures and Analysis of Indices

Automatic descriptive methods of Principal Component Analysis (PCA) and principal modelling procedures of EFA and CFA were applied in a parallel way by following methodological recommendations and practices of analysis on data reduction, scale construction and improvement, and evaluating a validity and psychometric utility of multiple variable set in the researches [12]. In detail, the research construct validity was determined by methods of the PCA extraction and varimax rotation. EFA techniques and indices were used to evaluate the received dataset structure, determine existing latent dimensions and identify common factors among the observed variables in the survey. The CFA techniques and indices were used both for a evaluation of the structure of factors and a research instrument and to test the validity of the dimensionality of the structure obtained after EFA procedures. Main indices of CFA are summarized in Table 5. The goodness-of-fit of the model with a given dataset was examined by evaluating global model fit indices of CFA and resulted in following findings: . The value of SRMR (0.049) as videlicet the absolute fit indices was lower than the recommended well-fitting threshold of 0.05 and confirms the model to be approximately well fitting. . The value of RMSEA (0.046) as a parsimonious indice was lower than the recommended good-fitting threshold of 0.06 and a convergence fit to the analyzed data of the model. . The values of CFI (0.917) and TLI-NNFI (0.901) as comparative indices are within the recommended threshold range from 0.90 to 0.95 and confirms a satisfactory fit of the model.

410

4.1.4

G. Baranauskas

Empirical Data Analysis and Modelling of Integrated Insurance Consumers Decision-Making Framework in Digital Platforms

The factor analysis completed within the principal analysis and modelling procedures of EFA and CFA resulted in following findings on factors and factor groups levels. Firstly, 6 interrelated factor groups were identified having a very strong positive or a strong positive correlation. In general, a statistically significant Pearson correlation of the strong positive level was identified in 7 pairs of factor groups: between F1 and F2 (0.0873), F1 and F5 (0.786), F1 and F6 (0.891), F2 and F6 (0.0861), F3 and F6 (0.818), F4 and F5 (0.702), F4 and F6 (0.825). In detail, calculations of Pearson correlations are presented in Table 6. Secondly, an interrelation from content and structure perspectives was identified in all factor groups: . The factor group F1, which consists of 7 combined internal and external variables from platform (technological) evaluation dimensions and defines the research subject from the technological implementation perspective. Moreover, an identified combination of the personalization and customization domain and technological features of a platform within one factor group confirms the vitality of a narrow traditional theoretical interpretation of the MCP concept as purely oriented to a technological setup. In this case, a very strong correlation of the factor group F1 and factor groups F2, F5, and F6 each individually were influenced by (technological) variables. . The factor group F2, which consists of 4 combined internal and external variables from dimension levels of personal and facilitating conditions. Here, traditional personal evaluation variables of perception of insurance need and affordability, perceived risk understanding and past usage experience are merged into one factor group with a financial factor of insurance price value and suggested terms and conditions. . The factor group F3, which consists of 7 combined internal and external variables from evaluation dimensions of personal, social and facilitating conditions. In this case, the decision of insurance purchase is defined as influenced by a combination of platform usage experience, loyalty and a brand of an insurer and recommendations from social network and media. . The factor group F4, which consists of 3 combined internal and external variables from personal and platform (technological) evaluation dimensions: task characteristics and user interface in the platform, and an evaluation of effort expectancy required to complete process in digital platform. Additionally, a very strong positive (0.909) and statistically significant correlation was identified between this factors group and the factor group F1. . The factor group F5, which consists of 2 internal variables from platform (technological) evaluation dimensions, which defines the level of a perceived process control by a user in digital insurance platform and interactivity in the process of

Digital and Customizable Insurance: Empirical Findings and Validation …

411

Table 6 The calculation of Pearson’s correlation.

Factor group F1

Factor group F2

Factor group F3

Factor group F4

Factor group F5

Factor group F6

Pearson correlation

Factor group F1

Factor group F2

Factor group F3

Factor group F4

Factor group F5

Factor group F6

1

0.873**

0.669**

0.909**

0.786**

0.891**

0.000

0.000

0.000

0.000

0.000

Sig (2-tailed) N

390

390

390

390

390

390

Pearson correlation

0.873**

1

0.652*

0.698**

0.611**

0.861**

Sig (2-tailed)

0.000

0.000

0.000

0.000

0.000

N

390

390

390

390

390

390

Pearson correlation

0.669**

0.652*

1

0.648**

0.627**

0.818**

Sig (2-tailed)

0.000

0.000

0.000

0.000

0.000

N

390

390

390

390

390

390

Pearson correlation

0.909**

0.698**

0.648*

1

0.702**

0.825**

Sig (2-tailed)

0.000

0.000

0.000

0.000

0.000

N

390

390

390

390

390

390

Pearson correlation

0.786**

0.611**

0.627**

0.702**

1

0.651**

Sig (2-tailed)

0.000

0.000

0.000

0.000

N

390

390

390

390

390

390

Pearson correlation

0.891**

0.861**

0.818**

0.825**

0.651**

1

Sig (2-tailed)

0.000

0.000

0.000

0.000

0.000

N

390

390

390

390

390

0.000

390

Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp) **Correlation is significant at the 0.01 level (2-tailed)

insurance purchase in a digital platform. Here, due to the similarity of the structure, a strong positive correlation (0.786) was identified towards the factor group F1. . The factor group F6, consists of 2 internal variables from platform (technological) evaluation dimensions, which define user habits on using digital platforms and existing general personal innovativeness and technology readiness level.

412

G. Baranauskas

On the factor level, next to the traditional evaluation from highest to lowest ranks, the interpretation of results was also carried out by following a benchmark of 6 points as an evaluation rank, allowing to identify and systemize an influential level of investigated variables from different angles. The establishment of such qualification requirement is grounded by both average results of evaluation ranks of variables, where a dispersion of evaluation ranks in the range from 5.2 to 6.7 points was identified, and methodological reasons. From the methodological perspective, it is recognized that within traditional nine-point scales for the Analytic Hierarchy Process (AHP), applied in a multi-criteria decision making (MCM) oriented research, the point 6 is a benchmark and an intermediate value between the point 5, which typically stands for a Neutral evaluation, and the point 7, which defines a Very strong importance [70]. The highest evaluation rank (6.7) was identified in cases of the platform (technological) feature of security and privacy and the facilitating (market) feature of the insurance price. The personal conditions dimension related variables of personal financial well-being (6.6), which refers to the conceptual construct Perception of Affordability, and the need for insurance (6.4), which refers to the conceptual construct Perception of Need, received a bit lower evaluation. Slightly lower ranks were identified within the following variables: . Variables of quality of the information in a digital insurance platform, personal experience, recommendations and feedback, which refer to the conceptual construct Word of Mouth, received an average evaluation of 6.2 points. . Variables of the acceptability of insurance product terms & conditions and the quality of support service in a digital insurance platform, which refer to conceptual constructs of Insurance service quality and Personalization, received an average evaluation of 6.1 points. . Variables of loyalty to insurance companies, insurance literacy, the quality of digital insurance platform features, and the consideration of lost and gains probability, which refer to the conceptual construct of Perceived Risks, received an average evaluation of 6 points. 14 variables in the survey did not receive any higher than 6-point evaluation and, thus, refer to past research in the field. The respondents’ priority to a platform (technological), especially to personal conditions and facilitating (market) features, corresponds to results of [46, 69, 78–81] studies and the cross-border empirical case study in Europe, conducted by order of the European Commission at [22]. In other words, higher ranks of financial (monetary) and personal evaluation-related variables confirm the validity and vitality of classical economics and rational behavior theories within the insurance decision-making process in a digital environment. A part of the platform (technological) and customization domain-related variables, such as insurance product customization (5.7), Perceived Interactivity (5.7), user interface design and framework in the platform (5.6), Perceived Behavioral Control (5.5), and Perceived Enjoyment (5.5), received lower than the benchmark evaluations. Such findings lead to a twofold interpretation, where lower ranks of these types of factors indicate that the Baltic non-life insurance market is still in a pre-stage and

Digital and Customizable Insurance: Empirical Findings and Validation …

413

a transition toward a full digitalization, integrated value co-creation or a bespoke level of customization insurance solutions. This lower level preparation for digitally customizable insurance products and personalized services reflects on consumers’ levels, who do not seem to prefer advanced technological solutions or empowerment within the insurance purchase process in digital platforms and prioritize a high level of personal data privacy, security, insurance service, and information quality. In order to provide comprehensive findings of behavioral patterns and influential factors of decision-making of Baltic insurance consumers in digital platforms, an additional statistical analysis of sociodemographic variables’ influence and their relation to factor groups and independent variables of the presence of digital insurance platforms (Q30), perceived usefulness of insurance (Q31), and attitude toward insurance (Q32) was completed. Statistical methods of Mann–Whitney U, Kruskal– Wallis H nonparametric tests, Pairwise Comparisons, and Binomial Logistic Regression, and the calculation of Point-Biserial Correlation coefficients were applied. The results of Mann–Whitney U and Kruskal–Wallis H tests confirmed that there are no statistically significant differences among the respondents’ genders, countries, and evaluations of 6-factor groups and variables Q30, Q31, and Q32. However, referring to the age group, a statistically significant difference was identified within groups F1, F4, and the construct Q32. Results of the Independent-Samples Kruskal–Wallis’ test application for the factor group evaluation is provided in Table 7, and for variables Q30, Q31, and Q32 in Table 8 respectively. The Pairwise Comparisons analysis was conducted to capture the spread of statistical differences among age groups and resulted in findings presented in Table 9 and in Table 10. As per Table 9, a major difference over the factors’ evaluation is observed between the age group 18–24 and 3 other age groups, 25–34, 35–44, and 45–54. This result can be explained by the well-known tech-savvy nature of this Table 7 Results of the Independent-Samples Kruskal–Wallis’ test application (Test Summary) Number

Null hypothesis

Sig.

Decision

1

The DISTRIBUTION of F1 is the same across categories of Age

0.037

Reject the null hypothesis

2

The distribution of F2 is the same across categories of Age

0.127

Retain the null hypothesis

3

The distribution of F3 is the same across categories of Age

0.917

Retain the null hypothesis

4

The distribution of F4 is the same across categories of Age

0.040

Reject the null hypothesis

5

The distribution of F5 is the same across categories of Age

0.482

Retain the null hypothesis

6

The distribution of F6 is the same across categories of Age

0.526

Retain the null hypothesis

The significance level is 0.050 Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

414

G. Baranauskas

Table 8 Results of Independent-Samples Kruskal–Wallis’ test application for analysis of statistically significant differences between the age group and Q30, Q31, Q32 (Test Summary) Number

Null hypothesis

Sig.

Decision

1

The distribution of Q30 is the same across categories of Age

0.098

Retain the null hypothesis

2

The distribution of Q31 is the same across categories of Age

0.845

Retain the null hypothesis

3

The distribution of Q32 is the same across categories of Age

0.027

Reject the null hypothesis

The significance level is 0.050 Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

age group and their lower dependency on personalized assistance in using digital insurance platforms. The statistical analysis of sociodemographic factors was also completed by conducting the Binomial Logistic Regression, whose results are provided in Table 11. Statistically significant differences were noticed in all six analysis pairs of dependent variables and respondents’ types Q1 (insurance and non−insurance consumers), Q2 (users and not users of a digital insurance platform), and independent variables of Q30, Q31, Q32. Such findings confirm the inclusion and investigation of conceptual constructs of Perceived Ease of Use of Platform, Perceived Usefulness of Insurance, and Attitude Toward Behavior. Table 9 Results of Pairwise Comparisons on the variable of age groups and evaluation of factor groups Age group

Test statistic

Std. error

Std. test statistic

Sig.

Adj. Sig*

18–24 – > 54

−23.040

22.583

−1.020

0.308

1.000

18–24–25–34

−32.076

16.282

−1.970

0.049

0.488

18–24–35–44

−33.026

14.793

−2.233

0.026

0.256

18–24–45–54

−53.925

19.100

−2.823

0.005

0.048

> 54–25–34

9.037

23.978

0.377

0.706

1.000

> 54–35–44

9.986

22.993

0.434

0.664

1.000

> 54–45–54

30.885

25.974

1.189

0.234

1.000

25–34–35–44

−0.949

16.846

−0.056

0.955

1.000

25–34–45–54

−21.848

20.731

−1.054

2.92

1.000

35–44–45–54

−20.899

19.583

−1.067

0.286

1.000

The significance level is 0.05 Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

Digital and Customizable Insurance: Empirical Findings and Validation …

415

Table 10 Results of pairwise comparisons of age groups Std. error

Std. Test statistic

Sig.

Adj. Sig*

−8.500

18.814

−0.452

0.651

1.000

18–24 – > 54

−9.306

22.245

−0.418

0.676

1.000

18–24–35–44

−30.229

14.572

−2.074

0.038

0.380

18–24–25–34

−48.612

16.038

−3.031

0.002

0.024

45–54 – > 54

−0.806

25.586

−0.032

0.975

1.000

45–54–35–44

21.728

19.291

1.126

0.260

1.000

45–54–25–34

40.112

20.421

1.964

0.050

0.495

> 54–35–44

20.922

22.650

0.924

0.356

1.000

> 54–25–34

39.306

23.620

1.664

0.096

0.961

35–44–25–34

18.384

16.595

1.108

0.268

1.000

Age group 18–24–45–54

Test statistic

The significance level is 0.05 Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

Table 11 Coefficients of the Logistic Regression analysis among variables Q1, Q2 and Q30, Q31, Q32

Dependent variable

Independent variable

Pr (>|z|)

Q1

Q30

0.000836

Q31

2.26*10^{−5}

Q32

0.000248

Q30

0.00079

Q31

0.000208

Q32

1.17e*10^{−5}

Q2

The significance level is 0.05 Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

Additionally, the relation among binary variables Q1 and Q2 toward identified factor groups was investigated by using the method of Point-Biserial Correlation. Calculations are summarized in Table 12. As per Table 12, a positive and statistically significant correlation in all cases is observed. On the factor level, a weak positive correlation coefficient, both in Q1 and Q2 cases, was identified toward the factor group F1. This result refers to the findings above of platform (technological) variables of technological and quality features and the personalization domain as among the most influential factors of the insurance purchase decision-making process. A very weak positive correlation among factors F3, F5, and the usage of the platform for non-life insurance purchase (Q2) partial confirms the findings above of a low interest of Baltic non-life consumers in advanced technological solutions and a received experience in digital insurance platforms as well as a low effect of digital branding activities and social media. In general, such findings at the factor groups’ level, together with the strength level of a moderate

416

G. Baranauskas

Table 12 Results of the Point-Biserial Correlation analysis among variables Q1, Q2, and factor groups Indices

Factor groups and variables F1

F2

F3

F4

F5

F6

Q1

0.351** 0.316** 0.219** 0.292** 0.213** 0.333** 1 Q1 Pearson Correlation Sig. (2-tailed)

0.000

0.000

0.000

0.000

0.000

0.000

N*

387

387

387

387

387

387

Q2 0.580** 0.000

387

387

Q2 Pearson 0.343** 0.289** 0.180** 0.313** 0.173** 0.304** 0.580** 1 Correlation Sig. (2-tailed)

0.000

0.000

0.000

0.000

0.000

0.001

0.000

N

387

387

387

387

387

387

387

387

*

Total sample was 390 but in 3 cases of Q1 and Q2 answers fell out of the binary selection ** Correlation is significant at the 0.01 level (2-tailed) Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

positive correlation (0.580), identified between respondent type Q1 and type Q2, also confirm earlier findings that insurance digitalization solutions are insufficiently widespread within sales distribution at the Baltic non-life insurance market. At the final stage of the data analysis, a causal modeling approach of the Path Analysis / Structural Equation Modeling (SEM) was used for twofold reasons. Firstly, as a confirmatory and data-driven method of the hypothetical integrated insurancedecision-making process framework. Secondly, as a statistical analysis approach, which allows unifying both observable and latent variables from the digital insurance decision-making process in a structural visual format as well as understanding and explaining patterns of a correlation among variables. Main statistical indices of SEM are provided in Table 13. The statistical analysis resulted in the finding of positive causal relationships and effects among variables towards the dependent variable of Insurance Purchase in Platform (IPP) as well as the strength of the path model. A standardized estimate of the path coefficient std all. resulted in the finding of a large effect both on a factor and on factor groups level with most influential factors of Perceived Interactivity (Q13), Insurance information quality (Q6), Perceived Behavioral Control (Q12), Task Characteristics (Q4), and Effort Expectancy (Q8) and factors groups of F1, F6, and F2. Such findings refer to the findings from the data analysis part of EFA and CFA principal procedures and indices, where a combined set of variables from personal and platform (technological) evaluation dimensions were recognized as leading factors in the insurance-decision making process in digital platforms. The Path Analysis/SEM process resulted in Fig. 1 and showed different practical interpretations on factor groups’ structure and influential level. In total, 6 unique factor groups were identified, where findings of the correlation analysis of leading

Digital and Customizable Insurance: Empirical Findings and Validation …

417

Table 13 Results of statistical analyses in SEM of the digital insurance decision-making process model Latent variables F1 = -

Estimate

Q3

1.000

Q5

1.143

0.126

9.063

Q6

1.301

0.135

Q7

1.153

0.129

Q10

1.126

Q11 Q17

Std. err.

z-value

Pr (>|z|)

Std.lv

Std. all

0.961

0.557

0.000

1.099

0.606

9.618

0.000

1.251

0.665

8.965

0.000

1.109

0.596

0.131

8.569

0.000

1.083

0.558

0.941

0.114

8.270

0.000

0.904

0.531

1.078

0.125

8.623

0.000

1.037

0.563

Q19

1.276

0.159

8.013

0.000

1.185

0.616

Q20

1.000

0.929

0.502

Q21

1.220

0.157

7.791

0.000

1.133

0.585

Q22

1.121

0.154

7.257

0.000

1.041

0.518

Q23

0.992

0.134

7.380

0.000

0.922

0.533

Q24

1.275

0.156

8.148

0.000

1.184

0.636

Q14

0.721

0.097

7.470

0.000

0.858

0.486

Q18

0.809

0.113

7.174

0.000

0.962

0462

Q25

1.000

1.189

0.601

Q26

0.893

0.114

7.855

0.000

1.062

0.520

Q27

0.988

0.123

8.058

0.000

1.175

0.538

Q28

0.921

0.107

8.594

0.000

1.095

0.590

Q29

0.784

0.100

7.875

0.000

0.932

0.521

F2 = -

F3 = -

F4 = Q4

1.000

1.169

0.642

Q8

0.959

0.108

8.910

0.000

1.121

0.637

Q9

0.781

0.102

7.638

0.000

0.912

0.506

1.048

0.646

1.212

0.714

0.999

0.504

0.979

0.553

1.618

0.920

F5 = Q12

1.000

Q13

1.156

0.149

7.774

0.000

F6 = Q15

1.000

Q16

0.979

0.153

6.417

0.000

BI = ATB

1.000

(continued)

418

G. Baranauskas

Table 13 (continued) Latent variables Purchase = F1

1.000

0.966

0.966

F2

0.800

0.114

7.029

0.000

0.800

0.800

F3

0.810

0.116

6.991

0.000

0.632

0.632

F4

0.995

0.127

7.833

0.000

0.791

0.791

F5

0.743

0.110

6.789

0.000

0.659

0.659

F6

0.872

0.133

6.563

0.000

0.810

0.810

BI

0.534

0.103

5.192

0.000

0.306

0.306

PEUP

0.081

0.048

1.697

0.090

0.081

1.697

PUI

0.349

0.046

7.604

0.000

0.349

7.604

Regressions ATB-

Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

factor groups F1, F2, and F3 were extended by the practical validation of factor groups F4 (0.79) and F6 (0.81) as strongly related and influential within the insurance decision-making process in digital platforms. From the theoretical perspective, the setup and influence level of the factor group F1 supports a continuous application of traditional and updated DeLone and McLean Information Systems (IS) Success Models within the modern insurance research domain. Similar conclusions can be made by following a practical recognition of the conceptual constructs of the TTF model and the UTAUT2 model accordingly in the factor group F4 and factor groups F3 and F6. Finally, an empirical confirmation of newly introduced conceptual constructs of Sustainability and Insurance Literacy illustrates a new value dimension, a feature of a modern insurance consumer profile and their practical requirements for insurers. The level of influence of 4 dependent latent variables of Perceived Ease of Use of Platform (PEUP), Perceived Usefulness of Insurance (PUI), Attitude Towards Behavior (ATB), Behavioral Intention (BI), and Insurance Purchase in Platform (IPP) were evaluated as well. The calculation of standardized regression coefficients resulted in a finding that indicates a small positive effect of PEUP (0.080) and a medium positive effect of PUI (0.359) towards the variable of ATB. Such result is related to a semantical interpretation of the conceptual variable ATB. The latent variable ATB in theory stands for a persistent assessment and mental consideration by combining and being affected by emotional, contextual, and situational factors and resulting in positive or negative preferences before making a final decision to purchase insurance in a digital platform [5, 25, 52, 69]. Therefore, the presence and usage of digital insurance platforms seem to be a less influential and measurable factor comparing to a personal experience-related factor of perceived usefulness of insurance. A medium effect of the latent variable BI (0.306) on the actual insurance

Digital and Customizable Insurance: Empirical Findings and Validation …

419

purchase decision-making behavior can be explained similarly. These findings of BI and ATB variables’ interrelationship strength and overall influence level on IPP confirm their latent and biased nature, which are difficult to explore compared to crystalized dependent variables. Therefore, these variables naturally require continuous and stand-alone scientific investigations of their place, outcomes, and influence on the insurance decision-making process in digital platforms.

Fig. 1 Path diagram of integrated digital insurance decision making process. Source Composed by the author by using software IBM SPSS Statistics 26 (Armonk, NY: IBM Corp)

420

G. Baranauskas

5 Discussion and Limitations The conducted research obtained valuable findings for researchers in the insurance field and service providers as they provide a holistic expert-level evaluation and understanding regarding the attitude and behavioral intentions of Baltic insurance consumers in digital platforms. The suggested and empirically validated integrated insurance decision-making process framework can be practically applicable within an analysis of the purchase process or the online customization framework in existing platforms as well as in modeling a new technology acceptance. An additional theoretical contribution to the field has been a validation of multiple and combined factor groups’ existence and a strong positive correlation identified on factor groups and factor levels. These validations support findings of the theoretical analysis part, where the combination of personal, including cognitive-emotional and experience factor, technological, monetary, and individual risk factors, appeared as a foundation of the modern insurance concept and insurance decision-making process in digital platforms. On the other hand, a lower prioritization of customization and personalization domain-related factors encourages a further scientific discussion to understand the direction, type, and outcomes of the insurance digitalization phenomenon in the Baltic insurance market. The ongoing insurance digitalization in the Baltics is worth challenging whether it has already shifted from an internal type organization-oriented transformation, which is technical-driven and has a focal point on back-end resource and operations optimization, to an external consumer-oriented type of transformation with a focal point of innovative, integrated personalization and customization front-end solutions. Moreover, the research gap is observed in the state-of-the-art analysis of the COVID-19 influence on the attitude and behavioral patterns of Baltic insurance consumers in the digital environment. The research not only contains theoretical and practical contributions, but also has a few limitations, which require a scientific discussion. The main methodological limitation is that the validity and reliability of results of the conducted research might be questioned due to a disproportional allocation of the received sample on the country level. The risk of biased or less valid results was rejected after completing the Kruskal–Wallis H test, resulting in no statistically significant differences among a residence country, evaluations on factor groups, and dependent variables of the presence of digital insurance platforms, perceived usefulness of insurance and attitude toward insurance. Key empirical limitations to consider are related to the selected research context of private (individual) consumers from the Baltic non-life insurance market and the subject of digital distribution channels. Therefore, results of investigations carried out might lack a strong confidence and qualitative imperative to apply in the case of the legal customers segment or the life insurance product line. In addition, the post-purchase stage and organizational benefits construct, identified in the theoretical analysis, are yet insufficiently investigated within the modern insurance research field.

Digital and Customizable Insurance: Empirical Findings and Validation …

421

6 Conclusions The statistical data analysis and modeling confirmed theoretical assumptions of the insurance purchase decision-making in digital platforms as being a heterogeneous and complex process, driven by multiple factor groups, consisting of a combined set of factors, including a mental consideration and financial affordability, experience, technological enablers, social and situational conditions. An online survey of 390 insurance consumers from the Baltic market and the statistical analysis resulted in an empirically validated process framework consisting of 6 combined factor groups of 27 factors, having a very strong positive or strong positive correlation. The suggested integrated research framework and subjects provide a holistic and prevailing scientific standpoint on subjects and drivers on the insurance decision-making process in a digital environment and a new evaluation perspective on the traditional trust, monetary and risk-related factors, insurance consumers’ attitude towards a behavioral intention, and actual behavior in a digital platform. The integrated insurance decision-making process framework extends current insurance research models by including new variables of Insurance literacy, Insurance digitalization and Sustainability. From the content perspective, the setup of most influential factors and their content orientation to a monetary-risk and personal condition evaluation indicates that traditional economic benefits and rational behavior theoretical constructs are relevant and applicable within the evaluation and modeling of the insurance purchase decision-making process in digital platforms. On the other hand, lower than a total average evaluations of the platform usage and framework-related factors can be summarized in the following assumptions. Firstly, this finding confirms a finding from an earlier study of [6] that the standardization domain is a predominant feature in current digital non-life insurance platforms in the Baltics. Secondly, the finding is influenced by the setup of technical innovations or customization options in current platforms as well as by a general insurance digitalization level in the market. Therefore, insurance consumers lack interest in a higher perceived control or enjoyment of the insurance purchase process or interactivity within digital insurance platforms. Here, an important finding was the identification of statistically significant differences among the respondents’ age, especially in the case of the age group 18–24 and factors’ evaluations as well as between the presence of digital insurance platforms and the purchase of non-life insurance products. In this way, identified evaluation differences in age groups confirm both the practical need for both more digitalized, customized, and personalized solutions of the insurance service and platforms in the Baltic region as well as a continuous scientific investigation through the glance of sociodemographic factors’ influence. In general, it is expected that the presented and practically confirmed Path Diagram of the integrated insurance decision-making in digital platforms would be a starting step for a further scientific discussion in the field of insurance digitalization and online customization as well as support practitioners on assessing the design and content of processes in digital service platforms or modeling the positive attitude and behavioral intentions of consumers.

422

G. Baranauskas

References 1. Ajzen I (1985) From intentions to action: a theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action-control: from cognition to behavior. Springer-Verlag, Heidelberg, pp 11–39. https://doi.org/10.1007/978-3-642-69746-3_2 2. Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. PrenticeHall, Englewood Cliffs 3. Albrecher H, Bommier A, Filipovi´c D, Koch-Medina P, Loisel S, Schmeiser H (2019) Insurance: models, digitalization, and data science. SSRN Electron J 19–26:1–3. https://doi.org/10. 2139/ssrn.3382125 4. Allodi E, Cervellati EM, Stella GP (2020) A new proposal to define insurance literacy: paving the path ahead. Risk Gov Control Finan Mark Inst 10:22–32. https://doi.org/10.22495/rgcv10 i4p2 5. Aziz Shahab, Husin Maizaitulaidawati Md, Hussin Nazimah (2017) Conceptual framework of factors determining intentions towards the adoption of family takaful- an extension of decomposed theory of planned behaviour. Int J Organ Leadership 6(3):385–399. https://doi.org/10. 33844/ijol.2017.60430 6. Baranauskas G (2021) Application of customization and personalization in digital solutions of non-life insurance market: a case study of Lithuanian, Latvian and Estonian e-sales platforms. Eng Manag Prod Serv 13(2):68–82. https://doi.org/10.2478/emj-2021-0013 7. Baranauskas G, Raišien˙e AG (2021) Reflections on the customer decision-making process in the digital insurance platforms: an empirical study of the baltic market. Appl Sci 11(18):8524. https://doi.org/10.3390/app11188524 8. Baranauskas Gedas, Raišien˙e Agota Giedr˙e (2021) Expert-based evaluation of digitalization and Mass Customization in the Baltic non-life insurance online platforms. J East Eur Central Asian Res (JEECAR) 8(2):184–201. https://doi.org/10.15549/jeecar.v8i2.644 9. Barwitz N, Maas P (2018) Understanding the omnichannel customer journey: determinants of interaction choice. J Interact Mark 43:116–133. https://doi.org/10.1016/j.intmar.2018.02.001 10. Bohnert A, Fritzsche A, Gregor S (2019) Digital agendas in the insurance industry: the importance of comprehensive approaches. Geneva Pap Risk Insur Issues Pract 44:1–19. https://doi. org/10.1057/s41288-018-0109-0 11. Chopra S (2016) How omni-channel can be the future of retailing. Decision 43:135–144. https:// doi.org/10.1007/s40622-015-0118-9 12. Chumney F (2012) Principal components analysis, exploratory factor analysis, and confirmatory factor analysis, pp 1–14 13. Cortina JM (1993) What is coefficient alpha? An examination of theory and applications. J Appl Psychol 78(1):98 14. Davis FD (1986) A technology acceptance model for empirically testing new end-user information systems: theory and results (Thesis). https://dspace.mit.edu/handle/1721.1/15192 15. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008 16. Delone W, McLean E (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3(1):60–95. https://doi.org/10.1287/isre.3.1.60 17. DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manage Inf Syst 19(4):9–30. https://doi.org/10.1080/07421222. 2003.11045748 18. Eckert C, Osterrieder K (2020) How digitalization affects insurance companies: overview and use cases of digital technologies. ZVersWiss 109:333–360. https://doi.org/10.1007/s12297020-00475-9 19. Eckert C, Eckert J, Zitzmann A (2021) The status quo of digital transformation in insurance sales: an empirical analysis of the german insurance industry. ZVersWiss 110:133–155. https:// doi.org/10.1007/s12297-021-00507-y

Digital and Customizable Insurance: Empirical Findings and Validation …

423

20. Eckert C, Neunsinger C, Osterrieder K (2022) Managing customer satisfaction: digital applications for insurance companies. Geneva Pap Risk Insur Issues Pract. https://doi.org/10.1057/ s41288-021-00257-z 21. Eling M, Lehmann M (2018) The impact of digitalization on the insurance value chain and the insurability of risks. Geneva Papers Risk Insur Issues Pract 43:359–396. https://doi.org/10. 1057/s41288-017-0073-0 22. European Commission (2017) Study on consumers’ decision making in insurance services: a behavioural economic perspective. Final report, London Economics, Ipsos and VVA Europe. https://ec.europa.eu/info/sites/default/files/finalreport_en.pdf 23. Fishbein M, Ajzen I (1975) Belief, attitude, intention and behaviour: an introduction to theory and research. Addison-Wesley, Reading 24. Gatzert N, Reichel P, Zitzmann A (2020) Sustainability risks & opportunities in the insurance industry. ZVersWiss 109:311–331. https://doi.org/10.1007/s12297-020-00482-w 25. Gbongli K, Xu Y, Amedjonekou KM (2019) Extended technology acceptance model to predict mobile-based money acceptance and sustainability: a multi-analytical structural equation modeling and neural network approach. Sustainability 11:3639. https://doi.org/10.3390/su1 1133639 26. Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236. https://doi.org/10.2307/249689 27. Grewal D, Roggeveen AL (2020) Understanding retail experiences and customer journey management. J Retail 96(1):3–8. https://doi.org/10.1016/j.jretai.2020.02.002 28. Hajdas M, Radomska J, Silva SC (2020) The omni-channel approach: a utopia for companies? J Retail Consum Serv 65:1–15. https://doi.org/10.1016/j.jretconser.2020.102131 29. Heinonen K, Strandvik T (2020) Reframing service innovation: COVID-19 as a catalyst for imposed service innovation. J Serv Manag 32(1):101–112. https://doi.org/10.1108/JOSM-052020-0161 30. Hooper D, Coughlan J, Mullen MR (2008) Structural equation modelling: guidelines for determining model fit. Electron J Bus Res Methods 6(1):53–60 31. Hu T-I, Tracogna A (2020) Multichannel customer journeys and their determinants Evidence from motor insurance. J Retail Consum Serv 54:102022. https://doi.org/10.1016/j.jretconser. 2019.102022 32. Jimenez IAC, Garcia LCC, Marcolin F, Violante MG, Vezzetti E (2021). Validation of a TAM extension in agriculture: exploring the determinants of acceptance of an e-learning platform. Appl Sci 11(10):4672. https://doi.org/10.3390/app11104672 33. Juaneda-Ayensa E, Mosquera A, Murillo YSs (2016) Omnichannel customer behavior: key drivers of technology acceptance and use and their effects on purchase intention. Front Psychol 7(1117):1–11. https://doi.org/10.3389/fpsyg.2016.01117 34. Kaigorodova G, Mustafina A, Alyakina D (2018) Directions of improving information system of insurance company. J Phys Conf Ser 1015(4):042016. https://doi.org/10.1088/1742-6596/ 1015/4/042016 35. Kaigorodova Gulnara, Mustafina Alfiya, Pyrkova Guzel, Grzebyk Mariola, Belinskaja Larisa (2021) Digitalization of the insurance business: systematization of net effects through the example of Russia. Ins Mark Compan 12(1):32–42. https://doi.org/10.21511/ins.12(1).2021.03 36. Kamis A, Koufaris M, Stern T (2004) Online mass customization and the customer experience. In: AMCIS 2004 proceedings, vol 481, pp 3818–3821 37. Kamis A, Stern T, Ladik DM (2008) A flow-based model of web site intentions when users customize products in business-to-consumer electronic commerce. Inf Syst Front 12:157–168. https://doi.org/10.1007/s10796-008-9135-y 38. Keong KQC, Kumar MK, Abbasi GA (2020) An extended TPB model to predicting consumer acceptance towards remanufactured goods: a case for Malaysia. J Entrepr Bus Econ 8(2):93–119 39. Kim S, Kim B (2020) A decision-making model for adopting al-generated news articles: preliminary results. Sustainability 12(18):7418. https://doi.org/10.3390/su12187418 40. Kiyak Deimena, Pranckeviˇci¯ut˙e Linara (2021) Causal survey of purchase of non-life insurance products for Lithuanian consumers. Reg Form Dev Stud 3:112–122. https://doi.org/10.15181/ rfds.v14i3.868

424

G. Baranauskas

41. Klapkiv L, Klapkiv J (2017) Technological innovations in the insurance industry. J Ins Finan Mark Cons Prot 26(4):67–78 42. Klapkiv YM (2018) Mobile applications in the online distribution of insurance services. Sci J Black Sea Petro Mohyla Natl Univ Econ 302(290):42–47 43. Klapkiv Y, Lyubov K, Zarudna N (2018) Online distribution of insurance of civil liability of owners of vehicles: the experience of Poland, opportunities of Ukraine. Baltic J Econ Stud 4:195–201. https://doi.org/10.30525/2256-0742/2018-4-1-195-201 44. Koutsomitropoulos DA, Kalou AK (2017) A standards-based ontology and support for big data analytics in the insurance industry. ICT Express 3:57–61. https://doi.org/10.1016/j.icte.2017. 05.007 45. Koyuncu ˙I, Kılıç AF (2019) The use of exploratory and confirmatory factor analyses: a ˘ ˙IT˙IM VE B˙IL˙IM. https://doi.org/10.15390/EB.2019.7665 document analysis. TED EG 46. Kunreuther H, Pauly M (2015) Behavioral economics and insurance: principles and solutions. In: Schwarcz D, Siegelman P (eds) Research handbook on the economics of insurance law. Edward Elgar Publishing Ltd., Cheltenham 47. Küster I, Vila N, Canales P (2016) How does the online service level influence consumers’ purchase intentions before a transaction? a formative approach. Eur J Manag Bus Econ 25:111– 120. https://doi.org/10.1016/j.redeen.2016.04.001 48. Lanfranchi D, Grassi L (2021) Examining insurance companies’ use of technology for innovation. Geneva Pap Risk Insur Issues Pract. https://doi.org/10.1057/s41288-021-00258-y 49. Lee ZWY, Chan TKH, Loong-Chong AY, Thadani DR (2019) Customer engagement through omnichannel retailing: the effects of multichannel integration quality. Ind Mark Manag 77:90– 101. https://doi.org/10.1016/j.indmarman.2018.12.004 50. Lezgovko A, Lastauskas P (2019) Expansion of insurance business: theoretical aspects and assumptions of market development. Bus Theory Pract 9(2):125–36. https://doi.org/10.3846/ 1648-0627.2008.9.125-136 51. Lin S-Y, Juan P-J, Lin S-W (2020) A TAM framework to evaluate the effect of smartphone application on tourism information search behavior of foreign independent travelers. Sustainability 12(22):9366. https://doi.org/10.3390/su12229366 52. Lin X, Wu R, Lim Y-T, Han J, Chen S-C (2019) Understanding the sustainable usage intention of mobile payment technology in korea: cross-countries comparison of chinese and korean users. Sustainability 11(19):5532. https://doi.org/10.3390/su11195532 53. Łyskawa K, K˛edra A, Klapkiv L, Klapkiv J (2019) Digitalization in insurance companies. In: Paper presented at: international scientific conference contemporary issues in business, management and economics engineering 2019, Vilnius, Lithuania. https://doi.org/10.3846/cib mee.2019.086 54. Momani A (2020) The unified theory of acceptance and use of technology: a new approach in technology acceptance. Int J Sociotechnol Knowl Dev 12(3):79–98. https://doi.org/10.4018/ IJSKD.2020070105 55. Mustafina AA, Kaigorodova GN, Alyakina PD, Velichko NY, Zainullina MR (2020) Digital technology in insurance. In: Ashmarina S, Mesquita A, Vochozka M (eds) Digital transformation of the economy: challenges, trends and new opportunities. Advances in intelligent systems and computing, vol 908. Springer, Cham. https://doi.org/10.1007/978-3-030-11367-4_65 56. Naffa E (2019) Testing the Main Drivers of Intention to Use E-Insurance: An Empirical Study from the Jordanian Clients’ Perspective. In: Paper presented at: Aqaba Conference, Israel 57. Nicoletti B (2016) Innovation in insurance. In: Nicoletti B (ed) Digital insurance: business innovation in the Post-crisis era. Palgrave Macmillan, , pp 6–46. https://doi.org/10.1057/978 1137553270 58. Nicoletti B (2021) Place or channels in insurance 4.0. In: Nicoletti B (ed) Insurance 4.0. Palgrave Macmillan, , pp 147–172. https://doi.org/10.1007/978-3-030-58426-9 59. Nunnally J (1978) Psychometric methods, 2nd edn. McGraw-Hill, New York 60. Nurhayati S, Hidayat N (2018) Acceptance measurement of health insurance information system based on technology acceptance model. KEMAS Jurnal Kesehatan Masyarakat 14(2):254–263. https://doi.org/10.15294/kemas.v14i2.11374

Digital and Customizable Insurance: Empirical Findings and Validation …

425

61. Osborne JW (2014) Best practices in exploratory factor analysis. CreateSpace Independent Publishing, Scotts Valley 62. Pallant J, Sands S, Karpen I (2020) Product customization: a profile of consumer demand. J Retail Consum Serv 54:1–10. https://doi.org/10.1016/j.jretconser.2019.102030 63. Parasuraman A, Zeithaml VA, Malhotra A (2005) E-S-QUAL: a multiple-item scale for assessing electronic service quality. J Serv Res 7(3):213–233. https://doi.org/10.1177/109467 0504271156 64. Parasuraman A (2000) Technology readiness index (TRI): a multiple-item scale to measure readiness to embrace new technologies. J Serv Res 2(4):307–320. https://doi.org/10.1177/109 467050024001 65. Peculis R, Rogers D, Campbell P (2007) A task model of software intensive acquisitions: integrated tactical avionics system for a maritime helicopter case study. In: Proceedings of the twelfth australian aeronautical conference, Melbourne, Australia, pp 510–522 66. Porrini D (2018) The effects of innovation on market competition: the case of the insurance comparison websites. Mark Manag Innov 3:324–332. https://doi.org/10.21272/mmi.2018.3-29 67. Pousttchi K, Gleiss A (2019) Surrounded by middlemen—How multi-sided platforms change the insurance industry. Electron Mark 29:609–629. https://doi.org/10.1007/s12525-019-003 63-w 68. Quirós Pelayo, Alonso Jose M, Pancho David P (2016) Descriptive and comparative analysis of human perceptions expressed through fuzzy rating scale-based questionnaires. Int J Comput Intell Syst 9(3):450. https://doi.org/10.1080/18756891.2016.1175811 69. Rocha AQ, Botelho D (2018) Attitudes towards insurance for personal assets: antecedents and consequents. Eur J Bus Soc Sci 6:62–80 70. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York 71. Santouridis I, Trivellas P, Tsimonis G (2012) Using E-S-QUAL to measure internet service quality of e-commerce web sites in Greece. Int J Qual Serv Sci 4(1):86–98. https://doi.org/10. 1108/17566691211219751 72. Shubenko I (2020) Trends of digitization in the insurance market of Ukraine. Bus Inf 2:273–279. https://doi.org/10.32983/2222-4459-2020-2-273-279 73. Standaert W, Muylle S (2022) Framework for open insurance strategy: insights from a European study. Geneva Pap Risk Insur Issues Pract. https://doi.org/10.1057/s41288-022-00264-8 74. Stoeckli E, Dremel C, Uebernickel F (2018) Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electron Mark 28:285–307. https://doi.org/10.1007/s12525-018-0304-7 75. Stoeckli E, Uebernickel F, Brenner W (2016) Digitalization in the insurance industry. themes and decisions that matter: insights from a multiple-case study in Germany and Switzerland. In: Series on research in information systems management and business innovation, vol 6 76. Taherdoost H (2016) Sampling methods in research methodology: how to choose a sampling technique for research. Int. J. Acad. Res. Manag. 5:18–27. https://doi.org/10.2139/ssrn.320 5035 77. Taylor S, Kevin C, Goodwin S (2002) Technology readiness in the e-insurance industry: an exploratory investigation and development of an agent technology e-consumption model. J Ins Issues 25(2):142–165 78. Ulbinait˙e A, Kuˇcinskien˙e M (2013) Insurance service purchase decision-making rationale: expert-based evidence from Lithuania. Ekonomika 92:137–155. https://doi.org/10.15388/ Ekon.2013.0.1409.0 79. Ulbinait˙e A, Moullec YL (2010) Towards an ABM-based framework for investigating consumer behaviour in the insurance industry. Ekonomika 89:250–257. https://doi.org/10.15388/Ekon. 2010.0.983 80. Ulbinait˙e A, Kuˇcinskien˙e M, Moullec YL (2011) Conceptualising and simulating insurance consumer behaviour: An agent-based-model approach. Int. J. Modeling Optim 1:250–257. https://doi.org/10.7763/IJMO.2011.V1.44 81. Ulbinait˙e A, Kuˇcinskien˙e M, Moullec YL (2013) Determinants of insurance purchase decision making in Lithuania. Eng Econ 24(2):144–159. https://doi.org/10.5755/j01.ee.24.2.3439

426

G. Baranauskas

82. van Osselaer SMJ, Fuchs C, Schreier M, Puntoni S (2020) The Power of Personal. J Retail 96(1):88–100. https://doi.org/10.1016/j.jretai.2019.12.006 83. Veki´c A, Borocki J, Fajsi A, Moraca S (2018) Adapting business model and strategies for next wave of mass customization. In: Proceedings of 8th international conference on mass customization and personalization – community of Europe (MCP-CE 2018), Novi Sad, Serbia, , pp 316–321 84. Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x 85. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540 86. Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178. https://doi.org/10.2307/41410412 87. Verhoef Peter C, Kannan PK, Jeffrey Inman J (2015) From multi-channel retailing to omnichannel retailing. J Retail 91(2):174–181. https://doi.org/10.1016/j.jretai.2015.02.005 88. Walker J, Ben-Akiva M (2002) Generalized random utility model. Math Soc Sci 43:303–343. https://doi.org/10.1016/S0165-4896(02)00023-9 89. Warg M, Zolnowski A, Frosch M, Weiß P (2019) From product organization to platform organization - observations of organizational development in the insurance industry. In: Proceedings of conference The 10 years naples forum on service, Ischia: Italy, pp 2–16 90. Weingarth J, Hagenschulte J, Schmidt N, Balser M (2019) Building a digitally enabled future: an insurance industry case study on digitalization. In: Urbach N, Röglinger M (eds) Digitalization cases. management for professionals. Springer, Cham, pp 249–269. https://doi.org/10.1007/ 978-3-319-95273-4_13 91. Wiesböck F, Matt C, Hess TH, Li L (2017) How management in the German insurance industry can handle digital transformation. Manag Rep, 1–26 92. Yao JM (2017) Supply chain resources integration optimization in B2C online shopping. Int J Prod Res 55(17):5079–5094 93. Zarian C¯ırule I, Voronova I, Pettere G (2019) Internal model for insurers: possibilities and issues. In: Paper presented at: international scientific conference contemporary issues in business, management and economics engineering 2019, Vilnius, Lithuania. https://doi.org/10.3846/cib mee.2019.026 94. Zari¸na C¯ırule I, Voronova I, Pettere G (2018) Assessment of the stability of insurance companies: the case of baltic non-life insurance market. Econ Bus 32:102–111. https://doi.org/10. 2478/eb-2018-0008 95. Zhao H, McLoughlin L, Adzhiev V, Pasko A (2018) Why do we buy mass customized products? – consumer’s perspective of success factors for online mass customization. In: Proceedings of 8th international conference on mass customization and personalization community of Europe: digital customer experience, Novi Sad, Srbia, pp 1–8 96. Zimmermann R, Weitzl W, Auinger A (2021) Identifying sales-influencing touchpoints along the omnichannel customer journey. Procedia Comput Sci 196:52–60. https://doi.org/10.1016/ j.procs.2021.11.072 97. Zolnowski A, Warg M (2017) Let’s get digital: digitizing the insurance business with service platforms. Cutter Bus Technol J 30(9):19–24

Education

Digital Transformation in Higher Education: Intelligence in Systems and Business Models Albert Rof, Andrea Bikfalvi, and Pilar Marques

Abstract Purpose—The purpose of this chapter is to analyze how higher education institutions can successfully manage digital transformation. Originality/Value—The future of higher education depends on the ability of its managers to envision and manage the digital transformation process in the new competitive landscape, which is further challenged with frequent and varied “crisis” situations such as COVID-19. The originality of this chapter is in its providing a review and reflection of the emerging digital transformation trends and an illustration of good practices in higher education. To this effect, it contributes to the on-going debate about why and how the higher education sector should adopt a business mentality and embrace business model innovation as a key driver to sustain the future competitiveness of higher education institutions. Keywords Higher education · Higher education institutions · Digital transformation · Business model innovation · Competitive landscape · COVID-19 · Customized learning strategy · Edtechs

1 Introduction Digital transformation (DT) requires disruptive changes that are increasingly necessary for organizations in any sector to remain competitive in their markets. The higher education (HE) sector, including universities, colleges, and polytechnics that offer degrees beyond secondary education, is also significantly affected by the effects of DT, especially since the COVID-19 pandemic. Some voices point particularly to A. Rof (B) · A. Bikfalvi · P. Marques University of Girona, Girona, Spain e-mail: [email protected] A. Bikfalvi e-mail: [email protected] P. Marques e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_18

429

430

A. Rof et al.

the disruption and transformation of the sector [37, 52] due to the combination of major changes that are occurring, including the impact of globalization, digital technologies, the irruption of new players from outside the HE sector, a rising labormarket trend towards hiring for “what you know” rather than “what degrees you have”, and ultimately the overall questioning of the role of HE institutions. COVID-19 has undoubtedly contributed to advancing this transformation as an extreme trigger accelerating an already on-going DT process in HE [70] and forcing the digitalization of most universities [53] which, prior to the shock, were largely operating using face-to-face modes of learning. The pandemic caused a drastic shift in the scale of change [7] in a sector that was already immersed in a continuous digitalization process at risk of being disrupted [38, 39, 61]. Although DT is strongly impacting HE, the reality is that the digitalization process still needs to be further explored, especially because not all HE institutions (HEIs) have the same readiness for DT [60] and neither do they have the same or enough digital maturity [5]. This variability necessitates different priorities to face differential DT challenges [5]. The most important challenge is how to implement DT since this transformation is a significant organizational change subject to barriers and risks that cannot be solved by simply copying processes that work in other HEIs [81]. However, following proper benchmarking processes via understanding and reflecting on DT practices that work in other HEIs, or even other sectors, can be a good starting point to manage the required DT change [47, 51]. The objective of this chapter is to contribute to the clarification of the DT concept in the context of HE, and to explain why it is necessary, why it is difficult, and how to manage the DT process by means of leveraging on emerging trends and good practices in the sector. This analysis is relevant because there is no going back in the digitization process of the university sector. The originality of this analysis, based on an exhaustive review of the literature, is to establish a roadmap for the DT of HEIs, is to provide a framework that can help structure decision-making in a context of uncertainty and profound changes. This introduction is followed by a literature review section that sets the framework to understand DT, its importance, and the barriers of it. The following section on managing the digital transformation describes seven ordered recommendations for managing DT in an HEI. In the next section, a 5-stage roadmap is proposed to successfully manage the DT process in an HE organization. Last, a concluding section provides an overall assessment of the chapter, highlighting its contribution and making future research proposals.

Digital Transformation in Higher Education …

431

2 Literature Review: Understanding Digital Transformation, Its Importance, and Barriers to It 2.1 Understanding Digital Transformation a) The concept of digital transformation In a recent attempt to reach a unified definition of DT based on a review of 124 articles, DT has been defined as “a fundamental change process enabled by the innovative use of digital technologies, accompanied by the strategic leverage of key resources and capabilities aimed at radically improving an entity (an organization, a business network, an industry, or society) and redefining its value proposition for its stakeholders” [28], p.12). Based on this definition, it is important to distinguish between DT, digitalization and digitization. DT is a profound change process that radically transforms and improves an organization with a redefined value proposition [28]. DT results in new capability-driven outcomes that redefine the value proposition, leading to business model innovation, new revenue streams, radical changes in offerings, new leadership organization and culture, and new sources of competitive advantages, among others [28]. Conversely, digitalization entails incremental, not radical, improvement to reinforce an existing value proposition [28]. Digitalization results in some economic-driven outcome, such as process automation, efficiencies and productivity improvements, cost reductions, error elimination, and improvements in the customer experience, products or services [28]. Organizations can pursue DT directly or indirectly, first implementing some digitalization projects before achieving DT [28]. Last, as regards digitization, this term essentially refers to the process of changing from analog to digital forms [28] and is a common operative process needed within wider and more strategic digitalization or DT processes. b) The higher education sector The HE sector, also known as tertiary education, includes a set of institutions (universities, colleges, and polytechnics) that offer degrees beyond secondary education. The reality is that the typology and profile of institutions included under this acronym is diverse and includes public universities, private universities and business schools, affiliated university centers, and corporate universities, among others. The academic offer in HE is generally organized in undergraduate and postgraduate studies (master’s and doctorate levels) in their different modalities (face-toface, semi-face-to-face, online). HEIs have the primary mission of teaching and research, but in recent years there has been increasing pressure to include a third mission, contribution to society [18], which stresses the capability of HEIs to further engage in the socio-economic and cultural development of local areas, employment promotion, and knowledge transfer to industry and to society at large, among others.

432

A. Rof et al.

c) Digital transformation in higher education The impact of digital technologies in HE, such as MOOCs and social media, among others, has been felt for some time, with studies indicating the risk of disappearance of the non-adapted players [40]. A decade after MOOCs’ popularization, Class Central reported that they had reached 220 M learners in 2021, with 950 participating universities, 19,400 courses, 1,670 micro-credentials, and 70 MOOC-based degrees [17]. It is estimated that the top four online educational platforms, Coursera, edX, FutureLearn, and Swayam are together serving more than 178 million learners [17]. However, this digitalization does not come without challenges. Of particular importance is how DT impacts teaching staff and students. While students are very motivated to use digital tools for learning, there is a considerable digital gap for academics, which requires developing teachers’ digital skills [11]. Although DT is an “ongoing process”, the context shows incipient threats of digital technologies disrupting universities [61] and rising pressures to adapt to technological changes to stay relevant [44, 83]. In fact, the HE sector has been immersed in a continuous digitalization process for some time, with DT perceived as positive and necessary, a facilitator for professionalization that affects and transforms all the main HE areas of activity, namely teaching, research and transfer, and administration [67]. DT impacts HEIs at several levels, including education and services to students, the deployment or creation of new technological applications, and initiatives to change the organizational culture, among others [22]. Further, DT involves digitizing and automating all the processes, and generating opportunities for increased data-based business intelligence and customer-centered approaches to better satisfy students’ needs and digital expectations [67]. DT in the HE sector is mainly associated with four digital facilitators: i) digitization of processes (i.e., the HEI becomes paperless); ii) data-based decisions (i.e., automation of decisions and data-enhanced decisions made by different HE stakeholders); iii) connectivity (i.e., the university in the pocket); and iv) digital innovation (i.e., virtualization of teaching) [67]. Although DT was already considered important and “necessary” in the prepandemic context, it was not perceived as a “game changer” [67]. The HE organization was already facing sustained pressure to adopt new technologies and develop new digital capabilities. The ongoing DT process makes tensions inevitable but, by facing the situation and finding appropriate solutions, the business model had started to be innovated in all its dimensions, namely value creation, value proposition, and value capture, even if the process was to some degree emergent and unplanned [67]. Therefore, even without a specific DT strategy, the tensions appearing in the digitalization process were forcing HEIs to progressively and emergently innovate their business models [67]. As an unexpected and unprecedented exogenous shock, COVID-19 destabilized the HE sector and the entire system where the sector is embedded [25]. The pandemic was an extreme trigger, accelerating the DT process in HEIs [39, 70] and forcing the digitalization of most universities [53] which, prior to this shock, were largely

Digital Transformation in Higher Education …

433

operating using face-to-face modes of learning. In the “digitalize now or stop operating” situation [70], HEIs demonstrated a huge capacity for rapid adaptation to preserve their operations [35, 3]. A total of 67% of HEIs were able to replace classroom teaching with online distance teaching and learning at the beginning of the pandemic [53]. This percentage rose to 89% during 2021 [77]. However, inequality of learning opportunities is a concern because of the remaining HEIs not being able to adapt. While the pressure to digitally transform to adapt to the pandemic scenario worked initially [5], it is still uncertain whether these changes will be permanent and will evolve into strategic capacities that are fully integrated and remain in the organization [7]. Digital Technologies in Higher Education: Educational Technology Educational technology or “edtech” is defined as the ‘technological resources and processes for learning and teaching purposes’ [35]. There is a wide range of technological solutions education can use, including educational applications, platforms, and resources, which are available to help the main stakeholders (parents, teachers, schools, and school administrators) to facilitate student learning [78]. During the COVID-19 shock, and with little reflection, HE institutions massively adopted educational technologies to continue operating [49], and many students were satisfied with how the situation was handled. According to a survey of students at Meiji University (Tokyo), conducted just after the lockdown, on-demand (distribution of recorded video lectures) and real-time delivery of lectures (synchronous and interactive) were highly valued, while student satisfaction with the lectures consisting in documents and assignments was low. A prominent 85% of students were highly satisfied with the on-demand lectures because they could repeatedly listen to them anytime and anywhere [43]. Another survey of private university students in Bangladesh found that student acceptance of online classes during the emergency phase was associated with their perceived advantages, the sudden change in their “confined lifestyle”, the user-friendliness of technology, the social influence on students, the support received, and the experience enjoyed [48]. Bearing in mind that there are no conclusive studies relating educational technologies with better learning results, what seems clear is that digital technology is necessary but not sufficient for the DT of HE [15, 23]. However, it has indeed been concluded that technology fuels innovation, and education technologies are a means to support the learning process, offering varied alternatives (e.g., digital pedagogies, adaptative learning, open technologies such as MOOCs, etc.). Adopting digital technologies demands and promotes the development of digital literacy, that is, the need for continuously acquiring and updating knowledge and skills to effectively use these new technologies for learning [22]. On the whole, it is undebatable that digital technologies are becoming fundamental for HEIs, and some of the main challenges they face, such as personalizing education on a large scale, would be unthinkable without them [73].

434

A. Rof et al.

2.2 The Importance of Digital Transformation DT has the potential to be used to build competitive advantages for universities [56], with DT strategies aimed at increasing revenues and productivity, creating new value and developing brand reputation [55, 71]. Nonetheless, and despite all the potential benefits, HEIs are still behind in DT in comparison to other sectors such as manufacturing, services, and commerce [66], representing an opportunity for the HEIs that most actively lead this DT. A combination of different factors, such as new student expectations, growing pressure to connect more with the labor market, the search of competitiveness, and fulfilment of the societal role of the university, are among the elements that make DT necessary in the HE sector. a. Meeting student expectations To meet student expectations beyond just providing good teaching, HEIs must start focusing on offering a global student experience, understood as the total interactions between the student and the HE institution during all the stages of the relationship, from before to after becoming a student, including engagement as alumni [27]. The pandemic introduced a new expectation, which is providing virtual experiences (e.g., digital student recruitment, teaching, graduation, and advancement without requiring an on-campus presence), which can be a key differentiator within HEIs [57] and has the purpose of meeting the evolving digital demands of students [32]. New technologies, and Artificial Intelligence (AI) in particular, can be used to predict and improve student satisfaction along the entire student journey, and provide a better understanding of student expectation and satisfaction patterns [27]. For example, a satisfaction study of medical students with their online learning during the emergency phase of COVID-19 found that educational processes must be learning and learner-centered, with students taking a leading and participatory role in the online teaching and learning processes, learning interactively, collaborating and sharing ideas with other students [34]. Figure 1 represents the change in the key success factors, from the perspective of an 18-year-old student who must choose an HEI. b. Bridging education and the labor market Universities have proven to be capable of transferring changes in scientific knowledge into course content. However, they are currently not applying changes in the skills demanded from the labor market into course content with the same effectiveness [19]. In this respect, COVID-19 accentuated the gap between the digital skills and competences of graduates and those required by industry, an aspect that should act as an incentive to increase university-business collaboration [54]. By including more digital skills development in curricula, HEIs could satisfy industry’s needs for better student readiness for the job market [54]. Fortunately, the COVID-19 shock changed the digital mentality of teachers, which in the future could be translated into exploring more flexible learning paths that include online learning, contributing to building student’s digital skills [70].

Digital Transformation in Higher Education … From

University quality

435 To

Degree certification

Certification of competences

Limited and proprietary teaching resources

Unlimited teaching resources (curation)

Rigid course design and teaching methods, and limited and fixed assessment methods

Flexible course design and learning and assessment methods

Attractive learning environment

Limited personalization, physical proximity, limited digital accessibility

Offering a student-centric personalized experience with digital proximity

High employability

Low support

High support, providing more services to increase studentsí employability and success

Limited cost

Limited options

More free options (e.g., MOOCs) and flexibility (e.g., subscription-based)

Faculty quality

Limited digital attitude and skills of faculty and expertise measured academically

Include expert and digital native profiles with high social impact

Technology availability

Basic technological infrastructure and technology integration with limited student experience

Sophisticate technology (e.g., through technology partnerships) to be able to always offer the student the best experience

Program quality

Fig. 1 Expected evolution of key critical success factors for students Source Authors’ own elaboration based on Rof et al. [69]

With the intention of better connecting with the demands of employers, the postsecondary education offer is changing, with the emergence of the so-called alternative credentials, also known as micro-credentials, digital badges, and industryrecognised certificates [42]. The term “alternative credentials” is relatively recent and there is still no commonly accepted definition, although a recent OECD working paper has pointed to the “credentials that are not recognised as standalone formal educational qualifications by relevant national education authorities” [42], p.9). Both HEIs and new entrants to the HE sector, such as EdTech companies, offer alternative credentials to help students acquire or upgrade skills, as well as accredit the skills they already have. This training and recognition helps students develop skills closer to the ones demanded by the labor market in a clear trend of moving from degrees to competence certificates. As reported by Class Central, in 2021 there were a total of 1,670 micro-credentials, with approximately 500 of them added during the year. Coursera is the biggest online provider, with 910 microcredentials units, followed by edX with 480 units, and FutureLearn with 180 [17]. Further confirming the attractiveness and disruption of education, Google has entered the micro-credentials market by offering Google Career Certificates, a selection of professional courses that train students on how to perform jobs demanded by the labor market. b. Evolving to be competitive In recent years, the HE sector has been faced with an increasingly competitive environment [62], with rising threats from powerful global competition and the trend of “free education”, with “digital” often being associated with “free of charge” [67]. However, the pandemic-accelerated DT process completely transformed the competitive landscape in HE by reducing mobility barriers between the two main

436

A. Rof et al.

types of existing competitors, namely traditional face-to-face universities and borndigital universities. The COVID-19 shock also propelled the growth of new entrants, such as the EdTechs (e.g., Coursera). By developing fully digital and innovative business models [69] based on a set of different resources and capabilities, such as databased teaching (e.g., adaptive teaching), these EdTech players are permanently transforming the market [65]. The EdTech sector is experiencing a boom and investors, especially venture capital companies, tripled the volume of investments in Europe and North America during 2021 [75]. Investments are not made solely by private initiative. China, for example, is strongly supporting Chinese EdTech startups that want to develop AI-based projects, increasing the number of new contestants in the HE sector [37]. These EdTechs start from scratch and have no legacy systems to protect, facilitating their ability to deploy innovative digitalized business models that include technologies such as learning analytics and artificial intelligence [65]. The end result of these combined forces is an entirely new configuration of the competitive landscape [69]. When comparing how COVID-19 has impacted the perception and importance of DT in a traditional HE organization versus a digital-born HE organization, [68] found that in both cases there is an accelerating effect of DT, although the magnitude of the expected impact is distinct. The traditional HE organization in Spain, a mid-sized public university with regional focus, had strong confidence in the value of its assets based on the physical campus. However, forced digitalization removed internal resistance and opened eyes to the possibilities of permanently extending to digital or hybrid models [84] in the future. Contrarily, the born-digital HE organization, a pioneering world leader in online education, noticed the pressure of this new competitive landscape to a greater extent. On losing differentiation from traditional universities that suddenly went online, and vis-à-vis the direct impact of EdTechs’ innovative digitalized business models, the only possible path they could contemplate was a radical innovation in the current business model [84] and migration towards a customized multimode learning strategy [70]. Figure 2 structures the impact of DT for HEIs for the three types of HEIs analyzed, namely face-to-face HEIs, born-digital HEIs, and EdTechs, for each of the elements of the DT concept stylized by [28]. d. Fulfilling the societal role of higher education Digital technologies will be fundamental in helping to achieve the goal of ensuring universal access to knowledge [73]. According to estimates from the UNESCO Institute for Statistics, by 2030 there will be 377.4 million students globally enrolled in HEIs. Online learning will be needed to meet this demand, mainly concentrated in emerging economies, because providing access to digital education can be a sustainable alternative to only relying in building physical campuses [73]. DT can make a globalized education possible if full online programs are expanded, moving closer to SDG target 4.3 [79], which seeks equal access for women and men to affordable tertiary education [46]. Beyond technological evolution, DT in industry also throws up many social, cultural and economic challenges, such as companies’ need to reskill their employees with digital skills, a task that the HE sector is trying to take on as part of

Digital Transformation in Higher Education …

Face-to-face HEIs

437

Born-digital HEIs

EdTechs

Innovative use of digital technologies

Semi-attendance / online Everything from the mobile

Student identification / authorship Personalization, mobile-first mentality

Flexible online training / studentís own pace Learning analytics and artificial intelligence

Strategic leverage of resources and capabilities

Continuous training in new digital capabilities. Continuous investments in digital technologies

Artificial intelligence / data analytics skills Continuous investments in digital technologies

Huge financial resources Digital mindset AI/data-based skills Social media skills

Economic-driven outcome

ìPaperlessî / cost savings Automation / no errors Customer-centricity / UX

Scalable automation Student lifelong learning Student digital experience (SX)

Student digital experience Significant cost advantage versus traditional HEIs

Capability-driven outcome

Technological model Carrying out pilots New organization Networking Business intelligence

Personalization (BMI) Job market-orientation Multimodality Plug in technologies Digital mindset

Short professional courses Job services High adaptability Startup mentality Digital competitive advantage

Reinforcing an existing value proposition (digitalization)

Extension to hybrid models

Mobile-first

Individualization

Redefining value proposition (DT)

Take advantage of the hybrid model and/or the 100% online model to attract new segments.

Envisioned Business Model based on a customized multimode online learning strategy

Individual career certificates, not degrees

Fig. 2 Impact of DT by types of HEIs

its public service [76]. Furthermore, in pursuit of its social missions, HEIs contribute to the digital development of regions and to the digital evolution of society while trying to satisfy the demand for “digital skills” from the labor market [76] and to reduce the risk of inequality due to lack of digital literacy [49]. DT therefore has a high potential to contribute to society, expanding HE missions beyond teaching and research [18].

2.3 Barriers to Digital Transformation in Higher Education DT in HE is not only about technology; it is basically and more about strategy, culture, and change management. The starting point of this change is understanding the organization’s digital maturity and its readiness for DT, as will be analyzed in the following sections. a. DT requires digital willingness and capabilities New technologies are emerging with more frequency, pushing HEIs to adopt them due to two types of pressures. First, the internal pressure of their own stakeholders, especially students who demand a more digital experience; and second, the competitive pressure of competitors, especially the newly entered EdTech companies. In this line, COVID-19 has caused a hyper acceleration of digital capabilities development among faculty members and students, including changes in the pedagogical models

438

A. Rof et al.

to adjust to the situation. The expectation is that the new hybrid models will be maintained in the future because of their flexibility and capacity to adapt to the changing environment [59]. If DT is an ongoing process, and digital technologies are the engine of this transformation, all the relevant HE stakeholders must continuously develop their digital skills. This development is especially important for educators because of the specific role they have in online teaching. However, given that DT affects the entire organization, the acquisition of digital competences must include all the relevant stakeholders, including administrative staff, researchers, and students. More particularly, these new technology deployments (e.g., new digital systems for student identification), the new digitalization of processes (e.g., self-registration), and the digitalization of resources (e.g., the digital library) are ongoing, requiring strategies to facilitate their new adoption by students, employees, and other users [22]. Table 1 presents the main outstanding digital trends and how they can be applied to HEIs. b. DT is a change process The digitalization of HE is much more than simply moving offline courses to the online environment. It demands real innovation such as pedagogical innovation if HE institutions really want to benefit from their DT [39]. Primarily, however, DT is a “fundament change process” that seeks to “radically improve an entity” [28]. These potential improvements will be achieved through execution, and this is where part of the difficulty of DT lies, in the “how” of the DT implementation, since this transformation implies a significant organizational change subject to barriers and risks which cannot be overcome by simply copying processes that work in other HEIs [81]. The “how to” of DT implementation in HEIs raises many challenges at different levels. For example, how should the DT process be carried out?; how will the learning value proposition—customization or personalization—be impacted?; what organizational changes are needed?; how will stakeholders be impacted?; how will the business model change?; and how will the risk of inequality be managed? [70]. To address these relevant questions, following proper benchmarking processes by means of understanding and reflecting on DT practices that work in other HEIs can be a good starting point to manage the needed DT change [47, 51]. c. DT is a cultural transformation In the context of DT during the COVID-19 lockdown, extant research shows that the organizations with higher digital maturity tended to be more flexible, and the ones with less digital maturity were more fragile [24]. But although the pandemic caused a drastic shift in the scale of change [7], with the HE sector proving high adaptive capacity [53], it is still not clear if this cultural digitalization transformation process will be permanent [70]. There is a common resistance to change. For example, research in traditional universities before the pandemic showed that face-to-face educational technology used for teaching and learning was limited, with Learning Management Systems perceived as the most useful tool [11]. In another

Digital Transformation in Higher Education …

439

Table 1 Outstanding digital trends and application to higher education Digital trend

Definition

Examples of application in higher education

Artificial intelligence (AI) AI is “a system’s ability to – Natural language interpret external data correctly, understanding (e.g., to to learn from such data, and to participate in international use those learnings to achieve programs in a context where specific goals and tasks through there is a language barrier); flexible adaptation” [41, p. 17] machine learning (e.g., when the machine learns from One of the big promises of AI is student interaction and results to enhance the quality of and adapts the content); speech education by providing and face recognition (e.g., personalization at scale [12] student identification in virtual exams); and holograms (e.g., a virtual avatar acting as a tutor), among others [12] – As an example, the Georgia Institute of Technology has used an AI-based teaching assistant called “Jill Watson” to answer students, without them knowing that they are talking to a machine [37] Machine learning

Machine learning consists in algorithms that learn to recognize complex patterns from massive data [33]

– Grading students automatically (avoiding human biases); improving student retention (e.g., by identifying and managing “at risk” students early on); predicting student performance (offering recommendations based on student behavior and patterns); and testing students (and automatically sending results and feedback to the different stakeholders, such as teachers, students and parents) [33]

Cloud computing

Virtualization of intellectual resources and software applications, propitiating information exchange via the Internet [56]

– Used to deliver educational services in a simple, fast and secure way, while optimizing the required technological resources [56] – Favoring a ubiquitous learning mode, making learning possible any time and in any place [70] (continued)

440

A. Rof et al.

Table 1 (continued) Digital trend

Definition

Examples of application in higher education

Internet of Things (IoT)

IoT connects different hardware – IoT allows automated devices, operating systems, and administrative activities (e.g., browsers, offering new monitoring attendance), opportunities for personalized providing an appropriate and more engaging interactions learning environment (e.g., with students [56] connecting to the campus from many different devices), and With IoT, the delivery of any innovating in how students program can be executed interact to learn (e.g., while intelligently and remotely [56] driving) – As an example, Xian University has installed facial recognition cameras supported by AI and has made it necessary to pass facial scanners to register attendance or to enter a particular area, such as the library [37]

Big data

“Approaches that enable – The potential benefits of organizations to analyze any real-time data could help HEIs and all data with greater speed to improve educational and agility. Data sets may be learning decisions, by large or small, legacy sources or predicting and stimulating new sources, structured or students’ behavior [56] – “Data-based” teaching unstructured data” [20] improvement, instead of Techniques to gain relevant, depending solely on the applicable and accurate results teacher’s intuition [70] in real time [56] – UOC University developed an app that analyses student’s interaction in online forums, producing reports for teachers using data analytics. The results showed a 6% decrease in the dropout rate and an increase by almost one point in the average grade of students [80] (continued)

Digital Transformation in Higher Education …

441

Table 1 (continued) Digital trend

Definition

Examples of application in higher education

Blockchain

Due to blockchain technology – Potential applications of records are impossible to be blockchain technology in the altered, which can be very HE sector include sharing useful in the educational academic records and student context, for example, by making achievements, verifying the it possible to collect all courses, academic credentials and and extracurricular or certificates submitted by professional experience of a students when enrolling on a candidate [37] new program [64], and making it easy for potential employers to verify the authenticity of the issued micro-credential certificates [63]

study focused on the COVID-19 context in Saudi Arabia, the most important challenge for students to adopt DT was the fear of change, and for teachers it was also fear of change, lack of experience, and privacy concerns [6]. d. DT is strategic DT is a fundamental change process aimed at radically improving an organization [28], which affects all areas of the entity, including strategy, leadership, talent management, technology, and organizational structure. As such a radical change, DT requires a fully committed leadership and will be subject to major resistances in the organization. Any DT project must be approached from a global perspective of the organization, and it would be a mistake to think that the required decisions are solely or mainly technological. In this line, a research study in the HE sector of UAE has found that an increase in team integration is associated with greater successful digital transformation [9].

3 Planning Digital Transformation This section offers seven ordered recommendations for planning digital transformation in HEIs.

3.1 Establishing a Realistic Diagnosis Knowing where a university is in its DT readiness level is the first step to start this transformation. To this effect, there is a wide spectrum of tools that can help HEIs in their innovation and DT processes, among which we can highlight HEInnovate [31].

442

A. Rof et al.

Promoted by the European Commission in collaboration with the OECD, HEInnovate is a self-assessment tool that helps to identify, prioritize, and plan actions in eight key areas: leadership and governance, organizational capacity (funding, people, and incentives), entrepreneurship teaching and learning, preparing and supporting entrepreneurs, DT and capability, knowledge exchange and collaboration, the internationalised institution, and measurement and impact. Specifically, the HEInnovate digital transformation self-diagnosis tool allows a particular HE organization to reflect on its digital capacity, defined as the “the ability to integrate, optimise and transform digital technologies to support innovation and entrepreneurship” [31]. Self-assessments are intended to help HEIs explore their innovative potential and prioritize and plan for improvement.

3.1.1

Identifying Barriers

Extant research has identified 22 barriers to the effective digital transformation of HE institutions, categorized in 9 areas, namely, vision, strategy and policy, resources, leadership, digital skill and knowledge, technology, adaptability, resistance to change, and government and economics [1]. Findings state that DT barriers depend on the context of a particular HEI, which means that identifying, first of all, particular barriers, and then managing these barriers, is crucial for a successful DT [2]. Barriers to digital education must be analysed both at individual-level (e.g., teachers) and institutional-level (e.g., lack of resources), and can be derived from many factors such as the digital divide (e.g., lack of training for teachers), resistances to change, and inequalities in student access [49]. As an example, teachers’ lack of digital competences is one of the main barriers when introducing new learning technologies. It has been found that 74% of teachers positively appreciated the introduction of digital tools for education, but only 31% felt they were properly trained [72].

3.1.2

Identifying Levers

The European Framework for Digitally Competent Educational Organisations (DigCompOrg) can be used by educational organizations to guide a process of self-reflection on their progress towards comprehensive integration and effective deployment of digital learning technologies [21]. Digital learning technologies act as a key enabler for educational organizations, whose integration requires innovation at pedagogical, technological, and organizational levels. Innovation driven by digitalization at the level of leadership and governance practices, teaching and learning practices, professional development, assessment practices, content and curricula, collaboration and networking, and infrastructure, act as a lever for the DT of the HE, according to the DigCompOrg framework [21].

Digital Transformation in Higher Education …

443

3.2 Innovating the Mission for the New Digital Environment There are calls for a renewed vision of HE and its role in the world, with a priority to adopt the sustainable development goals in all HE activity, i.e. teaching, research, and service functions [10]. Sustainable Development Goal 4 is to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” [79], and its target 4.3 states that “By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university” [79]. One of the big challenges of the HE sector is how to leverage DT in pursuit of the HE missions [5, 13], especially those concerning the digital divide that is affecting HE [53], understood as the inequality in access to the technical resources and different levels of digital skills necessary for digital learning [49]. Since the inequality problem is global, to address it HEIs need to partner with other similar institutions around the world, establishing an equitable global partnership of institutions that represent the global diversity [29]. The learnings from the COVID-19 forced digitalization should provide HE leaders with an opportunity to develop inclusive and long-term visions for digital education through collaboration [49]. In this line, a research study on how the forced DT was experienced among 85 HE leaders in 24 countries provides some recommendations for closing the digital divide, such as boosting collaborative learning (e.g., between students), collaborating on an international basis (as inequality is a global problem), and cooperation between HEIs in terms of resource sharing and open pedagogy. Voices from the EdTech sector have noted that digital education enhances access, learning, and collaboration [49], which is a way for HEIs to be better fulfilling their mission.

3.3 Strengthening the Business Mentality of Higher Education Institutions The challenges derived from this DT are huge, requiring strong leadership and solid ability in implementing business practices, one of the weaknesses associated with HE [62]. Managing DT challenges requires a greater orientation of HE towards adopting business practices, especially towards envisioning and preparing the organization to implement a new business model that responds to the new competitive landscape, the new technological context, and the expectations of students [69]. DT will transform the HE sector and its executives will have to lead the innovation of the business model to position HEIs as relevant players, making important decisions about the change model necessary to transform their business model [84].

444

A. Rof et al.

3.4 Becoming a Student-Centric Organization: Multimodality and Personalized Learning In this more competitive and digitalized context, the student-centric perspective (as the main target, client, and user) must be adopted [62]. This forces HEIs to reconsider who students are and what their new needs, expectations, and preferences in the current changed digital context are. A more student-centric lifelong learning relationship model has been found to be a key factor for the student to experience an increasingly demanded excellent digital experience [70].This student-centric approach fits very well in the digital environment and is in line with a constructivist view [4] since e-learning is more learner-centric and learner-personalized [58]. To make education truly continuous and to establish a lifelong learning relationship with students, it is essential that HEIs develop portfolios of additional courses [45]. An example of course development with a student-centric approach is the growth of micro-credentials and the paradigm shift that they represent, since they convert HEIs from degree-centric (undergraduate and graduate) to lifetime of self-directed student-centric learning [63]. As a result of this major student-orientation and aligned with students’ evolving digital demands [32], HEIs are expected to leverage digital educational technologies, develop new learning value propositions based on multimodality, and allow customized and personalized learning [70]. The concept of multimode digital learning can be defined as “the matrix of digital methods, forms, and tools, including direct instruction via synchronous video conferences and asynchronous videos, group-project-based learning, and online exams, that can be used for digital or digitally enhanced learning” [70, p. 125]. This matrix of multimode options allow HEIs and students to respectively offer and choose from a very large set of learning combinations, which will eventually lead to HEIs offering a customized learning value proposition that will change the what, when, how, and where of the learning journey [70]. Digital technologies have a significant role to achieve this greater studentcentricity, offering possibilities for personalized learning, customized learning, and hybrid models, depending on who controls the personalization, the technologies or the students. Figure 3 compares the nature and differences between personalized and customized learning. As an example of customer-centric initiatives, the Knewton adaptive-learning technology collects user behavioral learning data and defines different learning types. By leveraging this database through complex algorithms, the HEI can define individual learning packages where both the content and pace of learning are continuously adapted [65]. As another example, Arizona State University is using an algorithmbased solution, named eAdvisor, designed to support students through their studies to achieve their academic and professional goals. All student behavioral data is recorded and a suite of online and interactive tools is provided [65], gradually converting HEIs into data-driven organizations [14].

Digital Transformation in Higher Education …

445

Fig. 3 Personalized versus customized learning

3.5 Engaging the Main Stakeholders While a student-centric strategy is a necessary condition, it further requires the collaboration of all the key stakeholders for its success [68]. Stakeholders are defined as ‘groups and individuals that can affect, or are affected by, the accomplishment of organizational purpose’ [26], p. 25). Even if HE’s main stakeholders are students, teaching and research staff, and administration and services personal, the reality is that an expanded view of this concept is increasingly gaining in importance, including companies and society and many other participants that are involved in the DT process, such as educational technology companies. In this line, some investigations [74] have emphasized the importance for both public and private universities of carefully managing and building long-term relationships with key stakeholders, which can be grouped into categories: internal stakeholders (current students, employees, management team); main external stakeholders (alumni, prospective students, parents, donors, organisms such as the Ministry of Education, etc.); and secondary external stakeholders (the media, the community, local authorities, competitors, employers, the government, and high schools).

446

A. Rof et al.

3.6 Overcoming DT Barriers Overcoming DT barriers is fundamental to progress in the transformation. Having identified the key barriers to DT, and the relationships and hierarchy between them, researchers have proposed a four-phase strategy to improve the success of implementing DT: i) government support to overcome barriers related to a lack of public sector vision, planning, and policies; ii) strategic management to overcome barriers related to a lack of planning, insufficient funds, lack of commitment, and reluctance to move out of the comfort zone; iii) functional management to overcome barriers related to a lack of human resources or expertise in digital transformation; and iv) operational management to overcome the barriers related to a lack of shared vision when translating strategy into actions, including a lack of institutional policy, the difficulty to keep up with technological changes, and embedding ICT into educational systems [2]. These phases can be used to develop better strategies to diminish or eliminate the identified barriers and successfully execute the DT plans. As an example of another methodology that can be used to lead DT processes in the context of HE, there is the case of the transformation of the Universidad Francisco de Vitoria into a data-driven organisation. Based on a qualitative methodology, barriers and facilitators were identified and actions to overcome these barriers defined, leading to the proposal of a four-phase plan to manage this DT: diagnostic, preparation, implementation, and improvements and optimisation [14].

3.7 Adopting Change Management Methodologies DT is about changes driven by technology, but its impact influences organizations as a whole [30], producing changes at different organization levels, such as business operations, products and processes, organization structures and management [55], innovation processes, and business models [8]. In this way, DT must be understood as a complex and strategic process that embraces the entire organization [24], and as necessary for HEIs to develop capabilities to lead change [47].

4 Implementing Digital Transformation: A Roadmap Following the previous planning, we propose a five-stage roadmap to successfully implement the DT process in an HEI (Fig. 4). The first stage is to make a realistic diagnosis of the current digital position of the HE organization under study, using systematic tools for internal and external analysis and the scanning of digital possibilities. Second, the digital initiatives or digitalization projects with the greatest potential to fit with the diagnosis made are assessed and selected. Some of the projects can be aimed at achieving economic outcomes by enhancing the existing value proposition

Digital Transformation in Higher Education …

447

and thus be a digitalization change, while others may be aimed at achieving different capabilities to the current ones, leading to a redefinition of the value proposition, thus involving a digital transformation. Some examples of this typology of projects developed in the higher education sector have already been shown in Fig. 2. The third stage focuses on testing and validating the chosen digital projects to establish their fit with the market and internally. Based on this validation process, a decision is made as to which transformative digital initiatives will be fully implemented. The final stage focuses on defining and implementing the plan, with the use of a systematic change management methodology as a good practice (e.g., [47]. While any major digitalization or DT process needs to be comprehensive, from diagnosis to implementation, it must be subsequently repeated whenever new inputs are available, such as new technological possibilities, internal readiness, barrier reduction, or renewed leadership. To this effect, the digital transformation process can be understood as a continuum of waves of permanent change and transformation. The execution and implementation phase of the changes is key to advancing in the digital transformation, and it can fuel new waves of future changes based on the learning and results obtained. This wave view is visualized in Fig. 4. For each stage, the main resources and capabilities that are strategically leveraged are detailed [28]: human resources, financial resources [50], digital capabilities [16], and dynamic capabilities [82].

Innovative use of digital technologies Strategy Human Marketing IT Resources Operations

DIAGNOSIS

SELECTION

TESTING & VALIDATING

DECISION

IMPLEMENTATION

Discovery of the potential of different digital technologies

Selection of digital technologies

Experimenting with selected digital technologies

What digital technologies to use and how

Implementation of selected digital technologies

Financial resources Knowledge resources Digital capabilities

Dynamic capabilities

Orientation knowledge, reference knowledge Information, communication

Scanning for technological trends Screening of digital competitors’ trends Sensing customer-centric trend

Explanatory knowledge

Operational knowledge

Explanatory knowledge

Operational knowledge

Information, communication, production

Information, communication

Information, communication, production

Information, communication, production, safety

Analyzing scouted signals Interpreting digital future scenarios Formulating digital strategies

Long-term digital vision Entrepreneurial and digital mindset Creating minimum viable products Joining the digital ecosystem Considering a lean start-up Multiple external partners methodology Exploiting new ecosystem capabilities

Balancing internal & external options Scaling up innovative business models Speed of execution Identifying digital workforce maturity

Fig. 4 Managing organizational waves of digital transformation in a higher education institution

448

A. Rof et al.

5 Conclusions The process of transformation of the HE sector is unstoppable. A combination of forces is at play, including changes in student expectations, new digital technologies, new EdTech competitors, and new labor market expectations, all combined to accelerate the transformation of the sector. To continue operating in this context, HEIs need to develop their change management and business capabilities and to establish a roadmap for digital transformation. The digital transformation process can be understood as a continuum of waves of change and permanent transformation, each wave entailing a process with analysis, selection, test, decision, and implementation. This chapter contributes with an extensive literature review of the state of the art of digital transformation in HEIs, analyzing trends, barriers, best practices, and recommendations for change. The main originality offered is the proposal of a roadmap that is useful for understanding the strategic and continuing process of DT and to assist HEIs in their DT to enhance their chance of success in this endeavor. Further research exploring how the DT roadmap proposed fits the reality of DT and the degree to which it enhances DT success would be interesting, as would further exploring how the competitive landscape in the sector is taking shape and the extent to which some of the new technologies such as AI fulfill the high expectations raised. Other fields of interest are the challenge of personalized learning and the role of teachers in a new context of algorithms, robots, and avatars.

References 1. Aditya BR, Ferdiana R, Kusumawardani SS (2021) Categories for barriers to digital transformation in higher education: an analysis based on literature. Int J Inf Educ Technol 11(12):658–664. https://doi.org/10.18178/IJIET.2021.11.12.1578 2. Aditya BR, Ferdiana R, Kusumawardani SS (2021) Barriers to digital transformation in higher education: an interpretive structural modeling approach. Int J Innov Technol Manag 18(5). https://doi.org/10.1142/S0219877021500243/ASSET/IMAGES/LARGE/S02 19877021500243FIGF4.JPEG 3. Agasisti T, Frattini F, Soncin M (2020) Digital innovation in times of emergency: reactions from a school of management in Italy. Sustainability (Switzerland) 12(24):1–17 4. Albirini A (2007) The crisis of educational technology, and the prospect of reinventing education. Educ Technol Soc 10(1):227–236 5. Alenezi M (2021) Deep dive into digital transformation in higher education institutions. Educ Sci 11(12):770. https://doi.org/10.3390/EDUCSCI11120770 6. Alhubaishy A, Aljuhani A (2021) The challenges of instructors’ and students’ attitudes in digital transformation: a case study of Saudi Universities. Educ Inf Technol 26(4):4647–4662. https://doi.org/10.1007/S10639-021-10491-6/TABLES/10 7. Alvesson M, Sveningsson S (2015) Changing organizational culture: cultural change work in progress, second edition. In: Changing organizational culture: cultural change work in progress, 2nd edn. Taylor and Francis. https://doi.org/10.4324/9781315688404 8. Appio FP, Frattini F, Messeni A, Politecnico Di Bari P, Neirotti P (2018) Digital transformation and innovation management: opening up the black box motivation for the special issue. http://www.radma.net/wp-content/uploads/2018/04/CFP-JPIM-SI_Digital-Inn ovation_March-2018_.pdf

Digital Transformation in Higher Education …

449

9. Bettayeb A, Al Marri K (2021) Does team integration effect the successful implementation of digital transformation?, pp 1–5. https://doi.org/10.1145/3485557.3485583 10. Blessinger P, Sengupta E, Makhanya MS (2021) Unlocking the potential of higher education by serving the common good. Prom Higher Educ, 337–343. https://doi.org/10.1007/978-3-03067245-4_51 11. Bond M, Marín VI, Dolch C, Bedenlier S, Zawacki-Richter O (2018) Digital transformation in German higher education: student and teacher perceptions and usage of digital media. Int J Educ Technol High Educ 15(1):48 12. Cantú-Ortiz FJ, Galeano Sánchez N, Garrido L, Terashima-Marin H, Brena RF (2020) An artificial intelligence educational strategy for the digital transformation. Int J Interact Des Manuf 14(4):1195–1209. https://doi.org/10.1007/S12008-020-00702-8/FIGURES/4 13. Carayannis EG, Grigoroudis E, Stamati D, Valvi T (2021) Social business model innovation: a quadruple/quintuple helix-based social innovation ecosystem. IEEE Trans Eng Manag 68(1):235–248. https://doi.org/10.1109/TEM.2019.2914408 14. Carnicero I, González-Gaya C, Rosales VF (2021) The transformation process of the university into a data driven organisation and advantages it brings: qualitative case study. Sustainability (Switzerland) 13(22):12611. https://doi.org/10.3390/SU132212611 15. Castañeda L, Selwyn N (2018) More than tools? making sense of the ongoing digitizations of higher education. Int J Educ Technol High Educ 15(1):1–10. https://doi.org/10.1186/S41239018-0109-Y 16. Center for Digital Dannelse (2022) The Digital Competence Wheel. https://digital-competenc e.eu/ 17. Class Central (2022) By The Numbers: MOOCs in 2021—Class Central. https://www.classc entral.com/report/mooc-stats-2021/ 18. Compagnucci L, Spigarelli F (2020) The third mission of the university: a systematic literature review on potentials and constraints. Technol Forecast Soc Chang 161:120284. https://doi.org/ 10.1016/j.techfore.2020.120284 19. van Damme D (2021) Transforming universities for a sustainable future. Prom High Educ, 431–438. https://doi.org/10.1007/978-3-030-67245-4_64 20. Davenport TH, Bean R (2019) Big data has been a big success. https://www.tomdavenport. com/big-data-has-been-a-big-success/ 21. European Commission (2022) DigCompOrg Framework | EU Science Hub. https://ec.europa. eu/jrc/en/digcomporg/framework 22. Farias-Gaytan S, Aguaded I, Ramirez-Montoya MS (2021). Transformation and digital literacy: systematic literature mapping. Educ Inf Technol, 1–21. https://doi.org/10.1007/S10639-02110624-X/FIGURES/8 23. Fischer G, Lundin J, Lindberg JO (2020) Rethinking and reinventing learning, education and collaboration in the digital age—from creating technologies to transforming cultures. Int J Inf Learn Technol 37(5):241–252. https://doi.org/10.1108/IJILT-04-2020-0051/FULL/XML 24. Fletcher G, Griffiths M (2020) Digital transformation during a lockdown. Int J Inf Manag 55:102185. https://doi.org/10.1016/J.IJINFOMGT.2020.102185 25. Fligstein N, McAdam D (2011) Toward a General theory of strategic action fields. Soc Theory 29(1):1–26. https://doi.org/10.1111/j.1467-9558.2010.01385.x 26. Freeman RE (1984) Strategic management: a stakeholder approach. Pitman Publishing Company. 27. Gallastegui LMG, Forradellas RFR (2021) Business methodology for the application in university environments of predictive machine learning models based on an ethical taxonomy of the student’s digital twin. Adm Sci 11(4):118. https://doi.org/10.3390/ADMSCI11040118 28. Gong C, Ribiere V (2021) Developing a unified definition of digital transformation. Technovation 102:102217 29. Habib A (2021) Knowledge and partnerships for sustainability and equality. Prom High Educ, 377–381. https://doi.org/10.1007/978-3-030-67245-4_56 30. Hausberg JP, Liere-Netheler K, Packmohr S, Pakura S, Vogelsang K (2019) Research streams on digital transformation from a holistic business perspective: a systematic literature review

450

31. 32.

33.

34.

35. 36.

37. 38. 39.

40.

41.

42. 43. 44. 45. 46.

47. 48. 49.

50. 51.

A. Rof et al. and citation network analysis. J Bus Econ 89(8):931–963. https://doi.org/10.1007/S11573-01900956-Z HEInnovate (2021). https://www.heinnovate.eu/en Hervás-Gómez C, Díaz-Noguera MD, De la Calle-Cabrera AM, Guijarro-Cordobés O (2021) Perceptions of university students towards digital transformation during the pandemic. Educ Sci 11(11):738. https://doi.org/10.3390/EDUCSCI11110738 Iatrellis O, Savvas IK, Kameas A, Fitsilis P (2020) Integrated learning pathways in higher education: a framework enhanced with machine learning and semantics. Educ Inf Technol 25(4):3109–3129. https://doi.org/10.1007/S10639-020-10105-7/FIGURES/10 Jiménez-Bucarey C, Acevedo-Duque Á, Müller-Pérez S, Aguilar-Gallardo L, Mora-Moscoso M, Vargas EC (2021) Student’s satisfaction of the quality of online learning in higher education: an empirical study. Sustainability (Switzerland) 13(21):11960. https://doi.org/10.3390/SU1321 11960 Kaplan A (2020) Call for chapters: digital transformation and disruption of higher education. https://escp.eu/news/digital-transformation-and-disruption-higher-education Kaplan A (2020) Universities, be aware: start-ups strip away your glory - EFMD global blog. https://blog.efmdglobal.org/2020/05/11/universities-be-aware-start-ups-strip-awayyour-glory/ Kaplan A (2021) Higher education at the crossroads of disruption: the university of the 21st century. Emerald Publishing Limited. Kaplan A (ed) (2022) Digital transformation and disruption of higher education. Cambridge University Press, Cambridge Kaplan A (2022) Nothing is constant except change: academia’s digital transformation. in digital transformation and disruption of higher education, vol. 1. Cambridge University Press, Cambridge Kaplan A, Haenlein M (2016) Higher education and the digital revolution: about MOOCs, SPOCs, social media, and the Cookie Monster. Bus Horiz 59(4):441–450. https://doi.org/10. 1016/j.bushor.2016.03.008 Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62(1):15–25. https://doi.org/10.1016/J.BUSHOR.2018.08.004 Kato S, Galán-Muros V, Weko T (2020) The emergence of alternative credentials (No. 216; OECD Education Working Papers). https://doi.org/10.1787/b741f39e-en Katsu E, Katsu E (2021) Universities in an era of post-corona “New Order.” Prom High Educ, 239–243. https://doi.org/10.1007/978-3-030-67245-4_37 Khalid J, Ram BR, Soliman M, Ali AJ, Khaleel M, Islam MS (2018) Promising digital university: a pivotal need for higher education transformation. Int J Manag Educ 12(3):264 Klyachko T, Mau V (2021) Modern challenges for universities. Prom High Educ, 233–237. https://doi.org/10.1007/978-3-030-67245-4_36 Kohl K, Hopkins C (2021) Aligning higher education’s promise of well-being with sustainability as a purpose of education. Prom High Educ, 355–361. https://doi.org/10.1007/978-3030-67245-4_53 Kotter J (1996) Leading change. Harvard Business School Press Lamagna CZ, Khan MH (2021) Is the pandemic leading to a paradigm shift in higher education? Prom High Educ, 263–268. https://doi.org/10.1007/978-3-030-67245-4_41 Laufer M, Leiser A, Deacon B, Perrin de Brichambaut P, Fecher B, Kobsda C, Hesse F (2021) Digital higher education: a divider or bridge builder? leadership perspectives on edtech in a COVID-19 reality. Int J Educ Technol High Educ 18(1):1–17. https://doi.org/10.1186/S41239021-00287-6 Leidig T (2001) L 3-towards an open learning environment. ACM J Educ Res Comput 1(1):7 Lewin K (1997) Frontiers in group dynamics (Originally published in 1947). In: Resolving social conflicts and field theory in social science. American Psychological Association, pp 301–336

Digital Transformation in Higher Education …

451

52. Makhanya MS (2021) The constant of change: remaining relevant in 21st century higher education. Prom High Educ, 221–226. https://doi.org/10.1007/978-3-030-67245-4_34 53. Marinoni G, van’t Land H, Jensen TT (2020) COVID-19: higher education challenges and responses - IAU. https://www.iau-aiu.net/COVID-19-Higher-Education-challenges-andresponses 54. Márquez-Ramos L (2021) Does digitalization in higher education help to bridge the gap between academia and industry? an application to COVID-19. Ind High Educ 35(6):630–637. https://doi.org/10.1177/0950422221989190 55. Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5):339– 343. https://doi.org/10.1007/S12599-015-0401-5 56. Hashim MAM, Tlemsani I, Matthews R (2021) Higher education strategy in digital transformation. Educ Inf Technol, 1–25. https://doi.org/10.1007/S10639-021-10739-1/FIGURES/5 57. Morgan G, Lowendahl J-M, Thayer T-L, Yanckello R, Sheehan T (2021) Top Technology Trends Impacting Higher Education in 2021. Gartner. https://www.gartner.com/en/doc/742 584-top-technology-trends-impacting-higher-education-in-2021 58. Nguyen QLHTT, Nguyen PT, Huynh VDB (2019) Roles of e-learning in higher education. J Crit Rev 6(4):7–13 59. Núñez-Canal M, de Obesso M, de las M, Pérez-Rivero CA (2022) New challenges in higher education: a study of the digital competence of educators in Covid times. Technol Forecast Social Change 174. https://doi.org/10.1016/J.TECHFORE.2021.121270 60. Pham H, Tran QN, La GL, Doan HM, Vu TD (2021) Readiness for digital transformation of higher education in the Covid-19 context: The dataset of Vietnam’s students. Data Brief 39:107482. https://doi.org/10.1016/J.DIB.2021.107482 61. Posselt T, Abdelkafi N, Fischer L, Tangour C (2018) Opportunities and challenges of Higher Education institutions in Europe: an analysis from a business model perspective. High Educ Q, hequ.12192 62. Pucciarelli F, Kaplan A (2016) Competition and strategy in higher education: managing complexity and uncertainty. Bus Horiz 59(3):311–320 63. Ralston SJ (2020) Higher education’s microcredentialing craze: a postdigital-deweyan critique. Postdigital Sci Educ 3(1):83–101. https://doi.org/10.1007/S42438-020-00121-8 64. Reis-Marques Carlos, Figueiredo Ronnie, de Neto MC (2021) Applications of blockchain technology to higher education arena: a bibliometric analysis. Eur J Invest Health Psychol Educ 11(4):1406–1421. https://doi.org/10.3390/ejihpe11040101 65. Renz A, Hilbig R (2020) Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies. Int J Educ Technol High Educ 17(1):14 66. Rodríguez-Abitia G, Bribiesca-Correa G (2021) Assessing digital transformation in universities. Future Internet 13(2):1–17. https://doi.org/10.3390/FI13020052 67. Rof A, Bikfalvi A, Marques P (2020) Digital transformation for business model innovation in higher education: overcoming the tensions. Sustainability 12(12):4980 68. Rof A, Bikfalvi A, Marques P (2021) From heaven to hell, the struggle of Higher Education Institutions in the midst of COVID-19. In: EDULEARN 2021 Proceedings, vol 1, pp 3014– 3021. https://doi.org/10.21125/EDULEARN.2021.0641 69. Rof A, Bikfalvi A, Marques P (2022) Born-digital universities: facing the new competitive landscape. In: Kaplan A (ed) Digital Transformation and disruption of higher education. Cambridge University Press, Cambridge, pp 269–288 70. Rof A, Bikfalvi A, Marques P (2022) Pandemic-accelerated digital transformation of a born digital Higher Education Institution: towards a customized multimode learning strategy. Educ Technol Soc 25(1):124–141 71. Shaughnessy H (2018) Creating digital transformation: strategies and steps. Strategy Leadership 46(2):19–25. https://doi.org/10.1108/SL-12-2017-0126/FULL/XML 72. Shurygin V, Ryskaliyeva R, Dolzhich E, Dmitrichenkova S, Ilyin A (2021). Transformation of teacher training in a rapidly evolving digital environment. Educ Inf Technol, 1–20. https://doi. org/10.1007/S10639-021-10749-Z/FIGURES/2

452

A. Rof et al.

73. Sigalés C (2021) The role of digital technologies in the new present and future of higher education. Prom High Educ, 417–422. https://doi.org/10.1007/978-3-030-67245-4_62 74. Slabá M (2015). Stakeholder groups of public and private universities in the Czech Republic - identification, categorization and prioritization. Rev Econ Perspect. https://doi.org/10.1515/ revecp-2015-0022 75. Spence R (2022) European, North American edtech startups see funding triple in 2021 | TechCrunch. TechCrunch. https://techcrunch.com/2022/01/27/european-north-american-edt ech-startups-see-funding-triple-in-2021/ 76. Teixeira AF, Gonçalves MJA, Taylor M de LM (2021). How higher education institutions are driving to digital transformation: a case study. Educ Sci 11(10). https://doi.org/10.3390/EDU CSCI11100636 77. Jensen TT, Marinoni G, van’t Land H (2022). The second IAU global survey report on the impact of COVID-19 - IAU. International Association of Universities. https://www.iau-aiu. net/The-Second-IAU-Global-Survey-Report-on-the-Impact-of-COVID-19 78. UNESCO (2022) Distance learning solutions. https://en.unesco.org/covid19/educationres ponse/solutions 79. United Nations (2022) Goal 4 (sustainable development goals), Department of Economic and Social Affairs. Sustainable Development. https://sdgs.un.org/goals/goal4 80. UOC (2022) Big data improves university students’ performance. https://www.uoc.edu/portal/ en/news/actualitat/2022/030-elearning-bigdata-research.html 81. Visnjic I, Jovanovic M, Raisch S (2021) Managing the transition to a dual business model: tradeoff, paradox, and routinized practices. https://doi.org/10.1287/ORSC.2021.1519 82. Warner KSR, Wäger M (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Plan 52(3):326–349. https://doi.org/10. 1016/j.lrp.2018.12.001 83. Wildan U, Hashim A, Dahlan A (2018) A business case for digital transformation of a malaysian-based university. In: 2018 international conference on information and communication technology for the muslim world (ICT4M), pp 106–109 84. Wirtz BW (2020) Business model management. Springer, Heidelberg

Digital Transformation in Education: Relevant Paradigms and Theories of Teaching and Learning in the Industry 4.0 David Mhlanga

Abstract The study explores the paradigms and theories of education that will help the education sector to move in the right direction in industry 4.0. The study takes issue with the rate and magnitude of disruptions brought by technology as a result application of the right policies and principles in the management, teaching and learning in the education sector could not only enable the sector to minimise the effects of disruptions but better position the sector towards the digital transformation. The study’s major point is that due to rapid changes in political, cultural, social, economic, and technological situations, 21st-century society places a high demand on its members, and the situation is exacerbated by the presence of the COVID-19 epidemic. Using conceptual and content analysis of peer-reviewed journals, reports and books, the study found that the application of connectivism theory of learning, engagement theory, and cognitive theory of multimedia learning among others can help to move the education sector in the right direction despite the challenges brought by the digital transformation. The application of the theories and paradigms can help to minimise the effects of disruptions brought by technology in industry 4.0. The study concludes it is important to adopt a constructivism type of teaching to minimise the negative effects of technological advancement than steaking to a behaviourism type of teaching. Keywords Digital transformation · Education · Industry 4.0 · Learning · Paradigms · Theories

1 Introduction Every culture, region, or country’s strengths and shortcomings are shaped by education among other factors [19]. With tremendous developments in technology, economics, and industrial expertise, [19] suggest that the success of any society around the globe is dependent on the quality of education. [2] agreed, stating that D. Mhlanga (B) Department of Accountancy, The University of Johannesburg, Johannesburg, South Africa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_19

453

454

D. Mhlanga

education is a vital and critical component for a country’s economy to compete in the global marketplace. Education, according to [14], is one of the elements that can assist connect individuals in the workplace, and it is correct that education functions as a building brick that aids employment. [14] continued by arguing that education is the instrument through which people in any society can participate actively in the economy and become economically self-sufficient. Traditional employment possibilities, on the other hand, are increasingly endangered in Industry 4.0, and certain industries are eroding. This alone necessitates a shift in how people think about education. Education has evolved over the years and decades, and each revolution has necessitated the implementation of a certain approach to reach its final purpose, and industry 4.0 is no exception [19, 26]. Traditionally, education has followed a monodisciplinary path, requiring students to narrow their perspectives and focus as they progress through their courses. As a result, traditional education would provide people with a narrowly specialized skill that would allow them to qualify for a specific trade or job (Gwata [14]. The idea behind following a monodisciplinary trajectory was that specialisation was viewed as an instrument that can help to improve the productivity of an individual as well as their economic value. This notion often influenced the amount of reward for human capital. One example is a doctor with a specialisation in a particular area often earns a lot of money more than a general practitioner. This trend is common in many occupations. However, industry 4.0 is now different from the traditional model of teaching and learning, it now demands interdisciplinary and T-shaped people who are well equipped in various fields outside of their specialised occupations. Thornburg, (2000) argued that” we are on the cusp of a completely new era, and changes must be made in education to ensure that all students leave school prepared to face the challenges of a redefined world”. Because of the rapid changes in political, cultural, social, economic, and technological situations, 21st-century society places a high demand on its people. Personal computers, social networks and platforms, and cell phones, which were once deemed frivolous, are now having such an impact on society’s culture that it is nearly impossible to live without them [22]. These technologies have an impact on practically every aspect of our everyday life, including education, business operations, and even how people are governed. Hundreds of millions of young people around the world, particularly in Africa, are unemployed or underemployed, while employers are unable to fill positions [8]. It’s a problem that’s exacerbated by the rising gap between young abilities and employer demands. If left neglected, the situation would most likely worsen as the technological revolution accelerates [8]. As a result, society must adjust its abilities and knowledge in all parts of life to keep up with these changing circumstances [22]. Many people in many societies throughout the world believe that educational institutions must give adolescents the necessary skills and competence for this change. This raises the question of whether the quality of learning and instruction meets the needs of the times. As a result, educational institutions are under tremendous pressure to keep up with rapidly changing societal requirements and expectations. The spread of new technologies in the communication industry has had a significant impact on educational techniques all over the world. Students today require

Digital Transformation in Education …

455

different abilities than they did 20 or so years ago to be equipped for the twentyfirst century [27, 31]. Employers today are searching for young individuals who have developed new abilities such as problem-solving, interpersonal, and teamwork. Lifelong learning and its role in the development of a knowledge society are also high on the agenda [29]. Any learning that is done should aim to support the development of the competencies listed above. As a result, if the government is to equip students to live, work, and succeed in the twenty-first century, it must invent new means of teaching and learning. Self-directed learning, collaborative learning, experimentalbased learning, and active learning are examples of modern educational practices [16]. There is a dispute among educators about whether technology can alleviate many of the challenges linked with societal shifts in attitudes and educational delivery [22]. As new educational technology becomes more widely available, rethinking traditional teaching and learning processes becomes increasingly important as resources grow scarce and demand for the higher education of higher quality rises substantially. Hence the current study is exploring the paradigms and theories of education that will help the education sector to move in the right direction considering the digital transformation of the education sector in industry 4.0. The rest of the chapter is organised as follows: The following section will give the background of industry 4.0 and the education sector, followed by an empirical literature review on the digital transformation of education. The chapter will discuss the various theories and principles of education which include the Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning, equivalency theory and behaviourism. The Study will end with the conclusion and policy recommendation.

2 The Historical Background of Education Through the Industrial Revolutions According to Barlow (1967). J.R Buchanan stated that “education should prepare students for life and should be like the life to which it prepares. Stakeholders and scholars have been advocating for a change in the way education is being delivered from memorization to hands-on activities. The emphasis that Pestalozzi gave was the importance of considerable experience in training individuals to become productive (Smith 2002). The ideas around education and the theories were mainly shaped by the methods and forms of human survival. Industries were not in existence until the middle of the eighteenth century even intersocietal trading was mainly premised on agricultural activities. Manual labour was the main source of power and societies were mainly depending on animal power. Children who were from poor families had no education at all. As clearly put by Yuko (2016) the fact that many children were poor and could not have access to education forced the families to depend on farming as a source of livelihood. The coming of the first industrial revolution changed societies from depending mainly on agriculture to industrialization. The invention of the

456

D. Mhlanga

steam engine and machine tools made the journey towards industrialization possible. Societies began to have factories in numbers which made engineering and science important. The number of engineers and scientists as well as the schools that provide this type of education became a reality in many communities that aspired to be civilized and progressive. Emphasis on education became strong in the communities (Aggarwal 2019). In the years 1848, the number of industries that were devoted to making machines was still very small much progress was experienced in 1848 with the advent of railroads. This made the engineering techniques and emphasis on education to be more common. There was an introduction of public education, and it was now possible for poor children to be able to have access to education. The “prototype for the modern educational system presents today was also introduced”. Sakhapov (2018) the emergence of specialization in work and division of labour made the establishment of professional schools possible and universities were established. All these events took place during the first industrial revolution. The second industrial revolution appeared with mass advancement in the agricultural sector, manufacturing, and transportation. These advances in the different sectors started in Britain and spread to Europe and the rest of the world. The transition from steam power to electricity in the second industrial revolution allowed the development of transport, communication, and the development of high-tech industries a reality. Education also advanced in this era where science, technology, engineering, mathematics, business, and economics were brought into the factory. Some scholars argued that during this period qualified foremen, supervisors and technicians who were supposed to use these technologies were few due to shortages in technical education (Richard 2009). It was also highlighted that the shortages of key workers caused a gradual industrial and economic decline as it took some time to come up with strategies for technical education and training. In the early nineteenth century, technical and scientific institutions were established to make sure that the development of technical skills and knowledge is possible. The idea was to create a population of technocrats with the capacity to lead their nations in industrial development. In education, there was a paradigm shift towards the development of a multilevel training system for industry, “standardization of education, and the prestige growth of engineering education”. The welcoming of the third industrial revolution was driven by digital transformation with massive use of computers, transition to “telecommunication technologies, automation of production and rapid development of services”. These transitions made society move from mechanical devices to “pervasive digital technology with the introduction of semiconductors, mainframe computing, personal computing and the internet”. Penprase (2018) stated that the emergency of internet technology and renewable energy merged, and new infrastructure was created that changed many aspects of the distribution of power and this resulted in the expansion of access to higher education with the increase in campuses and globalization of academic research spearheaded by online technologies. The Internet made online education more popular and possible with educators ushered into a learning environment where access to information was free and immediate. The need for interdisciplinary education became a reality with liberal arts and interpersonal skills training being more emphasized.

Digital Transformation in Education …

457

Independent education in time and space Continuous improvement

Education 4.0

Interpretation of Data Project-based learning Flexibility in Learning Personalised Learning Guidance-oriented Student participation in the Curriculum Fig. 1 Education 4.0. Source Author’s Analysis

3 Industry 4.0, Education and Digital Transformation Industry 4.0 is normally defined as “a name for the current trend of automation and data exchange in manufacturing technologies, including cyber-physical systems, the Internet of things, cloud computing and cognitive computing and creating the smart factory”. [30] on other hand defined education 4.0 as one of the “new experiencebased education systems that use digital technologies instead of the rote-based system and responds to the needs of the new world through personalised education”. Education in industry 4.0 combines technology, individuality, and discovery-based learning with the idea of preparing learners for future jobs. One of the key concepts of education 4.0 is personalised education and success in life than paying more attention to exams [28, 30]. The transformation of the education system in industry 4.0 will lead to several important aspects which are clearly outlined in figure one below. Education 4.0 in industry 4.0 is associated with new aspects that are different from the traditional form of learning which is mainly based on the rote-based system. These aspects are clearly shown in Fig. 1 above which include independent education in time and space, Continuous improvement, Interpretation of Data, Projectbased learning, Flexibility in Learning, Personalised Learning, Guidance-oriented and Student participation in the Curriculum. Time and space-independent education is a situation where students are exposed to conditions where they can learn wherever and whatever they want. The interactive learning tools are making education to be space and time-independent. Issues related

458

D. Mhlanga

to theoretical dimensions of learning can be conducted outside the classroom while practical learning is conducted through face to face in the classroom. The existence of time independence implies that the learner can undertake learning even in their room due to the availability of various e-learning tools. The effect of all these initiatives is making the dependence on buildings diminish. When learners manage to learn theoretical parts of their studies on their own in a digital environment allows them to “transform their knowledge into real-life experiences through practical project-based learning activities in the classroom”. Personalised learning is viewed as a situation where learners receive personalised learning through various digital tools adjusted to their situations. Capabilities and needs. Personalised learning will allow students who are slow in learning to be able to improve their learning. Again, personalised learning has the advantage that it can make learners with difficulties feel supported in overcoming their challenges. With technology and the use of software, teachers can easily monitor the performance of learners and easily understand the predisposition of students towards subjects which will give the teacher the ability to see which subjects the student is stronger and weaker in. Learning flexibility is different from the traditional education system where the same model is applied to all the learners. With an education in the Fourth Industrial Revolution learning is flexible where different models and paths are applied if the same goal is reached. The same curriculum was offered to students from education 1.0 to education 3.0 applying the same styles. In education 3.0 there were applications of different efficient and effective methods, but flexibility was not attained. “In Education 4.0, a flexible global education model is recommended for every student”. Project-based learning is also part of education 4.0 where students are required to apply the knowledge acquired on real projects instead of responding to questions in an exam. In the class of project learning there is a field of learning called Maker where the “individual is transformed into a self-sufficient person through the use of the person’s talents in a fun way in various areas, especially technology”. Maker culture aims to prepare learners through fun. The skills that learners can develop in project-based learning areas outlined in the figure below include problem-solving, solution-oriented, collaboration and teamwork and even time management (Fig. 2). As outlined above project-based learning helps in developing the following skills, problem-solving, collaboration and teamwork, time management, and being solution-oriented. Data interpretation is also another aspect of education 4.0 due to the increase in the importance of subjects like mathematics in the lives of the people. The existence of robots that will do more of the mathematical calculations demands that people be able to draw insights based on the realised data. In education 4.0 students and graduates should be able to set up, manage, develop, collect, process, and interpret data. Students should be able to recognise trends in data and come up with recommendations from the data. The other important aspect of education 4.0 is not relying on a single exam but having continuous improvement. With the traditional and the current type of learning students are supposed to respond to questions and answer in an exam. This type of learning and assessment is viewed as the

Digital Transformation in Education …

459

Problem-solving

Being solution-oriented

Time management

Collaboration and teamwork

Fig. 2 Project learning outcomes. Source Author’s Analysis

short term by many scholars because after the exam the students will forget everything memorised. With education, 4.0 evaluation is one of the critical aspects instead of depending on exams. Evaluation will depend on the entire period, not just one exam. Students are requiring coming up with various continuous activities to apply their knowledge to practice. For instance, [30] gave one example where “children learning to code can develop a calculator or a game that they can use in their daily lives, instead of just mastering theoretical knowledge”. These projects will transform the theoretical knowledge into the practical experience which can be stored in long term memory. Curriculum with student participation is another important aspect of education 4.0 where students will be involved in the development of the curricula. To match the interests of the learners it is very important to involve them and consider their inputs. This process will assist to ensure that the content in the curricula is relevant to the aspirations of the students not only to pass the exam. Education 4.0 had another important aspect of being guidance oriented. This aspect is critical because educators will take the role of mentorship rather than distributors of knowledge. As put clearly by Gwata [14] “teachers are not going to be replaced by technology but technology in the hands of great teachers will be a transformational phenomenon. Dabbagh (2018) also stated that digital transformation of the education sector is important because it motivates “accessibility, collaboration, communication, value diversity, active and social learning, self-direction, content engagement, project learning and global exposure”. [19] also stated that the changes in science, technology, engineering and mathematics (STEM) have been necessitated by the existence of technology, machines, scientific studies, and inventions. Sahin (2018)

460

D. Mhlanga

also argued that education systems across the globe are adopting these new technologies and STEM education is one of the new approaches used in the education system. Even in America, the changes in education are being embraced, in 2013 the former United States of America President Barack Obama stated that “one of the things that I have been focused on as president is how we create an all-handson-deck approach to science, technology, engineering and mathematics. We need to make this a priority to train an army of new teachers in these subject areas and to make sure that all of us as a country are lifting these subjects for the respect that they deserve” [19]. The other argument from scholars is that STEM education should be encouraged to produce come up with scholars with skills that match the 21st -century demands. Some of the skills as put across by [19] include “collaboration, communication skills, creativity, problem-solving, perseverance information literacy, media literacy, global awareness, self-direction, social skills, literacy skills, civic literacy, social responsibility, innovation skills, critical thinking, technical skills and digital literacy among many other skills. The World Economic Forum came up with tipping points where 4IR technologies will be dominant in the day-to-day operations of human beings to create significant changes in society. The changes brought by the 4IR will have a huge influence on our lives to the extent of shifting education and employment. One of the surveys of eight hundred high-tech experts and executives came up with some dates that were some of the tipping points of technological changes to human life and existence. It was estimated that by 2025 implantable cellphones will be commercially available, the survey also revealed that eighty per cent of people in the world will have a digital presence in 2023, again ten per cent of reading glasses will be connected to the internet by 2023, the other results was that ten per cent of people will wear internet-connected clothes by 2022, the other result is approximately ninety per cent of the world population will have access to the internet by 2024, also there will be approximately 90 per cent of the population that will use smartphones by 2023, there will be 1 trillion sensors connected to the internet by 2022, over fifty per cent of internet traffic directed to homes and appliances by 2024 and ten per cent of cars will be driverless cars in the United States by 2026. The other predictions relate to the integration of technology in the Fourth Industrial Revolution workforce like “AI members of corporate boards of directors, AI auditors and robotic pharmacists, the proliferation of bitcoin in the economy, 3D printed cars by 2022 and transplants of 3D printed organs such as livers by 2024”. These changes will have enormous implications on education which include changes in the paradigms and theories of education that will help the education sector to move in the right direction considering the digital transformation of the education sector in industry 4.0. “Aligning education with Industry 4.0, can assist in creating more ingenious minds, with the capacity to fulfil new job needs.

Digital Transformation in Education …

461

4 Brief Review of Important Empirical Literature Review on Digital Transformation of Education In this second decade of the twenty-first century, digital transformation has become a top concern for the education sector [3]. As a result, several authors have documented this phenomenon from various perspectives. [4] stated that digital technology in the modern world is not only a tool but also a living environment that opens new opportunities: learning at any convenient time. [4], argued that the benefits and hazards to students and educators should be prioritized while implementing digital transformation in education. Technology allows teachers to try out new pedagogies and receive immediate feedback,it also aids in ensuring students’ active participation in the learning process. Other benefits mentioned by [4] include the notion that there are numerous resources for arranging pupils’ positive educational activities, and that technology will assist the teacher in automating or simplifying the execution of a variety of laborious tasks. Again, technology gives us fast access to the knowledge we need and teaches us how to deal with a variety of sources. On the other hand, [4] list some of the issues as having a negative impact on health, a drop in interpersonal skills, device addictions, a lack of writing abilities and so overall consequence, a lack of innovation. [12] also claimed that technological innovation constantly promotes transformation, causing culture, societal, and technological changes in organizations. [12] refer to these modifications as “digital transformation”. Despite digital innovation, [12] believe that the COVID-19 epidemic transformed the world in 2020, reorganizing society in terms of thinking, behaving, producing, consuming, and starting new businesses. Higher education institutions were also impacted by the epidemic, according to [12], therefore they were to implement adjustments to pupil professor engagement, teaching–learning, and academic entrepreneurship, where digital transformation played a vital role. Universities and colleges were also impacted by the epidemic, according to [12], therefore they were to implement adjustments to pupil professor engagement, instructional learning, and scholarly entrepreneurship, where digital transformation played a vital role. Despite the pandemic’s pressures, [12] found that digital transformation had already been emerging as a fundamental aspect of academic entrepreneurship, but the process had accelerated by the pandemic. [12] arguments were backed up by [32]. [32] argued that the COVID-19-related lockdowns have impacted human labour patterns in many aspects of life, especially postsecondary learning. According to [32] traditional classrooms were to be converted to online classrooms which necessitated technological infrastructure to be important. Due to COVID-19 instabilities, [32] contended that most learners in Saudi Arabia were negatively impacted by online courses. [3] also suggested how the progress of technology that Industry 4.0 provides has entered universities and colleges, forcing them to cope with a digital revolution in all aspects. [3] further suggested that applying digital transformation concepts to the university education domain is a new topic that has sparked attention in recent years since it allows us to characterize the intricate connections among players in

462

D. Mhlanga

a digitally enabled educational field. Furthermore, [3] revealed that digitisation in higher education is a new topic with few suggestions produced comprehensively. [15] also suggested that today’s kids, who have constantly been influenced by digital technologies since birth, are not equally prepared for their technology-rich future: many types of digital gaps still exist in society, affecting the youth of today and our digital futures. [15] went on to say that to satisfy the aspirations of the younger population and its digitalisation destiny, colleges and children’s safety must embrace a significant digitalisation. The COVID-19 outbreak has compelled education systems to undergo such a transition quickly and dramatically. In their paper, [15] looked at how the COVID-19 pandemic prompted a digital change in younger people’s basic schooling, the various digital gaps that emerged and were perpetuated, and the potential roadblocks encountered along the way., [15] believe that information management research should pay more attention to children, their digitised daily lives, and their fundamental education as major concerns.

4.1 Underlying Theories and Principles of the Study In the process of innovating the education sector, there is no single learning theory to follow. However, the theoretical framework in this study was built on a variety of already established theories and principles of learning that were developed over the years. These theories include the following, Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning, equivalency theory, American theory of independent study, European theory of independent study, the theory of industrialization of teaching and the theory of interaction and communication. These theories were used to shape the understating of how innovation can influence education considering the technological revolution. For purposes of this study, a summarise of the above theories was provided to shape the trajectory of the study. Some other theories like behaviourism, cognitivism, constructivism activity theory, situated learning and the cognitive load theory helped the study in understanding some of the guiding principles of learning. Some of the principles of instruction include Merrill’s first principles of instruction, Seven Principles for good practice in online courses, Laurillard’s Conversational Framework, Bloom’s (1956) taxonomy of learning outcomes, Variation theory of learning, and Keller’s ARCS model of learner motivation (Fig. 3). The Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning, equivalency theory and behaviourism were the theories described in summary as taken from the figure. Other theories though their descriptions were not given in this section like the American theory of independent study, European theory of independent study, the theory of industrialization of teaching and the theory of interaction and communication helped to shape the trajectory of the study and the understating of how innovation can influence education considering the technological revolution.

Digital Transformation in Education …

463

Connectivism Theory Engagement Theory Cognitive Theory of Multimedia Learning Equivalency Theory American Theory of Independent Study Theory of Industrializationof teaching Theory of Interaction and Communication Behaviourism, cognitivism, constructivism activity theory Situated learning variation theory of learning and the Cognitive load theory Fig. 3 Theoretical framework. Source Author’s Analysis

4.1.1

The Connectivism Theory of Learning

Many learning theories were concerned with learning from printed text without alternate interactive and multi-sensory learning contexts (Magout and [22]. Connectivism, a new learning paradigm for the digital age claimed by George Siemens, explains how technology affects how we learn, live, and communicate [10]. Connectivism is a strong theoretical construct for the digital age that combines significant parts of numerous learning theories, social structures, and technology. [34] argued that all learning theories were formed before technology had an impact on learning. Learning is no longer under the control of the learner, as technology now performs many of the activities that were previously performed by learners, such as information storage and retrieval [20]. As a result, some knowledge will be held by machines and some by humans [20, 34]. The present problem for educators is to create training for both machines and people, as well as how the two can interact [10]. Siemens believes that learning is much more than just acquiring information,it must be structured around the flow of information through networks (Magout and [22]. Learning and knowledge are based on a plurality of perspectives, learning is a process of linking specialized nodes or information sources, learning may live in non-human appliances, and the potential to know more is more critical than what is currently known, among other principles [6, 11].

464

4.1.2

D. Mhlanga

Engagement Theory

The engagement theory proposes that student participation should be at the heart of learning, with students engaging in meaningful interactions with other students [1, 7]. The term “engaged learning” refers to the importance of social interactions and collaborations for learners to become involved in a community of practice [9]. According to this approach, all students must participate in activities that include active cognitive processes including producing, problem-solving, thinking, making decisions, and evaluating [33]. Furthermore, due to the nature of the environment produced and its activities, engagement fosters intrinsic drive-in in students to study [33]. According to [22], technology can assist in facilitating all aspects of participation in ways that are difficult to achieve without it. The utilization of online conversations, conferencing, emails, chat, and teleconferencing is thought to create an environment where all participants may easily and creatively engage. In the digital age, engagement theory is one of the new paradigms of teaching and learning theories that highlights the beneficial role that technology may play in human interaction and evolution [22, 31]. This theory is not founded on other learning theories, although it is connected to constructivism, contextual learning, and experimental learning, as these concepts emphasize collaborative efforts and project-based assignments that result in innovative, meaningful, and authentic work [18, 22].

4.1.3

Cognitive Theory of Multimedia Learning

The cognitive theory of multimedia learning by [23] reasons that multimedia learning can be created in ways that can lead to a reduction in a reduction in cognitive load and optimize the use of working memory. The cognitive multimedia learning theory is based on the following assumptions. The theory assumes dual-channel where it assumes that humans possess two independent channels which they use to receive, and process visual and verbal information [36]. The cognitive theory also assumes that the working memory of humans has limited capacity. The theory argues that there is a limit on the amount of information the human brain can process at a time [24]. The theory also posits that the human brain has a limitation in that it cannot concentrate on two things at once [24]. Also, the cognitive theory of multimedia learning assumes active processing where it is argued that cognitive processing should be the core of learning and attention should be given to the presented material where it should be mentally organized in a coherent structure and is integrated with the existing knowledge. Cognitive theory of multimedia learning influences e-learning environments to provide a reduced cognitive load which will be vital to learners and more important to low ability learners.

Digital Transformation in Education …

4.1.4

465

Equivalency Theory

The equivalence hypothesis explains how different telecommunication technologies affect education, particularly distance education [35]. Instructors can now be seen, seen, and heard in an online classroom in the same way they can in a traditional classroom [35]. According to this theory, virtual education should be based on the concept of learning equivalency [25]. This idea goes on to say that the more similar distance education learning experiences are to face-to-face learning, the more similar the outcomes of the learners’ educational experiences will be [17]. According to [35], the equivalency theory proposes that virtual learning should be designed in such a way that, its learning experiences are like face to face for each student so that students enjoy appropriate learning experiences. According to this theory, virtual learning is defined as “formal, institutionally based educational activities where the learner and teacher are separated from one another, and where two-way interactive telecommunication systems are used to connect them synchronously and asynchronously for the sharing of video, voice, and data-based instruction” [35].

4.1.5

Behaviourism

Behaviourist theory insinuates that learning is a process of change in observable behaviour which is a result of external forces in the environment (Magout and [22]. The behaviourist school of thought is accredited to the influence of Thorndike (1913), Pavlov (1927), and Skinner (1974). According to [5], behaviourism focuses only on the objectively observable and measurable changes in the behaviour which is acquired through a condition being repeated until it becomes automatic. Psychological behaviourism has its foundations in part in classical associationism where intelligent behaviour is the result of associative learnings [21]. According to [13], behaviourism is made up of various theories that make three assumptions about learning: Understanding internal activity is less significant than observable behaviour. Simple elements should be the centre of behaviour: stimuli and responses that are specific,To learn, it is necessary to adjust one’s behaviour. The most common behavioural approaches to learning are shown in Fig. 4 below.

466

D. Mhlanga

The learner adopts a mostly passive and reactive posture.

The instruction is well-organized and systematic. Concrete and well-defined teaching goals, objectives, and strategies aimed at all learners, not just one in particular. Learning can be measured. Assumes that it is presumptively true that a well-thought-out instructional intervention can produce the desired learning outcome. Focus on simplicity (start with easy and work your way up) and repetition (rote learning/drill and practice) are the keys to success. Instruction is instructor-controlled.

Fig. 4 Common behavioural approaches to learning. Source Author’s Analysis

The figure above outlines the common behavioural approaches to learning which include “the learner takes on a predominantly passive and reactive role. Instruction is structured and systematic, with concrete and defined instructional goals, objectives and strategies aimed at learners in general and not on the individual learner, learning can be measured, it assumes that a well-planned instructional intervention can result in the desired learning outcome, focus on simplification (start with easy and progress to more difficult) and repetition (rote learning/drill and practice) and instruction are instructor-controlled”.

4.1.6

Paradigms and Theories of Education that Are Suitable to Move in the Right Direction in Industry 4.0.

Figure 5 below gives a summary of the Paradigms and theories of education that can help the education sector to move in the right direction in industry 4.0. These theories include the Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning, and equivalency theory.

Digital Transformation in Education …

467

Connectivism theory of learning

Equivalency theory

Theories of Education for Industry 4.0 Engagement theory

Cognitive theory of multimedia learning Fig. 5 Paradigms and theories of education that are suitable to move in the right direction in industry 4.0. Source Author’s Analysis

The theories shown in figure five above the Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning, and equivalency theory, are some of the theories which can help to allow education in the industry 4.0 to build the skills that can meet the challenges of the industry 4.0. The study takes issue with the rate and magnitude of disruptions brought by technology as a result application of the right policies and principles in the management, teaching and learning in the education sector could not only enable the sector to minimise the effects of disruptions but better position the sector towards the digital transformation. The main argument of the study is that the 21st-century society is demanding a lot of its members due to the rapid changes in the political, cultural, social, economic, and technological situations and the situation is worsened by the existence of the COVID-19 pandemic. After a careful analysis of all the theories discussed in this study, the study concluded that the Connectivism theory of learning, engagement theory, cognitive theory of multimedia learning and equivalency theory if applied appropriately in industry 4.0, the disruptions brought by technology can be minimised as these theories clearly articulate the nature of education that should be delivered to learners in the twenty-first century.

468

D. Mhlanga

5 Conclusion and Policy Recommendation Education has evolved over the years and decades, and each revolution has necessitated the implementation of a certain approach to reach its final purpose, and industry 4.0 is no exception. Therefore, the current study explores the paradigms and theories of education that will help the education sector to move in the right direction in industry 4.0. Using conceptual and content analysis of peer-reviewed journals, reports and books, the study found that the application of connectivism theory of learning, engagement theory, and cognitive theory of multimedia learning among others can help to move the education sector in the right direction despite the challenges brought by the digital transformation. The application of the theories and paradigms can help to minimise the effects of disruptions brought by technology in industry 4.0. The study concludes it is important to adopt a constructivism type of teaching to minimise the negative effects of technological advancement than steaking to a behaviourism type of teaching. As a result, educational institutions should begin incorporating and focusing more on theories and paradigms that allow teaching and learning to be successful in industry 4.0.

6 Future Research The study primarily focused on investigating educational paradigms and ideas that will assist the education sector in moving appropriately considering industry 4.0 using secondary data. Experimental studies could be conducted in the future to study the theories, paradigms, and models of teaching and learning that are effective in industry 4.0.

References 1. Allen IH, Lester SM Jr (2012) The impact of a college survival skills course and a success coach on retention and academic performance. J Career Tech Educ 27(1):2012–2020. https:// doi.org/10.21061/jcte.v27i1.536 2. Au W (2008) Between education and the economy: high-stakes testing and the contradictory location of the new middle class. J Educ Policy 23(5):501–513. https://doi.org/10.1080/026 80930802148941 3. Benavides LMC, Tamayo Arias JA, Arango Serna MD, Branch Bedoya JW, Burgos D (2020) Digital transformation in higher education institutions: a systematic literature review. Sensors 20(11):3291 4. Bilyalova AA, Salimova DA, Zelenina TI (2019) Digital transformation in education. In: International conference on integrated science. Springer, Cham, pp 265–276 5. Capacho J (2016) Teaching and learning methodologies supported by ICT applied in computer science. Turk Online J Dist Educ 17(2):59–73. https://doi.org/10.17718/tojde.48315

Digital Transformation in Education …

469

6. Change PD-AF. and A. for S. and 2014, undefined (2015) Access to Education In Europe: A framework and agenda for system change, Springer. https://doi.org/10.1007/978-94-017-87956.pdf. Accessed 16 May 2020 7. Clarà M, Barberà E (2013) Learning online: massive open online courses (MOOCs), connectivism, and cultural psychology. Dist Educ 34(1):129–136. https://doi.org/10.1080/01587919. 2013.770428 8. Deloitte Global Business Coalition For Education (2018) Preparing tomorrow’s workforce for the Fourth Industrial Revolution | Deloitte | About, pp 1–58. https://www.voced.edu.au/con tent/ngv:85595. Accessed 5 May 2020 9. Dickey MD (2005) ‘Engaging by design: how engagement strategies in popular computer and video games can inform instructional design. Educ Tech Res Dev 53(2):67–83. https://doi.org/ 10.1007/BF02504866 10. Downes S (2007) What connectivism is. Accessed 18 Aug 2010 11. Dunaway MK (2011) Connectivism: Learning theory and pedagogical practice for networked information landscapes. Ref Serv Rev, 675–685. https://doi.org/10.1108/00907321111186686 12. Garcez A, Silva R, Franco M (2021) Digital transformation shaping structural pillars for academic entrepreneurship: a framework proposal and research agenda. Educ Inf Technol, 1–24 13. Gredler ME, Johnson RL (2001) Lessons learned from the directed evaluation experience. Am J Eval 22(1):97–104. https://doi.org/10.1016/S1098-2140(01)00115-1 14. Munozovepi G (2019). To flourish in the Fourth Industrial Revolution, we need to rethink these 3 things | World Economic Forum. https://www.weforum.org/agenda/2019/08/fourth-ind ustrial-revolution-education/ 15. Iivari N, Sharma S, Ventä-Olkkonen L (2020) Digital transformation of everyday life–How the COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int J Inf Manag 55:102183 16. Imenda S (2014) Is there a conceptual difference between theoretical and conceptual frameworks? J Soc Sci 38(2):185–195. https://doi.org/10.1080/09718923.2014.11893249 17. Karata¸s S, et al (2009) Comparisons of internet-based and face-to-face learning systems based on “equivalency of experiences” according to students’ academic achievements and satisfactions. Q Rev Dist Educ 10(1):65–74. https://eric.ed.gov/?id=EJ904041, Accessed 16 May 2020 18. Kearsley G, Shneiderman B (1998) Engagement theory: a framework for technology-based teaching and learning. Educ Technol 38(5):20–23. https://doi.org/10.1145/1121341.1121392 19. Kehdinga D, Fomunyam G (2019) Education and the fourth industrial revolution: challenges and possibilities for engineering education. IJMET_10_08_022 Int J Mech Eng Technol 10(8):271–284 20. Kop R, Hill A (2008) Connectivism: Learning theory of the future or vestige of the past?. Int Rev Res Open Dist Learn 9(3). https://iaeme.com/Home/article_id/IJMET_10_08_022. Accessed 16 May 2020 21. Kravchenko A, Kyzymenko I (2019) The Fourth industrial revolution: new paradigm of society development or posthumanist manifesto. Philosophy Cosmol 22:120–128. https://doi.org/10. 29202/Phil-cosm/22/10 22. Magout M, Magout M (2020) Chapter two – theoretical framework and literature review. In: A Reflexive Islamic Modernity, pp 29–56. https://doi.org/10.5771/9783956506376-29. 23. Mautone PD, Mayer RE (2001) ‘Signaling as a cognitive guide in multimedia learning. J Educ Psychol 93(2):377–389. https://doi.org/10.1037/0022-0663.93.2.377 24. Mayer RE (2014) Cognitive theory of multimedia learning. In: The cambridge handbook of multimedia learning, 2nd edn, pp 43–71. https://doi.org/10.1017/CBO9781139547369.005. 25. Mead CE, et al (2007) Using equivalency theory in explaining the learning experience between originating and remote site students taking an interactive television (ITV) course. https://sha reok.org/handle/11244/7506. Accessed 16 May 2020 26. Mhlanga D (2020) Industry 4.0: the challenges associated with the digital transformation of education in South Africa. In: The Impacts of Digital Transformation, vol 13

470

D. Mhlanga

27. Mhlanga D (2021) the fourth industrial revolution and COVID-19 pandemic in South Africa: the opportunities and challenges of introducing blended learning in education. J Afr Educ 2(2):15–43 28. Mhlanga D (2022) The role of artificial intelligence and machine learning amid the COVID-19 pandemic: what lessons are we learning on 4IR and the sustainable development goals. Int J Environ Res Public Health 19(3):1879 29. Moon Y, Seol S-S (2017) Evaluation of the theory of the 4th industrial revolution 1. Asian J Innov Policy 6(May):245–261. https://doi.org/10.7545/ajip.2017.6.3.245 30. Costley P (2021) What is Industry 4.0? everything you need to know about industry 4.0’s impact on education. https://www.mentalup.co/blog/industry-4-and-its-impact-on-education 31. Reaves J (2019) 21St-century skills and the fourth industrial revolution: a critical future role for online education. Int J Innov Online Educ 3(1). https://doi.org/10.1615/intjinnovonlineedu. 2019029705 32. Saqib M, Nasir T, Gull H, Alabbad DA, Iqbal SZ (2021) Challenges and implications of digital transformation in higher education: a student perspective from Pakistan and Saudi Arabia. In: Pandemic, lockdown, and digital transformation. Springer, Cham, pp 159–173 33. Schneiderman B (1994) education by engagement and construction: can distance learning be better than face-to-face? http://www.hitl.washington.edu/scivw/EVE/distance.html. Accessed 16 May 2020 34. Siemens G (2006) Global summit 2006 : technology connected futures Connectivism : learning and knowledge today george siemens. http://www.mmiweb.org.uk/egyptianteachers/site/dow nloads/Siemens_2006.pdf. Accessed 16 May 2020 35. Simonson M, Schlosser C, Hanson D (1999) Theory and distance education: a new discussion. Int J Phytorem 21(1):60–75. https://doi.org/10.1080/08923649909527014 36. Wodak R, Meyer M (2009) Critical discourse analysis: history, agenda, theory, and methodology. - Research Portal | Lancaster University. In: Methods for critical discourse analysis, pp 1–33. http://www.research.lancs.ac.uk/portal/en/publications/critical-discourse-analysishistory-agenda-theory-and-methodology(d30211d8-a9e4-48ca-bce6-f5c067d3fffa).html. Accessed 16 May 2020 37. Goldie JGS (2016) Connectivism: A knowledge learning theory for the digital age? Med Teach. 38(10):1064–1069. https://doi.org/10.3109/0142159X.2016.1173661

Digitalization Maturity Model Development for Higher Education ˙ Nursel Buse Ulufer, Ikra Tuba Dolgun, Sevval ¸ Birinci, Atalay I¸sık, Semiha Bal, Gül T. Temur, and Alper Camcı

Abstract This chapter attempts to provide a measurement tool to determine the digitalization maturity level for universities. The objectives of this chapter can be listed as follows: (1) to discuss the relevant literature to reveal all criteria affecting the development of digitalization maturity in higher education, (2) to figure out the importance of these criteria by the help of a traditional multi criteria decision making method, and (3) to apply the proposed methodology on a university to validate how it is successfully implemented for determination of digitalization maturity level. After reviewing the literature, for the methodological structure, four main criteria (strategic, organizational, technological, and data management) are selected with their twenty sub criteria. The methodology of the paper is applied by conducting surveys with valuable experts on this area, and the evaluation of one of Turkey’s best universities is succeeded. It is expected that the outputs of the study will be beneficial guide for both of governmental and consultancy companies that aim to figure out digitalization maturity of higher educational system and aim to give guidance them on how to improve the weaknesses in current practices to level up. ˙ T. Dolgun · S. N. B. Ulufer (B) · I. ¸ Birinci · A. I¸sık · G. T. Temur · A. Camcı Bahcesehir University, Istanbul, Turkey e-mail: [email protected] ˙I. T. Dolgun e-mail: [email protected] S. ¸ Birinci e-mail: [email protected] A. I¸sık e-mail: [email protected] G. T. Temur e-mail: [email protected] A. Camcı e-mail: [email protected] S. Bal London School of Economics, London, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_20

471

472

N. B. Ulufer et al.

Keywords Digital education · Digitalization · Higher education · Maturity level · Multi criteria decision making

1 Introduction In the twenty-first century, the developments on digitalization have resulted in rapid and effective changes in all areas of life. The education and learning opportunities offered by digitalization have also affected the education processes of new generations dramatically, and this has made the innovations brought by digitalization even more substantial for both of education and training processes. The digital technologies are meaningful if they are helpful to increase the efficiency of learning and teaching systems in which the educators and students are able to use these technologies and their resources to increase the co-occurrence [4]. Especially during the Covid 19 pandemic, new hardware and software technologies offer numerous development opportunities to the education sector. Educational institutions have recently been in a constant effort to transform their systems in order to adapt their systems in line with the demands of current educational environment. Because of these transformation efforts, educational institutions began to reap benefits from the new resources and opportunities provided by digitalization. Furthermore, these efforts enable instructors to be involved during the preparation of learning materials [14]. Educators feel obliged to use information and communication technologies (ICT) because the students attach high importance on using social network, internet, and other high technology tools [12]. However, besides the obvious advantages of incorporating digital technologies into education; strategic and organizational challenges also remain. It should be noted that, the investments on high technology and utilization of the best hardware and software solutions do not guarantee the increase in the efficiency of education and training processes in digital era. It is also required to improve digitalization strategies and transform the organizational culture that provides a digitally ready culture and top management support the digital practices. According to [27], educational processes depend on not only physical solutions but also psychological infrastructure. Therefore, in this study, a comprehensive digital maturity model for higher education is developed including not only technological and data-based improvements, but also strategic and organizational necessities. The motivation of the study is to establish a comprehensive measurement tool that shows the digitalization maturity level of higher education institutions. For this reason, firstly, a traditional multi criteria decision making model named Analytic Hierarchy Process (AHP) is applied to these four main criteria (strategic, organizational, technological and data) to prioritize them according to their importance showing their impacts on contributing digital maturity level. Then, a real application is carried out for one of Turkey’s well-known universities to see in which level (0 (initiation)—5 (optimized)) it takes place. The main purpose of this study is not to create innovation in the method, but to apply an existing and popular method to a new field, so it has been chosen as one of the most

Digitalization Maturity Model Development for Higher Education

473

applicable and most understandable MCDM methods in the field of social sciences. The proposed methodology contributes to the literature by offering a support tool to determine the digital maturity level of universities, and university management can use the results to check (1) how to handle weak points, (2) how to level up, (3) how to keep the strong points and (4) how to create a road map to improve the organization’s digital maturity. For this aim, first of all, digitalization in education (also known as Education 4.0), the effect of digitalization on education area and determination of maturity model are explained in Sect. 2. Subsequently, the details of the proposed digital maturity model and its application are given in Sect. 3. We concluded the study with main outputs and further research opportunities.

2 Literature Review on Digitalization in Education 2.1 Definition of Education 4.0 Education 4.0 characterizes the phenomenon of digital integration in education and learning processes, where people, tools and machines gain momentum to produce solutions, troubleshoot and, of course, introduce new innovations [14]. Education 4.0 is a formation that aims to adapt the education environment to the digital transformation of the present and future. It generates solutions to the digitalization needs of today’s education and training sphere and adds a whole new dimension to the modern education approaches. Education 4.0 enables lifelong learning for individuals, and it provides utilization of potential digital technologies, personalized data, open-source content and other possibilities that technologically and globally connecting world. As [11] mentions, the new perspective on learning motivates students to understand and focus on the main sources to gain the new skills and knowledge. The today’s students who are between 18–23 years (also called Generation Z) are fully adapted to the technology, and compared to the previous generations, they have experienced practice-based learning process with high participation [15]. To reach the digital capability of the new student generation, the teachers must improve some main skills related with digitalization [9], such as video and visual content engagement, utilization of web sites and social media, blogs, creation of presentations, preparation of digital portfolio, and creation of creative exams. However, there are some debates on changes in new education era, the trends can be categorized as follows [11, 13, 23]: 1. Education 4.0 makes learning and teaching possible in any place and anytime: It eliminates space constraints in education. In other words, by minimizing the need for being physically at school, it provides students with the opportunity to benefit from online education opportunities regardless of time and place. 2. It provides personalized education: Education 4.0 allows students acquire advanced learning skills with different study tools according to their interests

474

3.

4.

5.

6.

7.

8. 9.

N. B. Ulufer et al.

and proficiency levels. In this way, it will be possible to determine the subjects, on which the students are insufficient, and subjects, on which they are more competent. It provides learning flexibility: Through Education 4.0, students can find the opportunity to concentrate on improving their weaknesses while achieving success in their own competence areas, thanks to the flexible education plan. It provides project-based learning: The digital contributions of Education 4.0 to the field of education focus on increasing the productivity of individuals in the new learning process, equipping them with age-appropriate skills, and encourage applied education. It is about data interpretation: All these digital possibilities that come with Education 4.0 make possible to perform all calculations and data management processes via computers. Therefore, unlike in the past, individuals can focus only on interpreting big data rather than calculating it. It provides applied training: Development of opportunities for students such as internship, mentoring and simulation are offered so that the skills gained through current training methods can be long-lasting and effective. No exam-based grading system with projects: Beyond the ongoing classical examination methods, it is aimed to acquire knowledge permanently through project-oriented assessment methods, including long-term, applied preparation processes. It is student-oriented system and includes intense student participation: Students can now have a say in determining their curriculum. Mentor/consultant teacher will gain importance: Counseling/mentoring support is vital for students in the process of defining how to proceed in a stable and meaningful way in their education journey. Another important benefit of adapted education methods is that it establishes the importance of counseling/mentoring support provided to students.

The role of Education 4.0 is highly important for today’s systems but in the near future, it will also take role on shaping the economic usage rate of knowledge, that means higher education systems have to recognize the importance of enhancement of teaching and learning process to by the help of innovative solutions continuously [14]. As [27] also highlight, future learning systems will be full of economically and efficiently advantageous digital technology. Recognizing these, our aim is to propose a measurement tool that brings out the necessities for the digital transformation in higher education. Before the explanation of the proposed methodology, it will be helpful to understand the history of education across time.

Digitalization Maturity Model Development for Higher Education

475

2.2 Education 1.0 to Education 4.0 The beginning of the innovations in this field is based on the industrial revolution and the developments have continued unabated until today. The history is summarized below by considering the studies of [16, 17, 21]. Education 1.0 started in the 1970s and was mostly shaped by traditional education techniques. This system was based on a teacher-centered approach, which proceeded in a flow of knowledge from teacher to student. Therefore, the position of the students was more submissive. The main source of knowledge was the teacher, and the learning method was focused on memorizing. In other words, teaching was considered as a knowledge transfer only from the source which is teacher. Education 2.0 brought the approach of integrating technological developments into the education system and aimed to move beyond the traditional understanding of education in this respect. To be more precise, Education 2.0 has taken steps towards being more technological by incorporating the e-learning system and by including communication and cooperation from student side in its education methods. Although the education system has evolved to be student-centered, the transformation period has not been entirely prompt. Nevertheless, Education 2.0 is marked as a period in which phenomena such as communicating, generating ideas, and contributing to teaching and learning methods are included in the education system. Moreover, the concepts of obtaining information and learning from other sources, not merely from the teacher came to the fore. For instance, the creation of websites and the inclusion of the blessings of technological innovations in learning made the education process more social and effective. In this period, exams are seen as quality control documents and diploma guarantee certificates. By 2000s, a new phase begins in which the basic dynamics in the field of education are shaped under the influence of the education 3.0 period, and this approach brings along the inclusion of educational methods that will meet the needs of the technology society. The Education 3.0 approach enables the formation of knowledge generations by supporting self-learning practices. In this period, the digitalization of education and training materials intensified. Moreover, the effect of social media was strongly seen in education, with the increase in internet use. With use of technology in all fields in the Education 3.0, learning has become multidimensional, and it has been observed that students’ participation in the education process is more active in this period. In line with this, students have started to be generators of “new information”. Furthermore, the formation process of multinational education programs was tried to be established in this phase. The phase we have reached in the world of education today is a setting shaped by the Education 4.0 approach. Education 4.0 transformation aims to equip new generations with life skills that will make easier to adapt them to our age in every field. With the facilities brought by Education 4.0 phase, students will be equipped by various abilities and thinking skills. Trainings in this period, in which the visualized education tools started to be used more intensively introduced the "Lifelong Learning" phenomenon into the lives of individuals. As [21] mention, Education 4.0 consists of

476

N. B. Ulufer et al.

four core components such as: soft and hard competencies, learning methods, information and communication technologies (ICTs) and infrastructure that rare utilized to understand the user requirements, handle data, measure the applications and provide the prototypes.

2.3 Digital Transformation on Education It has been widely thought that societal digital transformation has been revising the perceptions, acts and occasions of students [10], and digitization in universities generally reflects a positive student attitude towards current opportunities [26]. By the effect of these, as [25] mentioned, less than half a year, online education has been increased 10 times approximately and it is expected that it will move on to increase more dramatically than past. As it is given in the report of Technavio, the British Distance Market is expected to increase more than expected up to 2023. Researchers highlight that by 2030, 25% of the digital technology market will be from higher education segment [25]. Unfortunately, the current education system is not adequately equipped to satisfy the new modern trends in ICTs, and digital technologies are accepted as supportive tools. In the future, it is expected to change this perception [12]. There has still been a significant risk causing from the lack of digital literacy. One of the most important obstacles of digitalization in high education is digital literacy. In order to deal with this risk, the roles of instructors are highly critical. It will not be possible to standardize the main requirements of digitalization without the active participation of people who will be affected by digitalization and without increase their interest in digital learning [12]. Digital transformation should be handled within the scope of the “system approach”, and all stakeholders should be involved in the transformation process. The way to achieve this is high communication, coordination, but most importantly, strategic planning and putting these plans into practice. It is an essential need for learners and teachers to develop their digital literacy skills and to increase their digital competencies by considering national and international frameworks [5].

2.4 Determination of Maturity Model From past to present, all forms of organizations such as corporations, charitable entities, governments have made considerable efforts to identify their long-term or short-term objectives or targets, and to devise the plans. Nonetheless, strategies frequently fail to deliver the positive results they were expected to produce or gain. Maturity models are strategic models which describe the qualitative aspects used to classify an object or construct into one of many clearly defined domains. Maturity models help people and organizations to self-assess the maturity of assorted

Digitalization Maturity Model Development for Higher Education

477

conditions of their processes against benchmarks. From a technological perspective, technology adaptation capability is called “readiness”, or in most resources it is called “maturity”. In order to determine the maturity of the capability on new requirements, measurement of readiness is used on technological changes [19]. [19] groups the technology readiness into nine dimensions and conducts a descriptive analysis of each dimension which starts from reporting and concludes with proving actual system. The higher educational systems in which there is high adaptation need to high tech and smart solutions, have begun to attach high importance on maturity level measurement. Because the maturity model facilitates colleges and schools assess their resources in relation to best practice and grade course development actions. Maturity model also provides a scientific establishment to wind up benchmarking and the improvement of efficiency. As with other organizations, educational institutions are increasingly projecting their activities and processes to prepare, execute and more effectively complete projects. Maturity models may be applied to an organization, a business unit, or a team to provide a performance enhancement roadmap [7]. Many maturity models have been developed in recent years by both practitioners and researchers. Among the maturity model types, one of the best-known maturity models to correct the software development process is the Capability Maturity Model (CMM) [24] developed by Carnegie Mellon University Software Engineering Institute (SEI). SEI first identified five levels of processing maturity. These are altered from [6] Quality Management Maturity Grid (QMMG), first, repeatable, defined, man-aged and optimized. SEI successor launched CMM Integration (CMMI) in 2001. In addition, the QMMG and the CMM(I), [20] Hierarchy of Individual Needs and [22] Stage Theory are often best described of today’s numerous maturity models. The implementation of maturity models continues to rise in volume and depth of knowledge in procedure, software firms and consulting firms have yet to present a multitude of maturity models [3]. Briefly, there are various maturity models in the world according to certain topics as following: – – – – – – – – – – –

Safety Culture Maturity Model (SCMM) The Capability Maturity Model (CMM) Project Management Maturity Model (PMMM) Business Process Maturity Model (BPMM) Cognizant Enterprise Maturity Model (CEMM) Knowledge Management Maturity Models (KMMM) Agile Maturity Model (AMM) Capability Maturity Model Integration (CMMI) Quality Management Maturity Grid (QMMG) Service Integration Maturity Model (SIMM) Big Data Maturity Model (BDMM)

478

N. B. Ulufer et al.

3 The Proposed Methodology Considering the relevant literature, a digitalization maturity model is developed for higher education. For this reason, three steps-based methodology is proposed as: Step 1) Determination of the Main and Sub Criteria: Listing the important main and sub criteria affecting digital maturity level of higher education. Step 2) Prioritization of Criteria by AHP Method: Revealing the different weights (in other words, importance) of each sub criterion by the help of AHP. Step 3) Calculation of Digital Maturity Score for a Real Case: Applying simple weighted average method by using the global weights of each sub criterion and the score of the related cooperation for finding the total maturity score.

3.1 Step 1: Determination of the Main and Sub Criteria The criteria that have impact on increasing the maturity of digitalization in higher education have been considered and analyzed belongs to related literature and experts’ opinions. The criteria are analyzed, and they have been divided into four main dimensions: (1) Strategic, (2) Organizational, (3) Technological, and (4) Data Management. Each of main criteria divided into more specific sub-criteria. The structure of main and sub criteria is given at Table 1. In real world, it is expected that the effectiveness of these criteria on digitalization maturity level will not be equal. Therefore, a multi criteria decision making methodology will be applied in Sect. 4. But first, it is required to determine all criteria in detail. Strategy: The methods and approaches that have been applied to reach a goal is essential in life for both individuals and institutions. Every strategic move made to adapt to a process successfully has great importance. Moving from this, having a strategy-oriented vision contributes to making a strong additive to the development process of education in universities [8]. While evaluating our criteria in strategic terms, we handled top management support and investment on digital improvements to be advanced in digital education. Also, adaptation of partnership is scrutinized for being effective in digitalization process. It is also observed that projects and patents may support universities in the process of acquiring more domain in this digital world. In addition, being more integrated into this process requires to digest the digital culture existence. Furthermore, setting goals towards the potential student group, which is the target audience of digitalization culture, can strengthen the bond between universities and digitalization. Organizational: Today’s organizations have complex structures, and all have their own target units. In order to achieve their specific objectives, they must have been sufficiently equipped. By talking about sufficiency, we may refer to having necessary

Digitalization Maturity Model Development for Higher Education

479

Table 1 Digitalization maturity model criteria structure Strategic

Organizational

Technological

Data management

• Top management support

• Existence of digitalization working group

• Existence of digital infrastructure

• Cyber security

• Investment on digital education

• Existence of IT department

• Utilization of digital devices for students

• Educational data mining

• Adaption of partnership

• Employee training

• Utilization of digital devices for academic staff

• Optimization, diagnostic evaluation

• Projects on digitalization in education

• Digital collaboration with all stakeholders

• Utilization of digital devices for administrative staff

• Patents on digitalization in education

• Infrastructure-accessdistributions tool

• Information presentation control

• Potential of student group • Existence of digital culture

equipment, capacities of required units or in-house support mechanisms. In connection with this, universities are educational institutions, and their main purpose is to provide their students with a high-quality education that meets the needs of this era. Aforesaid quality is measured by the efficiency, reliability, and successful usability of their operations. Improving the quality of education is always possible thanks to the opportunities offered by the developing technology and having a high standard of education can contain many sub-criteria that must be met. To be more specific, it is important to have the necessary units in the university for the development of students and to establish productive partnerships in this regard. In addition, universities should have distant education and informatics units in order of easing the run of a smooth process in operation of organizational functions. Furthermore, providing employees with developing training programs and establishing digital connections with partners are among the important criteria that demonstrate the high standard of education. Besides, the situation is considered in the infrastructure-access-distribution tools hierarchy is whether the infrastructure hierarchy is accessible within the organization. Technological: Digitalization in education has direct impact on the methodological sphere of education that determines the way by which information reaches individuals. This further provides fast access to information and facilitates the selection of learning methods. The rapid adaptation of digitalization in education settings

480

N. B. Ulufer et al.

obliges educational institutions to provide digital platforms with various tools for students, academic staff, and administrative staff. In recent years, as part of the studies involving the use of modern technologies such as the internet, interactive media and communication technologies, digitalization necessitated developing the infrastructure in digitalization and providing the innovation of appropriate educational software. Thus, bringing technical innovations along with increasing technical capacity of institutions play crucial role in the digitalization process in education. While evaluating our criteria from a technical point of view, we focused on the development of digital infrastructure and the importance of checking the accuracy of the information presented in digital education. In this context, we examine the use of digital devices by students, academic staff, and administrative staff as follows: – For Students: face ID, virtual and augmented reality, availability of online learning options, hologram utilization, multi touch equipment, online feedback survey for development etc. – For Administrative Staff: e-signature utilization, online registration, online payment, online support units, online feedback survey for development, block chain etc. – For Academic Staff: e-database attainableness, effective communication tools (echat, e-talk), availability of online learning options, hologram utilization, multi touch equipment, online feedback survey for development and such as should be considered to have in universities. Data Management: The data is one of the most important criteria for digitalization in education. We divided the data management into three main items. Cyber security is an application to protect systems, networks, and programs against digital attacks. Cyber security information collects, processes, stores and protects data on computers and similar devices. It provides a sound infrastructure for the security of data and continuity of education quality in the digitalization process in education [18]. Educational data mining has led to the emergence of a new data analysis method that enables to analyze the data produced by information and communication technologies used in education with different methods and techniques and to summarize this data by converting it into useful information. In addition, data mining is used in education to protect data against errors and sudden changes in data that may be encountered in online education. Following issues can be examples of educational data mining: – Massive data in the public and private universities’ own school systems can be processed and recycled in terms of internal control and audit. – It can be created by modeling success or failure situations with preliminary predictions at private or public universities. Optimization and diagnostic evaluation are applied to achieve better results in digitalization in education. This evaluation is used to collect data about what students know about the topic. Diagnostic evaluations are written questions that evaluate the

Digitalization Maturity Model Development for Higher Education

481

student’s views on the subject they are familiar with or that will be covered in the course. The aim is to make analyze student’s situation, to make the teacher to choose how to teach the new course content and which teaching method to use accordingly. Generally, it is used before and after teaching, where the same pre and posttests are given to the students before and after the lesson. This method allows instructors and students to graph their learning progress by comparing the pre and post test results [1].

3.2 Step 2: Prioritization of Criteria by AHP Method In this chapter, prioritization of the criteria that selected for the digitalization maturity model is conducted by the help of Analytic Hierarchy Proses (AHP) method which is one of the popular and traditional multi criteria decision making methods. AHP was developed by [28] to handle quantitative and qualitative criteria simultaneously to decide on an alternative by also figuring out the weights of each affecting criteria. Our study is not concerned with explaining all details about AHP, because it is highly popular, but a summary is shared below. Besides that, our study is mainly motivated to offer a maturity model with a known and practically usable method, therefore AHP is selected. Firstly, specific surveys matching with the requirements of AHP are prepared and they are filled by three experts who have valuable experiences on student affairs and educational sciences. The following process is followed to figure out the different weights of each such criterion: 1. By using Saaty’s 1–9 Scale given in Table 2, experts are informed to compare pair wisely each main and sub criterion with the other taking place in the same group. 2. After gathering the answers given to surveys, matrices comparing criterion to each other are fulfilled. The pair wise comparisons are applied in terms of which Table 2 Saaty’s preference index [29] Intensity of importance

Definition

Explanation

1

Equal importance

Two activities contribute equally to the objective

3

Moderate importance

Experience and judgment slightly favor one over another

5

Strong importance

Experience and judgment strongly favor one over another

7

Very strong importance

Activity is strongly favored, and its dominance is demonstrated in practice

9

Extreme importance

Importance of one over another affirmed on the highest possible order

482

N. B. Ulufer et al.

Table 3 Randomness index [28] Element number

1

2

3

4

5

6

7

8

9

RI

0

0

0.52

0.89

1.11

1.25

1.35

1.41

1.45

criteria dominates the other criteria. A set of pair wise comparison matrices (n × n) is constructed (A). Then, each value is normalized by dividing the related value to the relevant column’s sum (W ). 3. Now, it is necessary to verify if the consistency occurs or not. The priority vector and the pairwise comparison matrix are multiplied to obtain the weighted total vector. It is calculated by dividing each element of the weighted total vector by its corresponding priority value. The arithmetic mean of these values is calculated. The index score is calculated with the consistency index (CI) formula given below (where CI addresses to the consistency index, λmax addresses the largest eigenvector of the matrix, n is the number of criteria, and RI is the random index that differs for each n). Finally, the consistency index score is divided by the randomness index and the consistency ratio (CR) is calculated as given below. The fact that the calculated consistency ratio is less than 0.10 shows that the pairwise comparisons made by the expert are consistent. Table 3 shows the randomness index value table.

CI =

λmax − n n−1

(1)

CI RI

(2)

CR =

4. If there are inconsistent results, the questionnaires are sent back to that expert and the questionnaire is repeated until the value becomes less than 0.10. 5. When all matrices become consistent, all matrices are aggregated by using geometric mean. 6. Previous steps are performed for all levels in the hierarchy. Then multiply the local weights with the weights of the parent main criteria to end up with the global weights of the sub criteria. These steps are conducted after receiving the answers of the surveys. Three experts who have deep experiences on education systems have selected and surveys including pairwise comparison of criteria are conducted with them. Their consistencies were checked, and it was proved that all of them are satisfactorily consistent. As an example, calculations to gather one expert’s weights and consistency ratio is given. In Table 4, the matrix of the pairwise comparison of one expert in terms of main criteria is given with the summation of the columns.

Digitalization Maturity Model Development for Higher Education

483

Table 4 The matrix of pairwise comparison of one expert in terms of main criteria Strategic

Organizational

Technological

Data management

Strategic

1

0.14

5

1

Organizational

7

1

9

5

Technological

0.2

0.11

1

0.2

Data management

1

0.2

5

1

Summation of columns

9.2

1,45

20

7.2

Table 5 The values of the matrix of pairwise comparison of one expert in terms of main criteria Average of each row

Normalized values 0.11

0.09

0.25

0.14

0.15

0.76

0.68

0.45

0.7

0.65

0.02

0.07

0.05

0.03

0.04

0.11

0.14

0.25

0.14

0.16

In Table 5, the normalization matrix with the average of each row is given for the selected expert’s comparisons. Then, arithmetic averages are calculated, and the weights are gathered. To show how the consistency is checked, the calculations are shown for one expert. The values in the matrix given in Table 4 (A), are multiplied with W, then the retrieved matrix is divided by 4 (n). Table 6 gives the results of these steps. Next, the largest eigenvector of the matrix (λmax ) calculated by getting the average = of the values 4.16, 4.44, 4.03 and 4.13. In our case, λmax is 4.19. So, C I = 4.19−4 3 0.06 0.06 and C R = 0.89 = 0.07. Because it is lower than 0.10, it is accepted as consistent. After proving that all experts are consistent, their pairwise comparison matrices are aggregated by applying geometric mean for each criterion set. For instance, the aggregated comparison matrix for main criteria is revealed as shown at Table 7. The global weights of main criteria are given at Table 8. Also, in Table 9, the local and global weights of each sub criterion are given. Table 6 A consistency calculation for one expert A

W

AXW

(A × W)/priority vector element

1.00

0.14

5.00

1.00

0.15

0.62

4.16

7.00

1.00

9.00

5.00

0.65

2.88

4.44

0.20

0.11

1.00

0.20

0.04

0.18

4.03

1.00

0.20

5.00

1.00

0.16

0.66

4.13

484

N. B. Ulufer et al.

Table 7 The aggregated matrix of pairwise comparison of main criteria Strategic

Organizational

Technological

Data management

Strategic

1

0.62

4.71

1.71

Organizational

1.61

1

5.73

4.22

Technological

0.21

0.17

1

0.28

Data management

0.58

0.23

3.55

1

Table 8 The weights of main criteria Main criteria

Weight

Strategic

0.29

Organizational

0.48

Technological

0.06

Data Management

0.17

3.3 Step 3: The Calculation of Digital Maturity Score for a Real Case The purpose of this sub section is to show how the proposed methodology is performed in a real case. Therefore, a real application is conducted for one of Turkey’s best universities. This university has a mission of “being higher education institution dedicated to teaching, research, and service to our society, and to educate the leading work force of future who have an inquiring mind and a critical thinking ability; are sensitive to local and global issues; achieve international standards; contribute to scientific, technological, and cultural knowledge; are strong supporters of universal ideas and values”. Parallel to this vision, the university management aims to adapt themselves to high tech education and training system with high efficiency, therefore they have just opened a new digital education center. As a first step, the university has been scored by using 0–5 Likert scale in terms of given sub criteria in the methodology. The score assignment was made by six experts of the selected university. Experts were asked to evaluate the success of applying each sub criterion, in other words, how much the university is good at applying these sub criteria. The average of the scores were taken into consideration, and they were multiplied by the weights computed by AHP. The scores and statuses given at [2] are considered to determine the maturity level. They are given below: – Maturity Level 1- Initiation) The basic practices planned in the process with maturity level 1 have been largely implemented. The organization generally does not provide a stable environment. Success in these organizations depends not on the use of proven processes, but on the competence of the people in the organization. – Maturity Level 2- Managed) The maturity level 2, called managed process, indicates that there is infrastructure to support the existing process, the performance

Digitalization Maturity Model Development for Higher Education

485

Table 9 The local and global weights of sub criteria Main criteria

Sub criterion

Local weights Global weights (wi )

Strategıc

• Top management support

0.082

0.023

• Investment on digital education

0.056

0.016

• Adaption of partnership

0.079

0.022

• Projects on digitalization in education

0.112

0.032

• Patents on digitalization in education

0.029

0.008

• Potential of student group

0.267

0.076

• Existence of digital culture

0.372

0.107

• Existence of digitalization working group

0.204

0.098

• Existence of IT department

0.201

0.097

• Employee training

0.186

0.089

• Digital collaboration with all stakeholders

0.156

0.075

• Infrastructure-access-distributions tool

0.251

0.121

• Existence of digital infrastructure

0.086

0.005

• Utilization of digital devices for students

0.178

0.113

• Utilization of digital devices for academic staff

0.191

0.012

• Utilization of digital devices for administrative staff

0.161

0.011

• Information presentation control

0.383

0.024

Organızatıonal

Technologıcal

Data management • Cyber security

0.325

0.053

• Educational data mining

0.426

0.071

• Optimization. diagnostic evaluation

0.248

0.041

process in the main applications has been successfully managed and the related business process has been completed to the expected extent. At this level, it is aimed to create the needs of the organization’s projects and to plan, implement, measure, and control the processes. – Maturity Level 3- Defined) Processes at level 3 are clearly defined, understood and supported and expanded by various standards methods and procedures. The aims, inputs, input features, activities, roles, measurements, outputs and output features are clearly stated in the processes at this level. At this level, processes are defined in more detail than level 2. – Maturity Level 4- Quantitatively Managed) Level 4 manages the processes and sub processes of a project statistically. Quantitative targets are determined according to the needs of the institution, organization and process implementers. In order to

486

N. B. Ulufer et al.

evaluate the process quality and performance, data is taken from the sub-processes and analyzed numerically. Variations in the process are found and their causes are identified. Precautions are taken to prevent certain situations from recurring in the future. – Maturity Level 5- Optimized) Level 5 is a constantly improving level of maturity. It focuses on continuously improving process performance by using innovative technological developments. The global weight values of each criterion (wi ) which are calculated by the help of AHP are multiplied by the scores marked by six experts (ei ) separately. The average of six experts’ evaluations are counted finally to find the overall digital maturity level of the selected university. Overall weight of the selected university was computed as 3.88 that takes place in Maturity Level 4. That means, this university is “quantitatively managed”. Transition from level 4 to 5 can be succeeded by development of the information technology continuously. Also, technology-supported development and the creation of new learning systems should be ensured to level up. Besides that, errors need to be early detected and resolved.

4 Conclusion The main purpose of this study is to provide a supportive tool to evaluate the digitalization maturity level for higher education in terms of four criteria: strategic, organizational, technological and data management. The other important goal is to prioritize digitalization needs in higher education with a methodically accepted and easy-to-understand tool, and then to find the total score by multiplying the differing degrees of importance by the success score of the university on the basis of each subcriterion. “Infrastructure-Access-Distributions Tool” is found as the most important sub-criterion, on the other hand, “Existence of Digital Infrastructure” is found as the least important sub-criterion. Among the main criteria, “Organizational” criteria is accepted as the most important one. With this tool, the university administration can determine where it is in the digitalization journey and look at how it can create a strategic roadmap in terms of progress. Also, considering its weaknesses and strengths, it can analyse and determine to protect and strengthen the points where it has a competitive advantage. In the future, other MCDM techniques can be used, and the results can be compared. Furthermore, sensitivity analysis can be conducted to see how the model is sensitive to changes on the evaluations. Across time, it is expected to see that new digitalization concepts and technologies will be applied in education systems, therefore the proposed methodology can be extended by adding new criteria in different hierarchical levels of the model.

Digitalization Maturity Model Development for Higher Education

487

References 1. Akgün DE (2018) E˘gitim 4.0 ve E˘gitsel Veri Madencili˘gi 2. Alparslan SA (2017) CMMI ˙Ile Yazilim Süreçlerinin ˙Iyile¸stirilmesi ve Yazilim. Antalya 3. Becker J, Björn N, Jens P, Alexander S, Liechtenstein V (2010) Maturity models in IS research. In: 18th European conference on information systems 4. Bejinaru R, Mare S (2019) Impact of digitalization on education in the knowledge economy. Manag Dyn Knowl Econ 7(3):367–380 5. Bozkurt A, Hamuto˘glu NB, Kaban AL, Ta¸sçı G, Aykul M (2021). Dijital bilgi ça˘gı: Dijital toplum, dijital donu¸sum, dijital e˘gitim ve dijital yeterlilikler. Acıko˘gretim Uygulamaları ve Ara¸stırmaları Dergisi 7(2):35–63 6. Crosby PB (1979) Quality is free: the art of making quality certain. McGraw-Hill, New York 7. Demir C, Kocaba¸s ˙I (2010) Project management maturity model (PMMM) in educational organizations. Procedia Social Behav Sci 9:1641–1642 8. Donovan MS, Wigdor AK, Snow CE (2003) Committee on a strategic education research partnership. National Research Council, vol 13 9. Education Technology and Mobile Learning (2016) Fundamental digital skills for 21st century teachers. https://www.educatorstechnology.com/2016/12/9-fundamental-digital-ski lls-for-21st.Html 10. Esteve-Mon F, Llopis M, Adell-Segura J (2020) Digital competence and computational thinking of student teachers. Int J Emerg Technol Learn 15(2):29–41 11. Fisk P (2017). Education 4.0 … the future of learning will be dramatically different, in school and throughout life. http://www.thegeniusworks.com/2017/01/future-education-youngeveryone-taught-together 12. Frolova EV, Rogach OV, Ryabova TM (2020) Digitalization of education in modern scientific discourse: new trends and risks analysis. Eur J Contemp Educ 9(2) 13. Göç B (2018) Endüstri 4.0 ile Gelen E˘gitim Düzeni: E˘gitim 4.0. MENTALUP 14. Halili SH (2019) Technological advancements in Education 4.0. Online J Dist Educ e-Learn 7(1) 15. Hussin AA (2018) Education 4.0 made simple: ıdeas for teaching. Int J Educ Liter Stud 6(3):92– 98 16. Jamaludin R, McKay E, Ledger S (2020). Are we ready for Education 4.0 within ASEAN higher education institutions? thriving for knowledge, industry and humanity in a dynamic higher education ecosystem? J Appl Res High Educ 12(5):1161–1173. https://doi.org/10.1108/ JARHE-06-2019-0144 17. Kuuk Ö (2019) E˘gitim 1.0, 2.0, 3.0 ve 4.0. Ankara: Nobel Yayın 18. Lord N (2019) What is cyber security? definition, best practices & more. DigitalGuardian 19. Mankins JC (2009) Technology readiness evaluations: a retrospective. Acta Astronaut 65:1216– 1223 20. Maslow A (1954) Motivation and personality. Harper, New York 21. Miranda J, Navarrete C, Noguez J, Molina-Espinosa JM, Ramírez-Montoya M-S, Navarro-Tuch SA, Bustamante-Bello M-R, Rosas-Fernández J-B, Molina A (2021) The core components of education 4.0 in higher education: three case studies in engineering education, Comput Electr Eng 93:107278 22. Nolan RL (1979) Managing the crisis in data processing. Harv Bus Rev 5(2):115–126 23. Öztemel E (2018). Yeni Yönelimlerin De˘gerlendirilmesi ve E˘gitim 4.0. Üniversite Ara¸stırmaları Dergisi 1(1):25–30 24. Paulk MC, Curtis B, Chrissis MB, Weber CV (1993) Capability maturity model for software, version 1.1. Software Engineering Institute, CMU/SEI-93-TR-24 25. Petrusevich DA (2020). Modern trends in the digitalization of education. J Phys Conf Ser 1691 26. Ronzhina N, Kondyurina I, Voronina A, Igishev K, Loginova N (2021) Digitalization of modern education: problems and solutions. iJET 16(04):122–135

488

N. B. Ulufer et al.

27. Qureshi MI, Khan N, Raza H, Bahru J, Imran A, Ismail F (2020) Digital technologies in education 4.0. does it enhance the effectiveness of learning? a systematic literature review. iJIM 15(04) 28. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York 29. Saaty TL (1994) How to make a decision the analytic hierarchy process. Interfaces 24:19–43

Smart Cities

Smart City and Smart Communities: Emerging Conditions for Digital Transformation Aleksey N. Raskhodchikov

and Maria Pilgun

Abstract The article presents an algorithm for analyzing the communicative behavior of actors in cyberspace to determine the perception and track opinions and attitude changes of metropolitan residents in terms of digital transformation during pandemic. In this study, authors focused of negative reactions of residents of the metropolis to the transformation of IT technologies. The study involved a crossdisciplinary approach. The materials for the study were data from instant messengers, microblogging, social networks, blogs, online media, forums, thematic portals, print media, TV, reviews, shops, video hosting services. The results of the study show that it is necessary to be made to the existing urban system of governance, and new methods for linking big data to findings of opinion polls on socially relevant issues need to be developed, urban communities have to be involved in the discussion of digital transformation of cities, and that a compromise has to be made between the implementation of new technologies and the protection of citizens from unwarranted interference with their private lives and abuse of their digital identities. Keywords Smart city · Smart communities · Digital transformations · Neural network technologies

1 Introduction Digital transformation and development of smart city technologies have become an increasingly popular way to address the growing complexity of urban systems and processes.

A. N. Raskhodchikov (B) Moscow Center of Urban Studies “City”, Moscow, Russian Federation e-mail: [email protected] M. Pilgun Russian State Social University, Moscow, Russian Federation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_21

491

492

A. N. Raskhodchikov and M. Pilgun

1.1 Literature Review Authorities and technology-based businesses actively promote facial recognition systems, taxi and car sharing apps, platforms for city traffic control, online medical appointments, medical diagnostic systems using artificial intelligence algorithms, and multiple other solutions [2, 3, 7, 20, 22, 24, 26, 27]. Systems of security measures, especially those that involve blockchain technologies, are of particular importance [19]. Development and implementation of technologies that are beyond the abilities of mere humans are evidently a mission of the new industrial revolution 4.0. However, that might be possible only after a complete digital transformation, which would create new economic sectors and deeply affect the existing ones [6, 11, 25]. In the meantime, the society is naturally ambivalent towards the changes that involve digital technologies deeper and deeper penetrating the people’s private lives. Smart city technologies are most vigorously adopted in metropolises and large agglomerations. This can be explained by both the larger economic potential of big cities and the higher complexity of urban processes management in fast-growing metropolises. The article analyzes general approaches to digital transformation in these cities, describes specific features of implementing various technologies, and assesses the impact of innovations on the quality of life as well as risks arising from the implementation of digital services. It is worth recalling that the original definition of smart cities centered on the implementation of information and communication technologies in urban infrastructure. Later, the overly technical concept was adjusted to focus more on management and social capital. Today, a smart city is more about a system, in which information and communication technologies facilitate overall balance and intergenerational equity of urban life [13]. Thus, the role of information and communication technologies in enhancing the efficiency of urban management and governance is thoroughly acknowledged in the report of the United Nations Economic Commission for Europe on socially smart and sustainable cities published in 2020. However, an apt conclusion is made in the report that the success of smart cities is defined by leadership, good intraorganizational coordination and the involvement of citizens, rather than by the level of technologies or technical capital [13, 23]. According to Nikos Komninos’s now classic definition, a smart city is a combination of ‘smart management’, ‘smart technologies’ and ‘smart communities’ [14]. However, in reality, the technologybased approach to digital transformation is still the predominant one. The analysis of news stories on smart cities in mass media shows that experts most frequently talk about BIM technologies, AI-based control and regulation systems, big data, artificial intelligence, mobile apps and Internet of Things. Possibilities of adjustment of urban management systems to new digital services are rarely mentioned, and cooperation with urban communities for smoother digital transformation of cities are totally excluded from the agenda (Fig. 1). The definition of a smart city as a system in which people directly transform their living environment by investing in urban innovations is, in fact, somewhat far-fetched. Development and implementation of technologies for smart cities require substantial

Smart City and Smart Communities …

493

Fig. 1 Key development trends for smart cities according to experts

investments. Tech businesses and municipal governments are capable of making such investments, but common citizens and urban communities are not. No wonder that it is tech businesses and city authorities that benefit from the implementation of smart technologies and not the citizens. That said, big data, artificial intelligence and mobile apps offer new opportunities for smarter urban planning. For instance, we are used to assessing the density of urban districts based on the number of residents; the local economies of such districts and the efficiency of retail and consumer services facilities depend on the population density. That approach seems somewhat old-fashioned, though, since the residents of a district are not the only ones who make use of its infrastructure. There are also people who live somewhere else but work nearby, as well as tourists and those who come to that particular district for a walk in a park, for a dinner in a local café, to see a movie or an exhibition. If we keep that in mind, we will have an entirely different picture of the population density and economic efficiency of that district. It used to be quite difficult to calculate it all together, but now, using big data technologies, we can get more precise information about various population groups and build a reliable multifunctional infrastructure in various urban districts. Visitor analysis algorithms based on big data allow us to calculate the efficiency of the most large-scale open-air events. Thus, during a study by the Moscow Urban Center “City” in Kaliningrad, we were able to obtain useful information about the participants of a major theater festival. The data provided by mobile operators allowed us to determine the cities that the participants came from, as well as their levels of income and their strategies for renting or acquiring housing. (Fig. 2). Such analysis methods help calculate real efficiency of event tourism, which could be instrumental in economic development of cities and regions. Mobile apps are also capable of making cities more comfortable for people. Apps that allow users to order a taxi, to arrange a car sharing or to pay for parking, as well as more sophisticated systems that can control traffic lights remotely could solve the pressing issue of traffic jams in busy streets. Security cameras make urban areas

494

A. N. Raskhodchikov and M. Pilgun

Fig. 2 The results of a major event audience study performed using big data [17]

safer, while emission control sensors help indicate toxic emissions and thus keep an eye on the cities’ environment. Considerable progress has also been achieved in state and municipal services. Digitalization of most of the official papers allows citizens to receive necessary certificates, file documents and get new passports quickly and without standing in lines for a better part of the day. Online services facilitate the work of commercial companies, helping to avoid bureaucracy and corruption while incorporating a new company, registering a land plot, getting a construction permit, receiving a license or getting connected to utilities. The pandemic has accelerated the digital transformation considerably. It became obvious that digital transformation would be a major challenge of the years to come [12]. Transition of businesses and government authorities to remote work, increasingly popular online conferences and meetings, digital passes during lockdowns as well as mobile apps for contact tracing have boosted online communications and speeded up the development of digital services and platforms [5, 15]. At the same time, urban communities have voiced growing concerns over enhanced governmental control and tech companies’ ready access to digitalized personal data of citizens. At the time of AI and digital transformation, identity issues may have an unexpected development [21]. In mass and social media, implementation of information and communication technologies is often discussed in terms of violation of the human right to privacy, and the future of digital transformation is increasingly associated with a ‘digital concentration camp’ [4, 8]. The digital transformation in the smart city has taken off rapidly during the pandemic, causing mixed perceptions among residents. Artificial intelligence technologies and large datasets collected in real time make it possible to take the management of urban systems to a fundamentally new level [16]. Meanwhile, the introduction of new technological solutions can cause problems related to the residents’ reactions.

Smart City and Smart Communities …

495

It is clear that technologies related to artificial intelligence and, in particular, to smart cities have a huge potential to improve the efficiency of services. However, the rapid development of technological solutions that are difficult to understand can cause a negative perception of the population, with unpredictable consequences. Thus, according to previous studies, citizens are seriously concerned about ethical issues, the lack of transparency and the consequences of the artificial intelligence development on the labor market [1]. The purpose of the study is to analyze the communicative behavior of Russianspeaking actors in cyberspace to determine the perception of users and to track the opinions and attitude changes of the metropolis residents in order to timely response and forecast emerging and/or developing conflict situations related to digital transformation during the pandemic. The authors will examine the impact of digital transformation on the welfare and quality of life of large urban agglomerations using the example of Moscow. This city has been picked deliberately, since it hit the top of the UN-HABITAT City Prosperity Index in 2022. The article analyzes general approaches to digital transformation in that city, describes specific features of implementing various technologies, and assesses the impact of innovations on the quality of life as well as risks arising from the implementation of digital services.

1.2 Methods The study involved a cross-disciplinary approach. To interpret the content, neural network text analysis, content analysis, sentiment analysis and analysis of lexical associations were used [10]. Sentiment analysis, associative network analysis and word association (WA) analysis reveal implicit information in textual content generated by users and help single out reactions that most accurately demonstrate citizens’ attitude towards digitalization during the pandemic. An important part of the research is the analysis of Internet users’ emotional reactions to the actions of authorities in text messages [9].

496

A. N. Raskhodchikov and M. Pilgun

1.3 Procedures 1.3.1

Digital Content Analysis

(See details [9, 10]).

Smart City and Smart Communities …

1.3.2

497

Offline Sociological Survey

1.4 Data The materials for the study were data from instant messengers, microblogging, social networks, blogs, online media, forums, thematic portals, print media, TV, reviews, shops, video hosting services etc., related to the first wave of pandemic in Russia. The data was collected from March 1, 2020 until June 1, 2020. The common data set includes 10,100,253 word, or 65,483,615 characters. The organization of the chapter: 1. Introduction 1.1. Literature review 1.2. Methods 1.3. Procedures 1.3.1. Digital Content Analysis 1.3.2. Offline Sociological Survey 1.4. Data 1.5. The organization of the chapter 2. Results and discussion 2.1. General description 2.2. Reactions of residents of the metropolis 2.2.1. Analysis of residents’ sentiments 2.2.2. Neutral cluster 2.2.3. Negative cluster 2.2.4. Positive cluster 2.3. Sentiment analysis

498

A. N. Raskhodchikov and M. Pilgun

2.4. Associations network analysis 3. Conclusion References

2 Results and Discussion 2.1 General Description At the beginning of the crisis in Russia, it became necessary to quickly digitalize numerous daily procedures. Despite the fact that the need for digitalization arose due to the external circumstances beyond people’s control, not all transformations were well-received by the society. We have already discussed changes in old and appearance of new digital apps and services during the first wave of pandemic n Russia in our previous paper [9]. In this study, we would like to focus of negative reactions of the citizens of Moscow to the transformation of IT technologies. According to the specific audience analysis, at the beginning of the pandemic, people preferred to get information about the current events from instant messaging services. The outreach of instant messengers (87,158,009) was much greater than that of microblogs (21,370,253) or social media (6,951,832) (Fig. 3). Data analysis showed that when discussing the problems associated with the spread of the pandemic at the beginning of the 3rd wave in Russia, the content was generated mainly by actors with personal profiles (710 471 050); media accounts were also actively used (194 243589); discussion in communities was represented by the smallest quantitative indicators (84,119,589) (Fig. 4). Meanwhile, in terms of the number of views, communities (4,983,737) are far ahead of personal profiles (3,213,738). It is in the communities that the most interested and reasoned communication of active citizens and specialists takes place, which arouses great interest of the audience. Media accounts have a minimum value in terms of views, which may Fig. 3 Types of information sources by outreach

Smart City and Smart Communities …

499

Fig. 4 Actors’ types

indicate that media content is the least in demand in the virtual space as a source of information (1,099,034) (Fig. 5). According to the digital footprints analysis, people much preferred to discuss their pandemic-induced problems, using instant messengers and social media (Fig. 6). Fig. 5 Views for various actors’ types

Fig. 6 Digital footprints by source type

500

A. N. Raskhodchikov and M. Pilgun

2.2 Reactions of Residents of the Metropolis Most of the people’s negative reactions to that radically new situation in the society caused by emerging conditions were demonstrated in instant messengers (3,212,391) and microblogs (2,304,261), the reason for that being that instant messengers and microblogs were the people’s main sources of information (Fig. 7).

2.2.1

Analysis of Residents’ Sentiments

Content sentiment analysis showed a significant predominance of the neutral cluster (Fig. 8). It is women that generated neutral (287,536,959) and positive content (8,102,850) more actively than men, and men generated mostly negative content (20,198,096) (Fig. 9). After sentiment analysis, the database was divided into 3 clusters according to the sentiment type (neutral, negative, positive). The content analysis results, the study of the topic structure and summarization made it possible to formulate the key topics of each cluster. Fig. 7 Content sentiments by source type

Fig. 8 Content sentiment

Smart City and Smart Communities …

501

Fig. 9 Sentiment of the content depending on the actors’ gender

2.2.2

Neutral Cluster

This analysis shows the quantitative advantage of the neutral cluster over the rest of the clusters. The neutral cluster consists of the content dedicated to discussing various issues related to the new outbreak of the disease. A significant part of the neutral cluster is taken up by official information. Key topics: . Vaccination. . Medical exemptions from vaccination. . Introduction of mandatory vaccination against coronavirus for certain groups of citizens in the regions of Russia. . Labor vaccination: Moscow will expand and simplify the vaccination scheme for migrants. . Vaccine production and effectiveness. . Health management during the pandemic. . PCR tests. . Introduction of non-working days in Moscow on June 15–19, 2021. . Quarantine restrictions . Problems with spreading false information . Operation of airports. . For the purpose of the fight against emerging conditions, “free” zones in Moscow restaurants have been introduced in Moscow. To enter such establishments, one will need a QR code confirming vaccination.

502

2.2.3

A. N. Raskhodchikov and M. Pilgun

Negative Cluster

The negative cluster consists mainly of information that is generated by vaccination opponents and opposition-minded citizens who disagree with the actions of state structures and anti-pandemic measures. Key topics: . . . . . . . .

Vaccination. Criticism of the actions of state and administrative structures. Introduction of an electronic QR code. Expanding infodemic. Information war between vaccination supporters and opponents. Operation of the healthcare system. Increase in revenues of the global pharmacological business Imposing restrictions on the work of cafes, restaurants, shopping centers, parks, etc.). . Compliance with sanitary standards. 2.2.4

Positive Cluster

The positive, as well as the neutral cluster, is represented mainly by messages that were generated by official sources or media affiliated with the federal and Moscow authorities, bloggers, etc., which contain information about effective anti-crisis measures. Key topics: . . . . . . .

Vaccination. Opening of health posts for labor migrants by Moscow Mayor Sergey Sobyanin Raffle of cars among people who receive the vaccine. Delivery of expensive medicines to children’s hospitals in the regions. Development of telemedicine. Expansion of services that can be obtained remotely. The Mayor of Moscow approved a package of anti-crisis support measures for public catering enterprises. . Actions of federal and regional authorities to support business in the context of the pandemic. . Measures to support the regions in terms of the development of infrastructure facilities. It is small surprise that after the appearance of “vaccines” in all three clusters, the main topic is vaccination. Studies of the perception of the pandemic after the introduction of vaccination have already been presented in a large number of works. The cognitive representation of the disease, which was formed among the actors, largely depended on the emotional and behavioral reactions of the members of society. Peculiarities of the crises perception became one of the important factors in deciding on vaccination and implementation of preventive measures, determined the situation

Smart City and Smart Communities …

503

Fig. 10 Sentiments of digital footprints

with the spread of the infection and played a significant role in the fight against the pandemic at a certain stage (Pilgun et al. 18). The best indicator of the power of those negative reactions to the catastrophic and life-changing events was, i.a., the fact that users expressed their emotions by liking/disliking (458,906) and reblogging (158,911) messages of others, while original negative comments made up only a small part of the digital footprints (1,625) (Fig. 10).

2.3 Sentiment Analysis Sentiment analysis of the content according to the type of source and audience coverage confirms the conclusion about the predominance of a neutral sentiment type, which is generated in social networks (Fig. 11). Sentiment analysis of the content generated on various digital platforms indicates the undisputed leadership of the social network VKontakte. YouTube and Facebook, respectively, are the second and third most popular platforms among actors discussing the problems of the third wave of the crises (Fig. 12). Meanwhile, in terms of user involvement, Instagram ranks first, thus driving out VKontakte, YouTube and Facebook (Fig. 13). Negative content is created by users in communities that unite people with a proactive approach to life and a desire for a rational understanding of what is happening (Fig. 14). The most vividly expressed reactions of users, which testify to their assessment of the situation, are digital footprints (likes, comments, reposts, duplicates). When discussing issues related to the spread of the coronavirus infection in June 2021, Russian-speaking users preferred social networks. Actors also left comments on

504

A. N. Raskhodchikov and M. Pilgun

Fig. 11 Content sentiment in accordance with the type of source and audience coverage

Fig. 12 Sentiment of the content generated on various digital platforms

video hosting sites and made duplicates and reposts in microblogs (Fig. 15). Sentiment analysis shows that the consolidated database is dominated by neutral digital footprints (Fig. 16).

2.4 Associations Network Analysis Neural network semantic content analysis and semantic associations analysis allowed us to identify negative events that caused most of the users’ anger and discontent. It is revealing that the digital transformations connected to the restrictions on movement and movement monitoring caused most of the dissent. Digital and electronic passes

Smart City and Smart Communities …

505

Fig. 13 Actors’ involvement on various digital platforms

Fig. 14 Sentiment of the content generated by various types of actors

became a symbol of the ‘digital slavery’ and were met with heated negative reactions (stimuli: Digital pass (10/66 653). Despite the fact that digital monitoring allowed the authorities to control the events in real time and take timely and efficient actions to prevent the spreading of the disease, a considerable part of users viewed such measures as an attack on their freedom and human rights (Fig. 17). Such measures as social distancing and quarantine made it necessary to increase the number of security cameras on the streets and to enlarge their surveillance coverage, which also caused general dissent of users (stimulus: Security cameras (10/35468) (Fig. 18). According to our analysis, users found it the most difficult to accept drastic changes in their daily routines, including temporary disruption of their social ties

506 Fig. 15 Actors’ digital footprints on various types of platforms

A. N. Raskhodchikov and M. Pilgun

Pilgun

Fig. 16 Sentiment types of actors’ digital footprint

and isolation. Therefore, it is quite natural that Distance learning (10/77 131) and Remote work (10/75 473) were on the top of the most negatively assessed stimuli (Figs. 19 and 20).

Smart City and Smart Communities … Fig. 17 Associative network of the Digital pass stimulus

Fig. 18 Associative network of the Security cameras stimulus

Fig. 19 Associative network of the Distant learning stimulus

507

508

A. N. Raskhodchikov and M. Pilgun

Fig. 20 Associative network of the Remote working stimulus

3 Conclusion Digitalization gets into all aspects of daily life in big cities by means of broadband and wireless internet access, online rent and delivery services, digital services and security and surveillance cameras. These innovations make cities more safe, comfortable and smart. At the same time, society demonstrated growing concerns about the government and tech companies invading citizen’s private lives. The collected data is not only analyzed by the authorities; it can also leak and get to the black market, and so be used by lucrative purposes. Besides, the implementation of smart technologies can sometimes turn against the citizens directly. A good example of that is parking or speeding tickets issued automatically based on surveillance camera footage. That can sometimes cause mistakes that have to be disputed in court. Experts are concerned about data protection: are such automatic systems really secure enough and can they not be hacked by ill-minded persons and used for profit? Based on this research, authors come to the conclusion that urban communities have to be involved in the discussion of digital transformation of cities, and that a compromise has to be made between the implementation of new technologies and the protection of citizens from unwarranted interference with their private lives and abuse of their digital identities. A separate issue is that significant changes are needed to be made to the existing urban system of governance, and new methods for linking big data to findings of opinion polls on socially relevant issues need to be developed. Such issues would include urban planning, operation of public transport and social security of citizens. Authors suggest in future studies to develop new assessment techniques for economic and social development programs that can really make our cities smarter, more comfortable and more competitively viable than they are today.

Smart City and Smart Communities …

509

References 1. Carrasco M, Mills S, Whybrew A, Jura A (2019) The citizen’s perspective on the use of AI in government: BCG digital government benchmarking. Boston Consulting Group, Boston. https://image-src.bcg.com/Images/BCG-The-Citizens-Perspective-on-the-Use-of-Art ifical-Intelligence-Mar-2019_tcm27-215068.pdf. Accessed 08 Jan 2022 2. Deng Z, Chen Y, Yang J et al (2022) Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets. Build Simul 15:1547–1559. https:// doi.org/10.1007/s12273-021-0878-4 3. Digital transformation (2020). In: Carayannis EG (eds) Encyclopedia of creativity, invention, innovation and entrepreneurship. Springer, Cham. https://doi.org/10.1007/978-3-319-153476_300446 4. Ferrer JN, Taranic I, Veum K, van den Oosterkamp P, Cordelia W (2019) The making of a smart city: policy recommendations. EU Smart City Information System. European Commission. https://smartcities-infosystem.eu/sites/default/files/document/the_making_of_a_smart_c ity_-_policy_recommendations.pdf. Accessed 04 Jan 2022 5. Garg PK, Tripathi NK, Kappas ML, Gaur L (2022) Geospatial data science in healthcare for society 5.0. In: Part of the disruptive technologies and digital transformations for society 5.0 book series (DTDTS), Springer, Singapore 6. Graham P (2019) Digital transformation. In: Dastbaz M, Cochrane P (eds) Industry 4.0 and engineering for a sustainable future. Springer, Cham. (2019) https://doi.org/10.1007/978-3030-12953-8_5 7. Han X, Wu Y, Zheng J (2020) Disruptive innovation through digital transformation. In: Multisided platforms of e-health in China. Springer: Singapore 8. Janssen M, Kuk G (2016) The challenges and limits of big data algorithms in technocratic governance. Gov Inf Q 33(3):371–377 9. Kharlamov AA, Raskhodchikov AN, Pilgun M (2021) Smart city data sensing during COVID19: public reaction to accelerating digital transformation. Sensors 21(12):3965. https://doi.org/ 10.3390/s21123965 10. Kharlamov A, Pilgun M (eds) (2020) Neuroinformatics and semantic representations: theory and applications. Cambridge Scholars Publishing, Newcastle upon Tyne 11. Kim J, Lee R (2021) Data science and digital transformation in the fourth industrial revolution. Springer, Singapore 12. Kleinert J (2021) Digital transformation. Empirica 48:1–3. https://doi.org/10.1007/s10663021-09501-0 13. Komeily A, Srinivasan RS (2017) Sustainability in smart cities: balancing social, economic, environmental, and institutional aspects of urban life. In: Smart cities: foundations, principles, and applications. Wiley, pp 503–534 14. Komninos N (2015) The age of intelligent cities: environments and innovation-for-all strategies. Routledge. London, New York, p 278 15. Kose U, Watada J, Deperlioglu O, Saucedo JAM (2022) Computational INTELLIGENCE for COVID-19 and future pandemics. In: Emerging applications and strategies. Part of the disruptive technologies and digital transformations for society 5.0 book SERIES (DTDTS). Springer, Singapore 16. Maisonobe M (2022) The future of urban models in the big data and AI era: a bibliometric analysis (2000–2019). AI & Soc 37:177–194. https://doi.org/10.1007/s00146-021-01166-4 17. Management of tourist development in regions: a report of the Moscow Centre of Urban Studies “City” presented at the World Urban Forum (WUF10) UN-Habitat in Abu Dhabi (2020) 18. Pilgun M, Raskhodchikov AN, Koreneva Antonova O (2022) Effects of COVID-19 on multilingual communication. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.792042 19. Saxena U, Sodhi JS, Tanwar R (2020) Augmenting smart home network security using blockchain technology. Int J Electron Secur Dig Forensics 12(1):99–117 20. Neugebauer R (ed) (2019) Digital transformation. Springer-Verlag GmbH Germany, part of Springer Nature, GmbH

510

A. N. Raskhodchikov and M. Pilgun

21. Shibuya K (2020) Digital transformation of identity in the age of artificial intelligence, Springer Nature Singapore Pte Ltd, Singapore 22. Smart city (2020) In Encyclopedia of wireless networks. Springer, Cham. https://doi.org/10. 1007/978-3-319-78262-1_300599 23. Socially Smart and Sustainable Cities (2020) A report of the United Nations Economic Commission for Europe. Published by the UN. eISBN 978–92–1–005266–5 24. Spena TR, Bifulco T (2021) Digital transformation in the cultural heritage sector. In: Challenges to marketing in the new digital era. Springer, Cham 25. Sun K, Hong T, Kim J et al (2022) Application and evaluation of a pattern-based building energy model calibration method using public building datasets. Build Simul 15:1385–1400. https://doi.org/10.1007/s12273-022-0891-2 26. Takeda K, Ide I, Muhandiki V (2021) Frontiers of digital transformation. In: Applications of the real-world data circulation paradigm. Springer, Singapore 27. Tian C, Ye Y, Lou Y et al (2022) Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network. Build Simul 15:1685–1701. https://doi.org/10.1007/s12273-022-0887-y

Digital Transformation for Intelligent Road Condition Assessment Sicen Guo, Yue Bai, Mohammud Junaid Bocus, and Rui Fan

Abstract Recently, governments have been resorting to cutting-edge artificial intelligence technologies to facilitate the digital transformation of smart cities. Remarkable progress has been made to strengthen smart city governance and sustainability, especially in road condition assessment. Road data acquisition and defect detection, two major processes of intelligent road condition assessment, play an important role in ensuring road maintainability while providing maximum traffic security and driving comfort. Traditional manual visual inspection is inefficient and lacks objectivity. Therefore, intelligent road condition assessment systems developed based on datadriven techniques have received increasing attention. This chapter presents the stateof-the-art intelligent road condition assessment systems, the existing challenges, and future development trends.

1 Introduction We have ushered in the digital age since the mid-20th century [1]. In the context of the digital revolution, society will undergo a comprehensive upgrade and transformation S. Guo and Y. Bai—Joint first authors of this chapter. S. Guo (B) · Y. Bai · R. Fan Tongji University, Shanghai, China e-mail: [email protected] Y. Bai e-mail: [email protected] R. Fan e-mail: [email protected] M. J. Bocus University of Bristol, Bristol, England e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_22

511

512

S. Guo et al.

through modern digital technologies [2]. The increasing digitalization of various processes and manufacturing is resulting in increasing sensor data and researchers’ participation [3]. The growth of data networks and the rise of digital processes have eventually led to the creation and development of a digital environment [4].

1.1 Digital Transformation Digital transformation (DX) aims to drive faster product innovation and improve service delivery [5]. The realization of DX requires not only the support of data science technology [6], but also the unification of city management strategy and cooperation between departments [7]. DX has permeated many aspects of our daily life, such as medical care, industrial allocation, traffic control, and urban management. For instance, it has been playing a significant role in healthcare, providing services such as electronic health records, digital imaging, and e-prescribing services [9]. This not only improves the efficiency of healthcare operations but also helps to ensure the high quality of patient care [10]. To improve the technical efficiency of flow monitoring, many airports have introduced new digital solutions in their operations [11]. In the aspect of urban governance, city managers collect data from daily activities, such as parking lots, to generate data networks. After analyzing them in real-time, the generated predictive models will be used for future planning [12]. Similarly, road data are collected for its condition assessment, which helps eliminate potential safety hazards and ensure driving safety [13]. Figure 1 shows the basic concept of the future smart road technology, consisting of a combination of information, electrical, and transportation networks [14]. DX has also transformed the way many industries operate today. It helps ensure a business’s competitive advantage, productivity, and execution efficiency [15]. The technologies used in DX can improve production efficiency, enhance industrial value, and build innovative models of business operations [16]. Data in agriculture, transportation, and other industries can be obtained more accurately and efficiently, mak-

Fig. 1 An illustration of future smart road technology [8]

Digital Transformation for Intelligent Road Condition Assessment

513

Fig. 2 An example of the DT application in fruits and vegetable transportation [21]

ing the systems automated and more intelligent [17]. Many industries have been affected by the pandemic of COVID-19 in recent years. Digitalized systems requiring minimal human intervention have, therefore, become more crucial than ever [18]. Digital twins (DT) technology, which is a crucial component of the digital transformation, uses innovative approximate methods to extract, analyze and upgrade relevant features [19]. DT technology has been extensively applied in many fields. It consists of (1) the physical information in real scenarios, (2) virtual replicas of the real scenarios, and (3) the relationship between these two states [20]. Learning and predicting the behavior of physical entities is typically implemented by artificial intelligence and other information technology [18]. Figure 2 illustrates an example of DT technology applied in the agricultural field, where smart electronic devices are transported alongside the fruits and they constantly monitor the state of the fruits in real-time through sensors. The collected information is then sent to the cloud platform for fault detection and defects analysis.

1.2 Smart City As one of the most important applications of DX, smart city has been a feasible solution to modern infrastructure maintenance, such as intelligent road condition assessment. A smart city is typically a city in which information and communication technologies (ICTs) are combined with traditional infrastructures, allowing intelligent planning and management of its resources [22]. Smart city technology allows city managers to monitor the city’s development in time, to know what is going on in the city, and to regulate the infrastructure [23]. The quality of urban services and the linkage between citizens and government have been enhanced with ICT [24]. At the same time, as an essential strategy, the smart city will also integrate the Internet of Things (IoTs) technologies to improve public infrastructure, services, and utilities.

514

S. Guo et al.

IoTs can connect numerous aspects of life into an organic entirety. Incorporating data-driven applications can help provide safe and intelligent services in smart cities [25]. Smart cities are expected to improve liveability and make production management more efficient and accurate [26]. The key success of smart city mainly relies on improving fundamental public services and utilities, developing an intelligent city network platform and improving public transportation [27]. Among these, a reliable and high-performing telecommunication infrastructure is an essential component of the smart city that will help in monitoring resources and providing quality services such as medical treatment. Built upon fast optical fiber networks, wireless broadband technologies such as WiMAX provide comprehensive coverage, which will facilitate the management of city’s resources and allow citizens to access services and information wherever they are [28]. The construction of smart cities also plays an influential role in the field of innovative medical care [29]. Hospitals can establish digital systems to manage medical supplies [30]. Regarding the management of resources, city managers can implement a green city platform to monitor resources. In conclusion, smart cities have a bright prospect [31]. Driven by the DX technology, extensive interdisciplinary research on smart cities using data science techniques is receiving increasing attention, especially in computer vision-based intelligent road condition assessment. Each city establishes an intelligent traffic management system according to its traffic conditions. In such a system, the use of sensor networks, IoTs, and other technologies can realize adaptive traffic signal control as part of urban construction, and ultimately facilitate the life of citizens [31].

1.3 Intelligent Road Condition Assessment Road condition assessment is a requisite task to ensure maintainability of road networks while providing maximum security for users [32]. Manual visual inspection by professional inspectors is still the main form of road condition assessment [33]. However, this process is subjective, costly, tedious, and time-consuming [13]. Therefore, there is an increasing demand for intelligent road condition assessment systems developed based on digital twin techniques [34]. An intelligent road condition assessment system typically consists of two main components: (1) road data acquisition, and (2) road defect detection, as illustrated in Fig. 3. Remote sensing, vibration sensing, and computer vision are commonly used for intelligent road condition assessment [32]. The remote sensing methods which have been utilized in unmanned aerial vehicles, satellites, multi-purpose survey vehicles, or aeroplanes have indeed reduced the workload of road inspectors. Nevertheless, the traditional geotechnical methods can never be entirely replaced by the remote sensing approaches [32]. Vibration-based methods generally use accelerometers and GPS for multi-modal road data collection [35, 36]. However, such methods

Digital Transformation for Intelligent Road Condition Assessment Road Data Acquisition

515

Road Defect Detection

2D Imaging 2011 2D Image Processing-Based

Camera

2014

3D Point Cloud Modeling & Segmentation-Based

3D Imaging

Machine/Deep Learning-Based

2018 Image Classification

Laser Scanner

2020

Object Detection Microsoft Kinect 2021 Stereo Camera

Semantic Segmentation

Fig. 3 Computer vision techniques for road data acqusition and defect detection [37]

always suffer from defect misdetection in spite of their advantages in terms of costeffectiveness, small storage requirements, and real-time performance [32]. Before assessing the condition of the road, it is necessary to acquire road data using 2D imaging technology through cameras and distance sensors [38]. However, for 2D road images without overlapping regions, it is not easy to acquire the 3D geometry of the road surface [39]. Furthermore, these methods are severely affected by the environment, especially the lighting conditions [40]. In this regard, some researchers [32, 33] use laser scanners, Microsoft Kinect sensors, and stereo cameras to collect road data with the help of 3D imaging technology. In addition to the aforementioned three common types of 3D imaging technologies, photometric stereo [41, 42], interferometry [43, 44], structured light imaging [45, 46], and time-of-flight (ToF) [47, 48] are other alternatives for 3D geometry reconstruction. However, they were not widely used for 3D road data acquisition. The interested reader is referred to [49] for a detailed description of these technologies (both theoretical and practical aspects are covered). Road defect detection is intrinsic to the intelligent road condition evaluation system. The road defect detection methods can be divided into: 2D image processingbased [50], 3D point cloud modeling and segmentation-based [51], and machine/deep learning-based [52]. Algorithms based on 2D image processing process road RGB

516

S. Guo et al.

or parallax/depth images through enhancement, compression, transformation, segmentation, etc.. 3D road point cloud modeling and segmentation-based algorithms build a geometric model from the observed road point cloud which is segmented by comparing the fitted and observed surfaces [53]. Machine/deep learning-based algorithms use image classification, object recognition, or semantic segmentation networks to solve the problem of road pothole detection.

2 Road Data Acquisition The application of 2D imaging technologies for road data collection started as early as 1991 [39, 54]. However, the spatial structure cannot be explicitly illustrated in 2D road images [32]. Therefore, many researchers [32–34, 55] have resorted to 3D imaging technologies, which can better reflect the geometric characteristics of road defects and improve the robustness of intelligent road condition assessment systems. With the development of digital transformation, various 3D imaging techniques have been used to calculate the depth of a road scene and reconstruct its 3D geometry model. The first reported effort [56] on leveraging 3D imaging technology for road data collection can be traced back to 1997. This section discusses four prevalent 3D road imaging technologies: laser scanning, infrared sensing, multi-view geometry, and shape (depth) from focus. Other 3D road imaging technologies are detailed in [49].

2.1 Laser Scanning Laser scanning is a well-established imaging technology for accurate 3D road data acquisition. This technology is developed based on trigonometric triangulation [49]. As shown in Fig. 4, the laser receiver is located at a known distance from the laser’s emitter. Accurate point measurements can be made by calculating the reflection angle of the laser light. Auto-synchronized triangulation [56] is a popular variation of classic trigonometric triangulation, and it has been widely utilized in laser scanners to capture the 3D geometry information of near-flat road surfaces [49]. However, laser scanners must be mounted on dedicated intelligent road condition assessment vehicles, such as a Georgia Institute of Technology Sensing Vehicle [57]. Nevertheless, such vehicles are not commonly used for intelligent road condition assessment because they are costly, and so is their routine maintenance [34].

Digital Transformation for Intelligent Road Condition Assessment

z

Sensor

Δx’

Laser Cylindrical lens

517

Lens

Δz

Image

Δz Δx Profiled surface x Fig. 4 Triangulation in laser scanners [58]

2.2 Infrared Sensing Microsoft Kinect sensors [40], as illustrated in Fig. 5, are the most frequently used infrared sensors for road data acquisition. It was initially designed for the Xbox-360 motion-sensing games. The operating range of Microsoft Kinect sensors is 800–4000 mm, making it suitable for road imaging when mounted on a vehicle. There are three reported efforts [40, 59, 60] on 3D road data acquisition and defect detection using Microsoft Kinect sensors. However, the main downside of Microsoft Kinect sensors is that they suffer considerably from infrared saturation in direct sunlight [53].

Fig. 5 Microsoft Kinect sensor

RGB Camera

3D Depth Sensor (IR Camera + IR Emitters)

Multi-array MIC

518

S. Guo et al.

Camera 0

X

Camera 1

Fig. 6 Multi-view geometry [49]

2.3 Multi-view Geometry The 3D geometry of a road surface can also be reconstructed using multiple images captured from different views [32]. The theory behind this 3D imaging technique is typically known as multi-view geometry [61], as shown in Fig. 6. These images can be captured using a single movable camera [62] or an array of synchronized cameras [33]. In both cases, dense correspondence matching (DCM) between two consecutive road video frames or between two synchronized stereo road images is the main problem to be solved. Structure from motion (SfM) [63] and optical flow (OF) estimation [64] are the two most commonly used techniques for monocular correspondence matching, as illustrated in Fig. 7. SfM method estimates camera pose and 3D points of interest from a series of images taken from different viewpoints [65]. SfM typically starts with feature extraction and matching, followed by geometric validation. It then uses the resulting scene graph as the basis for the 3D reconstruction procedure that triangulates scene points, filters out outliers, and refines the reconstruction using bundle adjustment (BA) [66]. OF describes the motion of pixels between consecutive frames of a video sequence. OF method obtains the motion speed (or distance in recent deep learning-based works) of pixels between images based on the assumption that the correspondence intensity is constant. OF estimation methods replace the calculation of keypoints and descriptors in SfM, demonstrating superb (dense) results even in the case of limited texture information [67]. Stereo vision (also known as binocular vision, stereo matching, or disparity estimation) [32, 34] is typically employed for binocular DCM. Stereo vision acquires depth information by finding the horizontal positional differences (disparities) of the visual feature correspondence pairs between two synchronously captured road images [32]. Stereo vision has high research value and broad application fields because it can calculate the absolute scale to establish more accurate 3D models, without having to consider the complex environmental hypotheses [38]. The traditional stereo vision algorithms formulate disparity estimation as either a local block matching problem [32] or a global/semi-global energy minimization problem (solvable with various Markov random field-based optimization techniques) [68, 69], while data-driven algorithms typically solve stereo matching with convolutional neural networks (CNNs) [70]. Despite the low cost of digital cameras, stereo vision accuracy is always affected by various factors, most notably by poor illumi-

Digital Transformation for Intelligent Road Condition Assessment

519

Fig. 7 3D road imaging with camera(s) [37]

nation conditions [53]. Furthermore, self/un-supervised learning [70, 71] that does not rely on human-labeled training data is the future of deep stereo matching. Moreover, the feasibility of stereo vision algorithms relies on the well-conducted stereo rig calibration [32]. Therefore, some systems incorporate stereo rig self-calibration functionalities to ensure their captured stereo road images are always well calibrated.

2.4 Shape (depth) from Focus As shown in Fig. 8, the position of point O can determine the depth z 0 when fully focused. Point A and point B are located at positions ∆z and −∆z on either side of the focal plane. There are two situations when the point source is imaged through the lens: focusing on the image plane or out of focus. When a point light source cannot be focused, its image on the imaging plane is no longer a point, but a circle called the circle of confusion. In Fig. 8, points A and B show ‘circle of confusion circles’ with radii c1 and c2 in the imaging plane respectively. Although the circle of confusion makes the image appear blurry, the image at point O is obvious. We can use this feature to identify clear points in the image, called Shape from focus (SFF). SFF uses many 3D images taken at different focal lengths [72]. Among them, SFF is based on the same scene, and the depth information is estimated by taking images with different settings of cameras to ensure that the focal length of each image has a different value. At the same time, local focus changes in the image are used as depth cues. The focal length measurement (FM) operator is usually used to measure the focal length level of each image pixel [74]. The length between any two planes on either side of the focal plane is usually defined as the depth of field [75]. Any blur smaller than the pixel size will not be detected, and the confusion circle is within the acceptable range. Therefore, a sufficiently low depth of field is necessary to make the SFF system more accurate. The SFF method is widely used in autofocus cameras today. This approach tends to rely on surface textures to perceive depth information. However, in many practical

520

S. Guo et al.

Fig. 8 Depth variation and resulting circles of confusion [73]

applications, there may be cases where the object’s surface is smoothly shaded or lacks detectable texture. This situation can lead to poor results generated by SFF, resulting in inaccurate and sparse disparity maps. In this situation, the original SFF method can be extended to force a firm texture on the imaging surface by using dynamic lighting, making it suitable for weakly textured or untextured surfaces [76].

3 Road Defect Detection In our recently published survey paper article [37], the state-of-the-art (SOTA) road defect detection approaches are categorized as (1) traditional 2D image processingbased, (2) 3D road surface modeling and segmentation-based, (3) machine/deep learning-based, and (4) hybrid.

3.1 Traditional 2D Image Processing-Based Approaches The traditional image processing-based road defect detection approaches typically consist of four main procedures: (1) image pre-processing, (2) image segmentation, (3) shape extraction, and (4) object recognition [77]. [78] is a representative example of this algorithm type. It first uses a histogram-based thresholding algorithm [50] to segment (binarize) the input gray-scale road images. The segmented road images are then processed with image filters, such as a median filter and morphology operators, to reduce the redundant noise. Finally, the defective areas are extracted by analyzing a pixel intensity histogram. Additionally, [50] solved road defect detection with similar techniques. It first applies the triangle algorithm [79] to segment gray-scale road images. An ellipse then models the boundary of an extracted potential road defect. Researchers also performed image segmentation algorithms on depth/disparity images for road defect detection. This allows the intelligent road condition assessment system to identify road defects with a higher accuracy [40, 57, 80]. For instance, [40] acquires the depth images of pavements using a Microsoft Kinect sensor. The

Digital Transformation for Intelligent Road Condition Assessment

521

depth images are then segmented using the wavelet transform algorithm [81]. In their practical experiments, the Microsoft Kinect sensor must be mounted as perpendicularly as possible to the pavements. In 2018, [57] proposed to detect road defects from depth images acquired using a highly accurate laser scanner mounted on a road inspection vehicle. The depth images are first processed with a high-pass filter so that the depth values of the undamaged road pixels become similar. The processed depth image is then segmented using the watershed method [82] for road defect detection. In 2019, [80] leveraged a so-called disparity transformation algorithm to process dense road disparity images, making the road defects highly distinguishable. The road defects are then detected by applying a histogram-based thresholding method on the transformed disparity images.

3.2 Machine/Deep Learning-Based Approaches The CNN-based approaches typically address the road defect detection problem in an end-to-end manner, based on image classification, object inspection, or semantic segmentation networks [37].

3.2.1

Image Classification Networks

Since 2012, deep CNNs have been the first choice for many image classification problems. The image classification networks have been widely used to detect, recognize and localize road cracks, as the features learned by CNNs can replace the traditional hand-crafted features [83]. For example, in 2016, [84] proposed a robust road crack detection network. An RGB road image is fed into a CNN consisting of a collection of convolutional layers. The learned feature serves as input to a fully connected (FC) layer to produce a scalar indicating the probability that the image contains road cracks. In 2019, [85] proposed a self-supervised monocular road defect detection algorithm, which can not only reconstruct the 3D road geometry models with multi-view images captured by a single movable camera (based on the hypothesis that the road surface is nearly planar) but also classify RGB road images as either unimpaired or defective with a classification CNN (the images used for network training were automatically annotated via a 3D road point cloud thresholding algorithm). Recently, [87] conducted a comprehensive comparison among 30 SOTA image classification CNNs for road crack detection. The extensive experiments demonstrate that (a) the performances achieved by these deep CNNs are very similar [87]; (b) Learning road crack detection does not require a large amount of training data (10,000 images are sufficient to train a well-performing CNN [87]). However, the pre-trained CNNs typically perform unsatisfactorily on additional test sets. Therefore, unsupervised domain adaptation (UDA) [88], capable of aligning two different domains, has become a hot research topic that requires more attention. Furthermore,

522

S. Guo et al. Input

Resize

Layer1Layer2 ×4

Layer3

Layer4

×23

×3

FC Layer Activations

Non-crack

×3

Crack

ResNet-101

Rectified Conv Feature Maps

Output

ReLU

+

Layer1_x conv1 1×1, 64 conv2 3×3, 64 conv3 1×1, 256

Layer2_x conv1 1×1, 128 conv2 3×3, 128 conv3 1×1, 512

Layer3_x conv1 1×1, 256 conv2 3×3, 256 conv3 1×1, 1024

Layer4_x conv1 1×1, 512 conv2 3×3, 512 conv3 1×1, 2048

Fig. 9 An example of CNN’s decision-making explanation with Grad-CAM++ [86]

explainable AI algorithms, such as Grad-CAM++ [89] (an example is shown in Fig. 9), have become essential for image classification applications to understand CNN’s decision-making.

3.2.2

Object Detection Networks

Region-based CNN (R-CNN) [90–92] series and you only look once (YOLO) [93– 96] series are two representative groups of modern deep learning-based object detection algorithms for road defect detection. As discussed above, the SOTA image classification CNNs can categorize an input image into a specific class. However, we sometimes attempt to obtain the exact location of a specific object in the image, which requires the CNNs to have the ability to learn bounding boxes around the objects of interest. A naive but straightforward way to achieve this objective is to classify a collection of patches (usually taken from the original image) as negative (the object is absent from the patch) or positive (the object is present in the patch). However, such an approach is usually costly as it selects many regions of interest (RoIs). To solve this dilemma, R-CNN [90] employs a selective search algorithm [97] to extract only 2,000 RoIs (generally known as region proposals). Such RoIs are then fed into a classification CNN to extract visual features connected by a support vector machine (SVM) to produce their classes, as shown in Fig. 10. In 2015, Fast R-CNN [91] was introduced as a faster object detection algorithm based on R-CNN. Instead of feeding region proposals to the classification CNN, Fast R-CNN [91] directly feeds the original image to the CNN and produces a convolutional feature map (CFM). The region proposals are identified from the CFM using selective search, which are

Digital Transformation for Intelligent Road Condition Assessment

523 CNN

Extracted region proposals

Warped region

Road defect

Others

Compute CNN features

Classify regions

Fig. 10 R-CNN [90] for road defect detection

then reshaped into a fixed size with an RoI pooling layer before being fed into an FC layer. A softmax layer is subsequently utilized to predict the region proposals’ classes. Nevertheless, both R-CNN [90] and Fast R-CNN [91] employ a selective search algorithm to determine region proposals, which is time-consuming and slow. To overcome this disadvantage, [92] proposes Faster R-CNN, which leverages a network to learn the region proposals. The YOLO series, on the other hand, is very different from the R-CNN series. YOLOv1 [93] formulates object detection as a regression problem. It first splits an image into an M × M grid, from which B bounding boxes are selected. The network then produces class probabilities and offset values for these bounding boxes. Since YOLOv1 [93] makes a significant number of localization errors and its achieved recall is unsatisfactory, YOLOv2 [94] was proposed to improve YOLOv1 [93] in the following respects: (1) it fine-tunes the classification network at the full 448 × 448 resolution; (2) it adds batch normalization on all the convolutional layers; (3) it removes the fully connected layers from YOLOv1 [93] and uses anchor boxes to predict bounding boxes. In 2018, [95] made several tweaks to further improve YOLOv2. The aforementioned object detectors have been extensively applied for road defect detection. [98] used a Faster R-CNN [92] to recognize road potholes, while [99] trained a YOLOv2 [94] object detector to achieve the same objective. [100] utilized YOLOv3 [95] to detect road potholes from RGB images captured by a smartphone mounted on a car. [101] employed three different YOLOv3 [95] architectures to detect road potholes. Recently, [102] compared the performances of YOLOv2 [94] and Mask R-CNN [103] for road defect detection. Nonetheless, these approaches typically leverage an unmodified object detector which has been specifically designed for the task of road defect detection. Furthermore, these object detectors can only provide instance-level predictions instead of pixel-level predictions. Therefore, in recent years, semantic segmentation has become a more desirable technique for pixel-level road defect detection.

3.2.3

Semantic Segmentation Networks

Semantic segmentation aims at assigning each image pixel with a specific class [104]. It has been widely used for road defect detection in recent years. According

524

S. Guo et al. Single-Modal Network

RGB Image

Data-Fusion Network

RGB Image

Encoder

Encoder

Decoder

Decoder

Road Defect Detection Result

Road Defect Detection Result

Transformed Disparity Image Feature Fusion

Encoder

True Positive False Negative False Positive

Ground Truth Road Defect Background

Fig. 11 Single-modal semantic segmentation versus data-fusion semantic segmentation for road defect detection

to the number of encoders, the SOTA semantic image segmentation networks can be grouped into two categories: (1) single-modal [105] and (2) data-fusion [106, 107], as shown in Fig. 11. The former inputs only one type of vision sensor data, such as RGB images or depth images, while the latter typically learns visual features from different types of vision sensor data, such as RGB images and transformed disparity images [108] or RGB images and surface normal information [106]. Researchers have employed both types of semantic segmentation networks to detect road defects/anomalies (the approaches designed to detect road defects can also be leveraged to detect road anomalies, as these two types of objects are below and above the road, respectively). Recently, [108] developed a data-fusion semantic segmentation CNN for road anomaly detection. The authors also conducted a comprehensive comparison of data fusion performance for different modalities of visual features, including (1) RGB images, (2) disparity images, (3) surface standard images [109], (4) elevation images, (5) HHA images, and (6) transformed disparity images [80]. The extensive experimental results demonstrated that the transformed disparity image is the most informative visual feature in terms of road defect/anomaly detection.

Digital Transformation for Intelligent Road Condition Assessment

525

X

Fig. 12 An example of 3D road pothole cloud acquired using the stereo matching algorithm introduced in [34]. The accuracy of the 3D road point cloud is around 2.23 mm

Y Z

3.3 3D Road Surface Modeling and Segmentation-Based Approaches The 3D road surface modeling and segmentation-based approaches [53, 110–112] typically formulate the 3D road point clouds (an example is shown in Fig. 12) as an explicit model f (p, a), where p = (X ; Y ; Z ) is a 3D point on the road surface in the camera coordinate system and a stores the model coefficients. The optimal a can be obtained by minimizing [110]: n ∑ E= ( f (pi , a) − Yi ).

(1)

i=1

By comparing the difference (namely, f (p, a) − Y ) between the actual and modeled road surfaces, the road defects can be extracted. Nevertheless, the 3D road surface modeling process is very sensitive to outliers. Hence, [112] employs random sample consensus (RANSAC) [113] to further improve its robustness. Furthermore, the actual road surface is sometimes uneven, which makes quadratic surface modeling somewhat problematic [34].

3.4 Hybrid Approaches In 2017, [114] proposed an intelligent road defect detection system based on the analysis of 2D LiDAR data (providing road profile information) and RGB road images (providing road texture information). To obtain a large field of view, this system uses two LiDARs. This hybrid system is pretty robust to electromagnetic waves and poor road conditions. Such a hybrid system can combine multi-modal vision sensor data’s advantages to improve the accuracy of road defect detection. In 2019, [53] also introduced a hybrid framework for road defect detection. It first applies disparity transformation, discussed in Sect. 3.1, and histogram-based

526

S. Guo et al.

thresholding method to extract potential road defects. In the next step, a quadratic surface was fitted to the original disparity image, where the RANSAC algorithm was employed to enhance the surface fitting robustness. Road defects can be successfully detected by comparing the actual and fitted disparity images. This hybrid framework uses a 2D image processing algorithm along with a 3D road surface modeling algorithm to significantly improve the road defect detection performance.

4 Future Insights DX technology has a bright future in many fields of study. With the use of DX techniques, smart cities can optimize their service efficiency. Ericsson, IBM, Cisco, Microsoft, and many other companies are also primarily involved in the digital revolution. These companies are conducting innovative research on how to apply digital technologies (such as wireless network sensors, digital twins, semantic web, and cloud computing) to address the challenges facing future cities [115]. [116] defined ten pillars of smart city that drive benefits to the digital age, as shown in Fig. 13. There are five fundamental pillars– governance, economy, talent, funding, and infrastructure [117]: • Building an intelligent city first requires wise governance. Creating a technologyenabled vision and developing a coherent implementation plan is the beginning of a smart city. • Only when smart cities have a practical economic development plan can they find their place in global trade and promote a good economic environment. • Smart cities are centers of talent. How to attract talents and establish a creative cultural center is the lifeblood of innovative city development. • To keep pace with the digital age, cities must be more innovative in their financing techniques, capital sources, budgeting methods, and business models. • The construction of interconnected and well-maintained infrastructure is the foundation for smart cities to expand their digital areas.

Fig. 13 The ten pillars of smart city transformation [117]

Digital Transformation for Intelligent Road Condition Assessment

527

In addition to the foundational pillars, the five science and technology pillars are also critical– mobility, environment, public safety, public health, and payment systems [117]: • Smart city layouts in the future should be multi-modal, involving various options that are fully integrated and connected. • As smart cities mature, environmental sustainability issues, energy use, resource allocation, and climate hazards should also be addressed. • The creation of a harmonious, safe, and healthy environment also underpins a flourishing smart city. • Moreover, Digital payment systems will not only help businesses to reduce costs but also increase revenue. Transportation is the artery of modern society and the economy [118]. After obtaining the road data using the sensing technologies mentioned above, the department of highways and surrounding traffic management can use the data for digital transformation [119]. Extensive interdisciplinary research using data science technologies is receiving more and more attention [120]. However, the following issues need to be taken into consideration in the future DX of smart city transportation [121]: • Improve the efficiency of the entire process. In order to make the traffic assessment prediction more timely, the time spent on data collection and analysis should be as short as possible [122]. Dedicated intelligent road condition assessment vehicles were devised to make data collection more accessible and faster. After obtaining road data, relevant departments should also be able to carry out digital management, where road restoration and traffic control work should be performed collectively to benefit end-users. • Improve the accuracy of intelligent road condition assessment. The road defect detection algorithms with deep learning models have achieved appealing results. However, such models are generally developed based on supervised learning. Labeling road data is a highly labor-intensive and time-consuming process [34]. Therefore, developing un/self/semi/low-shot learning strategies for road defect detection has become a popular area of research that requires more attention. • Expand the service scope for the digital outreach. The service group is not only limited to roadway engineers but also to all groups involved in the whole road condition assessment process. Ultimately, strategic decisions can be transparent and open, and the strategic decision implementation results can be traced. • Enhance information sharing and cooperation while establishing data fusion models [123]. By adopting a unified modular data interface, participants can implement the interoperability and consistent data standards to share data resources from the city context [124]. In the future, an interdisciplinary and trans-department digital urban transport system will be established.

528

S. Guo et al.

• Improve network security. There are many privacy and security risks in smart cities, which need to be rethought and improved [125]. The rise of smart cities entails considerable risks. In order to prevent malicious use of road data, it is imperative to enhance the anti-attack capability of the gateway. Therefore, in the early stage of innovative city development, smart cities must be fully prepared for cyberattacks.

5 Summary Digital transformation has been extensively used to improve the efficiency and accuracy of road condition assessment. This chapter gave readers an overall picture of the state-of-the-art road condition assessment systems. The state-of-the-art road data collection technologies, including remote sensing, vibration sensing, laser scanning, infrared sensing, multi-view geometry, and shape (depth) from focus, were first discussed. The existing machine vision and intelligence approaches, including classical 2D image processing algorithms, 3D road surface modeling and segmentation algorithms, machine/deep learning algorithms, and hybrid algorithms, designed to detect road defects were then presented. Finally, future insights on the digital transformation of smart cities were discussed. Establishing the digital transformation of road inspection with 3D road imaging technology and combining different types of machine vision and intelligence algorithms for road defect detection are the future trends in this research area.

References 1. Bohnsack R et al (2022) Sustainability in the digital age: intended and unintended consequences of digital technologies for sustainable development, pp 599–602 2. Elliott A (2021) Contemporary Social Theory: An Introduction. Routledge, London 3. Piepponen A et al (2022) Digital transformation of the value proposition: a single case study in the media industry. J Bus Res 150:311–325 4. Lasi H et al (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242 5. Nandico OF (2016) A framework to support digital transformation. In: El-Sheikh E, Zimmermann A, Jain LC (eds) Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures, vol 111. ISRL. Springer, Cham, pp 113–138. https://doi.org/10.1007/978-3319-40564-3_7 6. Henriette E et al (2015) The shape of digital transformation: a systematic literature review. In: MCIS 2015 Proceedings, vol 10, pp 431–443 7. Goran J et al (2017) Culture for a digital age. McKinsey Q 3(1):56–67 8. Davenport TH et al (2019) Artificial Intelligence: The Insights You Need From Harvard Business Review. Harvard Business Press, Boston 9. Kodama M (2020) Digitally transforming work styles in an era of infectious disease. Int J Inf Manag 55:102172 10. Haggerty E (2017) Healthcare and digital transformation. Netw Secur 2017(8):7–11

Digital Transformation for Intelligent Road Condition Assessment

529

11. Gardy A et al (2016) Digital trends & opportunities for airports. In: ACI-NA World Annual Conference 12. Scriney M, Roantree M (2016) Efficient cube construction for smart city data. In: EDBT/ICDT Workshops, vol 2016 13. Fan R, Wang H, Bocus MJ, Liu M (2020) We learn better road pothole detection: from attention aggregation to adversarial domain adaptation. In: Bartoli A, Fusiello A (eds) ECCV 2020, vol 12538. LNCS. Springer, Cham, pp 285–300. https://doi.org/10.1007/978-3-030-668235_17 14. Yesner R (2017) Accelerating the digital transformation of smart cities and smart communities, Microsoft, October 2017 15. Bordeleau F-È, Felden C (2019) Digitally transforming organisations: a review of change models of industry 4.0. In: European Conference on Information Systems 16. Schmarzo B (2017) What is digital transformation. Accessed 8 Mar 2018 17. Ebert C, Duarte CHC (2018) Digital transformation. IEEE Softw 35(4):16–21 18. Erol T (2020) Digital transformation revolution with digital twin technology. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp 1–7. IEEE 19. Tao F et al (2019) Digital Twin Driven Smart Manufacturing. Academic Press, Cambridge 20. Catarci T (2019) A conceptual architecture and model for smart manufacturing relying on service-based digital twins. In: 2019 IEEE International Conference on Web Services (ICWS), pp 229–236. IEEE 21. The digital twin IoT platform. https://www.digitaltwincorporation.com/. Accessed 12 May 2022 22. Batty M et al (2012) Smart cities of the future. Eur Phys J Spec Top 214(1):481–518 23. Zhao F et al (2021) Smart city research: a holistic and state-of-the-art literature review. Cities 119:103406 24. Forward N (2016) Building a smart city, equitable city 25. Sarker IH (2022) Smart city data science: towards data-driven smart cities with open research issues. Internet Things 19:100528 26. Zhang Y (2010) Interpretation of smart planet and smart city [j]. China Inf Times 10:38–41 27. Nam T, Pardo TA (2011) Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp 282–291 28. Yovanof GS, Hazapis GN (2009) An architectural framework and enabling wireless technologies for digital cities & intelligent urban environments. Wirel Pers Commun 49(3):445–463 29. Chu Z et al (2021) A smart city is a less polluted city. Technol Forecast Soc Chang 172:121037 30. Xue Q (2010) Smart healthcare: applications of the internet of things in medical treatment and health. Inf Constr 2010(5):56–58 31. Su K (2011) Smart city and the applications. In: 2011 International Conference on Electronics, Communications and Control (ICECC), pp 1028–1031. IEEE 32. Fan R et al (2018) Road surface 3D reconstruction based on dense subpixel disparity map estimation. IEEE Trans Image Process 27(6):3025–3035 33. Fan R et al (2019) Real-time dense stereo embedded in a UAV for road inspection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society, pp 535–543 34. Fan R et al (2021) Rethinking road surface 3-D reconstruction and pothole detection: from perspective transformation to disparity map segmentation. In: IEEE Transactions on Cybernetics, 2021. https://doi.org/10.1109/TCYB.2021.3060461 35. De Zoysa K, Keppitiyagama C, Seneviratne GP, Shihan W (2007) A public transport system based sensor network for road surface condition monitoring. In: Proceedings of the 2007 Workshop on Networked Systems for Developing Regions, pp 1–6 36. Eriksson J, Girod L, Hull B, Newton R, Madden S, Balakrishnan H (2008) The pothole patrol: using a mobile sensor network for road surface monitoring. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, pp 29–39

530

S. Guo et al.

37. Ma N et al (2022) Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms. In: Transportation Safety and Environment. https://doi. org/10.1093/tse/tdac026 38. Fan R (2021) Long-awaited next-generation road damage detection and localization system is finally here. In: 2021 29th European Signal Processing Conference (EUSIPCO), pp 641–645. IEEE 39. Mahler DS et al (1991) Pavement distress analysis using image processing techniques. Comput Aided Civ Infrastruct Eng 6(1):1–14 40. Jahanshahi MR et al (2013) Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor. J Comput Civ Eng 27(6):743–754 41. Woodham RJ (1980) Photometric method for determining surface orientation from multiple images. Opt Eng 19(1):191139 42. Barsky S, Petrou M (2003) The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Trans Pattern Anal Mach Intell 25(10):1239–1252 43. Fujimoto JG et al (2000) Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2(1–2):9–25 44. Walecki WJ, Van P (2006) Determining thickness of slabs of materials by inventors, 3 Oct 2006, uS Patent 7,116,429 45. Scharstein D, Szeliski R (2003) High-accuracy stereo depth maps using structured light. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol 1, p I. IEEE 46. Muzet V et al (2009) Surface deflection measurement using structured light. Testing in Civil Engineering, Nantes, France 47. Oggier T et al (2004) An all-solid-state optical range camera for 3d real-time imaging with sub-centimeter depth resolution (SwissRanger). In: Optical Design and Engineering, vol 5249, pp 534–545. International Society for Optics and Photonics 48. Anderson D et al (2005) Experimental characterization of commercial flash ladar devices. In: 2005 International Conference of Sensing and Technology, vol 2, pp 17–23. Citeseer 49. Mathavan S et al (2015) A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Trans Intell Transp Syst 16(5):2353–2362 50. Koch C, Brilakis I (2011) Pothole detection in asphalt pavement images. Adv Eng Inform 25(3):507–515 51. Chang K et al (2005) Detection of pavement distresses using 3D laser scanning technology. Comput Civ Eng 2005:1–11 52. Lin J, Liu Y (2010) Potholes detection based on SVM in the pavement distress image. In: 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp 544–547. IEEE 53. Fan R et al (2019) Pothole detection based on disparity transformation and road surface modeling. IEEE Trans Image Process 29:897–908 54. Koutsopoulos HN et al (1993) Primitive-based classification of pavement cracking images. J Transp Eng 119(3):402–418 55. Fan R et al (2018) Real-time stereo vision for road surface 3-D reconstruction. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST), pp 1–6. IEEE 56. Laurent J et al (1997) Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces. In: Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No. 97TB100134), pp 303– 310. IEEE 57. Tsai Y-C et al (2018) Pothole detection and classification using 3D technology and watershed method. J Comput Civ Eng 32(2):04017078 58. IIT: Lasers in quality controlline. http://www.iitk.ac.in/celt/lecture_laser/. Accessed 2 Apr 2022 59. Joubert D et al (2011) Pothole tagging system. In: 2011 th Robotics and Mechatronics Conference of South Africa, CSIR International Conference Centre, Pretoria, pp 23–25

Digital Transformation for Intelligent Road Condition Assessment

531

60. Moazzam I et al (2013) Metrology and visualization of potholes using the microsoft kinect sensor. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp 1284–1291. IEEE 61. Andrew, AM (2001) Multiple view geometry in computer vision, Kybernetes 62. Jog G et al (2012) Pothole properties measurement through visual 2D recognition and 3D reconstruction. Comput Civ Eng 2012:553–560 63. Ullman S (1979) The interpretation of structure from motion. Proc R Soc Lond Ser B. Biol Sci 203(1153):405–426 64. Wang H et al (2021) CoT-AMFlow: adaptive modulation network with co-teaching strategy for unsupervised optical flow estimation. In: Conference on Robot Learning (CoRL), pp 143–155. PMLR 65. Schonberger JL, Frahm J-M (2016) Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4104–4113 66. B. Triggs et al., “International workshop on vision algorithms,” Bundle adjustment–a modern synthesis, pp. 298–372, 1999 67. Fleet D, Weiss Y (2006) Optical flow estimation. In: Paragios N, Chen Y, Faugeras O (eds) Handbook of Mathematical Models in Computer Vision, pp 237–257. Springer, Boston, MA. https://doi.org/10.1007/0-387-28831-7_15 68. Hirschmuller H (2007) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341 69. Sun J et al (2003) Stereo matching using belief propagation. IEEE Trans Pattern Anal Mach Intell 25(7):787–800 70. Wang H et al (2021) PVStereo: pyramid voting module for end-to-end self-supervised stereo matching. IEEE Robot Autom Lett 6(3):4353–4360 71. Wang H et al (2021) Co-Teaching: an ark to unsupervised stereo matching. In: 2021 IEEE International Conference on Image Processing (ICIP), pp 3328–3332. IEEE 72. Danzl R et al (2009) Focus variation–a new technology for high resolution optical 3D surface metrology. In: The 10th International Conference of the Slovenian Society for Non-destructive Testing, pp 484–491. Citeseer 73. Vilaça JL et al (2010) 3D surface profile equipment for the characterization of the pavement texture-TexScan. Mechatronics 20(6):674–685 74. Pertuz S et al (2013) Analysis of focus measure operators for shape-from-focus. Pattern Recogn 46(5):1415–1432 75. Conrad J (2006) Depth of field in depth. Large Format Photography, pp 1–45 76. Sundaram H, Nayar S (1997) Are textureless scenes recoverable? In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 814–820. IEEE 77. Buza E et al (2013) Pothole detection with image processing and spectral clustering. In: Proceedings of the 2nd International Conference on Information Technology and Computer Networks, vol 810, p 4853 78. Ryu S-K et al (2015) Image-based pothole detection system for its service and road management system. Math Probl Eng 2015 79. Zack GW et al (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25(7):741–753 80. Fan R, Liu M (2019) Road damage detection based on unsupervised disparity map segmentation. IEEE Trans Intell Transp Syst 21(11):4906–4911 81. Beylkin G et al (2009) Fast wavelet transforms and numerical algorithms. In: Fundamental Papers in Wavelet Theory, pp 741–783. Princeton University Press 82. Najman L, Schmitt M (1994) Watershed of a continuous function. Signal Process 38(1):99– 112 83. LeCun Y et al (2015) Deep learning. Nature 521(7553):436–444 84. Zhang L (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp 3708–3712. IEEE

532

S. Guo et al.

85. Hu Y, Furukawa T (2019) A self-supervised learning technique for road defects detection based on monocular three-dimensional reconstruction. In: 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol 59216, p V003T01A021. American Society of Mechanical Engineers 86. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 618–626 87. Fan J et al (2021) Deep convolutional neural networks for road crack detection: qualitative and quantitative comparisons. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE 88. Hoffman J et al (2018) Cycada: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp 1989–1998. PMLR 89. Chattopadhay A (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 839–847. IEEE 90. Girshick R et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580–587 91. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448 92. Ren S et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99 93. Redmon J et al (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788 94. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263–7271 95. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement, CoRR 96. Bochkovskiy A et al (2020) Yolov4: optimal speed and accuracy of object detection, CoRR 97. Uijlings JR et al (2013) Selective search for object recognition. Int J Comput Vis 104(2):154– 171 98. Wang W (2018) Road damage detection and classification with faster R-CNN. In: 2018 IEEE International Conference on Big Data (Big Data), pp 5220–5223. IEEE 99. Suong LK, Kwon J (2018) Detection of potholes using a deep convolutional neural network. J Univ Comput Sci 24(9):1244–1257 100. Camilleri N, Gatt T (2020) Detecting road potholes using computer vision techniques. In: 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pp 343–350. IEEE 101. Ukhwah EN (2019) Asphalt pavement pothole detection using deep learning method based on yolo neural network. In: 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp 35–40. IEEE 102. Dhiman A et al (2019) Pothole detection using computer vision and learning. IEEE Trans Intell Transp Syst 21(8):3536–3550 103. He K et al (2017) Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2961–2969 104. Fan R et al (2020) Computer stereo vision for autonomous driving, CoRR 105. Fan R et al (2021) Learning collision-free space detection from stereo images: homography matrix brings better data augmentation. IEEE/ASME Trans Mechatron 27(1):225–233 106. Fan R, Wang H, Cai P, Liu M (2020) SNE-RoadSeg: incorporating surface normal information into semantic segmentation for accurate freespace detection. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) ECCV 2020, vol 12375. LNCS. Springer, Cham, pp 340–356. https://doi. org/10.1007/978-3-030-58577-8_21 107. Wang H et al (2021) SNE-RoadSeg+: rethinking depth-normal translation and deep supervision for freespace detection. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1140–1145. IEEE

Digital Transformation for Intelligent Road Condition Assessment

533

108. Wang H et al (2021) Dynamic fusion module evolves drivable area and road anomaly detection: a benchmark and algorithms. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021. 3064089 109. Fan R et al (2021) Three-filters-to-normal: an accurate and ultrafast surface normal estimator. IEEE Robot Autom Lett 6(3):5405–5412 110. Ozgunalp U (2016) Vision based lane detection for intelligent vehicles, Ph.D. dissertation, University of Bristol 111. Zhang Z (2013) Advanced stereo vision disparity calculation and obstacle analysis for intelligent vehicles, Ph.D. dissertation, University of Bristol 112. Wu R et al (2021) Scale-adaptive road pothole detection and tracking from 3d point clouds. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE 113. Hast A, Nysjö J (2013) Optimal RANSAC-towards a repeatable algorithm for finding the optimal set. J WSCG 21(1):21–30 114. Kang B-H, Choi S-I (2017) Pothole detection system using 2D lidar and camera. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp 744–746. IEEE 115. Clohessy T et al (2014) Smart city as a service (SCaaS): a future roadmap for e-government smart city cloud computing initiatives. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 836–841. IEEE 116. ThoughtLab E (2018) Smarter cities 2025 building a sustainable business and financing plan. https://econsultsolutions.com/wp-content/uploads/2018/11, ESI-ThoughtLab_SmarterCities-2025_ebook_FINAL. pdf (letöltve: 2019.07. 26.) 117. Yoon SY et al (2020) Smart city pathways for developing Asia: an analytical framework and guidance, ADB Sustainable Development Working Paper Series 118. Zhe W et al (2015) Traffic patterns in the silk road economic belt and construction modes for a traffic economic belt across continental plates. J Resour Ecol 6(2):79–86 119. Tsakalidis A et al (2020) Digital transformation supporting transport decarbonisation: technological developments in EU-funded research and innovation. Sustainability 12(9):3762 120. Parti K, Szigeti A (2021) The future of interdisciplinary research in the digital era: obstacles and perspectives of collaboration in social and data sciences-an empirical study. Cogent Soc Sci 7(1):1970880 121. Singh S et al (2020) Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustain Urban Areas 63:102364 122. Leduc G et al (2008) Road traffic data: collection methods and applications. Work Pap Energy Transp Climate Chang 1(55):1–55 123. Sidek O, Quadri S (2012) A review of data fusion models and systems. Int J Image Data Fusion 3(1):3–21 124. Jeong S et al (2020) City data hub: implementation of standard-based smart city data platform for interoperability. Sensors 20(23):7000 125. Al-Turjman F et al (2022) An overview of security and privacy in smart cities’ IoT communications. Trans Emerg Telecommun Technol 33(3):e3677

Transportation

Digital Maturity Assessment of Ship Management Companies Towards Organizational Intelligence: Blue Digital Focus Kadir Cicek, Metin Celik, and S. M. Esad Demirci

Abstract Digital maturity assessment is one of the pioneering studies of digital transformation. It is a stepwise approach to support the organizational awareness to establish the link between technology and business in different levels. It is a step-bystep approach to support organizational awareness and even achieve organizational intelligence in order to establish the link between technology and business. This chapter develops a novel self-assessment methodology, namely Blue Digital Focus, to conduct digital maturity analysis of ship management companies. The assessment involves seven dimensions such as strategy, organization, customer, technology, operations, innovations, process improvement. The implementation phase of Blue Digital Focus is conducted with an illustrative case study to demonstrate the model. Completing the assessment, a comprehensive analysis and reporting, including maturity scores, rankings, recommendations, is conducted. The proposed approach is recognized as useful tool both to ship managers and to maritime researchers interested in understanding the digital readiness level in practice. Blue Digital Focus has great potential to investigate the future needs of key maritime stakeholders responding to priorities of smart, green and sustainable transportation system. A further study through an interactive platform supported with digitalization survey visits is planned.

K. Cicek Marine Engineering Department, Maritime Faculty, Istanbul Technical University, Tuzla, 34940 Istanbul, Turkey M. Celik Basic Science Department, Maritime Faculty, Istanbul Technical University, Tuzla, 34940 Istanbul, Turkey S. M. E. Demirci (B) Maritime Transportation Engineering, Istanbul Technical University, Tuzla, Istanbul, Turkey e-mail: [email protected] Maritime Vocational School, Sakarya University of Applied Sciences, Sakarya, Turkey K. Cicek · M. Celik · S. M. E. Demirci Blueanalytica Maritime R&D and Information Technologies Inc., Teknopark, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_23

537

538

K. Cicek et al.

Keywords Maritime · Ship management · Digital transformation · Digital maturity · Organizational intelligence

1 Introduction Maritime transport systems are responsible for the transport approximately 90% of the international trade in goods. The recent statistical reports, published by key authorities such as International Chamber of Shipping (ICS) and The United Nations Conference on Trade and Development (UNCTAD), highlight the vital importance of shipping to the global economy [1]. On the other hand, digitalization, accelerated with COVID-19 pandemic, is a new paradigm to shape the future of key maritime organizations [2]. In fact, advancements in digital technologies advance the abilities to collect, store and process large amounts of data. So, digitalizing the maritime transportation processes would bring wide-ranging economic benefits, contribute to a stronger, more resilient supply chain and help to improve the organizational intelligence of ship management companies. The organizational intelligence can be defined as the ability of the organizations to acquire, create, and apply and transfer knowledge between individuals in their businesses. With the help of the data technologies, employees can be able to access and use the entirety of the organization’s knowledge. That is why, nowadays digital transformation turns into a crucial issue not only a stronger, more resilient supply chain but also an improved organizational intelligence in maritime industry. Despite the rapid spread and uptake of digital technologies, the challenges with skills shortages, system interoperability, and cybersecurity significantly stem digital transformation processes in maritime environment. Considering the mentioned challenges, it seems a growing interest in future skill development along with the digitalization in shipping [3]. On the other hand, unprecedented world events enforce maritime organizations to strengthen the maritime supply chain resiliency through adopting digital technologies. This challenge is become a significant competitive factor for the relevant parties. By these reasons, it is observed that the number of studies at academic, entrepreneurship and industrial level has been promoting [4–6]. Maritime security/safety enhancement, reducing environmental damage, fuel efficiency, information integration, traceability and transparency are considered as the potential needs and motivation behind the digital transformation (DX) [7]. In this circumstance, maritime organizations concentrate on adoption digital technologies into their core operations in order to prepare themselves for the unforeseen disruptions. Among the maritime stakeholders, especially the ongoing efforts and pursuits of ship management companies in digital transformation have been stand out in the market [8]. Additionally, international authorities have addressed the digitalization as a key milestone for the intelligent and resilient maritime organization in future perspective.

Digital Maturity Assessment of Ship Management Companies …

539

This book chapter proposes a novel self-assessment methodology, namely Blue Digital Focus, to conduct digital maturity analysis of ship management company organizations. The assessment involves seven dimensions such as strategy, organization, customer, technology, operations, innovations, process improvement. The main aim in producing the chapter is to promote the initial phase of digital transformation program specified to maritime organizations, particular to ship management companies. The objectives of the chapter are given as follows: i) investigating the existing digital maturity models, ii) understanding the core dimensions, maturity levels and enablers, iii) developing a self-assessment mechanism, iv) demonstrating the digital maturity assessment in ship management companies. Considering the fourth industrial revolution trends, the originality of this chapter is to design and develop a maritime-centered digital maturity assessment tool. The organization of the chapter is given as follows: The chapter plainly begins with short keynotes on maritime digitalization. Then, a literature review is conducted on digital maturity assessment approaches applied in different industries to identify the critical issues to be improved. Particular to maritime, Blue Digital Focus is introduced, as a new digital maturity assessment methodology. Subsequently, the analysis and reporting section of the Blue Digital Focus is demonstrated. The last chapter gives a few concluding remarks and further research agenda.

2 Literature Review on Digital Maturity Assessment Digital maturity assessment is a stepwise approach to support the organizational awareness in digital transformation in order to establish the link between technology and business in different levels (i.e. strategic, tactical, etc.). With the increasing interest in digitalization, the maturity assessment studies cited in the literature have upgraded to a reasonable number. Among them, Maturity Model for Digital Strategy Assessment (MMDSA), offering KPI oriented digital strategy monitoring tool, stands out as an effective approach [9]. To achieve a successful transformation, understanding the transformational goals and enablers, as key elements, is so critical. For instance, transformation capability of a production company highly depends upon the strategy management, value management, risk management, business process management, IT management, organizational change management, training management, program and project management, meta management [10]. Digital investigators are mainly referring capability maturity models (CMMs), originally initiated to systematically monitoring of contracted software project [11]. Because it has a holistic structure, the use of the CMM concept has become quite common in various fields. Referring CMMs, there are numerous models are developed and cited in literature. Just to name a few, Digital Investigation Capability Maturity Model (DI-CMM), Process and enterprise maturity model (PEMM), Business process maturity model (BPMM), Capability maturity model integration (CMMI), etc. can be addressed.

540

K. Cicek et al.

In the first round of review, it was determined that the good practice of BPM is being carried out in higher education institutions [12]. In manufacturing industry, Digital Readiness Assessment Maturity (DREAMY) is recognized as a very strong tool. It is developed based on CMMI framework applied to key processes categorized under design, engineering, production management, quality management, maintenance management, and logistics management [13]. As an alternative to DREAMY from future perspective, smart manufacturing readiness level (SMSRL), and manufacturing operations management capability maturity model (MOM/CMM) was studied to extend maturity tools in smart manufacturing era [14]. It is stated that a comprehensive investigation through smart products/services, smart business, strategy and organization are required to conduct maturity assessments in retail industry [15]. With the increasing digitalization trends globally, maturity assessment approaches in the literature are diversifying. Briefly, the following models can be addressed; Industry 4.0 Maturity Model, IMPULS Industrie 4.0 Readiness Model, Three-Stage Maturity Model in SMEs, Maturity and Readiness Model for Industry 4.0 Strategy, Maturity Model for Digitalization, Industrie 4.0 Maturity Index, Digital Enterprise Model, etc. [16]. On the other hand, prestigious management and consultancy companies (i.e. Deloitte, TM Forum etc.) have closely interested in delivering practical assessment tools. Digital Maturity Model (DMM) of Deloitte, with high visibility in the market, is becoming widespread. There are publicly available reports, keynoted the digitalization success levels of different industrial disciplines, written by the mentioned global companies [17, 18]. It is seen that the reports derived by the worldwide consultancy companies have a high utility to practically produce digital transformation solutions through the various organizations. In the literature, very recent applications of digital maturity can be found in hospital organizations [19], banking [20], airport [21], education enterprise [22], retail companies [23], small and medium-sized enterprises, [24], workforce management [25], etc. Considering the applications in different disciples, benchmarking of digital maturity models according to the dimension component is so important [26, 27]. In the last phase of the review, the digital maturity assessment studies along with the maritime industry are considered. Despite the recent attempts at maritime digitization, a dissertation on a comprehensive review of digital maturity models for maritime companies [28] has addressed only one research paper [29] in this area. The mentioned study is relevant to publish a digital readiness index utilized in smart port development [29]. When the review progressed on this subject, it was determined that industrial initiatives were more advanced when compared to academic studies. As a very distinctive tool, Maritime Digitalization Playbook (MDP) has been offered to maritime organizations with a close collaboration between the Maritime and Port Authority of Singapore (MPA) and Singapore Shipping Association (SSA). MDP promotes a systematic approach to organize and execute the digital transformation plans that have followed up by maritime organizations of Singapore [30]. Especially in the early stage of digitalization initiatives, significant contributions from this application are expected in regional manner.

Digital Maturity Assessment of Ship Management Companies …

541

Reviewing the existing models in literature and industrial level, the additional critics on the maturity levels definitions and dimension structures will be a good reference to extend new practices. In this manner, the structural frameworks (i.e. dimensions) of academic and industry-origin models were benchmarked. For example, dimensions of DREAMY are organization, technology, monitoring and control, process while digital-oriented, integrated and interoperability, defined, managed, initial are the defined as maturity levels. IMPULS is constructed based on six levels such as outsider, beginner, intermediate, experienced, expert, and performer whereas strategy and organization, smart products, smart operations, smart factory, employees, and data-driven services are the dimensions. The Deloitte-DMM of provides a holistic view of digital maturity across the organization based on 5 dimensions: customer, strategy, technology, operations organization & culture. The assessment results reveal three-stages action map including imagine, deliver, run. As a result of these comparisons, the importance of configuring the interfaces to be used in digital maturity analysis has been understood. When the review is completed, it is clearly stated that the number of digital maturity analysis approaches and applications in different industries has been increasing in recent years. In the meantime, digital maturity assessment studies specific to maritime field are very limited. This chapter particularly fulfils the gap on awareness of ship management companies in the subject of digital maturity.

3 Blue Digital Focus Framework 3.1 Framework Blue Digital Focus is a digital maturity self-assessment tool through ship management companies. The tool evaluates digital maturity of ship management companies across seven clearly defined dimensions. The dimensions are strategy, organization, customer, technology, operations, innovations, process improvement. In Fig. 1, the conceptual framework of Blue Digital Focus is provided. Compared to current digital maturity approaches, Blue Digital Focus is more customized and tailor-made approach. It was designed by taking the organizational structures of modern ship management company into account. It will reveal the transformation roadmap for the operational elements that are anticipated to be needed especially in the early stages of the transformation process. The application of Blue Digital Focus is conducted in two phases; Module#0 Executive Development Phase, Module#1 Managerial Development Phase. In executive level, the self-assessment seeks for the response to strategy, organization, customer, technology, operations, innovations. The managerial level investigates the process improvement potential of ship management companies. Figure 2 schematizes the application phases of Blue Digital Focus.

542

K. Cicek et al.

Fig. 1 Conceptual framework of blue digital focus Source Figure produced by authors

Fig. 2 Application phase of blue digital focus Source Figure produced by authors

Digital Maturity Assessment of Ship Management Companies …

543

3.2 Strategy To remain competitive in their industries and to take necessary actions through the digital transformation, as a primary step, business leaders and managers must formulate and execute digital strategies that drive better operational performance [31]. In this sense, this dimension represents vision, governance, planning, and management processes that will support the implementation of the digital strategy [32]. With the formulation and execution of the digital strategy, business leaders and managers specify their path through the digital transformation process and through navigation in this path, they can gather results from the integration and the use of digital technology with a broader impact on companies [33].

3.3 Organization To stay competitive, companies need to change and adapt their technology, their systems, their traditional ways of working, their approaches to solutions and as an overall their organizations [34] to navigate into digital transformation. In other words, the digitalization in the business processes, it radically enforces the redesigning of the organizations as well. So, the organization dimension focuses on identifying changes in communications, culture, structure, training, and knowledge management that will help the companies to become a more credible digital transformation player [32].

3.4 Customer Customer experience is an essential factor in business that plays a vital role in digital transformation. Therefore, it is quite important to understand the needs of expectations of the customer from the perspective of digital transformation. So, this dimension focuses on the issues related with the enhancing customer experience, improving customer engagement, analyzing customer behavior, and build a digital trust with customer [35].

3.5 Technology Digital transformation involves using digital technologies to redesign a process to become more effective, to create value and new services for the stakeholders. Digital transformation can involve many different technologies such as artificial intelligence, machine learning, augmented reality, virtual reality, block chain, drones, internet of things, robotics, 3-D printing, mobile applications, cloud technologies, etc. In such

544

K. Cicek et al.

a diverse technology landscape, this dimension representing the capabilities that enable effective technology planning, deployment, and integration to support the digital business [32].

3.6 Operations Digital transformation helps companies to improve nearly every operation in the value chain, from customer and supplier relationships to marketing to products and services with the aim of enhancing the operational flexibility in the digital era. Within this scope, this dimension focuses on the capabilities of the companies that support the productivity, service management, and flexibility [32]. Increased maturity within this dimension demonstrates a more digitized, automated, and flexible operation.

3.7 Innovation Digital innovation is the use of digital technology and applications to improve business processes and workforce performance, improve customer experience, and introduce new products or business models. So, innovation dimension represents the capabilities of the companies that enable more flexible, agile and adaptable ways of working [32]. The digital maturity of each dimension, strategy, organization, customer, technology, operations, innovations, are assessed across six levels (level#0, level#1, level#2, level#3, level#4, level#5), as presented in Fig. 3, considering the suitable existing and targeted options. In level#0, the option is selected as “NOT DEFINED” if the maritime organization has not taken any actions for digital transformation in the relevant dimension [32]. In level#1, the option is selected as “INITIATED” if the maritime organization has decided to move toward a digital business and is taking initial steps in that direction [32]. In level#2, the option is selected as “ENABLING” if the maritime organization is implementing initiatives within the dimension that will form the foundation of its digital business [32]. In level#3, the option is selected as “INTEGRATING” if the maritime organization’s initiatives are being integrated across the organization to support end-to-end capabilities [32]. In level#4, the option is selected as “OPTIMIZING” if the maritime organization’s digital initiatives within the dimension are being fine-tuned and used to further increase overall performance [32]. In level#5, the option is selected as “PIONEERING” if the maritime organization is advancing the state of the practice within the dimension [32].

Digital Maturity Assessment of Ship Management Companies …

545

Fig. 3 Evaluation and action framework of process improvement dimension Source Figure produced by authors

3.8 Process Improvement This dimension is focused on investigating the digital maturity levels of key processes regarding with crewing & training management, technical management, operational management and HSQE management in ship management companies. It is asked to respond to 30 processes executed by ship management company representatives. Under crewing & training management, the following processes are considered: crew evaluation and selection, crew familiarization and wellbeing monitoring, crew scheduling, competency monitoring and improvement, training planning and delivery, operational crew reliability assessment. On the other hand, safety and environmental performance trend analysis, emergency preparedness level prediction, remote failure diagnostics support, docking planning, marine service performance monitoring, maintenance planning and management, continuous fleet reliability analysis are the selected processes under technical management dimension. The operational management deals with the key process such as remote navigational safety support, real-time monitoring of critical operations, ship operating cost analysis, fuel consumption monitoring, EU MRV / IMO DCS reporting, bunker planning, voyage

546

K. Cicek et al.

reporting and analysis, ship routing and speed optimization. HSQE management includes SMS documentation, inspection and audit (PSC, SIRE, etc.) report analysis, safety culture improvement, hazard identification and risk assessment, accident, near miss and nonconformity records analysis, safety management system effectiveness review, regulatory & third-party requirements compliance analysis, KPIs management, management of change implementation. Table 1 lists the classification of key processes on which maturity assessment is conducted. The processes are assessed considering the suitable existing and targeted options to describe the level of digitalization in process management. In level#0, the option is selected as “Process is NOT DEFINED” if the conditions meet i) The process is not defined, ii) the process is not controlled at all. In level#1, the option is selected as “Process is proceeding in INITIAL digital maturity level.” if the conditions meet i) The process is poorly controlled, ii) Process management is reactive, iii) Process management is not supported with the basic organizational and technological tools to ensure repeatability, usability, extensibility of the utilized solutions. In level#2, the option is selected as “Process is proceeding in MANAGED digital maturity level.” if the conditions meet i) The process is partially planned and implemented, ii) Process management is weak due to lacks in the organization, iii) Process management is weak due to the lack of information technologies, iv) The methods are usually developed by the experience of the maritime executives of the company. In level#3, the option is selected as “Process is proceeding in DEFINED digital maturity level.” if the conditions meet i) The process demonstrates and disseminates good practices, ii) The process supports the planning of effective management procedures, iii) Process management is limited by some minor restrictions on integration, iv) The process implementations reveal the need for interoperability. In level#4, the option is selected as “Process is proceeding in INTEGRATED digital maturity level.” if the conditions meet i) The process is consistently planned and implemented, ii) Process management is substantially based on the information exchange, iii) The enhancement of process is mostly supported with integrated digital tools, iv) The process simultaneously follows-up third-party requirements to deliver continuous transparent solutions, v) The solutions are fully meet the maritime stakeholders’ expectations. In level#5, the option is selected as “Process is proceeding in DIGITALORIENTED digital maturity level” if the conditions meet i) Process is structured based on a solid technology infrastructure, ii) Process supports high level of integration and interoperability, iii) Process management enables speed, robustness and security in information exchange, iv) The process is executed in collaboration among the departments to achieve effective decision-making with group consensus, v) The enhancement of process is mostly supported with an advance predictive model to shape policies, culture, strategies, technologies in accordance with the future maritime trends. Figure 3 addresses the evaluation and action framework utilized in process improvement dimension of Blue Digital Focus self-assessment program.

Digital Maturity Assessment of Ship Management Companies … Table 1 Classification of key processes

Crewing & Training Crew evaluation and selection Crew familiarization and wellbeing monitoring Crew scheduling Competency monitoring and improvement Training planning and delivery Operational crew reliability assessment Technical Safety and environmental performance trend analysis Emergency preparedness level prediction Remote failure diagnostics support Docking planning Marine service performance monitoring Maintenance planning and management Continuous fleet reliability analysis Operational Remote navigational safety support Real-time monitoring of critical operations Ship operating cost analysis Fuel consumption monitoring EU MRV / IMO DCS reporting Bunker planning Voyage reporting and analysis Ship routing and speed optimization HSQE SMS documentation Inspection and audit (PSC, SIRE, etc.) report analysis Safety culture improvement Hazard identification and risk assessment Accident, near miss and nonconformity records analysis Safety management system effectiveness review Regulatory & third-party requirements compliance analysis KPIs management Management of change implementation

547

548

K. Cicek et al.

After conducting a self-assessment in process improvement dimension, the ship management company organizations ranked and clustered in different digital maturity levels such as initiated, developing, defined, integrated and optimized. The Blue Digital Focus recommended strategies in five different segments; i) Building awareness, ii) Implementing best practices, iii) Standardization and continuous improvement, iv) Integration and alignment, v) Continuous innovation. Then, managerial development phase is completed.

4 Illustrative Analysis and Reporting The developed digital maturity assessment model: “Blue Digital Focus” is presented to the use of the ship management companies by developing a web-based interface. To illustrate the model, a sample database for 50 ship management companies, given in Table 2, is studied. Followingly, to demonstrate digital capabilities of the ship management companies, we conduct analysis and reporting respect to the digital maturity self-assessment results. Str.: Strategy, Org.: Organization, Cust.: Customer, Tec.: Technology, Opr.: Operation, Inn.: Innovation, CTM: Crew & Training Management, TM: Technology Management, OM: Operational Management, HM: HSQE Management. The defined maturity level for each dimension help the ship management companies to identify their existing position in digital transformation and helps to define deficiencies in an area that may adversely affect the overall effectiveness of transformation efforts. Additionally, the defined target maturity level for each dimension guides the ship management companies to define appropriate actions and initiatives in each dimension to establish a recommended path towards improvement. Also, among the results obtained with self-assessment, the digital maturity levels of all processes in process improvement dimension are also analyzed. To illustrate the analysis, Fig. 4 illustrates a sample strengths and weaknesses of ship management company in process improvement dimension while Fig. 5 shows existing and target situations. After the company-based analysis, the distribution of the companies was made according to their maturity levels for each digital maturity dimension. In Fig. 6, the distribution of the companies in strategy dimension is presented. As seen from Fig. 6, 6 of the companies participating in the maturity assessment do not have a strategy related to digital transformation. On the other hand, the remaining 44 companies have a digital transformation strategy at different levels.

Digital Maturity Assessment of Ship Management Companies …

549

Table 2 Sample database of 50 ship management companies Company

Str.

Org.

Cus.

Tec.

Ope.

Inn.

CTM

TM

OM

HM

C1

4

2

3

3

3

3

3

3

3

2

C2

2

1

2

2

1

0

2

2

3

0

C3

1

2

2

3

2

2

2

2

2

1

C4

0

1

1

1

1

0

1

1

1

2

C5

2

1

2

2

2

2

1

1

2

2

C6

1

2

1

3

2

2

2

2

2

3

C7

1

1

0

1

2

1

1

1

0

1

C8

2

2

1

2

2

2

2

2

3

2

C9

4

4

4

4

4

3

4

5

4

4

C10

0

0

2

2

1

2

0

2

1

2

C11

0

0

2

0

0

0

1

1

1

0

C12

1

1

1

1

2

1

1

0

1

0

C13

3

2

2

3

3

3

3

2

3

3

C14

3

3

3

3

2

2

2

2

3

2

C15

2

2

1

2

1

1

0

0

2

0

C16

3

4

3

4

3

3

4

4

4

4

C17

2

2

1

3

3

2

2

2

3

2

C18

3

3

3

3

3

3

4

4

3

3

C19

2

3

2

3

3

3

2

2

3

2

C20

0

1

2

2

1

2

0

0

1

2

C21

1

1

1

2

2

0

0

2

3

2

C22

1

2

2

3

3

2

2

2

3

2

C23

3

3

1

3

2

1

2

2

2

3

C24

2

2

1

1

1

1

1

1

0

1

C25

4

3

4

3

3

2

3

3

3

3

C26

2

2

0

0

1

2

1

0

2

1

C27

3

2

2

3

2

3

3

3

3

3

C28

2

3

3

3

3

2

3

3

3

2

C29

2

3

2

2

3

1

2

2

2

0

C30

1

1

0

1

2

1

1

1

1

1

C31

2

2

2

3

3

2

3

3

3

3

C32

1

2

1

2

2

0

0

1

0

1

C33

1

1

0

0

2

1

0

1

2

1

C34

3

3

4

4

4

4

4

4

4

4

C35

4

4

3

4

4

3

4

4

4

4

C36

0

0

1

1

0

2

0

2

1

0 (continued)

550

K. Cicek et al.

Table 2 (continued) Company

Str.

Org.

Cus.

Tec.

Ope.

Inn.

CTM

TM

OM

HM

C37

3

2

3

3

2

2

3

3

3

2

C38

2

2

2

2

2

1

3

1

3

2

C39

4

3

3

4

3

2

3

4

3

3

C40

1

1

2

2

1

1

1

1

1

2

C41

3

3

3

4

3

2

2

3

3

3

C42

3

4

4

4

5

4

4

5

5

5

C43

2

1

1

1

1

1

1

0

2

1

C44

1

2

2

3

2

2

2

2

2

0

C45

2

3

2

3

3

2

2

3

3

3

C46

1

1

1

1

2

1

2

1

2

0

C47

0

1

2

2

0

2

0

2

1

1

C48

2

1

2

1

1

0

1

2

2

2

C49

2

2

1

1

1

1

2

1

1

1

C50

2

2

2

3

1

0

1

1

2

1

Fig. 4 Strengths and weaknesses of ship management company in process improvement dimension Source Figure produced by authors

As seen from Fig. 7, in Organization dimension, while only 3 of the companies do not have any action, 14 companies have digital maturity at the initiated level, 18 companies at the enabling level, 11 companies at the integrated level and 4 companies at the optimizing level.

Digital Maturity Assessment of Ship Management Companies …

HSQE Management

Operational Management

Strategy 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

551

Organisation

Customer

Technical Management

Technology

Crewing & Training Management

Operations Innovations

EXISTING

TARGET

Fig. 5 Maturity levels of the dimensions for ship management company-1 Source Figure produced by authors

17

18 16 14 12 12

10 10 8 6 5

6 4 2

0 0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 6 Distribution of the ship management companies in the Strategy dimension Source Figure produced by authors

According to the Fig. 8, it can be said that the distribution in the customer dimension is very similar to the distribution in the organization dimension. In the technology dimension, it is seen from Fig. 9 that the digital maturity level of the ship management companies is higher than the other dimensions and 18 ship management companies are at the integrating level.

552

K. Cicek et al.

20

18

18 16

14

14 11

12 10 8 6 4

4 3

2

0

0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 7 Distribution of the ship management companies in the Organization dimension Source Figure produced by authors

19

20 18 16

14

14 12 9

10 8 6

4

4

4 2

0

0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 8 Distribution of the ship management companies in the Customer dimension Source Figure produced by authors

As seen from Fig. 10, unlike other dimensions, it is seen that the digital maturity level is at the highest level in the operations dimension of 1 company. On the other hand, 3 of the companies do not have any action in terms of operations. In Fig. 11, in the innovation dimension, it is seen that 7 of the ship management companies don’t have any action, 13 of the ship management companies at initiated level, 20 of the ship management companies at enabling level, 8 of the companies at integrating level and 2 of the companies at optimizing level.

Digital Maturity Assessment of Ship Management Companies … 20

553

18

18 16 14

12

12

10

10 7

8 6 4

3

2

0

0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 9 Distribution of the ship management companies in the Technology dimension Source Figure produced by authors

17

18 16

14 14 12 12 10 8 6 4

3

3 1

2 0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 10 Distribution of the ship management companies in the Operations dimension Source Figure produced by authors

As of digital maturity level in process improvement, while general overview of the ship management companies displays relatively low and similar distribution in HSQE, Crew & Training, and technical management, have higher level in operational management. As seen in Fig. 12, 8 of the companies has no action in order to improve process of Crew & Training Management. 12, 15, 9, and 6 of the companies are at initial, managed, defined, and integrated level, respectively. Besides, no company is in level of digital-oriented.

554

K. Cicek et al.

25 20 20

15

13

10

8

7 5

2 0 0 NOT DEFINED

INITIATED

ENABLING

INTEGRATING OPTIMIZING

PIONEERING

Fig. 11 Distribution of the ship management companies in the Innovation dimension Source Figure produced by authors

16

15

14 12 12 10

9 8

8 6 6 4 2 0 0 NOT DEFINED

INITIAL

MANAGED

DEFINED

INTEGRATED

DIGITAL-ORIENTED

Fig. 12 Distribution of the ship management companies in the crewing & training management dimension Source Figure produced by authors

Maturity level of the 2 companies in Technical Management, as seen in the Fig. 13, is at digital-oriented level. 5 of the companies is at integrated level, 8 of defined, 17 of managed, 13 of initial, and 5 of not defined levels. As per operational management level of the companies presented in Fig. 14, most of companies, except 3 of them, have been attempt to improve own operational process. 10 of the ship management companies are at initial level, 13 of the companies are at managed level, 19 of the companies are at defined level, 4 of the companies are at integrated level, and one of them is at digital-oriented level.

Digital Maturity Assessment of Ship Management Companies …

555

17

18 16 13

14 12 10

8 8 6

5

5

4 2 2 0 NOT DEFINED

INITIAL

MANAGED

DEFINED

INTEGRATED

DIGITAL-ORIENTED

Fig. 13 Distribution of the ship management companies in the technical management dimension Source Figure produced by authors

Operational Management 19

20 18 16 13

14 12

10

10 8 6 4

4 3 1

2 0 NOT DEFINED

INITIAL

MANAGED

DEFINED

INTEGRATED

DIGITAL-ORIENTED

Fig. 14 Distribution of the ship management companies in the operational management dimension Source Figure produced by authors

In the Fig. 15, maturity level of the companies in HSQE management dimension, one of the companies has been reached to digital-oriented level, whereas 8 of the companies remain the lowest level. The other 11, 16, and 10 companies are in the level of initial, managed, and defined, respectively. After examining the distribution of companies according to each digital maturity dimension, the digital maturity scores of the companies were calculated and presented in Fig. 16. As can be seen from the related figure, the score of the company with the lowest digital maturity score was calculated as 0.5, while the score of the company with the highest digital maturity score was calculated as 4.3.

556

K. Cicek et al. HSQE Management

18 16 16 14 11

12

10 10 8 8 6 4 4 1

2 0 NOT DEFINED

INITIAL

MANAGED

DEFINED

INTEGRATED

DIGITAL-ORIENTED

Fig. 15 Distribution of the ship management companies in the HSQE management dimension Source Figure produced by authors

3.8 3.8

4.0

4.3

5

2.9 2.9

3.1 3.2 3.2

3.6

4

1.7 1.8 1.9 1.9 2.0 2.0 2.0

2.2 2.2 2.2

2.5 2.5 2.6 2.6 2.6 2.7 2.7 2.7

3

0.9 0.9 0.9 0.9 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.4 1.5 1.5

2

0.5

0.7

1

0

Fig. 16 Digital maturity scores of ship management companies

Finally, the overall averages of the companies in each dimension were examined and presented in Fig. 17. As a result of the examination, it is seen that the ship management companies got the lowest score with 1.700 points in the innovation dimension. Afterwards, they got the second lowest score in the crewing & training management dimension with 1.860 points. On the other hand, ship management companies had the highest score in the technology dimension with 2.320 points. Companies got the second highest score in the operational management dimension with 2.280 points.

Digital Maturity Assessment of Ship Management Companies …

557

HSQE Management

1.880

Operational Management

2.280

Technical Management

2.020

Crewing & Training Management

1.860

Innovations

1.700

Operations

2.100

Technology

2.320

Customer

1.900

Organisation

1.980

Strategy 0.000

1.920 0.500

1.000

1.500

2.000

2.500

Fig. 17 Average digital maturity scores in dimensions

5 Conclusion and Discussion The increase in digital transformation needs in the maritime industry is remarkable. In this process, shipping companies play the leading role as key stakeholders. Besides shore-based organizational development with excellent communications among divisions, process improvement is considered as an important element of digitalization. At this insight, a digital maturity assessment is the first stage work of digital transformation identifying existing situations and prerequisites of the targeted achievements. This chapter offers Blue Digital Focus to conduct digital maturity analysis of ship management companies. It follows a self-assessment approach in both executive and managerial phases. The first phase has a holistic structure and is applicable to other maritime organizations as well. It investigates the organizations along with the strategy, organization, customer, technology, operations, innovations dimensions. On the other hand, the managerial development phase of Blue Digital Focus concentrates on the key processes so that the approach is customized. In this cycle, the processes under the responsibility of core divisions such as crewing & training, technical, operational, and HSQE are involved. Then, an illustrative case study is conducted on a randomly assigned sample database to test and demonstrate the model. The illustrative results obtained with the implementation of the digital maturity assessment model proposed in the study by ship management companies contain valuable information. So much so that, the results obtained reveal the strengths and weaknesses of companies in each digital maturity dimension. In this way, the companies can experience which digital maturity dimension/s they need to take special actions. In addition, when the average digital maturity scores of the companies for each digital dimension are examined, it is possible to see the actions that should

558

K. Cicek et al.

be taken in the sector. According to the the average values, it can be said that ship management companies have weaknesses in innovation dimension and therefore they are insufficient in providing digital services that can produce benefits for their customers and interested parties. On the other hand, ship management companies have a higher average value in technology than other dimensions. Although ship management companies, which have an average value in technology dimension, use technology successfully in the management of business processes, they need to think more about developing services that their customers can benefit from. Consequently, Blue Digital Focus supports awareness and accelerates the strategies throughout digital transformation of ship management companies. The tailormade assessment structure enables an accurate data collection from responders to publish reliable results. Considering the digital maturity levels predictions (i.e. initiated, developing, defined, integrated, optimized), the strategies on awareness, best practices, standardization, continuous improvement, integration, innovation are recommended. The utility of follow-up actions, progressed by relevant managers, promotes the digitalization potential of ship management companies. In addition, self-assessment practice is also expected to contribute managerial development in the companies. Indeed, this study will also bring a new understanding to the terms of C-level executives in ship management companies in future perspective. Furthermore, there is a need for additional efforts on increasing the visibility of Blue Digital Focus. An interactive platform supported with company-based digitalization survey visits will be useful. Future studies might include the use of statistical learning analysis in digital maturity reporting in case the collected data reaches a sufficient level. Acknowledgements This book chapter is produced from an initial phase of research project entitled “STB 073033-Blue Digital Focus (2021–2022)” which has been executed by Blueanalytica Maritime R&D and Information Technologies Inc. at Teknopark Istanbul Cube Incubation Center. The authors acknowledge to the Turkish Chamber of Shipping for their industrial leadership, collaboration and financial support.

References 1. Kechagias EP, Chatzistelios G, Papadopoulos GA, Apostolou P (2022) Digital transformation of the maritime industry: a cybersecurity systemic approach. Int J Crit Infrastruct Prot 37:100526 2. Ichimura Y, Dalaklis D, Kitada M, Christodoulou A (2022) Shipping in the era of digitalization: mapping the future strategic plans of major maritime commercial actors. Digit Bus 2:100022 3. Baum-Talmor P, Kitada M (2022) Industry 4.0 in shipping: Implications to seafarers’ skills and training. Transp Res Interdiscip Perspect 13:100542 4. Sanchez-Gonzalez PL, Díaz-Gutiérrez D, Leo TJ, Núñez-Rivas LR (2019) Toward digitalization of maritime transport? Sensors 19:926 5. Ichimura Y (2021) Evaluating the impacts of digitalization on ship operation: examining how to enhance maritime safety. Dissertation, World Maritime University 6. Yang CS (2019) Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use. Transp Res Part E Logist Transp Rev 131:108–117

Digital Maturity Assessment of Ship Management Companies …

559

7. Babica V, Sceulovs D, Rustenova E (2020) Digitalization in maritime industry: prospects and pitfalls. ICTE Transp Logist,.20–27 8. Nikitakos N, Lambrou MA (2007) Chapter 12 digital shipping: the greek experience. Res Transp Econ 21:383–417. 9. Korachi Z, Bounabat B (2019) Towards a maturity model for digital strategy assessment. In: Ezziyyani M Advance Intelligent System on Sustainable Devices (AI2SD 2019) AI2SD 2019 Advance Intelligent System on Computing. Springer, Cham, pp 456–470 10. Kozina M (2019) Framework for assessing the company’s maturity of digital business transformation. In: 40th International Science Conference on Economic and Social Development, pp 80–87 11. Kerrigan M (2013) A capability maturity model for digital investigations. Digit Invest 10:19–33 12. Matkovic P, Pavlicevic V, Tumbas P (2017) Assessment Of Business Process Maturity In Higher Education. In: INTED 2017 Proceedings of IATED, pp 6891–6898 13. De Carolis A, Macchi M, Negri E, Terzi S (2017) A maturity model for assessing the digital readiness of manufacturing companies. In: Lödding H, Riedel R, Thoben KD, von Cieminski G, Kiritsis D (eds) Advances in production management systems. The path to intelligent, collaborative and sustainable manufacturing. APMS 2017. IFIP advances in information and communication technology, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-669 23-6_2 14. De Carolis A, Macchi M, Kulvatunyou B, Brundage M, Terzi S (2017) Maturity models and tools for enabling smart manufacturing systems: comparison and reflections for future developments. In: PLM 15. Akdil KY, Ustundag A, Cevikcan E (2018) Maturity and readiness model for industry 4.0 strategy. In: Industry 4.0: managing the digital transformation. Springer, Cham, Pp 61–94 16. Gök¸sen H, Gök¸sen Y (2021) A review of maturity models perspective of level and dimension. Multidisc Dig Pub Inst Proc 74(1):2 17. Kane GC, Palmer D, Phillips AN, Kiron D, Buckley N (2017) Achieving digital maturity research report in collaboration with. MIT Sloan Manag Rev 18. Hupfer S, Loucks J, Sallomi P (2018) Succeeding in the age of digital transformation, TMT Frontrunners lead the way for the Industry 4.0 revolution. Deloitte In-sights 19. Duncan R, Eden R, Woods L, Wong I, Sullivan C (2022) Synthesizing dimensions of digital maturity in hospitals: systematic review. J Med Internet Res 24(3):e32994 20. Von Solms J, Langerman J (2022) Digital Technology adoption in a bank treasury and performing a digital maturity assessment. Afr J Sci Technol Innov Dev 14(2):302–315 21. Halpern N, Budd T, Suau-Sanchez P, Bråthen S, Mwesiumo D (2021) Conceptualising airport digital maturity and dimensions of technological and organisational transformation. J Airp Manag 15(2):182–203 22. Al-Ali M, Marks A (2022) A digital maturity model for the education enterprise. Perspect Policy Pract High Educ 26(2):47–58 23. Salume PK, Barbosa MW, Pinto MR, Sousa PR (2021) Key dimensions of digital maturity: a study with retail sector companies in Brazil. RAM. Revista de Administração Mackenzie 22 24. Sándor Á, Gubán Á (2021) A measuring tool for the digital maturity of small and medium-sized enterprises. Manag Prod Eng Rev 12(4):14 25. Häberer S, Arlinghaus J (2022) maturity evaluation for workforce management. an integrated approach to assess digital maturity of workforce management systems. In International workshop on service orientation in holonic and multi-agent manufacturing. Springer, Cham, pp 303–316 26. Ochoa-Urrego RL, Peña-Reyes JI (2021) Digital maturity models: a systematic literature review. Digitalization, 71–85 27. Barry AS, Assoul S, Souissi N (2022) Benchmarking of digital maturity models according to the dimension component. In: 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET). IEEE, pp 1–8 28. Rakoma SK (2021) A review of digital maturity models for shipping companies. Dissertations, World Maritime University

560

K. Cicek et al.

29. Philipp R (2020) Digital readiness index assessment towards smart port development. In: Sustainable Management Forum, Nachhalt 2020, vol 281, no 28. Springer, Heidelberg, pp 49–60 30. MPA (2022) Maritime and Port Authority of Singapore. https://www.mpa.gov.sg/web/portal/ home. Accessed 08 Apr 2022 31. Hess T, Matt C, Benlian A, Wiesböck F (2016) Options for formulating a digital transformation strategy. MIS Q Exec 15:123–139 32. Valdez-de-Leon O (2016) A digital maturity model for telecommunications service providers. Technol Innov Manag Rev 6:19–32 33. Salume PK, Barbosa MW, Pinto MR, Sousa PR (2021) Key dimensions of digital z sector companies in Brazil. Rev Adm Mackenzie 22:1–29 34. Gupta S (2018) Organizational Barriers to Digital Transformation. KTH Royal Institute of Technology, School of Industrial Engineering and Management 35. Grover B, Damle M (2020) Comparison of select digital maturity models for digital transformation dynamics. PalArch’s J Archaeol Egypt/Egyptol 17:4836–4856

Digital Transportation Maturity Measurement Bilge Varol, Gulfem Er, and Gül Tekin Temur

Abstract From the past to the present, there are radical changes and developments available in technology. Therefore, following these technological developments become essential for companies and organizations for coping with competitors. Transportation is a system that is in every business and can be used in various fields, and therefore, digitalization in transportation can be very essential for companies to adapt to Industry 4.0. In this study, a novel maturity model is proposed with the help of the literature and the experiences of experts. Within the scope of the proposed maturity model, five main criteria (material flow, business culture, organization & strategy, customer satisfaction & marketing, smart logistics) are proposed. In addition, the proposed model is solved by a multi-criteria decision-making (MCDM) approach called hesitant fuzzy analytic hierarchy process (HFAHP). In the HFAHP method, uncertainties, which is the nature of this problem, are handled with fuzzy logic. Finally, a real-life case study is applied to the proposed model and methodology in a logistic company in Turkey. The results of this study show that the company needs to improve its capabilities for the digitalization of its transportation system, especially for customer satisfaction & marketing and organization & strategy criteria. Keywords Digitalization · Hesitant fuzzy analytic hierarchy process · Industry 4.0 · Logistics · Multi-criteria decision making

B. Varol (B) · G. Er · G. T. Temur Bahcesehir University, Istanbul, Turkey e-mail: [email protected] G. Er e-mail: [email protected] G. T. Temur e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_24

561

562

B. Varol et al.

1 Introduction In the 17th century, steam engines were invented and this invention leads huge changes in the industry. The invention of the steam engine created different energy source needs in the industry like coal, petroleum, etc. [17]. These changes in the industry cause new manufacturing systems and it was called the first industrial revolution, namely, Industry 1.0. The second industrial revolution (Industry 2.0) began with the invention of electricity at the end of the 19th century [17]. Different machine tools were developed during this revolution which causes mass production in manufacturing systems [31]. The development of the first programmable logic controller (PLC) in 1969 was considered the beginning of the third industrial revolution (Industry 3.0) [21]. Moreover, Industry 3.0 was related to automation, electronic and information technology [21], and mechanical manufacturing systems were converted to automated manufacturing systems with Industry 3.0. The beginning of the fourth industrial revolution, namely, Industry 4.0 goes back to the 21st century. Industry 4.0 focuses on end-to-end digitization and completely integrated digital industrial systems [32]. With the emergence of Industry 4.0, IoT, cloud computing, big data, simulation, augmented reality, autonomous robots, and cyber security technologies have also emerged [31]. Along with all these technological developments, the need for digital transformation has also become an integral part of Industry 4.0. As a result of Industry 4.0, digital technologies are rapidly integrated into many processes including supply chain management (SCM) [27]. Digitalization in SCM has various benefits such as real-time data collection, information availability, and optimization in logistics applications [4]. In this digital transformation process, mostly used technologies are cloud computing, Big Data, artificial intelligence (AI), and the Internet of Things (IoT) technologies such as radio-frequency identification (RFID) [8]. These are helping to transfer real-world data into the virtual environment which makes SCM more efficient, flexible, and easy to interfere with if there are any difficulties. There are some other technologies used in the production stage which is part of a supply chain. These are technologies for automation that decrease the need for workers such as 3D printing and robotics [24]. The automation of processes will reduce errors caused by humans and will provide high-quality and faster production/service. Transportation is one of the important parts of a supply chain. Digital transformation in transportation is becoming a crucial research area nowadays. The integration of digital technologies in transportation processes has created a new concept: Logistics 4.0 [9]. The technologies that are mentioned for SCM are also significant for transportation processes. For instance, Big Data has an important role in optimal scheduling and routing since it keeps and provides data related to previous operations. Another point mentioned previously was IoT which is one of the components of Industry 4.0. It provides real-time data from devices connected to the internet. For example, RFID tags inside a container can provide instant information about the fill rate, humidity, temperature, etc. All collected information can be reached easily by

Digital Transportation Maturity Measurement

563

IoT technologies. These technologies save time and by increasing the traceability make it easy to control the processes. In addition to the technologies mentioned for SCM, some technologies are especially important for the digitalization of logistics. One of them is autonomous vehicles (AV) which are used for transportation processes. These vehicles are using optimized routes that are the fastest and shortest [28]. AV usage is increasing safety, and efficiency and decreases the negative impact on the environment [33]. Also, it is adaptable to the changes if there is any problem in the optimal route. Another technology is augmented reality, with this application smart glass usage is getting widespread [12]. Some of the usage areas of smart glass are scanning a barcode, loading products optimally with the help of the information seen through the glass, and during transportation, it can guide the driver as new-generation navigation. There are some other wearable technologies such as exoskeletons and bionic arms. They increase efficiency and safety by reducing the probability of having an industrial injury. In addition, they minimize the errors caused by human mistakes. In this chapter, a novel maturity model is utilized for digitalization in the transportation activities of a company in Turkey. In this model five main criteria—material flow, business culture, organization $ strategy, customer satisfaction $ marketing, and smart logistics—are employed. The model is solved by applying the decision-making method named HFAHP. The uncertainties in the problem are considered by the fuzzy logic approach. The remainder of this chapter is organized as follows: In Sect. 2 maturity models are presented. Literature review is presented in Sect. 3. The proposed model is introduced in Sect. 4 including description of the criteria. In Sect. 5 research methodology and application is described and the conclusions & future research directions are presented in Sect. 6.

2 Maturity Models Since digitalization is one of the key factors to develop a company, maturity models become essential. Because of this reason, various maturity models have been developed and implemented from past to present. Software Engineering Institute started to develop a process-maturity framework for improving the software processes for the first time [20]. After this study, they extended their maturity framework as the methodology called Capability Maturity Model (CMM) [20] and these studies form the basis of maturity models. Maturity models have been used for a variety of applications after these studies like Industry 4.0. In detail, maturity models are explained as the development of an entity over time [10]. For organizations, the abilities are controlled by maturity, and maturity models are the methodologies for evaluating the readiness of organizations. The current level of an organization or process can be measured and conceptualized by applying maturity models as a tool for comparing with the desired maturity

564

B. Varol et al.

level. An organization’s aim needs to be continuous improvement if continuous success is desired. Therefore, maturity models can be used to reach the aims of the organizations and help decision-makers with how and when to start the transformation. In addition, companies need to compare themselves to their competitors and follow the applications of business best practices.

3 Literature Review In the literature, there are studies that deal with maturity models in the transportation activities of the firms. In [3], there are three dimensions of the model which are order processing, warehousing, and shipping. Another study has four dimensions in the maturity model. These dimensions are communication, data, intellectual capital, and processes [11]. In order to assess the maturity of Logistics 4.0, in [18], three dimensions are covered: the flow of material, management, and the flow of information. Similarly, in [6], dimensions are management and flow of material and information. In [34], a maturity model is constructed for internal logistics. Five dimensions are identified which are material identification, manipulation, supply, packaging, and storage. Another study assessed ten dimensions, some of them are digital management, innovation and change, customer orientation, information technology, and new strategies [2]. In [13], a digital maturity model is created for logistics companies with four dimensions: technology and applications, organization, data, and integration. In this paper, a maturity model for Logistics 4.0 is constructed with five dimensions. The dimensions are material flow, business culture, organization & strategy, customer satisfaction & marketing, and smart logistics. In [5], an Industry 4.0 maturity model for smart operations and supply chain management is proposed. They handled sales/service digitalization, customer data usage, logistics systems and model automation, optimization of material flows and logistics information, relationship with suppliers, purchase and order digitalization etc. as the dimensions of this maturity model. In addition, monte carlo simulation and fuzzy logic are incorporated in order to solve real-life applications.

4 Proposed Model All main and sub-criteria are defined in the proposed model by considering related literature, the opinions of experts, and studies of some companies. The hierarchy of the digital transformation in transportation is illustrated in Fig. 1.

Digital Transportation Maturity Measurement

565

Fig. 1 The hierarchy of the digital transformation in transportation

4.1 Sub-criteria of Material Flow Automation Degree: Automation is one of the most important criteria in measuring the applicability of Industry 4.0. Therefore, it is an important criterion to be able to use automation at the stage of transporting materials, which is one of the most basic movements in logistics. The importance of an automation degree is mentioned [18] and robotization in warehouse and transportation is presented as an evaluation of logistics 4.0. Internet of Things (IoT): Connecting devices and smart systems together can be done by applying IoT systems. With the use of IoT, each step of the material flow can be included in smart systems thanks to instant data exchange, which is essential for the applicability of Industry 4.0. In addition, the importance of the usage of IoT is mentioned in the Pwc maturity index [1]. Control Systems: In each step of the material flow, materials need to be controlled by smart technologies for not to make any mistakes at any time. These control systems

566

B. Varol et al.

can be included in warehouses and transport vehicles to control every step of the material flow. Traceability of the System: In a transportation system, each step of the material flow needs to be tracked by experts, customers, or smart systems to check the state of the materials. Smart sensors, and location detection technologies can be used to track the materials. The traceability of the system is not directly mentioned as a maturity criterion in any index, but the importance of the sensors and location detection technologies are handled in the Pwc maturity index [1].

4.2 Sub-criteria of Business Culture Adaptive Workplace: The motivation of employees is directly affected by the workplace and therefore, designing a workplace that causes reduced stress levels for employees becomes essential. Moreover, the motivation of employees increases productivity and performance. In addition, virtual business work solutions became a necessity when designing adaptive workplaces after the COVID-19 pandemic. Companies began to apply technological business solutions to their workplaces based on their own culture. Support New Technologies: As noted in [26], new technologies need to be supported by leaders in the company. Due to the developing new technologies, the trends in Industry 4.0 for transportation should be followed and applied. The implementation and support of new technologies should also be integrated into the company culture and considered a mission. Talent Acquisition: In IMPULS Industry 4.0 readiness assessment, talent acquisition is handled as a criterion for this assessment. Continuous learning should be a mission for a company if continuous development and innovation are wanted to be implemented in the company. In addition, developing technologies bring the need for new abilities and software. Therefore, employees need to follow these innovations with the help of companies talent acquisition mission.

4.3 Sub-criteria of Organization and Strategy Strategic Planning: Strategic planning is one of the main issues that should be applied in a company to see the future of the company. Therefore, making strategic plans about how to implement industry 4.0 technologies should be one of the main tasks of a company. In strategic planning, the allocation decisions of the resources that will be required for the implementation of Industry 4.0 and the basic decisions in this area should be decided.

Digital Transportation Maturity Measurement

567

Investments: Investments should be planned and implemented in the company to achieve targets within the scope of the digital transformation roadmap. Since Industry 4.0 technologies are quite costly, it is necessary to be prepared for the necessary investments. Investments should not be considered only as technological equipment purchases, but also as talent acquisition, new software, and management cost. In addition, investments are considered a criterion in the IMPULS Industry 4.0 maturity index [14, 15]. Innovation Management: Organization’s innovation system should be managed from the beginning of the idea to the implementation of the innovation. This process is called innovation management in a company and this process should be maintained by experts in this company. According to IMPULS Industry 4.0 readiness assessment, innovation management is considered one of the sub-criterion of this assessment [14, 15].

4.4 Sub-criteria of Customer Satisfaction and Marketing Digital Marketing: Digitalization is one of the main criteria for Industry 4.0. In addition, digital marketing has a huge increase in use by various companies day by day. Companies that can apply digital marketing successfully gain many advantages compared to their competitors. Therefore, a company that wants to increase its demands should increase its investments in digital marketing. Customer Relationship Management (CRM) Systems: CRM systems are handled as a criterion in Acatech Industry 4.0 maturity model [25]. Since data of customers can be tracked by CRM systems, these systems help increase customer satisfaction degrees. Indirectly, by increasing customer satisfaction, sales increase, and thus the overall growth of the company is ensured. Industry 4.0 Technologies for Marketing: Marketing procedures should also be digitalized to implement Industry 4.0 in a company. With the help of digitalization in marketing processes, developing customer-specific products and data-driven marketing strategies can be developed. Acatech and Schumacher Industry 4.0 maturity models stated that the digitalization on marketing strategies should be a criterion for fulfilling customer requirements [25].

4.5 Sub-criteria of Smart Logistics Cloud Systems: Cloud systems including cloud computing and cloud data storing technologies are helpful and essential systems for the applicability of Industry 4.0. All users of the overall systems are connected by using cloud systems and cloud systems are one of the main elements of IoT which means every user and data are

568

B. Varol et al.

connected to the internet. Cloud systems are considered as a maturity index for applying Industry 4.0 by IMPULS and Pwc readiness assessments [1, 14, 15]. Big Data Analytics: Developing technologies bring big data with them and therefore, there will be a need to apply big data technologies in companies. The use of Industry 4.0 technologies in transportation will also require dealing with big data. This emerging need in big data analytics can be used in business planning, customer relations, strategic planning, transportation operations and thus increases the efficiency of the company. As mentioned in the Pwc and IMPULS Industry 4.0 readiness assessments, big data analytics need to be applied by experts or smart technologies [1, 14, 15]. Data-driven Services: According to Industry 4.0 maturity models of IMPULS and Pwc, data-driven services are handled as a criterion for evaluating the readiness for digitalization [1, 14, 15]. End-user satisfaction can be increased with the help of datadriven services by processing the historical data to meet customer needs. In addition, strategic decisions can be designed based on data analysis and interpretation by using data-driven services. Digital Technologies (RFID, RTLS): Digitalization in transportation can be achieved by integrating digital technologies into systems. Radio Frequency Identification (RFID) and real-time locating system (RTLS) etc. can be implemented in transportation systems for the traceability of items and vehicles. In Pwc Industry 4.0 maturity model, implementing digital technologies is handled as one of the criteria of this model [1].

5 Research Methodology and Application In this section, the research methodology proposed to determine the weights of the main and sub-criteria is given. As a first step of the methodology, HFAHP is applied to reveal the weights; then the simple weighted sum method is applied to find the maturity level, which means these weights are multiplied by the scores of the companies in terms of each criterion as given in Eq. (1) wi is the global weight of each affecting sub-criterion Si is the score of the companies referring their success on performing each sub-criterion: Digitali zation Maturit y Level =

i 

wi si

(1)

n=1

We applied the HFAHP approach that is given in [19] for the evaluation of wi . The reason for preferring this approach is that it allows researchers to deal with contradictions. For instance, an expert can tend to express her/his score as: “Between 7 and 9”, rather than expressing an exact value as “8”. To handle this situation, HFAHP is more beneficial to use. In our case, because digitalization is a new concept and it

Digital Transportation Maturity Measurement

569

Table 1 Linguistic scales for hesitant FAHP [19] Rank Linguistic term 10 9 8 7 6 5 4 3 2 1 0

Absolutely high importance Very high importance Essentially high importance Weakly high importance Equally high importance Exactly equal Equally low importance Weakly low importance Essentially low importance Very low importance Absolutely low importance

Abbreviation

Triangular fuzzy number

AHI VHI ESHI WHI EHI EE ELI WLI ESLI VLI ALI

7,9,9 5,7,9 3,5,7 1,3,5 1,1,3 1,1,1 0.33,1,1 0.2,0.33,1 0.14,0.2,0.33 0.11,0.14,0.2 0.11,0.11,0.14

can cause contradiction during the evaluation of the criteria, HFAHP is found as the most appropriate MCDM tool. Hesitant fuzzy set (HFS) as a novel fuzzy set that is introduced by [30] and [29] is applied in this study because it is beneficial method to deal with conflicts. For hesitant fuzzy linguistic terms sets, [22] proposed the expression list to allow decision makers select their expressions which are hesitating. On the other hand, Analytic Hierarchy Process (AHP) is one of the traditional MCDM techniques, in which pairwise comparisons between main criteria, subcriteria and alternatives have been conducted. For the cases in which the decision makers have conflicts, that means, if they do not prefer to use an exact value for scoring, usage of hesitant fuzzy AHP (HFAHP) becomes meaningful. In this study, the HFAHP procedure constructed by Buckley’s theory is followed. As a first step, the hierarchical structure including main and sub-criteria is prepared. Then, main criteria, sub-criteria and alternatives are assessed by decision makers by using the linguistic terms given in Table 1. After gathering pairwise comparison assessments, the fuzzy envelopes approach [16] is applied to compute trapezoidal fuzzy numbers. The lowest scale is s0 the highest scale is sg , and the assessments differ between two terms; si to s j . It can be referred as s0 ≤ si < s j ≤ sg . To accomplish the fuzzy envelopes approach, OWA operator of dimension n is utilized as given in Eq. 10. O W A(a1 , a2 , ..., an ) =

n 

w j .b j

(2)

j=1

where b j is the largest of the aggregated arguments a1 , a2 , . . . , an and W = n (w1 , w2 , . . . , wn )T is the associated weighting vector where i=1 wi = 1. The

570

B. Varol et al.

values of a, b, c, and d constituting the trapezoidal fuzzy number N˜ (i.e., N˜ = (a, b, c, d), can be found by using Eqs. (3–6): j

j

(3)

j

j

(4)

i a = min{a Li , a iM , a i+1 M , ..., a M .a R } = a L

i a = max{a Li , a iM , a i+1 M , ..., a M .a R } = a R

⎧ ⎫ ⎪ a iM , ifi +1 = j ⎪ ⎨ ⎬ j (i+ j)/2 i f i + jiseven b = O W Aw2 (am ,...,am ⎪ ⎩ O W A 2 (amj ,...,am(i+ j+1)/2 i f i + jisodd ⎪ ⎭ w

(5)

⎧ i+1 ⎫ ifi +1 = j ⎬ ⎨ aM , c = O W Aw2 (amj ,amj−1 ,...,am(i+ j)/2 i f i + jiseven ⎩ ⎭ j j−1 (i+ j+1)/2 m ,am ,...,am O W A2(a i f i + jisodd w

(6)

α is in the unit interval [0, 1], first (W 1 = (w11 , w21 , . . . , wn1 )) and second (W 2 = types of weights are shown with α [7] as in Eqs. (7) and (8).

(w12 , w22 , . . . , wn2 ))

w11 = α2 , w12 = α2 (1 − α2 ), ...wn1

(7)

w12 = α1n−1 , w22 = (1 − α1 )α1n−2 , ...wn2 = 1 − α1 , w

(8)

where α1 = g − ( j − i )/g − 1 and α2 = ( j − i ) − 1/g − 1 (g is the top rank number of evaluation scores, j is the rank of highest evaluation and i is the rank of lowest evaluation). The decision matrices are also checked if they are consistent or not. To do this, the matrices are defuzzified to gather crisp values. If the trapezoidal fuzzy number is equal to d˜ = (l, m 1 , m 2 , u), the crisp number (μd˜ ) can be computed as given in Eq. (9). μd˜ =

l + 2m 1 + 2m 2 + u 6

(9)

To calculate the consistency ratio (CR), Eqs. (10) and (11) are used. where CI refers to the consistency index, λm ax addresses the largest eigenvector of the matrix, n is the number of criteria, and R I is the random index that differs for each n. If C R value is not equal or over than 0.1, it is accepted as consistent [23]. After receiving consistent matrices, they are aggregated into one matrix by applying geometric means. All the fuzzy numbers found are defuzzified by using Eq. (13). And the defuzzified weights are then normalized to obtain the local weights of the criteria. To calculate the global weights of the sub-criteria, their local weights are multiplied with the weight of the main-criteria they depend on.

Digital Transportation Maturity Measurement

571

Table 2 Fuzzy envelops of the main criteria evaluation C1 C2 C3 C1 C2

EE

Between ESLI ESLI and ELI EE EE

C3 C4 C5

EE

D=

C4

C5

Between ESLI and ELI Between ESLI and ELI EE EE

Between WLI and EE Between EHI and ESHI ESHI ESHI EE

cl + 2cm1 + 2cm2 + cu 6

(10)

For the application, as a first step, the questionnaire is designed in accordance with the HFAHP requirements and forwarded to a leading expert group in the logistics industry to obtain consensus results. The experts are informed that they do not have to set an exact. It was also noted that if they choose to give a range rather than a single value, that range should be no more than 2 units. After the consensus solution is obtained, the HFAHP procedure is started to be applied. First of all, the fuzzy envelope of five main criteria evaluation is prepared as given in Table 2. As an illustrative example, let’s see how one score is computed. As shown in Table 2, the linguistic scale of “C2 on C5” is “Between EHI and ESHI” and the fuzzy envelope of this evaluation is computed as (1, 2.78, 3.22, 7) as shown in Table 3. α1 and α2 values are counted first as follows: α1 = (10 − (8 − 6))/(10 − 1) = 0.89

(11)

α2 = ((8 − 6) − 1)/(10 − 1) = 0.11

(12)

j

Since a = a Li and d = a R ; so a=1 (minimum value of EHI shown as 1, 1, 3) and d = 7 (maximum value of ESHI shown as 3, 5, 7). If i + j is even, then b = O W Aw2 (am j . . . , am (i+ j )/2 ). For our case, i + j = 14; it is even, so b has to be computed as follows: b = α2 ∗ 3 + α1 ∗ 1 = 0.89 ∗ 3 + 0.11 ∗ 1 = 2.78 Because i + j is even, then c = O W Aw2 (am j am j−1 . . . , am (i+ j )/2) ) ward. Therefore c has to be computed as follows: c = 2 ∗ 3 − 2.78 = 3.22 So, the fuzzy envelope is computed as (1, 2.78, 3.22, 7) as given in Table 3. These computations are performed for all main and sub-criteria evaluations separately. After the trapezoidal fuzzy set matrices are converted to crisp values and the consistencies are also controlled to see if the consistency value is lower than 0.10 or not as stated in [23]. If there is inconsistency, the related part of the comparison is repeated until reaching a satisfactory result.

572

B. Varol et al.

Table 3 The trapezoidal fuzzy sets of main criteria C1

C2

C1

1

1

C2

1

C3 C4 C5

1

C3

C4

C5

0.14 0.32 0.35 1

0.14 0.20 0.20 0.33 0.14 0.32 0.35 1

1

1

2.87 3.14 7

1

1

1

1

1

1

1

1

0.14 0.32 0.35 1

1.

2.87 3.22 7

3

5

7

1

1

1

1

1

1

1

1

1

1

1

1

3

5

1

2.87 3.14 7

1

2.87 3.14 7

1

1

1

1

1

1

1

1

1.

2.87 3.14 7

1

0.93 1.08 5

0.14 0.31 0.36 1

0.14 0.20 0.20 0.33 0.14 0.20 0.20 0.33 1.

0.93 1.08 5

5

1

1 5

1 7

At a later stage, the geometric mean is applied to the trapezoidal fuzzy sets. Then, normalization is made by dividing all values by “9” which is the highest score. The same procedure is conducted for main and sub-criteria and each value set for each row is converted to crisp values. The crisp values are then divided by the sum of the values, to reach the weights of the main and sub-criteria. The local weights indicating the importance of the sub-criteria in their parent sets are multiplied by the local weights related main criteria. The obtained value is called “global weight” that will be used in the maturity level determination. Table 4 indicates all weights regarding main and sub-criteria. To conclude the methodology and to determine the maturity level, it is necessary to multiply the global weights with the performance scores as given in Eq. (1). Assume that there is a company and has the performance scores regarding each sub-criterion given in Table 5 (1: absolutely lowly successful; 5 = absolutely highly successful). So, the maturity level is found by making the following computation: T heMaturit y Level = 0.009 ∗ 3 + 0.016 ∗ 4 + ... + 0.044 ∗ 2 = 2.16

(13)

The maturity level of the company is calculated as 2.16 out of 5 which means the company has scored less than 50% of the allowable maximum score. Based on this information, the maturity level of the company is evaluated with the IMPULS readiness level [14, 15]. According to the IMPULS readiness level, there are 5 levels for organizations which are Level 0 to Level 5. Organizations that are included in Level 0 are called outsiders, Level 5 are called top performers, and these levels can be seen in Table 3. As mentioned before, an overall maturity level calculation is evaluated by a MCDM technique called HFAHP, and the maturity level is calculated as 2.16 out of 5. Therefore, it can be concluded that the company is in Level 2 from IMPULS maturity levels which also means the maturity level of the company is intermediate and in the learners group. The maturity level of the company is not acceptable enough to call the company as successful in digitalization. According to the weights of the main criteria, customer satisfaction & marketing criterion has the highest weight which means this criterion affects the overall maturity score the most. Therefore, the company needs to improve its capabilities regarding this criterion. Under the customer satisfaction & marketing

Digital Transportation Maturity Measurement Table 4 Weights of main and sub-criteria Main criteria Weights of main Sub criteria criteria Material Flow (C1)

Business Culture (C2)

Organization & Strategy (C3)

0.076

0.205

0.298

Customer Satisfaction & Marketing (C4)

0.343

Smart Logistics (C5)

0.079

Autonomation degree Internet of things Control systems Traceability of the system Adaptive workplace Support in new technologies Talent acquisition Strategic planning Investments Innovation management Digital marketing CRM systems Industry 4.0 technologies for marketing Cloud systems Big data analytics Data-driven services Digital Technologies (RFID. RTLS)

Table 5 IMPULS maturity levels [14, 15] Top performer Level 5 Expert Level 4 Level 3 Experienced Intermediate Level 2 Level 1 Beginner Outsider Level 0

573

Sub criteria

Sub criteria

0.117

0.009

0.214 0.12 0.549

0.016 0.009 0.042

0.639

0.131

0.167

0.034

0.194 0.475

0.040 0.142

0.403 0.122

0.120 0.036

0.48 0.199 0.321

0.165 0.068 0.110

0.142 0.139 0.165

0.011 0.011 0.013

0.555

0.044

Leaders

Learners Newcomers

574

B. Varol et al.

Table 6 The performance scores of a company Sub criteria Performance scores Autonomation degree Internet of things Control systems Traceability of the system Adaptive workplace Support in new technologies Talent acquisition Strategic planning Investments Innovation management Digital marketing CRM systems Industry 4.0 technologies for marketing Cloud systems Big data analytics Data-driven services Digital Technologies (RFID. RTLS)

3 4 2 3 2 1 2 3 4 2 1 1 1 4 3 5 2

criterion, the digital marketing sub-criterion has the highest weight among the rest of the criteria which means digital marketing techniques should be developed in this company. After the customer satisfaction & marketing criterion, organization & strategy has the second-highest weight among the rest of the criteria. After improving their capabilities of customer satisfaction & marketing criterion, various development studies should be carried out regarding this criterion. The sub-criterion that has the highest weight under the main strategy criterion is the strategic planning subcriterion. Therefore, strategic planning studies should be carried out very carefully. If the company takes these improvements into account, it will increase its overall maturity level score, thereby achieving a higher position in the IMPULS maturity levels. This will mean that the company has developed itself in the field of Industry 4.0 in transportation (Table 6).

6 Conclusions and Future Research Directions Digital transformation is necessary for companies that want to improve themselves for competing with their competitors. In addition, following the new technologies, especially for applying Industry 4.0 will become a need day by day. Also, the speed of digital transformation directly affects the increases in companies’ efficiency and profits. Based on this information, a novel maturity model for the digitalization of

Digital Transportation Maturity Measurement

575

transportation systems is proposed in this study. The main criteria and sub-criteria of the proposed model are developed with the help of literature and experts from the industry. With the help of this study, companies will have a roadmap for applying digitalization to transportation and this roadmap will show the experts how and in what order of importance they need to apply these digitalization processes. Because of the nature of this problem, uncertainties need to be included in this problem. Therefore, the proposed model is solved by a MCDM technique called HFAHP. The proposed model is tested on a real-life case study from a logistic company in Turkey. The maturity level is calculated as 2.16 out of 5 for this logistic company. According to the maturity level of this company, it can be seen that this company is in the Level 2 from IMPULS maturity levels. As mentioned before, if the maturity level of the company is evaluated as Level 2, then it means that the company is in the learners group which means the group of intermediate. It can be concluded that this company needs to improve its capabilities for improving its overall score which is called the maturity level. Especially, customer satisfaction & marketing criterion which has a weight of 0.343, and organization & strategy criterion, which has a weight of 0.298, needs to be improved the most for increasing their maturity level. Finally, it can be seen that this study proposed a general maturity model for the digitalization of transportation and each company can use this model for evaluating its maturity levels. After evaluating the maturity levels, they need to change their roadmaps with respect to the criteria that they are not strong enough. In the future, the proposed maturity model will be extended by considering supply chain criteria for handling more complicated scenarios for companies. After this extension, the proposed model will be solved by various MCDM techniques and they will be compared to each other. Finally, a better maturity model and a solution technique will be proposed and presented.

References 1. PwC and GMIS: Industry 4.0: Building the digital industrial enterprise (2016) 2. Asadamraji E, Rajabzadeh GHatari A, Shoar M (2021) A maturity model for digital transformation in transportation activities. Int J Transp Eng 9(1):415–438 3. Asdecker B, ve Felch V (2018) Development of an industry 4.0 maturity model for the delivery process in supply chains. J Model Manag 13(4):840–883 4. Bigliardi B, Filippelli S, Petroni A, Tagliente L (2022) The digitalization of supply chain: a review. Procedia Comput Sci 200:1806–1815. https://doi.org/10.1016/j.procs.2022. 01.381. https://www.sciencedirect.com/science/article/pii/S1877050922003908. 3rd International Conference on Industry 4.0 and Smart Manufacturing 5. Caiado RGG, Scavarda LF, Gavião LO, Ivson P, De Mattos Nascimento DL, Garza-Reyes JA (2021) A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. Int J Prod Econ 231:107883 6. Facchini F, Ole´sków-Szłapka J, Ranieri L, Urbinati A (2019) A maturity model for logistics 4.0: an empirical analysis and a roadmap for future research. Sustainability 12(1):86 7. Filev D, Yager RR (1998) On the issue of obtaining OWA operator weights. Fuzzy Sets Syst 94(2):157–169

576

B. Varol et al.

8. Han L, Hou H, Bi ZM, Yang J, Zheng X (2021) Functional requirements and supply chain digitalization in industry 4.0. Inf Syst Front. https://doi.org/10.1007/s10796-021-10173-1 9. Henke M, Besenfelder C, Kaczmarek S, Fiolka M (2020) A vision of digitalization in supply chain management and logistics, pp 277–286. https://doi.org/10.15488/9669 10. Klimko G (2001) Knowledge management and maturity models: building common understanding. In: Proceedings of the 2nd European Conference on Knowledge Management, vol 2, pp 269–278. Bled, Slovenia 11. Krowas K, Riedel R (2019) Planning guideline and maturity model for intra-logistics 4.0 in SME. In: Ameri F, Stecke KE, von Cieminski G, Kiritsis D (eds) APMS 2019, vol 567. IAICT. Springer, Cham, pp 331–338. https://doi.org/10.1007/978-3-030-29996-5_38 12. Kuhlmann AS, Klumpp M (2017) Digitalization of logistics processes and the human perspective 13. Kusters A (2022) Relating digitization, digitalization and digital transformation: a maturity model and roadmap for dutch logistics companies. B.S. thesis, University of Twente 14. Lichtblau K et al (2015) Impuls, industry 4.0 readiness. Impuls-Stiftung des VDMA, AachenKölb 15. Lichtblau K et al (2015) Impuls industry 4.0 readiness. Industrie 4.0 Readiness Study 16. Liu H, Rodríguez RM (2014) A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Inf Sci 258:220–238 17. Nayyar A, Kumar A (2020) A roadmap to industry 4.0: smart production, sharp business and sustainable development. https://doi.org/10.1007/978-3-030-14544-6 18. Ole´sków-Szłapka J, Stachowiak A (2019) The framework of logistics 4.0 maturity model. In: Burduk A, Chlebus E, Nowakowski T, Tubis A (eds) ISPEM 2018, vol 835. AISC. Springer, Cham, pp 771–781. https://doi.org/10.1007/978-3-319-97490-3_73 19. Öztaysi B, Onar SÇ, Boltürk E, Kahraman C (2015) Hesitant fuzzy analytic hierarchy process. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–7. IEEE 20. Paulk M, Curtis B, Chrissis M, Weber C (1993) Capability maturity model, version 1.1. Softw IEEE 10:18–27. https://doi.org/10.1109/52.219617 21. Poor P, Žení¸sek D, Basl J (2019) Historical overview of maintenance management strategies: development from breakdown maintenance to predictive maintenance in accordance with four industrial revolutions 22. Rodriguez RM, Martinez L, Herrera F (2011) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119 23. Saaty TL (2001) Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. RWS publications, Pittsburgh 24. Schlüter F, Hetterscheid E (2017) Supply chain process oriented technology-framework for industry 4.0. In: T.R.C.M Kersten W, Blecker T (ed) Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), vol 23, pp 275–299. epubli GmbH, Berlin. https://doi.org/10.15480/882.1467. http://hdl.handle.net/ 10419/209313. Urn:nbn:de:gbv:830-88217645; 10419/209192; https://econpapers.repec.org/ bookchap/zbwhiclpr/23.htm 25. Schuh G, Anderl R, Dumitrescu R, Krüger A, Ten Hompel M (2020) Using the industrie 4.0 maturity index in industry. Current challenges, case studies and trends 26. Schumacher A, Erol S, Sihn W (2016) A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia Cirp 52:161–166 27. Seyedghorban Z, Tahernejad H, Meriton R, Graham G (2020) Supply chain digitalization: past, present and future. Prod Plan Control 31(2–3):96–114 28. Sorkun M (2020) Digitalization in logistics operations and industry 4.0: understanding the linkages with buzzwords, pp 177–199. https://doi.org/10.1007/978-3-030-29739-8_9 29. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539 30. Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decision. In: 2009 IEEE International Conference on Fuzzy Systems, pp 1378–1382. IEEE

Digital Transportation Maturity Measurement

577

31. Vinitha K, Ambrose Prabhu R, Bhaskar R, Hariharan R (2020) Review on industrial mathematics and materials at industry 1.0 to industry 4.0. Mater Today Proc 33:3956– 3960. https://doi.org/10.1016/j.matpr.2020.06.331. https://www.sciencedirect.com/science/ article/pii/S2214785320348045. International Conference on Nanotechnology: Ideas, Innovation and Industries 32. Xu LD, Xu EL, Li L (2018) Industry 4.0: state of the art and future trends. Int J Prod Res 56(8):2941–2962. https://doi.org/10.1080/00207543.2018.1444806. https://doi.org/10.1080/ 00207543.2018.1444806 33. Yu JJQ, Lam AYS (2018) Autonomous vehicle logistic system: joint routing and charging strategy. IEEE Trans Intell Transp Syst 19(7):2175–2187. https://doi.org/10.1109/TITS.2017. 2766682 34. Zoubek M, Simon M (2021) Evaluation of the level and readiness of internal logistics for industry 4.0 in industrial companies. Appl Sci 11(13):6130

Transport Digitalization G. V. Kuznetsova and G. V. Podbiralina

Abstract The rapid development of digital technologies, accelerated by COVID-19 effects tends to undermine the established business models and existing sectors of the economy, including the transport sector. Artificial intelligence, robotics technology, the Internet of Things, big data drastically transform all types of transport and logistics operations. Moreover, in addition to digitalization, this sector turned out to be influenced by a wave of public concern over the ecological state of the planet. A powerful impetus was given by a recent climate change conference in Glasgow, following the results of which the governments of many countries undertook a commitment to ensure a net zero carbon footprint over the coming decades. Thousands of companies have declared their allegiance to ESG principles and readiness to work for a green economy. The prospects for the development of the industry are also influenced by changes in consumer behavior. New generation—digital natives, are ready to give up private cars, using instead car sharing service, switch to bicycles and kick scooters and buy environmentally friendly electric cars. However, in the beginning of 2022 the world has met new challenges and risks brought by the new geopolitical situation after the start of the war of Russia in Ukraine. The event resulted in the immediate disruption of many logistic channels, the consequences of which are beyond prediction at the moment. Even after the military activities cease it may take significant time to restore the disrupted connections. The study is based on the use of qualitative comparative analysis that identifies the impact of digitalization on the subsectors of the transport industry. The purpose of the study is to analyze the impact of the main digital transformation tools, as well as other impact factors (decarbonization, changes in behavioral stereotypes, COVID-19, etc.) on the development of transport: road, rail, sea and air transport. The transport industry is viewed through the lens of “Mobility as a Service” (MaaS). The research is focused on revolutionary changes in the development of the industry, which will result in structural changes in value added chains, shifts in the G. V. Kuznetsova (B) · G. V. Podbiralina Plekhanov Russian University of Economics (PRUE), Moscow, Russia e-mail: [email protected]; [email protected] G. V. Podbiralina e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_25

579

580

G. V. Kuznetsova and G. V. Podbiralina

geography of the deployment of production facilities, the further spread of electronic document flow and payments, the growth in the use of electric cars and autonomous vehicles, robotization of warehouses and port infrastructure, the introduction of the Internet of Things in traffic control. The result of the study was the conclusion that the digitalization process in transport will develop, having an impact on other sectors of the economy. It was determined that these processes are irreversible, and companies engaged in transport operations and those working in related fields must adapt to changes: develop their own business models or adapt to existing ones. At the same time, the industry is only in the early stages of digitalization, the rate of changes and their consequences are not yet determined. Keywords Digitalization · Transport · The fourth industrial revolution · Innovations · Road · Air · Sea · Rail transport

1 Introduction The second decade of the XXI century was marked by radical shifts in all areas of social, political and economic life caused by the digital revolution, which is characterized not only by the introduction into production of new technologies, but also by a switch to new models of business organization along the entire perimeter of companies’ operations, with cardinal restructuring of production as well as management processes, the creation of new supply chains (Korovkin, V., [29]). Activities involving artificial intelligence (AI), the Internet of Things (IoT), cloud technologies, online platforms, outsourcing and business offshorization are set to increase in ever greater extent. This results in drastic changes in the structure and geography of the deployment of production facilities and the direction of international trade links. A multimodal smart transport system is taking shape, which provides digital interaction of infrastructure objects (traffic lights and semaphores, CCTV cameras, lighting and signaling systems), roadways, vehicles, online traffic control applications (Kyoungho Ahn et al., [30]). The trend for creation of driverless transport is becoming a key one. According to McKinsey estimates, by 2025 the digital economy will be able to deliver 19%–34% of global GDP growth (Digital McKinsey, [37]). Covid-19, despite the enormous and apparent damage caused to the economic development of the countries of the world, has become a powerful driver for acceleration of digital transformation processes. Over several months of the pandemic, a breakthrough was made in the level of digitalization of production and sales processes, as well as product lines in different sectors of the economy, comparable to similar changes in the previous 3–4 years. There are new areas and tools for the use of digital technologies (CovidTech), new business models and approaches to company management organization. It is apparent that the digital revolution is becoming the dominant vector in shaping of the modern image of international economic links, which requires an ever more

Transport Digitalization

581

detailed consideration of both the factors influencing these processes and the possible consequences of current and future transformations. The development of transport services in recent decades has undergone a significant transformation, which largely contributed to the globalization of world business processes. Scientific-and-technological advances have made it possible to speed up transportation, while improved marketing and management operations have led to a reduction in the cost of auxiliary operations. An instrumental role was played by containerization and the introduction of multimodal shipping or transport corridors, which made it possible to use for the delivery of goods all types of transport, including water, air and land transport and combine them in a continuous transport process to entrust the transportation to a single transport company. Global navigation satellite systems, such as GPS, GLONASS, BEIDOU, Galileo, along with regional systems Indian IRNSS and Japanese OZSS enable to keep track of location of any vehicle. Thus, economic distances have reduced noticeably and global supply chains are transforming the world into a “global factory”. At the moment, the transport services sector is one of the fastest growing sectors of the economy. Moreover, it is evident that the production performance of other sectors of the economy, and, consequently, the well-being of the population, will also depend on the degree and rate of its digitalization. The global digital freight forwarding market is estimated to be approximately $1,2 billion in 2018. By 2026, the global market is estimated at $3,1 billion, with a compound annual growth rate of 49.78 %m (Maia Research, [33]). Digitalization is radically changing the technologies used in transport and the way people and goods are transferred. For this purpose, one can single out a few general trends which affect all types of transport services: Electronic document flow, electronic payment, electronic data interchange (EDI) have already become widespread in much of the world and became a kind of platform for the development of other areas of digitalization. Transactions involving purchase and sale of transport services are increasingly being made in electronic form, not least because of the pandemic. The spread of robotics technology has covered the entire transport services sector, but especially warehousing and port facilities, cargo-handling operations, and the delivery system. There is an explosive growth in the use of unmanned vehicles, especially unmanned aerial vehicles, for the delivery of goods, products, medicines, and mail. Robotaxis and drones make it possible to more effectively resolve the problem of the “last mile”, reduce the cost of services, and improve their quality. The introduction of three-dimensional printing (3D) enables to reduce the cost and improve the quality of repair work and aftersales service. Further spread of these technologies will change the geography of the deployment of production facilities, bring them closer to the largest consumer markets, and will enable to reduce the volume of world trade in finished goods, and, consequently, cargo traffic. The use of artificial intelligence (AI) and big data makes it possible to improve the speed and increase the level of decision-making. Mobile data traffic in quarter 3 2021 alone turned out to be bigger than traffic generated in mobile networks for the entire period until the end of 2016; taking into account fixed-wireless access

582

G. V. Kuznetsova and G. V. Podbiralina

(FWA) networks, global traffic in quarter 3 2021 amounted to 78 EB (exabytes) per month, the volume anticipated by the end of 2027—370 EB per month.1 AI relieves production of routine operations, improves the efficiency of supply chains, and transport safety. The Internet of Things (IoT) is widely used in transport and constitutes a network of devices capable of collecting data on urban and intercity traffic of trucks and light vehicles, public transport vehicles, water and air transport vehicles, railway rolling stock. The Internet of Things improves transport efficiency by providing cost savings, including through fuel saving and increase in speed. The increasing use of RFID sensors ensures transparency of operations, allows to optimize routes, handle traffic jams in megacities, manage traffic flows, improve the quality of repair and maintenance, and minimize the impact of the human factor, thereby reducing accident rate. RFID tags make it possible to save on vehicles up to 40%, cut the labor costs of logisticians in half. There is a case of Walmart, which describes the results of digital technologies use in the procurement of mango. The introduction of IoT made it possible in a matter of seconds to receive information about the farm where the fruit was grown, the storage warehouse, transportation routes, and shelf locations. The test audit conducted by Walmart showed that the time required for tracing the route of a mango package significantly reduced from a few days or even weeks to 2 s (Roberts, I., [44]). “Smart” roads enable to track traffic offenders, “illegal” carriers, identify faults in railway tracks, emergency situations. As estimated by IoT Analytics, in 2021 the global number of connected devices will grow to 12.3 billion of active endpoints. By 2025, their number may reach more than 27 billion.2 Digital platforms are becoming ever more widespread in transport. The wellknown and ubiquitous Uber platform literally exploded the city taxi business. Similar platforms however are used for public transport and cargo traffic as well. The 5PL platform (Fifth Party Logistic Model) constitutes a full package of transport and logistics services. VR/AR virtual/augmented reality—technologies that are increasingly used in the transport services sector. First of all, they are used for training, because they allow to simulate a training environment for those activities for which preliminary preparation is necessary and important. Such activities include equipment management. The second area is the architecture and design of vehicles. VR/AR enables to create fullsize objects in virtual space for demonstration. BMW was the first to use AR in the automotive industry. According to the company’s analytics, more than 40% of consumers are willing to pay for an augmented reality compatible product (Peters, S., [42]). Of course, all these advances are based on the Internet, which is also revolutionized on an ongoing basis. The era of 5G draws near. According to CSS Insight, in 2021

1

Ericsson Mobility Report https://www.ericsson.com/en/reports-and-papers/mobility-report/rep orts/november-2021. 2 IoT Use Case Adoption Report 2021. https://iot-analytics.com/product/iot-use-case-adoption-rep ort-2021-2/.

Transport Digitalization

583

Table 1 Fossil primary factors and GHG emission factors for selected fuels Fuel

Fossil PEF (MJ/MJfuel)

Well-to-Tank EF (gCO2eq/MJ)

Total EF (gCO2eq/MJ)

Gasoline

1.18

14

87

Diesel

1.21

16

89

LPS

1.11

8

74

Diodisel

0.45

55

55

Natural gas

1.16

14

70

Hydrogen-from natural gas 2.20

125

125

Hydrogen-from electrolysis, EU mix

2.22

230

230

Eletricity—from EU max, low voltage

1.70

150

150

there will be 340 million 5G connections in the world, and 2.7 billion by 2025.3 As compared to 4G technology, which can support 10 thousand devices per each square kilometer, the 5G network supports a million devices. The success of digitalization in transport is underpinned by great advances in international certification. ISO has developed and is implementing an ITS standard— intelligent transport systems (ISO 21217), which is applicable to almost the entire field of mobility, including: public transport, road safety, transportation and logistics, electronic document flow and electronic payments. ITS services standard covers the exchange of data between the means of transport themselves (light vehicles and trucks, buses, specialized vehicles, etc.), road and city infrastructure (traffic lights, road signs, various kinds of signal lights, etc.), as well as cloud service and control centers which monitor (traffic, operators, locations, road users, including pedestrians and cyclists).4 The trend towards transport digitalization has coincided with the demand for an ever greater greening of the economy. Transport generates approximately 16% of global greenhouse gas emissions, with the most significant part of them coming from motor vehicles (11.9%). Air and sea trades generate 1.9 and 1.7% of global emissions, respectively.5 The research of the use of various power sources in the countries of EU illustrates the comparative volume of emissions generated by them (see Table 1) (Noussan & Tagliapietra). In this regard, the trend towards the use of electric cars is becoming mainstream in terms of changes in transport. It is calculated that the hydrocarbon footprint of 3

5G Networks. https://www.ccsinsight.com/research-areas/5g-networks/. Cooperative intelligent transport systems (C-ITS) Guidelines on the usage of standards. ISO. Edition 26. June 2020. https://isotc.iso.org/livelink/livelink/fetch/-8846111/8847151/8847160/CITS-Brochure.pdf?nodeid=21466865&vernum=-2. 5 Climate-Centered Mobility for a Sustainable Recover. https://www.transformingtransportati on.org. 4

584

G. V. Kuznetsova and G. V. Podbiralina

300 273.7

250

207.4

200

169.1

150

120.3

100 76.4

73.5 60

50

45.4

0.2

0.07

0.2

0

1.2

2.1

3.8

6

8.8

12

37.4 17.1

42.5 31.2 36.5 22.9 33.5

0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 Gljbal Market Size, BUSD

Growth Rate %

Fig. 1 Global market size (bill. USD) and grows rate of digital freight forwarding (%) (Maia Research, [33])

an electric car, taking into account all stages of car production and CO2 emissions when generating electricity, is 19–69% smaller than that of cars with an internal combustion engine (ICE) (Bieker, G., [4]). In addition to conversion of land transport to electric traction, work is being underway to switch air and sea transport to alternative fuels based on biofuels (plant- and organic waste-based fuels). Boeing, for example, intends to convert aircraft to biofuels by 2030. As estimated by BCG experts, the use of biofuels will allow to reduce carbon dioxide emissions by 85% compared to fossil fuels (Jameson, P.et al., [27]).

Transport Digitalization

585

Abandonment of hydrocarbon fuels could generate an additional $ 2.6 trillion a year, reduce CO2 emissions by 1.8 gigatons and road fatalities by 800,000.6 Reduction of carbon footprint is known to be one of the Sustainable Development Goals proclaimed by the UN. The development of transport is influenced by changes in consumer behavior. The consumer is becoming more and more “digitalized”, having at his disposal an ever greater deal of information thanks to mobile devices. He easily finds information on prices, conditions, discounts, looks for attractive offers all over the world. Digital generation entry with a focus in consumption on healthy lifestyle, ecology and advanced technologies noticeably changes the demand for transport services. Further development of transport is associated with the prospects for building ecosystems of logistics corridors, new energy sources and the use of unmanned systems. The proposed study is dedicated to these matters. The rest of the chapter proceeds as follows: the next section provides a literature review, while Sect. 3 propose the analysis methods. Section 4 analyzes our results which are presented in four areas of research: seaborne trades, road transport, rail transport, and air transport. Section 5 provides discussion. Section 6 concludes with thoughts for future research.

2 Literature Review This section contains a brief review of academic research covering digitalization issues and the impact of these processes on the prospects for the development of transport services. It is noteworthy that there is already a gigantic body of literature in this field. The proposed research rests on the foundation laid by many scientists who study this issue. Among literature in review there is Clayton Christensen’s work “The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail”, which deals with a disruptive impact of new technologies on company operations (Christensen, C., [8]). Among the latest works it is worthwhile to single out “Competing in the Age of AI” by Karim R. Lakhani, Marco Iansiti who, using the examples of the most advanced companies, lay bare the influence of digital networks, AI, big analytics on the shaping of new business models (Lakhani, K. and Iansiti, M.). Carliss Baldwin, Kim B. Clark in “The Power of Modularity” also showed a huge impact of information technology manufacturing on the nature. Michael A. Cusumano, Annabelle Gawer, David B. Yoffie in “The Business of Platforms. Strategy in the Age of Digital Competition, Innovation, and Power” revealed the importance of platformization for business development. The work by Perego and Perotti “ICT for logistics and freight transportation: A literature review and research agenda” (Perego A. et al., [40]) is dedicated specifically to the impact of digitalization on the transport sector. 6

Transforming Transportation 2020 https://www.wri.org/events/2020/01/transforming-transport ation-2020.

586

G. V. Kuznetsova and G. V. Podbiralina

Research by Ljubo Vlacic “Shall the Rail Lines in Isolated Territories Be Replaced by “Autonomous Vehicle Trains”? is dedicated to assessment of satisfaction of constantly growing requirements for reduction of traffic congestion and travel times through technological advances in rail operations (Vlacic, L., [53]). Selected elements of a scientific approach to the transition to digital media in transport services, as well as the study of potential problems associated with digitalization and possible solutions for them are set out in the work by Anne Durand and Toon Zijlstra. “The impact of digitalisation on the access to transport services: a literature review” (Durand, A. and Zijlstra, T., [14]). Recently, there has been much concern about creating a sustainable transport system and the impact of digitalization on this process. This problem, as well as the opportunities opened by digitalization to improve efficiency, reduce costs and improve the level of service of road freight transport are set out in the study “How Will Digitalization Change Road Freight Transport? Scenarios Tested in Sweden” (Pernestål, A. et al., [41]). The EU’s Joint Research Center (JRC) report (2020) made a significant contribution to the study of the impact of digital transformation on a range of policy areas spanning transport, construction, energy, digital government and public administration. The report, in particular, analyzes the profound changes taking place in all sectors of the economy and society as a result of introduction and integration of digital technologies (Baldini, G.et al., [5]). Especially noteworthy is a document developed by European researchers dedicated to the summary of results of support for research and innovation in the field of transport in the context of European Union (EU) policy (Tsakalidis, A. et al., [52]). One of the serious problems originated by the digitalization process is the creation of an appropriate IT infrastructure for the future allocation of resources and the formation of automated tools for forecasting passenger traffic flow. These issues are also reflected in modern studies. For example, the problems of digitization at airports are covered in the work “Challenges in airport digital transformation. Transportation Research Procedia” (Zaharia, S. and Pietreanu, C., [58]), and the role of digital innovation in revenue optimization from air freight—in the study “Digital Innovation Will Take Air Freight Revenues to New Heights” (Ferri, G., [18]). Especially noteworthy is the work of Iulia Poulaki and Vicky Katsoni “Current Trends in the Strategy of Air Transportation Distribution Channels: Evolution Through Digital Transformation”, in which the authors not only elaborate on the positive aspects of the digitalization process in relation to transport systems, but also touch upon opportunities of the airlines to develop sales channels, maximize earnings, reduce costs and so on (Poulaki, I. and Katsoni V., [43]). Apart from individual authors, close attention to transport digitalization is paid by international organizations. For example, a report, prepared by the World Bank, jointly with the International Association of Ports and Harbors, entitled “Accelerating Digitalization: Critical Actions to Strengthen the Resilience of the Maritime Supply

Transport Digitalization

587

Chain”7 provides a comprehensive analysis of digital technologies (big data; Internet of Things; blockchain-based solutions; unmanned aerial vehicles, etc.), which are currently used by the world’s leading transport and logistics companies. An important contribution was made in the paper by Michel Noussan, and Simone Tagliapietra “The effect of digitalization in the energy consumption of passenger transport: An analysis of future scenarios for Europe”, which analyses the possible scenarios of development of the European market of passenger cars from the perspective of the interplay of digitalization and ecological requirements. At the same time, the rapid dynamics of technological changes in the world requires constant study and rethinking of the impact of digitalization processes on various spheres of economic and social life. The chapter below contains an attempt to elaborate on the above ideas, enhancing them with an analysis of new factors influencing the transport services sector, in particular taking into account the Covid effect.

3 Analysis Methods The task at hand requires an integrated research approach. On the one hand, the available expert assessments of researchers and authoritative analytical agencies allow to conduct a detailed analysis of the dynamics and directions of transport digitalization, on the other hand, they are not enough to identify trends in the development and restructuring of the industry sector in response to the digital challenges of the era. The available statistics is rather a matter of judgment and poorly founded. Some additional information can be obtained from corporate reviews of large companies. However, these data are clearly not enough, the more so that the introduction of complex digital technologies is characterized by a significant time interval between the start of implementation and getting significant economic effects.8 Accordingly, current estimates may not fully reflect the digitalization picture of the industry sector. For this reason, in the context of the object in view, an important research task is to describe the effects of digitalization at a qualitative level. Throughout this chapter the Intuitive Logic (IL) technique is used as well. The method is based on the analysis of trends that are believed to affect the area of research. The trends are identified and analyzed from the standpoint of assessing the expected power of their impact. Trends that are assessed to have high impact and low uncertainty constitute “definite events”. 7

Digitalizing the Maritime Sector Set To Boost the Competitiveness and Resilience of Global Trade. https://www.worldbank.org/en/news/press-release/2021/01/21/digitalizing-the-mar itime-sector-set-to-boost-the-competitiveness-and-resilience-of-global-trade. 8 For example (Wang, Zhao and Zhang, [56]) found based on the material of Chinese enterprises that the peak financial return on investment in innovation occurs in the twelfth year after investment; at the same time, Chinese enterprises are considered in the world as a model of the speed of obtaining a return on investment in innovation.

588

G. V. Kuznetsova and G. V. Podbiralina

The sources of this description are research literature, assessments of experts and business consultants as well as materials prepared by reputable analyst companies. Important analytical documents are the reports of UNCTAD, World Recourses Report, ITATA and others. McKinsey reports constitute sources of cross-country comparative statistical analysis and interesting practical recommendations. A comprehensive analysis of the above sources (statistical data at the industry and individual company level, research literature, publications by business consultants and articles in business media) allows to achieve the research goal to be sought of analyzing the degree of conformity to the global digitalization trends.

4 Results Based on the vast experience of transport development research, it can be argued that transport services constitute one of the most dynamic sectors of the world economy. This conclusion is confirmed by the high growth rates of both freight and passenger traffic in the pre-COVID period. At the same time, several trends were characteristic: the strengthening of the role of the so-called global transport chains in international transportation, a grow in the share of developing countries on the international transport market, an increase in the importance of the Asia–Pacific direction, outstripping growth rate of South-South bound traffic, primarily between China and the countries of South America, Asia and Africa (Afonin A., [2]). In 2020, due to the pandemic, closure of borders and a sharp decline in international trade, the volume of transport operations decreased. A particularly strong decline was observed in the sector of air transportation, warehousing and port facilities. Many global supply chains were disrupted and logistics channels fully or partially closed. Western companies were particularly affected, they urgently had to quickly identify secondary suppliers shifting their foreign trade relations from the Asian direction and involve more actively local companies in the supply chains. In 2021 came significant recovery of all sectors of the global economy including transportation services, which returned to the pre-COVID turnover or even exceeded it (UNCTAD. Global trade update). Yet the launch of the war of Russia in Ukraine and the international sanctions that followed led to a new disruption of logistics. The crisis had three industrial dimensions: food, energy and finance.9 There were also changes in tax and legal environment that impact organizations who operate in or in cooperation with Russia and Ukraine. Obviously the lost supply chains cannot be re-established overnight—especially complex, globalized material supply chains of the kind required for large-scale capital projects. The redesign of supply chains is

9

UN crisis response group calls for immediate action to avert cascading impacts of war in Ukraine/UNCTAD. 13 April 2022 https://unctad.org/news/un-crisis-response-group-calls-immedi ate-action-avert-cascading-impacts-war-ukraine.

Transport Digitalization

589

2.35%

3.84%

33.85%

60%

Roadway

Seaway

Airway

Railway

Fig. 2 Global digital freight forwarding market value share by type, %, in 2018 (Maia Research, [33])

unlikely until the end of the military “hot phase” of the crisis, forecasts differ on the possible time span of this.10 The term transport services covers all types of passenger and cargo transportation, related and auxiliary operations. In accordance with the GATS classification11 , the main services are considered in relation to the modes of transport: sea, inland water, rail, road, pipeline, air, space transport. The auxiliary or related operations in the GATS documents include: cargo-handling operations at terminals, warehouses, ports, airports; storage; insurance; operations related to document flow, the activities of forwarding and customs agents; operations in connection with the adoption of measures to ensure the safety of cargoes and reduction of losses as a result of theft of goods during transportation; emergency repair work; refueling, etc. Needless to say, that digitalization extends to these areas to varying degrees. In the proposed study, due to the limited scope of the article, we shall confine ourselves to considering the situation in sea, road, air and rail transport. The level of digitalization differs markedly in different sectors of transport services. Seaway and airway occupied the main market in 2018 (Fig. 2). We will try to assess the impact of digitalization on each of the sectors.

10

The global forces shaping the future of infrastructure. https://www.pwc.com/gx/en/capital-pro jects-infrastructure/pdf/global-infrastructure-trends.pdf. 11 General Agreement on Trade in Services (GATS). URL: https://www.wto.org/english/tratop_e/ serv_e/gatsqa_e.htm.

590

G. V. Kuznetsova and G. V. Podbiralina

4.1 Seaborne Trades Seaborne trades are a key factor in the development of world trade. 80% of all foreign trade cargo is shipped by sea. Merchant marine and seaborne cargo trades play a decisive role in the development of foreign economic relations, however, in recent years, the growth rate of shipments has slowed down, which is associated with the general instability of international trade. The 2020 saw a serious decline in this area as affected by Covid. At the same time, the share of developing countries in the transportation of foreign trade cargoes continues to increase (Fig. 3). In terms of passenger traffic, the role of sea transport is insignificant and in general is on the decrease. The digitalization of maritime transport, due to its overall high cost, requires huge investments, which not every company can afford. The biggest progress is observed in the spread of the use of GPS, GLONASS, BEIDOU and Galileo navigation systems. The use of industrial robots is expanding. HullBUG, developed by SeaRobotics, is a small, self-contained device weighing between 30 and 40 kg, which cleans the ship hull and helps reduce fuel consumption. Navigation is by means of sensors. In economic terms, the introduction of robotics technology into the field of maritime transport is justified, provided that the computer software controlling the robot provide performance at least equal to that of man. Digital platforms—marketplaces—also find use in maritime transport. Currently, there is a Port Community System (PCS), which is an open and neutral platform

12,000 10,000 8,000 6,000 4,000 2,000 0 2010

2011

2012

2013

2014

Developing countries

2015

2016

2017

2018

2019

2020

Developed countries

Fig. 3 World seaborne trade by types of cargo and by group of economies, annual, metric tons in millions12 12

https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=32363.

Transport Digitalization

591

for the electronic interaction of information systems belonging to various companies and organizations operating in seaports. Its use has significantly increased the efficiency of port services and the port as a whole. Amazon organizes its cargo delivery services through Prime Air. Alibaba in partnership with COSCO created a logistics platform for small and medium-size enterprises. Uber and Tencent (WeChat owner) are expanding logistics and transportation services through their platforms (Baburina O. N., Kuznetsova G. V., 2020). A Scandinavian Blockshipping company created a Global Container Platform (GSCP), which has become a registry for nearly 27 million shipping containers and a marketplace for all participants in container transportation. It is estimated that the use of GSCP may help to reduce the annual costs of world shipping by $ 5.7 billion as well as global CO2 emissions by more than 4.6 million tons.13 There are also FLEET, FREIGHT HUB, FLEXPORT, Haven platforms. All of them are transport management systems (TMS). AllContainerLines, an online freight forwarder, is also working on its own TMS for forwarding companies, integrated with all container lines (Deryabin A., [12]). Artificial intelligence (AI) also finds an ever-growing use in maritime transport. It provides recommendations to a shipmaster on optimum engine performance, keeping the course and rate of sailing, which in turn saves fuel and optimizes the shipping route. AI is forcing the forwarder out from the market and limits the scope of business of other traditional professions in transport. Autonomous marine surface vessels have become a reality. In 2019, the US tested a fully-autonomous masterless and crewless Sea Hunter vessel, which sailed 4,000 km across the Pacific Ocean and reached Pearl Harbor. The US Navy is planning to build an entire flotilla of unmanned ships of this type.14 Norway completed tests of the Wärtsilä automatic mooring system for Folgefonn ferry. The vessel independently touched at a port, maneuvered in narrow Norwegian fjords, came into the berth and berthed. In general, the competitive advantage of robotic ships, according to the developers, will be based on two directions: reducing the cost of design by abandoning the crew quarters and life support systems and reducing operating costs by saving on crew payroll. The pandemic has increased interest in the use of AI in ship navigation. The bans on port calls, due to the threat of the viral spreading (in March–April 2020, 120 countries imposed restrictions on the entry of ships into their ports and 92 countries imposed total bans) did not allow to change crews in a timely manner, which in turn resulted in an increase in the number of accidents of the sea—up to 96% of accidents were attributed to the human factor. The need to transfer some of the employees to a remote work also contributed to the acceleration of the digitalization of the transportation organization and management. The Internet of Things (IoT) also becomes widespread in transport. By means of IoT, the largest ports in the world are being gradually transformed into unified digital ecosystems. A case in point is the digitalization of the port of Rotterdam, where 13

https://www.eg-online.ru/article/374710/. https://www.naval-technology.com/features/sea-hunter-inside-us-navys-autonomous-submar ine-tracking-vessel/. 14

592

G. V. Kuznetsova and G. V. Podbiralina

a digital copy of all services and territories is being developed to try out various scenarios: coordinating loading and unloading traffic, scheduling ships’ arrival and sailing, managing the actions of port teams, etc. One of the innovations in the port is the installation of “digital dolphins”, that is, IoT sensors that support the traffic flows of ships. “Dolphins” estimate the number of free and occupied mooring terminals, and also display real-time information about the status of port operations.15 There is a Rotterdam Additive Manufacturing LAB R&D Center, considered to be the world’s first 3D printing field laboratory, providing a wide range of certified metal ship spare parts. The Qingdao port has a fully automated container terminal. There is automatic mooring of vessels, delivery of containers, unmanned intelligent gate system, etc. Comprehensive digitalization creates conditions for the use of autonomous vehicles both on land for loading and unloading, as well as afloat. Even these few examples show that the digitalization of maritime transport enables to be more effective in addressing emerging challenges, as well as helps to gain immediate benefits for the maritime sector, by eliminating delays and reducing high logistics costs. An important factor influencing scientific and technological progress in maritime transport is the general trend towards greening, as mentioned above. That is particularly in evidence in demands for improved hydrodynamic parameters, the creation of more energy efficient engines, the use of low-carbon and zero-carbon fuels for ships. For example, the Green Maritime Methanol consortium, comprised of leading international shipping companies, ship owners, shipyards, manufacturers, ports and research institutes, supported by the Ministry of Economy and Climate Policy of the Netherlands is exploring the possibility of using methanol as a sustainable alternative transport fuel in the maritime sector. LNG-fuelled ships are becoming a kind of alternative to diesel fuel. Today, there are 135 LNG-battery hybrid vessels and one cruise liner fully powered by LNG (AIDANova, owned by Carnival Cruises) in the world. In addition, another 140 LNG-battery hybrid vessels will be built by 2025. In the period from 2014 to 2019 Maersk Corporation (A.P.M) had been investing nearly $1 billion per annum in projects to improve the technical and financial reliability of zero-carbon solutions, the development and implementation of energy-efficient alternatives for ship engines.16 A serious approach towards greening is characteristic of the EU. The European Union stated the goal of a carbon–neutral policy and a full transition to its implementation is planned by 2050, and it is water transport that will play a fundamental role in the European economy. An important place in solving the stated goal is given to innovations that will contribute to the growth of the use of composite materials, innovative methods for hull repair and the use of renewable energy sources. Combined with non-technological measures (e.g. improved systems), these measures have significant potential to reduce CO2 emissions and the negative impact of water

15

https://www.eg-online.ru/article/374710/ Review of sea transport. 2019. UNCTAD. p.39 https://unctad.org/en/PublicationsLibrary/rmt 2019_ru.pdf. 16

Transport Digitalization

593

transport on the environment (Grosso, M. et al., [22]). It is significant that the official websites of all companies leading in the field of maritime transport contain declarations of the focus on greening of their activities within the framework of the implementation of the Sustainable Development Goals proclaimed by the UN. However, the use of modern technologies for the transportation of goods by seagoing vessels and measures to digitize ports to increase the efficiency of this area are not enough. It is important to improve the existing or develop a new legal framework that meets modern requirements, in particular, the rules governing marine unmanned aerial vehicles. In addition, measures are needed to promote collaboration between private and public stakeholders in maritime supply chains. These measures, together with the digital transformation in countries and regions of the world in the medium and long term, will reduce trade costs, increase competitive advantage, and ensure high economic growth rates (Eric Johnson, [28]).

4.2 Road Transport Road transport is one of the most developed and affordable means of transport. An increase in the driving speed, safety level and carrying capacity of transport vehicles contribute to the growth of the attractiveness of this mode of transport for freight and passenger transportation. An additional incentive for their development is the possibility of door-to-door delivery and solution of the “last mile” problem. Modern road transport is becoming ever more multifunctional and its technical capabilities make it possible to transport a wide variety of goods: bulk, liquid, hazardous, etc. Road transport is also currently undergoing a digitalization phase, induced by accelerated urbanization (Ðào Thu H`˘ang, [11]). Until recently, this type of transport has developed quite steadily. But now it is becoming a clear example of the effect of “creative destruction” according to J. Schumpeter (Schumpeter, J. A., [46]). What seemed stable and evident a few years ago is undergoing fundamental changes. In 2020, global car sales fell by 16% from 90.4 million units to 77.6 million.17 And this is not caused by COVID-19 alone. Road transport market was subjected to destructive effect of growing global concern about climate change and emissions and pollution control associated therewith. For example, in the European Union, vehicle emissions account for 23% of total CO2 emissions: light vehicles account for over 70% and oversize vehicles— for 25% of emissions (Pernestål, A. et al. [41]). Thus, the current demand for road transportation is under the influence of increasingly stringent emission controls. The fight for the environment compels countries to proceed with the development of legislative norms that restrict the use of vehicles with an internal combustion engine (ICE) and stimulate the use of electric cars that consume electricity generated from renewable sources. The EU has unveiled a “Fit for 55” program, a package of measures aimed at reducing greenhouse gas (GHG) emissions by 55% by 2030 17

https://www.oica.net/category/production-statistics/2020-statistics/.

594

G. V. Kuznetsova and G. V. Podbiralina

and, ultimately, achieving a carbon–neutral future by 2050. Similar measures have been taken by the Biden administration in the United States. Governments of many countries propose measures to stimulate the transition from internal combustion engine to electric cars. 150 European cities have prepared local legislation for the tasks of reducing the carbon footprint (Conzade, J.et al., [7]). 11,722 cities with a population of more than 2 billion people joined the Cities Race to Zero—net zero emissions initiative. An important step in this direction was the enactment of Euro 6d environmental standards in the EU in 2021. Work is underway on Euro 7 standards, which, most likely, will compel to completely give up the use of internal combustion engines and eventually change over to the use of electric motors. By 2030, 60% of all new cars sold in the EU are estimated to be electric cars. Several countries have already announced the end of sales of ICE light vehicles by 2030, prompting a number of traditional auto groups (OEMs) to declare their intention to completely change over to the production of electric cars alone. It is also anticipated that in China 70% of all new cars sold in 2030 will be electric cars, in the US—65%. By 2035, the EU, PRC and the US automotive markets will be represented exclusively by electric cars (Conzade, J.et al., [7]). Plans of this kind require a complete restructuring of the auto industry and road infrastructure. Considering that 70 million electric vehicles may appear on the roads of Europe, it is planned to build 24 new factories for the production of batteries for the automotive industry, and millions of charging stations. Battery production should increase 20-fold by 2030 and reach a capacity of 965 GWh. At the same time, new production facilities are expected to move closer to final assembly sites. If now huge cargo flows originate from Asian countries, in the coming years new production facilities will be deployed in Germany, the UK, France, as well as in Norway and Sweden—leaders in the fight for the environment. The light vehicle market is greatly influenced by changes in consumer behavior caused by the emergence of a generation of digital natives with demands for clean environment and a healthy lifestyle. The sharing economy (carsharing, carpulling, ride sharing—search for fellow travelers, pulling—sharing logistics resources and capacities) is developing at a quick pace. Micromobility vehicles are introduced and there is an increasing interest in electric cars and unmanned vehicles. All of this makes potential buyers wonder should they need to buy a car now, or even own a car at all (Llopis-Albert, C., et al., [32]). To answer this question, it is important to analyze the influence of digitalization on the possible trajectory of development of the passenger car market in the EU by 2030 or 2050. The authors estimate the dynamics of demand for cars in the context of growing penetration of the following factors: mobility as a service (MaaS), shared mobility, autonomous vehicles, and complete digitalization of the segment (See Table 2). The decrease in number of personally owned cars is forecasted. The growing penetration of MaaS will stimulate the use of public transportation and the development of car sharing will increase the number of users per car to 1,3 in 2030 and to 1,5 in 2050; the availability of fully autonomous general purpose vehicles can bring the figure up to 3. The growth of e-commerce and agile working will also be factors in decline of personal ownership of cars.

Transport Digitalization

595

Table 2 Main hypotheses underpinning the digitalization scenario (Michel Noussan & Simone Tagliapietra) Digitalization Mobility as a Service

Modal shift from private car share to public transport in cities (5% in 2030, 15% in 2050) Optimized use of urban public transport thanks to AI-driven mobility platforms (+5% load factor in 2030, + 10% in 2050)

Shared mobility

Development of private carpooling, thus increasing average passenger/car (1.3 in 2030, 1.5 in 2050) Car sharing substitutes private car in cities (reaching 10% in 2030, 20% in 2050) Bike sharing for last mile in cities decreases other modes (1% in 2030, 5% in 2050)

Autonomous vehicles

AVs penetration in private cars that increases mileage by 50% (5% in 2030, 20% in 2050) Car sharing by AVs with optimized operation leads to 3 passenger/car (25% of car sharing in 2030, 80% in 2050)

Extra-sector digitalization

Decrease of urban demand due to agile working and e-commerce (2% in 2030, 10% in 2050)

Micromobility—traveling short distances using compact vehicles (unicycle, segway, electric kick scooter, bicycle or electric scooter) is becoming one of the fastest growing segments of the urban transport system. The explosive development of this market began very recently in 2018, when the Lime and Bird shared scooter companies appeared in the United States. These companies, just within a year upon their foundation, reached a value of USD1 billion, becoming unicorns in record time. According to McKinsey forecasts, by 2030 micromobility vehicles rental market will grow to USD 200–300 billion in the US, USD 100–150 billion—in Europe and USD 30–50 billion—in China (Heineke, K. et al., [23]). Authorities in many cities are working towards reducing the use of private cars by creating conditions for the introduction of micromobility vehicles. For example, the Paris Mayor’s office intends to invest USD 300 million in the upgrading of the road network and transform 50 km of car lanes into bicycle lanes (Conzade, J., Cornet, A., Hertzke, P., et al., [7]). Road transport, compared to other modes, is most influenced by the invasion of the Internet of Things. Numerous sensors, transponders, radio frequency identification devices (RFID) create an integrated network, link the vehicle to the road infrastructure, as well as vehicle to vehicle (V2I and V2V), which enables to improve traffic, prevent accidents and improve the road traffic situation. Data4Cities, Innovate4Cities, Invest4Cities systems came into existence which aim to better enable GCoM cities.18 According to the report of the Organization for Economic Cooperation and Development “Big Data and Transport”, —artificial intelligence, machine learning and big data analytics help to optimize traffic flows, and logistics companies—delivery routes.19 18 19

https://www.globalcovenantofmayors.org/our-initiatives-new/. https://www.itf-oecd.org/sites/default/files/docs/big-data-travel-demand-modelling.pdf.

596

G. V. Kuznetsova and G. V. Podbiralina

In the future, there will be a spread of driverless vehicles, robotaxis and robotrucks. Apple, Google, Tesla, VW, Audi, BMW are working towards this goal. According to the forecasts of the Boston Consulting Group (BCG) experts, in 2025 about 600 thousand driverless vehicles will be produced in the world, and by 2035 their number will grow to 21.1 million. By 2035, more than a third of the vehicles produced will be autonomous (Forth,P. et al., [17]). There are already examples of the actual work of a robotaxi. In Arizona, residents can make use of self-driving taxi services from Waymo (a subsidiary of Google), a driverless taxi from Baidu can be called in a business and shopping center in Beijing, and an Auto X taxi can be booked in the suburb of Shenzhen. Robot buses are being tested within FABULOS project in five European cities under the sponsorship of the European Union. In May 2021, the lower house of the German parliament authorized the travel of self-driving cars on public roads. Digitalization drastically changes cargo transportation. Here, as well, in addition to the “digitalization” effect, the Covid factor and the trend towards a new energy transition make themselves felt. Driverless transport is being tested in cargo transportation. For example, TuSimple, manufacturer of self-driving trucks conducted an experiment in the United States to deliver goods along a route of just over 1,500 km from Arizona to Oklahoma. Despite the fact that, in accordance with local laws, part of the route was traversed with the participation of an engineer-driver, the cumulative delivery time was reduced from 24 to 14 h 6 min, or by 42%. Aurora, Tesla, Waymo and other companies also conduct their developments in the field of autonomous cargo transportation. The Internet of Things also finds use in the organization of cargo transportation. Great opportunities are offered by the “virtual rails” project—a system of sensors installed on highways that allows to select optimal driving speed, taking into account the weight and dimensions of the cargo, the lay of the roadbed, the general road traffic situation. This makes it possible to save up to 30% of fuel. Systems of sensors read from intersections, traffic lights and public transport stops allow to determine traffic density, track speed, check visibility and temperature conditions on the road, which enables to more effectively prepare for changing weather conditions, make decisions on the implementation of transport projects and repairs. Sensors installed on trucks themselves allow to control the temperature conditions, which is important for perishable goods, and to prevent theft (Hunt, S., [24]). The Internet of Things will also solve another problem: it will help to reduce no-load trips and use transport in a more efficient way. For example, it is known that up to 50% of trucks go empty on their return trip. Big Data use allows to resolve this problem and increase the efficiency of transportation while reducing energy costs and carbon emissions (The digital transformation of logistics). Digital transformations in road freight transportation result in significant changes in the destinations of commodity flows. With the spread of electric cars and autonomous vehicles, up to 52% of trade in units and components will fall upon batteries, sensors, detectors, etc. At the same time, the supply chains themselves,

Transport Digitalization

597

will in all likelihood be decreasing, and the distribution costs will decline. It is estimated that digitalization has extended to half of road transport (CCOO, [6]), and in the future, digital technologies shall become even more widespread.

4.3 Rail Transport Rail transportation, as affected by competition from road and air transportation, has lost its significance in recent years. However, scientific-and-technological advances, which made it possible to build and operate high-speed railways, create new conditions for increasing the attractiveness of this mode of transport. The trend towards the greening of the economy is also valid here: electric trains do not leave a carbon footprint, which increases the chances for a growing use of rail transportation. In addition, it should be borne in mind that, on average, rail transport consumes 12 times less energy and emits 7–11 times less greenhouse gases per passenger-kilometer than private cars and aircraft, which makes it the most efficient type of motorized passenger transport as well as energy efficient and the least carbon-intensive way for the transportation of goods (International Energy Agency, [25]). A huge role in improving rail transportation is played by the development of digital integrated communication between railway companies, which is based on a single information and communication space and a universal time system involving the use of geotechnical monitoring, geoinformation technologies, space monitoring technologies. This is especially important for Europe, where dozens of railways and hundreds of operators are integrated into a single system. There is a unified European Rail Traffic Management System (ERTMS) in the European Union, which includes the European Train Control System (ETCS), both enabling to synchronize and coordinate railway traffic. The Internet of Things—“smart railways”—is becoming more and more widespread on the railways of different countries. For example, in India, where 115 out of 131 derailments are due to human factor, Skylo Technologies has been developing the Skylo Hub system since 2020 to collect data from trains and transport infrastructure. Indian Railways has announced that it intends to equip around 350,000 locomotives and cars with RFID tags by 2021. This technological development will help the company to track each car, keep a record of and protect the cargo. The spread of smart railways goes hand in hand with the development of smart workshops for the repair and maintenance of cars and engines, railway tracks. Especially noteworthy is the experience of China in the revamp of the railway infrastructure. Just ten years ago, there were practically no high-speed lines in the PRC. By now, the situation has changed radically: more than 600 billion passenger-kilometers pass annually along two high-speed lines located in China. In 2020, China announced far-reaching plans to double its high-speed rail network from 36,000 km to 70,000 km over the next 15 years, which will enable to provide rail service for all cities with population over 200,000 people (International Energy Agency, [25]).

598

G. V. Kuznetsova and G. V. Podbiralina

A highly publicized trend for the development of railways is the use of autonomous trains equipped with advanced sensor technology and a real-time data transfer system. The operation of autonomous trains helps to reduce technical errors, improve traffic data flows and increase passenger confidence in rail transit. Special mention should go to the 4 Grade of Automation system (GoA4), which autonomously processes emergency situations, identifies obstacles and monitors train speed, operation of brakes and doors (Top 10 Rail Industry Trends in 2021). According to forecasts, the global automatic train control market will be growing by an average of 10.7% per annum from 2020 to 2025, which is largely due to the growth of government support. Unmanned passenger trains operate in 23 cities around the world. The top ten includes: Dubai (line length—80 km), Vancouver (68 km), Singapore (65 km), followed by Lille, Busan, Paris, Kuala Lumpur, Toulouse and then Taipei and Tokyo (25 km each). The biggest achievements in unmanned freight transportation were accomplished by Australia. In 2018, Rio Tinto began operating unmanned trains to transport iron ore. The project is based on the AutoHaul system, which allows to process data on the train location, length and weight, fuel consumption (Franz, J., [19]). The development of railway transport, as well as other means of mobility, is linked to the course towards a general greening of the economy. Electrified high-speed railways create a low carbon footprint and many countries added to their plans activities involving provision of incentives for the development of the next generation railway service. This, in particular, the EU’s Strategic Research and Innovation Program (SRIA) is targeted at. An example of this approach is the railway hub of the port of Hamburg. The “intelligent railroad point” system operating there makes it possible, through the installation of sensors, to analyze the busiest points on the port railroad. The trend towards greening of transportation encourages the search for alternative fuels for diesel locomotives. There are the so-called hybrid propulsion systems that maintain a real-time optimal control of the locomotive, ensuring a minimum consumption of diesel fuel. According to preliminary estimates, fuel savings will be up to 30% compared to existing diesel locomotives. Global locomotive manufacturers, such as Toshiba, Alstom, Gmeinder Lokomotiven GmbH and others are working on the creation of hybrid models. Several companies are working on hydrogen-based engines. Such zero-emission trains are already available in Europe, Asia and North America. Given the low energy and CO2 emissions intensity of rail transport, change over of passenger traffic from more energy-intensive modes of transport (such as private cars and aircraft) to rail transport is a key strategy for net zero implementation. It is also noteworthy that despite the rapid global expansion of the metro and high-speed rail network over the past ten years, the share of rail transport in passenger traffic has remained approximately constant and accounted for just under 10% over the past two decades. In general, as for other modes of transport services, there are several sectors on the railway that have good development prospects due to the spread of digital transformation. They are, in the first place, the further improvement of multimodal transportation, ensuring cost and time saving, increasing the level of cargo safety,

Transport Digitalization

599

optimizing cargo and passenger traffic, and resolving many transport and logistics problems. Implementation of new business models, spread and improvement of digital platforms, development of the Internet of things, and so on.

4.4 Air Transport Air transportation plays an important role in modern international economic relations, providing transportation of passengers, luggage, cargo, mail. The air transportation system comprises air transport enterprises and airports. Air traffic control systems, service and maintenance sectors of the aviation industry. About 70% of air services falls upon passenger transportation, 28%—cargo transportation and 2%— postal transportation services. The low share of cargo transportation is associated with its elevated cost, which is usually 4–5 times higher than the cost of transportation by road and 12–16 times higher than the cost of transportation by sea. That is why air transportation is used for perishable agricultural commodities and seafood, documents, pharmaceuticals, fashion garments, product samples, consumer electronics products, urgent goods such as emergency supplies of spare parts (Ferri, G. et al., [18]). The air transportation industry (including air transport) has been successfully developing for ten years: passenger traffic flow growth during this time accounted in average for over 6% per year. However, in 2019, air cargo traffic went down by 3.2% against the same period of 2018. The main reasons for this are the world trade wars and the moderate economic downturn. The biggest damage however was caused by COVID-19. As a result, in 2020, this sector was among the worst affected by the pandemic. The total losses amounted to 35%, the number of passengers using air transport services fell by 70% (Fig. 4). Passenger terminals and airport services were closed. As a result, passenger revenue fell 69% in 2020. At the same time, revenue from cargo transportation went up by 27%. On some trade routes between certain regions, air transportation tariffs increased by 100% or more. As prices for air transportation were rising, so did the number of orders for cargo transportation in the spot market (in 2020, spot sales accounted for up to 90% of bookings of individual airlines) (Ferri, G. et al., [18]). Scientific and technological progress has a huge impact on the development of air transportation. In addition to an increase in speed and carrying capacity of aircraft, there is an active introduction of digital technologies into the industry. According to estimates of the International Civil Aviation Organization (ICAO), AI and other areas of innovation have the potential for gaining significant positive advantages in terms of safety of flights, aviation security, efficiency, and sustainability of performance (Hermes Report Committee, [45]). We are talking about Aviation 4.0, aimed to reshape the design of cyber-physical systems which can support humans by aviation information systems and by helping them to make decisions and to complete tasks autonomously. Within this context, robotics, augmented reality, radio frequency identification, internet of things are introduced and these connected systems can interact

600

G. V. Kuznetsova and G. V. Podbiralina

5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Fig. 4 Number of scheduled passengers boarded by the global airline industry, mln20

with each other using standard internet protocols and analyze data to predict failures, configure themselves, and adapt to changes (Aydın, S., Kahraman, C. [1]). The pace of change in aviation as a result of digital transformation is accelerating, with changes affecting almost all aspects of the air transport services sector— not only flights themselves, but also ground services: air traffic control operations, airport services (airports, passenger and cargo terminals, security services, etc.). Some technologies have already gained wide use. For example, today most of the air passengers use online flight check-in—through the website, mobile application, kiosk at the airport or automatic check-in services. Considerably increased a number of passengers using automated passport control through special turnstiles or kiosks. More than half of the world’s airlines have introduced support chatbots that answer frequently asked questions in several foreign languages. Air traffic control itself relies on a GPS-based ADS-B system (Automatic Dependent Surveillance-Broadcast). The use of ADS-B enables to improve safety, provide flexibility and efficiency in air traffic control, reduce separation intervals, noise, emissions and fuel consumption. The aviation industry is among the leaders in the generation and accumulation of digital data, comprising: aircraft condition, fuel consumption, customer data, etc. The potential for their use is enormous as they can be used to optimize maintenance and repair, ensure efficient fuel use, create personalized offers for passengers, and so on. Augmented and virtual reality technologies have come into use in aircraft construction, transport maintenance, personnel training. They are also used for 20

https://www.statista.com/statistics/564717/airline-industry-passenger-traffic-globally/.

Transport Digitalization

601

creation of aircraft digital twins to resolve various problems and real-time traffic simulation. Platformization of the industry’s services is developing at a quick pace Applications like Inflyter and Grab allow to pre-order items from Duty-Free shops, or pre-order meals at airport restaurants. A number of passenger airlines have their own web-based platforms. For example, SITA has a Day of Travel platform that allows to see real-time status of each flight. Ample opportunities are provided by Expedia and Trip.com marketplaces. Cargo.one is used for cargo transportation. The use of the Internet of Things at airports makes it possible to combine warning and monitoring systems for all objects, make passengers safer and more comfortable by transferring data relevant for navigation to their portable electronic devices (smartphones, tablets, etc.). Aviation nodes can more effectively control the number of passengers at any point in the airport and prevent the accumulation of large queues. RFID sensors placed on luggage monitor its movement, which reduces the risk of its delay or loss. Unmanned small aircraft is on the upswing—drones are used for the delivery of goods. The latest models allow to transport loads up to 150 kg. Drones are becoming a good alternative to road transportation. Thus, digital technologies find an active use in the air transport industry, which includes digitalization of airlines, airports and airport services, thereby making air transportation more accessible to customers and passengers, allowing them to quickly and conveniently execute contracts and trips through online channels. Customerfocused digitalization in air transportation has been further improved to meet new patterns of consumption and demand for goods and services (Poulaki, I. and Katsoni, V., [43]). It should also be borne in mind that effectiveness of transport operations depends on four factors: customs clearance procedures, cargo screening procedures, the efficiency of carriers and the layout of storage facilities (World Bank, [57]). The advantages offered by digitalization make it possible to meet these challenges.

5 Discussion We proceed from the assumption that the digital transformation of various sectors of the economy, including transport services, is an irreversible process that is developing progressively. COVID-19, despite the undoubted damage it caused to the national and global economy, accelerated digitalization processes in many industry sectors. At the same time, moving along the “digital” path is not a linear process. It is influenced by many factors, including a slowdown in economic development, trade wars and conflicts between countries, changes in demography and consumer behavior, the fight for the environment and many other circumstances. This leads to uncertainty in estimating digital outlook for humanity. At the moment, the main uncertainty factor continues to be Covid epidemic, and the forecasts for the recovery of both the global and national economies are contingent on an amount of time required to overcome it, and also the results of military actions in Ukraine that may bring prolonged global

602

G. V. Kuznetsova and G. V. Podbiralina

economic crisis regardless of their outcome. This makes any predictions of the time span of the digital transformation in transport highly volatile. However, this transformation is irreversible and despite all its challenges it contributes to the solution of key problems that face humanity in our era. Digital transformation is uneven across countries and regions. If the developed countries of Europe, the US, Canada, Japan, Singapore, China, South Korea have made significant progress towards the digitalization of business processes, the majority of countries in Africa, South America, and some Asian countries are only at the beginning of the way. The issue whether digital transformation is leading to an increase in digital inequality or gives a chance to backward countries to cross an abyss separating them from the developed world at a “quick jump” remains controversial. This fully applies to the transport services sector. While some countries are launching satellites into space and have covered their entire territory with a GPS system, the population of other countries does not have access to a paved road network. The same inequality exists at the company level: not all businesses, firms and organizations are ready for digital transformation. Until now, some firms use outdated production lines that, for various reasons, cannot be automated, corporate information management systems in companies remain underdeveloped, and information technologies are not used. Digitalization of transport services enables to cover the entire supply chain from manufacturers to last-mile delivery, including the services sector, state and municipal regulations, road infrastructure, dealer networks, service platforms and centers. At the same time, the impact of digitalization on different segments of this transport chain is unequal. For example, electronic ticket sales, online payments for services are well-developed, digital platforms and AI are rapidly spreading, however AR/VR technologies in transport are only making their first steps. There are also big differences in the depth of penetration of digital technologies into different types of transport services. It is evident that space transportation leads the way in the use of digital technologies. The digital transformation processes have achieved great success in the passenger road transport services sector, where one can talk about “Uberization” of the sector. Achievements on the railway are yet fragmentary. Movement along the path of digitalization requires huge financial investments. For example, in order to put into operation driverless trains, it is required to install special fences, sensors, cameras. At the same time, in many countries and in many sections, railways are not even electrified, overloaded and have a worn-out fleet of cars and locomotives. Against this backdrop, digitalization projects do not always look realistic. Transport services digitalization processes run across a number of barriers. One of them is the growth in electricity consumption, which in turn affects the environment. Certain problems are associated with the trend for robotization of transportation and transfer of vehicles to an unmanned mode. Such issues as the safety of passengers and goods during transportation by an autonomous vehicle at this point have not yet been resolved. It is also necessary to think about the problem of unemployment, which will inevitably arise upon automation of cargo-handling and warehousing operations, and the introduction of autonomous vehicles. Many jobs may be lost

Transport Digitalization

603

and more workers will need to be retrained to adapt to new technologies: “Digital transformation requires workers to possess a different set of skills in today’s economy and creates new types of jobs.“ Despite all the advantages of digitalization, there are problems as well (for example, managing the impact of automation on employment, retraining of industry-specific personnel for the digital economy) that require to be dealt with expeditiously (Hermes Report Committee, [45]). Digitalization processes give rise to fears about the growing power of digital TNCs, the growth of cybercrime, total control and many other fears. These fears, due to the speed and revolutionary nature of the transformations, look quite justified, however they are fraught with growth of tension and multiplication of contradictions, which is expressed in a rise in protectionism, the spread of trade wars and sanctions. Overall, the digitalization of transport services is essential to the achievement of the Sustainable Development Goals proclaimed by the UN. However, this requires a radical restructuring of the entire transport system, the development of appropriate legislation. It is also necessary to fundamentally revise the business models of enterprises and organizations in all sectors of the economy, as well as change the nature of interaction in the supplier–consumer chain. Researchers point out that digital technologies can make a significant contribution to the development of all sectors of the economy for the benefit of humanity, however warn that positive results are by no means guaranteed.

6 Conclusions The course towards digital transformation is currently in the focus of governmentsponsored development programs in much of the world as well as at the core of business strategies of the business sector. Big success have already been achieved along this path. The pandemic, for all damage it caused to the economy and social life, has become a powerful driver for acceleration of all transformation processes. The transport services sector turned out to be quite susceptible to digital transformation. All known digital technologies have found their use here. The introduction of digitalization technologies in transport is not a linear process; there are many barriers to its successful implementation. These include the unwillingness of both entrepreneurs and the authorities, and the consumers themselves, to abandon the traditional perception of reality, to acknowledge irreversibility of changes. At the same time, not all participants in the transport and logistics chain get equal benefit from digitalization. On the way to digitalization, there are common problems involving underdevelopment or even lack of transport infrastructure in most countries of the world. Millions of people simply do not have access to modern transport services and for them the main issue is not about improving the service offer through digitalization, it is rather a simple ability to cover a distance to the city, transport hub or center. Inequality of opportunity continues to persist, and the ability of digitalization to overcome it remains to be seen. But in any case, all participants in

604

G. V. Kuznetsova and G. V. Podbiralina

the transport and logistics chain stand to gain from digitalization through an increase in the speed of transportation and reduction of costs. Overall the effectiveness of transportation is a function of four factors: customs documentation, customs inspection, effectiveness of carriers and planning of warehouse facilities (World Bank, [13]). The digitalization of transport services is developing slowly and is still fragmentary due to the need to raise a huge amount of investment for the introduction of expensive technologies. Based on the results of the research “Industry 4.0. Creation of a digital enterprise”, the share of transport and logistics companies that assess the level of their digitalization as “advanced” is only 28% (Fedotova S.N., [16]). Therefore, if large companies are able to make a digital breakthrough quickly enough, small and medium size businesses have big problems to achieve this. According to a McKinsey & Company report, “tech giants and digital natives are investing in and deploying technology at scale, but widespread adoption among less digitally mature sectors and companies lags behind” (McKinsey Global Institute, [36]). The effectiveness of digital transformation is not yet obvious to many entrepreneurs. Only 16 percent of respondents McKinsey say their organizations’ digital transformations have successfully improved performance and also equipped them to sustain changes in the long term. An additional 7 percent say that performance improved but that those improvements were not sustained (McKinsey Global Institute, [37]). There are regulatory barriers. Legal norms for regulating new types of activities arising in connection with the development of digitalization processes (for example, the limits of the use of AI, driverless transport, blockchain, etc.) are still under development. There are no universal documents for multimodal and international transportation. Until now, there are significant differences in shipping documents, formats and requirements to them, which is associated with differences in regulation, tariff policy and other issues of the development of various modes of transport. For example, if for seaborne trades there is the bill of lading—a unique single document that enables to trace the route of the cargo, there is yet no such document, that would link sea, air, rail and road transportation. In this regard, it is difficult to combine various modes of transport into a single environment by combining information, organizational and technical elements and participants in the transportation process, and multimodality of transport and continuous, seamless cargo tracking do not work. For all that, according to expert forecasts, as a result of the digital transformation of the industry until 2025, it will be possible to generate $ 1.5 trillion in value for participants in transport and logistics operations and another $ 2.4 trillion—in social benefits for the population (World Economic Forum, [13]), which is indicative of significant potential and the high value of this process for society. It should be borne in mind that the acceleration of transport digitalization will help to ensure connectivity of territories, increase safety during transportation, improve efficiency of freight and passenger traffic, reduce the environmental load, as well as satisfy consumer demand through the provision of various high-quality services.

Transport Digitalization

605

References 1. Aydin S, Kahraman C (2022) Aviation 4.0 Revolution. In: Kahraman C, Aydın S (eds) Intelligent and fuzzy techniques in aviation 4.0, vol 372. Studies in Systems, Decision and Control. Springer, Cham, pp 3–19. https://doi.org/10.1007/978-3-030-75067-1_1 2. Afonin A (2018) Digitalization in shipping as a tool for the development of the industry. Economy and life, #23 (9739) 2018. https://www.eg-online.ru/article/374710/ (in Russian) 3. Baburina ON, Kuznetsova GV (2020) Prospects for the digitalization of international sea transportation. Marine Intell Technol 4–4(50), 67–72 (in Russian) 4. Bieker GA (2021) Global comparison of the life-cycle greenhouse gas emissions of combustion engine and electric passenger cars. https://theicct.org/publications/global-LCA-passengercars-jul2021 5. Baldini G et al (2019) Digital transformation in transport, construction, energy, government and public administration. In: Desruelle P (eds) EUR 29782 EN, 2019. Publications Office of the European Union, Luxembourg. ISBN 978–92–76–08614–7, https://doi.org/10.2760/058696, JRC116179. https://www.sciencedirect.com/science/article/pii/S0040162520311690 6. CCOO (2018) Situación y Perspectivas en el Sector del Automóvil. Medidas Ambientales, Digitalización y Automatización de la Industria. Confederación Sindical de Comisiones Obreras. Área de Estrategias Sectoriales. https://industria.ccoo.es/9ddeee3ef0745110d18ae92f9a4bc7 06000060.pdf 7. Conzade J, Cornet A, Hertzke P et al (2021) Why the automotive future is electric. McKinsey center for future mobility. Mainstream EVs will transform the automotive industry and help decarbonize the planet IAA. https://www.mckinsey.com/industries/automotive-and-assembly/ our-insights/the-future-of-mobility-is-at-our-doorstep 8. Christensen MC (1997) The innovator’s dilemma: when new technologies cause great firms to fail. Harvard Business School Press, Boston, MA 9. Climate-centered mobility for a sustainable recovery (2021). https://www.transformingtransp ortation.org 10. Cooperative intelligent transport systems (C-ITS) Guidelines on the usage of standards. ISO (2020). https://isotc.iso.org/livelink/livelink/fetch/-8846111/8847151/8847160/CITS-Brochure.pdf?nodeid=21466865&vernum=-2 11. Ðào Thu H`˘ang Seven transformations for more equitable and sustainable cities. World recourses report towards a more equal city. https://files.wri.org/d8/s3fs-public/2021-10/tra nsformations-equitable-sustainable-cities.pdf?VersionId=eDW1GgIXYhwUmdNluUMul9Lu Msns3MRX 12. Deryabin A (2020) Marine Uber: under what conditions will digitalization transform sea freight? http://www.rostransport.com/article/200420/ (in Russian) 13. Digitalizing the maritime sector (2021) Set to boost the competitiveness and resilience of global trade. World Bank. https://www.worldbank.org/en/news/press-release/2021/01/21/dig italizing-the-maritime-sector-set-to-boost-the-competitiveness-and-resilience-of-global-trade 14. Durand A, Zijlstra T (2020) The impact of digitalisation on the access to transport services: a literature review. https://doi.org/10.13140/RG.2.2.22686.97600. https://www.researchgate. net/publication/342735768_The_impact_of_digitalisation_on_the_access_to_transport_ser vices_a_literature_review 15. Ericsson mobility report (2021). https://www.ericsson.com/en/reports-and-papers/mobilityreport/reports/november-2021 16. Fedotova S.N. (2019) Digitalization of transport and logistics services. J Econ Bus 11–3 (57), S. 124–127. https://doi.org/10.24411/2411-0450-2019-11407 (in Russian) 17. Forth P, de Laubier R, Chakraborty S, Charanya T, Magagnoli M (2021) Performance and innovation are the rewards of digital transformation. Boston Consulting Group https://www. bcg.com/en-ru/ 18. Ferri G, Rocchi S, Dirk-Maarten M, Guerrini A, Dinges V (2021) Digital innovation will take air freight revenues to new height. https://www.bcg.com/publications/2021/digitization-in-airfreight-value

606

G. V. Kuznetsova and G. V. Podbiralina

19. Franz J (2020) How autonomous freight trains powered by artificial intelligence could come to a railroad near you. https://www.seattletimes.com/seattle-news/how-autonomous-freight-tra ins-powered-by-artificial-intelligence-could-come-to-a-railroad-near-you/ 20. General agreement on trade in services (GATS). https://www.wto.org/english/tratop_e/serv_e/ gatsqa_e.htm 21. Global covenant of mayors common report framework (2018) Global covenant of mayors for climate and energy. https://www.globalcovenantofmayors.org/wp-content/uploads/2019/04/ FINAL_Data-TWG_Reporting-Framework_website_FINAL-13-Sept-2018_for-translation. pdf 22. Grosso M, Marques Dos Santos F, Gkoumas K, Stepniak M, Pekar F (2021). The role of research and innovation in europe for the decarbonisation of waterborne transport. Sustainability 13 (18):10447, JRC126454, ISSN 2071–1050 23. Heineke K, Kloss B, Scurtu D (2020) The future of micromobility: ridership and revenue after a crisis. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-fut ure-of-micromobility-ridership-and-revenue-after-a-crisis 24. Hunt S (2021) The Internet of Things (IoT) software market. https://www.datamation.com/app lications/internet-of-things-iot-software-market/ 25. International energy agency (2021) Rail tracking report. https://www.iea.org/reports/rail 26. IoT use case adoption report (2021). https://iot-analytics.com/product/iot-use-case-adoptionreport-2021-2/ 27. Jameson P et al (2021) Global shipping’s net-zero transformation challenge. https://www.bcg. com/en-ru/publications/2021/global-zero-carbon-shipping-transformation-challenge 28. Johnson E (2021) IAPH urge rapid maritime digitization. World Bank. https://www.joc. com/technology/logistics-technology-providers/world-bank-iaph-urge-rapid-maritime-digiti zation_20210121.html 29. Korovkin VV (2017) Kak ponimat’ tsifrovuyu transformatsiyu? Moskovskaya shkola upravleniya SKOLKOVO, 2017. [How to understand the digital transformation?] Moscow school of management SKOLKOVO, 2017. https://iems.skolkovo.ru/downloads/documents/ SKOLKOVO_IEMS/Research_Reports/SKOLKOVO_IEMS_Research_2017-05-18_ru.pdf (in Russian) 30. Kyoungho A, Hesham AR, Kyungwon K, Govindarajan V (2016) Multimodal intelligent traffic signal system simulation model development and assessment. Transp Res Rec J Transp Res Board 2558(1), 92–102. https://doi.org/10.3141/2558-09 31. Lakhani KR, Iansiti M (2020) Competing in the age of AI. From the Magazine. https://hbr.org/ 2020/01/competing-in-the-age-of-ai 32. Llopis-Albert C, Rubio F, Valero F (2021) Impact of digital transformation on the automotive industry. Technol Forecast Soc Chang 162, 120343, ISSN 0040-1625. https://doi.org/10.1016/ j.techfore.2020.120343 33. Maia research (2019) Global digital freight forwarding market professional research report 2014–2026, segmented by players, types, end-users in major 40 countries or regions. https:// www.maiaresearch.com/Press_Release/Global_Digital_Freight_Forwarding_Market_Pro fessional_Research_Report_2014_2026__Segmented_by_Players__Types__End_Users_in_ Major_40_Countries_or_Regions-753520.html 34. Mazareanu E (2020) Rail industry worldwide—Statistics & Facts. https://www.statista.com/ topics/1088/rail-industry/#dossierKeyfigures 35. Michel N, Simone T (2020) The effect of digitalization in the energy consumption of passenger transport: an analysis of future scenarios for Europe//Journal of Cleaner Production. Volume 258, 10, 120926. https://doi.org/10.1016/j.jclepro.2020.120926 36. McKinsey Global Institute (2017) Artificial intelligence the next digital frontier? Discussion paper. McKinsey Global Institute. https://www.mckinsey.com/~/media/mckinsey/industries/ advanced%20electronics/our%20insights/how%20artificial%20intelligence%20can%20deli ver%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx 37. McKinsey Global Institute (2018) The rise of Digital Challengers. https://www.mckinsey. com/~/media/mckinsey/featured%20insights/europe/central%20and%20eastern%20europe% 20needs%20a%20new%20engine%20for%20growth/the-rise-of-digital-challengers.ashx

Transport Digitalization

607

38. McKinsey Global Institute (2018) Unlocking success in digital transformations. https://www. mckinsey.com/~/media/mckinsey/business%20functions/people%20and%20organizational% 20performance/our%20insights/unlocking%20success%20in%20digital%20transformations/ unlocking-success-in-digital-transformations.pdf?shouldIndex=false 39. OECD/ITF (2021) Big data for travel demand modelling summary and conclusions. https:// www.itf-oecd.org/sites/default/files/docs/big-data-travel-demand-modelling.pdf 40. Perego A, Perotti S, Mangiaracina R (2011) ICT for logistics and freight transportation: a literature review and research agenda. Int J Phys Distrib Logist Manag 41:457–483 41. Pernestål A, Engholm A, Bemler M, Gyözö G (2020) How will digitalization change road freight transport? Scenarios tested in Sweden. Sustainability 13, 304. https://doi.org/10.3390/ su13010304 42. Peters S (2019) BMW applies VR and AR technology to production processes. https://www. enginetechnologyinternational.com/features/bmw-applies-vr-and-ar-technology-to-produc tion-processes.html 43. Poulaki I, Katsoni V (2020) Current trends in air services distribution channel strategy: evolution through digital transformation. In: Katsoni V, Spyriadis T (eds) Cultural and tourism innovation in the digital era. Springer Proceedings in Business and Economics. Springer, Cham, pp 257–267. https://doi.org/10.1007/978-3-030-36342-0_21 44. Roberts I (2017) Walmart and block chain: it takes two to mango. https://digital.hbs.edu/pla tform-rctom/submission/walmart-and-block-chain-it-takes-two-to-mango/ 45. Recommendations. Digitalization, AI in aviation and the human factor. 2020. R20-I 01/04/2020. 2020 Hermes report committee. https://hermes.aero/wp-content/uploads/2020/05/HERMESRECOMMENDATIONS-2020_Fin.pdf 46. Schumpeter JA (2003) Capitalism, Socialism and Democracy. Taylor & Francis e-Library, London 47. Turner J (2018) Sea hunter: inside the US Navy’s autonomous submarine tracking vessel, May 3. https://www.naval-technology.com/features/sea-hunter-inside-us-navys-autonomoussubmarine-tracking-vessel/ 48. The global forces shaping the future of infrastructure. https://www.pwc.com/gx/en/capital-pro jects-infrastructure/pdf/global-infrastructure-trends.pdf 49. The digital transformation of logistics: threat and opportunity. https://reports.weforum.org/dig ital-transformation/the-digital-transformation-of-logistics-threat-and-opportunity/ 50. Top 10 rail industry trends in 2021. Research blog. https://www.startus-insights.com/innova tors-guide/top-10-rail-industry-trends-innovations-2021-beyond/ 51. Transforming—Transportation 2020. https://www.wri.org/events/2020/01/transforming-tra nsportation-2020 52. Tsakalidis A, Boelman E, Marmier A, Gkoumas K, Pekar F (2021) Horizon scanning for transport research and innovation governance: a European perspective. Transp Res Interdisc Perspect, ISSN 2590–1982 (online), 11, p. 100424, JRC122968. https://publications.jrc.ec.eur opa.eu/repository/handle/JRC122968 53. Vlacic L (2021) Shall the rail lines in isolated territories be replaced by “autonomous vehicle trains”?. IEEE Intell Transp Syst Mag 13(3), 26 July 2021. https://doi.org/10.1109/MITS.2021. 3081857 54. UNCTAD. Review of sea transport (2019), p 39. https://unctad.org/en/PublicationsLibrary/rmt 2019_ru.pdf 55. UNCTAD. Global trade update February 2022. Global trade trends and nowcast. https://unc tad.org/system/files/official-document/ditcinf2022d1_en.pdf 56. Zhu G, Zhang X, Wang Y, Xiong H, Zhao Y, Sun F (2016) Effects of exercise intervention in breast cancer survivors: a meta-analysis of 33 randomized controlled trails. Onco Targets Ther 2153. https://doi.org/10.2147/OTT.S97864

608

G. V. Kuznetsova and G. V. Podbiralina

57. World Bank Publications (2021) Air Freight: a market study with implications for landlocked countries. https://www.worldbank.org/en/topic/transport/publication/air-freight-study 58. Zaharia S, Casandra P (2018) Challenges in airport digital transformation. Transp Res Procedia 35. 90–99. https://doi.org/10.1016/j.trpro. 2018.12.016 59. 5G Networks. https://www.ccsinsight.com/research-areas/5g-networks/

Future of Digital Transformation

Future of Digital Transformation Elif Haktanır, Cengiz Kahraman, Sükran ¸ Seker, ¸ and Onur Do˘gan

Abstract Digital transformation is a concept that defines the process of finding solutions to social and sectoral needs with the integration of digital technologies, and accordingly the development and change of workflows and culture. Digital transformation includes many concepts such as virtual reality, augmented reality, drones, 3D printing, cloud, big data, internet of things (IoT), and artificial intelligence (AI). Digital transformation, which is developing at an increasing pace today, has affected many sectors and forced them to adapt to digital transformation developments in order to maintain their existence and competitive advantages. While digital transformation has provided numerous advantages to the industries, it has also caused some implementation difficulties and risks. In this chapter, the current situation of digital transformation is discussed one by one on the basis of sectors such as automotive, energy, smart manufacturing, white goods, health, banking, tourism, insurance, digital education, smart cities, and transportation. The trends that await these sectors in the future are examined and finally, future-oriented digital transformation expectations are given. Keywords Digital transformation · Manufacturing industry · Service industry · Future trends

E. Haktanır (B) Department of Industrial Engineering, Bahcesehir University, Besiktas, 34349 Istanbul, Turkey e-mail: [email protected] C. Kahraman Department of Industrial Engineering, Istanbul Technical University, Besiktas, 34367 Istanbul, Turkey S. ¸ Seker ¸ Department of Industrial Engineering, Yildiz Technical University, Esenler, 34220 Istanbul, Turkey O. Do˘gan Department of Industrial Engineering, ˙Izmir Bakircay University, 35665 Menemen, ˙Izmir, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Kahraman and E. Haktanır (eds.), Intelligent Systems in Digital Transformation, Lecture Notes in Networks and Systems 549, https://doi.org/10.1007/978-3-031-16598-6_26

611

612

E. Haktanır et al.

1 Introduction Digitalization refers to the transfer of objects and attributes in business processes to a digital environment in order to meet changing business and market needs. Everything that can be digitized can be easily integrated into the digital environment. In other words, digitalization is one of the most important trends of this century, dramatically transforming various sectors and industries. Manufacturing, health, food, and transportation systems are being digitalized with the help of digital infrastructures and big data [1]. With the development of technology, digital transformation goes one step further and big companies release some new apps, new videos, and new features every year to protect the status and reputation of their companies. Digitalization has accelerated 10 times since 2018 [2]. Digitalization continues to take place in our lives at an increasing rate, and while it touches our daily lives, it also changes the way we do business and affects almost all sectors. We see that digital transformation transforms and facilitates many things including business models, revenue streams, operational processes, customer relations, or the services offered. According to the literature, the positive effects of digitalization on human life are most evident in the health, banking, and education sectors. According to the Accenture Digitization Index in 2016, financial services is the sector with the highest digitalization performance with a score of 81%, where banking and the insurance sectors also stand out. It is followed by service activities, retail trade, trade, and repair of motor vehicles, respectively. In recent years, many sectors and businesses have started to adopt digitalization and it has become inevitable for them. Digitalization has become a key to guide the future and stay competitive not only for businesses but also for all sectors. Digitalization has benefits for both employees and businesses such as increasing operational efficiency, creating sustainability, taking and giving quick actions, and error-free work progress. Numerous digital transformation studies have been carried out in the literature. Khan et al. [3] showed the role of robotics in healthcare sector considering the minimization effects of the life threat of medical staff and doctors. The storage of personal health data and the development of predictive and preventive systems for diseases provide cost-effective solutions in healthcare industry [4]. Gebre-Mariam and Bygstad [5] illustrated the causal chain led to the digitalization of the health management information system in a developing country. The study developed a model to explain the process of digitalization considering Archer’s morphogenetic approach. They identified four multiplicative tools of health management information system: projectivization, informatization, embedded inscription, and scaling. Pappas et al. [6] argued that the benefits of digitalization in healthcare should be seen in terms of treating patients and co-creating sustainable societal benefit. Digital technologies have fundamentally changed the way of people and goods are transported, due to the increasing demand for transportation and significant impacts on the associated energy consumption and environmental impacts. Noussan and Tagliapietra [7] performed sensitivity analysis to evaluate the electricity generation

Future of Digital Transformation

613

mixes and efficiency improvements of electric vehicles. The results showed that to utilize from digitalization, alternative transport options need to be optimized and appropriate policies are needed to support the efficient and effective use of digital technologies. Internet of Things (IoT) as a technology for waste management is suggested by several authors for smart cities [8–10]. Ringenson et al. [11] aimed to find digital solutions to environmental problems that are important for municipalities depending on EU policies. Digital solutions are beneficial for introducing solutions that will support European Union policies. Digital twin technology has advantage for the current production systems, processes, and instruments to be integrated and harmonized using small equipment such as sensors and application software. Thus, it helps to increase productivity by reducing errors and breakdowns by applying predictive maintenance [12]. For the supply chain activities, Simchenko et al. [13] proposed the use of digital twins to conduct activities through the entire supply chain. Once the customer orders are dropped into the system, order details and related communications are seen by the digital supply chain twin, leading real-time simulation and decision making. With the COVID-19 pandemic, the adoption of digital technologies has played a significant role for responding to the crisis. In China the government has promoted to perform big data, artificial intelligence (AI), cloud computing, and other digital technologies in epidemic monitoring, virus monitoring, illness treatment, and job retention. Big data technology has provided strong assistance for monitoring the pandemic in real time. Thanks to its online office software, it has allowed employees to work remotely flexibly [14]. With remote working, organizations have carried out their activities in a virtual location away from the office. Along with virtual offices, companies recruited employees by accessing unlimited new workforce pools around the world [15]. Digital technologies offer capabilities that synchronize logistics processes including warehouse and shipping systems. It is characterized by excellent cooperation and communication between the networks used, the hardware and software of the parts of the chain. The aim is to synchronize the interaction between digital services and organizations. Sensor-based datasets, new processes, digital tools, and every part of the supply network are integrated, providing fast and efficient responses to risky situations and changes [16]. In the following the current digital transformation status of the sectors included in this book will be analyzed in detail.

1.1 Digital Transformation in Manufacturing Industry 1.1.1

Automotive Industry

In the automotive sector, which is one of the sectors that adapt to technological developments the fastest, companies that invest the most in technology stand out in

614

E. Haktanır et al.

the competition. Competition in the sector is increasing, trends are changing, and innovative developments such as electrical and 5G supported autonomous vehicles are triggering technology investments in the sector. The use of digital technologies in manufacturing facilities is also inevitable for this sector. According to the literature, it is predicted that businesses that cannot adapt to digital transformation will face very serious risks in the next few years. Digital transformation changes almost everything in a company’s business processes, from production methods to customer expectations and distribution channels in the automotive industry. Technological developments, which now have a very important share in every stage of the automotive production segment, provide digital transformation and offer business efficiency, quality, and savings opportunities to its users. Starting the journey of digitalization with the right technology is the most critical issue. Automotive companies that adopt IoT technology and the right business partnership models can provide savings, efficiency and quality with the big data they collect in this context. The benefits that companies provide by integrating IoT technologies into their existing operations are one of the biggest indicators that this development process in the automotive industry is now inevitable. In addition, the investments made by many companies in the automotive industry around the world to implement IoT and big data & analytics solutions are increasing day by day. So, the automotive industry should see digital transformation as an opportunity to update and renew its infrastructures and operations in order to remain competitive in both local and global markets. The effects of IoT technologies, especially on the production segment, increase efficiency considerably. Instant and real-time monitoring of all operations on the production line makes great contributions to the automotive industry, as it does in every other industry. Thanks to the most advanced IoT, real-time locating system (RTLS) and AI technologies and their integrations, processes can be accelerated, and efficiency can be increased in businesses. For example, RTLS technology has replaced the traditional barcode reading method in the production phases. In addition, with RTLS technology, which is integrated with IoT technology, it is possible to easily find products and devices in the automotive industry, to identify and correct unnecessary processes in real time, to optimize task sequence and material routing, to make better use of workspaces and to prevent unnecessary movement waste, to prevent errors and to keep employees safe. These solutions, which are produced in order not to cause unintentional stoppage of the production line due to any disruption, are programmed to work with automatic calls, work order arrangements and instant notifications, and to prevent data loss. These technologies are very important in sectors such as the automotive industry that need to work autonomously and cannot tolerate any data loss. In parallel with the increasing importance of digitalization in the automotive industry, intelligent production functions such as automatic production, stock planning, real-time traceability and forward forecasting with data have also begun to play critical roles. Smart production, which is a result of digital transformation, contributes to manufacturers in the automotive sector, especially in the form of the following benefits:

Future of Digital Transformation

615

. Production monitoring and assembly management with production information systems, tracking of vehicles at every stage of production areas with RTLS technology, and digitalization of processes that operate manually with barcodes with RTLS, . When the raw material is low on the production lines, work orders are automatically sent to the nearest forklift, preventing stops caused by waiting for raw materials and ensuring the continuity of production, . Notifications can be sent to the relevant units when product shipments are not made in accordance with the key performance indicators (KPIs) defined for internal logistics and to ensure that the shipments are carried out correctly, . Creation of digital twins that enable the production or collection of digital data representing a physical object of factories with IoT technology and increasing quality, savings, and efficiency by creating insights with these data, . Implementation of digital quality systems with error tracking, which enables the process to be further developed by making continuous changes or updates in the system, and minimizing the losses that may arise from potential errors, . Digitalization of new product development stages and increasing efficiency with the test and prototype management system that provides in-depth analysis to understand customer expectations and to design and produce products and services that can exceed these expectations, . Organizing and controlling production shifts with smart algorithms to obtain maximum benefit. 1.1.2

Energy Industry

While the sectors continue to prepare and adapt for the new normal and recovery process resulting from the impact of Covid-19, the energy sector stands out as a helpful and necessary resource among all sectors. As in other sectors, it has been understood more clearly what the necessary transformations are for companies in the energy sector. The necessity of transformation, which has become increasingly important in this period, emerges as trends that will determine the development of the energy sector in the future. Among the rising trends after Covid-19, energy efficiency and green energy, digital transformation and automation, and the use of blockchain technology in the energy sector stand out. In KPMG’s 2021 CEO Outlook Pulse Survey, energy industry leaders state that the most important risks facing the industry in the next three years are environment and climate change. The increasing environmental and climate change risk will also cause the trends that will determine the development of the energy sector. In this respect, the importance of energy efficiency and green energy are gradually increasing. Digital transformation and automation have become an inevitable necessity for the energy sector, as in every sector. Companies operating in the energy sector have to benefit from technology not only in their operations, but also in the monitoring of internal financial and non-financial data. Thanks to the analysis competencies that energy infrastructure companies will gain through digital transformation, they will

616

E. Haktanır et al.

be able to monitor their financial performance in a better way, and they will have the opportunity to provide a stronger analysis to the regulatory institutions for tracking and meeting their costs. Another important concept that comes with digital transformation and automation is cyber security. Cyber security, which is one of the strategic areas for the energy sector, will become a more important topic with the increasing technology and digitalization in the future. According to KPMG’s 2021 CEO Outlook Pulse Survey, energy industry leaders cite cybersecurity risk as one of the top risks facing the industry in the next three years. In the energy sector, the cyber security risk should not be considered as a risk only for energy lines. In the sector, the cyber security risk regarding the collections of companies and the payments of the users in the system is on an increasing trend. Another important factor that will affect and even guide the development of the energy sector is blockchain technology. Blockchain technology appeals to many different areas, especially in the energy sector. Among these areas, there are innovative processes such as the ability of consumers to purchase energy directly on the grid with crypto money, the ability to trade energy between micro-grids in which energy producers are located, and solutions for making the supply chain efficient. It is seen that the subject that should be emphasized in this title is the use of blockchain technology for the collection and analysis of information on decarbonization, which is another trend that will increase its importance in the future. One of the most important issues facing companies in the decarbonization process, which begins with the reduction of carbon footprint, is to document whether their energy consumption and offset calculations are correct. This process requires a wide variety of data input not only from IoT sensors and other direct measurement devices, but also from external sources. In addition, protocols are needed to gather and organize this information. At this point, blockchain emerges as a solution that records all transactions in this area and gives confidence to stakeholders.

1.1.3

Smart Manufacturing Industry

One of the biggest changes that digital transformation will make in our lives will be in production centers, workshops, and factories. As a part of the change in technologies such as AI, cloud systems, 5G, wi-fi and li-fi in recent years, factories continue to get smarter. These factories, which will require the arm power of very few people and almost do not even need light, will also eliminate all the risks that may arise from human beings. Accordingly, these factories, which will create significant changes in terms of occupational health and safety, are also called “Dark Factories” because they work with the zero-light principle. These factories, where control mechanisms are provided completely digitally and many interconnected machines operate autonomously and produce, are expressed as the points where the most basic and advanced technology emerges, which is described as Industry 4.0.

Future of Digital Transformation

617

In smart factories, all processes are kept under control 24 h a day, 7 days a week by different technological systems. A communication system is activated without the need for human beings in these centers where production is almost never interrupted, and machines operate in a clockwork order. This process, which operates autonomously and on its own, is controlled by a human at any time, and all data can be captured and processed as desired. Protecting human health, preventing possible injuries, accidents or deaths, and reducing human-induced errors whenever possible lies behind the emergence of these factories and their increasing preference day by day. Although the idea of the Smart Factory may seem like an expression or formation from the new era, its history goes back to the early 1980s. At that time, factories with the power and number of workers were in the majority, and a system that worked with semi-automatic and semi-human power was imagined as the basic idea. However, no factory could imagine a structure that was fully controlled by digital systems and operated autonomously as we understand it today. In order for this to be imagined and implemented, digitalization and, accordingly, Industry 4.0 would have to come into play. The reason behind the design of this structure and then its implementation lies in the excessive increase in consumption and the inability to keep up with the production speed and capacity. Factories that work with the logic of continuous production and do not need to rest, as the manpower is very low, are designed to meet this consumption capacity. Smart factories undoubtedly bring many benefits and completely change production systems. The first example of dark factories, which will intensify in the future with Industry 4.0, was held in China. In this factory, which produces mobile phone modules, many robot systems have been installed that can do the job of 6–8 workers alone. In addition, while the number of workers working in the factory was 650 before this technique was adopted, the number decreased to 60 after the system. The dark factory technique has already gained popularity and signals that it will be used in the factories of big brands day by day. In the near future, factories with this system will make a difference to their competitors by producing continuously and without stopping, and they will add new values to the system. Although the number of dark factories is few today, it seems to be among the concepts that we will hear frequently in the developing technology and production sector with the clue and foresight it gives us about the future. Dark factories will be very beneficial to the production power and increase efficiency. At the same time, it will reduce expenses in labor costs, and decrease the rate of worker health and death. Although this technique, which will be produced without manpower, is considered to reduce employment, it will allow the use of high-skilled jobs and technical knowledge and skills of workers and will shift employment to the areas of control and maintenance needed by “continuous production”. One of the biggest advantages of smart factories is that they eliminate labor costs to a large extent. In addition, all systems from production to distribution work in communication thanks to automation and any problems that may occur become predictable. Likewise, being able to control all the systems in the factory from a single point means that time losses are prevented. Each stage is constantly kept

618

E. Haktanır et al.

under control with digital systems. In addition to production, smart factories work by combining many necessary systems such as business management, purchasing, warehouse, shipping, supply, maintenance, energy, software, machine connections. Due to the advantages it provides, the smart factory market receives more and more investments every day. According to the researches, the smart factory sector will grow by 10% every year and reach 391 billion dollars by 2024. Today, many business owners are transitioning to smart factories. It is envisaged that business owners who receive technological assistance in areas such as production, distribution and inventory management will completely switch to smart systems in a few years. 90% of the industrial systems currently used are wired and fixed. It is also very costly to bring intelligence to these inflexible systems. In this way, the data cannot be analyzed correctly, and a complete intelligence cannot be achieved. On the way to Smart Factories, within the scope of technological and scientific developments, studies should be carried out on 4 main subjects: . Electrical, electronics, control, computer, and software engineers should work together in the design of sensors, actuators, and human/machine interfaces in enterprises, in the design of smart devices to be used for digital communication with machines. These devices are devices that can implement IoT, IoS (Internet of Services) type concepts. . Computer/software/industrial engineers should construct and implement flexible ERP and MES software that can meet ideal workflow algorithms, and integrated work of both in-plant and customer and supplier systems should be ensured. . As a sub-branch of industrial engineering, it is necessary to develop different areas of expertise under the name of “Smart Factory Management”, where complete IT systems can be used while managing production, and the business can be managed by establishing close cooperation with customers and suppliers by using IT supported management tools. . Machinery manufacturers should produce machines in a way that will serve the new flow to be constructed in the enterprise and have smart sensors, controllers, internet, and computer facilities that can communicate with the surrounding machines. 1.1.4

White Goods Industry

In the white goods sector, which is one of the sectors that most feel the need for digital transformation brought by the Industry 4.0 revolution with its productionoriented perspective, companies that apply new generation technologies at every step, from R&D to the point where the product reaches the consumer, stand out. Increasing competition environment and cost-oriented production perspective have brought digitalization along in every process in white goods companies that aim to create perfect processes from supply chain management to after-sales services. End-to-end traceability and management has become a necessity within the scope of Industry 4.0, from spare parts manufacturers to distributors.

Future of Digital Transformation

619

The first step in creating 360° digitalized processes in the white goods industry is smart supply management. IoT based solutions find their place in the process, which starts with the supply of materials needed for production at the right time. Systems that place orders at the right time to keep minimum stock by controlling stock, track average supply times, monitor the vehicles of materials transferred between warehouses, plan daily shipments, and provide mobile integration with drivers are of great importance for increasing efficiency and reducing costs. Smart white goods are becoming an integral part of consumers’ lives day by day. An example of this trend is the demand for online mobile devices (online dishwasher) or communication with other smart devices (communication between washer and dryer, between household appliances and voice assistant and smart home systems). In the white goods sector, five products stand out as having the highest potential among online devices: washing machine, dryer, refrigerator, oven, and dishwasher. Smartphones will be the most important coordination device when it comes to managing online devices. Some of the examples for usage of smartphones in white goods’ digitalization are as follows. . Diagnosis of technical problems in devices: Informing the authorized service before their arrival, thus saving cost and time . Remote monitoring of devices: Monitoring which stage of the program the washing machine is in from the application on the mobile phone and signaling the smart device when the washing is completed . Communication between devices: Setting the most suitable program and drying time of the dryer with the signal sent from the washing machine to the dryer, thus reducing the program duration and saving money

1.2 Digital Transformation in Service Industry 1.2.1

Health Industry

In today’s digital age, technological development and digital transformation processes are experienced in many sectors including the health sector, which is one of the most basic needs of human beings. Digitalization is revolutionary for the health sector, which is struggling with problems such as access to health, quality and increasing spending burden. The health sector is one of the sectors that need to rapidly implement digital transformation in terms of data. The developments that occur in the world of science and technology every day ensure that the standards of health services are gradually increased. Technology has a great priority in processes such as diagnosis, treatment, posttreatment, and preventive health, which are vital in the health sector. In order to increase service efficiency, patient safety, diagnosis, and treatment accuracy, and to provide better care, innovative solutions can be produced in parallel with the data collected with mobile communication technology and infrastructure.

620

E. Haktanır et al.

In treatment and care services, there are developments regarding instant tracking and remote monitoring of personal clinical data in order to enable patients to continue their care and treatment outside the hospital. Technologies that support preventive care and follow-up systems have also come to the fore instead of treating in health systems, and it has become inevitable for every human being to experience developments in technologies that keep health under control and follow up. On the other hand, with the increase in computer-aided robotic applications, there has been a reduction in processing time and efficiency in operations. In parallel with the increase in video-consultation and tele-visit applications, tests with mobile applications, the flow of treatment and care services from hospitals to homes, computerassisted operations and applications, the use, and types of medical devices at every stage of health services are increasing to a greater extent. However, applications such as chronic disease management are becoming widespread and technologies such as digital twin, blockchain, command centers, virtual reality, 3D printing, wearable technology and AI, which are the building blocks of digital transformation, create significant opportunities in health. With these developments in the health sector, the need arises to transform not only diagnosis and treatment, but also operational processes and to obtain the outputs of the technological transformation with the same organizational quality. At this point, business applications should be supported by smart software such as robotic process automation and business process management. Today, Hospital Information Management Systems are not only a structure that affects and is affected by in-hospital processes, but also turns into systems that can exchange data with other systems. For this reason, it is necessary to transfer all data in the database to another database to be used, when necessary, with the prescribed content and scope, to integrate with all relevant systems, to improve in-hospital management, decision support and workflow processes, and to meet expectations such as resource management and saving. In addition, it is critical that concepts such as “paperless digital hospital” become widespread within the scope of technological and digital transformation. The paperless hospital model offered by the digital hospital enables patients and healthcare professionals to access the data they want easily and quickly. This seamlessly integrates patients, doctors, staff, and information throughout the hospital. Alongside digitalization in healthcare, innovation is critical. The development and maintenance of the quality created in this regard is of vital importance. Studies have shown that innovative applications shorten the hospital stay of patients, reduce service costs, and provide patients with a more qualified treatment process and a comfortable recovery period during their hospital stay. At the same time, innovative practices in nursing services increase the quality and effectiveness of health care services, leading to increased productivity and profitability.

Future of Digital Transformation

1.2.2

621

Banking Industry

In today’s world, where developments that make our lives easier thanks to computers, mobile phones and other digital devices are experienced rapidly, the banking sector also gets its share from these developments. The digital change and transformation in finance and banking is leading the rapid digitalization of individuals and society. For individuals keeping up with the digitalizing world, it becomes attractive to handle all banking transactions with one click. In our world where all life processes are carried over the web, cost-reducing developments in the banking sector attract everyone’s attention and innovations in digital banking affect customer preferences. While the concepts of the 4.0 revolution, AI and the IoT come to the fore, banks are also following these developments. It seems that it will be possible for us to follow the developments over the years by creating automatic channels suitable for all kinds of electronic devices, not just phones. Banks are working hard to meet the needs of their customers in the digital environment. One of the first transactions that come to mind when it comes to digital banking, banking transactions from computers was designed only functionally at the first stage. Nowadays, prominent, advanced interface designs and user-friendly applications attract the attention of customers. In the digitalization process, banks that want to attract more customers through mobile applications compete fiercely in this sense. All banks bring new alternatives to payment channels with the use of digital wallets and encourage their customers to use digital applications. Banks promise that transactions such as opening and closing accounts, getting loans, getting insurance, applying for and purchasing banking services are easier in the digital environment, enabling bank customers to prefer mobile applications. Banks stand out with their ability to enter faster, easier, and more securely without using a password with eye scanning technology on smartphones. It tries to satisfy its customers with features such as fast cash withdrawal without going to an ATM with a QR code and direct connection to the call center. While banks offer smart solutions, needs-oriented analysis also come to the fore. Responding to customers’ demands becomes easier with analytics. The customer representation system, on the other hand, gains a new dimension. Thanks to AI, chatbots and voice recognition technologies are reshaping to respond to customers much faster.

1.2.3

Tourism Industry

As the digital world evolved, customer behavior began to change. This change had an inevitable effect on the tourism sector as well. Customer experience, including customer service, events, room features, and other services the business offers, became important as people easily select travel destinations with a few clicks. Nowadays, everyone has at least one digital device for personal use. Technology has begun to change people’s habits. Hotel room reservations can now be made

622

E. Haktanır et al.

quickly through the mobile application or the hotel website. From checking in with mobile devices to ordering room service, choosing room on arrival, and unlocking hotel room door without a key, customers can easily do many things with digital transformation developments. These advantages enable hotels to quickly adapt to the latest technology by distinguishing themselves from their competitors. With the development of technology, the tourism industry has also included IoT in its processes. In this way, while keeping manual check-in and cleaning processes in sync with the front desk, there is no need for paperwork. In addition, hotels started to answer guest questions with in-room voice recognition system, while installing built-in sensors for room light and TVs. While these eliminated the workload of the front desk, they also reduced hotel costs and expenses. With the development of technology, hotels find the opportunity to collect data while measuring the satisfaction of their guests. Analyzing this data also offers a great opportunity to improve the efficiency of hotel operations. With the results achieved, the service offered to the guests can be personalized, while providing a better service on their next visit and enabling them to visit the hotel again. The personalized services and experiences that the facilities will offer to their customers become important. In other words, the fact that the hotel to be accommodated or the facility to be used knows its customers in advance and offers an experience tailored to their needs and expectations will affect the preferences. While planning a holiday, there is now a chance to compare hotel preferences on the internet. It is possible to make an evaluation about a hotel from the comments where thousands of users share their photographic experiences about a hotel. Photography is a powerful marketing tool that influences potential guests in their decision making and allows them to involve themselves in the stay before they arrive to the hotel. Drones enhance digital marketing by capturing eye-catching photos of travel destinations. In addition to services and user experiences, the differentiation of payment options and the presentation of technological payment alternatives can also be reasons for preference. Payment options with installments and campaigns are almost everywhere now. In addition, alternative currency options seem to be preferred. For example, it is predicted that Bitcoin can be used as a payment alternative in the near future. Tourism industry leaders see the popularity of IoT, especially in the hotel experience, and provide the data needed to fully personalize the guest experience. Hotels can access the IoT system via in-room tablets that connect to various elements in the hotel room and surrounding amenities.

1.2.4

Insurance Industry

The insurance industry receives support from the information network at every step of the value chain, such as data collection, processing and storage, coverage creation, damage measurement, using actuarial methods to create risk pools, as well as serving the customer.

Future of Digital Transformation

623

Changes brought by digital applications in the industry are generally divided into three categories: changes in the interaction of companies and customers; automation and / or standardization of changes in business processes, thereby improving their effectiveness and efficiency; and the development of new products or modification of existing products. Process improvement often takes place in service management, such as developing online and automated sales channels, policy management or claims resolution. Chatbots, robotic process automation and social media are examples of tools that change the way customers and companies interact. As examples of the steps that benefit most from digital transformation, we can give underwriting and risk management, where individual risk characteristics are calculated, and policy and claims management, which determines the quality of the service provided by the insurance company. In addition, support functions, on which business steps depend heavily, also need solid digitization. For example, if the link between accounting and policy management is broken and claims cannot be resolved due to underpayment, the result may be losing a customer or losing money. Big data can be used extensively in the marketing of products, market and consumer research, analyzing target groups, developing pricing strategy and designing communication strategies. Digital tools such as videos, social networks and websites are also widely used in marketing to reach audiences. In processes for product development and pricing, big data can be used to better understand risk pools, the IoT to focus on private insurance, or blockchain for contract creation. Big data can facilitate behavioral data collection and enable personalization of the service. Digital tools can be used in sales by using big data in Customer Relationship Management, by storing policies in the cloud, or by using chatbot and AI in sales activities. All social networking platforms including mobile apps, websites, online video channels can be used in the sales service. Developments in digital transformation are making room for new players in the industry. These include smaller startups as well as larger players like Amazon and Google. However, these digital giants are more reluctant to enter the insurance industry due to the stricter insurance legislation with different rules in different parts of the world regarding their strongest muscle, data collection, processing and storage. On the other hand, insurtech start-ups appear in three ways; first, companies aiming at the customer experience and creating products accordingly, for example entering and editing contracts and creating an online tool that can send and receive messages at the same time; secondly, companies that target their business processes by remaining an interaction medium between the insured and the insurance company; and third, companies that aim to create new products in the market and do not undertake an insurable risk across the entire spectrum. The advantage of start-ups is that they have a direct connection with the consumer thanks to automation; optimizing time, place and consumer needs, for example, via chatbot. For this reason, start-ups also base their marketing strategies on a process-oriented approach. One of the biggest challenges will be changing traditional business models. To compete in the digital marketplace, companies will need a good strategy that will enable them to integrate digital technologies into their existing processes.

624

E. Haktanır et al.

Apart from that, data security and privacy are becoming important issues as data transparency increases. There is a lot of data transfer between service providers such as customer, insurance companies, agents, experts, data aggregator platforms and medical services. This data transfer poses a legal threat. A data breach is also vulnerable to reputational hazards, as it can be easily spread and learned among consumers. This creates a burden for insurance companies to manage their data transactions. As technological developments come with many opportunities for digital channels, they also run the risk of losing customer loyalty, especially in societies that are more reluctant to cultural changes. Losing direct contact is a problem that cannot be neglected. It is very important to balance digital interaction with personal relationship. Apart from these, reluctance and resistance in an organization are among the most fundamental difficulties that the company will face. If companies don’t paint a clear picture of what they want or need in the new era in which they grow up digitally, they can fail to go digital. A company cannot achieve its goals without the support of the entire organization.

1.2.5

Education Industry

Since education is an interdisciplinary field, it is especially high in the digital transformation agendas of countries. New educational approaches and understandings reveal an interactive, two-way environment where the learner is active, and the teacher is the guide. Now, online courses, distance education, smart boards, innovative and personalized learning experiences, virtual environments where students and teachers change roles will be common. With the widespread use of computers at the end of the 1900s, we encounter computer-aided applications in education. This period can be called the beginning of digital transformation. The first signs of digital transformation stand out as the transfer of libraries in higher education institutions from print to digital media. Information technology has started to be used effectively in the execution of formal, distance and virtual higher education programs. When talking about education and digitalization, the concept of university 4.0 is one of the first to come to mind. This new era, which is a necessity of Industry 4.0, is a period that includes continuous learning in the short term and focuses on the development of skills and foresees the development of innovative and sustainable digital strategies. If we look at the digital transformation trends in education from around the world, personalized and individualized learning experiences come first. Personalized and special programs are widely used in the world, as student needs, levels and preferences, capacities, skills, learning speeds and methods, which are among the basic elements of education, are different. The simplest examples of these can be digital devices such as laptops, tablets, and some software and programs provided by educational institutions to their students.

Future of Digital Transformation

625

Learning on the go can be defined as learning done while you go about your daily life and work. The action here is connected to the philosophy of “lifelong learning”. Universities have made many attempts in this regard and separate units have already been established for this. The main purpose is to extend learning to life by removing the four walls from schools and classrooms. Resources you can use in this context include podcasts, audiobooks, applications, and software that support mobile and electronic learning. One of the terms that comes to mind when it comes to digitalization is “big data”. The role of big data in education is undeniably important. Because the source used both in personal education experience and gamification technology is big data. Big data is used for many purposes such as increasing the quality of education, providing personalized learning, improving student performance, planning the education curriculum, restructuring the course content, and monitoring student performance by instructors and administrators. One of the remarkable digital steps is the use of gamification in education. Gamification, which aims to increase motivation with the reward system, is used in education as well as in many sectors. Elements such as points, rewards, storytelling, mystery, collecting collections, levels, randomness, and feedback used in gamification reinforce learning. Oxford University Press has partnered with a game developer company to take an initiative to adapt certain classic novels, such as Alice in Wonderland, into web and mobile games specifically for children. Again, Stanford University hosts gaming initiatives that people of all ages can benefit from in its academic networks. Wuzzit Trouble, which claims to increase the ability to solve mathematical problems by up to 16% and this is proven by the research of the university, includes scientific experiments, and anyone can develop models by RNA folding with EteRNA, and these models are synthesized in the laboratories of Stanford University and users are given feedback. Other gamification initiatives of the university include Septris, SICKO, Biotic Video Games. Another transformation brought by digital is undoubtedly distance education and certificate programs that have become a sector with it. The industrialization of distance education has enabled the interaction of the stakeholders who produce and consume in the new structure by taking advantage of the full potential of digital technology. For example, Open University in England, which is one of the oldest open universities, has been keeping pace with this change and has initiated start-ups and spin-offs in higher education institutions or research laboratories. In addition, thanks to the augmented reality applications developed with the spread of mobile devices, digital rich content is offered to students both on and off campus, and some higher education institutions allow learners to explore the campus with augmented reality applications. Again, with the participatory learning approach in social networks accessed by mobile devices in some universities, learners can share their thoughts, discuss, and comment on their projects in online learner communities.

626

1.2.6

E. Haktanır et al.

Smart Cities Industry

The world’s growing urban population brings new challenges in how to make cities smarter and safer. With the increasing population, the importance of the management of issues such as traffic, air pollution and carbon footprint is increasing. Innovative ways are needed to help gather evidence and information that can make city governments, incident prevention, emergency response and law enforcement more effective. Smart solutions and new technologies bring breakthrough solutions to this issue in terms of time, workforce, and efficiency. Smart city systems help keep people safe by ensuring critical infrastructures are safe, pollution is under control, transportation systems are operating effectively, and disasters and emergencies have minimal impact on the public. The origins of the smart city concept lie in the smart growth movement of the 1990s, which advocated new policies for urban planning. This concept was later adopted by technology companies to market solutions that implement complex information systems to integrate and operate urban infrastructure. Today, instant monitoring of many activities through the IoT, sensors, cameras and data sets collected in various ways makes preventive-regulatory activities possible. The term smart cities refers to cities that use Big Data technology as a means of increasing efficiency, improving the quality of settlements, optimizing municipal operations and, of course, protecting the environment. More recently, the concept of smart cities has become a call to action, with a focus on creating improvements in sustainability and resilience. According to the United Nations data, by 2050, 68% of the world’s population will live in urban areas. The World Health Organization, on the other hand, states that 91% of the world’s population lives in places where air quality exceeds the organization’s directive limits, and that there are nearly 4.5 million deaths each year as a result of exposure to outdoor air pollution. These data require smart cities to incorporate data and integrated technologies, as well as innovative ways to engage society, collaborative leadership methods, and forms of collaboration across disciplines and independent systems. All these enable local governments to provide better service and improve the quality of life of the community. City budgets may not always allow for extensive investment in technology resources. Therefore, municipalities wishing to transition to a data-driven structure should start by determining how to maximize the existing infrastructure to obtain this information. Experts accept the most important safe city problems as traffic monitoring, public safety, crime and counterterrorism, incident response and operational efficiency. Many major cities around the world now use advanced technology to ensure that cities are characterized as safe and hassle-free, thanks to advances in AI, machine learning, data, cloud computing, and powerful smart city applications. The technologies that make up the smart city ecosystem are vast. To give examples of the solutions offered for the priority problems mentioned above are as follows.

Future of Digital Transformation

627

. In traffic monitoring, with smart city applications using AI, it is possible to detect traffic density, vehicle type and license plate, number of people traveling in it, stopped vehicles, slowing or waiting vehicles, vehicle queues, those entering the wrong direction, speeding more or less than they should be, those falling on the road / spilling in seconds. . In public safety, smart city applications allow to monitor the crowd and count how many people are in an area, monitor and analyze the past footage in the same place and automatically monitor unusual behavior thanks to algorithms that know what should normally happen at the scene, detect and find a person or child in a previously known location. It offers the opportunity to detect when a person has entered a sensitive or dangerous area. . In the fight against crime processes, AI and video are used in smart data collection, joint operations, automatic alerts, and smart research tools. Incident Response, on the other hand, goes beyond security with functions such as the ability of smart city applications to visualize data, seeing real-time information, presenting traffic conditions, data on a map, and automatic warnings. For example, video cameras at a train station can be transmitted to an advanced smart city application that detects a pedestrian who has suddenly fallen onto the tracks and notifies the station’s security team with an audible and visual warning displayed on the control center screen. This allows authorities to respond quickly to incidents without having to constantly monitor footage from the camera. With the power of video, all these insights can be gleaned from across the city. Systems that integrate and talk to each other, a shared infrastructure, sensors connected to a common network, and joint activities of institutions gain importance in making use of this data. With a platform that offers a consistent end-user experience, systems that previously worked independently, such as databases of law enforcement agencies, can be connected to each other, allowing rapid access to data that can present cause and effect, and enable legal prosecution. This significantly reduces both incident response and investigation processes. Thus, operational efficiency increases noticeably. It is aimed to realize collaborations that will increase stakeholder participation with projects carried out for safe cities by using smart city applications in different cities of the world, and to find permanent and sustainable solutions in the rapidly changing world order by keeping up with the renewed technology.

1.2.7

Transportation Industry

Signs of change for the transportation sector stand out in the public sector’s investments in smart streets and digital railways, the focus of automotive manufacturers on producing new generation vehicles and smart transportation services, and the inevitable reality that the information age will sooner or later change the status quo in the transportation sector.

628

E. Haktanır et al.

The population living in cities is increasing around the world and this increase is happening faster than the increase in the capacities of existing highways, railways, and other transportation channels. This pressure on transportation infrastructure requires investments of over $1 trillion per year. However, more investment and construction do not always mean that more capacity can be produced. Technology stands out as a very important factor that comes into play at this point and will change the mode of transportation. With the digital age, technology offers smartphones, real-time planning, accessible traffic data and social consumer services. Now, passengers have all kinds of information. This important change offers real choices to consumers by showing alternative routes, offering price comparison, and showing the status of the transport network. As transportation companies adapt and new players enter the industry, new business models will evolve and transform areas such as consumer information usage, payments, integration, and automation. These changes point to five main trends that will lead to fundamental changes for smart transportation solutions: . Thanks to user-oriented transportation services, control is now personal to passengers and public transportation. The approach to operations and planning in the industry is changing, considering users’ choices, priorities, and big data. For example, employees will wear ‘digital uniforms’ to provide helpful information to passengers. . Integrated and intelligent transportation networks will forecast demand, measure performance, and monitor the health of tangible assets. Intelligent systems will be activated in real-time and provide services for capacity management, forecasting and problem prevention. . Pricing and payments will be revolutionized in the next five years. Digitization of tickets and payments will transform metro services; all rail operators will adopt online-ticket applications like airlines. Beyond contactless payments, pay-as-yougo applications will be based entirely on location. . Automation and security will benefit greatly from the tremendous progress in cognitive technologies; will have the potential to save millions of lives around the world, especially on the roads. The increase in security practices and changes in liability will deeply affect the insurance industry. . Innovation in the public and private sectors will, together, overcome the transportation challenges of the 21st Century. The public sector will play a critical role in promoting development and protecting citizens. New entrants to the private sector will benefit from digital and mobile technologies, low costs of reaching global scale, and peer-to-peer models. . The scale and pace of these anticipated radical changes will vary. The digital age will empower travelers and transform the traditional operating and management models of players in the transportation industry. The need for various transportation systems that will facilitate and intelligently integrate connecting journeys will come to the fore. In order to have these systems, all public and private sector

Future of Digital Transformation

629

players, from public institutions to automotive manufacturers, must think innovatively and differently and join forces for the growth and sustainability of the transportation sector. The remaining of the chapter is designed as follows. Section 2 presents the future trends in digital transformation. Section 3 gives insights on the possible digital technologies in the future. Section 4 concludes and summarizes the paper and gives directions for further research studies.

2 Future Trends in Digital Transformation Today’s emerging conditions have demonstrated how necessary it is for organizations to shape their operational models to deal with the crisis. Being ready to meet the world requirements is critical to combat the long-term devastating effects of these conditions. The conditions accelerated digital transformation all over the world and proved once again how important technological solutions are to develop and support social life and to sustain and grow businesses. Digital transformation is necessary not only to counter the devastating effects of the crisis, but also for organizations to gain efficiency and develop the ability to adapt to similar crises and new waves in the long run. In today’s business world, the four areas where digitalization will be felt most are customer interactions, people management, supply chain, and new operating models. Digitalization is rapidly gaining importance in interactions with customers. Online sales and distribution models through digital channels are replacing traditional instore sales. Delivering an advanced digital customer experience is more important now to protect profits and retain customers. One of the sectors that is expected to be most affected by this situation is the consumer products sector. The online shopping trend, which grew rapidly during the crisis, seems to have a permanent place with the changing customer habits after the crisis. The dominance of major online platforms with the prominence of online and omnichannel sales requires resizing physical spaces and reviewing store portfolios. Another sector where the transition to the digital channel will be felt heavily is expected to be financial services. The rise of digital channels is expected to be less felt in sectors such as oil and gas, energy and infrastructure, mining and metals. In addition, using advanced analytics to understand and appropriately respond to new consumer needs and values is essential for long-term customer satisfaction. On the human management side, it is aimed to increase productivity by bringing business processes and employee experiences to the digital dimension. In this period when remote working is increasing, providing flexible conditions to employees, and providing the necessary technological infrastructure are some of the first steps to be taken in order to catch up with rapidly changing workforce trends. In this context, creating a flexible working culture and workforce resilience in the long term in the consumer products, financial services, real estate, and construction sectors come to

630

E. Haktanır et al.

the fore. Developing the digital competencies of the workforce is also a necessary element in order to adapt to digitalization, which has an increasingly important place in the business world. It is essential that the mining and metals, oil and gas, energy and infrastructure, and health industries provide technical training to their employees so that they are prepared for the rapidly changing workforce dynamics. In the production sector, digital factory employee initiatives on this issue come to the fore. In addition, it is a necessary step for organizations to automate their existing processes with digital solutions such as robotic process automation, machine learning and AI, and to restructure their workforce in parallel to ensure efficiency and maintain competition. While this is expected to be dominant in the manufacturing, consumer products, technology media and telecommunications, energy and infrastructure and public sectors, it is likely to be felt more mildly in the financial services and real estate and construction sectors. Attracting a workforce with advanced digital competencies during recovery is an issue for almost every industry. In the near future, where technology will dominate, it is critical to have digitally competent employees at all levels of the organizational chart. With the integration of technology into supply chain processes, it is expected to have a more flexible, more efficient network and to reduce dependency on processes that require more workforce. In addition, increasing the end-to-end visibility by improving the agility and transparency of the supply chain with digital tools is of great importance in order to identify possible risks and take the necessary actions. Industries expected to be most impacted by supply chain digitalization include manufacturing, technology media and telecommunication, real estate accommodation construction, mining and metals, oil and gas, and energy and infrastructure. Finally, the transition to new operating models stands out as a necessary element for the adaptation process to the new conditions. Institutions should improve their innovation capabilities and improve their products and services while maintaining their agility and stability. One of the sectors where the innovation speed was felt the most was the health sector. Technology-based services such as virtual care and telemedicine, which started to develop with the crisis, form the basis of future operating models within the framework of the health sector. In addition, the application of advanced data analysis and AI to products and services are among the expected developments. 3D printing is another innovation fueled by the crisis in the health industry. The use of this technology to print parts of medical equipment has made unique contributions to the industry. Other sectors expected to adopt new technologies and operating models include technology, media and telecommunications, manufacturing, mining, and metals. Figure 1 shows the industries most affected by digital transformation (McKinsey Global Institute). Companies are now aware that they can gain a much better competitive advantage by realigning their strategic plans with the real-time forecasting data produced by new age digital technologies. Digital technologies and their applications have increasingly become an integral part of corporate products, processes and even people’s lives [17].

Future of Digital Transformation

631

(% of executives indicating big impact) Information technologies Telecominication Leisure, publishing an the media Retail activity Financial Services Life sciences Education Health Manufacturing Government 0

10

20

30

40

50

60

70

80

Fig. 1 Industries most affected by digital transformation

Possible future developments in areas such as big data, IoT, metaverse, AI, blockchain, and digital twin which are components of digital transformation, are discussed below. These discussions were supported by sources from the literature. Big data analytics will be developed as an online service in the future, with cloud computing replacing by the centric system [18]. Data analytics will become more involved in the market in the near future. The management, analysis, and security of large amounts of both structured and unstructured data generated by IoT will increase its importance and the competitiveness of companies. Companies will more clearly record their demands and increase their financial returns by increasing the future use of data analytics [19]. Metaverse uses AI, blockchain, and IoT technologies in virtual workstations [20]. People will be able to come into the metaverse completely virtually (i.e., with virtual reality) or relate with parts of it in their physical area with augmented and mixed reality. For educational use of the metadata repository, it is expected for teachers to design lessons for students to solve problems or carry out projects collaboratively and creatively, which will lead students to be more interested in the lesson by seeing objects in three dimensions. In health education, students will examine inside of the human body as an anatomy lab. On the other hand, due to the COVID-19 pandemic, it has not been easy to join meetings with many people or to have a meal together at a restaurant. Thanks to the metaverse, thousands of people will be able to come together at a festival or watch the concert of their favorite singer [21]. In the future, with a digital supply chain twin, different projections will be created with financial analysis, enabling them to improve and optimize forecasting, pricing and upsell opportunities. The organizations will use block chains to check, manage and confirm the information entering the digital twin simulations to take the results more efficiently, dynamic, and transparent. While the current digital twin technology

632

E. Haktanır et al.

is applied more to production processes, it will be integrated and processed with the supply chain in the future [12]. Digitalization will make significant contributions to the future of the automotive industry. Such concepts as autonomous driving, customized insurance contracts, remote diagnostics and repair, and predictive maintenance will enable to develop new business models in automotive industry. With increasing restrictions on carbon emissions, the development of electric cars will be increased [22]. By leveraging AI algorithms in autonomous driving, combined sensor data and real-time HD maps, the “advanced driver assistance system” will provide a safer knowledge for both the drivers and the passengers traveling in the vehicle. Using cloud technology, specialized, powerful tools and services will be provided for analytical evaluation by collecting big data needed to gather customer insights [23]. Lyu and Liu [24] showed that evolving digital technologies such as AI, big data, IoT, robotics, blockchain, and cloud computing will become more and more important in the energy sector and will be used more widely. It has shown that IA will be ahead of the energy sector in the future compared to other digital technologies emerging in the energy sector. According to Adamson et al. [25] cloud computing will combine with other technologies in product design and production processes to form a cloud-based design and production platform. The platform will be managed by mobile devices and assist users to handle complex information.

3 Possible Digital Technologies in the Future Instead of humans working with machines, automation is likely to make some jobs redundant: . Taxi drivers are being replaced by self-driving Uber cars. . Robots replaced receptionists. . Doctors are outnumbered by algorithms that can connect to large medical databases. Web services destroyed travel agencies because intelligent services on the web handle some critical activities such as travel planning and flight booking. Even sportswriters are threatened by companies like Narrative Science, which are currently using AI to automate the generation of sports reports and financial updates. Technology will create opportunities for new occupations and specialties as it continues to disrupt and eliminate jobs, creating new occupations we cannot yet envision such as: . Computer engineer/mechanic fixing of self-driving Uber taxis . Genome mappers and bioengineers . Space tour guides and vertical farmers

Future of Digital Transformation

633

Employees probably won’t do their jobs in a traditional office. We have already seen a change in the definition of work with the effect of the pandemic. Work is no longer a place you go; it is a task you perform. Efficiency is no longer measured sitting at a desk. There is no such thing as nine to five or no lifetime job. In MYOB’s report [26], chief technology officer Simon Raik-Allen suggests we will return to more vibrant local communities as people work within walking distance of their homes. Hospitals are the most costing element in a country’s healthcare system. Hospital costs account for 40% of Australia’s annual healthcare expenditures [27]. No wonder future healthcare strategies seek to keep people away from them. Disease prevention will come into focus as we gain greater control over our health information by using self-monitoring biosensors and smartwatches to collect fitness data continuously. Web applications will synchronize data with electronic health records. Using this information, companies will be able to predict future problems and build a model of our overall health. Forewarned patients will be able to take early action by changing their lifestyle habits or taking designer drugs that fit their DNA. Technology will also be fundamental in healthcare. "Tele-health platforms will make patient monitoring at houses the norm for those who need it." wrote CSIRO healthcare research leader Dr. Sarah Dods for CSIRO [28]. Genome mapping will lead to personalized medicines and 3D-printed replacement organs. Meanwhile, unmanned aerial vehicle (UAV) technology will be used in driverless ambulance drones. Of course, greater awareness of what we need to do to stay healthy will be just as important as avoiding weird detox rituals like juicing, eating clay, and temporary health fads like weight loss supplements. After all these developments, the services offered by health insurance companies will naturally change. With telemedicine, health insurance will allow you to ask questions to the doctor you want, to take priority while waiting in line. Within the scope of the protection of personal data, there may be insurance companies that give us a unique drug development guarantee, considering our DNA sequence. In Star Trek, where many of our ideas of future technology are sprouting, people can enter the medbay and digitally scan their entire bodies for signs of illness and injury. The makers of Q Bio say doing this in real life will improve health outcomes and ease doctors’ burden. An US company has developed a scanner that will measure hundreds of biomarkers in about an hour, from hormone levels to fatty deposits in your liver, markers of inflammation or any number of cancers. It aims to use this data to produce a 3D digital avatar of a patient’s body, known as a digital twin, that can be tracked over time and updated with each new scan. Q Bio CEO Jeff Kaditz hopes that the massive amount of data collected will usher in a new era of preventive, personalized medicine, where not only will it help doctors prioritize which patients need to be seen most urgently, but also develop more sophisticated methods for diagnosing diseases. Technology underpins everything, including food, health, human relations, and work. We’re headed to a future where advanced battery technology will likely deliver better electric cars, personal flying machines, Hyperloop transportation systems,

634

E. Haktanır et al.

private space tourism, and drone delivery services. We’ll likely wear Band-Aidstyle fitness sensors on our skin, charge all kinds of devices using wireless power, let algorithms optimize and protect our homes, and have virtual assistants (nextgeneration Google Now, Siri, and Cortana) to help us manage them. It could be some of them or none. But three things are certain: technology will get smaller, smarter, and cheaper. In fact, it will likely become so small, smart, and inexpensive that we can put computers and sensors on almost anything. It will notify the municipality when the boxes are full, the 4 K televisions will notice when we stop watching and turn themselves off to save power. We are not only on the internet but on the way of the IoT, where everything is interconnected. Ray Kurzweil, futurist and director of engineering at Google, doesn’t like the idea of people he loves dying. He thinks, “We can’t keep them from dying, but we can better protect their memories from faded photographs”. We are heading towards an era where we can create virtual reality avatars of our deceased loved ones and interact with them. “This technology will be a way to bring it back,” he says, citing his father. Bringing these people back with AI will be realistic. No longer a science fiction metaphor, the use of brain-reading technology has dramatically improved in recent years. One of the most exciting and practical uses we’ve seen tested so far comes from researchers at the Swiss Federal Institute of Technology in Lausanne (EPFL). Thanks to a machine learning algorithm, a robotic arm, and a brain-computer interface, these researchers created a tool for tetraplegic patients (those who cannot move their upper and lower bodies) to interact with the world. The robot arm performed simple tasks such as navigating around an obstacle in tests. The algorithm will then use an EEG cap to interpret the signals from the brain and automatically identify when the arm makes an action that the brain considers wrong, such as getting too close to an obstacle or going too fast. Over time, the algorithm can be adjusted according to individuals’ preferences and brain signals. In the future, this could lead to brain-controlled wheelchairs or assistive machines for tetraplegic patients. Bionic eyes have been a mainstay of science fiction for decades. Real-world research is starting to catch on to far-sighted storytellers. Some technologies are coming to market that restores vision to people with different types of visual impairment. In January 2021, Israeli surgeons implanted the world’s first artificial cornea in a bilaterally blind, 78-year-old man. The patient could read and recognize family members when the bandages removed. The implant also fuses naturally with human tissue without rejecting the recipient’s body. Likewise, in 2020, Belgian scientists developed an artificial iris attached to smart contact lenses that corrects a range of visual impairments. And scientists are working on wireless brain implants that bypass the eyes altogether. Researchers at Montash University in Australia are working on trials for a system in which users wear glasses with a camera. This work can send data directly to the implant located on the brain’s surface, giving the user a primitive sense of vision. As a result of these studies becoming widespread, it is easy for artificial eyes to record what they see. Recording everything we see in the following periods may lead to a decrease and perhaps even disappearance of the disease of forgetfulness.

Future of Digital Transformation

635

Considering that forgetfulness is sometimes necessary, it is possible to say that using this technology would be more accurate for those in need. Artificial eyes can also appear as someone else’s eyes. They can be used to see not only us but also others following us, with the help of lenses to be attached to them and other devices to which the lenses are attached. By adapting artificial eye and lens technologies to robots that will be developed in the future, we will be able to handle a job outside by sitting at home. We will be able to see, talk and attend meetings with robots that will travel to our place without the need to physically go to school and work. Even those who want can travel the world with these robots. Carbon emissions are a big concern for commercial flights. Still, there is a potential solution, and it has received a lot of funding. A £15 m UK project has announced plans for a hydrogen-powered airplane. This project is known as Fly Zero and is managed by the Institute of Aerospace Technology in conjunction with the UK government. The project developed a concept for a medium-sized aircraft powered entirely by liquid hydrogen. It would have the capacity to fly around 279 passengers non-stop to the other side of the world. If this technology can be implemented, it could mean a one-stop zero-carbon flight from London to West America or London to New Zealand. Our cramped cities urgently need a pause, and relief may come from the air rather than the roads. It’s becoming a reality in Coventry, the UK, as the first Urban Air Port, a different kind of transport hub plan for delivery aircraft and electric air taxis, receives funding from the UK government. Powered entirely offgrid by a hydrogen generator, the idea is to replace them with a clean alternative in a new type of small aircraft by improving designs, eliminating the need for numerous delivery trucks and personal vehicles on our roads. Infrastructure will be necessary for this technological development. Authorities are investigating the establishment of air corridors that can connect a city center to a local airport or distribution center. The future is unpredictable. Predicting the future is notoriously risky, especially if you claim to be an expert and then get it horribly wrong. “X-rays will prove to be a hoax,” said Lord Kelvin, president of the British Royal Society in 1883. In 1959, US Postmaster General Arthur Summerfield predicted that the mail would be delivered in a guided manner from New York to Australia in a few hours. No one can predict the future exactly, but it can still be a good idea to be prepared for your own future.

4 Conclusion Digital transformation is the holistic transformation of people, business processes and technology elements in order to provide more effective and efficient service and to ensure beneficiary satisfaction, in line with the opportunities offered by rapidly developing information and communication technologies and changing social needs. It is not possible to reduce digital transformation to a few technologies, but the groundbreaking influence of web 2.0, mobile, broadband internet, cloud computing, digital media, big data, AI, augmented reality, IoT, and 3D printers has started a new era. Although the beginning of digital transformation is associated with the

636

E. Haktanır et al.

emergence of the internet, it is known that it dates back to earlier times. Many scientists have ideas and research on new technologies and a digital world. Among the most famous of these ideas and works is the idea of creating a “wireless world”. Nicola Tesla has studies on wireless lighting and wireless electrical energy distribution at the beginning of the twentieth century. It is known that Tesla put forward the idea that wireless communication could be possible in 1893, but he could not complete his work. About 50 years after Tesla, the world met with internet technology. In 1969 ARPANET succeeded in transmitting messages between two computers. Then, with the establishment of the “World Wide Web” system in 1989, the inevitable rise of digital transformation began. With digital technologies, firstly, analog records were processed in the digital environment (automation) and the processes were transferred to the digital environment (e-service). At this point, all corporate assets and stakeholder relations are redefined in the digital environment. The digitalization process is not one-sided, organizations can always make their automation more efficient with new technologies and improve the digital technology experience in their services. It is predicted that the most important shortcoming of digital transformation for individuals and institutions will be overcome by 2030 and the whole world will be automated with the IoT. This rapid rise of digital transformation, of course, seriously affects societies. The process that changes the perception of time and space. It turns the world into a global village. Today, it is possible to be aware of events happening in any part of the world simultaneously. Of course, this situation changes and transforms sectors, production, consumption, socio-economic structure, and cultures. The impact of digital transformation on societies also affects the daily lives of individuals. Technology use, learning styles, habits are being restructured and shaped. Since digital transformation is often referred to with the concepts of R&D and innovation, it may only be perceived that it creates a change in business-related issues as a misunderstood. However, digital transformation brings many innovations in all areas of life and for everyone. The number of wearable devices increased from 325 million in 2016 to 711 million in 2019. Many examples, such as the digital TV platform Netflix reaching 15.7 million new users in the last quarter, show that the transformation continues in individual life and will continue to revolutionize. In this chapter, we touched upon the revolutionary effects of digital transformation and shared insights on how our lives and business industries may change in the future. Although the future is uncertain, it is a proven fact in many studies that digital transformation will undoubtedly develop and bring irreversible, permanent and revolutionary changes in the coming decades.

References 1. Catal C, Tekinerdogan B (2019) Aligning education for the life sciences domain to support digitalization and industry 4.0. Proc Comput Sci 158:99–106

Future of Digital Transformation

637

2. Khaleej Mag (2018) Five amazing trends of digital transformation in 2018. https://khaleejmag. com/technology/five-amazing-trends-digital-transformation-2018/ 3. Khan ZH, Siddique A, Lee CW (2020) Robotics utilization for healthcare digitization in global COVID-19 management. Int J Environ Res Public Health 17(11):3819 4. Balta M, Valsecchi R, Papadopoulos T, Bourne DJ (2021) Digitalization and co-creation of healthcare value: a case study in occupational health. Technol Forecast Soc Chang 168:120785 5. Gebre-Mariam M, Bygstad B (2019) Digitalization mechanisms of health management information systems in developing countries. Inf Organ 29(1):1–22 6. Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G (2018) Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. IseB 16:479–491. https://doi.org/10.1007/s10257-018-0377-z 7. Noussan M, Tagliapietra S (2020) The effect of digitalization in the energy consumption of passenger transport: an analysis of future scenarios for europe. J Clean Prod 258:120926 8. Nair AR, Valarmathie P (2017) IOT based waste management system for smart cities. Int J Adv Res Ideas Innov Technol 3(3):1276–1280 9. Kasliwal MH, Suryawanshi S (2016) A novel approach to garbage management using internet of things for smart cities. Int J Current Trends Eng Res 2(5):765–769 10. Nirde K, Mulay PS, Chaskar UM (2017) IoT based solid waste management system for smart city. Int Conf Intell Comput Contr Syst (ICICCS), 666–669 11. Ringenson T, Höjer M, Kramers A, Viggedal A (2018) Digitalization and environmental aims in municipalities. Sustainability 10(4):1278 12. Kamble SS, Gunasekaran A, Parekh H, Mani V, Belhadi A, Sharma R (2022) Digital twin for sustainable manufacturing supply chains: current trends, future perspectives, and an implementation framework. Technol Forecast Soc Chang 176:121448 13. Simchenko NA, Tsohla SY, Chyvatkin PP (2019) IoT & digital twins concept integration effects on supply chain strategy: challenges and effect. Int J Supp Chain Manag 8(6):803–808 14. Guo H, Yang Z, Huang R, Guo A (2020) The Digitalization and public crisis responses of small and medium enterprises: implications from a COVID-19 survey. Front Bus Res China 14(1):1–25 15. Amankwah-Amoah J, Khan Z, Wood G, Knight G (2021) COVID-19 and digitalization: the great acceleration. J Bus Res 136:602–611 16. Marmolejo-Saucedo JA, Hartmann S (2020) Trends in digitization of the supply chain: a brief literature review. EAI Endorsed Trans Ener Web 7(29):164113 17. Chawla RN Goyal P (2020) Emerging trends in digital transformation: a bibliometric analysis. Benchmarking: Int J, 1–44 18. Cozmiuc D Petrisor I 2018 Industrie 4.0 by Siemens: steps made next J Cases Inf Technol 201 31 45 19. Ohri A (2020) Scope and future of data analytics in 2021. https://www.jigsawacademy.com/ blogs/business-analytics/future-of-data-analytics/ 20. Mozumder AI, Sheeraz MM, Athar A, Aich S, Kim HC (2022) Overview: technology roadmap of the future trend of metaverse based on IoT, blockchain, AI technique, and medical domain metaverse activity. Int Conf Adv Commun Technol (ICACT) 2022:256–261 21. Kye B, Han N, Kim E, Park Y, Jo S (2021) Educational applications of metaverse: possibilities and limitations. J Educ Eval Health Prof 18(32) 22. Savanovic S The future of automotive–preparing for transformation. https://www2.deloitte. com/nl/nl/pages/consumer/articles/the-future-of-automotive-preparing-for-transformation. html/ 23. Embedded Computing Design (2020) How cloud drives digital transformation across the automotive industry. https://www.embeddedcomputing.com/application/automotive/adas-aut onomous-drive/how-cloud-drives-digital-transformation-across-the-automotive-industry 24. Lyu W, Liu J (2021) Artificial intelligence and emerging digital technologies in the energy sector. Appl Ener 303:117615 25. Adamson G, Wang L, Holm M, Moore P (2017) Cloud manufacturing–a critical review of recent development and future trends. Int J Comput Integr Manuf 30(4–5):347–380

638

E. Haktanır et al.

26. MYOB (2015) The future of business Australia 2040. https://www.myob.com/content/dam/ myob-redesign/au/docs/business-monitor-pdf/2015/3-MYOB-Future-of-Business-ReportAU.pdf 27. The Australian institute of health and welfare (2016). Australia’s health 2016. https://www. aihw.gov.au/getmedia/9844cefb-7745-4dd8-9ee2-f4d1c3d6a727/19787-AH16.pdf.aspx 28. Dods S (2014) Telehealth Beyond Videoconferencing. https://cdn.ymaws.com/members.digita lhealth.org.au/resource/resmgr/telehealth2014/Dr-Sarah-Dods.pdf