Integrated Business Models in the Digital Age: Principles and Practices of Technology Empowered Strategies [1st ed. 2022] 9783030978761, 9783030978778, 3030978761

Table of contents :
Editors
Editorial Advisory Board
List of Reviewers
Preface
Organisation of This Book
Acknowledgements
Contents
Editors and Contributors
About the Editors
Contributors
Key Terms and Definitions
List of Figures
List of Tables
Part I The Emergence of Tech-Trends and Practices of Technology-Empowered Strategies
1 Cybersecurity and Integrated Business Models
Introduction
Technology, Entrepreneurship, and Business Models
Prospects of Technology in Businesses
Launching New Businesses
Fostering Communications
Flexibility in Business Operations
Promotional Marketing
Increase Productivity and Profits
Suggestions for Entrepreneurs
Appropriate Technology
Adaptive with Change
Trendy and Timely Innovations
Data Compliance
Meaning of Cybersecurity
Business Models, Technologies, and Cybersecurity
Business Models Integration
Emerging Technologies and Cybersecurity
Artificial Intelligence
Internet of Things (IoT)
Robotics
5G Technology
Quantum Computing
Regulating Cyberattacks: Law and Policy Perspectives
Way Forward
Legal Measures
Technical Measures
International Cooperation
Conclusion
References
2 Exploring the Sectoral Patterns of Possible Applications of AR as an Important Ingredient of New Business Models: The Bangladesh Perspective
Introduction
Concept of AR
AR in Business
AR Business Model
Possible Business Applications of AR in Bangladesh
Managerial and Policy Implications
Conclusion and Suggestions for Future Research
References
3 Integrating Gig Economy and Social Media Platforms as a Business Strategy in the Era of Digitalization
Introduction
Methodology and the Schema
COVID-19 and Gig Economy
Blending Varied Business Strategies to Cope with Post-Pandemic Economic Problems
Role of Social Media Platforms in Enabling Gig Economy as an Important Engine of Growth
Data Analysis
Cloud Chart: 1
Cloud Chart: 2
Cloud Chart: 3
Summing Up
References
4 Setting the World in Motion: Blockchain Redefining Transport and Logistics
Introduction to Blockchain
Bitcoin
Current Widespread State
Key Concepts
Blockchain Types
Distributed Ledger
Nodes and Miners
Verifying New Transactions
Consensus
Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT)
Smart Contracts
Non-Fungible Tokens
Blockchain in Transport and Logistics
Blockchain Establishing Itself
Benefits and Applications in Transport and Logistics
Business, Social and Environmental Benefits
Enabling IoT in Logistics and Supply Chain Management
Reducing Customers’ Perceived Risks
Challenges in Blockchain Adoption
Use Cases and Platforms
IBM, Maersk, GTD Solution: The TradeLens Platform
SITA: From Baggage and Air Cargo Tracking to Drone Registries
Walmart: Produce Tracking with Hyperledger Fabric
SkyCell: Ensuring Temperature in Pharmaceutical Logistics
ZIM, Wave: Shipping and Trade Document Management
Brilliant Earth: Everledger for Transparency and Responsible Sourcing
Princes Group: Product Passports Using the Provenance Platform
Other Companies and Applications
Blockchain Implementation in Modern Businesses
The Use Case—Is Blockchain the Right Tool for the Job
Do You Actually Need It
Barriers in the Implementation Process
Cost Estimation and ROI
Integration with Existing Infrastructure
Expertise and User Training
Assurance of Performance and Scalability
Manager & Stakeholder Resistance
Regulations and Cross-Country Legislation
The Key Stages of Blockchain Implementations
Stage 1: Evaluation—Determining Use Case, Feasibility and Sustainability
Stage 2: Development—Build and Test the Blockchain Solution
Stage 3: Rollout—User Training and Go-Live
Stage 4: Onwards—Debug and Optimise
Future Evolution
Conclusion
References
Part II Data Analytics Strategies and Technology-Enabled Integrated Business Models in the Digital Age
5 Industry 4.0 Driven Supply Chains—Technological Advancements Regarding Logistics Service Providers
Chapter Introduction
Outsourcing
Logistics Outsourcing
Logistics Service Providers
Service Characteristics of LSP Model and Service Provider
Service Provided in Logistical Model by LSPs
Technologies Used by LSP
Logistics Model for Multi-site Manufacturer
Kuehne + Nagel’s Integrated Logistics Model
DHL’s Logistical Model for Industrial Equipment Manufacturer
Chapter Summary and Key Lessons
References
6 Impact of Predictive Analytics on the Strategic Business Models of Supply Chain Management
Introduction
Literature Review
What Is Big Data Analytics?
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
Importance of Predictive Analytics
Predictive Analytics Process
Requirement Collection
Data Collection
Data Analysis and Massaging
Statistics and Machine Learning
Predictive Modelling
Predictions and Monitoring
Predictive Analytics and Supply Chain Management
Procurement
Manufacturing
Logistics and Distribution
Warehousing
Consumers
Research Methodology
Data Collection
Data Analysis
Findings and Discussions
Conclusion and Recommendations
References
7 Challenges for the Adoption of Data Analytics Strategies by Small, Medium-Sized Enterprises in Singapore
Introduction to Data Analytics Trends
Industrial Trends
Benefits of Data Analytics
Small and Medium-Sized Enterprises (SMEs)
Challenges of Data Analytics Implementation
Promoting Data Analytics to SMEs
Definitions of Similar Terms
Data Analytics, Analysis, Mining, Warehousing, Science
Historical Development of Data Analytics
Types of Data Analytics
Frameworks for Small, Medium-Sized Enterprises
Research Gaps and Methodology
Implementation Experience from Case Study Organisation
Five Recommendations for the Small, Medium-Sized Enterprises
Recommendation 1: Policymakers to Raise Awareness
Recommendation 2: Leaders to Make Proactive Exploration
Recommendation 3: Make a Business Case for Data Analytics Strategies
Recommendation 4: Start Small
Recommendation 5: Identify Data Required at the Onset
Research Limitations and Recommendations for Future Research
Conclusions
References
8 How Can Luxury Fashion Brands Create a Multisensory Environment Online to Improve Customer Experience?
Introduction
Luxury Experience
Multisensory Marketing
Sight
Hearing
Touch
Smell
Taste
Multisensory Experience in the Online Space
Theoretical Framework
What Are Luxury Fashion Brands Implementing at the Moment?
Research Methodology
Data Collection Method
Data Analysis Methods
Data Analysis and Results
Discussion of the Results
Final Considerations
Limitations and Recommendations for Future Research
References
Part III Digital Transformation in Cyber Business Village, Privacy, Cybersecurity Consciousness and Entrepreneurship Business Models in Different Sectors
9 Video Camera in the Ambient Assisted Living System—Health Versus Privacy
Introduction
Medical Data Registration in AAL
Vital Signs
Heart Rate
Breath Rate
Blood Pressure
Body Temperature
Physical Activity
AAL for MCI and Dementia Patients
Diagnostic Aspects
ADL
Spatial Movement
Sleep Abnormalities in MCI
Mental Health AAL Applications
Video Observation in AAL
AAL Evaluation Methods
Conceptual Stage
Model
Prototype
Impact Assessment
Questionnaires
Questionnaire Framework
Types of Questionnaires
Questionnaire: Statistical Analysis
Reliability
Cronbach’s Alpha
Revelle’s β
McDonald’s Omega
Guttman's λ2
Correlation: Non-Parametric Methods
Mann–Whitney-Wilcoxon Test
Kruskal–Wallis Test
Spearman’s Rho
Research
Method
Reliability
Focus Group
Age and Gender Structure
Medical Profession
Health Versus Privacy: Results
Descriptive Statistics
Sensors in AAL, Answers’ Frequency Table
Sensors in the Ambient Assisted Living System for Patients with Mild Cognitive Impairment: Statements and Results
Privacy of the Patient with Mild Cognitive Impairment in a Home Equipped with the Ambient Assisted Living System: Statements and Results
Conclusion
References
10 Mobile Applications in Urban Ecotourism: Promoting Digitization and Competitive Differentiation
Introduction
Urban Green Spaces
Urban Ecotourism
Contribution of Mobile and Digital Technologies for the Development of Urban Green Spaces and Urban Ecotourism
Storytelling
Mobile Projects Related with Urban Ecotourism
Boosts, Constraints and Future Steps
References
11 Improving Learning Experience and Privacy in Education Using the Power of Big Data and Artificial Intelligence
Big Data
Definition
Characteristics
Challenges of Big Data
Artificial Intelligence (AI)
History of Artificial Intelligence
General Classification of Artificial Intelligence
AI and AI-Enabled
Technology-Oriented
Artificial Intelligence Major Branches
Expert Systems
Robotics
Natural Language Processing (NLP)
Fuzzy Logic
Machine Learning
Neural Networks and Deep Learning
Big Data & AI Transforming Education
Big Data & AI in Unison
Applications of Artificial Intelligence in Education
Sample ITS Architecture
Evaluation of AIED
AI Benefits to Education Business Model
Challenges
The Role of IoT
Massive Online Open Courses
AI Establishing Itself
Expansion of AI in Education and Other Industries
AI Industry Market Share and Estimated Growth
The Exponential Growth of Data
Conclusion
References
12 Digital Trends in Education: Marketing of the Online Teaching
Introduction
Defining the Concepts
A Review of the Literature and Previous Research
Key Theories
Higher Educational Institutions Pedagogical Reconsideration
Repositioning the Roles of Educators
University Students
Technological Innovation and Disruption
Methodology
Data Findings
Self-Portraits
Collaborative Debate
Synthesis of Ideas
Data Discussions
Reflections on Step 1: Flipgrid Introductions
Reflections on Step 2: Thematic Analysis
Reflections on Step 3: The Future of HE
What Digital Trends Emerged?
Conclusion: The Future of Higher Education
References
13 The Emergence of Technopreneurship for Sustainable and Ethical Economic Growth: Theory, Research and Practice
Introduction to Entrepreneurship
Entrepreneurship and the Economy
Technology in the Business World
Internet of Things
Big Data and Big Data Analytics
Blockchain Technology
Technology in the Business World
Entrepreneurship and Technology
Nascent Entrepreneurs and Technology
Technology Entrepreneurship–Technopreneurship
Technological Entrepreneurship Ecosystem/Digital Entrepreneurship Ecosystem
Digital Transformation and Entrepreneurship
Entrepreneurial Opportunities and New Technology
Sustainability in Technology Entrepreneurship
Change, Innovations and Entrepreneurship Through Technology
Entrepreneurial Education and Technology
Entrepreneurial Universities
Entrepreneurial Academics and Academic Entrepreneurs
COVID-19 Pandemic and Technology Entrepreneurship
Conclusions
References
Index

Citation preview

Edited by Sumesh Singh Dadwal Hamid Jahankhani Azizul Hassan

Integrated Business Models in the Digital Age Principles and Practices of Technology Empowered Strategies

Integrated Business Models in the Digital Age

Sumesh Singh Dadwal · Hamid Jahankhani · Azizul Hassan Editors

Integrated Business Models in the Digital Age Principles and Practices of Technology Empowered Strategies

Editors Sumesh Singh Dadwal Northumbria University London, UK

Hamid Jahankhani Northumbria University London, UK

Azizul Hassan Glasgow, UK

ISBN 978-3-030-97876-1 ISBN 978-3-030-97877-8 https://doi.org/10.1007/978-3-030-97877-8

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 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 Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Editors

Dr. Sumesh Singh Dadwal, Northumbria University London, UK. [email protected] Professor Hamid Jahankhani Ph.D., FHEA, FRSA, Professor of Information Security and Cyber Criminology, Northumbria University London, UK. [email protected] Dr. Azizul Hassan, Tourism Consultants Network, The Tourism Society, UK. [email protected]

Editorial Advisory Board Dr. Anil Angrish, National Institute of Pharmaceutical education and Research, India. [email protected] Dr. Arshad Jamal, Northumbria University, London; UK. Arshad.Jamal @northumbria.ac.uk Dr. Bilan Sahidi, University of Sunderland in London, UK. sidibilan@ hotmail.com Dr. Ben Binsardi, Glyndwr University, UK. [email protected]

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Editors

Dr. Gordon Bowen, Northumbria University, London, UK. Gordon. [email protected]

List of Reviewers Mr. Anwar Haq, Northumbria University, London; UK. anwar.haq@ northumbria.ac.uk Mr Azharul Islam, University of wales, Trinity Saint David, UK. [email protected] Prof. Hamid Jahankhani, Northumbria University, London; UK. hamid. [email protected] Dr. Farooq Habib, Cranfield University, UK. farooqhabib1969@gmail. com Mr. Imad Nawaz, Northumbria University, London, UK. imad.nawaz@ northumbria.ac.uk Dr. Gordon Bowen, Northumbria University, London, UK. Gordon. [email protected] Dr. Lillian Clark, QA Higher Education, UK. [email protected] Dr. Manpreet Arora, Central University of HP, India. arora.manpreet3@ gmail.com Dr. Trevor Gerhardt, (QAHE), UK. [email protected] Mr. Usman Javed Butt, Northumbria University, London; UK. usman. [email protected] Dr. Vipin Nadda, University of Sunderland in London, UK. nkv1@live. co.uk

Preface

The purpose of business is to deliver customer value and innovate. The net customer value for a business is the difference between demand-side revenue streams and supply-side-cost streams. Most of the technopreneurs have improved their business models by improving efficiencies on the demand side as well as supply side and deliver better customer value. The business models help not only convey an idea better but also develop new business strategies. On the demand side it requires right customer segments, value propositions, channels, customer relationships, revenue streams and on the supply side the main focus is on organising the key resources, key activities, key partnerships, cost structure, etc. Chandler released ‘Strategy and Structure’ in 1962, in which he outlined how the alignment of plan, environment, and resources required to achieve this strategy produce value for all stakeholders. This logic has proven to be one of the fundamental building elements of modern business model thinking. The business modelling improves knowledge of the company, enables for easier sharing of the underlying business logic, aids in the analysis of the business logic, aids in the management of the business logic, and may encourage innovation and boost future preparedness.

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The business model is built on four pillars: product, customer interface, infrastructure management, and financial factors; and these four pillars come together to form nine business model elements: value proposition, target customer, distribution channel, relationship, value configuration, capability, partnership, cost structure, and revenue model leading to the development of the integrated business model. This book is integrating elements of business models in order to create, develop, and deliver customer value. Technological disruption is of key interest and concern to researchers, governments, entrepreneurs, and partners. The entrepreneurs, in particular those who have developed unicorns are more interested to know more about what is an in-trend and what will be the trend? How can they remain ahead of the Game in their sphere of activity? Indeed, many researchers have argued that technology, entrepreneurship, integrated business models marketing, etc. are key to the success of any business in general and creating unicorns in particular. However, there is still a lack of research and awareness about interdisciplinary approaches to techno-entrepreneurial business strategy. New technological applications, entrepreneurship skills, innovations in the business models, the emergence of technologies including Blockchain, social media, Big data, Augmented Reality, virtual reality, the community of consumers, etc. are the uptrends in business. Thus, the technology is offering so many interesting trends that there is a real need for interdisciplinary research that explores how business can be driven by entrepreneurs’ using technology. Research that converges the divergences of technologies such as Big data, AI, cybersecurity strategies, and techno-entrepreneurs is needed in the fields of technology and business together. The economy of the world is emerging and merging at a tremendously fast pace. This is a result of disrupting technologies, globalisation and connected consumers. The consumers are using a range of devices like smartphones, iPads, laptops, glasses, VR equipment, and watches—to undertake e-commerce. The researchers, business leaders, and entrepreneurs need to design, develop, and utilize integrated business models to become proficient in using technology. They need

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answers regarding the principles, protocols, and practices for successfully applying and implementing integrated technology-enabled business models. Researchers, entrepreneurs, strategists, and business model developers need to work closely with IT departments and technologists. They need to understand the theories and processes behind the development of integrated technology-enabled entrepreneurial business models and marketing across sectors. Multidisciplinary researchers and professionals need an answer to new principles and practices of technologyempowered strategies in managing integrated business models in the digital age. Researchers, entrepreneurs, and professionals who have commenced to work in this global cyber village are not clear about how to play technology-enabled business strategies offensively and defensibly! There is a need for research to explore this interdisciplinary area where sociology, technology, and business meet together. The book is an attempt to explore and develop principles, models, and other theoretical and practical concepts, to provide better guidance to techno-entrepreneurs on how to adopt and adapt the business models (such as sharing economy based business models) using new technologies such as AI, cloud computing, Blockchain, cybersecurity and infrastructure, and to develop integrated business models.

Organisation of This Book This book also has taken an approach of integrating various areas of management using technology. The book not only discusses the technology and business models in general but has also considered a number of specific industries such as tourism, supply chain management, transportation, shared economy, gig economy and education industry, Small and medium enterprise, etc. This book is organised into 13 chapters. A brief description of the chapter is given in the next section. The first chapter by Islam and Karim explains the interrelationships between business models, technological adoption, and cybersecurity. The second chapter by Ali PK and Rakib examines the utilities of Augmented Reality (AR) practises in various sectors and identifies business areas

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where Augmented Reality technology may be used in improving business models. The third chapter by Arora and Sharma theorises how combining various business strategies with entrepreneurial spirit and technology might emerge as a solution to the large-scale disruption produced by the COVID-19 epidemic. Such strategic integration can strengthen the gig economy (despite its drawbacks, such as the lack of permanent security) to significantly help the short-term job sector, so offering a powerful push to global economic activity. The fourth chapter by Butt, Ntavelis, Abbod, and Hussein presents a thorough study of the causes behind Blockchain’s rapid adoption across numerous business sectors, with a particular focus on the transportation and logistics industries. Real-world applications, which are now in use by a number of international corporations, demonstrate how this technology aims to revolutionise the way transactions, contracts, and data tracing are handled in the digital age. The fifth chapter by Dahibhate, Habib, and Ali, Khan examines the innovations in business models with use of Industry 4.0-driven supply networks, with an emphasis on logistics service providers’ technology developments. The fifth chapter analyses the service characteristics of logistics service providers (LSPs) as well as their technical improvement in order to improve supply side of the business models and to meet the demands of their customers. The sixth chapter by Pradhan, Sarwar, and Hosseinian-Far looks at and analyses the impact of predictive analytics on supply chain management and related business models. Predictive analytics is gaining traction, and many firms are using it to improve their performance and profits. The seventh chapter by Sia, Hosseinian-Far, and Toe examines the obstacles that Singapore’s small and medium-sized businesses confront while implementing data analytics initiatives in improving their business models. It reviews data analytics trends, the historical evolution of data analytics, kinds of data analytics, obstacles, and frameworks for adopting data analytics for organisations. The eighth chapter by Stancescu, Clark, and Redolfi explores how luxury fashion firms can improve their online consumer experience with innovations and business models. The ninth chapter by Herzog explores the application of. Ambient Assisted Living in Mild Cognitive Impairment (MCI) or dementia. The majority of individuals with MCI and dementia live at home. Ambient Assisted

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Living can enhance the health of patients and their families without significantly increasing healthcare costs. The chapter proposes innovative business models with use of video camera for observations of patients. The role of video observation in AAL as well as the potential for future of digital health business models is explored. The tenth chapter by Cardoso, Sousa, and da Cunha examines how the mobile and digital developments in urban ecotourism are changing business models, it also highlights several ground-breaking projects from around the world. The eleventh chapter by Butt, Ntavelis, Abbod, Eghan, and Agbo takes a deep dive into history, current condition, and actual or possible uses of Artificial Intelligence (AI) in business and in the context of education in particular. The importance of these technologies, the advantages of adopting them into education, and the issues they solve are all discussed in depth. The twelfth chapter by Gerhardt is an exploratory study of new business models in education sector. It examines university tutors’ online teaching experiences, evolving digital patterns and innovation. The last chapter, by Rathnayke and Roca, provides an inclusive discussion about the value of studying entrepreneurship and technology for both, higher education students and education policymaker. It has examined how entrepreneurs bring in revolutionary changes in business methods and practices and business models. Creating a business model is more practical side of a business than deciding on a business strategy with the support of ICT. This book thus is expected to benefit the governments, marketers, managers, brand teams, students, technologists, application developers, and IT technologists to understand and apply the principles and practises of technology-empowered business strategies and business models. London, UK London, UK Glasgow, UK

Sumesh Singh Dadwal Hamid Jahankhani Azizul Hassan

Acknowledgements

It is our pleasure to thank numerous individuals, who have directly or indirectly supported me on this book project. Many thanks to almighty God for always being with us and also giving us opportunity to become a link pin between knowledge source and seekers. We would like to thank all contributing authors, who have very diligently and patiently given their valuable time to write, and revise their work a number of times, so as to produce a well-researched and insightful high-quality piece of work. Thanks to all of the Editorial board team members, and all of the; for reading other authors’ works and providing very constructive feedback. Your support has made it possible to deliver a high-quality output that fits with the purpose of the book and is in line with the needs of the target audiences. Millions and Zillions of thanks to our dear family members for sparing us time and providing their continuous love and support.

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Acknowledgements

Last but not least we would like to convey special thanks to Palgrave publishing and their team for their continued support and for shaping this project into a final unique piece of work. Thanks to the readers, as your needs have been the motivations for this book. Enjoy reading. Sumesh Singh Dadwal Hamid Jahankhani Azizul Hassan

Contents

Part I

The Emergence of Tech-Trends and Practices of Technology-Empowered Strategies

1

Cybersecurity and Integrated Business Models Md. Toriqul Islam and Ridoan Karim

2

Exploring the Sectoral Patterns of Possible Applications of AR as an Important Ingredient of New Business Models: The Bangladesh Perspective Uzzal Ali Pk and Md. Rakibul Hafiz Khan Rakib

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4

Integrating Gig Economy and Social Media Platforms as a Business Strategy in the Era of Digitalization Manpreet Arora and Roshan Lal Sharma Setting the World in Motion: Blockchain Redefining Transport and Logistics Usman Javed Butt, Aristeidis Davelis, Maysam Abbod, and Khaled El-Hussein

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Part II

Data Analytics Strategies and Technology-Enabled Integrated Business Models in the Digital Age

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8

Industry 4.0 Driven Supply Chains—Technological Advancements Regarding Logistics Service Providers Ajinckya Dahibhate, Farooq Habib, Abdul Ali, and Murtaza F. Khan Impact of Predictive Analytics on the Strategic Business Models of Supply Chain Management Ishwari Pradhan, Dilshad Sarwar, and Amin Hosseinian-Far Challenges for the Adoption of Data Analytics Strategies by Small, Medium-Sized Enterprises in Singapore Nam-Chie Sia, Amin Hosseinian Far, and Teoh Teik Toe How Can Luxury Fashion Brands Create a Multisensory Environment Online to Improve Customer Experience? Laura Stancescu, Lillian Clark, and Carolina Redolfi

Part III

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Digital Transformation in Cyber Business Village, Privacy, Cybersecurity Consciousness and Entrepreneurship Business Models in Different Sectors

Video Camera in the Ambient Assisted Living System—Health Versus Privacy David Josef Herzog

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Mobile Applications in Urban Ecotourism: Promoting Digitization and Competitive Differentiation Ana Filipa Silva Cardoso, Bruno Barbosa Sousa, and Ana Cristina Gomes da Cunha

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Improving Learning Experience and Privacy in Education Using the Power of Big Data and Artificial Intelligence Usman Javed Butt, Aristeidis Davelis, Maysam Abbod, Caleb Eghan, and Haiiel-Marie Agbo Digital Trends in Education: Marketing of the Online Teaching Trevor Gerhardtl, Anu Laitakari, Michael Rice, and Chandra Bhasham

13 The Emergence of Technopreneurship for Sustainable and Ethical Economic Growth: Theory, Research and Practice Dinusha Maduwanthi Rathnayake and Teresa Roca Index

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Editors and Contributors

About the Editors Dr. Sumesh Singh Dadwal is an academician and consultants based in London, UK. Currently, he is a faculty member, program leader, dissertation research lead for Master’s Programs at London Campus of Northumbria University London, UK. He has 21 years of experience in business academic research, teaching, e-learning, quality assurances and in a wide range of business, technology-enabled business models, tourism, and healthcare subjects. He is actively engaged in postgraduate and Ph.D. research supervision. Sumesh’s core areas of expertise include International Marketing and Globalisation, Contemporary and Technological trends in the marketing and consumer behaviour, strategic and technological innovation management. He is an active researcher undertaking analysis in the service sector, promotional strategies in the emerging markets, digital marketing, Augmented Reality Marketing, and consumer behaviour, utilising various qualitative and quantitative techniques. He has written

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various research papers, co-edited a book, written book chapters, and has led research activities at various levels. Dr. Dadwal has also had been associated with the Quality Assurance Agency (QAA) of UK. and has also worked as a freelance consultant with a number of institutions and an awarding organisation (OTHM, UK) for the development of new educational programmes. Dr. Dadwal has taught at a number of London campuses of the UK Universities such as University of London (Birkbeck College), University of Glyndwr, Roehampton University, Plymouth University, Ulster University, University of West London, New Bucks University, Northampton University, and Northumbria University UK. Professor Hamid Jahankhani gained his Ph.D. from the Queen Mary College, University of London. In 1999 he moved to the University of East London (UEL) to become the first Professor of Information Security and Cyber Criminology at the university in 2010. Hamid’s principal research area for a number of years has been in the field of cybersecurity, information security, and digital forensics. In partnership with the key industrial sectors, he has examined and established several innovative research projects that are of direct relevance to the needs of the UK and European information security, digital forensics industries, Critical National Infrastructure, and law enforcement agencies. Professor Jahankhani is the editor-in-chief of the International Journal of Electronic Security and Digital Forensics, www.inderscience.com/ijesdf, International Journal of Electronic Democracy, www.inderscience.com/ ijed, both published by Inderscience, and general chair of the annual International Conference on Global Security, Safety and Sustainability (ICGS3). Hamid has edited and contributed to over 15 books and has over 150 conference and journal publications together with Various BBC radio interviews. Hamid has supervised to completion of 13 Ph.D. and professional doctorate degree students and overseen 67 Ph.D. students progressing. In summer 2017 Hamid was trained as the GCHQ ‘cyberis’ to train the next generation of cybersecurity experts through GCHQ CyberFirst initiative.

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Dr. Azizul Hassan is a member of the Tourism Consultants Network of the UK Tourism Society. Hassan did Ph.D. from Cardiff Metropolitan University, UK. His main areas of research are: technology-supported marketing in tourism; innovative marketing dynamics; destination branding in tourism; cultural heritage tourism; heritage interpretation; and sustainable management/marketing alternatives for cultural heritage industries. He is a regular reviewer of Tourism Analysis, the International Journal of Human Resource Management, the International Journal of Ecotourism, the eReview of Tourism Research (eRTR), and the International Interdisciplinary Business-Economics Advancement Journal.

Contributors Maysam F. Abbod received a Ph.D. degree in control engineering from The University of Sheffield, UK, in 1992. He is currently a reader of Electronic Systems with the Department of Electronic and Computer Engineering, Brunel University London, UK. He has authored over 50 papers in journals, nine chapters in edited books, and over 50 papers in refereed conferences. His current research interests include intelligent systems for modelling and optimization. He is a member of the IET (UK), and a chartered engineer (UK). He is serving as an associate editor of the Engineering Application of Artificial Intelligence (Elsevier). Haiiel-Marie Agbo has been an M.Sc. cybersecurity student at Northumbria University London. Haiiel-Marie Agbo completed his master’s in cybersecurity with distinction from Northumbria University London. He is currently doing master’s in project management with Northumbria University London. His research areas are mainly in penetration testing and Artificial Intelligence. Haiiel-Marie Agbo Is a focused, detail-oriented IT Security Specialist with an academic background in both developing security measures, policies, procedures (ISMS, SIP, IGP) and completing Penetration Testing procedures. Haiiel is proficient in the use of code management tools for the automation/ optimization of critical deployments and developing CI/CD pipelines.

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Dr. Abdul Ali is a lecturer in Operations and Supply Chain Management at the University of Greenwich. He was a full-time faculty member at the University of Northumbria-London Campus (QA). Abdul has completed his Ph.D. from the University of Bedfordshire where he also worked as a visiting lecturer and researcher. He was also a visiting lecturer & dissertation supervisor with QA at the University of Ulster London Campus, London Metropolitan University and Roehampton University London. Abdul is a fellow of the UK Higher Education Academy (Advance HE). He is a member of various professional bodies and serves as a reviewer of many prestigious journals. His research interests are in the areas of Operations, Logistics, and Supply Chain Management. Manpreet Arora is an assistant professor of Management in the School of Commerce and Management Studies, Central University of Himachal Pradesh Dharamshala, India. With twenty years of teaching experience, she has varied areas of interest. A gold medalist at undergraduate and distinction holder at postgraduate level, she obtained her Ph.D. in International Trade from Himachal Pradesh University, Shimla, India. Her areas of research interest include Accounting and Finance, Strategic Management, Entrepreneurial Leadership, Qualitative Research, Communication Skills, and Microfinance. She has published in category journals as well as Scopus and WOS indexed journals. With four edited books to her credit, she is a persistent researcher in the field of management Chandra Bhasham MIB, MHRM, MCMI, Chartered MCIPD is the Head of Global Partnership Development (Business and Management) and course leader for apprenticeship programmes at the Claude Littner Business School, University of West London. As an experienced academic practitioner, he has overseen various developments across business courses which have matched industry and student expectations. He is a champion of improving graduate attributes especially on enhancing employability skills in the current business context; a firm believer of Industry-University collaborations with an excellent track record of embedding real business issues as part of UG/PG assessment strategy. He holds Chartered Membership of CIPD.

Editors and Contributors

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Usman Javed Butt received Bachelor of Computer Science (Hons) degree in 2003, M.Sc. Degree in Computer Networks and Systems Security from University of Greenwich in 2010 and now doing Ph.D. in cybersecurity from Brunel University. He is a certified ethical hacker, Certified CISMP and Certified ISO 27001 practitioner. He is also trained on GCHQ CyberFirst initiative and have delivered it successfully. Along with the academic experience, he also possesses commercial experience and have worked in the role of Systems Administrator and Network Engineer. He is currently Associated Dean and Head of Department Computing with Northumbria University London and holds External Examiner position for UK HEI. He has also authored number of book chapters on Ransomware, Cyberwarfare, and Smart Homes Security and his research interests include malware analysis and use of machine learning for cyber defence. Dr. Ana Filipa Silva Cardoso holds a master’s degree in Plant Molecular Biology, Biotechnology, and Bioentrepreneurship from Universidade do Minho, focusing in improving visits to urban green spaces with digital technologies. Currently studying Design for Sustainability in Universidade de Lisboa. Dr. Lillian Clark is a researcher and programme leader in Digital Marketing for QA Higher Education Ltd. Her research interests include online consumer behaviour, culture and digital marketing, mobile advertising and the use of social media in B2B activities. Dr. Ana Cristina Gomes da Cunha is an assistant professor at the Department of Biology, School of Sciences, University of Minho (UM), Portugal, and Ph.D. in Sciences-Biology. Head of the undergraduate course in Applied Biology and member of the steering committee of the Master Course Plant Molecular Biology, Biotechnology and Bioentrepreneurship (UM), teaches in plant physiology, biotechnology, and biochemistry. CBMA research full member published more than 30 papers in international peer-reviewed journals, books, and book chapters and supervised more than 60 students (B.Sc., M.Sc., and Ph.D.). Ajinckya Dahibhate has recently completed his M.Sc. in Procurement and Supply Chain Management from Cranfield University and currently

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Editors and Contributors

working as a material planner at Hyster-Yale Group in Northern Ireland. Prior to this Ajinckya has completed his M.Sc. in Logistics and Supply Chain Management from Sheffield Hallam University and completed his bachelor’s in Mechanical Engineering from University of Pune and Later worked for a manufacturing firm called Force Motors Limited. During his bachelor’s degree Ajinckya has published a scientific research paper at 2nd National Conference on Mechanical Engineering, Material Science and Mechatronics (NCMMM) on ‘Experimental Investigation of an Anaerobic Digester with Co-digestion of food Waste and Animal Manure for Improved Bio-gas Production. Aristeidis Davelis completed his B.Sc. in Computer Science with the Athens University of Economics and Business in Greece, and his M.Sc. in Web and Mobile Development with distinction, from the Northumbria University in the UK. He is a member of QAHE UK and works with Northumbria University and the University of Bedfordshire. Alongside his academic career, he has strong professional experience in ERP Systems Development, Web Engineering, and Integrated Systems, areas in which he is actively engaged. He has also had extensive experience in IT Management roles in senior positions, as well as Operational and Strategic Business Administration. His research interests include Software Engineering, the Internet of Things, machine learning and emerging technologies. Caleb Eghan has been an M.Sc. cybersecurity student at Northumbria University London. He is currently working in Cloud Security and his research interests are in Artificial Intelligence and smart algorithms. Caleb Eghan earned his B.Sc. in Electronic Information Engineering from Nanjing University of Posts and Telecommunication in China and his M.Sc. in cybersecurity from Northumbria University in the UK with distinction. He is an Azure certified cloud practitioner with a skill set that includes cloud security and software deployment. He has extensive practical expertise in API integration, microservices, software testing, and the creation of application containers and pipelines in addition to his academic work. He has also secured professional roles in DevOps and DevSecOps through his considerable experience in software deployment security and architecture.

Editors and Contributors

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Khaled El-Hussein is a cybersecurity trainer at QA Ltd and has been associated as a lecturer with Northumbria University London. Khaled El-Hussein completed his B.Sc. in Computer Science with University of Plymouth in England, and his M.Sc. in Network security and Penetration Testing with a distinction, from Middlesex University, London. Khaled is a member of advanced HE, QAHE UK and currently holds a lecturer position at Northumbria University and University of Bedfordshire. He has been involved in delivering numerous cybersecurity programmes in multiple regions, such as Europe, Asia and the middle east. Alongside his academic career, he has a strong experience in carrying out penetration testings and Computer security assessments. He is actively engaged in ethical hacking projects parallelly with his lecturing job at Northumbria University. His research interests include cybersecurity awareness, IoT, data science, AI, and machine learning. In the past, he has worked in cybersecurity areas at the University of Bradford and also worked as a cybersecurity journalist. Dr. Trevor Gerhardt has over two decades of experience in Leadership, Training and Development and Strategic Management within the Third Sector and over a decade of experience in Higher Education including publication, teaching, speaking, curriculum development, and programme assessment. He is an active member of the European Society for Research on the Education of Adults (ESREA) and a convener of the Working Life and Learning Network of ESREA. He is active as Dr. Trevor Gerhardt on LinkedIn, Twitter and YouTube with a podcast ‘Dr Trevor Gerhardt PhD’ and courses on Udemy within the field of WIL. His research interests are WIL, reflection, professional/career development, and adult education. Farooq Habib currently teaches Strategic Procurement, Industrial Negotiation and Commercial Contract Management, Inventory and Operations Management, Lean Six Sigma and Supply Network Resilience at Cranfield University (UK and Oman Campus). Farooq holds or has held Visiting Faculty positions at business schools in Birkbeck College, University of London (UK), University of Buckingham (UK), and University of Bedfordshire (UK and Vietnam Campus). Prior to these roles he held senior management positions for more than 15 years in

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Editors and Contributors

export-oriented organisations operating globally within the textiles, food, and beverage sectors. Farooq has co-authored various academic papers, book chapters, and project reports. He regularly engages in high-quality research, evidenced by publications in leading academic and practitioner journals and conferences in the arena of logistics, procurement, and supply chain management. Farooq is a member of review boards of leading academic journals. Dr. David Josef Herzog qualified as an M.D. in 1995 and worked in general medicine and psychiatry. Interest in new medical technologies led him to collaborate with the high-tech industry in prospective areas. In 2010 Dr. Herzog obtained M.Sc. in Structural Molecular Biology at the Department of Crystallography of Birkbeck University and Institute of Structural and Molecular Biology, collaborative body for Birkbeck and UCL. Currently, David finalizes his Ph.D. research at the Faculty of Science and Technology, University Fernando Pessoa, Porto, Portugal. The area of his research is the interaction of cognitively impaired patients with the ambient assistive environment of the specially designed smart home. The wider interests include smart supportive environment, telemedicine, AI-enabled healthcare, human-machine interaction, cyberbiology, artificial consciousness, and transhumanism. Prof. Amin Hosseinian-Far is a professor of Systems Thinking in the Department of Business Systems and Operations at the University of Northampton. He is also the chair of the research Centre for Sustainable Business Practices (CSBP) at the University. Prof. Hosseinian-Far has more than 80 peer-reviewed publications that are mainly disseminated as journal articles, conference papers, and book chapters. His research interests are at the intersection of systems thinking (mainly hard systems thinking), information systems, analytics, and modelling, with applications in resilience assessment and sustainability, information systems and security, management, business systems, global business operations, supply management, engineering, health, and education. Moreover, he has been an editor of two books and two conference proceedings. Prof. Hosseinian-Far is an ex-associate editor for the International Journal of Systems and Society, the founding editor and the editor-in-chief of the International Journal of Strategic Engineering (IJoSE), and a guest

Editors and Contributors

xxvii

editor for a Special Issue in the International Journal of Environmental Research and Public Health (MDPI). Dr. Md. Toriqul Islam completed his Ph.D. from the Faculty of Law, University of Malaya, Malaysia, and worked as an assistant professor of Law at Bangladesh University of Business and Technology (currently on leave). He graduated with a Bachelor of Laws (LLB Hons) and Master of Laws (LLM) from Islamic University, Bangladesh. Being a passionate researcher, Dr. Toriqul has widely published in peer-reviewed journals and presented papers in several national and international conferences in his areas of interest, e.g., data protection law, public international law, constitutional law, and human rights law. Dr. Islam reviewed academic articles for International Data Privacy Law, Oxford University, and Utrecht Law Review, Netherlands. He is currently working as an editorial board member at Lampung Journal of International Law, and Indonesian Journal of Law and Society, Indonesia. Dr. Ridoan Karim is a lecturer at the Department of Business Law & Taxation, School of Business, Monash University Malaysia. He has taught and researched in the fields of business and international trade law. Being a passionate researcher and academic, Ridoan has widely published in peer-reviewed journals and presented papers in several national and international conferences in his areas of interest, i.e. ‘legal and regulatory aspects of energy’, ‘science, technology and law’, ‘privacy and data protection law’, ‘human rights law’, ‘health and medical law’, ‘legal research’, and ‘Asian and comparative law’, etc. Ridoan had acted as a Consultant and Fellow in projects funded by the University of Malaya, Malaysia. Murtaza Farooq Khan is an aspiring barrister, with a particular interest in Company, Revenue, and IT law. He holds an LLB Single Honours from SOAS University of London and is presently reading the Bar Professional Training Course at the University of Law, London. In pursuit of his career at the Bar, Murtaza holds two major scholarships, from the Middle Temple and the University of Law. He is also involved in blockchain technology—having produced a White Paper, a

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Editors and Contributors

self-balancing index, and a variety of research reports for organisations involved in the private sector. Anu Laitakari (B.Sc., P.G.C., M.Ed.) is the Head of Technology for Kaplan Open Learning. She is a highly skilled technical platform manager with considerable experience in managing learning technology platforms and developing learning content for a variety of audiences. She has worked in Further Education and Higher Education (HE) as well as for commercial and charity sectors, and now focuses on online learning within HE. She is especially interested in online pedagogy, fair assessment, and learning design. She is also a champion for accessibility. Anu has recently completed an M.Ed. with her dissertation focusing on the role of tutor self-efficacy and Emotional Intelligence in online learning. Uzzal Ali Pk is a BCS (Bangladesh Civil Service) cadre officer (BCS— General Education), now working as a lecturer of Marketing at Dinajpur Government City College, Dinajpur, Bangladesh. He completed M.B.A. and B.B.A. in Marketing from the University of Rajshahi, Bangladesh. He has more than four years of teaching experience in theories and practices of Marketing in the college education level. Before joining the present position, he worked in the Research and Development Unit of a leading private commercial Bank in Bangladesh for two years. His areas of research interest are Marketing, E-Commerce, Tourism & Hospitality Marketing, Entrepreneurship Development and Technology in Business. Ishwari Pradhan is a graduate of M.Sc. Logistics and Supply Chain Management at the University of Northampton, UK. Apart from this, she also holds a master’s degree in Fashion Merchandising and Retail Management from LIM College, New York. She has previously worked with many global fashion and furnishing brands, mostly in the product development, production, and supply chain domains. Her research interests are in the areas of supply chain management, related technologies that enhance supply chain functions, and the application of different analytics techniques for improving pertinent supply chain decisionmaking processes. Md. Rakibul Hafiz Khan Rakib is a lecturer at the Department of Marketing, Begum Rokeya University, Rangpur-5404, Bangladesh.

Editors and Contributors

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Rakib completed M.B.A. and B.B.A. in Marketing from the University of Rajshahi, Bangladesh and obtained 1st position in both the examinations. He also worked as the coordinator of M.B.A. (Professional) 6th batch of the Department of Marketing, Begum Rokeya University, Rangpur. He has more than five years of teaching experience at the University level. His areas of interest for teaching and research include Marketing, Tourism and Hospitality Marketing, Sustainable Tourism Development, and Sustainable Entrepreneurship Development. He authored more than 15 articles and book chapters published in toptier journals, edited books, and conference proceedings from publishers like Routledge, Springer, Sage, Inderscience, etc. He has expertise in SPSS, Structural Equation Modelling (SEM), PLS-SEM, and AMOS. Currently he is trying to develop a framework for sustainable tourism development in Bangladesh. Besides, he is engaged in extra-curricular activities and consequently affiliated with many social and cultural organizations supporting community empowerment, cultural diversity, and environmental up-gradation. Dinusha Maduwanthi Rathnayake is a lecturer in Business at Asia Pacific Institute of Information Technology (APIIT) in Sri Lanka, which is affiliated with Staffordshire University, UK. She is passionate about lecturing and research on Entrepreneurship, Higher Education, Management, and HR, and, experienced in supervising undergraduate students since 2018. Dinusha has two bachelor’s degrees, one in ‘Entrepreneurship and Management’, and one second in ‘Human Resources Management’ and also she has a dual award master’s degree in ‘Business with International Management’ from Northumbria University, UK and Heilbronn University, in Germany. Dr. Carolina Redolfi is the programme leader in MA Luxury Brand Management and lecturer in Marketing at Northumbria University. Carolina believes that her anthropological background helps her understand market dynamics and consumers from a holistic point of view. In addition, she finds it interesting to blur the lines between anthropology, economy, and marketing. Her research projects are in consumer culture, luxury and macromarketing. Within that, she is interested in the impact of marketing practices in our society and consumer daily life and how

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Editors and Contributors

marketing interconnects to other aspects of our social life. Her research interests are consumption and financial practices, social relationships and interaction, consumer culture, social distinction, sharing/gift-giving, historical marketing and luxury. Michael Rice is the module leader for a number of business modules at Pearson Business College in London. Before joining the College, he worked as a board-level marketing practitioner, supplying blue-chip retailers and brands such as Tesco, Boots, Diageo, and J and J for over 25 years. He offers students a unique insight from a UK-based manufacturing company perspective, one that succeeded in launching many innovative and sustainable solutions in the retail sector in EMEA. Since selling his company, Michael has invested in a number of UK and Irish start-ups and has worked in education for more than a decade as a visiting lecturer in subjects such as Strategy, Entrepreneurship, and Marketing. Michael is a fellow of the Higher Education Academy, has completed his PGCHE, holds an MBA from Kingston University and a B.Sc. (Marketing) degree from Trinity College Dublin. Dr. Teresa Roca holds a multidisciplinary Ph.D. in Occupational Psychology, Policy and Education, from Loughborough University UK. Currently, a senior lecturer in Leadership and IHRM, at Northumbria University, and guest professor for Hochschule Kaiserslautern, Germany. Current research interests include: the impact of digitalisation on entrepreneurship, organisations design, structure, effectiveness, learning and development; occupational well-being; organisational leadership, trust, behaviour and change; employability and career development; accountability, school improvement and educational policy development; and multidisciplinary research. Dr. Roca has wide Organizational and Government report publications, presented several papers at conferences, currently building a multidisciplinary research portfolio. With a Chartered Occupational Psychologist background, Dr. Roca has extensive international business and consultancy experience, academic experience of Programme Design and Leadership, Ph.D. supervision, and over 4 years as an External Examiner; member of the British Psychological Psychology, Division of Occupational Psychology, EMONET-L,

Editors and Contributors

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Independent Consultants Support Group, Leaders-In, and Radical Innovation Research Group among others. Dr. Dilshad Sarwar is working within the area of Business Systems and Operations as a Subject Group Leader, within the Faculty of Business and Law at the University of Northampton. In her previous roles she has taught at both undergraduate and postgraduate levels and has actively been involved in Ph.D. supervision. Dilshad’s Ph.D. was gained from Leeds Beckett University titled: ‘Critical Race Theory—A Phenomenological Approach to Social Inclusion of BME Non-Traditional Students’. Dilshad has a Postgraduate Certificate in Research, M.A. in Education Management, and M.Sc. in Information Systems, Dilshad is also a Senior Fellow of the HEA. Dr. Sarwar’s current research is broadly within the area of Information Systems and Business Information Systems as a discipline with a focused research interest in the social influences and domains of Internet of Things and Disaster Management Systems, which entails social computing and managing information in the digital age. Dr. Nam-Chie Sia holds a DBA from the University Northampton, and Amity Business School. His research interests relate to implementing data analytics strategies for not-for-profit organisations. Academically, Nam graduated with Bachelor of Accountancy (Hons) from Nanyang Technological University and M.B.A. (Distinction) from Manchester Business School. He has professional qualifications as CA (Singapore), CIA, CISA, CRMA, and CFA. While he works in the financial industry, he is an active volunteer in a few not-for-profit organisations in Singapore. Roshan Lal Sharma is a professor in the Department of English, School of Languages, Central University of Himachal Pradesh, Dharamshala (HP). He has been a senior fulbright fellow at the University of Wisconsin-Madison (USA) during 2007–2008. He has authored Shorter Fiction of Raja Rao (2009), and Walt Whitman (2000); co-authored Som P. Ranchan: Dialogue Epic in Indian English Poetry (2012); and co-edited, Communication in Contemporary Scenario: Its Multiple

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Dimensions (2017), Mapping Diaspora Identities (2017), and Communication, Entrepreneurship and Finance: Renegotiating Diverse Perspectives (2018). He has more than seventy published papers and book chapters to his credit. Dr. Bruno Sousa is an adjunct professor of Marketing at Polytechnic Institute of Cavado and Ave (IPCA), Portugal and Ph.D. in Marketing and Strategy in Universidade do Minho, Portugal. Head of Master Program—Tourism Management (IPCA) and Tourism Marketing (IPCA). CiTUR research member and UNIAG research member. He has published in the Journal of Enterprising Communities, Tourism Management Perspectives, Current Issues in Tourism, Journal of Organizational Change Management, World Review of Entrepreneurship, Management, and Sust. Development, Annals of Leisure Research, among others. Laura Stancescu has been a student of M.A. Luxury brand management at Northumbria University London. She has experience in luxy=ury branding, personal branding, and social media advertisements. Dr. Teoh Teik Toe is an adjunct professor in Henan University, Shandong Institute of Technology, and Han Chiang University. He has published 3 books and more than 50 publications including journals and conference. He has more than 25 years of research experience including Big Data, Deep Learning, Cyber-security, Artificial Intelligence, Machine Learning, and Software Development. He also has more than 15 years of teaching experience in AI, Data Science and Analytics, Statistic, Business, Finance, Accounting and Law. He has obtained Ph.D. in Computer Engineering from NTU, Doctor of Business Administration and M.B.A. from University of Newcastle, Master of Law from NUS, LLB and LLM from UoL. He is also the chartered holder of CFA, ACCA, CIMA, Chartered Accountant Singapore, Chartered Tax Practitioner, Chartered Valuer and Appraiser, Chartered Accountant Malaysia and CPA Australia. He is also a member of Mensa.

Key Terms and Definitions

Asynchronous mode

Blended Learning (BL) Bloom’s Digital Taxonomy (BDT)

Community of Inquiry (CoI)

Connectivism

Teaching mode taking place at any time and any place convenient to students using online resources. The use of synchronous and asynchronous modes of teaching due to the use of digital tools and resources. Provides a framework to teach and assess the teachers’ and students’ understanding and usage of associated digital tools in the academic and non-academic contexts. A model that describes the combination of presences that will help assure a good online experience encompassing the idea of Cognitive Presence (CP), Social Presence (SP), and Teaching Presence (TP). A theory that explains how learning happens in an online network community in the digital age.

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Key Terms and Definitions

E-literacy, digital literacy, ICT literacy and multiliteracies

Flipped Classroom

LMS ODL OMO OnO Synchronous mode TPACK Transactional Distance (TD)

The awareness, skills, understandings, and reflective approaches necessary for an individual to operate comfortably in informationrich and IT-enabled environments. Moving more subject based content to a asynchronous mode where students can read and research this content as a self-directed learning process allowing the discussion and application of this content in the synchronous space. Learning management Systems such as Blackboard and Moodle. Online Distance Learning (ODL). Online-Merge-Offline (OMO). The combination of online and offline. real time, face-to-face teaching mode taking place in a specific location. Technology, Pedagogy and Content Knowledge. Ideas of dialogue, structure, and autonomy, where dialogue is a unique segment of the educational conversation that leads to the construction of knowledge, structure is a measure of responsiveness to a learner in terms of meeting learning needs, and autonomy the degree that a student was dependent or independent (self-directed and self-regulating).

List of Figures

Fig. 1.1 Fig. 2.1 Fig. Fig. Fig. Fig. Fig.

2.2 4.1 4.2 4.3 4.4

Fig. 4.5 Fig. 4.6

Fig. 4.7 Fig. 4.8 Fig. 4.9

Projected global sales activity of 5G by 2035 Reality-Virtuality Continuum (Milgram and Kishino 1994) The Osterwalder’s Business Model Canvas Basic Bitcoin block architecture Timeline of blockchain technology development The concept of the distributed ledger Global industrial blockchain revenues (ABI Research 2020) Blockchain-enabled provenance knowledge (Montecchi et al. 2019) Survey (base: 600): Which of the following will be the biggest barriers to blockchain adoption in the next 3-5 years? (% of respondents ranking top 3 barriers) (PwC 2018) Overview of use case exemplars (Hackius and Petersen 2017) (Icons: Vecteezy.com) Benefits of TradeLens (IBM Corporation 2018) (Icons: Vecteezy.com) Walmart case study (Hyperledger 2019)

24 50 54 89 91 95 103 110

113 114 115 116

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List of Figures

Fig. 4.10 Fig. 4.11

Do you really need a blockchain? (Peck 2017) Common barriers in the implementation process (Icons: Vecteezy.com) Stakeholders in Supply Chain blockchain systems (Hastig and Sodhi 2020) Framework for Enterprise Systems (Markus and Tanis 2000) Key stages of Blockchain implementations (Icons: Vecteezy.com) Framework for defining innovation (Henderson and Clark 1990) Future growth in IOT devices (Atlam et al. 2020) The value chain (Porter 1985) Difference between 3PL and 4PL (Ciemcioch 2018) The 4PL concept (Christopher 2016) Four key components of a 4PL (Christopher 2016) 4PL services (Ça˘glar Kalkan and Aydın 2020) Cloud computing (AWS 2019) Digital twins (Gesing and Kückelhaus 2020) Blockchain in logistics (Heutger and Kückelhaus 2018) Supplier selection process (Tay and Aw 2021) Two-level hierarchy of LSP selection criteria (Hwang et al. 2016) Supplier selection methods (Taherdoost and Brard 2019) Multicriteria supplier selection model (Yadav and Sharma 2016) E-procurement tools (Mena et al. 2018) General E-auction framework (Habib 2020) Key reasons behind not renewing existing LSPs Contract (Rushton et al. 2017) LSP transition process (Barry 2019a) Responses to the first question on PA role in optimization Responses to the second question on the requirement to have highly skilled workers Responses to the third question on the role of PA for insight provision

Fig. 4.12 Fig. 4.13 Fig. 4.14 Fig. 4.15 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.16 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

Fig. 5.9 Fig. 5.10 Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 5.15 Fig. 5.16 Fig. 6.1 Fig. 6.2 Fig. 6.3

120 121 124 125 127 128 136 153 155 156 157 158 161 162 163 170 172 176 179 180 181 187 190 230 232 233

List of Figures

Fig. 6.4 Fig. Fig. Fig. Fig. Fig.

6.5 6.6 6.7 6.8 6.9

Fig. 6.10 Fig. 7.1 Fig. Fig. Fig. Fig.

8.1 8.2 8.3 10.1

Fig. 10.2

Fig. 11.1 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9

Fig. 11.10 Fig. 11.11

Responses on the comparison between PA and other analytics categories Descriptive statistics on the fifth question Responses to questions on PA and ROI Responses on the value proposition of PA Perceptions on PA reliability PA and competitive advantage; descriptive statistics of responses Responses of participants on the suitability of PA for SC forecasts Four Steps Framework for SMEs, adapted from Alibaba Cloud (2018) Gucci AR Snapchat filter Multi-gesture interface on touchscreen device Conceptual model Benefits of urban green spaces arranged by environmental (green), economic (yellow) and socio-cultural (light blue) domains, as well as health (dark blue) as a socio-cultural subdomain. Connections show how a benefit from a domain can have an impact on other domains (using respective colours) Major opportunities/strengths (highlighted in dark blue) and resultant benefits (highlighted in light blue) of urban ecotourism, along with their integration concerning sustainable development (in green) Definitions of Big Data (online survey of 154 global executives in April 2012) Big Data 5V characteristics Challenges of Big Data General classification/types of Artificial Intelligence Major branches of Artificial Intelligence Hierarchy between AI-ML-DL Machine Learning types Machine Learning algorithms Example of MLP network architecture (Iman et al. 2020) Standard, fully connected DNN (Kowsari et al. 2019) The roles of AIED (Hwang et al. 2020)

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233 234 236 237 238 238 240 261 288 289 291

352

356 372 375 377 380 384 386 387 388 389 391 393

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Fig. 11.12 Fig. Fig. Fig. Fig.

11.13 11.14 11.15 11.16

Fig. 11.17 Fig. 11.18 Fig. 11.19

Fig. Fig. Fig. Fig. Fig. Fig.

12.1 12.2 12.3 12.4 12.5 12.6

Fig. 12.7 Fig. 12.8

List of Figures

Model-based ITS architecture (Holmes et al. 2019; Luckin et al. 2016) Benefits of AIED Challenges of AIED The regulatory problems of AI (Scherer 2015) 2020–2024 growth rates of (Left) AI Software Platforms/(Right) AI Application Development and Deployment Global Business AI and IT AI service growth projection (x-axis: Billion $) Global revenue from Big Data and Business Analytics, 2015 to 2022 (y-axis: Billion $) Estimated Volume of data captured, captured, consumed, and created globally from 2010 to 2024 (x-axis: data volume in Zettabyte) Web 0.0 to Web 5.0 (www.trendONE.de) Taxonomies compared (Amin and Mirza 2020) Top 10 EdTech Unicorns (HolonIQ, no date) Generational disruption (Deshmukh 2021, p. 144) Disruption of space (Deshmukh 2021, p. 144) OMO classroom framework based on PST (Xiao et al. 2019) Technological educational innovation strategy (Adapted from Amin and Mirza 2020) Technological educational innovation model (B adapted from A—Goold et al. [1994])

395 397 402 405

411 412 413

414 429 435 442 443 444 459 459 460

List of Tables

Table 1.1 Table 4.1 Table 4.2 Table 4.3 Table 5.1

Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table Table Table Table

5.6 5.7 8.1 8.2

Impacts of technology in business Main elements of blockchain Comparisons among public, consortium and private blockchains (Zheng et al. 2017) How does blockchain address common issues in Transport and Logistics systems Service characteristics of LSPs (Alvarez 2020; Grant 2019; Hofmann and Osterwalder 2017; Jurczak 2018; Kennedy 2020; Nailwal 2021) Emerging digitalisation technology in FLT (Wang and Sarkis 2021) LSP evaluation criteria (Akman and Baynal 2014; Hwang et al. 2016) Definition of 21 sub-criteria (Hwang et al. 2016) Critical analysis of scorecard system (Finch 2017; Ramos 2020) MCDM methods available in literature RFP checklist (Author 2021) Sensory cues summary Top brands summary

7 92 93 105

160 164 171 173 175 177 190 290 293

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List of Tables

Table Table Table Table Table Table

8.3 8.4 8.5 8.6 8.7 8.8

Table Table Table Table Table Table Table

8.9 8.10 8.11 9.1 9.2 9.3 9.4

Table 9.5 Table 11.1 Table 11.2 Table 12.1 Table Table Table Table

12.2 12.3 12.4 13.1

Table 13.2

Gender frequency Statistics of senses Summary table added sensorial inputs Age * channel crosstabulation Chi-square test age * channel Relationship between the importance of seeing how the product fits and the impact of added visual cues Coefficients of regression model Coefficients of regression model hypothesis 6 Coefficients of regression model hypothesis 7 Age and gender structure Age distribution for professional groups Sensors in AAL, answers’ frequency table Descriptive statistics by statements, “Sensors in the AAL for patients with MCI” Descriptive statistics by statements, “Privacy of the patients with MCI in homes with AAL” AI enabling key attributes of Big Data (O’Leary 2013) Educational scenarios and applications of AIED Learning theories compared (Pillai and Sivathanu 2020) Summary of self-portraits Thematic analysis based on teams debate Advantages and disadvantages Four main domains in technological entrepreneurship ecosystem Entrepreneurial academics vs academic entrepreneurs

295 295 296 297 298

298 300 301 302 336 337 338 340 342 392 394 433 450 451 452 486 503

Part I The Emergence of Tech-Trends and Practices of Technology-Empowered Strategies

1 Cybersecurity and Integrated Business Models Md. Toriqul Islam

and Ridoan Karim

Introduction In the last three decades, cyberspace is used widely to handle vital infrastructures in diverse industries. Undoubtedly, the increasing reliance on multiple digital platforms has made life easier, faster, and smarter. Contrarily, this ecosystem entails huge challenges too, as evidenced by an account of a data breach incident that took place in this decade. In 2010, Julian Assange leaked around 750,000 secret diplomatic or military documents of the USA with the assistance of one U.S. Army intelligence officer Chelsea Manning (2015). In 2011, Sony PlayStation Network and Sony Pictures witnessed a massive cyber-attack, which Md. T. Islam (B) University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected] R. Karim School of Business, Monash University, Subang Jaya, Malaysia

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_1

3

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M. T. Islam and R. Karim

compelled the company not only to incur a fine of $400,000 but also to compensate the victims. Global Payment Systems experienced a data breach in April 2012, which resulted in a loss of $93.9 million. From 2013 to 2014, Yahoo! experienced a huge data breach incident amounting to 500 million of its users. The incident provoked in filing above 40 lawsuits against Yahoo! bringing down its sale price to an amount of $350 million (De Groot 2019). In 2015, unscrupulous foreign miscreants successfully transferred $80 million to their accounts from the account of the central bank of Bangladesh at the New York Federal Reserve Bank (Kuner et al. 2017). Panama Papers leakage in 2016, Paradise Papers disclosure in 2017, and Facebook Cambridge Analytica scandal in 2018 have shocked the global community, and made them aware of cybersecurity (Islam 2018). In 2019, another data leakage of above 540 million Facebook users was revealed, and until April 2019, nearly 1.885 million customers’ financial data was leaked from First American (Holmes 2019). In March 2020, around 5.2 million guests’ information was leaked from hotel Marriott International. In April 2021, the sensitive personal data of around 500 million users of LinkedIn was allegedly scraped (Brown 2021). The above incidents make it apparent that cybersecurity appears as one of the hot button issues in contemporary global policies, politics, and businesses. In the words of Kuner et al. (2017), ‘cybersecurity is attracting more attention than ever—not just in headlines, but among policymakers, industry leaders, academics, and the public’ (Kuner et al. 2017). This chapter outlines the interrelations among technology, entrepreneurship, and business models in section ‘Technology, Entrepreneurship, and Business Models’; the meaning of cybersecurity in section ‘Meaning of Cybersecurity’, and the co-relation among integrated business models, emerging technologies, and cybersecurity in section ‘Business Models, Technologies, and Cybersecurity’. Section ‘Regulating Cyberattacks: Law and Policy Perspectives’ analyses the regulatory aspects of cyberattacks in legal and policy perspectives, while section ‘Way Forward’ explores three policy measures as the way forward. Section ‘Conclusion’ includes a precise conclusion for the entire chapter.

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Technology, Entrepreneurship, and Business Models Almost all firms carry out business operations for making profits by outperforming their competitors in the market. However, it is not easy for entrepreneurs to turn their businesses into successful ventures. For a successful business project, an entrepreneur has to take the right decision in every sphere, choose the appropriate business model, and keep watch on the performance of the firms. The performance of the firms is based on three determinants, such as business models, business environment, and change (Afuah and Tucci 2003). In the digital age, all these determinants of business performance are more or less affected by emerging technologies. Hence, in the current global information economy, technology, entrepreneurship, and business models are tightly coupled with one another. Technology refers to the knowledge and use of the systems, techniques, tools, material products, or methods of an organisation (Kremer 1993). The growing advancement of information and communication technologies, their commercial viability, and resultant adoption by businesses in the last few decades have immense implications for businesses and society. Hence, there is hardly any business nowadays that does not rely on modern technologies for its operations. Some businesses are significantly based on technologies, such as Apple; Samsung Electronics; Alphabet; Microsoft Corporation; Tencent Holdings; Facebook; Intel Corporation; IBM; Taiwan Semiconductor Manufacturing Co., and Oracle Corporation (Ponciano 2021). Sometimes technology itself affects business model prospects (Baden-Fuller and Haefliger 2013). Due to the pandemic COVID-19, many companies suspended their operations; employees lost their jobs, and eventually, unemployment was rampant. Despite the pandemic situation, technology help many businesses, e.g., e-commerce, gaming, subscription-based streaming service, outsourcing, video content creation, education service, and marketing to make huge profits (Ponciano 2021; Fleming 2021; Daugherty et al. 2021). While some US tech giants, such as Facebook, Amazon, Apple, Netflix, and Google (alternatively known as FAANG) also flourished

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more during the pandemic than ordinary period (Pisal 2021). With technological innovations, Apple, Facebook, Google, IBM, and Microsoft have changed the idea of world business and the means we communicate. It can be concluded that technology, entrepreneurship, and business models are mutually supportive. However, to dig into something more, let us describe the prospects of technology in business and suggestions for entrepreneurs.

Prospects of Technology in Businesses The revolutionary digital transformation has significantly changed the idea and forms of entrepreneurship and business models. In the early 1980s, people could hardly relate business models with technological innovation, but the way of doing business has drastically changed on 6 August 1991—when the World Wide Web came into being (Vibes 2015). Nowadays, one of the simple formulas for business success lies in the attachment with technological innovation. Technological attachment in the business has a similar effect that steam engine had on the industrial revolution. Recent research shows that technologies improve communication among employees, make employees more efficient, and ease the working environment within the framework of businesses (Table 1.1). Moreover, modern technologies equip businesses with tools for solving intricate problems; enable businesses of making the right decisions; improve marketing strategies; enhance customer supports; and advance resource management. It is tough to think of a business in the digital age that is not blessed with technological innovation. Even the farmers use computers nowadays for financial planning; understanding technical issues; production records; and procurement (Vibes 2015). Some modern technology, for example, blockchain enables businesses to ensure the security of their financial data, trade secrets, decisions, and other transactions (Hopwood 2019). However, among numerous benefits of technologies in businesses, a few are shared below.

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Table 1.1 Impacts of technology in business1 Issues of consideration

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

Total

Having continuous technological advancement in your company improves communication within the organisation The use of technology makes employees more efficient Technology has brought ease of work process for the company

1

1

14

21

108

145

2

2

18

24

99

145

2

1

12

25

105

145

Launching New Businesses It has been proven today that information is power. The rising information technologies are affecting today’s businesses in several ways. Among others, some cutting-edge technologies change the industry structure, and rules of competition. These technologies render companies competitive advantages by outperforming their competitors. Some technologies spawn entirely new business within the company’s existing operations (Porter et al. 1985). It is immaterial whether a company is big or small—emerging technologies render them tangible or intangible benefits; make them profitable; grow them in meeting the needs of the day, and demands of the customers. Hence, it is unthinkable nowadays to 1

The authors have adopted the above table and embedded information from the work of other authors. See Alheet, A. F., Hamdan, Y., & AL-Bazaiah, S. A. (2021). The Impact of Technology, Entrepreneurship and Consumer Attitudes on Firm Performance. Polish Journal of Management Studies, 23.

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ignore the massive implications of technologies in businesses. The development of some emerging technologies, such as virtual and augmented realities systems (VR and AR systems); artificial intelligence; robots; drones; blockchain, and driverless vehicles are impacting businesses at all levels (University of Queensland 2021). This trend will likely continue in the coming days. Thus, these five key emerging technologies are setting an ever-changing blueprint for all sorts of future businesses.

Fostering Communications For a successful business venture, effective communication among different stakeholders is crucially important. Advancements in information and communication technologies (ICTs) have made communications more effective, easier, and faster (BBC 2021). Land phone, fax, computers, internet, smartphone, etc. have facilitated all human beings to get connected in such a way that had never been earlier. Today, with the help of ICTs, co-workers, employees, and bosses across industries are getting closer anytime, anywhere. Eventually, technologies facilitate the culture of cooperation, collaboration, and solidarity among different stakeholders in businesses. Moreover, technologies significantly help diverse stakeholders in getting closer, engaging, and building a team within a workplace. By using communications technologies, especially, the Internet, social networking sites, etc. businesses can connect with their clients and consumers. Through these technologies, businesses can answer customers’ questions regarding products; make a good relationship with all stakeholders; and improve the company’s image (Doane 2018).

Flexibility in Business Operations Technologies help not only open up new businesses, and establish effective communication among stakeholders, but also render flexibility in business operations. For instance, in many businesses, robots are used for doing the most boring, repetitive, risky, unhealthy, and vulnerable jobs. Since robots work in place of human agents, they facilitate human beings

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ample leisure time and engage in creative activities (Pham et al. 2018). In almost all businesses, computers are used for numerous activities, including accounting, inventory, records keeping, etc. The internet is used for all sorts of communications, while automation for efficient business operations. Websites, blogs, social networking sites, and electronic media are used for advertising, marketing, and branding businesses. Sometimes, online-based outsourcing options are used to avoid the deficiency in the human workforce and reduce service, or manufacturing costs. Diverse online shopping platforms are used for e-commerce, viz. selling and buying of products. To put it simply, the roles of modern sophisticated technologies in business are beyond description.

Promotional Marketing One of the cornerstones of the strategic plans of all businesses is to reach the targeted customers. The customers are considered as the blood of all businesses, and the stairs of their success. Hence, it is the utmost priority of each business to have a good relationship with prospective customers. Generally, businesses can reach their targeted customers by producing quality products and services for them. Promotional marketing strategies play crucial roles for businesses to reach out to prospective customers. Numerous information and communication technologies may help businesses in getting closer to their customers. There are several widely used ways for digital marketing, such as advertising on websites; content marketing; email marketing; online branding; search engine optimisation (SEO); social media marketing, etc. (Razu 2019). Furthermore, by sharing the company’s products, or services on the company’s website, social media, electronic media, blogs, personal email, mobile, etc., businesses can perform promotional marketing. Due to the rapidity, reduced cost, and efficiency in digital marketing, almost all companies rely on modern technologies. Companies are generally using the Internet, mobile apps, and especially, social media for the promotional marketing of their business. For example, Facebook has more than 50 million registered business account holders, while above 88% of companies use Twitter accounts for promotional marketing purposes (Lister 2017; Dwivedi et al. 2021).

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Increase Productivity and Profits Technologies have also massive impacts on businesses, in terms of improving productivity and increasing profits. Recent research reveals that irrespective of size, digital transformation of businesses is possible, and the consequential result of which is sustainable (George 2012). Hence, almost all businesses are hugely relying on technologies in the twenty-first century. Globally, people can earn, learn, read, lead, advertise, advise, purchase, sell, and do many more activities by using numerous technologies. By breaking all barriers that human faces, challenging traditional business models, introducing immeasurable opportunities, technologies make human lives easy, comfortable, and smarter. Technology can increase productivity and profits by maintaining efficiency; reducing marketing costs; identifying probable challenges; better organising time management; easy reaching experts; improving business security; and reducing overall expenses (Cogent Analytics 2020), while another research concludes that technologies can help businesses to increase their productivity by blending team efforts; improving system and collaboration; reinforcing marketing; outsourcing skilled workforces, and leaving repetitive works for automation (Denton 2020). In essence, businesses can achieve the desired increase in productivity and profits by utilising fitting technologies; cutting extra costs; setting specific plans, right motivation, and realistic goals; recruiting better employees; training of employees; ensuring congenial working environment, and teamwork; evaluation of performance and targets, etc. It is worthy of note that using technologies in the workplace does not guarantee success for businesses. Entrepreneurs should learn something more in combining the trio, such as technology, entrepreneurship, and business models. Accordingly, a few important suggestions for the entrepreneurs have been shared below.

Suggestions for Entrepreneurs The integration of technology, entrepreneurship, and business models may be compared as the backbone of the current global business trends.

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Hence, the success of businesses depends much on the effective combination of this trio. The entity that can play the leading role to turn a business into a successful venture is the entrepreneur. Among many other necessary initiatives, the entrepreneurs have to take the right decision for both selecting the right business models and adopting suitable technologies for businesses. Above all, entrepreneurs should have the capacity to make appropriate decisions in every sphere of a company’s business. For example, keeping pace with the current trends of innovation, entrepreneurship, and business models, some countries, such as the USA, China, Germany, Russia, South Korea, Canada, Finland, Brazil, and India have managed to flourish in their economic growth and prosperity in the last few decades (I¸SIK et al. 2016). However, for technology selection, technology use, and resultant business success, the entrepreneurs should consider the following: • • • •

Appropriate technology; Adaptive with change; Trendy and timely innovations; Data compliance.

Appropriate Technology Although technology helps in both the growth and glow of a business, it is always challenging to pick up an appropriate technology for a particular business. For choosing the right technology, the entrepreneurs should analyse the type of their businesses, business’s needs, and select such a technology that is conducive to growing their businesses. In case of technology selection, the entrepreneurs should also consider the fundings of purchasing technologies, physical conditions of application, or installation, organisational capacity of handling those technologies, etc. (UN-HABITAT 1989). Appropriate technology refers to a manifestation, or process encircling technological choice and application, which is usually small-scale; decentralised; affordable to locals; energy-efficient; labour-intensive; environment friendly; and locally autonomous (Hazeltine and Bull 1999; Sianipar et al. 2013). By appropriate technology, we simply mean such a technology that is fitting for a particular business

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and is adapted to local conditions. By designing appropriate business models, and choosing appropriate technologies, the entrepreneurs can contribute not only to the interests of their businesses but also to sustainable development goals for global common goods.

Adaptive with Change Adaptability refers to such a quality that enables one to learn new skills and behaviours in response to the changing conditions. Although the traditional approach suggests that the universe is both static and predictable, new technologies, innovation, and globalisation have overthrown this orthodox conception. In the age of risk and uncertainty, many entrepreneurs discover competitive advantages in such organisational capacities that promote quick adaptation (Reeves and Deimler 2011). Apart from doing some good things in some particular ventures only, it is high time for businesses to do some more exciting things with the help of technologies, being adaptive to the change and demands of the day. The entrepreneurs should be ready to adapt and evaluate prevalent market strategies, organisational frameworks, and other transactions. Especially, the entrepreneurs should concentrate on changes in strategic behaviour to improve competitive posture, and achieve a perfect match between the organisation and the ecological niche thereof (Schindehutte and Morris 2001). In brief, adaptation fosters innovation; develops the skills of employees; offers huge opportunities for businesses; and improves team spirits. Hence, the entrepreneurs who are always ready to be adaptive with time, technology, and current trends make far better revenue than those who are reluctant about adaptation. For example, the Eastman Kodak Company, alternatively known as simply Kodak’s business model fails due to—sticking with old plans and strategies, not to adapt to change, and not to pay due attention to current trends (Baron 2014).

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Trendy and Timely Innovations From a business point of view, it is believed that adaptation of new technology would render better, cheaper, and faster service to the potential customer, investors, suppliers, partners, and the general public. With the help of new technologies, businesses could introduce a brave new world. Undoubtedly, technology adoption makes businesses capable of introducing or innovating new products, or services. But the entrepreneurs should make sure that their innovations are timely, and they are capable of meeting current demands, and compatible with recent trends. While thinking of a business idea, an entrepreneur should bear in mind that ‘a great idea does not always result in a successful product’ (Umesh et al. 2007), unless that is fitting with recent market trends and customer’s demands. Hence, before producing or manufacturing a new product, thing, or service, an entrepreneur should at least talk with different stakeholders, including investors, partners, and suppliers; advisors, clients, and employees; media, public, and even competitors. However, the most important thing is—an entrepreneur should talk with himself or herself. Before presenting the new products to the wider public, an entrepreneur should strategically think about his or her innovations. The entrepreneur must try to realise how his or her new product is minutely different from the products that already exist in the market (Douglas 2014). Moreover, an entrepreneur should also remind that innovating new products do not necessarily entail any success unless that add new value.

Data Compliance While using technologies, entrepreneurs should learn how to protect data privacy, data security, and data ethics for personal data of themselves, and others. Entrepreneurs should be data compliant. Data compliance refers to the practice of following regulations set out by the government, corporate governance, and industry organisations (Egnyte 2021). In the ubiquitous computing era, data privacy is vitally important for almost all types of businesses, as the lack of which leads to misuse of data, loss of

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identity, reputation, revenue, trust, and cause huge fines. Unfortunately, many businesses cannot realise the importance of data compliance. Privacy compliance of businesses can be compared with animal tendencies. Like turtles, some businesses may be too slow to adopt privacy compliance mechanisms. Others, like ostriches, may try to hide their heads into the sand, and neglect the compliance issue. Also, there remains another group that may try to outfox regulators through partial compliance. But businesses that are not serious about data compliance may face fatal consequences. For example, in recent years, the French DPA (the CNIL) inflicted the highest GDPR sanctions of e50 million against Google, because it failed to demonstrate the precise users’ guidelines for data sharing by many of its services (CNIL 2019). In July 2018, the Information Commissioner’s Office (ICO, UK) imposed a fine of £500,000 against Facebook because of the Cambridge Analytica scandal (Information Commissioner’s Office 2018a). Besides, the ICO also fined Equifax Ltd., a US-based credit risk assessment agency for an amount of £500,000 for its failure of protecting the personal data of around 15 million UK residents during a cyberattack in 2017 (Information Commissioner’s Office 2018b). In November 2018, the Dutch DPA [Autoriteit Persoonsgegevens (AP )] and the ICO combinedly fined Uber a total of $1.17 million for a data breach incident, which took place in 2016. In that fine, the ICO’s amount was £385,000 ($491,284), and the Dutch’s portion was up to e600,000 ($679,257) (Elizabeth 2018).

Meaning of Cybersecurity Until recently, the term ‘cybersecurity’ has evolved as a widely used phenomenon due to the increased adoption of allied issues by diverse industries. Nonetheless, there remains a lack of a precise, concrete, and universally accepted definition of the conception of ‘cybersecurity’. This happens due to the lack of authoritative definition, agreed-upon consensus, continental jargon, contextual reality, the different focus of diverse groups, and the like. Although the definitional issue is not a matter in an informal context but may have potential implications in

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the question of ensuring safety, security, organisational strategy, business policy, and governance. Being a generic term ‘cybersecurity’ refers to covering a wide array of issues, such as security or protection of data, systems, networks, hardware devices, etc. Generally, cybersecurity refers to the protection of hardware, software, online data, etc. from being destroyed, interrupted, infected, misused, or theft. For decades, many scholars particularly from the legal arena conflate cybersecurity with data security (Kosseff 2017). Being a widely discussed notion that is studied by many professionals from different studies, it is almost impossible to agree on a specific definition. This, however, does not mean that the term ‘cybersecurity’ is full of ambiguity, hence completely lacks a definition. Given that in the following, we will analyse some commonly used definitions along with their deficiencies to arrive at a more reliable definition. 1. Cybersecurity is the collection of tools, policies, 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 organization and user’s assets (ITU 2009). 2. 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, unauthorized use or modification, or exploitation (DHS 2014). 3. Prevention of damage to, protection of, and restoration of computers, electronic communications systems, electronic communications services, wire communication, and electronic communication, including information contained therein, to ensure its availability, integrity, authentication, confidentiality, and nonrepudiation (CSRC: NIST 2021). 4. The state of being protected against the criminal or unauthorized use of electronic data, or the measures taken to achieve this (Lexico 2021). 5. The ability to protect or defend the use of cyberspace from cyberattacks (CSRC: NIST 2021).

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The stated definition is either too short to include some important aspects of cybersecurity, or too wide to make the notion ambiguous. For example, Lexico aims to define cybersecurity in terms of data security but says nothing about relevant software, hardware, cyberspace, cybersystems, or critical cyber infrastructure, whereas the definition given by CSRC: NIST is too short to explain the issue properly. Other than the protection for the ‘use’ only, there should have an aim to protect the entire cyber environment, cyber system, cyberinfrastructure, relevant software, and hardware. Nonetheless, the definition falls short of covering all these allied issues. The rest of the definitions refer to both technical aspects and human interactions, their focus mostly on technical issues devoid of human actions. Consequently, although the stated definitions are useful, they rarely offer a holistic view that is essential to support interdisciplinarity. Cybersecurity is a complex issue that requires, among others, interdisciplinary reasoning. Hence, while defining the notion, an attempt should be made considering the aspects and interests of all relevant stakeholders. A useful definition must come up with an understanding of the relevant structure, subject, actor, object, and together with the allied purposes, intentions, and outcomes (Buzan et al. 1998). To better understand the notion of cybersecurity, one should consider the following cycle:

Cyber

Cyberspace

Cyberattack

Cybersecurity

The term ‘cyber’ means the networks covering both information and communications, whereas ‘cyberspace’ refers to a global domain of information technology infrastructures, usually run on the Internet, computer systems, telecommunications networks along with embedded controllers and processors. Cyberattack means an attempt of attack using cyberspace to disable, disrupt, destroy, or maliciously control a computing system, or destroy the integrity of the data, or steal preserved data. Realising all allied issues, it can be concluded that cybersecurity refers to a systematic arrangement, set-up, and establishment of means, methods, mechanisms, procedures, processes, systems, resources, and structures employed

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to protect cyberspace, allied systems, software, hardware, and persons concerned from diverse attacks, intrusions, and harmful activities. In short, cybersecurity denotes the capacity of persons, or organisations of protecting or defending their virtual existence, spaces, and properties from numerous unwanted or unlawful cyber-attacks.

Business Models, Technologies, and Cybersecurity Business Models Integration Nowadays, cybersecurity has emerged as one of the most pressing problems across the globe when a majority of our works are done online. Moreover, the pandemic Covid-19 has changed the global working environment in such a way that today we are virtually present everywhere, from educational institution to job places, and industrial sites to farmhouse. This landscape makes life smoother, but eventually, poses tremendous challenges to our online activities, digital assets, and properties. To get rid of the problems, relevant stakeholders should rethink, review, and revise their online policy measures. However, the current section sheds light on the nexus among integrated business models, emerging technologies, and cybersecurity. A well-formulated business model is crucial as it explains how and to what extent, a company is capable of creating and capturing value (Øiestad and Bugge 2014). Generally, a business model refers to a rationale for or process through which a company selects its market, and delivers its product, or services. Again, a business model determines the procedures of acquiring, creating, deploying, or selling goods, finance, human resources, or services to create value for its customers, owners, and other stakeholders (IFAC 2009). Further, a business model means the outlines of a company for making money by selling its product to a specific customer and market. In essence, the business model explains four specific elements of a business, such as specific products or services it will sell; marketing strategy for sale; operational or other costs, and turnover policy (Kriss 2020). Some other scholars opine, that although there

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may have disagreements, a business model consists of five critical components, such as value creation, value capture, value communication, value delivery, and value proposition (Rayna and Striukova 2014). Generally, business models are used to explore or commercialise a company’s mission, vision, innovations, products, or services to attract consumers’ attention, create value, and eventually, make money. It is a blueprint of a company for attaining certain strategic goals, such as making money, sustainable operation, and creating value for all stakeholders. However, the academic analysis of business models may have various focuses, purposes, and dimensions. For example, we emphasise integrated business models, emerging technologies, and associated cybersecurity challenges. Contemporary business studies reveal that while introducing a new service, or product in the market, especially, regarding the ICT products or services, most entrepreneurs suffer from major financial decisions, such as cost-sharing, investments, product positioning, revenue sharing, technical architecture, value networks, vertical integration, etc. (Poel et al. 2007). In a contemporary business model, a firm has to deal with some other issues, covering alliances and networking; marketing; market structure; strategic management; systems of innovation, etc. Thus, to mitigate such inherent complexities among the above interactions, the framework for an integrated business model has been evolved. Business integration refers to a linkup among all activities of a firm in a manner that may generate value. In other words, a business integration model is such a managerial tool that facilitates the management of a company to combine numerous activities and processes to obtain the highest production capacity. For example, a professional chef mixes all necessary elements while preparing delicious and healthy food. Although many people regard business integration as nothing but IT issues only, in the business integration process, IT is only a tool, not an end (Hybrid Accountant 2021). In essence, an integrated business model is designed to frame the complexity that a firm has to address in digital contexts. In the networked world, almost all scientific devices, appliances, and technologies working being connected to the Internet while using personal data. At all levels and spheres of national and international life, almost every service requires to have access to the personal data of

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the individuals. This results in numerous cyber-attacks, including identity theft, data breach, privacy intrusion, hacking, etc. Besides, almost all human activities are more or less connected with scientific technologies, which are, by default, prone to diverse cyber-attacks. Hence, there remains close connectivity between cybersecurity and the increasing usage and reliance of emerging technologies.

Emerging Technologies and Cybersecurity In our time, a cyber-attack is no more a hypothetical possibility, rather a harsh reality. For instance, a recent report of the World Economic Forum (WEF) (2020) reveals that the gross global expenses for cybersecurity reach now $145 billion a year, and this may exceed $1 trillion by 2035 (WEF 2020). Although almost all innovative technologies have the potentials of causing cyber-attacks, today the world community is worried about the cyber-attacks that may be posed by some emerging technologies, especially, artificial intelligence, the Internet of Things (IoT), robotics, 5G technology, and quantum computing. Emerging technologies refer to new digital technologies, which stir up the industry, or evolutionary services/products that set up an entirely new industry, dramatically change customers’ expectations, and reshape the marketplace (Aloulou 2021). In brief, emerging technologies mean new technologies or things that continue constant improvement. It cannot be claimed that the stated technologies have only immense impacts on human activities, nor do they deploy all risks humans might face in future. The said technologies have been chosen based on the potentials they could bring in the next five to ten years.

Artificial Intelligence Artificial intelligence (AI) means a type of computational technology, which aim to expand, extend, and stimulate human intelligence (Niu et al. 2016). In other words, artificial intelligence means the use and application of computational powers to perform some activities, such as decision-making, speech recognition, translation of languages, and visual

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perception that usually require human intelligence. In short, artificial intelligence (AI) refers to intelligence facilitated by machines, as against the natural intelligence characterised by humans or other animals. As a field of computer science, AI has been studied and practised for over 6 decades (Voda and Radu 2019), particularly since the 1950s (Brey and Soraker 2009). Among all emerging technologies, artificial intelligence (AI) is capable of performing genuine roles in diverse sectors, including business, commerce, finance, healthcare, manufacturing, and transportation. Accenture shares, AI can enhance the productivity of business up to 40%; Forbes reveals, AI increased the number of start-ups 14 times from 2000; Forrester predicts, AI will transform 16% of American jobs into automation; and Adobe explores, around 15% of businesses are employing AI, while 31% companies have taken AI as their scheduled program for the next 1 year (Adixon 2019). Together with numerous prospects, AI can be used for malicious intents. It is predicted that the AI-enabled systems, being facilitated by robotics technologies will play increasingly significant and unsupervised roles within the activities of the firms, especially, in manufacturing jobs (LR 2016). AI could pave the way for attackers of adversarial uses thereof by manipulating algorithms. Meanwhile, some attackers opened many AI algorithms for data-poisoning, and data manipulation (Biggio and Roli 2018; Biggio et al. 2012). The attackers may obtain the maximum benefit from stolen data, generate more harm, or increase cyberattacks by using AI. AI-enabled deep-fakes may also be used for ransomware or other cyberattacks. Meanwhile, voice-cloning software has allegedly been exploited for major thefts (Harwell 2019; Kwon et al. 2017). To minimise the AI-enabled risk factors, all stakeholders should employ quick response and operation services. To fight against AIenabled emerging attackers, it is essential to continue evolving technologies, enhance operational capabilities in line with the dynamism and pace, and build the capacity of all stakeholders (WEF 2020). The relevant stakeholders should foster traditional risk controls mechanisms as the baseline instrument together with AI-enabled security measures.

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Internet of Things (IoT) The phrase ‘Internet of Things’ (IoT) was first coined by Kevin Ashton to denote the RFID-enabled supply chain management (Ashton 2009), although it became commercially available in the market in 2014 (Kumar 2020). The Internet of Things (IoT) denotes one of the complex technological innovations that connect information and operational technologies with independent users through the Internet and other embedded technologies. In other words, the Internet of Things (IoT) means and includes a set of sensor-enabled physical objects; software; processing capacity, or other technologies having the capacity to connect and exchange information with allied systems and devices through the Internet or other communicating systems (ITU 2015). With over 7 billion inter-connected devices that is likely to increase up to 22 billion by 2025, IoT has emerged as one of the most important technologies of our time (Oracle 2021). Like most other emerging technologies, the IoT has both prospects and problems. The IoT helps businesses in diverse ways, by better handling business; increasing productivity and efficiency of business operations; creating new business models and revenue streams; and establishing seamless connectivity between the physical business world to the digital business world (Oracle 2021). AI-enabled IoT endpoint datasets and real-time analysis, together with deep learning help businesses to monitor industrial equipment automatically and proactively, track level of performance, and detect risk factors. Besides, through the IoT, businesses can avoid expensive repairing costs and losses, caused by faulty machines (Altynpara 2021). Due to overarching benefits and advantages, especially, in increasing productivity and reducing unnecessary costs about labour and energy, IoT is being employed in diverse businesses sectors. Recent statistics reveal that globally, 61% of businesses are using IoT, whereas the portion of IT and telecoms sectors reaches 71%, and the finance sector uses 68% (Kaspersky 2020). Despite immense potentials across industries, IoT entails massive challenges encircling safety, security, and unauthorised access. Some authors identify that dysfunction, privacy, security, and trust are the major threats posed by IoT (Sicari et al. 2015), whereas some focus on ethical

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issues (Nguyen and De Cremer 2016). There may be a lot of issues and challenges encompassing IoT platforms, covering cloud computing; interoperability of technologies; interoperability and standardisations; investment; jobs and skills; regulations and legal policy; unauthorised access to RFID; sensor-nodes security breach, etc. Sometimes hackers can obtain control of IoT-enabled premises to lock the doors remotely or access to thermostats and electrical system of a building. A recent cybersecurity report shows that above 36% of companies allow thirdparty access to their IoT platforms (Kaspersky 2020). Hence, probably, the most critical challenge of IoT is data breach incidents that may be caused by third-party access to IoT networks.

Robotics In last few decades, we have witnessed a remarkable development in robotics and increased applications thereof domestically, commercially, or militarily. Robots are playing diverse roles almost everywhere, being surgical robots, care robots, companion robots, military robots, etc. Over the years, the employment of robotics in different workplaces has become popular because of its inherent potentials. Unless malfunctioning, a robot can perform its assigned jobs non-stop, perfectly, and quickly. It can perform in extreme conditions, such as, deactivating explosives, exploring mines, finding sunk shipwrecks, rescuing survivors, etc. The term ‘robot’ has emerged from the Czech word ‘robota’ that was coined by Czech poet Karel Capek in 1924, the meaning of which is forced labour (Barthelmess and Furbach 2014). Subsequently, Isaac Asimov used the term ‘robots’ in the 40s, as one of the leading characters in his visionary novels. Thereafter, the industrial robots came into being in the 60s by Joseph F. Engelberger, who introduced the PUMA robot with a modern shape and efficiency (Schweitzer 2003). However, scholars have divided robotic history into four different periods, such as 1954, 1978, 1980, and 1995 to date. George Devol designed the first programmed robot in 1954 terming as the ‘Universal Automation’, subsequently, he abbreviated it to Unimation, and later, this turned into the name of the first robot company of the world. In

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1978, the Unimation company developed the ‘Programmable Universal Machine for Assembly’ (Puma) robot in association with General Motors (Kapila 1986). The third phase started in 1980 when the robotics industry had expanded in leaps and bounds. Many universities and institutions started to introduce programs and courses on robotics among a wide range of disciplines. The last phase has started in 1995, and the phase is continuing. To date, the use of robotics has dramatically expanded and spread out everywhere as an educator, entertainer, and executioner (Lin et al. 2011). These widespread human–robot interactions give rise to several ethical, social, and legal challenges that need to be resolved. In addressing those challenges, the regulators will have to devise workable solutions, although there is no one-size-fits-all solution. Therefore, scientists, manufacturers, policymakers, and regulators should work together for striking the balance between the problems and prospects encompassing robotics. Again, the challenges in the field are not against a particular nation or a region, but to the whole world at large, hence, international collaboration and cooperation led by the United Nations are strongly advisable.

5G Technology During the last couple of decades, the world community has noticed a continuous evolution of mobile network technologies. This paradigm shift has been started from the 2G GSM (Global System for Mobile) to the 3G UMTS (Universal Mobile Telecommunication System) to the 4G LTE-A (Long Term Evolution-Advanced) system to 5G (Mitra and Agrawal 2015). 5G refers to the 5th generation cellular network, piloted and rolled out by the telecom operators after 1G, 2G, 3G, and 4G networks. Hence, by definition, 5G refers to 5th generation mobile network technology that has been emerging since 1980 (Galal and O’Halloran 2020). With up to 100 times faster than 4G network, 5G is providing with such opportunities for business and people that have never been seen earlier.

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5G is not only to add extra G after 4G network but also more than that compared to 3G and 4G. 5G is a real game-changer since it is transforming the universe diversely as we know it (Ericsson 2021). The main features of 5G technology include, among others, the capability of higher data transfer; greater availability; massive network capacity; more reliability; dealing with more users, with ultra-low latency, etc. All these characteristics enable 5G technology to attain high performance, better efficiency, more user experiences, and eventually, connect more industries. Hence, with 5G technology, we can have a safer, smarter, and more sustainable future. Among others, 5G will enable $13.2 trillion in global sales activity by 2035 as shared in Fig. 1.1 by IHS Markit (IHS Markit 2019). Despite generating huge opportunities, 5G network entails massive security and privacy threats as well. With more than 7 trillion wireless devices for over 7 billion people that are likely to be connected, the mobile operators would have less control over 5G networks (Nokia 2021). Moreover, ensuring the protection of privacy and security among diverse actors by synchronising the mismatched policies may appear as another major issue in 5G networks. For ensuring better connectivity, 5G requires smaller antennas and base stations indoors and outdoors for the

Fig. 1.1 Projected global sales activity of 5G by 2035

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users. This inevitable installation of antennas will expose the precise location of its users. Besides, there are possibilities for attackers to track users’ activities by seizing subscribers’ IMSI of their gadgets (Nokia 2021). By nature, 5G operations are not restricted to any geophysical location as they generally use cloud-based data storage, located in different countries. There are differences in the level of protection for privacy because of variance in privacy measures and enforcement mechanisms in different countries. Eventually, ensuring privacy in the 5G network will presumably be challenged. To mitigate the above challenges and the like, there should have mutual cooperation, consensus, and agreement among all stakeholders in designing legal and policy measures. Technically, privacyby-design, obfuscation techniques, and Temporary Mobile Subscriber Identity (TMSI) might help reduce security and privacy threats in the sector (Nokia 2021).

Quantum Computing The phrase ‘quantum computing’ was coined by Paul Benioff in 1980 while introducing a quantum mechanical pattern of ‘Turing machine’. Physicist Paul Benioff felt the necessity of innovating quantum computing because the Turing machines were very slow, and required several steps to perform simple operations (Benioff 1980). Later, Richard Feynman and Yuri Manin shared the idea that a quantum computer had the potentials to simulate things at such a level that could not be feasibly done by a conventional computer (Feynman 1982; Manin 1980). Despite having huge experimental research, it is still to go too far to reach the optimum result in the field. Due to having enormous prospects of quantum computing technology, huge funds and investments are allotted from both the public and private sectors. The notion of ‘quantum computing’ still remains in an early phase of development. It is estimated that the quantum computer may emerge with a complete design in the next 5–15 years; some applications thereof may be practically possible within around a decade, although the timeframe may be shortened too (WEF 2020). Due to the lack of specific knowledge and information on quantum computation, most analysis

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become very speculative. Although goes through a nascent stage, it has already been demonstrated that quantum supremacy can do the impossible possible. For instance, Google AI Quantum team claimed in 2019 that through their 54-qubit Sycamore processor they could solve a calculation in 200 seconds that could require 10,000 years to solve by the most powerful supercomputer in the world (Arute et al. 2019). The basic building block of a classical computer is the bit that exists in either ‘0’ or ‘1’ of two different states, whereas a qubit (quantum bit) not only exits in both ‘0’ and ‘1’ of the classical states but also is in a coherent state (superposition) of both ‘0’ and ‘1’. In superposition things, for instance, an electron can exist concurrently in both ‘spin up’ and ‘spin down’ states until measured. If measured, then only it will decohere into a single one from those two states (Dunn 2008). The core concept of quantum computing lies in the fact that here information can be both manipulated and encoded based on qubits that existed in any physical system capable of generating quantum superpositions between two basic states (Aromí and Roubeau 2019). For example, in the case of using a register of 3 classical bits, a classical computer can present only anyone from 0 to 7 at a time, but in the case of a register of 3 qubits, a quantum computer can show every single register from 0 to 7 simultaneously. The quantum algorithms have the potentials to cause significant advances and transformational benefits in a wide spectrum of use cases in diverse industries (McKinsey 2020; Schatsky and Puliyakodil 2017; DeBenedictis 2018). The technology can be used to solve critical optimisation issues in financial services; improve AI capabilities; accelerate drug discovery; and could be used in some other areas, including aerospace; materials science, and molecular simulation. It can revolutionise the methods we encrypt information since quantum algorithms can break the encryption systems. Although the fullest extent of transformation has not yet been achieved in quantum computing, meanwhile, it has become explicit that the technology will introduce the breakthrough in value creation (WEF 2020). Thus, quantum computing technology could change the entire world. Hence, different nation States, and regions, including the USA, the EU, and China have invested billion dollars in advancing quantum research. Giant corporations, like Google, IBM, Microsoft, etc. are racing to assemble authentic quantum computers.

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Among others, a quantum computer may break the cryptographybased security system on which the entire digital economy relies (Chu 2016; Shor 1999). If that happens, the key security systems of the digital economy and infrastructures, such as encryption, digital signature, online verification, electronic voting, etc. will become useless. Cryptography refers to a technique of the protection of information and communications by the application of codes so that any information can be read and processed by those only for whom it was shared. Due to having inherent complexity in developing and managing quantum computing, expert hackers, or cybercriminals can easily design various cyberattacks on ordinary people, their digital assets, financial or other critical infrastructures, etc. Moreover, a few global giant corporations and some developed countries are only involved in research, innovation, or development projects encompassing quantum computing, which may, in turn, generate ‘quantum poverty’ for the wider community.

Regulating Cyberattacks: Law and Policy Perspectives Although organisations are currently aware of their vulnerability to possible cyber-attacks, most of them respond to the call when they are under attack (Pan and Yang 2018). Among others, the preventive measures are always effective compared to other cybersecurity measures. There cannot be any denial that the purposeful implementation of cybersecurity measures is essential to combat the dynamic vulnerabilities of cyber threats. An integrated action with the help of the experts’ groups from major disciplines, including engineering, politics, law, social science, economics, and technical studies might be fruitful in preventing diverse cyberattacks, although most of the studies have inherent shortcomings either to ensure cybersecurity or to integrate them with contemporary business models. Thomas Aquinas in his ‘magnum opus Summa Theologica’ mentioned, ‘law is an ordinance of reason for the common good, made by those who have care of the community’ (Aquinas 1981). Nevertheless, such a definition cannot properly connect with the international law on

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cyberspace, whereas the lack of international laws on cyberspace usually creates a legal vacuum and complications in the sector. In such an atmosphere, it is essential to harmonise the national and international laws on cybersecurity for securing the expected level of governance in cyberspace (Karim et al. 2019). Participants from national, regional, and international entities may play crucial roles in dispersing the realm against all odds. All academic discussions point out the inadequacy of international cybersecurity laws due to the complex questions over jurisdiction on the internet, and the absence of a global legal system encompassing cyberspace (Finnemore and Hollis 2020), whereas State actors sometimes promote the notion of digital sovereignty to regain control over information, communication, data, and the Internet infrastructure (Tikk and Kerttunen 2020). Consequently, any future international regulation on cybersecurity will be more challenging to implement. Hence, the crucial question is—whether international law applies to national cybersecurity issues? There is no much more simple answer to the question. There is nothing new about the concept of international law in cyberspace. For the application of international law, we need clarification for at least three things, such as multilateralism, cyber libertarianism, and statism (Jamart 2017). Multilateralism refers to cyberspace management that calls for a cyberspace governing body by or on behalf of the United Nations (UN). Cyber libertarianism is such a view, which nurses that all nations should have the ability to define their national laws, rules, and policies relating to cybersecurity. In addition, cyberspace should be free from all sorts of tyranny and repressive rules, and there should always be ways of ensuring personal liberty and other human rights, even on the Internet (Freedman 2016). The notion of statism refers to the idea that national and international laws must govern cyberspace based on real-life statists. They also believe that national laws must form strict connotations based on their cybersecurity threats (Franklin 2013). Territorial jurisdiction is another prime concern of international law relating to cyberspace. Hence, several treaties have emphasised that the views of the worldwide policymakers are much more important to overcome this challenge (Lastra and Allen 2020). Although inherently,

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cyberspace enables anonymity for its users, at the same time, the information of an individual is also vulnerable too. This makes it appear as a two-edged sword. Hence, every State has its own strategy to govern cyberspace and its allied challenges. This leads to the legal development of arbitration on cyber behaviour. However, arbitration relating to cybercrime is also complicated because of the intricacy of the actors and some other inherent problems (Walters 2016). To date, there is no widely agreed-upon legal standard in Internet law due to the diversity of its participants (Delerue 2020). Hence, cybercrimes are generally arbitrated in national courts rather than international courts, although international arbitration for cyberspace is also possible. For instance, the Permanent Court of Arbitration (PCA), located at the Hague, Netherlands, has jurisdiction over outer space, energy, and environmental disputes. The same can happen in adjudicating crimes relating to cyberspace as well. Despite the lack of binding dispute settlement mechanisms in international law, one cannot deny the reality of the State-centric focus on cybercrime and the lack of a binding conflict settlement mechanism for cyberspace. In recent years, it is being widely discussed that rules of customary international law might be the foundation for international cyberspace law. Arguably, to be effective, that law must be implemented through national legal frameworks. Various organisations, such as the ITU, ICANN, and the Internet Governance Forum are working to prepare basic rules, principles, and operationalities for cybersecurity law (Liaropoulos 2017). Nevertheless, none of them has reached their ultimate goals (Liaropoulos 2017). A new movement of digital sovereignty is eroding the development of cybersecurity law in the international arena. International players need to rethink digital sovereignty before planning to adopt international cybersecurity law. Meanwhile, China and Russia have formed a cyber alliance on digital sovereignty to safeguard their national interests and establish extended surveillance over their own cyberspace (Zeng et al. 2017; Claessen 2020). Some other countries like Saudi Arabia and Egypt are also walking towards the same paradigm (Anguelov and Kaschel 2017). Block-wise cybersecurity governance projects may also hamper the internationalisation process of cybersecurity law.

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Although not always on an equal footing, there are developments of legal frameworks for cybersecurity in the national spheres. Presumably, the developed nations have well-established and well-known legal frameworks for cyberspace. For example, HIPAA 1996, Gramm-Leach-Billey Act 1998, and Homeland Security Act 2002 are three major regulations in the USA dealing with cybersecurity. Similarly, France has also established and set up legislative frameworks for cyberspace with the help of its national authorities. In 2006, the Federal Law on Personal Data (No. 152 FZ) has been enacted by the federal authorities in Russia. However, Russia and the USA have distinct views on cyberspace; Russia places security concerns ahead of personal rights and vice versa. Noteworthy that the regulatory regimes for the protection of privacy and personal data are much more satisfactory. This has been evidenced by the global wave of enacting data privacy legislation since the 1970s. To date, a total of 145 countries have already passed data privacy laws, while the rest of the others are attempting to amend their relevant laws (Greenleaf 2021). To protect the privacy and personal data of their residents, meanwhile, the EU has adopted the General Data Protection Regulation (GDPR) in May 2018 with immense global implications. The above literature makes it explicit that although there are certain legal developments at the national level, still, there remains a dearth of cybersecurity legislation at the international level. Nonetheless, there are two recent United Nations-led forums to work on the application of international law in cyberspace, namely, the Open-Ended Working Group (OEWG), and the Group of Governmental Experts on Advancing Responsible State Behaviour in Cyberspace in the Context of International Security (GGE). In March and May 2021, respectively, both these forums published their final reports. In terms of the application of internal law in cyberspace, the final reports of both forums generally rely on the consensus-based approach. However, due to differences in opinions among State Parties on several questions, particularly, the applicability of international law, international humanitarian law, rules of customary international law, sovereignty, non-intervention, due diligence, etc., there is not much more room for optimism. Some scholars, for instance, Dan Efrony (2021) opines that both the results of the above two UN forums and

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the lack of trust among the State Powers led to the conclusion that the efforts of these two forums are also not suitable to address the pressing cybersecurity challenges. There are some optimistic views regarding the application of international law in cyberspace as well. Acknowledging the complexity in searching for rules of international law and their application in cyberspace, the GGE attempted to establish the legal framework for responsible behaviour in cyberspace from 2019 to 2020. It achieved a significant consensus on several issues, such as sovereignty and due diligence. States have started acknowledging that the norm of international law applies in cyberspace. Much more non-binding norms have been developed, and are yet to be developed in future, and eventually, to appear as the binding norms of customary international law. Despite having disagreements on the question of the application of international law in cyberspace, consensus would always be the best way to mitigate the issues of lawlessness in cyberspace (Schmitt 2021).

Way Forward The digital contexts arising out of constant progress and use of digital devices, modern business models, and lifestyles have radically changed the ways we work across industries. Today we are virtually present everywhere, from farmhouses to space stations. In such an atmosphere, we are leaving our digital footprints everywhere through many of our online activities. This situation shows the ubiquity of online activities making cyberspace and allied issues practically ungovernable. Although there is no one-size-fits-all solution in regulating cyberspace, this chapter outlines three specific policy options, including (1) legal measures; (2) technical measures; and (3) international cooperation.

Legal Measures Among all strategies and policy measures, probably, legal measures provide the most effective tools for ensuring cybersecurity. In many countries, the developments of most substantive and procedural laws

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dated back to the nineteenth century. All these laws cannot take cognisance of issues that are mostly intangible, for example, the ‘computer data’. Hence, in the era of information, such laws can be regarded as backdated and useless. The context requires that there shall have specialised legislation for the prevention and control of cybercrimes. The said law must equip with rules for the standards of the behaviour or conduct in the use of the computers, Internet, and relevant digital devices; the activities of the public, private and other entities; the evidence, criminal procedure, and criminal justice systems in the cyberspace, and the regulations essential for reducing the harms towards individuals, institutions, or critical infrastructure (UNODC 2013). In short, an adequate cybercrime law should have substantive, procedural, and preventive characteristics. The provisions of the traditional criminal code can rarely apply against activities committed via the Internet. Thus, it is essential to conduct a thorough analysis of the provisions of the existing criminal laws to identify the possible gaps. To fill that gaps, well-designed cyberlaw incorporates provisions to criminalise certain activities, including child pornography, computer fraud, copyright violations, data breach, illegal access, etc. (Gercke 2012). Due to the borderless characteristic of cyberspace, cybercriminals can commit any crime outside the national geographic boundary. In that case, investigating cybercrimes may have to be committed beyond the border that is not available in ordinary procedural laws. Hence, there should have provisions for the extraterritorial application of the cyber law so that criminals outside the country may also be brought within the domain of the national legal frameworks. Islam and Karim (2020) shared, extraterritorial applications of laws have become widespread nowadays, especially in the case of cybersecurity, data privacy, or ICT laws. For example, section 9 (1) of the Computer Crimes Act, 1997 of Malaysia; section 1302 (2) (ii) (II) and (iii) of the Children’s Online Privacy Protection Act (COPPA) of the USA; section 11 of the Computer Misuse and Cybersecurity Act, 2017 of Singapore; section 1.5A and 5B of the Australian Privacy Act, 1988; article 75 of Protection of Personal Information, 2017 of Japan, and article 51 (2) of the Personal Information Protection Act, 2012 of Taiwan

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incorporate provisions for the extraterritorial application of the national laws (Islam and Karim 2020). There should have preventive aspects of cyber law in regulating cyberspace and associated risks factors. Accordingly, preventive cyberlaw aims to prevent cybercrime, or mitigate the damage resulting from the commission of a cybercrime (UNODC 2013). In short, cyberlaw should incorporate essential provisions for identifying, investigating, and prosecuting cybercrimes. The other laws, for example, the telecommunication laws, should also incorporate provisions to prevent wiretapping and ensure data protection. It is worth noting that most data privacy laws of the world, such as the EU GDPR, 2018; OECD Revised Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, 2013; Updated APEC Privacy Framework, 2015; Data Protection Act 2018 (DPA 2018) of the UK; California Consumer Privacy Act 2018 (CCPA) of the USA; Privacy Act 1988 (Privacy Act) of Australia, etc. have provisions for the protection of privacy and personal data. In a traditional legal suit, it is essential to produce legal documents (often printed) before the court, but in case of cybercrime, there may have digital footprints of the criminals in cyberspace that need to be taken into cognisance as evidence. Hence, while preparing a cyber law, the policymakers must consider the admissibility of digital evidence as a crucial weapon in the battle against cybercrimes (Gercke 2012).

Technical Measures Technical measures may play also a very crucial role in combatting crimes. Through building capacity and making aware of the relevant stakeholders about the use of safe cyberspace; diverse cybercrimes; cybercriminals; technical preventive measures, etc., a significant portion of cybercrimes can be reduced. In the words of the Federal Bureau of Investigation (FBI) (2021), ‘taking the right security measures and being alert and aware when connected are key ways to prevent cyber intrusions and online crimes’ (FBI 2021).

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While there is cybersecurity in question, there remain four major stakeholders, such as the government entities, businesses, private individuals, and cybercriminals. In a broader context, there are two rival groups among these stakeholders; the first group consists of government entities, businesses, private individuals, and their counterparts are the cybercriminals. Cybercriminals refer to individuals who commit some sorts of illegal activities, including hacking, identity theft, malware attack, scams online fraud, etc. using the computer, other digital devices, or the Internet as either the tools or the targets (Sammons and Cross 2017). The issues encompassing cybercrime often appear too big and widespread to handle by the politicians and police. The former usually do not have the required technical competency to enact effective legislation, and the latter lack adequate training, time, and manpower (Shinder and Cross 2008). There is no one-size-fits-all solution, rather each of the stakeholders entails self-centric protections. To prevent, or reduce cybercrimes, the government should have specific strategic plans, including, but not limited to—(1) building infrastructures for cybersecurity operations; (2) encouraging cybersecurity culture; (3) establishing governance based on cooperation and trust; (4) increasing safety, security, and protection for national core infrastructures; (5) promoting international cooperation for cybersecurity, and (6) strengthening cyberattack response capacities (NCS, Korea 2009). To protect the digital assets, websites, and the interests of customers from various cyberattacks, businesses should take some specific measures concerning the—(a) risk management systems; (b) architecture and configuration management; (c) asset management systems; (d) data security; (e) engagement and training programs; (f ) identity and access management; (g) incident management; (h) logging and monitoring systems; (i) supply chain security management, and (j) vulnerability management (NCSC, UK 2021). To ensure personal safety and security, private individuals shall have to be alert and aware of some common cybercrimes and risk factors, including, but not limited to—(i) Business e-mail compromise (BEC), the most harmful financial cybercrimes; (ii) identity theft; (iii) ransomware, malware or malicious software that is designed to prevent a person from accessing to his own computer files, networks, or systems,

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but return to the owner on paying a ransom; (iv) phishing and spoofing that aim to trick a person to provide the sensitive personal data to the scammers, and (v) online predators, who usually pose threat to the young people (FBI 2021).

International Cooperation Due to the international character of the Internet, its services, service holders and the attackers in cyberspace, cybercrimes have also an international dimension. The mobility of cybercriminals, their borderless location, and the threats themselves also require the necessity for international collaboration among the wider global community. Further, due to divergences in domestic legislation and deficiencies in instruments, international cooperation is regarded as one of the most effective tools for cybercrime prevention. Nations that are willing to cooperate with other nations in conducting investigations for transnational crime feel necessary to utilise the instruments of international cooperation as well (Sussmann 1998). Due to having massive implications, the ITU Global Cybersecurity Agenda (GCA) 2007; UN General Assembly Resolutions 55/06; 56/121; 57/239 and 58/199; article 18 (1)–(2) of the United Nations Convention against Transnational Organized Crime (UNTOC); and articles 23–25 of the Convention on Cybercrime 2001 (Treaty No. 185 of the Council of Europe) emphasise on international cooperation mechanism in combating cybercrime. Likewise, the Economic and Social Council of the UN adopted a resolution on international cooperation in 2004 to prevent, investigate, prosecute, and ensure punishments for fraud, and stop the criminal abuse and fabrication of identity and the like (ECOSOC Resolution 2004). In 2007, the ECOSOC adopted another resolution that contains, inter alia, the provisions of international cooperation for the prevention, investigation, trial, and punishment for certain crimes, such as economic fraud, or identity-related offences (ECOSOC Resolution 2007).

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Conclusion Entrepreneurship, technology, and cybersecurity appear as a single concept in the understanding of integrated business models. The prime objectives of all businesses are—to achieve perpetual existence; make continuous progress; become profitable and leader in the relevant domain, etc. Being the leader of each business, an entrepreneur has to take the right decision in every sphere of business operations. In the ubiquitous computing era, almost all entrepreneurs increasingly depend on emerging technologies for the successful operations of businesses. In such contexts, this chapter aimed to analyse the following—technology adaptation in businesses; prospects of technology adaptation in businesses; problems of technology adaptation; entrepreneurs’ roles to technology selection, and the way forward. To put it simply, the main theme of this chapter is to analyse the impacts of technology in businesses, business models, and entrepreneurship. Consequently, cybersecurity has become one of the central phenomena of the discussion of this chapter. Cybersecurity appears as one of the buzzwords in today’s interconnected world. None and nothing can be said as safe, including business. Due to constant progress in information and communication technologies, especially, artificial intelligence, Internet of Things (IoT), robotics, 5G technology, and quantum computing, and businesses’ increasing reliance thereof, there remain massive cybersecurity challenges across industries. This situation calls for holistic solutions to diverse cybersecurity challenges. Despite the divergence in underlying policies, rules, or sprits, there has been a global trend of adopting cybercrime prevention legislation. Specific focus has been given to adopting cybercrime legislation and electronic evidence since the 1990s. As of June 2021, a total of 92% of Member States of the UN had either completed reforms in the field or worked on reforms. Noteworthy progress has happened regarding the provisions of the substantive criminal law, as 124 (64%) out of 193 of the UN Member States have undertaken reforms in the sector (CoE 2021). Moreover, as of June 2021, a total of 40% of Member States of the UN have been either Parties, Signatories, or had been invited to accede to the Convention on Cybercrime (CoE 2001). Beyond membership,

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82% of the UN Member States have adopted the Budapest Cybercrime Convention either as a guideline or as a source of inspiration to design the national cybercrime prevention framework (CoE 2021). Besides, two UN groups, such as GGE and OEWG are in operations in preventing global cyberattacks and cybercrimes. Nevertheless, cybercrimes situations are increasingly growing over the years. Against these backdrops, this chapter outlines three specific measures, such as legal measures; technical measures; and international cooperation. It is expected that the suggestions provided with this chapter may help in reducing cybercrimes at a significant level. It is also to be acknowledged that there is no one hundred per cent guarantee of success of any of the stated tools. Hence, an ethical consideration may also play a significant role in keeping cybercriminals within legal bounds. Ethics is such a branch of knowledge, standard, or touchstone that determines what is right or what is wrong. Ethical values can guide human beings to choose or pick the right decision in a given situation. Like the offline world, all stakeholders have an ethical role to play in each of their actions, particularly, in the questions of confidentiality, copyright, fraud and misuse, liability, piracy, privacy, responsible decision-making, sabotage, and trade secrets (NIATEC 2019). Arguably, to prevent cybercrime, ethical consideration seems to be a very weak tool due to the lack of a universal ethical standard and lack of enforcement mechanism.

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2 Exploring the Sectoral Patterns of Possible Applications of AR as an Important Ingredient of New Business Models: The Bangladesh Perspective Uzzal Ali Pk and Md. Rakibul Hafiz Khan Rakib

Introduction The ever-changing business world is becoming more technology oriented day by day. Every field of business is widely dependent on the technology in reshaping the business structure, promoting the brand and ensuring the customer satisfaction as well. Innovative marketers always seek a perfect blend of technology and customer experience which will guarantee more brand loyalty. Previous research also confirmed that, technology can be a good means to boost user engagement (tom Dieck et al. 2018) which will ultimately lead consumers to make added transactions per purchase, generate more repeated purchases, turn out to U. A. Pk Dinajpur Government City College, Dinajpur, Bangladesh Md. R. H. K. Rakib (B) Department of Marketing, Begum Rokeya University, Rangpur, Bangladesh e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_2

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be more fanatical for the brand (Rosetta Consulting 2014), build up long-term affiliations with the brand (Venkatesan 2017) and help firms attaining sustainable competitive advantage (Kumar and Pansari 2016). Among many of its kind, Augmented Reality (AR) is now considered as one of the leading and emerging technology to be used in the field of business and marketing around the globe. Competitive marketers are now using the AR as a tool to ensure expected level of brand experience, engaging consumers with the interactive advertising, and let the consumers to feel the products and services in a more realistic way (Scholz and Smith 2016). In addition, AR has the benefit of reaching a huge number of audience, and compatible to be run on the frequently used devices like existing smartphones or tablets (Hariharan et al. 2020). Technologically advanced economies are ahead of getting benefitted from the application of AR technology, whereas the rest of the economies are on the way. AR is the technology of creating the real-like set with the help of computer aided imagery components (3D) by imposing it to the real world in an interactive way (Azuma 1997; Milgram et al. 1995; Schmalstieg and Hollerer 2016). Objects and spaces are augmented here in the real world by using contemporary digital devices like smartphones, tablets and computers with the artificial and virtual components. The idea of AR is completely different from the Virtual Reality (VR) as the AR is not the substitute to the real world. In the process of AR, the real world is enhanced with the innovative and crafty use of computer aided digital components (Altinpulluk 2017). It is an interface between the human and computer which enriches the perceptions about the actual world by superimposing the creative digital context vivaciously (Ling 2017). Many business sectors are directly getting advanced by the successful implementation of AR technology including education, marketing, manufacturing, health service, tourism, social media, defense, aerospace, robotics, transportation, games, etc. (Mahmood et al. 2018). But the utmost utilization of AR technology is yet to come. The use of AR in the field of business in Bangladesh is still untapped but having immense potentials (Riffat et al. 2020). Business entities as well as consumers in Bangladesh are now well oriented with the use of modern technologies. Competition among the brands is too much high

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and the competing brands conscientiously search for newer strategy and technology to stay in advance of others. In this perspective, the AR is probably a phenomenal tool to be introduced in various business sectors of Bangladesh. As the application of AR technology is still seen absent in the field of business in Bangladesh, so competing brands can take it as an exclusive opportunity to introduce the AR technology in order to achieve competitive advantages over others. Bangladesh has a huge customer base having a total population of over 164 million (World Bank 2020). The number of tech-savvy customer is gradually increasing here. The total number of subscribers to internet in Bangladesh is now 120.95 million (BTRC 2021) and the number is still increasing. Around 41% of the total mobile phone users use smartphone (GSMA 2021) which will create a ground for introducing the Mobile AR (MAR) for commercial use in Bangladesh. This study will provide an overview of AR practices in the business sectors all over the world. Furthermore, it will focus on the probable business sectors for applying AR technology in Bangladesh based on the global AR practices. The authors believe that this study will assist entrepreneurs—especially the technopreneurs, marketers and relevant researchers while they are working on the AR technology in Bangladesh perspective. The chapter is structured as follows. The next section briefly discusses the concept of AR. Business applications of AR in different sectors across the globe are discussed in the following section. After that, a detail about the possible business applications of AR in Bangladeshis presented. In the next section, managerial and policy implications are offered. The final section provides concluding remarks and suggestions for future research.

Concept of AR AR is that magical technology which enables someone to be present, and act really and virtually at the same time. It is the blend of real and virtual world which improves the real world with virtual objects (Milgram and Kishino 1994; Pramanik et al. 2021). AR is the online superimposition of data and information on users’ instantaneous environment by using mobile phones, head-mounted displays, smart glasses and other

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digital devices (Georgiou and Kyza 2017; Guttentag 2010; Jung et al. 2018; Rauschnabel et al. 2015). AR is the process where digital elements like images, videos or graphics are craftily imposed on the real world, so that the real world is enhanced or augmented, which the audiences or consumers perceive as almost real with the help of digital screens (Pramanik et al. 2021; Scholz and Smith 2016). Like many other scientific invention, AR was also first fictionalized by science fiction (Farshid et al. 2018). It is believed that the AR was initially imagined by L. Frank Baum in his well-known book ‘The Master Key’ published back in 1901 (Altinpulluk 2017) and the term ‘AR’ was first coined by Tom Caudell in 1990 (Lee 2012) although the scientific journey of the idea of AR was first launched back in 1960s (Johnson et al. 2010). AR, VR and Mixed Reality (MR) sometimes sound similar. Experts often perceive AR as one kind of MR (Roxo and Brito 2018). Although these issues are closely identical; but some differences are still there. However, Farshid et al. (2018) specified a clear borderline among these issues. In their views, the AR is ‘Information and data overlaid on top of the actual world’, VR is ‘A complete digital representation of the actual world’ and the MR is ‘The introduction of possible elements into an actual world’. Hence, the idea of AR can be better understood by Milgram and Kishino’s Reality-Virtuality Continuum presented in Fig. 2.1. The Fig. 2.1 illustrates how different forms of reality exist with the blend of real and virtual environment. The left marginal part of the scale indicates the real environment whereas the right marginal part indicates the virtual environment. In between the real and virtual environment, augmentation is done with the presence of reality or virtuality. The closer the real environment, the AR is created and the closer the virtual environment, the Augmented Virtuality (AV) is created. And these two Mixed Reality (MR) Real Environment

Augmented Reality (AR)

Augmented Virtuality (AV)

Virtual Environment

Fig. 2.1 Reality-Virtuality Continuum (Milgram and Kishino 1994)

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augmented environments through their interactive overlapping construct the MR. The implementation of AR is widely seen in many business sectors like tourism, advertising, manufacturing, education, transportation, construction, space research, military, architecture, engineering, urban planning, health, online retailing, entertainment, theatre, journalism, games, etc. (Jung et al. 2020; Mekni and Lemieux 2014). Global business giants like McDonald’s have already started utilizing the idea of AR in their marketing strategies (Scholz and Smith 2016). The future of business and marketing will be more technology oriented where the AR will surely find a boost.

AR in Business The AR has already been materialized in the various field of business in technologically advanced countries. But still it’s considered as a nearfuture technology, though the non-commercial use of AR is widely practiced right now (Kleef et al. 2010). AR acts as a strong tool to ensure consumer engagement (tom Dieck et al. 2018). Consumer engagement is a process by which companies engage its consumers with the brand by initiating different activities to strengthen the consumer–brand relationship (Brodie et al. 2013) in order to secure repeat purchase, brand loyalty and competitive advantage (Kumar and Pansari 2016). Leading global brands like McDonald’s, Coca-Cola and General Electric have already started the application of AR in their competitive business strategies (Scholz and Smith 2016). Marketing, especially the advertising has a tremendous opportunity to utilize the AR technology in reaching its target audience perfectly. AR advertising is getting popular day by day (Meydano˘glu et al. 2020). In the AR advertisement, customers are enabled to interact lively with the AR components regarding a specific product or service. AR advertising is immersive and interactive in nature (Singh and Pandey 2014). Sometimes mobile apps are used to visualize the AR advertisement. For instance, IKEA launched an AR advertising campaign titled ‘Place a Tree on It’ (YouTube 2017) with the help of mobile app namely ‘IKEA Place’

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which enables its customers to visualize how the IKEA’s Christmas tree looks like with other furniture at their home. In this way, customers can visualize the scenery of their home before and after installing the tree. Tourism is another sector of business which utilizes the idea of AR in presenting a place, archaeological items, cultural heritage or even a whole museum mainly in the developed countries since couple of years (Dadwal and Hassan 2015; Han et al. 2014; Siang et al. 2019). Application of AR can make tourists’ experience more enjoying, learning, interactive and immersive (Pramanik et al. 2021; tom Dieck et al. 2018). AR technology is used in different stages of tourism like pre-booking, information search and on-site visiting (Cranmer et al. 2020). ExCORA, a pervasive AR game developed for the tourists to provide an enhanced idea about the Urgull Mountain of Spain (Linaza et al. 2014) is an instance of AR application in tourism context. AR backed education is gradually getting attention from scholars and educational researchers (Wu et al. 2013), academicians, students and education entrepreneurs. The coexistence of actual environments and virtual stuffs permits learners to predict multifaceted spatial associations and abstract impressions more conveniently (Chang et al. 2013). The application of AR technology makes the process of learning more enjoyable, easy and effective (Pranoto and Panggabean 2019). The use of smartphone-based AR apps has made the process of AR education more easy and accessible. Not only education but also different skills development training has been enhanced by the use of AR technology (Lee 2012). Google Expeditions now merged with Google Arts and Culture is an example of AR education in which different places and reading materials like DNA structure are presented in a three-dimensional way. In the Healthcare sector, AR technology has been emerged as a blessing. It offers very easy solutions to some complex problems. AR is now used as surgery assistant, doctor–patient communicator, rehabilitation provider and even it is used in phobia treatment also (Ferrari et al. 2019). Another tremendous contribution of AR in the healthcare sector is the healthcare education. It visualizes the anatomical parts of body so lively (Zhu et al. 2014). AccuVein (see www.accuvein.com) is an ARbased handheld scanner which can detect a vein very easily for inserting

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an IV (Intravenous) injection (Khor et al. 2016). Ultrasound imaging, Xray vision, cooperative surgery, etc., are also blessed by AR (Bajura et al. 1992; Blum et al. 2012; Sielhorst et al. 2008; Wen et al. 2014). In case of Online Retailing, the application of AR is seen growing rapidly (Javornik 2016). In this case, AR is mainly used for providing a better buying experience to the consumers. Consumers can have a comparative visual idea before buying the product. New York City-based global fashion brand Rebecca Minkoff ’s AR mirror, for instance, can help consumers get an idea about the clothes according to their body shape before making a buying decision (McCormick et al. 2014). AR can also be used to ensure intelligent transportation system through incorporating AR-based smart navigation system. It can take the driving experience to even more realistic level by developing ARbased road mapping and design for learners and drivers (Moussa et al. 2012). The traffic system can be more systematic and organized by the application of AR technology which can lower down the frequency of road accidents. AR is gradually opening up novel avenues for entertainment industry. Especially the gaming sector has got a serious boost by applying AR technology. Pokémon GO, an AR-based mobile game, first of its kind (Rauschnabel et al. 2017), has got tremendous response from the consumers when it was initially revealed back in 2016. Swimming pools, battle fields, football fields, cricket pitch, race tracks and other sports environments can be easily demonstrated through the use of AR technology (Van Krevelen and Poelman 2010). The AR technology has made Computer Aided Design (CAD) an obsolete one by replacing it with the AR 3D design implemented in product design (¸Sahin and Abdullah 2016) and manufacturing. Before going to final production, the product, any of its kind, is designed with the crafty use of AR technology. The manufacturing process is also enhanced and operated by the AR technology more efficiently.

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AR Business Model Different scholars and business practitioners have developed different business models to measure and visualize the implications and effectiveness of businesses. Osterwalder (2010) developed a business model based on four basic pillars namely Infrastructure Management, Product, Customer Interface and Financial Aspects comprising of nine diverse elements. Kleef et al. (2010) on the other hand explained the Osterwalder’s Business Model in the context of AR (Fig. 2.2). Infrastructure Management comprises of three elements that represents capturing and creating values with the help of available resources and partners’ activities. In the case of AR, the Strategic Partners may be technology and infrastructure providers, Key Activities may include developing AR software and Key Resources may refer the device (e.g. Smart Phone) to experience the AR environment. The Product represents what kind of value is actually created. Value Proposition can be happened in the context of AR through portraying the digital contents in the real world. Customer Interface shows the distribution channels through which the value is delivered to the ultimate users. In the case of AR, Customer Relationship is mainly maintained through online as the Infrastructure Management

Strategic Partners

Key Activities

Product

Value Proposition

Key Resources

Customer Interface Customer Relationship

Channels

Cost

Revenue Financial Aspects

Fig. 2.2 The Osterwalder’s Business Model Canvas

Customer Segments

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distribution Channel means mostly the internet and Customer Segments also represent the users having the ability to use the internet and sophisticated smartphone. Financial Aspects are simply the Cost and Revenue associated with the AR business. Integrating all these nine elements under the four pillars will decide the success of the AR business.

Possible Business Applications of AR in Bangladesh Bangladesh is a country having a huge customer base and the customers are gradually inclining to technology here. The use of Information & Communication Technology (ICT) in the various business fields has been tremendously increased, especially during the Covid-19 Pandemic in Bangladesh (Showrav et al. 2021). Bangladesh is projected to be the 24th largest economy by 2030 as ICT industry is booming and leading from the front here (World Economic Forum 2019). So this is the perfect time to think about the business applications of AR in Bangladesh. It will strengthen the process of achieving the Government’s various economic targets where the business entities will find the scope to flourish with their competitive market offerings and strategies as well. Aligning with the global practices and considering Bangladesh perspective, the AR can be utilized in the following business sectors a. AR Advertising: One of the core principles of advertising is to create a clear position of the product and service in the consumers’ mind. Engaging consumers in the advertising campaign can make it possible. In traditional advertising, there is a little chance to engage consumers in the process. AR Advertising has come up with an enormous opportunity to engage consumers in the promotional campaign by providing them a floor to interact directly (Singh and Pandey 2014). Currently there are so many global and local brands doing business in Bangladesh which can take the opportunity to ensure better consumer engagement by applying AR Advertising as a comprehensive promotional tool. They can use an interactive Magic

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Mirror, a TV screen disguised as mirror in front of which an audience, generally a consumer can see himself on the screen with an illustrated digital environment representing a product or service and interact lively. Mega shopping malls in Bangladesh like Basundhara City Shopping Complex and Jamuna Future Park are the suitable place for installing such Magic Mirror. Mobile applications can also be used to implement the AR Advertising in Bangladesh. It will require AR supported smartphone by which consumers can download the specific AR application from the brand’s website. By installing it on the smartphone, consumers will be able to see and interact with the AR objects, may be the product, surroundings or consumer himself, with the smartphone’s camera and screen. Wearable technologies like the smart glasses (e.g., Google Glass), smart lenses (e.g., Microsoft Hololense) and Head-mounted Displays (HMDs) can be used in the process of disseminating the AR Advertising to the target audience (Rauschnabel et al. 2015). b. AR Tourism: Technological advancement has been perceived as a blessing for the tourism industry (Sigala 2018) for the last several decades. Bangladesh is a country having a huge number of tourist destinations including natural beauties, archaeological sites, monuments, museums and world heritage sites which can earnestly attract tourists a lot (Pramanik and Rakib 2020; Rakib and Hassan 2020). There is an immense possibility to trigger the performance of tourism industry in Bangladesh by tactfully applying the emerging AR technology (Pramanik et al. 2021). The AR technology can enhance the total tourism experience by working in the following areas of tourism in Bangladesh. i. Augmenting the Archaeological Sites: Bangladesh is blessed with its many archaeological and historical sites including Somapura Mahavihara, Mahasthangarh, Sixty Dome Mosque, Shalban Vihara, Lalbagh Fort, Jagaddala Mahavihara, Mughal Tahkhana and a lot of Zamindar Bari. These archaeological sites can be better represented with the help of AR technology. An AR mobile application can be developed which will show the 3D structure including the history and all other relevant information of the

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site when a tourist will open his mobile camera in front of the site. Moreover, the tourists could be provided with the chance of experiencing himself through a virtual tour into a specific archaeological site, enhanced and transformed in its primeval look with all the furniture and even staffs developed by AR technology. The tourist can see and experience himself as may be a Mughal Emperor in an ancient Mughal archaeological site. This could be possible through the AR technology as it is the blend of the real and virtual world (Azuma 1997). ii. Augmenting the Natural Beauties: Bangladesh is famous for its natural attractions. But these natural beauties are yet to be experienced largely by the foreign tourists (Rakib et al. 2020). It could be possible through the intelligent use of AR technology. An AR mobile application can be developed based on a specific tourist place like the Cox’s Bazar which will provide the tourists with an enhanced digital environment where the tourists will experience even the roaring Cox’s Bazar with the wave. Being appealed, the tourists will be interested to give a real trip to Cox’s Bazar possibly. Moreover, some AR games based on the real natural beauty like the Chittagong Hill Tracts (CHT) can be developed where the gamer will find himself in the adventurous hilly area of Chittagong, Bangladesh. iii. Augmenting the Museums: There are many museums in Bangladesh where the AR technology can be utilized suitably to attract more visitors to its premises. It can enhance the visitors experience by making things in the museum more enjoyable. A lot of Buddhist and Hindu Idols/Sculptures are there in different museums in Bangladesh. What happens if these sculptures will tell their own tales? This is possible through the AR technology. An AR mobile application can be developed by which visitors will get to know the religious story by simply focusing the camera on the sculptures. In some museums, skeleton of different animals is preserved. With the help of AR technology, the 3D model of the animals can be visualized with all the relevant information by the visitors. Broken artifacts can be restored digitally in the same way so that the visitors can get the full picture of the broken artifacts

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or any other valuable broken items. Through all these initiatives, visitors can be provided with a completely novel experience by the crafty implementation of AR technology in the museums of Bangladesh. iv. Augmenting the Accommodation Facilities: Accommodation, simply stated, the hotels and resorts are considered as a leading revenue generating variable for the tourism industry. Bangladesh’s hotels and resorts are trying to uplift the services parallel to the global standard (Rakib and Hassan 2020). It can be possible to provide an enriched accommodation experience with the help of AR technology. Tourists will be enabled to take a virtual trip to the hotel rooms before making the booking decision through the AR mobile application. The local attractive tourist destinations can be displayed on the hotel’s wall with an AR map which will provide all the necessary information regarding a specific place. More personalized and interactive accommodation experience can be provided through the AR technology. The hotel’s mobile AR application can arrange even the transportation (on call), local guide and other recreation facilities for the tourists. c. AR Education and Training: AR technology can make the learning experience more effective and enjoyable. Enormous possibilities are there to apply the AR technology in the field of education to ensure quality learning for the students in Bangladesh. Many subject matters in the Physics, Chemistry or Biology can be better demonstrated in the 3D format by applying the AR technology which will make the learning easier, interactive and learnable for the students. Many Astronomical, Geographical and Historical subject matters like solar system, maps and any historical happening can be transformed into a digitally visualized format so that the learning experience will be more effective. The Covid-19 pandemic has proved that the education sector of Bangladesh requires more digitalization so that the learning can be continued even while the class room is closed. The application of AR technology can make the digitalization process of education sector in Bangladesh more strengthened. AR can also

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enrich the corporate, military and technical training in Bangladesh by providing more visualized contents in the aforesaid ways. d. AR Online Retailing: Online retailing or e-Commerce is gradually increasing in Bangladesh and it has got a serious momentum during the Covid-19 pandemic (Showrav et al. 2021). This momentum can be more strengthened by using AR in online retailing. Online retailers in Bangladesh can provide a different shopping experience to its consumers through the AR technology. Virtual trial room can be offered to compare the clothing items by developing an AR mobile application where consumers will be enabled to make their purchase decisions after taking a virtual trial. More personalized shopping experience can be provided through the AR technology supported by the Artificial Intelligence (AI). Product catalogs can be displayed with additional product information in an interactive and 3D preview enabled way, so that the consumers can evaluate the products more realistically and make the purchase decision comfortably. More interactive and profitable customer relationship can be established through the use of AR technology, where more brand recognition, brand association and consumer engagement will be achieved. e. AR Gaming: Freelancing has been emerged as one of the fastest growing sectors in Bangladesh. Back in 2017, the Oxford Internet Institute (OII) ranked Bangladesh as 2nd most favorite country in supplying online laborers (The Daily Prothom Alo 2017). The leading software companies working in the gaming in Bangladesh are Riseup Labs (https://riseuplabs.com), Azmi Studio (https://azm istudio.com), Dreamerz Lab (https://dreamerzlab.com), etc. The gaming industry of Bangladesh can utilize the AR technology to boost up its position as an emerging freelancing country. The AR games provide more exciting environment blending the real and virtual world to the gamers than the VR games. The demand for AR game is rising day by day and Bangladesh can take the opportunity to be a competent developer in the global gaming market. Bangladeshi culture, history, heritage and natural beauties can be synchronized while developing an AR game for the global market. It might be perceived as a unique feature.

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Managerial and Policy Implications The notion of ‘Digital Bangladesh’ will be more comprehensive by the successful adoption of AR technology in the policy implementation areas. Government can come up with the required logistic and financial motivations along with the necessary policy support. Extensive research initiatives can be taken in the academy level backed and directed by the government in order to explore the potentials of AR in Bangladesh perspective. The freelancing prospects can further be exploited by effectually working with AR technology assisted by the necessary government supports. Moreover, government has a significant role in implementing AR Education in Bangladesh to uplift the standard of education to the next level. Government should invest more in ensuring the technology backed education in this circumstance besides the widely used neonormal online education platforms like Google classroom, Google meet and zoom through making and customizing these platforms with more lively AR contents. Business entities have also their specific roles to play in initiating and implementing the AR based business practices to shape a sophisticated corporate structure in Bangladesh. In this regard, the implementation procedure suggested throughout the sectors identified in this chapter can be used as a general guide for initiation of AR applications in Bangladesh.

Conclusion and Suggestions for Future Research AR in coming future will become impossible to differentiate from our everyday life and reality. Therefore, technology and applications related to AR are expected here to reside. Bangladesh is not separate from this predisposition and the use of AR is expected to find a clear shot. As the application of AR in Bangladesh is still in its infancy, this chapter only shades lights on the sectoral patterns of possible business applications of AR. Hence, this chapter fulfills its aims through finding suitable sectors for business application of AR and discusses the theoretical ways in which

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the AR can be implemented in the aforementioned business sectors. But this chapter has a limitation as it is unable to offer a scientific roadmap for AR application in Bangladesh. Besides, the application of AR is supposed to generate long-term relationship, loyalty and profitability. But, how much economic value creation is possible through business application of AR in Bangladesh is still unknown. Thus, future research is suggested to find out whether AR provides economic or financial value in different business sectors and the extent of such value creation. Furthermore, research is recommended to focus on the probable applications of AR in areas including military operations, aerospace research, manufacturing, urban planning, etc., through scientific proposition of AR application blueprint.

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3 Integrating Gig Economy and Social Media Platforms as a Business Strategy in the Era of Digitalization Manpreet Arora and Roshan Lal Sharma

Introduction The whole world, as we all know, has been grappling hard with problems caused by COVID-19 pandemic for quite some time now. In economic and business arenas in particular, the supply chain activity has suffered a severe jolt. In fact, this is so as most of the countries were impelled to shut down international travel activity without allowing any business or trade activities to take place. As a consequence, businesses have suffered, M. Arora (B) HPKVBS, School of Commerce and Management Studies, Central University of Himachal Pradesh, Dharamshala, India e-mail: [email protected] R. L. Sharma Department of English, Central University of Himachal Pradesh, Dharamshala, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_3

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industrial growth has plummeted and supply chain life lines have been rendered lifeless. Moreover, the workforce across the world has shrunk to a negligible size due to large-scale laying off of the workforce. Since, the employers were forced to sit back home, the entrepreneurial curve of their thinking had yet to be activated so that they could start opting for short-term contracts, jobs, employment opportunities and other assignments that would fetch them at least some minimally sustainable amount of money. The thrust has thus been on somehow becoming part of the ‘gig economy’ which due to COVID-19 has become a new norm in the US and other countries in Europe. The same practice has gradually been gaining currency in developing countries. Gig economy, integrated with technology and varied social media and digital platforms, seems to be an effective business strategy to stay alive and grow sustainably in modern businesses. In a sense, ‘to gig’ is possible only if media multiplicity with its digital edge is dovetailed with existing business strategies that are still relevant. Without the above-mentioned integrated business strategies, ‘gigging’ may lose its meaning. The change in the nature of work will thus continue to take place as we may have no option but to move along. The newer concepts of Big Data, artificial intelligence, crowd funding and gig economy have changed the perception of labor market. With digitalization, higher accessibility to internet and excessive use of electronic gadgets has reduced the world market into a smaller space. If it has to be made workable, we need to judiciously integrate gig economy with technology and entrepreneurial thinking to pave way for integrated business strategies that may be employed in diverse business environments across the globe.

Methodology and the Schema The proposed chapter seeks to firstly explore post-COVID-19 pandemic scenario that has caused large-scale disruption in all arenas of human life, and secondly look into the meaning, nature and implications of the term ‘gig economy’. Emphasis will be given on how this phenomenon has suddenly gained relevance in the coronavirus-struck world. Thirdly,

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an attempt will also be made conceptually to work out the importance of social media platforms in boosting the growth of gig economy. The main objective will be to theoretically postulate as to how the blending of diverse business strategies with entrepreneurial spirit and technology can emerge as an answer to the large-scale disruption caused by COVID19 pandemic. Such strategic integration can boost gig economic arena (despite its downside as it offers no sense of permanent security) to substantially benefit short-term employment sector, and thus result in providing a paddle push to the larger economic activity across the globe. The method of research in this chapter is qualitative and the following research questions will be taken up for critical discussion: a. Why and how has gig economy assumed sudden relevance in postCOVID-19 scenario across the world? b. How can integrated and blended business strategies be the only answer to the complex economic problems that have arisen due to COVID-19 pandemic? c. What role does social media platforms play in enabling gig economy as an important engine of growth? While dealing with the third objective mentioned above, qualitative software NVivo will be used to analyze tweets on key words such as ‘gig economy’, ‘freelancers’ and ‘gig workers’ that are germane to the critical discussion in the paper.

COVID-19 and Gig Economy As stated above, COVID-19 pandemic has thrown in challenges of diverse sorts in most of the business arenas. The first and foremost is the unemployment as workers/employees had to be forcibly laid off in literally all the sectors of the world economy due to lack of work and wages. Needless to mention that the world is no more the same as it used to be two years back and so are the whole range of businesses across the world. There is considerable evidence to prove that quite a few business ventures crumbled totally whereas others suffered immense and irreparable losses.

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As a result of it, a sizable chunk of humanity was suddenly pushed out of the work sphere and rendered jobless. The degrees of distress and despair kept increasing exponentially and jobless workforce was impelled to think innovatively about the alternative ways of earning livelihoods. Amidst such large-scale gloom the world over, there have been instances of sheer inhumaneness vis-a-vis badly exploited labor and workforce. The daily wages were drastically curtailed and the people below poverty line along with those who were pushed to the margins due to pandemic, found it hard to make both ends meet. The third and fourth world nations were terribly affected and people came across horrendous situations wherein they had to struggle endlessly to somehow or the other survive penury and hunger more than the havoc caused by coronavirus pandemic. It is said that the best as well as the worst in human beings come to the fore during the most trying and distressing times. And such precisely was the case with those jobless people who had their entrepreneurial spirit intact, even though subdued, consequent upon the recurrent jolts given by the changing faces of COVID-19 in the form of its mutants such as Delta, Delta Plus along with others. One can arguably claim that amidst such gloom, social media played an extremely crucial role in dealing with the pandemic and its devastating effects. Therefore, the generation of youngsters who were active on diverse social media platforms and who despite all odds could keep their entrepreneurial spirits alive resorted to gigging, which initially didn’t pick up that well but later on changed the financial fortune of several jobless youth. This chapter, in fact, is premised on the belief that blending of gig economy, traditional business strategies, social media platforms, technology and entrepreneurial spirit in a considerably digitalized world of today can reap rich dividends. The idea is to conceptualize this blending/integration as a strategic tool to be employed in various damaged business sectors, and thus kindle a hope of their restoration as well as preservation in a sustainable manner. Such a strategy may have the potential to change the gloomy countenance of various economies in the world that have either crashed due to COVID19, or are about to give in. Gig economy was prevalent earlier also but the term gained prominence and became more relevant especially during the pandemic wherein

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the consumers as well as service providers were confined to their homes. To fulfill their requirements while they could not move out, many people came up with the solutions vis-à-vis delivering goods/products and services at their door steps. Gradually we adopted this new normal and the world also accepted and embraced the gig players as well as gig workers fully with the passage of time. Gig economy, in fact, tends to facilitate ultra-short-term contracts and there are very less/negligible long-term contractual/service commitments. The players in such an economy also call themselves ‘independent contractors’. Other buzzwords associated with gig economy are ‘sharing economy’, ‘ondemand workforce’ and ‘contingent workers’. The modern workforce has shown a paradigm shift during the pandemic and this era has not only liberated the workforce but has provided several opportunities and platforms to work from remote areas/their homes and at ‘their convenience’. Within such a structure of economy, autonomy of the workplace and freedom and flexibility to do the work is very important. With advancement in technology, various gig economy apps have mushroomed through which a plenty of very good, varied and flexible part-time jobs may become available. The beauty of these platforms/apps is that they connect gig workers (of all sorts who are skilled and resourceful) with the outer world via providing recommendations of households, and people who can work on weekends, or in the evenings, or even on daily basis, or anytime on-demand. These platforms not only help the unemployed but also facilitate students, other part-timers and household workers to find work as per their convenience and thus become self-sufficient. Earlier the gig platforms were majorly Uber and Airbnb which had a deep impact on the way the services were provided at the global platforms. In an economy, ‘gig workers’ are characterized as people who work on temporary or contingent basis. In global markets, these workers are also termed as ‘liquid workers’. They are associated with cloud-based platforms also which provide great access to the workforce of this kind with the use of technology. The rapid development and use of gig platforms are a clear indication of the paradigm shift brought forth by the economic crises caused by the pandemic around the globe. The industry working at the multinational levels was badly hit due to the disruptions of supply chain and the closure of the borders

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across nations. The accessibility to inputs like raw material, workforce and related operational requirements were badly hit. After the COVID19 pandemic hit the world, on-demand service provider apps shot to prominence and the popular and major players among these have been Uber, Lyft, Etsy, Amazon Flex, etc. Gig economy has a wider and greater scope than the on-demand industry simply because it includes a wide range of people as well as the services that they provide. Gig economy can prove to be useful in various ways to workers as it helps them find work simultaneously at different places and thereby also helps them hone their skills for a variety of jobs. Flexi hours of working, freedom of choice and least risk of losing the job are some of the hallmarks of the way gig workers function. They create their own clientele/market and get short-term contracts in a digital world where they can benefit from their knowledge and skill-sets. There are so many fields where gig workers can earn more than a regular employee only if they understand the nature of the market and job requirements therein to provide services accordingly. In fact, they are free to choose where, when and how to work. It is their expert services required for companies/service users, which make them successful. During the COVID-19 pandemic, the supply chain was hit badly and that was why several companies were forced to cut the costs on regular basis alongside hiring workers who could quickly meet the business demands. The gig economy is also associated with the existence of a free market system where temporary and flexible jobs are carried on with the help of independent contractors or freelancers. Reduction of the operational costs is one of the biggest reasons that the companies have started preferring independent contractors/freelancers instead of fulltime employees. It is totally against the conventional way of doing work and that is why the expectations of the companies as well as the employees have been redefined in the dynamic contemporary context. The concept of gig economy became very popular during the COVID-19 pandemic in the US. Around 30% of the US population is now involved in gig economy. Gig workers are generally divided into two categories—independent and contingent. The independent ones are their own bosses but the contingent workers work for another company. On the other hand, freelancing implies contracted services provided by the freelancers

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to either businesses or individuals. Freelancing has always been associated with certain professions/jobs like writing, photography or designing for app-developers in the digital world. The gig economy also includes certain people who provide production services like small businessmen or artisans. There are certain other components as well that could be associated with the big market of gig economy where people associate themselves by providing products and services through platforms like Amazon, eBay, etc. A unique trend can be seen due to the advent of this economic paradigm shift during the pandemic. So much so that the implications of terms such as ‘job’, ‘profession’, and ‘work-culture’ have undergone tremendous change. There is no doubt about the fact that gig economy provides less job security compared to the structured employment; however, the need for self-ownership and working for oneself has made this gigging extremely successful for those who aspire for higher degree of flexibility in the process of earning their livelihood. Those who favor structured employment, prefer big platforms and that is the reason that a trend can be seen in the workforce around the world wherein people are ready to quit their permanent jobs in order to become part of the gig economy. Technology in fact plays a very important as well as prominent role in the gig economy, as the very basis of gig economy is technologically developed tools and devices. The disruption caused by Covid-19 proved to be helpful for those who could not work as whole-timers due to various hindrances. A paradigm shift could be thus seen in the labor landscape and one cannot undermine the role of technology therein. Due to technological advancements and digitalization in almost all the sectors of economy, gig players have revolutionized the idea of how we think about employment. As disruptions are sometimes fruitful, rewarding and sustainably beneficial, the pandemic has given opportunities to coalesce technology and freelancers to get benefits in almost all the sectors of the economy. The disruptions caused by the pandemic resulted in unprecedented use of technology-based professions where time-zone and distance proved to be irrelevant as well as immaterial.

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The concept of gig economy is defined differently and the existing body of literature suggests that ambiguity is there in terms of defining this concept (Watson et al. 2021). The gig economy has also been termed as sharing economy where companies like Uber have been contributing but legal concerns are also arising in this economy in terms of the use of information accessed by the companies of their users (Calo and Rosenblat 2017). Due to digitalization and excessive use of internet, the work environment has also been affected and the gig economy has flourished in several parts of the world (Anwar and Graham 2021). The working conditions and the legal implications of the nature of work performed by workers in the gig economy have always been a debatable point and empirical evidences are available to substantiate the fact that the choice of products by the customers in a gig economy is influenced by the working conditions and relationship of the company with the gig economy players (Belanche et al. 2021). Despite the challenges as well as the criticism faced by the working of the economy the number of gig players is quite considerable even in countries like the US (Bracha and Burke 2021). The labor market has undergone serious change especially during the pandemic and job insecurity has increased manifold. Companies do not prefer fulltime workers and the demand for their skill-sets has been continuously changing. Studies highlight that in a few markets, the payments received in this system at times are also quite below average compared to the permanent jobs (Cairnduff et al. 2018). The major stakeholders of the gig economy came out to be workers who earn wages for housework in few countries. The ‘caregivers and mothers’ came out to be the gig players due to the available flexibility in this sector by which they can balance their home and paid works (Milkman et al. 2021). One of the interesting aspects which developed in the economy recently due to pandemic is dispersal of various activities on these platforms and the players of this economy are varied and diverse ranging from multinationals like Amazon and Alibaba to Uber and on-demand paid workers. The prevalence of gig workers can be seen in European countries like United Kingdom, Germany, Belgium and Italy. The digital platforms have been contributing significantly toward the growth of gig economy

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as for many, who want to work on part-time basis and do not have physical platforms to earn money, are the real beneficiaries of this economy. Uber has set an excellent example for us wherein anyone who is interested to earn during his available hours of working can offer his services and earn. Many part-time workers, students, householders have taken advantage of the platform and earned money. The work opportunities for many have increased due to the extensive use of internet and availability of various apps which facilitate the gig workers to connect with their potential clients. This is the advantage that technology and digital devices offer to the gig workers. The gig economy can serve as a platform to reduce the unemployment level across the world especially after COVID-19. The simple reason behind this statement is that longer commitment, engagement and employment in all businesses have been proving to be a costly affair and that is why business processes are getting hampered. Outsourcing and supply chain management has always been a challenge for any business firm in order to produce low cost but quality products. That is the reason why many companies opted for global supply chain management practices or global operations management. Some of the companies wanted to operate without inventory and therefore followed the practice of gig economy like Uber. The fact which is hard to deny is that the sourcing and supply chain management raised even a bigger challenge to the producers around the world especially during and after pandemic waves in the years 2020 and 2021, and it has changed the whole perspective about doing business. Every organization today, which is engaged in production of goods and services, faces certain important questions such as: (a) are the suppliers reliable? (b) what are the issues in quality management?; (c) how do we control the fixed and operational costs?; (d) how do we save inventory costs?; (e) will the suppliers deliver the quality products in time?; (f ) how efficient is the procurement process?; and (g) what can be the ways of getting benefits from the existing operations system? The excessive use of World Wide Web enabled the organizations to reach out

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to millions of customers as well as producers. Their search for brandbased suppliers sitting in various parts of the world was fulfilled before the restrictions were imposed during the COVID-19 pandemic. Even until now, there are several countries that have not opened their borders fully. And in case some of them have opened the borders, the losses suffered by the companies therein are so huge that they are impelled to depend on the sources available nearby to control the costs associated with the products. Moreover, the consumer-income has also reduced considerably, inflation is at the peak in developing countries like India, and the priority of the customers in terms of spending has also undergone a serious change. In such an abysmal scenario, the services provided by freelancers, independent contractors, local producers and suppliers have proven to be significantly important. The longer commitments and regular/permanent employment have been proving redundant owing to the serious economic downslide caused by the pandemic across the world, and therefore, gig economy has been proving to be really beneficial not only for the suffering populations across the world but also for the sagging economies that have been finding it hard to come to terms with the global business scenario.

Blending Varied Business Strategies to Cope with Post-Pandemic Economic Problems As discussed above, gig economy has been emerging as a remarkable coping strategy with the post-pandemic times particularly because it promotes self-employment and is good for the entrepreneurial growth and economic development. As the supply chain and operations management practices are changing globally so is the level of competition as well as the customer expectation. The demand pattern of the products has undergone a serious change in the pandemic-struck world. Consumer is now more health-conscious and is aware of the drastic consequences and ill effects of chemically processed, non-nature friendly, environmentally unsustainable use of products as well as processes. Customers are becoming more demanding and various firms are now required to respond to various market requirements more rapidly. Therefore,

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gig economy players who are easily available and can be hired at are becoming an important part of the economy as they are proving to be crucial for the success of rapidly growing industries and businesses where the basic thrust is on customer’s satisfaction. However, gigging is not enough as it is high time to blend/combine various strategies to be able to better respond to the economic situation across the world. Different business organizations, firms and even industries have to come together to respond collectively to the challenging situation that has greatly been addressed by gig economy. The point is simple whether it is possible to adapt the existing/conventional business practices in accordance with the way gig economy works. This is where the entrepreneurs and business leaders have to take the lead and offer a blended strategic business vision which can alleviate the suffering economies across the world. The traditional business practices will have to be rethought wisely in the light of the pandemic-struck, cost-driven, quality-oriented, challenging and customer-oriented sustainable markets. Good supply management practices are central to any quick response system. Organizations have less lead time to get the benefits from the operations management practices; moreover, they should be more flexible to react to the changes in the marketplace. Another aspect of a quick responding organization is that in order to be responsive to the market fluctuations closer ties with the suppliers, and the different players of the economy is required so as to match the requirements placed by the buyers. It is possible through the involvement of gig players along with the existing/traditional systems as a blended strategy. It also calls for alternative methods of cutting down the procurement time, in which again gig players having important stake in business and economy can play a crucial role. In a gig economy, there is a greater focus on flexibility, freedom and autonomy of the work as well as workers. In this regard, technology plays a crucial role from the viewpoints of providing faster means of communication, engaging workers from diverse and remote locations, providing smartphones, tablets, iPads and other mobile gadgets to work, and

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also providing advanced communication technologies enabling videoconferencing and online meetings and interactions. As evident during the pandemic, digital technology emerged as an effective tool to deal with the situation that had arisen wherein face-to-face interaction was choked due to stringent social distancing norms. Therefore, business organizations had no option but to switch gears and shift their work orientation entirely via opting to work from home, meet from home, monitor/track from home and manage from home depending on one’s job profile. The gig/contractual/contingent workers too benefited as they started getting paid assignments/jobs individually as well as in teams during and after the pandemic. Digital devices have a great role to play in the abysmal business scenario across the globe. Not that contingent workers have not been in existence prior to the pandemic; as a matter of fact, contractual/short-term jobs and assignments acquired different resonance after coronavirus struck and businesses floundered the world over. US Bureau of Labor Statistics (BLS) in its report released in 2018 revealed that more than sixteen million people have been working as temporary/contingent workers out of which nearly seven percent have been working as ‘independent contractors, independent consultants, or freelance workers’. Communication technologies along with media multiplicity can give tremendous boost to gig economy through integrated business strategies with a view to benefit digitalized gig workforce. The workers as well as the business firms must reinvent themselves to reap maximum benefit out of the situation that has arisen. If hiring at one level has become cheaper, business firms have been finding it hard to pay them due to substantial decline in their income/profit. Due to enhanced connectivity, business firms may rely on remotely located workforce. Digital/technological tools and devices may be instrumental in creating the ‘new normal’ characterized by need-based hiring on short-term basis, and once the job is done, both the employer and the employed in gig economy may simply move on. The gig workers in fact are at tremendous ease with the latest digital technologies and technological tools/gadgets and thus expect business firms to be amenable to such changes.

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Another blended mode strategy which the authors believe can come for the rescue of sagging economies is creating lean organizations by involving gig economy players in the existing network of supply organizations and co-partners. One of the underpinning blocks of a lean organization is the supplying organizations that co-partner with the manufacturing organizations in creating a value stream for wellorganized delivery of goods and services. The organizations that have been following purely traditional practices do not cater to resolve the issues/challenges of building such a network of organizations. It not only requires mutual trust and close connections, but long-term relationships among the participating organizations as well. In such a case scenario too, blended strategies choosing wisely the role of gig players/freelancers/contractors who can ensure the quality parameters can be associated with the organizations that are ready to fulfill their long-term commitments in the supply chain practices. It requires a considerable amount of give and take on the part of the organizations engaging themselves in such blended exercises in terms of preparing short-term tactics or long-term strategies whether they are pursuing the objectives of cost leadership or differentiation, or both. To create a niche, gig players can play a remarkable role as the concentration would be on a small region, with focused approach toward the achievement of medium-term objects and therefore the level of creativity required would be higher. So, based on gig players’ approach to quickly respond to the unprecedented economic crisis, a set of blended business strategies could be in place to decide about the course of action to be taken to achieve long-term goals in business as well as broader economic arenas.

Role of Social Media Platforms in Enabling Gig Economy as an Important Engine of Growth Though in many countries the gig players are underrated and made invisible owing to understandable legal reasons, yet their contribution in the economy cannot be underrated. At times, gig workers are underpaid too and are not considered as important as the permanent workforce, but to beat the pressure of competition, rising costs and quality, their role

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has been increasingly becoming important as the use of gig economy apps such as Swiggy, Ola, and Zomato proves. The need is to integrate these players with the working of the business along with traditional set of jobs. A growing discontent regarding various payment issues to the gig players has been observed on social media platforms. During the pandemic, when all human activity came to a standstill, social media emerged as a rescuing factor and kept the world connected. A huge increase has been seen in the use of platforms like Facebook, Twitter and Instagram. Earlier too the proportion of youngsters using social media was higher but during the pandemic it increased exponentially, and it not only provided the means to connect, express and share emotions of people of all age groups, but also became a medium for learning as well as earning. People with different skill-sets became visible through social media platforms and offered their valuable services when COVID-19 peaked. The qualitative impact of the use of social media by the gig players for earning relatively smaller amounts of money can never be measured, however, the fact remains that people felt extremely empowered because of the intervention of the social media platforms during the pandemic. People realized for the first time how important the emotional content associated with a person who had been making something with his own hands, or a housewife who had been cooking variety of food to sell for her family, or an artisan making a pot with clay and selling it to you to be able to buy a meal for himself and his family, is far more important in a pandemic situation compared to the quantification of the market share of a big multinational company. Moreover, the pandemic also taught us how only money and materialistic things cannot buy love and compassion, and what actually matters in life is humaneness and interpersonal relations among human beings who can stand by one another at the time of distress and despair. In research also, qualitative methods can provide us deep understanding of the phenomenon under study. The socio-contextual issues and elaborate interpretations become possible if visualization tools are used in qualitative studies. Analyzing the content available on social

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media platforms has gained considerable attention as it provides us insights about the market scenarios, the trends and public sentiments. To achieve the third objective of the paper, tweets on key words such as ‘gig economy’, ‘freelancers’ and ‘gig workers’ were extracted from Twitter by using NCapture—the utility offered by qualitative software NVivo. As a result, NCapture captured 6009 tweets on the keyword ‘gig economy’, 6647 tweets on ‘gig workers’, and 16,032 tweets on the keyword ‘freelancers’. It was done on October 19, 2021 and the following process was used: Twitter Data → Language Preprocessing → Data Mining → Sentiment Analysis → Deriving Insights

Data Analysis After data mining, the extracted tweets were analyzed by using the software, NVivo. The data-set on gig economy, gig players and freelancers revealed that public sentiments about freelancers are moderately positive. For better visualization of the results, a word cloud was prepared which is based on the percentage and frequency of words used in the tweets concerning the post. It gives us an idea about the topics/areas on which public shares their sentiments through certain keywords. The first cloud chart that has been given below, prominently displays the use of ‘gig economy’ in the tweets extracted through the keyword ‘gig economy’ which is surrounded by the related keywords of subsidiary importance such as ‘work’, ‘workers’, ‘months’, ‘essential’, ‘pandemic’, ‘companies’, ‘platform’, ‘healthcare’, ‘education’, ‘investment’, ‘Swiggy’, ‘Zomato’, ‘protection’, ‘Uber’, ‘money’, etc. This clearly indicates how critically crucial the concept of gig economy has been for Twitter users.

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Cloud Chart: 1

Source Software Output The words that are mentioned in the cloud chart above indicate about the tweets having the content relating to the work/job requirements, workers, various sectors and diverse players in the gig economy like Swiggy, Zomato, Uber, etc. The following cloud chart no. 2 shows the results of the search term ‘freelancers’ and the highest tweets have been found on this, which indicates that it is a buzzword in the social media. From this, we can also infer that freelancers have been using social media platforms like Twitter to talk about and share information about their services, skill-sets and expertise in various fields. To further analyze the public sentiment toward freelancing, a sentiment analysis has been done by using NVivo software on the tweets extracted on ‘freelancers’. The Sentiment Analysis Matrix extracted from the software clearly indicates that the public sentiment toward freelancing is more positive than negative.

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Cloud Chart: 2

Source Software Output

1 : Files\\freelancers - Twier Search ~ Twier

A : Very negative

B : Moderately negative

C : Moderately positive

D : Very positive

2064

6101

9304

2809

Source Software Output Out of the tweets extracted on the keyword ‘freelancers’, 2064 tweets have been found ‘very negative’, 6101 tweets are ‘moderately negative’, 9304 tweets are ‘moderately positive’ and 2809 tweets are ‘very positive’ about freelancers.

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The third cloud chart given below represents the search term ‘gig workers’. This cloud chart also reflects certain terms concerning issues related to the players in a gig economy. The other prominent words which appeared in the cloud chart extraction are ‘workers’, their ‘rights’, ‘labor’, ‘benefits’, ‘companies’, ‘huge profits’, ‘transparent’, ‘holy work’, ‘drivers’, ‘uber’, ‘law’, ‘contractors’, ‘jobs’, ‘time’, ‘money’, ‘power’, etc.

Cloud Chart: 3

Source Software Output

Summing Up The foregoing discussion thus suggests rather lucidly as to how crucial blending in the sense of integrating/combining of gig economy with technology and social media platforms is in view of the digital age that we are living in and also the unprecedented crisis that COVID-19 pandemic has caused in economic and business arenas across the globe. The problem of unemployment the world over has acquired catastrophic

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proportions. Amidst such a challenging situation, it is really hard to rely on the alternate means of seeking and providing employment particularly owing to the fact that there is the possibility of labor exploitation, below par payments of daily wages, injustice being done to the workforce in first world as well as the third world economies, etc. Nevertheless, the problem is right there, staring us in the face. We either adapt in accordance with the demands of the situation, or perish. Relying on the former, gigging seems to be the only way out as a short-term solution as gig economy, strengthened by communication technology and digital tools, ensures succor to the suffering labor force with variety of skillsets and a whole range of expertise in carrying out different types of jobs concerning businesses and day-to-day life. Thus, short-term jobs or opportunities to earn on contractual basis can provide workers with at least a minimally sustainable amount on their terms and conditions. This has been the reality even in the first world nations like the US and several other countries in Europe.

References Calo, Ryan, and Alex Rosenblat. 2017. “The Taking Economy: Uber, Information, and Power.” Columbia Law Review 117 (6): 1623–90. Anwar, Mohammad Amir, and Mark Graham. 2021. “Between a Rock and a Hard Place: Freedom, Flexibility, Precarity and Vulnerability in the Gig Economy in Africa.” Belanche, Daniel, Luis V. Casaló, Carlos Flavián, and Alfredo Pérez-rueda. 2021. The Role of Customers in the Gig Economy : How Perceptions of Working Conditions and Service Quality Influence the Use and Recommendation of Food Delivery Services. Service Business. Vol. 15. Berlin, Heidelberg: Springer. https://doi.org/10.1007/s11628-020-00432-7. Bracha, Anat, and Mary A Burke. 2021. “How Big Is the Gig? The Extensive Margin, The Intensive Margin , and The Hidden Margin.” Labour Economics 69 (January): 101974. https://doi.org/10.1016/j.labeco.2021. 101974.

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Cairnduff, Annette, Kelly Fawcett, and Nina Roxburgh. 2018. “Young Australians and the Disrupted Economy.” In The Wages Crisis in Australia: What It Is and What to Do about It, edited by Jim Stanford and Tess Hardy Andrew Stewart. University of Adelaide Press. https://doi.org/10.20851/ wages-crisis-18. Milkman, Ruth, Luke Elliott-Negri, Kathleen Griesbach, and Adam Reich. 2021. “Gender, Class, and the Gig Economy: The Case of Platform-Based Food Delivery.” Critical Sociology 47 (3): 357–72. https://doi.org/10.1177/ 0896920520949631. Watson, Gwendolyn Paige, Lauren D. Kistler, Baylor A. Graham, and Robert R. Sinclair. 2021. Looking at the Gig Picture: Defining Gig Work and Explaining Profile Differences in Gig Workers’ Job Demands and Resources. Group and Organization Management. Vol. 46. https://doi.org/10.1177/105 9601121996548.

4 Setting the World in Motion: Blockchain Redefining Transport and Logistics Usman Javed Butt, Aristeidis Davelis, Maysam Abbod, and Khaled El-Hussein

Introduction to Blockchain The technology that is likely to have the greatest impact for the next few decades has arrived, and it is not social media, not big data, not robotics, it’s not even AI. It is blockchain. –Don Tapscott U. J. Butt (B) · M. Abbod Brunel University, London, UK e-mail: [email protected] M. Abbod e-mail: [email protected] A. Davelis · K. El-Hussein Northumbria University, London, UK e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_4

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In a centralised system of digital economy, there are some intermediaries like banks or government agencies that we trust to properly manage our assets, and properly record any transactions of those assets (Kiayias et al. 2016). The trusted intermediaries help prevent what is called the ‘double-spend problem’, which is the act of spending the same unit of value more than once, by managing transactions and all related tasks including authentication, verification, fraud prevention, etc. However, in this manner, trusted intermediaries are needed to establish trust in the economy and in the transactional process, thus raising concerns associated with security, accessibility, privacy, cost and others. After the 2008 financial crash, and the entailing mistrust in the financial system and the government, an author under the alias ‘Satoshi Nakamoto’ published a white paper proposing an electronic cash system based on digital currency or ‘cryptocurrency’ called Bitcoin (Nakamoto 2008). Bitcoin was based on a novel and radical architecture called blockchain. In simple words, blockchain allows peer-to-peer transfers of digital assets without involving intermediaries. In blockchain, trust is established not by a known official body or institution, but by collaboration, cryptographic proof and underlying algorithms.

Bitcoin Bitcoin is considered as the largest cryptocurrency in the world and has a decentralised ledger system empowered by blockchain technology. It is considered as a peer-to-peer technology which facilitates instant payments and maintains independency of the consumers while processing transactions without the governance and centralised monitoring of banks and financial institutions. The two biggest contributions of Bitcoin are: K. El-Hussein e-mail: [email protected]

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a. A continuously flowing digital currency system b. Blockchain, the model for autonomous decentralised application technology Transactions are the basic elements of a Bitcoin blockchain, stored on a chain of linked blocks with digital signatures. Every transaction goes through the process of validation and broadcast. A number of transactions along with other information forms a block, and blocks are stored in the form of a chain through a digital data link with each block referencing the previous one, hence the name blockchain. Candidate blocks go through the consensus process, in order to elect the next block to be added to the chain (Pilkington 2016). The chosen blocks are verified and then added to extend the current blockchain. A timestamp is also added to each verified block to determine the exact time and date it was validated and mined by the blockchain network. The basic architecture of blocks forming the blockchain can be seen on Fig. 4.1.

Fig. 4.1 Basic Bitcoin block architecture

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In the concept of blockchain, network peers perform the validation and grouping of transactions to form candidate blocks. They also broadcast valid transactions and the consensus on which block will be created next, chaining blocks to create immutable records (Narayanan and Clark 2017). There are two major roles of peers in a Bitcoin blockchain; these are regular participants, that create transactions (send or receive cryptocurrency) and initiate a transfer of value, and miners (analogy to gold mining process). Miners perform tasks such as verifying/validating transactions, broadcasting them and competing to create new blocks among others by solving a computationally hard mathematical puzzle. Bitcoins are awarded to Miners as an incentive for their efforts in managing the blockchain, either with Bitcoins as transaction fees or through new coins flown to the system (Michele D’Aliessi 2021). Miners are also awarded Bitcoin for creating a new block, thus the effort to be the first to find the solution to the puzzle is the reason why the mining process is usually performed on powerful computers, running software indicated by the blockchain’s protocol. Participants reach a consensus to add a new block to the chain, using a proof-of-work protocol, named after the ‘work’ needed to find the solution to the puzzle. The first miner to solve the puzzle by finding a number called the nonce (abbreviation for ‘number used only once’), earns the right to add to the block and an announcement is broadcasted to the network along with the block. The other participants in the network will then verify the new block. This new block, with the new set of verified, recorded and confirmed transactions, is then added to every peer’s local copy of the blockchain, thus creating a new state of the blockchain network. Once created and added to the chain, blocks cannot be altered, thus ensuring that blockchain records are immutable and tamper-proof. Figure 4.2 depicts the timeline of blockchain technology. The basic concept was conceived in 1991 by Scott Stornetta and Stuart Haber. In 2000, Stefan Konst published the theory of cryptographic secured chains and in 2008, white paper by Satoshi Nakamoto on cryptocurrency is released (ICAEW 2021). In 2014, Blockchain 2.0 was created with the

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Fig. 4.2 Timeline of blockchain technology development

addition of Smart Contracts, and cryptocurrencies like Ethereum and Ripple were introduced (ICAEW 2021).

Current Widespread State Blockchain can enable an inclusive economy since anyone regardless their geographical location can join the blockchain network (Tripoli and Schmidhuber 2018). They will be considered as a peer and can create transactions without the need for a bank account or any other contractual commitment. However, apart from cryptocurrency, blockchain has been implemented in a wide range of applications across various industries including healthcare, manufacturing, finance, government and distribution (Rabah 2018). Thus, blockchain is in a position to redefine applications in multiple business sectors, due to its robust features. Some other potential applications include goods transfer, digital media transfer, remote service delivery, decentralised business logic, distributed intelligence, distributed resources, crowdfunding, identity management, government public records and so on (Sakız and Gencer 2019).

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Key Concepts There are various key components that make blockchain unique, whereas ongoing research is constantly striving to make the technology better and more robust. Some of the main elements of blockchain technology can be seen in Table 4.1. The key concepts of blockchain are discussed in greater detail in the sections below.

Blockchain Types Public blockchains are based on allowing any individual or group to participate in the transaction processes through verification, and consider themselves as a node of the blockchain (Chen et al. 2020). As Zheng et al. (2017) analyse, in a public blockchain, the consensus process (validating new blocks) is permissionless and all nodes take part, while everyone can read transactions. Some examples of public blockchains include Bitcoin, Ethereum, Litecoin and Monero. Public blockchains are decentralised, they store records on a large number of nodes and are Table 4.1 Main elements of blockchain Concept

Function

Cryptography

Cryptographic digital signature and cryptographic hash functions are used from identity management (node identity) to ensuring a tamper-proof data structure A tamper-proof record of the history of all transactions occurred, stored as an immutable data structure of linked chains of blocks Nodes in the distributed system, participating in the blockchain by holding copies of the ledger In cryptocurrency blockchains, miners manage the whole blockchain ecosystem and compete with each other to create new blocks by solving hard hash puzzles. Mining is not applicable to most non-cryptocurrency blockchains, as there are no hash puzzles or awards for solving them The protocol that decides how the peers agree on the value or state of the blockchain system

Distributed ledger

Normal peers Miners

Consensus protocol

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therefore immutable but also less efficient, with limited throughput and increased latency. The behaviour of public blockchains often receives a lot of criticism because it consumes a lot of electricity, as there is the use of ‘proof-of-work’ algorithm in building consensus (Cao et al. 2019). On Private blockchains, consensus is permissioned and controlled by a single individual or organisation, while read access can be public or restricted. Immutability is not guaranteed since the number of participating nodes is limited; this however ensures higher efficiency since there are less validators. These blockchains are not associated with cryptocurrency and are typical in business use cases, offering little decentralisation but providing high throughput. Multichain is an example of a private blockchain (Vizier and Gramoli 2018). In Consortium blockchains, consensus is controlled by a pre-selected set of nodes and control is distributed across multiple participants. Read access permissions to the blockchain may be public or restricted, and there are also hybrid routes (Buterin 2015). This type of blockchain is appealing to business applications due to its flexibility; it can be viewed as a semi-private blockchain, used for building consensus systems across different organisations or groups were key participants can be identified. Examples of consortium blockchains include Quorum and Corda. Similar to private blockchains, Consortiums do not provide cryptocurrency or financial incentives for transactions (Chen et al. 2020) (Table 4.2). Table 4.2 Comparisons among public, consortium and private blockchains (Zheng et al. 2017) Attribute

Public Blockchain

Private Blockchain

Consensus

All nodes/Permissionless Public Almost impossible to compromise Low No

One organisation/Permissioned Public or controlled Could be compromised

Read access Immutability Efficiency Centralisation

High Yes

Consortium Blockchain Selected nodes/Permissioned Public or controlled Could be compromised High Partial

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Distributed Ledger Most organisations record transactions in the form of ledgers. Those ledgers hold information on the participants involved in each transaction such as buyers, sellers and intermediaries. They also record the agreements or contracts between participants and other vital information, like the time of transactions (Abeyratne and Monfared 2016). In the traditional sense, businesses use multiple ‘ledgers’ to track asset ownership and transfers between the participants. Traditionally, this is managed centrally by some organisation and is stored in a central safe location (Burkhardt et al. 2018). However, there are some significant issues with this system, as it is inefficient, subject to misuse, tampering, corruption or fraud, while also lacking transparency which could lead to a central point of failure (El Ioini and Pahl 2018). To gain greater insight into the concept of distributed ledgers, it is crucial to understand that it is a form of database which is shared, replicated and synchronised among multiple participants of the distributed and decentralised network across several locations. Distributed ledgers are used to store the transactions and assets among the participants in the network (Bashir 2018). In the case of traditional ledgers, copies of the ledger can be stored in various places, although they are not truly distributed since they are managed by the same organisation (centralized). Distributed ledgers are decentralised, and hence do not require these central authorities or intermediaries to process, validate or authenticate transactions. Furthermore, transactions are only recorded or updated in distributed ledgers if the majority of participants in the network agree upon the common value through a consensus process (Maull et al. 2017). Transactions in the distributed ledger are timestamped and are given a cryptographic signature, for accurate and secure storage. The technology also makes it easy for participants to verify and audit the information stored in the database (Kuhn et al. 2019). In blockchain, information is recorded in a data structure that consists of a sequence chain of cryptographic hash-linked blocks. The blocks are nothing but a collection of transactions, grouped together for efficiency, which are arranged in a chain (Kaur et al. 2020). The records

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store the history of asset exchanges that took place among the peers in the network, permanently. Therefore, all transaction blocks that have been confirmed and verified are linked from the beginning of the chain (the ‘genesis block’) to the current block. Distributed Ledger Technology (DLT) is considered as the protocol and the technological infrastructure that is designed for providing access, validation and the simultaneous updating of the records in the distributed ledger (Kannengießer et al. 2020). The functioning of the distributed ledger is through a network which is spread across multiple geographic locations and involves sharing of databases as depicted in the forms of Nodes A, B, C and D in Fig. 4.3. The benefits of distributed ledgers include higher transparency, no dependency on the third party and inherent decentralisation (Liu et al. 2020). Traditional ledgers are centralised and do not involve much transparency. In other words, traditional ledgers were accessible only to a selected group of users and there was a higher potential of identity theft (Berger and Van de Looverbosch 2020). However, the distributed ledger is based on encryption technology which enables higher security and better accessibility to the users, without being dependent on a centralised institution or system (Rathee et al. 2020).

Fig. 4.3 The concept of the distributed ledger

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Nodes and Miners As mentioned earlier, blockchain is a system comprising a peer-to-peer network. In a typical public blockchain, anyone can join or leave the network without any hassle or fee. The only thing the participants have to do is run a program like the Bitcoin client (for the Bitcoin Blockchain) on their computer. The participants that initiate a transaction or are the recipients of the value of a transaction are called peers (Efanov and Roschin 2018). A recipient of the coin is not required to be logged in the network, for the transaction to be confirmed or to be valid. The sender can simply use the recipient’s address to transfer the coins and the transaction will be recorded in the ledger if it is valid. After this, the recipient can simply join in later or login later using his signature/address and confirm that the transaction has indeed happened by looking into the immutable ledger (Sabry et al. 2019). In noncryptocurrency blockchains, peers are considered all nodes that maintain copies of the ledger and smart contracts (discussed later). Besides these nodes, in cryptocurrency blockchains there are other special nodes or peers called miners, that perform the additional work to verify and broadcast transactions, compete for the right to create a block, work on reaching a consensus by validating blocks and broadcasting newly created ones, and finally confirming the transactions (Kaur et al. 2020). This mining process requires a high amount of capital investment as it requires powerful computing devices, and high electricity costs to run those devices. As an incentive to participate, Miners can be awarded cryptocurrency for their efforts in managing the blockchain. Moreover, the incentives can be given by two methods, in the first method, for every block the miners add, the first transaction inside the block is a special transaction which has no inputs (Mechkaroska et al. 2018). This transaction adds coins to the address of the creator of the block as an incentive. It is basically a way to introduce new money into the system and keep the coin flow. Once enough coins are introduced into the system, the incentives model can be shifted on a transaction fees basis, which is the second method of incentivizing the miners (Huynh et al. 2019). The incentive is a product of clever engineering, which

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encourages the peers to remain honest, play by the rules and participate in the system. The main takeaways from this are: • Mining is the actual process that is responsible for creating new cryptocurrency by resolving a complex mathematical puzzle (e.g. in the case of Bitcoin). • Mining has become more complex and sophisticated over time, using advanced machinery to speed up the mining process (Rathee et al. 2020). • Mining is important to sustain the ledger of the transactions on which cryptocurrency is based. • Public blockchains are not regulated by any central body of authority. They are backed up by many computers globally, which are referred to as nodes. • The main operation that nodes undertake is that they store information about previous transactions and assist in verifying their authenticity (Frankenfield and Mansa 2021).

Verifying New Transactions In this section, the working operation of Bitcoin with the abovementioned components will be discussed. Blockchain records transactions that occurred in its system in the form of a distributed ledger (chains of blocks) (Conoscenti et al. 2016). When a transaction is initiated from a node in a blockchain ecosystem, the node broadcasts the transaction to all the nodes that comprise peer-to-peer network. In this context, the transaction will have a digital signature of the peer who is initiating the transaction. This digital signature is used by the other peers in the network to know that the transaction really came from that particular peer node. Thus, this digital signature helps to protect the owner of the coin against stealing (Kuhn et al. 2019). Transaction validation is carried out independently by all the miners, and the validation process itself involves examining more than 20 criteria including

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size, syntax, header, etc., which are part of the transaction’s data structure. All invalid transactions are rejected and ignored, and will not be broadcasted. Valid transactions are added to the pool of transactions, from which Miners select a set of transactions to create a candidate block (Huynh et al. 2019). The key challenge to building a distributed e-cash system is the distributed consensus. In a scenario where every miner can add blocks to the chain, there will be many branches linked to the chain instead of a single consistent chain of individual blocks, which would result in an inconsistent state in the blockchain. To address this issue, blockchain relies on a decentralised consensus process.

Consensus Consensus in a distributed system is defined as a protocol or a set of protocols that helps the system decide on the same value or state in the system (Memon et al. 2018). There are two requirements for the distributed consensus. First, when a single consensus process terminates, all the correct nodes should decide on the same value. There is an emphasis on the correct nodes because some nodes might be faulty, delayed or even right out malicious. Second, this agreed-upon value should be proposed by some correct nodes, so, the value is not an arbitrary value. Consensus is done on a block-by-block basis, on the other hand each node also has a set of outstanding transactions that have been broadcasted but not yet added to the blockchain. Though, by definition each node might have a different version of the outstanding transactions, meaning that some nodes might have heard about the transactions while others might not have (Liu et al. 2020). This can be due to an imperfect peer-to-peer network, the fact that not all peers are connected all the time, potential faults in the network or high network latency. Any valid transaction that didn’t make it into the particular block that gets chosen as a result of a consensus protocol, can wait and be added into the next block. Only one of the miners is chosen by the consensus protocol at a

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time, and the valid block proposed by that miner gets appended to the existing blockchain. The miners are in constant competition with each other, in order to decide who will earn the right to create the next block (Lei et al. 2021). They compete by solving a computationally hard hash puzzle. If one of the miners solves the puzzle before the others, he earns the right to add to the block and an announcement is broadcasted to the network along with the new block. This process of selecting the peer for creating a new block is termed as ‘proof-of-work protocol’ (Nakamoto 2008). The other participants in the network verify the new block, and can also check whether the puzzle has been solved or not. Participants reach a consensus to add a new block which is then added to all peers’ local copy of the blockchain. As a result, this becomes the new state of the blockchain network. Assuming the majority of the nodes are honest, this consensus process in turn helps prevent double-spending problems and along with its immutable distributed ledger it also prevents stealing, fraud or tampering without the presence of a central authority or a mediator (Puthal et al. 2018).

Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) In the Proof-of-Stake consensus model of blockchain, validators maintain cryptocurrency by keeping their tokens as collateral (Ibrahim 2019). The probability that a node will hold the right to add a new block in the blockchain is proportional to how many coins or stakes they have, instead of computing power. So, the mining power of a miner is proportional to the number of coins they hold. In this type of consensus, mining does not require excessive computational power, making it much more efficient compared to Proof-of-Work; it could however benefit the ‘wealthy miner’ and cause oligopolies (Cao et al. 2019). PoS is considered more democratic, because it provides the opportunity to any validator with tokens to participate. In an evolved concept of PoS called ‘Delegated Proof-of-Stake’, validation of new blocks happens

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through network voting and election of delegates. The delegates are also considered as block producers, and they are assigned the authority to validate a specific block without being able to do the same for other blocks. The PBFT is an algorithm designed for the efficient working of the asynchronous systems (Wang et al. 2019). Blockchains with lower overhead times have been supported with the utilisation of PBFT as a consensus mechanism, which can be considered as a significant feature for acquiring consensus in distributed networks even when some nodes are not responsive or are providing incorrect responses. Apart from those mentioned above, several other consensus mechanisms exist like Proof-of-Capacity, Proof-of-Elapsed-Time and Proof-ofAuthority, with different purposes and uses depending on each scenario.

Smart Contracts Vitalik Buterin was one of the initial contributors to the Bitcoin codebase and believed that there was more potential in blockchain than just plain currency transactions. Buterin published a white paper on Ethereum blockchain in 2013. Ethereum blockchain extended the limited scripting feature of the Bitcoin blockchain to include a fully built-in programming language, which allowed anyone to write Smart Contracts (Buterin 2014), thus adding an extra layer of logic and computation. They are the centrepiece and thrust of the Ethereum blockchain and opened up the door for decentralisation where people can choose their own arbitrary rules of ownership, transitions and state transition functions. So, Ethereum could record other assets like loans and contracts, and not just currency (transfer of value). With Smart contracts, the efficient automation of decentralised applications can now be realised. This turned out to be a pivotal moment in blockchain history. The Ethereum project was initiated in 2014 via crowdfunding, and it was released officially on 30 July 2015 with an initial supply of 72 million coins (Marr 2018). Following this, Ethereum has been used as a reference implementation in various other blockchains.

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The execution of a smart contract is initiated by code included in the transaction. Whereas in the Bitcoin blockchain digital currency transfers perform the simple addition of value to payee and subtraction of value from payer, Ethereum blockchain enables transactions that may perform out more sophisticated operations. A transaction may require some conditional transfer, some form of evaluation, it may request signatures for the transfer of assets, or it may specify the need to wait for a specific time or date; all this can be achieved through a smart contract. One simple example of a smart contract is in auctions; if the bid of an item is less than some minimum bid amount and less than the highest bid, then the bid is rejected (Miraz and Ali 2018). Alternatively, if the bid of the item is greater than the minimum bid and the highest bid, then the bid is accepted. A smart contract can not only define rules and penalties around an agreement but also automatically enforce these obligations. Thus, in many situations or applications middlemen like lawyers are not required at all when using smart contracts. Specific programming languages have been designed to code smart contracts with Ethereum using the Solidity programming language to implement them. Every node in the blockchain network should be able to run any arbitrary code in a smart contract irrespective of the underlying hardware or operating system. To support this execution of code in the smart contract, Ethereum blockchain implements an abstraction layer called Ethereum Virtual Machine (EVM) on top of the peer-to-peer network. Smart contracts written in high-level programming languages like Solidity are translated into EVM bytecode and then deployed in the EVM, similar to the Java programming language (Wan 2020). Ethereum has also worked on other issues with the Bitcoin blockchain, such as privacy and scaling. The Enterprise Ethereum Alliance (EEA) has been working on the Ethereum protocol and maturing it into something that industries can use. Ethereum will also move to a proof-of-stake protocol for distributed consensus instead of proof-of-work used in the Bitcoin blockchain, via Ethereum 2 upgrades (Laabs and Ðukanovi´c 2018).

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Non-Fungible Tokens Bitcoin and Ethereum are a type of fungible cryptocurrencies. In Bitcoin, if an individual sends one coin and receives one back, there will be no difference. One coin is interchangeable with the other and has the same value. A Non-Fungible Tokens, or NFT, is a unit of data stored in a distributed ledger that certifies that the digital asset is unique and whose ownership and authenticity are tracked using the infrastructure of a blockchain (Mondragon et al. 2018). Most NFTs are implemented in Ethereum blockchain, while other blockchain ecosystems have their own protocol and standards for supporting NFTs. Non-fungible tokens are like baseball cards or books signed by the original author, in that the tokens have unique information and varying levels of rarity (Underwood 2016). Additionally, NFTs are not interchangeable. The tokens also cannot be divided or transacted in fractions, like Bitcoin or Ethereum. They must be bought as a whole. NFTs allow artists to monetise, market and protect their masterpiece in digital form without the need for intermediaries, while their use often extends beyond artwork (Barenji et al. 2018). Since the ownership can be tracked and verified in the blockchain, NFTs provide creators and inventors with a way to protect their intellectual property or claim their ownership. As an example of the potential of NFTs, on the 11th of March 2011, a digital artist named Mike Winkelmann sold an NFT of his work for $69 million (Kastrenakes 2021).

Blockchain in Transport and Logistics Blockchain Establishing Itself Blockchain and DLT in general, is finding applications on multiple industries that rely on the immutability of data, trust, security, trustworthiness and transparency. As Underwood (2016) notes, it allows companies and individuals to cut out intermediaries, thus reducing transaction costs and the time overhead of involving third parties. Blockchain,

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however, has just started to unlock its full potential in supply chains, logistics and transport (Pournader et al. 2020). The anticipated fastpaced establishment of blockchain in Transport and Storage, among other industries, can be seen on a projection by ABI Research (2020) on Fig. 4.4. Apart from its present state, however, one of the pivotal points behind the current and future evolution of blockchain in Transport and Logistics (T&L), as with any new technology, is the emergence of a standardisation body to coordinate development and adoption among interested parties. As Koh et al. (2020) mention, IEEE and ISO sought to set standards for stakeholders in industry, government and other areas; similarly, the Blockchain in Transport Alliance (BiTA) was founded in 2017, aiming to act as a standards group in Transport and Logistics. The following section focuses on the benefits of blockchain and DLT, its advantages over the previously established technology, and what contributed to its success in the Transport and Logistics industries.

Fig. 4.4 Global industrial blockchain revenues (ABI Research 2020)

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Benefits and Applications in Transport and Logistics Contemporary intelligent logistics and transportation systems face multiple issues which emerge not only because of each system’s own implementational features, but also due to its connection and coexistence with IoT and Big Data, who also play a dominant role in supply chains and the modern logistics industry (Fu and Zhu 2019). Subsequently, the traditional issues of T&L systems must be examined in close correlation to adjacent technologies and only as a whole, rather than as an isolated unit. As observed by Fu and Zhu (2019) and Pournader et al. (2020), significant issues in T&L systems encompassing IoT and Big Data are observed under the contexts of security, traceability, transparency/trust, efficiency, privacy and supervision. An analysis of these and other T&L system issues, alongside the attributes of DLT and blockchain that address them, are discussed in Table 4.3. Considering the issues blockchain addresses and the value it offers, we can now investigate its potential applications and benefits offering in Supply Chain, Transport and Logistics. These can be observed in various aspects of modern society and industry, for businesses that implement it in their supply chain.

Business, Social and Environmental Benefits As Saberi et al. (2019) analysed, blockchain provides the ability to instantly share information and data modifications among departments and stakeholders. This can aid business finances by offering rapid deployment of products and processes, reducing human errors, transaction times and security system costs, resulting in reduced security risks and overall increasing business reliability and customer trust because of transparency. Apart from its financial benefits, however, Saberi et al. (2019) also highlight the social and environmental supply chain sustainability that blockchain can offer. Its immutable nature can prevent corruption from individuals, organisations or governments, whereas its traceability can

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Table 4.3 How does blockchain address common issues in Transport and Logistics systems T&L systems issues

How does blockchain address this

Security Data must not be maliciously changed, destroyed or leaked during storage, transmission and processing

Cryptographic signatures on transactions and messages ensure security and protection against hacking, data manipulation and data compromise (DHL CSI 2018) Utilising private blockchains, only specific users can have permission to update the ledger. This way processing is limited to members or employees of an organisation and can still be open to consumers (Deloitte LLP 2016) As Cheney et al. (2009) note, provenance in legacy systems was incomplete, unreliable, insecure and heterogenous. With DLT and blockchain, however, attaching reliable, secure and immutable provenance information to products and shipments, is not only possible, but also one of the technology’s strongest advantages in transport and logistics. Blockchain technology can enable traceability and provenance knowledge, as a system that can certify the authenticity and the integrity of products, as well as track custody (Montecchi et al. 2019) Specifically surrounding authenticity, registering all transactions on a blockchain can protect against counterfeit products. Ownership and certificates can be stored and passed along with transactions, via the use of smart contracts (Hughes et al. 2019)

Traceability System and product states should be timestamped, geotagged and attributed to actors in an unalterable manner

(continued)

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Table 4.3 (continued) T&L systems issues

How does blockchain address this

Transparency Need for a single, undisputed, trusted source of transactional and informational truth

As Craig Fuller, managing director of BiTA in 2017, highlighted, the most powerful part of blockchain is eliminating fraud, since enormous amounts of money are tied up in payment disputes daily (fleetowner.com 2017) Blockchain ensures records are accurate, verifiable and tamper-evident. Everyone collaborating knows they are using the most recent and accurate dataset (DHL CSI 2018) One of its major attributes is that it is decentralised, so there is no need for trust in 3rd parties or mediators, creating a trustless environment. Essentially, blockchain defeats the need for a trusted central organisation that controls the system, allowing customers to ‘inspect the chain of custody and transactions from the raw materials to the end sale’, while discouraging opportunistic behaviours (Saberi et al. 2019). This ‘disintermediation’ means that blockchain transactions have their own proof of validity and also reduced costs associated with involving 3rd parties (Niranjanamurthy et al. 2019) As Pournader et al. (2020) note, Mutual Distributed Ledgers (MDLs) can be used in multi-tier supply chains. These are essentially ‘multiorganizational databases with a super audit trail’, which can ensure security when transferring or storing signed documents and consequently increase trust among members (continued)

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Table 4.3 (continued) T&L systems issues

How does blockchain address this

Efficiency Avoid inefficient logistics and supply chain management

Less-Than-Truckload shipments are one of the major inefficiency issues in T&L, where trucks are under-utilised. Blockchain can be used to enable a ‘collaborative shipping marketplace’ which will consolidate freights, lower costs and increase truck utilisation (Sampath et al. 2020) With the use of private, permissioned blockchains (as opposed to public ones), access can be controlled and restricted to specific users or organisations that need to be safe-listed (DHL CSI 2018) With consortium blockchains, consensus is controlled by a pre-set number of organisations, and a number of them must ‘sign’ each block for it to be considered valid. Read access might be public or restricted (Buterin 2015) Blockchain in coordination with IoT can securely monitor, store and report on system, cargo and environment states. This can be very important in assuring ideal transport conditions or ensuring product quality from origin to consumption/end-use Furthermore, supervision is particularly crucial in various supply chain industries, for example pharmaceuticals and the cold chain (vaccine transport) (Singh et al. 2020). As Pournader et al. (2020) mention, smart contracts in the cold chain can be utilised to establish that desirable temperatures and other critical conditions are maintained throughout manufacturing and transport, and warn whenever abnormalities are detected by sensors

Privacy Protected system areas and datasets should not be accessed without permission

Supervision Need for guarantee of continuous fit state and reportability

(continued)

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Table 4.3 (continued) T&L systems issues

How does blockchain address this

Costs Avoid high administration, processing and other costs

As mentioned above, (a) blockchain directly reduces costs linked to mediators/3rd parties and (b) can be used to address the LTL (less-than-truckload) issue Reliance on paper and manual coordination increases costs significantly. Based on a research by IBM (2018) and Maersk, expenses related to documentation required in trading processes can soon amount up to 20% of the total costs of the physical transportation itself. DLT comes to reduce these costs and eliminate the cost to move paper documents across international borders

help assure human rights, fair and safe work practices, as well as the ethical sourcing of goods and raw materials. Product tracking can help reduce substandard products and recalls, reducing unnecessary energy waste and enforcing environmentally conscious and greener processes. This can also aid accurately calculating carbon footprints of products and easily enforcing carbon taxes to companies with high carbon emissions, thus driving environmental change and greener philosophy in the industry, through the motive of reducing costs.

Enabling IoT in Logistics and Supply Chain Management IoT is widespread in supply chains, where RFID tags and wireless sensors are integrated and established technologies. This prevalence of IoT, however, also creates security, financial and administrative concerns which blockchain can help address, while in parallel creating new opportunities to further enable the potential of IoT. As Christidis and Devetsikiotis (2016) mention, the traditionally centralised nature of IoT incurs a high-maintenance cost, makes the device updating process inefficient and creates trust issues with

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customers. All these issues can be addressed with a ‘scalable, trustless peerto-peer model that can operate transparently and distribute data securely’, in this case blockchain and the use of smart contracts. Khan and Salah (2018) discuss several IoT optimisations and security enhancements emanating from the use of blockchain and smart contracts. These include: • Providing trustworthy identity registration, ownership tracking and asset monitoring. • Ensuring data authentication and integrity since all devices are cryptographically proofed and signed. • Enhancing authorisation and access control, while enforcing the ‘right to update or upgrade’ a device. • Securing communications by replacing reliance to traditional insecure or heavy protocols. Blockchain eliminates the need for key management and distribution, since each device will have its own GUID and asymmetric key pair. Utilising smart contracts can also automate the buying, selling and even renting of services and property for limited amounts of time using microtransactions. This could include storage space, computational power or even energy in the case of IoT- and blockchain-enabled solar panels, and as Christidis and Devetsikiotis (2016) suggest, create a ‘marketplace of services between devices’.

Reducing Customers’ Perceived Risks Furthermore, as Montecchi et al. (2019) point out, blockchain-enabled provenance assurance in the supply chain can indirectly benefit businesses by reducing the perceived risks of customers. Providing origin assurance can verify the potentially rare, expensive or premium quality materials that make up a product. Traceability in this case reduces the perceived financial risk associated with paying premium price for unique or rare materials. Additionally, using blockchain firms can produce and attach digital certificates of authenticity to products which can accompany them through their lifecycle. This can eliminate psychological and

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social discomfort that customers experience when potentially purchasing counterfeit products. Using blockchain, companies can also offer custody assurance and guarantee pre-purchase quality, resulting in reduced perceived performance or quality risks. For example, it can be used to ensure that proper humidity, temperature and packaging or other conditions were met during storage or transport, in the case of an expensive wine purchase. The same example applies in the case of pharmaceuticals, where mistreatment, improper storage or transport or even fake/counterfeit medicine can result in potential harm for patients, blockchain can help reduce perceived physical risks and increase trust in the vendor (Fig. 4.5).

Fig. 4.5 Blockchain-enabled provenance knowledge (Montecchi et al. 2019)

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Challenges in Blockchain Adoption Despite the numerous benefits of DLT and its huge potential in various industries, blockchain also faces significant challenges to its widespread acceptance and commercialisation. A considerable barrier to the adoption of public blockchains are transactional limitations surrounding volumes (number of transactions per second), latency (time to add to the blockchain) and size restrictions (maximum allowed transaction size) (Pournader et al. 2020). Increasing the size and number of blocks to accommodate this introduces a ‘bloat’ issue, and especially in the supply chain this could pose a serious concern, since the expected number of transactions and accompanying data is high (Saberi et al. 2019). This could be addressed partially by technological and cloud storage improvements or as Lo et al. (2017) suggest, by using fine-tuned consortium or private blockchains and by storing the bulk of data off-chain. Furthermore, to address these issues to a satisfying degree, as Deloitte (2018) analyses, new consensus mechanisms are emerging constantly, aiming to increase performance and throughput in the blockchain. These include Delegated Proof-of-Stake and Practical Byzantine Fault Tolerance (PBFT), already utilised by various platforms like Hyperledger and others. One of the pressing obstacles blockchain faces, similar to most emerging technologies, is the lack of standards and regulations. As Morkunas et al. (2019) emphasise, blockchain architectures are not yet standardised, and projects are ‘based on different protocols, consensuses, privacy measures, and written in different coding languages’. To this end, multiple consortia including the EEA, the Hyperledger Foundation and BiTA aim to provide standards and guidelines to aid adoption. Another potential issue with blockchain is related to its immutability, which can be a double-edged sword in data entry. Erroneous data entered into the blockchain, potentially due to human error, cannot be corrected and will linger as an ‘immutable scar’ (Palombini 2017). Although ‘editable’ versions of blockchains for permissioned systems and enterprises have been introduced (Accenture 2016), this might not be viable in various use cases where the benefit of immutability is paramount. In these cases, an approach similar to accounting systems can be followed,

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where reverse transactions can be created to correct the erroneous ones. Obviously, sufficient control mechanisms can reduce the margin of error, albeit not eliminate it entirely. Similar to the above, the transparency attribute of blockchain introduces a privacy trade-off, which some companies might regard as a barrier, especially since all nodes maintain a full copy of the ledger (Tijan et al. 2019). As Sayogo et al. (2015) mention, ‘hoarding or hiding information is seen by some traders as a competitive advantage and strategy to mitigate threat of substitution’. This advantage can obviously be lost with blockchain. The only solution to this is researching whether blockchain is the right choice for each type of business, based on its business approach, establishment and philosophy of work. An obvious challenge to blockchain, as with all digital systems, is cybercrime. This can result in security issues and threaten the blockchain and its immutability attributes. Although blockchain is considered secure, as Hofmann et al. (2017) suggest, potential issues like 51% attacks still exist on public blockchains, although they would need huge amounts of resources (in computational power, hardware and energy) to enact, making them unviable. Additionally, quantum computing in the future could in theory make cyberattacks easier, but it should also strengthen cryptography, counterbalancing the security of blockchain. Apart from cyberattacks, however, unnoticed bugs can also lead to serious financial loses such as the DAO hack in 2016 (Hofmann et al. 2017), although these can be minimised via proper implementation plans including testing and debugging processes. On the other hand, lengthy implementation can incur high implementation costs, especially when blockchain is integrated with Warehouse Management System (WMS), Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), other systems common in transport and supply chain businesses, or even other blockchains (Fig. 4.6).

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Fig. 4.6 Survey (base: 600): Which of the following will be the biggest barriers to blockchain adoption in the next 3-5 years? (% of respondents ranking top 3 barriers) (PwC 2018)

Use Cases and Platforms Multiple companies have already implemented blockchain, usually in coordination with IoT, to enhance their operations, establish provenance knowledge (Montecchi et al. 2019) and ensure data immutability, security and trust. Worldwide blockchain investments and revenues are growing fast as presented in Fig. 4.4 earlier. Based on research by Gartner Inc. (2019b), blockchain as a technology has gone beyond the ‘inflated expectations’ stage and into the stage of ‘disillusionment’, where companies and the rest of the world start viewing it realistically and understanding its actual potential. As Kietzmann and Archer-Brown (2019) point out, blockchain ‘enlightenment’

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Fig. 4.7 Overview of use case exemplars (Hackius and Petersen 2017) (Icons: Vecteezy.com)

and its ‘plateau of productivity’ follows next, and various multinational corporations have already invested in large blockchain projects to demonstrate their trust in the technology. Some sample use cases are presented below, from a range of market-leading companies in the supply chain, Transport and Logistic industries (Fig. 4.7).

IBM, Maersk, GTD Solution: The TradeLens Platform In 2018, IBM and the shipping giant Maersk, announced their collaboration on a new blockchain platform that aimed to ‘provide more efficient and secure methods for conducting global trade’. As a result, the TradeLens platform was developed and launched by IBM and GTD Solution Inc., which already since 2019 had onboarded hundreds of organisations and was publishing more than two million events per day (TradeLens 2021). The main benefits and issues the platform is aiming to resolve are presented in Fig. 4.8.

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Fig. 4.8 Benefits of TradeLens (IBM Corporation 2018) (Icons: Vecteezy.com)

SITA: From Baggage and Air Cargo Tracking to Drone Registries SITA, the global air transport solutions corporation, has developed multiple blockchain projects using Ethereum, Hyperledger Fabric and other platforms. These have been utilised to digitally track and log information on airline cargo containers throughout their journey, maintain drone registries and track baggage (SITA 2019). The company has also issued the FlightChain paper, exploring the benefits of smart contracts in the air transport industry, in a research conducted with British Airways and other companies (SITA 2017).

Walmart: Produce Tracking with Hyperledger Fabric Utilising Hyperledger Fabric, an open-source distributed ledger framework, Walmart ran two proof of concept (POC) projects to test the capabilities of blockchain. Provenance tracing on specific test products (mangos) was reduced from 7 days to 2.2 seconds, and as a result the company has rolled out an IBM Blockchain system built on Hyperledger Fabric on more products and suppliers since. The company is planning to further expand its use of blockchain in cooperation with IBM Food Trust (Hyperledger 2019) (Fig. 4.9).

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Fig. 4.9 Walmart case study (Hyperledger 2019)

SkyCell: Ensuring Temperature in Pharmaceutical Logistics The Swiss pharmaceutical supply company SkyCell is utilising IoT in combination with blockchain to ensure ‘tamper-proof collection and storage of data’. IoT sensors measure temperature deviations of even less than 0.1% and audit their performance. Furthermore, each container is equipped with a smart card chip which provides it with a digital identity (key). This unique and immutable identity is subsequently used to store the state of each container on a blockchain throughout transport. As the company highlights, this guarantees the ‘authenticity, integrity and confidentiality’ of the data gathered from its containers. As SkyCell (2021) states: Digital integration will generate more transparency in logistics processes, consequently reducing cost and increasing patient safety. Technologies such as blockchain or artificial intelligence have only started to enter this very conservative industry.

ZIM, Wave: Shipping and Trade Document Management In 2017 the shipping corporation ZIM, successfully piloted a blockchain-based paperless Bills-of-Lading project with a select group of customers. The project proved to be more than efficient and as ZIM stated, ‘original Bill of Ladings were transferred to the receiver within under two hours from Vessel’s departure, a process that ordinarily takes days or even weeks’ . But paperless Bills-of-Lading was not the only advantage the project introduced. The blockchain platform, developed by Wave Ltd., used the

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DLT to enable all stakeholders in the trading process to ‘issue, transfer, endorse and manage shipping and trade related documents through a secure decentralised network’ . Furthermore, it improved activities that relied on email and legacy tools. ZIM planned to roll the platform out to all their customers in selected trades as a next step (ZIM 2019).

Brilliant Earth: Everledger for Transparency and Responsible Sourcing The jewellery design company Brilliant Earth is focusing on ethically sourced diamonds and is using the Everledger blockchain platform to ensure transparency. Integrating the platform in their supply chain, allows the company to ‘seamlessly and securely track gemstone origin, and provide greater consumer assurance for responsible practices’ (Brilliant Earth 2018). Everledger, as a DLT solutions provider, focuses on solutions surrounding sustainable development, carbon footprint reduction and the Responsible Minerals Initiative (RMI). The company was named a ‘Technology Pioneer’ by the World Economic Forum in 2018 and has multiple supporters in various industries (Everledger (Foreverhold Ltd.) n.d.).

Princes Group: Product Passports Using the Provenance Platform Project Provenance is a UK-based company that has built the Provenance data sharing platform, which utilises blockchain to support transparency in commerce and the supply chain. The international food and drinks group Princes have partnered with Provenance to support traceability of their fish products. Princes have created the ‘Product Passport’ (Princes Group 2021) which shows the journey of tuna from sea to shelf, in an initiative aiming to increase shopper trust (Project Provenance 2021).

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Other Companies and Applications Among other established logistics companies, FedEx, UPS and DHL have all joined the BiTA and have been evangelising the future use Blockchain over the last few years. Robert Carter, CIO of FedEx has highlighted the importance of its immutable and trusted nature, where all participants in the supply chain can contribute to the custodial chain of the product (FedEx 2018).

Blockchain Implementation in Modern Businesses The Use Case—Is Blockchain the Right Tool for the Job As with all new enterprise & IT systems, the main consideration to make when evaluating their potential implementation is:

Do You Actually Need It The blockchain implementation process in a business environment would start in a fashion similar to most new system implementation projects—identifying the Use/Business Case (Markus and Tanis 2000). This means figuring out the exact issues and challenges that blockchain will solve and how. Evaluating and trialling the potential effectiveness of an existing system instead of a new one, or even a change in strategy and internal policies, can save the business a big investment in time, human and financial resources. After clarifying that existing systems or internal shifts in strategy cannot address the issue and that a new system is required, the next step is evaluating whether blockchain is the answer. Based on its basic attributes and capabilities, a blockchain might or might not be the right choice for the issue; a traditional database or a different system could be more suitable depending on the case. Typically, as Wüst and Gervais

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(2018) suggest, blockchain is fit-for-purpose when all of the following conditions exist: • The need to store a ‘state’ or data representing the state of a participant. If there is no data to be stored, then there is no need for either a database or a blockchain. • Multiple ‘writers’ (e.g., suppliers or logistics partners) that need to store data simultaneously. If there is only a single party/organisation writing information, a regular database can provide better performance than blockchain, with reduced latency and increased throughput. • There is no mutual trust between writers, and there is no Trusted Third Party that can be always available online, assume the responsibility of writing data to the system and verify state transitions. In contrast, if all writers trust each other, a database with shared write permissions can substitute the need for blockchain. As Lo et al. (2017) suggest, performance might be another deciding factor, especially in the supply chain industry where large amounts of data are exchanged. If consortium or private blockchains are out of the question, or if the bulk parts of the data that do not absolutely need to be immutable cannot be stored off-chain, a conventional database might be the faster solution in this scenario. The chart in Fig. 4.10, proposed by Peck (2017), provides a decision tree that can help verify the need for a blockchain in an organisation.

Barriers in the Implementation Process As discussed in the previous chapter, there are various challenges in the widespread adoption of blockchain, including performance, lack of standards, security and immutability/privacy trade-offs. Apart from these general hurdles in the overall establishment of blockchain in the industry, there are also a few potential barriers that could arise during the implementation of blockchain itself in an organisation.

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Fig. 4.10

Do you really need a blockchain? (Peck 2017)

Preparing for barriers with a clear implementation strategy is crucial to the success of the project, once blockchain has been identified as an appropriate solution. This section aims in identifying some of these barriers and highlighting them in isolation due to their importance, prior to encountering them later during the implementation process (Fig. 4.11).

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Common barriers in the implementation process (Icons: Vecteezy.com)

Cost Estimation and ROI First and foremost, the need for accurate evaluation of required financial resources, is a significant barrier and a pitfall common to all Information Systems development projects. Especially in blockchain projects, where the benefit of lengthy, trialled and established knowledge does not apply, a detailed cost estimation, risk assessment and Return-on-Investment (ROI) analysis is quintessential. As Legris and Collerette (2006) highlight, ‘in order to assess the feasibility of the project and its constraints, a reliable cost evaluation is required’. The costs associated to the solution should be well documented, in order to make sure that the investment is suited for the organisation’s capabilities and will not financially threaten it. Furthermore, all the following sections can also incur cost fluctuations and should be considered prior to implementation.

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Integration with Existing Infrastructure This is probably the most important barrier of the implementation process, especially for large organisations with ERP, WMS, CRM and other systems in place. There could also be a need for integration with other blockchains to ensure interoperability, further increasing risk and the potential for added—obvious or hidden—costs. As Holotiuk et al. (2018) note, the ‘challenge of incorporating blockchain into the existing infra-structure’ is viewed by businesses as the biggest challenge in blockchain projects. This issue is especially important on greenfield projects, where blockchain is being introduced for the first time with no previous working experience to drive policies and constraints. As a possible solution, the adoption of an intermediate ‘integration layer ’ is proposed, as a means to connect blockchain to existing infrastructure. Additionally, as Scriber (2018) outlines, adding an intermediary backed by a blockchain to existing disparate systems can lead to performance, communication and data availability or protection issues.

Expertise and User Training Insufficient blockchain literacy can be devastating and lead not only to a failed project but also to adoption failure after rollout due to inefficiencies. Carrying out the implementation in collaboration with an external consultancy or development agency might be advisable if the required expertise does not exist within the organisation. Furthermore, Legris and Collerette (2006) point out the significance of internal training to accumulate in-house know-how and overcome the feeling of inexperience, discomfort or confusion towards the new system. This is especially important and can ‘turn the usual failure experience into a successful one’, even if it entails additional investment in individualised training. Investment in User Experience and a friendly User Interface can also help towards users embracing and understanding the system faster.

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Assurance of Performance and Scalability Another important issue is making sure the system can accommodate future needs in transaction volumes and processing. Essentially, making sure the blockchain is not only capable of fully supporting the business as it is, but also ensuring the new system is future proof. As Altarawneh et al. (2020) note, ‘for a blockchain to be scalable, it must be able to handle a high number of transactions in a small amount of time. This means that, as more nodes enter the network, blocks need to be either created more quickly or become larger in size’ . However, the Scalability Trilemma (Ethereum Foundation 2021), states that blockchain systems ‘can only at most have two of the following three properties’—decentralisation, security and scalability. This means that any two among them can only be increased at the expense of the third, and a blockchain implementation should evaluate the required balance between these in the chosen solution.

Manager & Stakeholder Resistance As Walsh et al. (2020) mention, the Status Quo Bias should be considered, introduced by Samuelson and Zeckhauser (1988), where ‘Individuals can become resistant to adopting a new technology because of their bias or preference to stay with their current technology’. This could easily be the case with blockchain. As discussed, a higher ‘perceived need for new infrastructure’ among managers, will have a stronger positive effect on the blockchain’s perceived value in general and will better justify the associated switching costs. This of course applies both to managers and involved stakeholders. Based on interviews relating to stakeholders on a large scale blockchain project, Beck and Müller-Bloch (2017) note that the earlier you involve ‘more of the business’, the easier the journey will be. This is because more key stakeholders and parts of the business will be onboard with the new technology early, they will feel more connected to the changes, and the new solution will not need to be ‘sold’ to them at later stages (Fig. 4.12).

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Fig. 4.12 2020)

Stakeholders in Supply Chain blockchain systems (Hastig and Sodhi

Regulations and Cross-Country Legislation As Hastig and Sodhi (2020) discuss, blockchain implementations are subject to the wider social, legal and regulatory frameworks in which they reside; thus, blockchain projects should not be treated as if they were confined within their project limits or in isolation. Multiple actors beyond the implementing organisation can exert control over the way blockchain operates, including governments and non-governmental organisations, trade associations and other social or environmental regulators. This wider regulatory framework can be especially troublesome when suppliers or 3rd parties contributing to the blockchain operate in a

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variety of countries, or when multiple blockchains across different countries are connected. This could mean that legislations might also differ and affect what is considered acceptable operation for the project.

The Key Stages of Blockchain Implementations There are different approaches to implementing IT and blockchain projects and each individual implementation is unique. This section intents to highlight some of the critical decisions that need to be made throughout the process, act as a guideline to areas of focus and important milestones, assist decision making and help reduce implementation risks. For the implementation stages discussed here, a model similar to the one proposed by Markus and Tanis (2000) for Enterprise Systems was followed as a backbone as seen in Fig. 4.13, adjusted and accommodated to suit the specific needs of blockchain projects.

Fig. 4.13

Framework for Enterprise Systems (Markus and Tanis 2000)

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The project starts with the most important (and potentially most time consuming) part, the identification of a use case for blockchain and an accurate estimation of its feasibility and sustainability via a detailed POC. This stage includes thorough evaluation and analysis, as well as onboarding stakeholders and 3rd parties. After the Evaluation stage, the now well-defined and agreed project proceeds to the Development stage, where the blockchain ecosystem is materialised. This entails clearly defining policies and new relationships between stakeholders, assembling the required human and material/technical resources, and ultimately building and testing the project itself. Following the build-test iterative process, the Rollout phase includes training system actors and pushing the new system to the production environment. Finally, the Onwards phase ensures normal operation can be maintained moving forwards, and that the system is constantly evaluated, updated and improved to remain fit-for-purpose. The proposed framework for blockchain implementations can be seen in Fig. 4.14, divided in the Evaluation, Development, Rollout and Onward stages.

Stage 1: Evaluation—Determining Use Case, Feasibility and Sustainability This Evaluation stage is the cornerstone of any blockchain project, as it aims at clearly determining the details, the elements and feasibility of the entire implementation. This stage is the main focus of this study, since the success of the project depends on how well it is executed. It is the part where most decisions need to be made and where managers need to be involved to a larger extent. As discussed earlier, after the use case has been identified, the first step is deciding if blockchain is the right tool for the job; this will greatly depend on the use case. As Hughes et al. (2019) suggest, this involves considering problems in the organisation that need to be solved, and how they could be solved with blockchain technologies. Furthermore, a clear view of what needs to be achieved must be established, to determine the level of innovation and risk involved. Blockchain as a new technology can

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Key stages of Blockchain implementations (Icons: Vecteezy.com)

easily introduce new links between business concepts and components, and/or entirely redefine core concepts in the organisation. Introducing a high level of innovation in such a manner, also translates to high risk and ‘unmistakable challenges for established firms’ (Henderson and Clark 1990) (Fig. 4.15). Dobrovnik et al. (2018) suggest that single-use cases or internal (e.g., administration focused) implementations are a good starting point for most logistics companies, before moving on to more complex blockchain projects. The lower risk and less third-party coordination involved in these, can provide useful experience and an opportunity for stakeholders to test on a smaller scale before larger scale rollouts.

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Fig. 4.15

Framework for defining innovation (Henderson and Clark 1990)

After a clear use case has been defined, the exact actors and stakeholders should be examined and onboarded. Regardless of whether external consultants or IT experts are involved, or whether the project is contained within the organisation, picking the right people for the job based on their previous experience, portfolio and expertise can makeor-break a blockchain project. Furthermore, as Pinto and Slevin (1987) state, a successful project implementation does not only come down to top management support and competent team members, but also a competent project manager who is skilled interpersonally, technically and administratively. The next step is defining the actual solution via a Proof-of-Concept. Due diligence at this stage is very important to evaluate the projects: a. Feasibility This includes accurately defining the required current and future performance and scalability needs, as well as choosing the right platform to achieve this, commercial or bespoke/custom. There are numerous platforms available in the market, each with their advantages and disadvantages (Knirsch et al. 2019) and new platforms are developed constantly.

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Listing and comparing implementation platforms is beyond the scope of this study and should be discussed with implementation experts and in relation to the selected use case. In general, as ABI Research (2020) suggests, a number of factors should be considered when choosing the right partner and platform, most importantly their previous experience and commercial launches, integrations with industrial systems and IoT, ecosystem and partnerships, platform offering, developer/training resources and admin/management tools. The potential of BaaS (Blockchain-as-aService) solutions should also be evaluated depending on the project. A key decision also tied to the selection of platform at this stage includes the type of blockchain (public, private, permissioned, permissionless etc.) as analysed in previous sections. This again greatly depends on the use case and comes down to defining who can read and who can write to the blockchain, its immutability attributes and its consensus mechanisms. The appropriate type of consensus needs to be carefully decided prior to implementation, since it directly impacts scalability (Zhou et al. 2020). There are various different types of consensus as discussed earlier (Proof-of-Work, (Delegated) Proof-of-Stake, PBFT and more) with their individual attributes, usually linked to the implementation platform unless a fully custom approach is chosen. b. Sustainability As analysed in the previous chapter, blockchain can help reduce costs significantly. As an example, successful blockchain implementations can enable leaner, automated and error-free processes (Dobrovnik et al. 2018), improve inventory management, reduce fraud, waste and delays (Tijan et al. 2019) and as Christidis and Devetsikiotis (2016) observe, automate workflows and provide significant cost and time savings in combination with IoT. Blockchain implementation however can be costly in itself, especially when considering potential legacy data migrations involved in the process (Crosby et al. 2016) or integrations with other systems. In fact, as Deloitte Development LLC (2018) notes, the cost and complexity involved with building and deploying blockchain

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solutions is considered as one of the most significant obstacles in its adoption. Consequently, apart from the technical feasibility of the project, numerous other attributes should be considered in order to determine whether the project is viable and sustainable. These include investing special effort in risk assessment and accurate cost estimation for the entirety of the project. Cost estimation should not be limited to preimplementation and should also consider the ‘Onwards’ stage and future development, as well as worst-case scenarios throughout the process (points-of-failure, unavoidable iterations, 3rd party delays etc.). This should help provide a clearer picture and help assess ROI with a higher precision. Equally important for blockchain projects, is careful research surrounding internal and external stakeholders. As mentioned, privacy and security concerns from involved organisations can reinforce the Status Quo Bias (Walsh et al. 2020) and hinder the implementation. Furthermore, as Lo et al. (2017) note, the supply chain ‘consists of complex, dynamic, multi-party arrangements with regulatory and logistical constraints spanning across different jurisdictional boundaries’. This means that a careful study should be conducted to guarantee ‘adherence to regulatory and data protection requirements’ (Walsh et al. 2020) and also ensure the solution’s availability for all parties and in all countries involved.

Stage 2: Development—Build and Test the Blockchain Solution At this stage, all functional requirements of the project should be clarified in detail with the stakeholders. This will help to manage expectations and avoid cases of ‘scope creep’ (Garrett 2010), where new desired functionality is constantly discovered and added during development, delaying rollout significantly. As an outcome of the implementation process, blockchain will introduce new relationships between business components and internal or external stakeholders. Therefore, an ecosystem of policies and rules

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should be documented to accommodate these new relationships (PwC 2018). Niranjanamurthy et al. (2019) also note that blockchain introduces significant changes or complete replacements to existing systems, and organisations must strategize accordingly to accommodate the transition. The arsenal of all required human, software and hardware resources, needs to be assembled in order to proceed with project development. As highlighted, apart from the actual development of the solution itself, this stage should incorporate integrations, to ensure communication with other internal or external systems. As Pan et al. (2020) state, effective integration in blockchain projects plays an important role in improving operational capabilities and avoiding the ‘information island’ effect, which finds information ‘distributed among different enterprises in the supply chain, with low degree of sharing, slow operation and poor information authenticity and reliability’. Integration with other blockchains could also be required. Moreover, blockchain technology provides the underlying protocols but is not a complete application in itself. Other areas of development in a blockchain project might include decentralised applications executing smart contracts (dApps) (Cai et al. 2018), and incorporating User Interfaces (UI), business logic and data persistence, as discussed by Gartner Inc. (2019a). An integral part of the development process and equally important towards producing a working and sustainable solution is testing. As Porru et al. (2017) note, testing is required to ensure that BlockchainOriented Software (BOS) is trustworthy and ensures data integrity and uniqueness. To this end, Smart Contract Testing (SCT) and Blockchain Transaction Testing (BTT) should be conducted to ensure compliance to specifications and correct operation. Last but not least, it is imperative to test in coordination with all involved parties, including: (a) external stakeholders to verify cross-system compatibility and satisfaction, (b) internal stakeholders to verify internal systems coordination and that expectations are met and (c) User Acceptance Testing (UAT) to verify usability. The results of these tests will feed back into the iterative process of development/testing, until the project is ready for rollout.

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Stage 3: Rollout—User Training and Go-Live General guidelines and advice after this point should be tailored to each specific implementation. Nevertheless, it should be noted that an essential part of the blockchain system’s rollout preparation, is adequate user training. Although training needs are sometimes perceived as a barrier to blockchain implementations (Hoek 2019), user training is also one of the project’s critical success factors, similar to most Enterprise Systems (Ahmad and Cuenca 2013). As Kim and Kankanhalli (2009) point out, introducing new Information Systems introduces a new way of working, and without guidance, training and appropriate learning resources, user reactions can be negative and resistant to the change. This also shows that helpful documentation is also important alongside training, for users to embrace the new system. At the end of the day, proper know-how will increase efficiency, reduce errors and keep the system utilised to its full capabilities. Initial configuration and getting the system ‘up-and-running’ on the production/live environment follows next, alongside ensuring proper live operation without issues.

Stage 4: Onwards—Debug and Optimise Following system rollout in a live environment, establishing normal operation is important. This includes debugging where necessary; the first few weeks are crucial to successfully identifying and fixing code and system issues. This requires constant careful observation by the development team to avoid rollbacks and system failures. As highlighted by Porru et al. (2017), blockchain as a relatively new technology is in need of ‘enhanced testing and debugging suites’, and isolated testing might be hard due to the distributed nature of the blockchain. This lack of established platforms and increased testing and debugging difficulty increases the complexity of the project and thus, the responsibilities of the development team.

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Apart from debugging, system audits and internal/external user feedback will help identify quick-wins and areas of optimisation. Optimisation of course will be an ongoing process, to ensure the system remains fit-for-purpose, relevant, increasingly efficient and with a competitive advantage against alternative solutions. Other key post-rollout areas include establishing user support channels and teams for troubleshooting and regular maintenance. Continuous business improvement, ongoing skill building, as well as postimplementation benefit assessments also play a key role at this stage (Markus and Tanis 2000). Post-implementation assessments should also include thorough data analysis and regular reporting, based on system states and output, which is required to produce KPIs, add value in the long run, adapt to the new environment and utilise the benefits of blockchain to its full potential.

Future Evolution The concept of blockchain has attracted the attention of people in recent times due to its emerging popularity, as cryptocurrencies like Bitcoin have been successfully adopted as the mainstay of blockchain. The future of blockchain will revolutionise traditional business processes and blockchain technology can be applied to various industries, experts believe (Marcell 2020). In contrast, blockchain is a bit inadequate to meet investor expectations as blockchain, like other new technologies are still immature in its implementation, but the increase in funding for blockchain start-ups that took place over the past year cannot be overlooked (Nofer et al. 2017). Furthermore, time and patience are essential in the blockchain technology as this technology is based on a gradual process, however, there is no doubt that blockchain technology will affect every aspect of businesses in the upcoming times (Batta et al. 2020). As per the prediction of Gartner, most traditional businesses will still wait for the best applications of blockchain technology and just keep an eye out, but not make a plan of action. The banking and finance industries will not face many difficulties in adopting blockchain technology as other traditional businesses, and they may seriously consider adopting

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blockchain, if it is implemented successfully (Pilkington 2016). The concept of blockchain will bring considerable savings to banks, reduce excessive bureaucracy, improve the confidentiality of transactions, and provide speed, as the concept is simple and intuitive. In addition, new cryptocurrencies to be regulated and influenced by monetary policy can be launched through blockchain technology (Marcell 2020). The idea of the distributed ledger is extremely appealing even to government officials handling large amounts of data, as well as agencies in constant need of information about clients from each other as they each have their own separate databases. Hence such agencies can improve performance and do effective data management by implementing blockchain technologies. Blockchain is a relatively new technology, so opinions on its possibilities are varied. In a TechRepublic Research poll, 70% of experts claimed they had never heard of the blockchain. However, 64% anticipate blockchain to have an impact on their sector, with the majority expecting a good outcome (Rejeb et al. 2021). The following prediction was made in a modern Trend Making Judgments by research company Gartner: • The value of blockchain to businesses may have risen only to above $360 billion, and by 2030, it would increase $3.1 trillion by 2026. • Only 10% of businesses would use blockchain to accomplish any significant impact by 2022. • At least one creative blockchain-based firm will be valued at $10 billion by 2022 (Rejeb et al. 2021). One of the most promising areas for blockchain’s future growth is Cybersecurity. Data tampering is a persistent problem for organisations of all sizes. Blockchain technology may be utilised to avoid information manipulation and rules and strategies to confirm the validity of a document (Schulz et al. 2020). According to the prediction of Gartner, by the year 2022 more than a billion people will have some data stored on the blockchain without their knowledge (Marcell 2020). There is still a lack of blockchain experts in the job market while blockchain remains at the top of its popularity.

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Blockchain technology is new and has a limited number of knowledgeable engineers in the job market; as the online freelancing database ‘Upwork’ reports, there has been a rapid increase in demand for people with blockchain skills recently. International Data Corporation (IDC) was expected to deploy 20% of IoT services to blockchain services by the year 2019 and that is why many IoT companies are considering the implementation of blockchain technology for their solutions, as per the IDC (Zheng et al. 2018). Interestingly, according to Atlam et al. (2020) the blockchain technology is looking to be incorporated into IoT network architecture, as it could be a great solution to eliminate the implication of having a centralised architecture in the IoT domain. It is estimated that there will be around 80 billion IoT devices by 2025 and the presence of blockchain will play a crucial role in this upscaling. There is a great opportunity for the blockchain technology to take part in the continuous growth and development of the IoT domain, as well as Artificial Intelligence. The integration of IoT and blockchain brings along many advantages. For example, implementing and introducing the decentralisation and distribution of blockchain can tackle issues such as single point of failure and security, which is typically associated with a centralized IoT architecture, since there is no need for a central server to control IoT devices and the communications between them (Fig. 4.16). The whole system of transportation and logistics can collect and securely share data via the use of blockchain, while not being vulnerable to manipulation. Improved tracking may also have an influence on the preservation of items in transit (Ar et al. 2020). With the aid of IoT developments, blockchain can be especially beneficial for resource management. The cost of carrying freight is frequently determined by cargo volume. Moreover, in this manner, shippers and transportation businesses can identify a lot of bandwidth absorbed in a package and calculate pricing appropriately via deploying IoT sensors in vehicles and other transport devices and transferring all this data to the blockchain. Additionally, tracking is important for more than just supply success, as blockchain could be used to monitor the effectiveness of personal cars in a group (Rožman et al. 2019). When a major or smaller firm wishes to buy a used mail truck, the blockchain can assist validate data about

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Fig. 4.16

Future growth in IOT devices (Atlam et al. 2020)

the car’s prior productivity and service record. Blockchain could assist authenticate driver records of a national provider, even though it can simply confirm the records of a used car. This difficulty may be solved with blockchain, which creates a distributed system that stores all of the required data for providers in the transport and shipping sectors. It would be very hard to fake this data, and it would be virtually instantaneous to check it. Furthermore, blockchain technology could be utilised to increase traffic performance (Winnesota 2021).

Conclusion Blockchain would change corporate operations in several areas of information technology, but still needs time to be properly assimilated and implemented. Nonetheless, authorities could be anticipated to eventually acknowledge the advantages of blockchain technology and start using it to improve economic and organisational operations in the near future. Even if some blockchain businesses fail, individuals will get greater insight and expertise in using the technology. Customers are willing to learn new skills as a result of blockchain, whereas established firms will have to fundamentally rethink their operations. Overall, it is expected

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to see more effective uses of blockchain in the coming years, whereas blockchain’s potential future in the industry of Transport and Logistics is bright, as the revenue for blockchain within this particular industry could end up being enormous in the near future.

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Part II Data Analytics Strategies and Technology-Enabled Integrated Business Models in the Digital Age

5 Industry 4.0 Driven Supply Chains—Technological Advancements Regarding Logistics Service Providers Ajinckya Dahibhate, Farooq Habib, Abdul Ali, and Murtaza F. Khan

Chapter Introduction This chapter seeks to examine Industry 4.0-driven supply chains with specific focus on technological advancements relating to logistics service providers. The aim of this work is to identify the service characteristics of Logistics Service Providers (LSPs) and their technological advancement to satisfy their customer needs. Additionally, the chapter provides novel insights about Industry 4.0-driven Supplier Selection and Evaluation (SSE) processes and risks involved in changing LSPs, and how A. Dahibhate · F. Habib (B) Cranfield University, Cranfield, UK e-mail: [email protected] A. Ali University of Greenwich, London, UK M. F. Khan University of Law, London, UK

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_5

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organisations can plan a seamless LSP transition. In order to achieve the above, the following 4 research objectives are set: 1. To examine the service characteristics of current and alternative logistics providers 2. To examine the LSP selection process 3. To investigate the risks and barriers in changing the current logistics model and logistics provider 4. To develop a detailed transition plan for organisations to migrate from the current logistics provider to the proposed logistics provider. These research objectives are the basis of the key themes that will enable a deeper understanding of the research topic.

Outsourcing Businesses strive to employ strategies that provide competitive advantage for the organisation. Understanding organisational competitive advantages is dependent on the discrete activities performed by firms including designing, manufacturing, marketing, logistics and product support function. Therefore, Michael Porter (1985) developed the concept of ‘The Value Chain’ (Christopher 2016). Figure 5.1 displays the categorisation of value chain activities as primary and secondary activities. Therefore, the gain in competitive advantage depends upon how effectively and exceptionally companies perform these value chain activities over their rivals to deliver value to their customers. That is why porter suggests that the organisations assess each, individual activity in their value chain and gauge whether they experience a real competitive advantage in that activity. If not, the organisation should outsource the activity to a service provider to provide cost and/or value advantage.

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Fig. 5.1 The value chain (Porter 1985)

Logistics Outsourcing A logistics function of an organisation includes all levels of management and involves tactical, operational, strategic and collaborative decisions (Bartolacci et al. 2012). Worldwide, logistics has emerged as an evergrowing multi-billion-dollar business. According to Langley and Infosys (Langley and Infosys 2019), the national expenditure for logistics business is 10% of GDP. As a result, the organisation has progressively perceived outsourcing logistics activities as a strategic decision to move products efficiently in today’s increasingly complex supply chains. Similarly, the outsourcing level of logistics activities to LSPs has enhanced in recent years. According to the 2017 logistics outsourcing market reports, it has experienced revenues of $869 billion globally, and in upcoming years it might even exceed this (Prataviera et al. 2021). Logistics outsourcing has become mainstream; almost 80% of supply chain professionals indicate that they increased their outsourcing budget in 2020 to support activities beyond warehousing and fulfilment to support complex logistics operations. According to Gartner’s survey, 42% of businesses are looking to outsource their logistics functions to a 4PL service provider who can design, build, run and measure logistics functions (Bingelow 2019).

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Logistics Service Providers A big market is open to significant challenges; therefore, to maintain competitive advantages, businesses outsource their logistics functions to professional LSPs who have in-depth knowledge and expertise in carrying out complex logistics operations. There are plenty of LSPs available in the market who provide 1PL, 2PL, 3PL, 4PL and 5PL services. However, the focus of this thesis will be on 4PL LSPs (Christopher 2016; Ciemcioch 2018).

From 3PL to 4PL The primary difference between the 3PL and 4PL service providers is shown in Fig. 5.2. The 3PL service provider often operates distribution centres, delivers product through fleets, and sometimes undertake value-adding services like packaging and re-packing. However, the 4PL service provider enables their customers with a control-tower view of their SCM function, for instance providing oversight of the mix of shipping industries, warehouses and freight forwarders under management. Due to changing nature of supply chain networks and increasing globalisation, it has become difficult for one organisation to manage such complex supply chains. Therefore, organisations need a service provider that can contract a focal firm through a joint venture, who can use its knowledge and 3PL service providers to manage the end-to-end supply chain (Christopher 2016; Ciemcioch 2018). The 4PL assembles the best breed of service providers utilising IT capabilities to offer sustainable and cost-effective solutions. Figures 5.3 and 5.4 summarised the principle behind the 4PL.

Rise of 4PL In 2020 when the whole world was undergoing the COVID-19 pandemic, many organisations such as Opel and Vauxhall realised that they lack the computing infrastructure, resources and personnel to deal with disruption. Therefore, these organisations appointed a 4PL

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Fig. 5.2 Difference between 3PL and 4PL (Ciemcioch 2018)

provider offering sophisticated digital technologies (e.g. Big Data, cloud computing, GPS tracking) to streamline transportation, legal compliance, warehousing and supplier management (Logistics Bureau 2020). Insight Partners states (2020) that in 2019 the global 4PL market was valued at $56,472.1 million, and by 2027 it is predicted to reach $78,981.5 million, and that this industry will grow at a CAGR of 4.5% between 2020 and 2027 (The Insight Partners 2020).

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Fig. 5.3 The 4PL concept (Christopher 2016)

Service Characteristics of LSP Model and Service Provider The chapter revolves around the logistical models in the automotive industry, as companies therein have been considered the most extensive and multinational of all industries. Therefore, it is essential to understand the automotive logistics model. The automotive logistics process has two primary functions: inbound logistics of raw materials, manufacturing equipment, spare parts, loading, unloading and storage. Secondly, there exist outbound logistics aimed to deliver vehicles (finished product) from the manufacturing plant to the customers (Sabadka 2015). Industries now consider logistics as a critical pathway to achieving competitive advantages. Therefore, many automobile industries have started to outsource some or the entire logistics function of LSPs

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Fig. 5.4 Four key components of a 4PL (Christopher 2016)

(Zacharia et al. 2011a). For example, according to Rajahonka and Bask (2016), the Japanese car manufacturer Toyota has adopted a combination of in-house and outsourced logistics functions. As a Lead Logistics Provider (LLP), Toyota is responsible for logistics management. Similarly, Renault is also accountable for its logistics management but contracted 100 LSPs for transportation. However, Nissan has outsourced their entire logistics function between four LSPs. For the past two decades, services

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and strategies offered by LSPs have been in transition, and their role in logistics operation is expanding. Now LSPs can provide various services according to the customer’s logistics strategies and design service models related to 11.1.1.1.1.1.1.1 compile categorisations available in the LSP service strategies and service model in the literature.

Service Provided in Logistical Model by LSPs The following are the services provided by LSPs to their customers (Fig. 5.5)

˘ Fig. 5.5 4PL services (Çaglar Kalkan and Aydın 2020)

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• The main objective of the LSPs is to enable the executive management of the buying organisation to focus on organisational core competencies (Ça˘glar Kalkan and Aydın 2020). Furthermore, LSPs integrate the needs of customers and resources by contacting the business process management, the IT providers and 3PL providers (Win 2008). • Apart from playing the role of transactional centre, LSPs create values by managing information (Fulconis et al. 2006). Furthermore, LSPs build close relationships with stakeholders involved in the supply chain to initiate cost-cutting exercises and enhance flexibility to counter-balance the uncertainty in supply and demand (Ça˘glar Kalkan and Aydın 2020). • LSPs are the supply chain service providers that participate in supply chain coordination by providing value-adding services, such as IT integration, order tracking and tracing, transport planning, financial services and logistics consulting (van Hoek and Chong 2001; Vasiliauskas and Jakubauskas 2007). • LSPs provide global solutions to their customers by integrating the services and activities of diverse carriers, operations, storage, packaging companies and subcontractors. Furthermore, the service providers offer services related to continuous improvement of supply chain processes such as cost-optimisation, SLA and inbound-outbound delivery efficiency (Fulconis et al. 2006; Nowodzi´nski 2010). • Many LSPs offer sub-tier visibility to their customers. Hence, if an issue akin to the most recent Suez Canal crisis arises, the 4PL service provider can quickly assess the risk exposure and reroute the cargo or, in parallel, contact the alternative freight forwarder to deliver the critical products (Versed.AI 2021). During the Suez Canal crisis, the Kuehne + Nagel firm rerouted their shipments via services, combining sea, air and railroad, from China to Europe (Kuehne + Nagel 2021). Table 5.1 summarises the service characteristics of the LSPs represented in the existing literature.

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Table 5.1 Service characteristics of LSPs (Alvarez 2020; Grant 2019; Hofmann and Osterwalder 2017; Jurczak 2018; Kennedy 2020; Nailwal 2021) Service characteristics of LSPs

LSPs 1PL

2PL

Self-management of logistics function (own assets) Lease of own transportation (asset-based logistics services) Transportation and freight forwarding Warehousing, including inventory management Picking, packing and labelling Reverse logistics (returns), customer relationship management Full truckload and less truckload shipping Project management (PM), sourcing and negotiations Consultancy function End-to-end integration Logistics planning and control Transport management (3PL management) Procurement of stock The flow of the capital needed for planning, delivery, tracking Provision of IT platforms

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3PL

4PL

5PL

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Technologies Used by LSP Freight Transport Logistics (FTL) is a critical function in automotive supply chains, which involves moving materials and finished goods. As a result, the FTL sector is experiencing a wave of digitalisation (Tipping and Kauschke 2016). The following describes key technologies that LSP use in the FTL sector:

Cloud Computing The previously labelled ‘on-demand computing’ has become mainstream in the logistics industry. The technology allows LSPs to host an ICT system for their customers, usually using remote network servers via the internet to store, manage and process data. This technology offers

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Fig. 5.6 Cloud computing (AWS 2019)

FTLs the capacity to forego building EDI links and providing services like SaaS and PaaS. It creates flexibility in the FTL industry (Wang and Sarkis 2021). Notably, 5G Technology, alongside cloud computing, will provide increased connectivity. For example, Port of Livorno uses 5Gbased cloud computing technology that enhances efficiency, decreases the transit time of goods and generates real-time shipments data (Cavalli and Lizzi 2020). Edge computing complements cloud computing and helps LSPs resolve issues related to bandwidths and latency at the edge of upstream and downstream (Shi et al. 2016) (Fig. 5.6).

Digital Twins IoT and edge computing enable LSPs to implement digital twins; a virtual representation of real-time data, objects, processes and complex ecosystems of connected objects. It is not yet a widespread application in logistics. However, critical technologies such as shipment tracking devices, API strategies and cloud computing that enable digital twins are already in place. DHL has a digital twin in practice where they offer their automotive customers container packaging strategies by implementing sensors in containers that then consolidate the data of temperature, shocks and vibration. Furthermore, DHL creates a virtual model of their entire logistics network, including data of logistical assets and oceans, air,

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Fig. 5.7 Digital twins (Gesing and Kückelhaus 2020)

road freights and customers’ homes and workplaces. This allows the LSPs to use real-time data on physical networks to plan for future disruptions and alternatives routes (Gesing and Kückelhaus 2020; Wang and Sarkis 2021) (Fig. 5.7).

IoT and Big-Data Analytics IoT devices are perfectly embedded in transport networks, fleets and shipping containers; generating masses of structured and unstructured data. As a result of managing such massive amounts of data, LSPs use bigdata analytics, which provides transformative, strategic-level impact. For example, dynamic time-tabling and transport planning, rooted in decision mathematics, use real-time data to simultaneously optimise carrier and cargo-tracking transport infrastructure capacity (Wang and Sarkis 2021). In addition, NAEKO Logistics uses bid-data analytics for route optimisation, which determines the best-suited route among the other options and helps organisations refine transportation in terms of cost and time (Feliu 2018).

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Fig. 5.8 Blockchain in logistics (Heutger and Kückelhaus 2018)

Blockchains Every LSP considers logistics its lifeblood, though involving many parties with various priorities and diverse trade efficiencies. As a consequence, blockchain technology generates an attractive alternative—at its best, combining custom collaboration, transport management, trade finance and shipment tracking (Heutger and Kückelhaus 2018). For example, IBM and Maersk collaborated and established a blockchain system for end-to-end tracking and tracing shipments, and trade workflow, enabling all involved stakeholders to transparently track shipments in the deep sea (Armonk 2017) (Fig. 5.8). Summary of the selected papers for the emerging Digitalisation Technologies in FTL adopted from Wang and Sarkis (2021) is shown in Table 5.2.

The focus of research paper

Technology that focuses on supply chain collaboration (Platform Ecosystem)

Technology that focuses on supply chain collaboration (Platform Ecosystem)

Next-generation intelligent logistics operations (Metaheuristic, machine learning)

Research papers

Choi (2020)

Xu et al. (2020)

Fazi et al. (2020)

To resolve the issues related to optimal transportation selection with an internet-based Elastic Logistics Platform (ELP) This paper aims to resolve supply chain coordination problems of a manufacturer as they sell their products through an offline channel To resolve the issues involved in planning the transportation of containers between the sea terminals

Research objective

Table 5.2 Emerging digitalisation technology in FLT (Wang and Sarkis 2021)

The research paper has developed a local hybrid search Metaheuristic Algorithm combined through a branch-and-cut solver to resolve the issue. Therefore, the created framework contributes to developing the compressive set of decision support tools for planning transportation

The researchers have stated that the supply chain can still be coordinated irrespective of the platform power by cost-sharing contract when delivery sensitivity is high

Researchers built a model that explores the value of ELP and concerning orders and optimises the transportation mode selection decisions

Contribution to FLT sector

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The focus of research paper

Next-generation intelligent logistics operations (Bigdata Analytics, IoT)

Next-generation smart logistics

New supply chain models

Research papers

Miller et al. (2020)

Yavas and Ozkan-Ozen (2020)

Orji et al. (2020)

Research objective

Evaluation of factors that influence the adoption of blockchain in the freight logistics industry

To address inferring state-wide traffic patterns by using massive GPS trajectory data To investigate the essential criteria for logistics centres in Industry 4.0

The authors have introduced 12 crucial criteria which can shape the new logistics centre by developing a framework via Fuzzy Decision-Making Trial and Evaluation Laboratory DEMATEL To provide a theoretical TOE (Technology-Organisations-Environment) framework of critical factors and prioritise them using ANP (Analytical Network Process)

Scholars have proposed that the generated model has approximately reduced the median station error in all test locations from 18 to 32%

Contribution to FLT sector

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Logistics Model for Multi-site Manufacturer Kuehne + Nagel’s Integrated Logistics Model Kuehne + Nagel (KN) posited the following logistical model for leading construction original equipment manufacturers (OEMs) to overcome challenges related to decentralisation as they balanced the management work-load of multiple manufacturing plants across Europe.

Background Due to the global footprint, the organisation had a massive logistical requirement—the OEM dealt with 1500 first-tier suppliers who shipped materials to multiple manufacturing plants across Europe. Thus, the company experienced fragmented inbound logistics. The increased number of suppliers significantly affected the organisation, in areas such as decentralised transportation data management (lack of visibility). Moreover, manufacturing plants in Europe preferred different LSPs which led to inconsistent operations and processes, and individual inventory management and inbound logistics management, thereby affecting the scale of the economies in procurement for LTL and FTL. These scenarios negatively impacted the organisation’s Logistics Management Spend (LMS) and Logistics Operating Spend (LOS). Thus, the organisation wanted to implement a logistics model that would be variable, provide visibility across all the shipment flows, better SLA and service quality, and lower cost structure (Kuehne + Nagel 2017).

Solution KN designed an integrated logistical model and provided construction equipment manufacturers with a dynamic European inbound network centrally managed by a single LSP that offered transitional pricing to mitigate the aforementioned issues. In the model, KN offered their KN Control Tower Suite services which empowered the manufacturer to rectify issues related to inadequate visibility. Instead of giving a

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lump sum plan to ensure lower cost, KN gave a pricing model based on monthly orders. The pricing model was combined with centrally managed inbound logistics, giving construction equipment manufacturers higher visibility of Total Cost of Ownership (TCO). The model also provided continuous transportation cost reduction through frequent route/freight optimisation, dynamic planning of multiple picking/dropping services and direct FTL/LTL. Furthermore, the model provided end-to-end shipment flow visibility and continuous shipment monitoring through KN’s global IT platform, along with Enterprise-Resource-Planning (ERP) software. KN also included services such as customer-dedicated and business management teams, to act as a crucial point of contact ensuring improved financial and operational performance in the offered logistical model (Kuehne + Nagel 2017).

DHL’s Logistical Model for Industrial Equipment Manufacturer DHL provided the new logistical model for leading heavy industrial and construction equipment manufacturers that redesigned their global supply chain operations.

Background Due to explosive growth in business in past years, the organisation now has multiple manufacturing plants and distribution centres on four continents. However, the global expansion and increasing demand added excessive pressure on the then-existing logistics and global supply chain model. Simultaneously, inbound logistics costs were constantly rising. Due to increased suppliers worldwide, organisations started global part sourcing, and investing in overseas production facilities. However, due to the fragmented supplier base and manufacturing plants, organisations struggled with real-time visibility of shipment flows. Organisations offering customised machinery to their customers, and due to the volatile economic climate organisation, were struggling to complete their orders.

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Therefore, an overhaul was needed to design a robust, agile logistics model to better respond to customer demand (DHL 2016).

Solutions After spending a few months embedded within its manufacturers’ daily activities and global business units, DHL came up with a new logistics model, presenting significant supply chain improvements and cost-saving opportunities. DHL offered warehousing services, sea and air freight services and import/export centre management in the improved logistics model. For some UK manufacturing plants, DHL additionally offered in-plant logistics activities. DHL has now established logistics control towers in the UK, India, USA, Brazil and China for procurement and shipment management. This multinational control-tower setup and transportation management system provided the global manufacturer with premium freights solutions, a range of approved carriers and various delivery modes, such as motorcycle, van and charter aircraft. The improved model generated transport cost reductions of 16% for standard and 35% for expedited deliveries. This new model has cost-saving potential for diverse organisations. For instance, DHL has implemented the Ex-Works programme that provides a complete/delivered cost policy, with a staggering 75% cost reduction. DHL integrated their ERP, transport management and Red Prairie warehouse management systems with the manufacturer’s supply chain operations to provide end-to-end shipment visibility from finished goods to individual parts. The transport management systems enabled route optimisation, efficient transportation planning and enhanced container fill rate (DHL 2016). 1. LSP Selection Industries, including automotives, outsource their logistics function to LSPs for seamless operations and generate direct–indirect profits. Due to increased globalisation coupled with marketplace threats, it is essential to have appropriate LSP selection criteria. The appropriate selection can ensure optimal service lead-time, increased flexibility,

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reduced cost and concentration on core competencies, as stated by Tay and Aw (2021). 2. Supplier Selection and Evaluation Process (SSE) According to Nair et al. (2015), supplier selection criterion configure a series of qualifying operational and strategic expectations employed by the buying organisation to balance external resources with internal stakeholder objectives. The strategic performance of LSPs is judged by their capacity to provide and develop state-of-art technologies, abilities of service development, route optimisation, risk mitigation and adaption to technological changes. In comparison, the judgement of operation capabilities concentrate on the LSPs’ ability to provide low-cost, flexible and high-fidelity service and delivery efficiency (Monczka et al. 2016). Figure 5.9 demonstrates the supplier selection process. According to Tay and Aw (2021), organisations should clearly define all activities within the process before entering into the supplier selection process. The buying organisation generating its guidelines and supplier selection criteria should analyse the market condition. Furthermore, organisations should evaluate and select the best-suited LSP according to organisational, strategic and operational expectations, as well as pre-defined criteria and selection guidelines. The final step is establishing a formal supply chain cooperation agreement between the buying firm and LSP to finalise the working relationship. In later stages, the SSE’s information can also be shared with the open market via feedback which often results in knowledge and information sharing that further enhance LSPs’ abilities and capacities, as per Wu et al. (2008). 3. Supplier Evaluation Criteria Nowadays, organisations are especially responsible for their internal practices and suppliers’ working behaviour (Essien et al. 2019). Therefore, several industries have adopted supplier assessment schemes whose systematic approach, supplier evaluation criteria and practical framework enable organisations to make an adequate selection (Alkhatib et al. 2015; Winter and Lasch 2016). The standard criteria for supplier selection are cost, quality, technology, service, performance and intangibles (Baily et al. 2015).

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Start

Analyse Market CondiƟon

Ascertain Supplier SelecƟon Target

Make Supplier SelecƟon

Evaluate the Supplier

Select Supplier

EvaluaƟon Method Implement CooperaƟon

End Fig. 5.9 Supplier selection process (Tay and Aw 2021)

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Table 5.3 LSP evaluation criteria (Akman and Baynal 2014; Hwang et al. 2016) Category

LSP evaluation criterion

Performance

Shipment error rate, responsiveness, document accuracy, effectiveness, delivery efficiency Customer support service, problem solving capacity, value-added services, assets/equipment, flexibility, capability, service scope Price, cost control of value-added services, cost reduction Continuous improvement, regulations, KPI tracking, ISO compliance IT systems, capability to adopt the technological changes, system reliability, system stability, system scope, system scalability Global scope, labour relationships, client relationship, firm background, customer orientation, financial stability, experience, global footprint, firm reputation

Service

Cost Quality assurance Technology (IT)

Intangible

Table 5.3 consolidates the LSP selection criteria and further groups them into six major categories. 4. LSP Selection Criteria Two-Level Hierarchy Framework Hwang et al. (2016) performed the duties of external auditors and audited the six significant categories shown in Table 5.3 and developed a two-level LSP selection hierarchy framework with 21 sub-criteria, as shown in Fig. 5.10. Furthermore, definitions of this developed 21 sub-criteria are shown in Table 5.4. 5. Supplier Evaluation Scorecard Rajesh et al. (2012) believe that the scorecard is a high-profile supplier evaluation and performance measurement model that enables organisations to balance and link crucial perspectives, including the financial and non-financial, tangible and intangible, and internal and external. Scorecards help organisations to transform their visions and strategy into reality. Doolen et al. (2006) state that the supplier scorecard helps buying organisations in developing critical supplier metrics and evaluate diverse potential suppliers against them; thereby establishing an objective and quality-drive standard.

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LSP Selecon Criteria

Service

Cost

Quality Assurance

Document Accurancy

Cistomer Support Service

Connuous Cost Reducon

Connuous Improvement

On-me

Problem Solving Capcity

Cost Control of Value-added Services

Tracking

Value-added Services

Price

Delivery

Responsiveness

Shipment Eror Rate

KPI

Technology

Data Security

Funcon Coverage

Intangibles

Firm Experience

Financial Stability

System

Global

Scalability

Footprint

System Stability

Level 1

Level 2

Performance

Level 0

Profitability

Transportaon Safety

Fig. 5.10

Two-level hierarchy of LSP selection criteria (Hwang et al. 2016)

Moreover, the scorecard aligns buying firms’ strategic objectives with their service providers’ internal performance metrics. Therefore, a supplier scorecard is a document that stores the essential performance metrics and enables reliable decisions. Thus, supplier scorecards act as purchaser and supplier planning guides (Baily et al. 2015). The LSP selection and evaluation sample scorecard is shown in B.1, adopted from Purchasing Power Blog Procurement (2017). Table 5.5 shows the critical analysis of the scorecard system. 6. Supplier Selection Methods See Fig. 5.11. 7. Multicriteria Decision-Making (MCDM) Logistics outsourcing is receiving increased importance, and it has become essential for buying organisations to select an appropriate LSP. However, many factors might affect the selection of LSP. Therefore, selecting LSP is an MCDM problem. Various MCDM methods are available in the literature to choose the most suitable LSP (Hwang et al. 2016). Table 5.6 summarises the MCDM methods found in the literature. One of the MCDM methods is explained below:

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Table 5.4 Definition of 21 sub-criteria (Hwang et al. 2016) Criteria

Definition

Continuous cost reduction

Most of the automotive industries undergo the intense pressure of continuous cost reduction. Therefore, it is the measurement to request LSP to reduce the rates on some primary logistical services The ability of LSP to provide high-value services such as route optimisation, risk mitigation planning and proactive process improvements The ability of LSP to provide value-added services at the lowest possible rate Customer support is given from pre-transaction to the post-transaction LSP’s ability to protect the data from theft and make it available to its stakeholders It is a parameter to measure accuracy, consistency and completeness of essential documents (bill of lading, packing lists, invoices, etc.) among the freight forwarders, shippers, customs agents and customers LSP’s experience in automotive logistics Leverage ratios, operating profitability and liquidity to measure the financial situation of LSP

Continuous improvement

Cost control on value-added services

Customer support service

Data security

Document accuracy

Experience Financial stability

(continued)

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Table 5.4 (continued) Criteria

Definition

Function coverage

The functional scope of IT systems such as cloud computing, Bigdata Analytics, Digital twin and Blockchain The reputation of LSP in the FLT industry Global footprint refers to the LSP ability of global coverage, market coverage, geographical coverage and shipment destinations and distances The ability of LSP to meet KPI regularly and tracking it continuously It is the parameter to measure the punctuality of shipments, i.e. time taken from pickup to delivery of the shipment Competitive charges on the services such as warehousing, inventory management, packaging and freights The ability and flexibility of LSP to handle/solve unforeseen events and disruptions How quick the LSP responds to their customers logistical and business requirements It is a parameter to measure the accuracy of degree shipment The LSP’s ability to expand its IT systems without impacting the existing operations The ability of the IT systems to work without any errors and technical difficulties The ability of the LSP to deliver the goods safely to its end destination The ability of LSP to offer high-quality services that can enhance the customer’s logistics function

General reputation Global footprint

KPI tracking

On-time delivery

Price

Problem-solving capacity

Responsiveness

Shipment error rate System scalability

System stability

Transportation safety

Value-added services

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Table 5.5 Critical analysis of scorecard system (Finch 2017; Ramos 2020) Advantages Clarity

Negotiation support and decision making

Service provider justification

Limitations Data collection and analysis

High resource investment

Stakeholder acceptance

Scorecard provides a clear indication of metrics and KPIs which are essential to the buying firm. A well-balanced scorecard reflects the organisation’s intention of forming long-term partnerships The data collected from the supplier scorecard provides an evidence-based platform for ongoing negotiations with existing or future suppliers and simplifies the decision-making process for buying organisations After assessing sets of scorecard documents, the buying organisation can quickly verify whether the shortlisted supplier is an asset or liability Training of employees is essential to understand how and when to analyse the data. The scorecard provides the required information; however, one should identify those indicators and implement them appropriately. If not, then the organisation is at risk of getting an inaccurate supplier selection The scorecard demands significant time and financial cost investment as it is a long-term solution rather than short-term. Employees should understand the working of the system. Hence, it increases the employee training expenditure The stakeholders and purchasing team should be on the same page. If employees fail to understand the process, stakeholders will hesitate to invest. Those who are resistant to change will not accept the new system

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Supplier Selecon Methods

Cluster Analysis (Stascal/ Probablisc)

Fuzzy Set Theory

Mulcriteria Decision Making (Categorial Method)

AHP

ABC

ANP

TCO

TOPSIS

Fig. 5.11

Method Based on Costs

Mathemacal Programming

Arficial Inteligence

Combined Approaches

Linear Programming

CBR

MP+TCO

MOLP

ANN

AHP+LP

Global Programming

ANP+ TOPSIS

Supplier selection methods (Taherdoost and Brard 2019)

• Analytical Hierarchy Process (AHP) AHP is one of the MCDM methods based on the additive weighting process. In AHP, all the relevant metrics are represented as per their importance. Decision-makers determine the relative weighting and rank of each metric. Further, the decision-maker uses the pairwise comparison matrix to select the best-suited supplier. AHP is a widely used method, owing to its simplicity, flexibility and ability to manage qualitative and quantitative criteria. Therefore, the AHP method is used to select the LSP and the global supplier selection, strategic sourcing, inventory management, KPI prioritisation capacity planning and supply chain risk analysis (Hwang et al. 2016; Kumar et al. 2019). A company operating in the Indian automobiles industry adopted AHP for supplier selection. The organisation established six main criteria, including quality, cost, delivery, longevity, service, flexibility and 22 further sub-criteria in the hierarchy. The industry then established a pairwise comparison with the more essential criteria. Further, the obtained combination was assessed against the numerical value concerning their parent criteria. In conclusion, managers found local weights by pairwise comparisons between the parent criteria and sub-criteria, thereby generating value-driven rankings to choose one out of three shortlisted suppliers. Figure 5.12 represents a multi-criteria supplier selection model using the AHP method.

Fuzzy decision-making Grey decision-making trial and evaluation laboratory (DEMATEL) Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) Fuzzy analytical network process (ANP) Fuzzy analytic hierarchy process (FAHP) Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR)

MCDM methods

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Datta et al. (2013)

Authors Sharma and Kumar (2015)

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Prakash and Barua (2016)

Table 5.6 MCDM methods available in literature

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Govindan et al. (2016)

Haldar et al. (2017)

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Ilgin (2017)

Raut et al. (2018)

(continued)

Bianchini (2018)

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AHP TOPSIS Taguchi loss function ANP Data envelopment analysis (DEA) Linear programming (LP)

MCDM methods

Table 5.6 (continued)

Datta et al. (2013)

Authors

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Sharma and Kumar (2015) Prakash and Barua (2016) Govindan et al. (2016)

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Ilgin (2017)

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Raut et al. (2018) ✔ ✔

Bianchini (2018)

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Idenfying the Criteria

Weighng the Criteria

Ranking the Suppliers

Pairwise Comparision

Revision No

Is it Consistent? Yes Analyse with expert Choice Suppliers Final Priories of Alternave Suppliers Selecon of best alternave supplier

Fig. 5.12

Multicriteria supplier selection model (Yadav and Sharma 2016)

8. E-Procurement Tools In the modern day, supply chain procurement is no longer considered a merely transactional, conventional and administrative activity. In truth, it has pivoted towards becoming a more strategic and valueadditive role. E-procurement tool adoption is considered one of the fundamental drivers behind this transformation (Vanpoucke et al. 2017) (Fig. 5.13).

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E-procurement Tools for Decision OpƟmisaƟon

Cost ReporƟng and Spend Analysis

Supplier EvaluaƟon and Performance EvaluaƟon

Buyer-supplier Contract Lifecycle Management

Electronic Request for x (E-RFx)

E-bid

E-aucƟons

Purchase Order GeneraƟon

Goods Recieved and Quality InspecƟon Fig. 5.13

E-procurement tools (Mena et al. 2018)

9. E-Auction According to Qusa et al. (2020), e-auction is one of the oftenused E-Procurement tools which eliminates the barriers involved in a traditional auction-like location, small target audience, geography and presence. Figure 5.14 shows the generalised framework of e-auction. According to the buying organisation’s strategic and operational expectations, the procurement team shortlists the best-matched supplier and evaluates them per the supplier selection criteria, such as the Balanced Scorecard (Kaplan and Norton 1995). Further, the organisation publishes the Pre-Qualification Questionnaire (PQQ).

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General E-auction framework (Habib 2020)

Moving forward, buying organisations evaluate the PQQ response, operational and strategic performances. The successful candidates are further assessed by their Request for Proposal (RFP) and then called for an E-auction or negotiations (Habib 2020; Mena et al. 2018). For instance, one of the global leaders in subsea systems provides the oil and gas sector with its services through e-auction for strategic sourcing, logistics and international shipping (Air, Ocean and Land freights) (ProcurePort 2021). 10. LSPs Information Identifying reliable LSPs and aligning them with an organisation’s strategic and operational goals is fundamental. Interconnectivity has certainly streamlined the LSP identifying process. 11.1.1.1.1.1.1.1. Foundation of LSP Strategies and related Models

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Author Berglund et al. (1999)

Persson and Virum (2001)

Foundations of LSP strategies

Related strategies and service models

1. LSPs offer basic services (low-cost) to the customers. The solution provider provides complex and customised services to premium customers 2. Organisations are offering essential services like warehousing and transportation, and the organisation providing value-added logistical services 1. Service offering based on needs-based vs variety-based 2. Asset-based resources non-physical vs physical

1. Basic logistics service: Help customers being competitive 2. Value-added logistical services: Become value leaders by providing globally integrated logistics 3. Basic logistics solution: provide 3PL solutions in simple warehousing and transportation 4. Value-added solutions: consultancy services for customers 1. LSP owns physical assets and provides various logistical services 2. 3PL provide needs-based services for unique customers through their assets 3. Many LSPs offer non-physical asset-based services to their customers. They are often termed logistics agents 4. Some non-physical asset LSPs offer need-based services from their targeted services. They are often known as logistics integrator (continued)

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(continued) Author Bolumole (2003)

Foundations of LSP strategies

Related strategies and service models

1. Strategically outsourcing the organisation 2. The extent of outsourcing 3. 3PL relationship with the client 4. Customer’s perception of 3PL

1. Functional service provider: Cost-based perception, operational-level functions, transactional relationships, internal focus 2. Inhouse logistics departments: cost-based perception, tactical-level function, bilateral relationship, internal focus 3. Logistical joint venture: Partnership-type relationship, cost-based perception, internal focus, strategic-level function 4. 3PL provider: resource-based perception, operational-level function, transactional relationships, external factors 5. Supply chain logistics provider: bilateral relationships, operational-level function, resource-based perceptions, tactical-level functions 6. Logistics process integrator: resource-based perception, strategic-level function, partnership-type relationships, external focus (continued)

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(continued) Foundations of LSP strategies

Related strategies and service models

Hertz and Alfredsson (2003)

1. Problem-solving capacity 2. Customer adaptation

Stefansson (2006)

1. Service scope 2. Degree of customisation

Waters and Rinsler (2014)

1. Increased cost advantages 2. Increased differentiation

Cui and Hertz (2011)

1. Network 2. Capabilities

1. The 3PL provider provides general services such as distribution and warehousing 2. Service developers provide state-of-art value-added services 3. LSPs take over the clients entire logistics functions termed as customer adapters 4. LSPs take over entre logistics function by integrating themselves termed as customer developers 1. Freight forwarders with the simple transport-related services 2. LSPs with extended administrative service offerings 3. Logistics service intermediaries administer the logistics activities without handling the goods physically 1. Value and cost leadership provider 2. Cost leadership providers 3. Commodity provider 4. Added-value provider 1. 3PL firms 2. Logistics intermediary firms 3. Carriers

Author

(continued)

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(continued) Foundations of LSP strategies

Related strategies and service models

Zacharia et al. (2011b)

Degree of scope outsourced to the logistics provider

Soinio et al. (2012)

Asset vs non-asset-based services, strategic, operational and tactical tasks, relationship-oriented vs transactional-based services Asset intensity, global footprint and range of services, the scope of services, degree of customisation, types of goods handlings

Outsourcing engagements: 1. Out-tasking and standardisation of repetitive and specific tasks 2. Service management with shared responsibility for tasks and functions with the enormous scope 3. LSP takes the significant responsibility of function management, design and implementation 4. Complete outsourcing where LSP is entirely responsible for all outsourced tasks 1. Consulting 2. Transportation of goods 3. The outsourced chief logistical officer model 4. 3PL with planning

Author

Lampe (2014)

Narrow/broad perspective carriers/2PL, 3PL, 4PL, LLP and freight forwarder

The following may be described as key resources useful for identifying viable LSPs (Baily et al. 2015; Monczka et al. 2016): 11. Online Supplier Database/Catalogues Many websites or web portals store a particular file that consolidates LSPs’ offered services, contact details and price lists. For example, Inbound Logistics provides detailed information about the LSP’s geographic footprint, logistics, transportation, warehousing and a

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list of secondary value-added services and technologies (Inbound Logistics 2021). Trade Journals Trade journals provide information about the processes, services and technological advancements offered by LSPs—most of whom with a tendency to be affiliated with the publishing body. For example, Thigpen Library maintains articles and trade journals for logistics and supply chain management (Thigpen Library 2021). Trade Dictionary Service providers tend to publish their company dictionary, which reflects key information about their services. For example, Logistics List provides information about logistics services, logistics technology, logistics sponsors and resources and value-added services (Logistics List 2021). Trade Shows This is an especially effective means of generating massive exposure to diverse service providers in one setting. For example, Event Eye provides information relating to the logistics and transportation of engineering-handling-storage operations through its industryleading trade shows in Europe, connecting a diversity of leaders and hopefuls under one roof (Event Eye 2021). Risk and Barriers in Changing the LSP Why Change LSP? The following points given by Jones (2018) might be helpful towards understanding why organisations change their existing LSP: Changes in Order Quantity Suppose the organisation has achieved massive business growth; this would act as a highly visible, though not all-encompassing, indicator of the LSPs’ capabilities. Similarly, if the organisation faces volatile demand because of business fluctuations or seasonal promotions, it may gain visibility of its existing LSPs’ limitations (Jones 2018; Rushton et al. 2017). Increased Complexity Order fulfilment has become much more complex for contemporary organisations. Companies now look for an LSP with integrated

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Fig. 5.15 Key reasons behind not renewing existing LSPs Contract (Rushton et al. 2017)

strategic global footprint, state-of-art order management technologies and various logistical services to streamline order fulfilment (Jones 2018; Rushton et al. 2017). 19. Increased Shipping and Fulfilment Cost If the freight, technological requirement, warehouse space and labour cost a fortune to the organisation, finding a new LSP can sometimes minimise the overhead cost and offer better transportation costs by leveraging competitor advantage (Jones 2018; Rushton et al. 2017). 20. Service Failures The preferred LSP should ensure customer satisfaction by providing on-time deliveries, accurate orders, excellent inbound, outbound services and inventory management. However, if the LSP fails to deliver these services, without good reason, this is an indicator that the organisation should scrutinise and adapt (Jones 2018; Rushton et al. 2017). Figure 5.15 shows the critical reasons behind the changing existing LSP.

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21. Risks and Barriers 22. Dependency on Existing LSP To enhance supply chain efficiency and efficacy, and to gain competitive advantages, organisations depend on the LSPs. The vertical integration in the logistics industry happens through system integrations between buying organisations and LSPs. These integrations reduce the cost association with management processes, asset ownership and generally enhance financial performance (Zacharia et al. 2011b). The method and system of integration can sometimes prove prohibitive to the buying organisation. Therefore, many organisations do not risk shifting to a new LSP even if the existing LSP is not delivering the agreed service levels (Holt et al. 2010; Rushton et al. 2017). 23. Change in Management According to Holt et al. (2010), risk in changes to management is a combination of structural and psychological factors, indicative of the extent to which employees and organisations are willing to accept them. Therefore, if any organisation working with their LSP long-term, makes a sudden change, their employees might feel fear, uncertainty and frustration from this change, as they have to shift from their long-held practices, routine and working culture. Thus, senior management can be hesitant to make changes and risk losing spirit (Etokudoh et al. 2017; Rushton et al. 2017). 24. Supplier Switching Cost The overdependence on an existing LSP may generate fear within buying organisations in terms of losing pricing advantages. Therefore, senior management may be hesitant to lose a perceived advantage, especially against the backdrop of market complexity and risk-aversity (Baily et al. 2015; Monczka et al. 2016). 25. Supply Disruption Supply disruption is one of the potential risks which can occur while changing the LSP. While transitioning from a current service provider to another, the new LSP may not integrate processes, systems and technologies with the buying organisation until the hand-over time. This time lag can generate costly and scandalous supply distributions. Also, there are possibilities that the LSP may

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27. 28.

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not fully understand the intricacies of the business. Therefore, and at great cost, this situation can affect the manufacturing schedule and affect the shipments to end customers (Baily et al. 2015; Monczka et al. 2016). Services Provider’s Availability The unavailability of the most preferred LSP can be a potential risk. The whole process of SSE consumes massive organisational resources, including and beyond time and money. If the preferred service provider is unavailable, the entire process can be a costly failure, and the organisation may damage their relationship with their pre-existing supplier (Baily et al. 2015; Monczka et al. 2016). LSP Transition Planning When Should Change be Made? While changing the LSP, the organisation should carefully consider the timing of the transition. Often, the companies start searching for a new LSP at the start of the fiscal year, budgeting the change for the fourth quarter so that the newly appointed LSP may be operational at the start of the next year’s first quarter. It is wise to start the transition during the operationally quiet period, such as the last days before manufacturing shuts down. Otherwise, there are risks of supply chain disruption and service failures (Harps 2003; Jones 2018). Development of Effective RFP Developing an effective RFP is the next critical stage in transition planning (refer to 10). Developing an effective RFP provides a clear understanding of buyers’ ability to communicate, identify, quantify and define its sourcing and technical requirements. The RFP gives an overview of the buying organisation and includes product specification, order volume data, promotional/seasonal discount fluctuations. Inbound-outbound logistics delivery requirement and shipping timelines. The following is a checklist of points included in the RFP (Jones 2018; Monczka et al. 2016) (Table 5.7). Buying organisations should identify and define operating variances and assumptions. There is a positive correlation between the detail provided in an RFP and the relevancy of proposals received.

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Table 5.7 RFP checklist (Author 2021) RFP checklist Optimal network optimisation Technological requirement (WMS, TMS, etc.) Return logistics service Delivery requirement After sales market customer service Service quality management

LSP Transion Process

Project Management Task

Fig. 5.16

Inventory Movement

System Cosideraon/ Integraon

Seng up 3PL Process

LSP transition process (Barry 2019a)

30. Transitioning Process Once the buying organisation finalises the LSP, it is essential to develop a seamless transition plan. The buying organisation and LSP should spend some time understanding the requirements, project scope and assigning a qualified transition team that involves critical players from senior management, human resources and process engineering teams (Barry 2019a; Kao et al. 2019). The buying organisation can often involve a small team of the last LSP, handing over the process documents to the new LSP. Together this team should create a roadmap for the go-live date, identify necessary tools and available resources. While implementing the transition plan, both parties should keep track of the budget, construct and implement a contingency plan to ensure an easy and reliable transition. As shown in Fig. 5.16, four significant categories are critical for a smooth transition (Barry 2019a; Jones 2018). 31. Project Management Task Developing, managing and executing the transition plan requires massive resources like time, cost and energy. However, these efforts

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and good PM practices will ensure on-time and within-budget project completion, and minimise commercial and reputational risks to the organisation (Barry 2019a; Elmar and Hall 2020). 32. Development of Project Plan The project plan should include all the milestones and tasks that buying organisations expect from their LSP (Barry 2019a). The project schedule might include a brief meeting between the old and newly appointed LSPs for process hand-over procedures. Generally, project planning also involves start and end dates for each task, a list of responsible parties for completing each task, as well as a description of costs associated with each task. Furthermore, communication and coordination between all involved stakeholders is crucial to success (Elmar and Hall 2020; Kao et al. 2019; Maylor 2010). Often the work of developing and updating transition plans requires continuous communication between all involved stakeholders. Often, to save time, organisations delegate these processes to LSPs. However, it is advisable for an organisation to control, schedule and plan the transition project, rather than completely delegating outwards, as the LSP Logistics service providers (LSPs) might not be aware of all involved stakeholders. Furthermore, the LSP may not wish to involve itself in cost-control initiatives because these may give rise to conflicts of interest (Elmar and Hall 2020; Kao et al. 2019; Maylor 2010) 33. Go-Live Date According to Barry (2018) Jones (2018), all involved stakeholders should ensure an easy transition to minimise business interruptions by planning around the go-live date. It should be kept at the end of the season or the year-end when there are generally low inventory levels. Moreover, it is advisable for the PM team to factor in longer lead times for tasks such as IT system integration and inventory migration. 34. Project Timeframe The project’s timeframe depends on the number of SKUs the buyer has and the inventory’s movement from the previous LSP to the

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newly appointed LSP’s fulfilment centres. Furthermore, the project time frame is also dependent buyer’s complex and unique fulfilment processes. It is worth noting that generally, in PM, the timeframe for a simple project is 90 days and four-five months for some complex projects (Barry 2019a; Elmar and Hall 2020). Appointment of Project Manager Shaukat et al. (2021) state that a project’s success is a multidimensional, subjective, ambiguous and subtle concept. A project’s sustainability, as well as integration of economic, social and environmental issues are critical in PM. Thus, according to Barry (2019a), due to competitive pressures, continuous technological advancements, size, costs and risks involved in PM, organisations need to appoint a full-time project manager. The breadth of the project manager’s experience influences the on-time project completion, even under complex circumstances (Salvador et al. 2021). Inventory Movements The next step in the transitioning process is inventory movement. Quantities to Move Often, the organisation classifies their SKUs as per the ABC analysis, and 20% of the SKUs contribute towards 80% of the organisational revenue (Slack and Brandon-Jones 2019). Most of the time, while transitioning, some inventory is always available with the old LSP. Therefore, the organisation should evaluate their inventory at their old LSP before transferring it to the new LSP. Many researchers have advised that organisations should keep some inventory available at their fulfilment centre in case of unforeseen delays in the go-live date (Barry 2019a; Heizer 2020). Movement of Dead-stock Transitioning period is the ideal time for the organisation to assess their obsolete inventory and liquidate it. This is because slow, seller inventory-movement costs more money in terms of inventory preparation for transportation, monthly inventory storage costs and transportation cost (Barry 2019a). Receipt of Inventory at 3PL Inaccurate delivery of inventory to the LSP can cost a fortune to the organisation. Therefore, the organisation should discuss how LSP

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wants to receive the inventory. It gives the organisation an opportunity to accurately count inventory and maintain documentation regarding it. Documenting all the procedures will be beneficial for the organisation in the future as it creates a system of accountability and reference (Barry 2019a). Transport Arrangement and Scheduling Arranging a transportation service is one of the expensive things in transition. Organisations should discuss with their new LSP responsible for transporting the inventory available with the old LSP. If an organisation is responsible for this task, they have to arrange the trucks, ships and fulfilment centres to store the inventory (Barry 2019a; Kao et al. 2019). System Considerations Newly on-board LSP comes with all new IT systems and functions, and this IT platform requires integration with the buyer’s IT platform, and usually, this process has longer lead times. Therefore, all involved parties need good communication to make it accessible, simple and successful (Barry 2019a). LSP Data Mapping The buying organisation has to provide information about their SKUs and product line. With the provided information, LSP will map the product line on an excel sheet, and the service provider will assess the dimensions of the products so that they can arrange the packaging, loading and transportation of the product (Barry 2019a; Kao et al. 2019). Technological Integration Technological integration is one of the time-consuming processes in the LSP transition process. After the product mapping, often LSPs start implementing the track and trace and data-mapping technologies such as cloud computing, big-data analytics and blockchain-integration. However, before implementing these technological advancements, an organisation needs compliance certifications (Barry 2019b). Therefore, a project manager must plan related testing ahead of time so as to optimise the implementation cascade (Barry 2019a; Heutger and Kückelhaus 2018; Kao et al. 2019; Wang and Sarkis 2021).

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44. LSP Processes Setup 45. Define Procedures and Processes The buying organisation and their LSP should arrange meetings to discuss the implementation process of transition projects. If an organisation has a training and procedure manual documented, it becomes easier to transition from one LSP to another. If their processes and procedures are not documented, this is then a fresh opportunity for buying organisations to document all the actions and decisions taken so that an organisation can replicate them in future/as needed (Barry 2019a; Kao et al. 2019). 46. Shipping Orders in Transition Even in the transition phase, the buying organisation must ensure that they satisfy their customers with on-time delivery, good service quality and downtime reduction. Failing this, there can be averse and lasting commercial and reputational consequences. Therefore, for a smooth transition, a buying organisation can either ship themselves or use the services of their old LSP until transitioned (Barry 2019a; Kao et al. 2019). Transitioning from one LSP to other is a challenging task. Assigning a good project manager, with a well-developed plan, organisations can achieve seamless transitions. To ensure this, transparent and crossstakeholder communication is critical (Barry 2019a; Kao et al. 2019; Maylor 2010).

Chapter Summary and Key Lessons The chapter provides a thorough overview of relevant topics and ideas essential to achieving the research objectives. Therefore, the chapter has focused on the latest service characteristics and technological advancements that LSPs leverage for their customers. Further, the chapter emphasises the SSE as a vehicle for the seamless selection of new LSPs, as well as problems and pathways associated with making related changes. This chapter contributes to several key theoretical areas in the operations, logistics and supply chain management industry; focusing on

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Industry 4.0 in LSPs. This should supplement existing theoretical knowledge on industry 4.0 and its advancement in LSPs. It should also elevate the knowledge on 3PL, 4PL, LSP selection process and the technologies (i.e. IoT, Cloud computing, Big data Analytics) and their implications. As to practical consequence, this study should contribute to the understanding of contemporary managers on how Industry 4.0 technologies could help improve their operations, logistics and supply chain management. Given the impact of COVID-19, it is particularly important for companies to best utilise available technologies to make their supply chains more agile, resilient and future-ready. With a couple of case study examples (i.e. Kuehne-Nagel, DHL) this chapter provides some critical insights which could help cross-industry management achieve efficiency-optimised decisions. Future research could develop hypotheses/propositions and collect primary data to establish these empirical findings.

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6 Impact of Predictive Analytics on the Strategic Business Models of Supply Chain Management Ishwari Pradhan, Dilshad Sarwar, and Amin Hosseinian-Far

Introduction The advancement of newer and upcoming technologies such as Machine Learning (ML), Cloud Computing, Artificial Intelligence (AI), Data Analytics (DA), Internet of Things (IoT), etc. has created immense levels of discussions and arguments regarding their growth, advantages, and risks (Tuptuk and Hailes 2018). Everyday immense amount of data is I. Pradhan (B) · D. Sarwar · A. Hosseinian-Far Department of Business Systems and Operations, University of Northampton, Northampton NN1 5PH, UK e-mail: [email protected] D. Sarwar e-mail: [email protected] A. Hosseinian-Far e-mail: [email protected]

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created which is then stored, processed, managed, and then used to interpret and visualize information to improve organisational capabilities. To increase consumer base, productivity and innovation, it is crucial for data systems to advance their analysing capacity which require mathematical, statistical, and computational methods (Esmaeilbeigi et al. 2020). Thus, Big Data has been proved to add a lot of value and enhance any firm’s competitive advantage (Tiwari et al. 2018). It is a high-performance strategy where data can be converted into useful insights which can lead to great success. Thus, BD has many opportunities for new and upcoming entrepreneurs as well (Sedkaoui 2019). Predictive Analytics (PA) has been proving to be one of the most used and vital components in all fields. This analysis rests on statistical approaches along with other more recently developed tools of data mining. The purpose of these strategies is to anticipate predictions and projections about the organization’s operations in the future (Ouahilal et al. 2016). Data analytics enables businesses to make better decisions and implement them when it is most advantageous to the company, its end consumers, and partners (Ouahilal et al. 2016). Companies can improve their internal processes, enrich their products and services around data, and also market and supply their data to external services, thus monetizing themselves (Weiner et al. 2020). According to the depth of study, data analytics can be classified into four types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. This research aids in understanding the use of predictive analytics to help people make better decisions. PA helps to understand the following for any organisation: • What is predicted? • What can be done about it? These major concerns for any organisation can be dealt with the help of PA. The open-ended nature of these questions leads to a vast array of industries using this technology in all processes (Siegel 2016). Supply chain managers are using PA in forecasting demands, inventory and stock management, distribution and logistics processes, pricing strategies, predictive maintenance, etc. Some predictions even state that the

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market is supposed to grow at a CARG of 17.3% from 2019 to 2024. Thus, supply chain professionals can predict challenges and improve costs and service levels (Riverlogic 2021). Although large firms are the primary users of predictive analytics, there are various other industries and organisations where predictive analytics technologies can be beneficial to decision-makers (Attaran and Attaran 2019). Many companies have started extracting potential out of their strategic business models using big data, which is giving rise to big data business models (BDBMs). These BDBMs are not only just assets or resources but are strategic aspects that aid in organizational value creation and capture. But there are some companies like Facebook that have been facing challenges when it comes to BDBMs due to their privacy issues (Weiner et al. 2020). It is thus wise for organizations to plan and deploy BDBMs and other technologies strategically to avoid consequences. This research thus aims to understand the impact of PA on SCM. It will also help to understand its applications in all sectors of supply chains. As there has been very limited research done on the actual impact of PA on all sectors of Supply Chain Management, this paper will help to gain an overall insight into it. The topic of supply chain management predictive analytics offers a promising path for revolutionising supply chain management, and also an intriguing array of research prospects (Schoenherr and Speier-Pero 2015). Predictive analytics enables businesses to transition from a retrospective and intuitive decision-making process to one that is proactive and data-driven. It can be said that big data technology is still in its infancy, which combined with the initial financial costs and a lack of expertise about how to deploy it, makes it difficult to accept in the supply chain business (Raman et al. 2018). Hence, the limited amount of material and database available can be considered to be limitations to the study.

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Literature Review What Is Big Data Analytics? In recent times there has been tremendous growth of big data. There have emerged many breakthroughs that manage large transfer of data which is collected from different sources. These can be found in petabytes and beyond in structured, semi-structured, and unstructured formats (Acharjya and Ahmed 2016). The utilisation of tools and methods for analysing and processing variables and patterns from excessive amounts of data is required when using Big Data as a resource (Riahi and Riahi 2018). The range of applications of BD in SCM is quite vast and helps in consumer behaviour, demand and supply forecasting, trend analysis, etc. Accurate marketing along with forecasting models have helped understand customer needs and expectations. With the rise in information technologies and improved computational strategies, BDA can provide precise predictions in risk assessments, improve SC efficiency, and assess consumer needs and performance (Seyedan and Mafakheri 2020). Using data analytics and artificial intelligence, a supply chain unicorn, Convoy, based in Seattle, US, managed to reduce carbon emissions by providing real-time data to match shippers and carriers efficiently (Fortune 2021). Data analysis is revising the business and corporate world, allowing organisations to better their performance and overall processes, providing them with a competitive advantage (Sharma and Bhat 2014). Datadriven business models are proving to have 5–6% higher productivity. For example, publishers are able to target their demographics based on data in their digital distribution channels which then can be sold which enable more efficient targeting. Moreover, around 71% of banking firms have also seen to gain competitive advantage with the use of data (Brownlow et al. 2015). Radio-frequency identification (RFID), tracking devices, various sensory information, and other technologies are used in the supply chain business to collect massive volumes of data (Zhong et al. 2015). Data management can be said to be extracting and storing data along with preparing and retrieving it for analysis, using algorithms and accompanying technologies. Whereas analytics can be said to be the techniques used for analysing and extracting insights from enormous

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amounts of data. Thus, big data analytics can be considered to be a subordinate process inside the wider process of retrieving insights from enormous amounts of data (Gandomi and Haider 2015). Everyday huge amounts of data are generated, and Data Scientists are striving hard to come up with solutions to manage, control, and analyse this data for the benefit of companies and organisations (Nasser and Tariq 2015). Creating new-found ventures using data and novel digital technologies, many entrepreneurs can reap benefits and become successful (Chae and Goh 2020). In supply chain management, processes can be enhanced, and supply chain sectors can better track and trace every path of goods and services and can even mitigate and avoid risks on time. Big data has the following characteristics: Volume—Volume is the quantity of data which is generated and then stored and operated in systems. Organisations face hurdles to analyse and process data saved for many years due to lack of technological support. This can be considered to be a big benefit if companies can process this data (Ishwarappa and Anuradha 2015). As everything is now tracked and recorded every second, this massive amount of captured data cannot be processed using traditional systems. It is thus clear that the percentage of data which is generated is growing and the data that needs to be processed and analysed is decreasing resulting a blind zone (Nasser and Tariq 2015). Variety—Variety can be considered as the structural variability in a dataset. Thanks to technology improvements, businesses may now use a variety of organised, semi-structured, and unstructured data (Gandomi and Haider 2015). Most data generated is of unstructured form. So, most analysts should have the knowledge to deal with different types of data to avoid complexities when it comes to analysing it (Ishwarappa and Anuradha 2015). Structured data which is referred to as tubular data form is easy to process. Unstructured data includes images, text, videos, and audio files. Semi-structured comes between the two and does not conform to any standards. Due to the development of new data management techniques and data analytics, businesses can leverage data in their business processes, which is a unique feature (Gandomi and Haider 2015).

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Velocity—Velocity is considered to be the frequency with which data is extracted, stored, and then analysed. Most of the data needs to be processed and analysed in real time for good results (Riahi and Riahi 2018). Traditional data management solutions are unable to process huge data flows in real time. In this case, big data technology can help. They enable companies to derive real-time intelligence from massive amounts of perishable data. Data from mobile devices and applications generates massive quantity of data that can be used to develop real time, individualised offers towards regular consumers. This data comprises essential consumer information such as geospatial location, demographics, and historical purchasing behaviours, all of which may be evaluated in real time to provide meaningful customer value (Gandomi and Haider 2015). Veracity—This term can be said to be the data’s uncertainties, impressions, lies, and missing values. This aspect assesses the data precision and its ability to be used for analysis. The degree of correctness of the data sets that is collected in systems will decide whether the data is valuable. Many academics believe that this is Big Data’s largest hurdle (Nasser and Tariq 2015). It is thus vital to monitor and manage the quality and accuracy of the data to have the best results. Value—Value is also considered to be a crucial aspect of Big Data. Large volume of unstructured data is collected but most of it can be useless. This data can have value if it is properly analysed and made useful (Ishwarappa and Anuradha 2015). All these characteristics can be considered to be current challenges faced by big data. It is vital and necessary to understand that the defining limitations are determined by the firm’s size, sector, and location, and they change over time (Gandomi and Haider 2015). Data analytics can be divided into four categories (Rajaraman 2016), as outlined in the below sections.

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Descriptive Analytics Descriptive analytics makes use of historical or past data to determine the current state of a situation. It consolidates all the gathered data from various sources and presents them in analytical ways to have a better vision over the problem (Ouahilal et al. 2016). The data implications can be visualised using various charts, bar graphs, pie charts, scatter diagrams, etc. Decision-makers usually make use of these visualisation techniques to analyse the data and provide with accurate solutions to optimise processes. A good example of descriptive analytics is where the population of a country can be classified based on sex, age, income, density, education, etc. (Rajaraman 2016).

Diagnostic Analytics Diagnostic analytics determines the root cause of the problem by understanding why something happened in the first place. It tries to gauge the causes and behaviours of the events (Riahi and Riahi 2018). Diagnostic analytics help decision-makers to take the correct steps after they determine the errors as it provides them with the answer to the question why it happened. An example of diagnostic analytics can be that of a plane’s engine where it sends information to operational databases and then the maintenance department can devise solutions based on the data (Funmilola and David 2019).

Predictive Analytics Predictive analytics is one the most important types of analytics. It predicts what can happen in the future by analysing past data. It uses statistical algorithms and various machine learning techniques to determine and forecast the future steps to be taken (Ouahilal et al. 2016). It can help by processing and analysing historical data to predict patterns and behaviours which can be even days, months, or years from the present. Decision-makers thus make good use of predictive analytics to improve profit margins and propose accurate steps (Funmilola and David

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2019). An example of predictive analytics is in retail marketing where data from the consumers can be gathered and processed to market the correct and saleable merchandise in the stores (Rajaraman 2016).

Prescriptive Analytics Prescriptive analytics helps to achieve the answers towards a specific goal. It provides solutions to existing problems (Rajaraman 2016). The purpose of prescriptive analytics is to look at existing or present patterns and forecasts and make judgments based on that knowledge. The goal of prescriptive analytics is to recommend or prescribe the optimal decision possibilities based on enormous amounts of data so that companies can reap the benefits in the foreseeable future. It does so by combining the outputs of predictive analytics along with artificial intelligence, enhanced algorithms, and smart devices and systems in a probable environment that result in adaptive, automated, limited, time-sensitive, and optimal judgments (Lepeniotia et al. 2020). An example here can be that of airlines maximising profits by analysing data of past holidays, destinations, durations, etc. and providing prices and seat reservations (Rajaraman 2016).

Importance of Predictive Analytics Predictive analytics is a part of data analytics which provides future insights based on historical trends and data. The generation of these future outcomes can be very accurate and precise, and some may even provide a foresight which is seconds, days, weeks, or even years ahead. It uses many mathematical techniques along with statistical modelling, big data, machine learning, and many other algorithms to predict future events. As the prediction of the future is a crucial aspect to any business, predictive analytics has been considered to be an important part of many organisations (Sumithradevi and Raja 2019). Predictive analytics can cope with a variety of situations. There are both continuous and discontinuous changes (Mishra and Silakari 2012). Predictive analytics is composed of two words—predict and analyse.

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But it works reversely where it first analyses and then uses machine learning techniques to predict. Lead by data, predictive analytics is considered to be inductive. Creating these models can be very tricky as some might not give the correct results. Developing predictive models necessitates a lot of effort, and the outcomes are not always guaranteed to be useful (Eckerson 2007). PA is used in supply chains, healthcare, robotics, architecture, financial and insurance industries, retail, travel, social networking and telecommunication, IT, Government and Public sectors, Oil and Gas industries, etc. (Ongsulee et al. 2018). This applicability makes PA one of the most desirable and extensively used application in the current scenario. Many artificial intelligence (AI) applications use machine learning as a way of computational learning. ML algorithms are versatile tools that can make predictions and at the same time learn from trillions of data points. Machine learning can be thought of as a modern-day version of predictive analytics. While it’s critical for businesses to understand the differences between machine learning and predictive analytics, it’s also crucial to understand how they’re linked. Machine learning is a branch of predictive analytics (Ongsulee et al. 2018).

Predictive Analytics Process Predictive Analytics involves many processes by which analysts usually predict future events. It has a series of tasks that occur gradually:

Requirement Collection The main aim and objective of a project are defined here. The desired outcomes are discussed with the client according to their demands and requirements (Eckerson 2007). It also defines the knowledge that will be gained after prediction of insights. For developing the proper model, specific usage of data is also discussed with the client (Kumar and Garg 2018).

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Data Collection Once the specifications and requirements of the clients are met, the next stage involves acquiring data from respective datasets. Different organisations may use different datasets to gain data from consumers (Kumar and Garg 2018). Data may be structured, unstructured, or semi-structured. Data analysts then capture the data for analysis and the time taken depends on the type.

Data Analysis and Massaging For the creation of models, the data collected is extracted, cleaned, and then processed (Eckerson 2007). Here the extracted data is cleaned and made into a structured format. Sometimes the captured data may even have some errors, so it has to be tested before analysis. All the issues must be addressed beforehand as the quality of data is the most vital factor for the creation of the predictive model (Kumar and Garg 2018).

Statistics and Machine Learning There are many Machine Learning and statistical techniques which are used in PA. Regression analysis and probability theory are commonly used (Kumar and Garg 2018). The end result and the solution needed are dependent on the approach taken and the devised technique followed (Fuentes 2018). Statistical techniques have to be involved in the process even though they are traditional techniques compared to Machine Learning ones.

Predictive Modelling At this stage a model is developed and tested on a test data set which is derived from the main dataset. If the model is successful, it is considered to be a finalist. After investigating many techniques, approaches, and models, the finalists are then considered to be the promising ones which will give out the best solution. Once this model is made final, it

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predicts accurate results based on the dataset to meet the desired project specifications (Kumar and Garg 2018).

Predictions and Monitoring Once the model starts to predict insights, it is then monitored and checked. The accuracy and quality of the results are also monitored to improve and optimize performance (Eckerson 2007). All this can be done at the client’s location and according to their requirements so that any errors or issues can be dealt with at the very moment. This step-by-step path leads to the development of the final model which helps in predicting future insights and goals as required. The decision-making process utilizes all these processes, and all are a vital part of the whole circle from collection to final deployment in PA (Kumar and Garg 2018).

Predictive Analytics and Supply Chain Management Nowadays supply chain professionals are overwhelmed with massive amounts of data across all platforms. This large amount of data needs to be processed and analysed to be made useful to benefit supply chains (Hazen et al. 2014). Predictive analytics can be a useful tool for many supply chain businesses which can help in cost-effectiveness, data management, improved lead times, and better forecasting which all can improve performance. Many organisations are flooded with data, with a lot attempting to gain a competitive advantage through data analysis (Davenport 2006). The difficulties and consequences of poor data quality are becoming increasingly important to supply chain management. As the quality of data has been of immense importance in businesses, many suffer when it comes to analysing vast amounts of data as the demand and capabilities of these businesses keep growing (Parssian et al. 2004). Predictive Analytics and Big Data Technology (BDPA) can help in leading towards value creation in the entire cycle of the supply chain. It also enhances the resilience, robustness, risk management, and overall

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organizational performance (Gunasekaran et al. 2017). Even most practitioners have a tough time comprehending the skills that are needed to turn data into value. Despite its widespread use in the industry, many businesses are hesitant to engage in this technology because of the difficulty in identifying potential benefits. Furthermore, research on BDPA capabilities in the supply chain is scarce, necessitating a thorough examination of its capabilities so as to reap the benefits of big data. Various companies thus have to consider the organizational, human resource, and many other technical challenges to improve the value and to gain better future prospects in comparison to their competitors (Arunachalam et al. 2018). In order to absorb PA in different processes and to have a high supply chain and operational performance, the entire top management should not only gain resources but also give all their efforts to this process by organising and investing in gathering resources (Gunasekaran et al. 2017). So, the overall aim of the paper is to explore the impact that the use of PA has on supply chain organizations that implement it. There has been limited research done on the actual impact of PA on the overall organisational performance. So, this research will help in understanding the effect that these new technological advancements can change the way supply chains function and create outstanding results when implemented. To understand this, the use of predictive analytics in the various sectors of supply chains is necessary.

Procurement Procurement has been helping organizations to reduce costs with the help of tactful contract management and also improving value propositions. Most of the operations have been digitized and organizations are now focusing on developing strategic plans of action (Ate¸s et al. 2018). Technology in procurement enhances relationships and skills and capabilities, achieve cost-efficiency, optimise processes, and increase performance. It allows for new organisational configurations and it analyses by gaining procurement and organisational integration and alignment through the management of priorities into unambiguous procurement results that are

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always in line with the business strategy (Biazzin and Castro-Carvalho 2019). Massive datasets may be retrieved, aggregated, and transformed into valuable information and knowledge using BDPA, which can then be used as decision support systems to conduct deep analyses and improve operational performance, particularly in the field of smart manufacturing (Ranjan and Foropon 2021). Top management is responsible to align the firm’s procurement policies, corporate policies, and data analytics strategies to run parallel with the firm’s own strategic objectives (Gunasekaran et al. 2017). Businesses must focus on the strengths of human and technological roles, which necessitates a thorough grasp of human judgement and technological capabilities to support data management and have consistent insights (Schoemaker and Tetlock 2017). When it comes to procurement issues, common difficulties and decisions include supply and demand management, cost and budget reduction, developing costing models, innovations in procurement, market categorization strategies and intelligence, supply chain risks, supplier management, and stakeholder evaluation. To approach and solve these issues, procurement technology is required along with a vehicle for segregating data from multiple sources (e.g., ERP systems, procureto-pay systems, contract management systems (CMS), and third-party systems and devices), processing, and presenting information to users, who then act on it (Handfield et al. 2019). Statistics, graphical visualisation tools, simulation, and mathematical methods are all employed in PA. Increasing information transparency among supply chain actors can assist all stakeholders in achieving their overarching goal of enhanced stockholder value. Increased asset velocity (e.g. inventory turns) and the ability to move material swiftly through supply networks are both aided by improved access to supply chain data (Handfield et al. 2019). As a result, data visibility increases reaction to demand shifts and disruptions, assisting in the management of supplier risk and performance (Handfield and Linton 2017). Advanced analytics, risk and resilience management, sustainability, globalisation, adoption, finance, innovation, transparency, and people will be the greatest disruptive factor in the procurement department (Handfield et al. 2019).

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Most innovative procurement technologies are still in their early stages of development, and only a few major companies have successfully implemented them. Real-time spend analysis, in combination with visualisation tools and properly drafted contract inquiries, will add a new level of value (Handfield et al. 2019). Spend analysis can be paired with contract expiration data to yield valuable insights. Predictive analytics and cognitive technology may be able to link pricing differentials or other elements to unexpected changes in market conditions (Handfield et al. 2019). Risk analytics provide an initial stage of warning signs towards upcoming disruptions. Shorter product life cycles are associated with faster technical progress. This short-term and volatile product life-cycle has caused demand planning and procurement to be unreliable (Kuo, Chen et al. 2021). Procurement predictive analytics tends to have an impact on many different sectors like visibility in supply chains, social media, and information systems, flow of data between systems, standards of management and transmission, and cybersecurity of buyers and suppliers (Stevens and Johnson 2016). It can thus be seen that the future is all about predicting the accurate insights and will help in the success of procurement processes in supply chains (Kuo, Peng, et al. 2021).

Manufacturing With the introduction of internet of things (IoT), asset handling and monitoring sensors, weblogs, social media posts, product and components tracking, and other technologies, manufacturing, and service industries have to deal with increasingly large amounts of data in a limited amount of time. These businesses are no strangers to storing large datasets, but actionable and manageable insights from the data are frequently absent (Shukla et al. 2019). Predictive analytics helps to play a major role in manufacturing by improving product forecasting, performance management through various manufacturing sectors, product and service quality, operational visibility, customer preferences and purchasing patterns, real time or current manufacturing processes and asset maintenance, product design, customer service and consulting,

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and many others (Govindan et al. 2018). Although there are numerous advantages to employing PA, its acceptance in many organisations is still in its infancy. Making intelligent production systems that are “self-aware,” “selfpredict,” and “self-maintain” is one of the most difficult tasks facing the manufacturing industry today. The Predictive Manufacturing System (PMS) encapsulates a variety of technologies and methodologies, including statistics, data modelling and data mining, and artificial intelligence. These tools and methods are used to transform data into useful information and give predictions about the system under study (Nikolic et al. 2017). To achieve transparency, manufacturing companies must have advanced predictive analytics, as well as IoT, data mining, networks for intelligent sensors, along with big data, to transform a large amount of collected data into useful information and gather or store it in the cloud, so it can be available for use at any given point of time and place (Nikolic et al. 2017). Industry 4.0 has cyber-physical systems that are linked with the internet of things to create intelligent predictive systems, virtual presentation, and interfaces that can be operated from any point to support predictive manufacturing systems and organized industrial environments. The manufacturing process becomes more adaptable, and intelligent embedded devices begin to function in unison (Gunasekaran et al. 2018). The BDPA plays a critical role in an organization’s agility. By having an integrated and developed supply chain network, and collaboration among other sectors, along with giving timely and more precise information about product demand and having proper processes in place, it can aid in optimal performance (Gunasekaran et al. 2018). Despite the steady advancement of relevant technologies for the implementation of PMS, there are still a number of difficulties to address. The creation of algorithms that can learn from and handle a variety of scenarios can be considered as a major issue that has a broad array of hurdles in PMS and also Industry 4.0 entirely (Kang 2016). Data security in PMS is also a serious worry, as massive data is housed in virtual cloud platforms. Thus, more research is required in this field of improving storage of such massive amounts of data (Wuest et al. 2016). Moreover, top management has a critical role in the adoption of

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analytic tools as practical decision-making approaches in the application of PA technology. The analysts’ good thinking abilities and comprehension of statistics aid in making proper conclusions and thus optimizing operations (Raut et al. 2021).

Logistics and Distribution The logistics and transportation business are a blend of the networks, infrastructure, equipment, information technology, and employees required to convey a wide variety of items safely and effectively throughout the country and around the world (Stefan 2017). Trains, aircraft, ships, and trucks are all part of an interconnected transportation network, despite the fact that they are commonly thought of as independent entities. Data science, predictive analytics, and big data may be able to assist logistics managers in meeting internal needs and adapting to supply chain changes (Govindan et al. 2018). Access to real-time data is gaining a lot of importance in supply chain and logistics decision-making. A supply chain logistics manager can efficiently substitute flow of goods and products with information flow at a significantly lower cost if they have a better awareness of inventory location and demand information. Some sort of tracking technology is now most likely generating and delivering the most beneficial, real-time supply chain data (Basole and Nowak 2018). BDPA can be used in inventory management, marketing, analysing consumer demands and sentiments, optimizing transport, analysing traffic patterns and help track vehicles, and moving inventory with the help of tracking devices (Sodero et al. 2019). Given the availability of talent, BDPA implementation allows businesses to gain possible benefits connected with the development of value and the acquisition of a competitive edge (Gunasekaran et al. 2017). The study of logistics operations is based on the total cost concept, and its goal is to manage and handle the actual flows of raw materials, materials, and finite products, as well as international flows, efficiently and effectively. Under the farm of supply chain management, the competitive advantage is secured by

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harmonising the logistics function with other company functions and by integrating the logistics chains of all upstream and downstream organisations in order to ensure a high degree of customer service at low prices (Stefan 2017). It is thus vital for BDPA to consider all parameters when it comes to logistics to enhance and optimize the business overall. The logistics and transportation industries face a multitude of challenges. As a result, inbound and outbound logistics must adjust to the changing environment. It can no longer be managed using standard planning and control procedures due to its rising complexity (Premm and Kirn 2015). GPS, bar-coding, RFID, time stamping, and other technologies are allowing businesses to capture more data than ever before complicating decision-making. Accurate demand forecasting is a challenge for businesses; not knowing what is coming, when it will arrive, who will need it, and when can it cause resource constraints. Recruiting and training personnel is quite expensive (Sgarbossaa et al. 2020). With shifting fuel prices, the need to optimise routes and staff utilisation is becoming ever more critical. Companies are encountering declining profit margins and capacity restrictions in terms of both physical and human capital, notwithstanding the opportunity to pass part of the fuel costs on to customers via fuel clause adjustment (Stefan 2017). The emergence of Predictive Analytics can help companies to spot previously unnoticed trends and forecast variations, allowing them to better plan for the future. PA also provides a wide range of mathematical enhancements, projects and resource planning and management, simulation system management, decision-making analysis, and many other capabilities, allowing organizations to create detailed models of the business and get a clear picture of current, future, and potential performance (Winkelhaus and Grosse 2019). Predictive analytics can even aid in developing refuelling plans and maintenance schedules which can be implemented by all vehicle producers and manufacturers, resulting in lower customer fuel costs while also enhancing the performance, efficiency, and longevity of future cars (Hopkins and Hawking 2018). It also enables better reporting procedures as well as the detection of risk areas such as compliance violations, fraud, and reputational damage. Only a small percentage of businesses make full use of predictive analytics. On

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the other side, this approach frequently clashes with efforts to keep IT expenditures under control and reduce them. Smart logistics can better aid in improving the transparency and flexibility and be resilient to any consumer demands and market changes. Because “Smart Logistics” will evolve in response to actual technological advancements, it will be time-sensitive, making it necessary to define the current status of the technology (Barreto et al. 2017).

Warehousing For the past five years, multiple sources in warehouses have generated massive volumes of data every second (Ghaouta et al. 2018). These are RFID devices, pallet transfer information, weather data, ERP, GPS, and IT system data, along with many other business and external data. Companies which invest in BDPA platform and acquire expertise in efficient analytics can improve and enhance their WM operations and become cost-effective (Ghaouta et al. 2018). Receiving, put away, internal replenishment, order picking, accumulating and sorting, packing, cross-docking, and shipping are all examples of warehouse operations. Companies are utilising innovative approaches such as BDPA to gain improved insights which can be used as competitive advantage to reduce the likelihood of various risks, increase efficiency and growth by maximising frequency and transparency in operations, and improve agility to deal with hurdles and challenges (Gupta et al. 2020). BDPA can provide innovative solutions to deal with WM difficulties such as management of space in warehouses, handling resources, monitoring and overseeing operations, and reduce costs and optimize performances to make better decisions in the WM industry. In addition, data sources in WM can be divided into two categories: internal and external data. Data from ERP systems, pallets, carriages, GPS, weather and traffic data as well as other business, and EDI transaction data are all examples of internal data found in company IT systems and databases. External data sources include logistical networks, weather data, and mobile apps, such as those from social networking sites, and statistics from data portals (Ghaouta et al. 2018).

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Although BDPA adoption in WM is still not common, many logistics companies are using it for a variety of purposes like refuelling planning, pricing contracts and negotiations, minimising truck/driver waiting times, enhancing maintenance durations, route planning, deliveries within the business, and detection of drivers prone to cause more accidents (Ghaouta et al. 2018). Warehouse management systems aid in the collection of warehouse operational and process data. The data captured in Warehouse Management systems (WMS) is of three types. The Master data contains the following: • Supplier Master • Item Master • Customer Master The Transaction data includes the following: • • • • • •

Receiving Shipping Inventory tracking Cycle counts Inventory adjustments Value-Added services

Business data results in the understanding of WMS application and configuration based on client requirements. With these three forms of data, Predictive Analytics can extract, analyse, and predict a lot more important business information (Andiyappillai 2019). Due to the vast number of deliveries, space management is a key component of WM. PA can help in this regard by projecting and anticipating the supply and demand of brands and accordingly, space management can be implemented. Also, costs can be reduced by optimizing redelivery systems (Palakshappa and Patil 2018). If a company fails to meet the needs of its clients, it risks losing them. Furthermore, a company’s reputation can be spoiled as a result of incomplete or nonfulfilment of orders. Getting the right product at the right time to the

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right consumer is the need for today’s logistics environment. Smart businesses can use BDPA to gain a better visibility over the entire range of processes and understand consumer behaviour to provide the best customer service (Awwad et al. 2018). It is vital that warehouses and fulfilment centres extract as many benefits as possible from big data and PA to maximise profits and gain competitor advantage.

Consumers Consumers are the most important aspect of many industries and most definitely for supply chain organisations. Factors such as globalisation of supply chains, highly competitive marketplaces, volatile product variations, and short product life-cycle have made forecasting more challenging (Boone et al. 2019). Big data has led to a new image of Customer Relationship Management (CRM) strategies. Data analysis assists firms in describing client behaviour, understanding their habits, developing appropriate marketing plans, identifying sales transactions, and establishing long-term loyalty relationships (Wassouf et al. 2020). Companies must adopt new activities to capture and improve client happiness and retention as their businesses become more complex and competitive. As seen before, vast amounts of data are captured on a day-to-day basis from every sector of supply chains. Organisations should analyse data to gain insights into customer demographics and psychographics. Customer loyalty can be predicted to identify both consumers who are very loyal to their current provider and customers who are planning to switch to a competitor (Wassouf et al. 2020). Also, the impact of social media platforms like Facebook, Twitter, Instagram, Pinterest, etc. has been proving to have a huge impact on buying preferences and perceptions of consumers (Pantano and Gandini 2017). Today’s most prevalent method of tracking is through social media, credit and debit transactions, IP addresses, loyalty programs, customer emails registered on websites, etc. If the data captured has a variety of information about the consumers, it is considered to be a rich data set with the ability to predict more accurate set of insights using predictive models (Bradlow et al. 2017). Massive amounts of data acquired in

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near real time has the potential to increase understanding of demand and supply forecasting, customer behaviour, and supply chain management. The more a company understands its customers’ buying habits and preferences, the more accurate demand forecasts will be received which can help in efficient supply chain decision-making (Boone et al. 2019). There are many ways that the data is collected for consumer analytics and demand forecasting. There is point-of-sales data where modern point-of-sale systems can record precise information on sales timings as well as goods availability in inventory. This can result in a better view of sales as a pathway to calculate the required stock planning, giving a better sense of demand (Boone et al. 2019). Other ways also include the traffic data, in-store data, path data- which suggests how the consumer approaches products, and internet and social media data (Boone et al. 2019). But with such vast amounts of data come challenges. Most organisations still use traditional methods of capturing and forecasting data. More information should ideally lead to better forecasts. But the increasing level of variables and confusing functions lead to difficulties (Feng and Shanthikumar 2018). Biases from the planners and forecasters also play a major role. Newly accessible data has the potential to decrease the negative consequences of prediction adjustments. Companies are now integrating such systematic approach to have better forecasts (Sagaert et al. 2018). Customer experiences and preferences keep on changing due to impact of external influencing factors like publications, internet, discounts, etc. Connected supply chains which use techniques like targeted promotions and messages, accurate tracking of consumptions, online diagnosis, and risk management, can be used by demand planners to capture consumer attention and loyalty. But many organizations fail to inculcate these and thus get improper forecasts. Moreover, privacy and theft issues are increasing on a daily basis and forecasting communities need to take these factors under consideration as well (Boone et al. 2019). The literature review reflects that even though there are many challenges in PA, the benefits outweigh them in all levels. As predicting the future can reveal many important insights and avoid risks and errors, it is the next step for all organisations to use to their full advantage.

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Strategies such as just-in-time, improved data prediction and accuracy and lean manufacturing can help in quality management and ongoing market growth (Gupta et al. 2020). It is thus vital for this research to understand the concepts of Big Data Predictive analytics, its types, functions, characteristics, and also mainly about Predictive Analytics with this implications and impact on supply chain management and the current challenges faced by organizations in its implementation. It can also be seen that big data analytics and other technologies not only enhance operational effectiveness using analytics methods to gain value and capabilities but also can give rise to new entrepreneurial options where new businesses can rise with the strategic use of these technologies (Sedkaoui 2018).

Research Methodology The main objective of any research study is to find answers and solutions to problems using various scientific methods. It is a long and time-taking process and involves many activities to derive to final conclusion (Bairagi and Munot 2019). For this research, the researchers use qualitative research methodology. Qualitative methods usually provide descriptive data which is usually concerned with people’s feelings and experiences. It is a way to approach the empirical world rather than being just a set of data gathering forms (Taylor et al. 2015). Qualitative research is inductive and also works on theoretical frameworks and researchers use the data to reveal useful insights and understandings from the patterns in the data collected (Taylor et al. 2015). Also, this type of research is more complicated and requires guidance and concepts to back up theories. Everyone tends to react differently to different situations and hence it is difficult to derive predictive conclusions (Bairagi and Munot 2019). There are few theory-based aspects available to comprehend and then interpret the effect from a managerial view. As a result, qualitative approaches were employed for this research paper so as to better comprehend the variety of perspectives and impressions expressed by the respondents concerning the occurrence (Johnson

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and Bohle 2019). Qualitative research focuses on gaining a better understanding of processes, experiences, and the interpretations people place on objects (Aspers and Corte 2019). But many researches argue with this fact as people’s views and opinions are different depending on their personal perceptions and experiences (Taylor et al. 2015).

Data Collection The data that is gathered for this research is of primary and secondary types. The primary data for this paper is extracted through the use of questionnaire survey which has been specifically designed to understand and portray the main aim and objectives established. For the purpose of this research, the survey was distributed to a population of around 80 participants. The participants for the research consisted of Supply Chain Analysts and other Supply Chain professionals who have a knowledge about the use of PA in the field. Due to time restrictions and other factors, a total of 63 responses from the respondents have been collected from the surveys. All participants were provided with the consent form and were intimidated about the survey beforehand. The secondary data has been derived through many research papers and peer-reviewed journals and reports. In addition, the secondary data gathered for the literature study and supporting material has been correctly cited, with references to the relevant journals, reports, books, websites, publications, and so on. As a result, the study complies with the relevant legal and ethical considerations.

Data Analysis The data that has been captured through the JISC survey link was distributed among the respondents and has been analysed by the JISC system itself. To identify crucial issues, gather relevant and significant information and analyse the data helps in taking the right course of action (Sekaran and Bougie 2016). The questions asked were mostly close-ended and easy to interpret. Hence graphical representation of

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them was easily created. Pie-charts which are effortlessly created, give a very clear and crisp idea about the results. Because of the historically substantial dependence on quantitative methods, qualitative methods such as case studies and grounded theory have become more relevant to practise. Thus, for this research, interpretative approach is also employed to enhance the findings from secondary data.

Findings and Discussions This section illustrates the findings gathered from the primary research survey questionnaire which has been distributed among the participants. This section is useful in gaining a better knowledge of the actual impact that PA can have when it is applied in organisations and businesses. The first question that was asked to the participants was regarding optimized performance by the use of Predictive Analytics in organizations. Majority of the respondents have agreed that PA does help in optimizing the performance of an organization (Fig. 6.1).

Fig. 6.1 Responses to the first question on PA role in optimization

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Predictive analytics is built on people, tools, and algorithms, and operationalising it requires all three (Ogunleye 2014). Big data enhances organisational performance by complementing information technology with the ability to use it and at the same time aid in better decisionmaking. The enhanced operational performance results into costeffective solutions that can weather the storm and contribute to overall sustainability. Lean manufacturing, just-in-time strategies and the ability to predict precise data are all part of quality management and can aid in sustainable and long-term growth of the market performance (Gupta et al. 2020). Also due to cost constraints, most large organizations can afford the use of data-driven software. This is where cloud computing is now being used as a cost-effective means to use data to its full potential by smaller organizations and even upcoming entrepreneurial businesses, ultimately enhancing their business models (Naous et al. 2017). Business systems can thrive longer with strategic engineering techniques to develop their business models for cloud computing and data analytics (Hosseinian-Far et al. 2017). The next question which was asked of the participants was regarding the use of highly skilled workers in PA. The majority of responders agreed with this statement (Fig. 6.2). To attain overall dynamic capabilities, the total organisational performance needs a continuous upgrade of technology as well as people who can use that technology. This leads towards the human component, which not only aids in the establishment of technology but also plays a critical part in realising its full potential, and at times even outperforms as a competitive advantage for the firm. There are many disadvantages when there is a shortage of skilled labour in organizations like impact on leveraging the value of technological advancements and achieving overall performance. But at the same time, the presence of highly skilled human involvement can have a positive impact not only on developing the knowledge but also making sure that all resources are utilized at full capacity. This is something that cannot be acquired easily (Gupta et al. 2020). It is thus necessary for any organization to acquire a rich and skilled set of resources to have an enhanced environment.

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Fig. 6.2 Responses to the second question on the requirement to have highly skilled workers

Question three asked of the participants was if PA models provide companies with accurate future insights. Majority of responders agreed with the notion (Fig. 6.3). Predictive analytics predicts future insights by using various simulations, statistics, and programming. Most of the supply chains lack the capability to leverage the power of data to gain valuable insights for the organisations. The root causes include a lack of capacity to analyse massive amounts of data and also the ability to use erroneous data, both of which result in immense cost savings (Rowe and Pournader 2017). This type of erroneous data can even hamper the quality of the predictions generated. Recent technical breakthroughs in data collecting and storage, as well as powerful data analysis tools, particularly for unstructured data, have radically altered the nature of labour and the workplace. Also, it is very important to understand that no algorithm can give 100% accurate results (Tiwari et al. 2018). It is thus vital for organisations to improve on their analytics capabilities to gain more accurate and credible insights for optimized performance.

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Fig. 6.3 Responses to the third question on the role of PA for insight provision

Fig. 6.4 Responses on the comparison between PA and other analytics categories

The next query asked to the participants was if Predictive analytics is more effective than other forms of analytics which are—Descriptive, Diagnostic, and Prescriptive (Fig. 6.4). Descriptive analytics illustrates as to what happened in the past. Diagnostic analytics helps to understand the reason for why it happened in

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the past and in what way it actually occurred. Predictive analytics helps to anticipate and forecast what can happen in the future. Prescriptive analytics can help in devising solutions after receiving insights. Most of the managers usually prefer the idea of predicting to change than reacting to it (Halim et al. 2018). Therefore, most organizations prefer to use predictive analytics over others. From projecting consumer behaviour and purchase patterns to anticipating sales records, predictive analytics can be applied throughout supply chains. They also aid in forecasting demand for supply chain and inventory planning and also operational management. Predictive analytics can help predict the future or bridge the gap in between the organization’s knowledge (Tiwari et al. 2018). In the future, predictive data analytics will be the backbone of resilient and adaptable supply chains, allowing businesses to better prepare for changes and uncertainties. They can not only mitigate hazards but also respond proactively to supply and demand fluctuations (Majumdar 2021). The next query that the participants were asked was whether PA model is time-consuming. Here, majority of respondents agreed to the notion (Fig. 6.5).

Fig. 6.5 Descriptive statistics on the fifth question

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The process of prediction involves many steps. First, it involves defining the project’s business goals and intended outcomes, then translating them into predictive analytic goals and tasks. Then the source data is analysed to select the best data and model-building approach and define the scope of the project. The data is then prepared by selecting, extracting, and transforming it accordingly. The models are created, tested, and validated based on the data prepared to see if they meet the project criteria. The model results are then applied for making business decisions. They are then monitored and maintained to keep the efficiency high at all times. Many experts are of the opinion that the data preparation stage is very tasking and time-taking. As data comes in all types and forms, it needs to be properly segregated to have accurate prediction models. Moreover, the models that are developed also take time as there are many variables that have to be considered. As a result, most analysts reduce the number of variables in the final model from a few hundred in the initial version to a few dozen. To increase model accuracy, they may need to incorporate new data types or recombine current fields in innovative ways. Model creation is labour-intensive and time-consuming due to this iterative process (Eckerson 2007). The next question asked was if PA provides a good return on investment (ROI) to companies. Most of the respondents did agree to this (Fig. 6.6). Companies can use predictive analytics to improve existing processes, better understand, and gauge consumer behaviour and also spot unanticipated opportunities and predict problems before they even take place, providing the companies with a good ROI (Eckerson 2007). When it comes to PA, people, process and technology have to considered to acquire its full potential. PA has the capacity to provide organizations with a good ROI which depends on the three factors. Although the majority of predictive analytics projects are focused on enhancing individual business processes, having a long-term strategy allows for a more efficient utilisation of predictive analytics and supporting resources. The benefits of predictive analytics projects last for a long time, and companies who use analytics more frequently are more competitive. Predictive analytics projects yield a slew of intangible or difficult-to-quantify benefits, bolstering the case for investing in these tools. Even during difficult

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Fig. 6.6 Responses to questions on PA and ROI

economic times, investment in predictive analytics continues to grow at a strong rate (Vesset and Morris 2011). With respect to the pricing quotient of PA, most respondents agreed the fact that it is expensive. But at the same time, good number of respondents chose to opt the option to neither agree nor disagree (Fig. 6.7). As PA involves the use of many different aspects and requires skills to understand the technology, the overall investment needed is high. Pricing can depend on a lot many factors like the amount of data available, the extent to which it needs to be cleaned and prepared, the number of skilled employees, technologies used, software needed for visualization, etc. So, the price varies according to what is expected from a project. In the academic and industry communities, big data trading has become a developing and promising topic. Data is a virtual thing, unlike traditional commodities, with qualities such as variability, variety, volume, velocity, and complexity. Traditional pricing methods and strategies need to be modified and transformed to meet the current environment, so that the true value of big data can be revealed (Liang et al. 2018).

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Fig. 6.7 Responses on the value proposition of PA

Regarding the reliability of PA, most respondents were of the opinion that it is in fact reliable (Fig. 6.8). When it comes to PA, there are many factors that determine the accuracy and reliability of the technology. One of the factors that affect the accuracy is the quality of data. If the data quality is bad, it can affect future predictions which in turn will not be reliable. The influence of poor data quality on the validity and interpretations of BD procedures led to the conclusion that data quality and accuracy must be ensured in every proposed strategy (Mazzuto and Ciarapica 2019). Organisations should therefore invest in data quality control for enhanced results. The use of a BD strategy in the SCM is favourably connected to information interchange and SC connection, implying that BD can assist in overcoming crucial predictive analytics difficulties related to data capture, storage, transfer, and sharing (Gunasekaran et al. 2017). More data will be generated as supply chains become more integrated, resulting in more dependable outcomes. The next query was regarding whether PA provides companies with a competitive advantage over others. Most respondents agreed to the notion (Fig. 6.9).

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Fig. 6.8 Perceptions on PA reliability

Fig. 6.9 PA and competitive advantage; descriptive statistics of responses

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Predictive Analytics has become a popular issue in the analytics world as more companies understand that predictive modelling of accurate customer behaviour and business scenarios is the most effective technique to acquire insights from data analytics. Predictive analytics is also regarded as one of the most important foundations in allowing digital transformation efforts across sectors and business processes all over the world. Additionally, IT companies’ increased development and commercialisation of analytics solutions has aided in the expansion of predictive analytics capabilities. As a result, the number of people adopting the technology has increased. When effectively deployed, the technology can allow businesses to achieve business benefits from their data collection (Attaran and Attaran 2019). As predictive analytics becomes more sophisticated, it will be used to identify the most productive companies in the future. This indicates evolving technological and managerial frontier that increases firm performance while providing a fleeting competitive advantage: organisations that stay on the cutting edge achieve and sustain gains over less data-centric businesses (Brynjolfsson and McElheran 2019). The final question asked to the participants was if they would be willing to recommend PA to other businesses or not. Majority responded positively (Fig. 6.10). It is self-evident that, given the benefits that PA and big data can deliver to businesses, the vast majority of them will adopt it. Logistics managers can have a better visibility on many internal processes if technologies like data science and big data predictive analytics can be used at full potential. As companies begin to adopt BDPA, modest benefits gained through initial pilot projects frequently serve as the foundation for expanding implementation to other elements of their operations, allowing managers to rethink their potential capabilities. New goods, services, and even business strategies, along with this shift in perspective, drive enterprises and their SC relations to rethink their entire competitive spot in the whole chain in comparison to competitors (Sodero et al. 2019). As a result, businesses should push one another to utilise modern technology in order to improve performance and maintain good partnerships. This has the potential to improve both technological and organisational performance.

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Fig. 6.10

Responses of participants on the suitability of PA for SC forecasts

Conclusion and Recommendations As supply chains are very diversified and complex networks, it is quite essential for organizations and companies to inculcate newer technological developments to better their performance and results. Technologies like ERP systems, Human Capital Management, Predictive Analytics, Business Intelligence, and security systems can enhance business models and give better predictions. This can also help predicting employee attrition. All these are very useful tools for entrepreneurship as well (Chornous and Gura 2020). The goal of predictive analytics is to find deviations and patterns in data and convert into informative business ideas. It can be used to estimate delivery, asset utilisation, maintenance, productivity, and sales prediction, as well as sense and react to risks in supply, demand along with operations. When estimating demand, predictive analytics allows companies to take into account both endogenous and exogenous elements (Arunachalam et al. 2018). Overall, PA provides a vast array of benefits when used in a systematic manner. Supply chain sectors from procurement, manufacturing, logistics and distribution, warehousing, and the end consumers have to understand the technical aspects of big data and predictive analytics to reap the utmost benefits out of it. There have been some concerns and challenges

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that organisations have faced in appropriately implementing it. The main issues have been with the burdensome data generation, the expertise and skills required and the adoption by industries. Segregation is a time-consuming and difficult operation that necessitates precision and accuracy. As a result, firms must engage in data management techniques to ensure that data analysis runs smoothly. Big data can help in providing both vertical as well as horizontal vertices of flow of information, which must be supplemented with the technical and management skills required to process it. As a result, it is clear that highly skilled staff can assist in meeting PA needs. Organizations whose primary focus is on long-term business operations may consider adopting a dynamic capability approach so that they can establish not just distinct but also adaptive skills. These dynamic resource capabilities let businesses leverage themselves by allowing them to change their operations and adopt, build, modify and change their internal and external processes. New technologies have the power to reach successful heights with proper planning and development. With immense amount of data, newer business models can be developed to gain profits from multiple sources. Delhivery, Dmall, and Flexport are some of the examples of supply chain unicorns which have shown to provide cost-effective platforms for their consumers with the help of data. Young start-ups can make use of the ample amount of information on the internet and grow strategically. With proper guidance, BDPA can deliver competitive advantages in extremely dynamic market settings if nurtured over time.

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7 Challenges for the Adoption of Data Analytics Strategies by Small, Medium-Sized Enterprises in Singapore Nam-Chie Sia, Amin Hosseinian Far , and Teoh Teik Toe

Introduction to Data Analytics Trends Data analytics strategies impact all industries (Gartner 2018) and research in the area has been widely published in the last few years (Hosseinian-Far et al. 2017, 2018). Many organisations used such strategies to gain competitive advantage by creating new business models, N.-C. Sia (B) · A. H. Far University of Northampton, Northampton, UK e-mail: [email protected] A. H. Far e-mail: [email protected] T. T. Toe Amity Singapore, Singapore, Singapore e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_7

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successfully disrupting conventional business models, gaining monopolies, or a better competitive position within the operating market (Provost and Fawcett 2013; Gartner 2018; Hair et al. 2018). With digitalisation, more data are being collected digitally. The digital data are made available and prepared for analysis within all aspects of business, such as operations, marketing, manufacturing, and supply-chain management (Provost and Fawcett 2013; Marr 2016). The insights gained from the data analytics enable organisations to perform better targeted marketing, cross-selling, and online advertising (Provost and Fawcett 2013). There has been a rise in disruptive digital business models; Uber is an instance that uses data analytics resulted in the creation of a new business model which disrupted the traditional business process (Marr 2016). In essence, Uber is a taxi booking company that links passengers to drivers. It uses internal and external data, such as traffic conditions, drivers’ supplies, and passenger demand to calculate dynamic fares called “surge pricing” (Marr 2016). The concept is similar to those used by hotels and airlines. When there is high demand, the prices are raised to entice more drivers to make the trips. On the other hand, the prices would drop when the demand is low to encourage the drivers to take a time-out. Amazon is another company that uses data analytics to gain a competitive advantage and grew from an online bookshop to one of the largest retailers in the world (Marr 2016). It has achieved this by collecting customers’ data to understand and predict their needs (Marr 2016). For example, Amazon collected customers’ data such as the language used, shipping addresses, time spent on particular pages to map these to other consumers with similar characteristics. By using the purchase patterns of consumers with similar features, Amazon has predicted the customers’ preferences and requirements to create cross-selling opportunities, which contribute to one-third of the sales revenue (Lin 2014; Wills 2014). The above instances demonstrate that organisations can generate new ideas through insights from data analytics (Chang 2021) to enhance their target marketing, cross-selling, and consumer management (Provost and Fawcett 2013). However, not all organisations have successfully

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implemented data analytics strategies. Before exploring the challenges to implement data analytics, this chapter will examine the definitions of commonly used and similar terms and the historical development of data analytics.

Industrial Trends Advancements in technologies enable small and medium-sized enterprises to expand their local footprints to the international arena, which raises revenue (MacGregor 2004) while lowering costs (Jahanshahi et al. 2013). Therefore, small, medium-sized enterprises must use technologies to redefine competitive advantages, rethink business strategies, re-examine business models, and re-invent customer services (Lee 2001).

Benefits of Data Analytics Data analytics strategies present considerable opportunities to organisations (Lin 2014; Yeo and Carter 2017). Studies have shown that organizations adopting data analytics perform better in terms of profitability and productivity (McAfee and Brynjolfsson 2012; Capriotti 2014; Ang 2021). This is because they can meet customers’ needs better by using insights drawn from data analytics to design new products and services (Segarra et al. 2016), outperform competitors, and gain a competitive advantage (Lin 2014). Like what Uber has achieved, organisations can also deliver their products and services using new business models (Ang 2021). Besides creating opportunities to increase revenue, data analytics strategies also help organisations save costs (Ang 2021; Lin 2014). For example: • Aviva analyses their customers’ lifestyle data and waive the lab tests for new customers whose lifestyles contribute to a low risk of illness. This enables the organisation to save lab test fees (Lin 2014).

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• By monitoring the individual parts of their trucks, UPS replaces the parts on a “need to” basis instead of fixed mileages or durations. As a result, the company saves millions of dollars in maintenance costs yearly (Lin 2014). The benefits of data analytics are not confined to large organisations as they impact all geographies, industries, and consumers (Gartner 2018). Small, medium-sized enterprises can adopt data analytics strategies to gain competitive advantage (OECD 2017). For example, they can use insights from data analytics to: • identify customers’ needs (Pan and Sun 2018), • innovate customised products and services to meet customers’ needs (Coleman 2016; DBS 2018, 2019; Alibaba Cloud 2018), • create new business opportunities (Ogbuokiri et al. 2015; Microsoft and ASME 2018) to grow revenue (Pan and Sun 2018; Microsoft and ASME 2018). Similar to what the large organisations have done on the operational aspects, small, medium-sized enterprises can use data analytics to improve productivity and reduce costs (Pan and Sun 2018; Microsoft and ASME 2018; Alibaba Cloud 2018), strengthen the finance capability (Alibaba Cloud 2018; DBS 2018), deploy employees more efficiently (Alibaba Cloud 2018; DBS 2019), conduct marketing effort better (Alibaba Cloud 2018; DBS 2018), increase sales (Alibaba Cloud 2018; DBS 2018) and plan logistics more effectively (DBS 2018). Studies performed by the Singapore Ministry of Trade and Industry confirmed that small, medium-sized enterprises adopting digitalisation and data analytics had seen an increase in revenue by 25% and productivity by 16% (Enterprise Singapore 2020). Small, medium-sized enterprises, which used technologies to advance their business models, for example, by re-engineering their corporate website can collect customer data when interacting or transacting with customers (Lee 2001). These data could be analysed, using advanced data analytics tools, to devise new channels or business models to serve their customers better.

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With numerous advantages, small, medium-sized enterprises should embrace data analytics to gain a competitive advantage (DBS 2019; Shivkumar 2019; DBS 2018). However, it is noted that the extent of the competitive advantage derived from data analytics is diminishing (Ransbotham et al. 2016), and product life cycles are getting shorter (OECD 2017, 2018), there is urgency for small, medium-sized enterprises to embark on the data analytics journey soon to be data-driven. Organisations, especially those in Singapore, where the local market is tiny, have little choice but to embrace digitalisation. The government noted the importance of embracing data analytics strategies (Lung 2018), which can be a new growth engine for the country (Tan 2016; Ong-Web and Ang 2017). For instance, it highlighted that the local organisations could increase their value by 25% and productivity by 16% (Enterprise Singapore 2020) by adopting data analytics strategies. Conversely, Singapore organisations risk losing their competitive advantage if they hesitate to proceed (Lai 2020). This is because the costs for not doing so are increasing as the world economies are interconnected (OECD 2018). The COVID-19 pandemic, caused by the novel coronavirus has accelerated the speed of digitalisation (Subhani 2020). As the changes are permanent (Lai 2020), the digital economy will become more critical than before COVID (Skilling 2021). Therefore, small, medium-sized enterprises must digitalise and innovate (Tan 2020) to connect with the borderless global digital economy (Hussain 2020; Subhani 2020). Given the importance, the government has encouraged broad-based adoption of data analytics (Lung 2018), e.g., a SGD 20 million fund was launched to assist organisations in Singapore to defray the digitalisation costs (Ng 2020). Despite providing assistance through fundings, many small, medium-sized enterprises have not adopted data analytics as part of their strategies (Ramchandani 2017; Ang 2021). Thus the Singapore government recognised that these enterprises need help to digitalise. It has provided a new toolkit to help small organizations embark on data analytics strategies (Lim 2020). Small, medium-sized enterprises can proactively learn the government schemes and explore ways to tap the available supports to invest in

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data analytics technologies. This would equip them to implement technologies and data analytics strategies to expand their market place from Singapore to the Asian region, or even globally (Jahanshahi et al. 2013). After that, small, medium-sized enterprises must continue to innovate, for example, by creating new business models (Lee 2001) and embark on new technologies, such as artificial intelligence.

Small and Medium-Sized Enterprises (SMEs) While discussing data analytics for small and medium-sized enterprises, it is essential to review the term and identify them. In general, there is no universal agreement on the term (OECD 2017), which is used to describe organisations with less than 500 employees in the USA (OECD 2017), and those with less than 250 employees in the Organisation for Economic Co-operation and Development (OECD) (OECD 2000, 2017). In Singapore, it is used to describe organisations with less than 200 employees or less than SGD 100 million turnovers (Teo 2013; Enterprise Singapore 2019). Although the enterprises are individually insignificant, small, medium-sized enterprises are collectively an integral and vital part of Singapore’s economy. In total, they employ about two-thirds of the workforce (Teo 2013) and contribute to approximately half of the country’s gross domestic product (GDP) (Microsoft and ASME 2018). If such a massive proportion of the economy becomes uncompetitive, the country will face a dire future. Therefore, it is crucial to research the challenges these small, medium-sized enterprises face and make practical recommendations to guide them on the data analytics journey.

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Challenges of Data Analytics Implementation Promoting Data Analytics to SMEs Despite the benefits and incentives to adopt data analytics strategies, the Singapore government struggles to convince most small, mediumsized enterprises to onboard the data analytics bandwagon with urgency (Ramchandani 2017; Microsoft and ASME 2018; Shivkumar 2019; Ang 2021). As discussed in the prior section, this is a big concern because Singapore organisations risk losing their competitive edge to satisfy increasing customers’ expectations (OECD 2017; Alibaba Cloud 2018; DBS 2018; Ang 2021). The slow adoption of data analytics among the small, medium-sized enterprises is not unique to Singapore as it is the same in the United Kingdom (Coleman 2016) and the OECD countries (OECD 2000, 2017, 2018). Reasons behind the slow adoption include: Leadership—One of the leading causes for failing to adopt data analytics strategies is the lack of leadership to provide a clear purpose and commitment to adopt the strategy (Ransbotham et al. 2016). In fact, leadership is known to be more influential to drive digitisation in small, medium-sized enterprises than availability of resources (Sia et al. 2021; Sia and Hosseinian-Far 2021). The leaders, such as owners and key decision-makers in small, medium-sized enterprises have no urgency to explore, understand and adopt data analytics strategies; hence they are not aware of the benefits data analytics can bring (Microsoft and ASME 2018). This, in turn, yields incorrect perceptions that data analytics implementations are costly (Ogbuokiri et al. 2015; Microsoft and ASME 2018) and they are designed only for large organisations (Ogbuokiri et al. 2015). Talent shortages—As small, medium-sized enterprises generally have limited resources, they cannot afford expensive human capital. There is a lack of IT and statistical skills (Coleman 2016; Gartner 2018; Microsoft and ASME 2018). In particular, there are limited talents like data scientists in the market; small, medium-sized enterprises can hardly afford

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to attract, train, and retain them (Gartner 2018; Chong and Nippani 2021). Organisation culture—Implementations of data analytics strategies are akin to business transformation (Microsoft and ASME 2018) and require culture change that cannot be achieved in a short time (Shivkumar 2019). Before exploring solutions and frameworks to assist small and medium-sized enterprises in implementing data analytics strategies the following section will discuss similar terms, development history, and various types of data analytics to assist readers, especially those less familiar with the topics, to gain understanding.

Definitions of Similar Terms Data Analytics, Analysis, Mining, Warehousing, Science Data analytics uses applied analytical techniques, such as statistical, quantitative, predictive, and contextual models, to draw insights communicated for decision-making (Wills 2014; Ransbotham et al. 2016; Segarra et al. 2016; Sun et al. 2017; Larson 2018). It is argued that data analytics includes data analysis, data mining, data warehousing, and visualisation (Segarra et al. 2016; Sun et al. 2017). Data analysis uses tools to organise, examine, study, and organize data to reach conclusions or make predictions (Sun et al. 2017). Data analysis is different from data analytics because the latter emphasises communication of insights (Provost and Fawcett 2013; Sun et al. 2017), as telling stories to drive better decision-making is the most crucial stage of data analytics (Knaflic 2015). Data mining identifies patterns through machine learning and statistics (Segarra et al. 2016). There is no emphasis on communications and insights. A data warehouse is a term used to describe a massive database for data storage (Sun et al. 2017). This does not concern data analysis or analytics.

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Data science involve collecting, modelling, analysing, and applying data through statistical methods (Larson 2018). Although the two terms (data science and data analytics) may be used interchangeably, data science commonly refers to the data collection and manipulation processes, whereas data analytics draw insights to gain competitive advantages (Proost and Fawcett 2013; Larson 2018). Big data is commonly called massive in volume, high in velocity, and significant in variety (McAfee and Brynjolfsson 2012; Lemieux et al. 2014; Lin 2014; Campbell et al. 2015; Coleman 2016; Yeo and Carter 2017). However, there is no consensus on the definition (Lin 2014). For example, Chang (2020) argued that big data have four Vs. (velocity, variety, veracity, and value), while Cheng et al. (2015) revealed that there are five Vs. (volume, velocity, variety, veracity, and value). Veracity means data can be inconsistent, while value means there are values in the data (Campbell et al. 2015).

Historical Development of Data Analytics In the 1970s, organisations have started to use data analysis, known as decision support systems, to support simple decision-making processes (Davenport 2014). The decision support systems were enhanced to executive support systems in the 1980s to support more complex executive decisions. In the last decade, data analysis is supported by advanced technologies, such as Hadoop, to perform the analysis concurrently with data generated in huge volume, high velocity, or wide variety (Marr 2016).

Types of Data Analytics The term “data analytics” may be too broad for many small, mediumsized enterprises. This section illustrates various types of data analytics, to assist the understanding, especially by readers who are new to the concept.

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Capriotti (2014) suggested four types of data analytics: • • • •

Descriptive data analytics shows what has happened. Diagnostic data analytics assesses the causes. Predictive data analytics anticipates what is going to happen. Prescriptive data analytics proposed options. Wills (2014) classified data analytics into:

• Real-time analytics, which is the most advanced, provides immediately actionable information. • Predictive modelling analytics use models and algorithms to analyse data, find related patterns, and predict outcomes. • Small data analytics perform analysis over a small population of data. Small, medium-sized enterprises should actively pursue data analytics strategies (OECD 2000, 2017, 2018). They can start with the basic descriptive and diagnostic data analytics suggested by Capriotti (2014), or small data analytics suggested by Wills (2014), as relatively low investments are required.

Frameworks for Small, Medium-Sized Enterprises As discussed in the challenges section, many organisations have failed to launch data analytics strategies because they are perceived to be complex and expensive. Bump (2015) suggested organisations “start small” by piloting data analytics strategies in teams of less than five people. This can avoid huge lump-sum investments that many small organisations cannot afford (McAfee and Brynjolfsson 2012). Therefore, small organisations should not be too ambitious. They are advised to break their data analytics implementation projects into manageable chunks (DBS 2014; Alibaba

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Fig. 7.1 Four Steps Framework for SMEs, adapted from Alibaba Cloud (2018)

Cloud 2018), preferably piloting them in the departments that will reap the benefits within the shortest timeframe. Alibaba Cloud (2018) put forth four-step strategies—plan, assess, adapt and optimise—for SMEs to digitise their business operations (Fig. 7.1). The model is aligned with Bumps (2015) and McAfee and Brynjolfsson (2012), who advocated organisations to “start small”. Besides implementing data analytics strategies in stages, it is important to articulate the purposes of adopting them by linking them to business objectives. There must be explicit purposes for the projects, and metrics must be established to measure the successes, or lack thereof (Andersen 2012). In this regard, Segarra et al. (2016) proposed an integrated framework, which consists of the SWOT analysis, matrix of change, and balanced scorecard. They can be used as guidance to implement data analytics strategies.

Research Gaps and Methodology The literature review noted that small, medium-sized enterprises lag behind their bigger counterparts to launch data analytics strategies due to a lack of leadership support, talents, and financial resources. However, the existing studies do not provide comprehensive recommendations to guide SMEs to kick start the projects, especially for the enterprises in Singapore, where the business environments are different from those of the USA and Europe.

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The authors have attempted to fill the research gaps by: • identifying the barriers that prevent Singapore’s small, medium-sized enterprises from quickly adopting data analytics strategies. • providing recommendations to guide Singapore, small, medium-sized enterprises to launch data analytics strategies. Given there is limited similar research in Singapore, the above analysis is exploratory. Qualitative research, which tends to be an exploratory study that builds theory (Dasgupta 2015), was adopted. Moreover, a case study organisation was adopted as the setting to conduct the research. Case study is the chosen research strategy because it provides a richness of data through in-depth investigations (Dasgupta 2015) that generates insights when little is known (Lee et al. 2007; Noor 2008). More importantly, it contributes new, practical, and actional knowledge (Coghlan 2003; McDermott 2008; Mahani 2012) to solve practical problems (Anderson et al. 2015). The case study organisation offered a pool of participants that could provide their perceptions and understanding of the challenges in relation to the adoption of data analytics technologies. Due to the organisation size of the case study organisation, which employed only 10 staff, it was not practical to adopt random sampling. Instead, purposive sampling method was chosen. Although small sample size can be challenged in terms of generalisation (Tellis 1997; Marrelli 2007; Singh 2014), the authors opined that high-quality findings could be applied and generalised, as supported by Gerring (2004), Lee (2007), and Noor (2008). Semi-structured interview, which facilitates understanding of attitudes, opinions, or phenomena especially when there is little knowledge (Rowley 2012; Oplatka and Hemsley-Brown 2004; Shahaida 2009), was the main data collection method. The interviews were transcripted and sent to the participants for confirmation before they are analysed. The authors adopted the following steps, which are recommended by SAGE Research Methods Datasets (2015) during the analysis: • Identify initial themes raised by the participants using challenges noted in the literature review as reference.

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• Code the interview transcripts. • Review the codes and identify common themes. After the common themes were identified as a result of the above steps, a second round of interviews was conducted individually with all the participants. The second interviews serve as triangulation to confirm the accuracy of the findings.

Implementation Experience from Case Study Organisation Before embarking on the data analytics project, the case study organisation performed a SWOT analysis, which was participated by staff in all the departments. The wide participations created a sense of ownership throughout the organisation. The analysis concluded that the organisation should strengthen its marketing capability to tap into the opportunities. After evaluating various options to strengthen its marketing capability, the leaders in the organisation chose data analytics. Therefore, the organisation has a strong business case for its data analytics strategy, which has a clear purpose to strengthen its marketing capability. While organisation culture is commonly cited as a barrier to implement data analytics, it is not the same in the case study organisation, where the authors noted a vibrant and positive attitude towards changes. This could be attributed to the leaders and staff in the case study organisation attending conferences, seminars, and industry discussions regularly, where the latest developments, such as data analytics, are discussed. During the interviews, the participants demonstrated their awareness of data analytics and the importance to kick start the strategy soon. A few of them indicated that the organisation has no alternative but to embrace the changes. One participant said: “In this modern-day and age, we have no choice but to do so”. Contrary to the issues noted in the literature review where leaderships are commonly cited as the causes for failures, the participants

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viewed their leadership as their strength. They have unanimously agreed that “leadership is essential” for the successful implementation of data analytics strategies and they opined that leadership supports are not lacking in their organisation. One of them said that “The board is very supportive and aware that we need to move on. We need to be at the forefront, especially with the uncertainty because of COVID-19, the mindsets are all changed”. Most of the participants noted that skill shortages exist in their organisation. However, they pointed out that they are not trained in data analytics because they are not needed to perform their current jobs. With the implementation of the strategy, they are confident that the skills can be acquired quickly without incurring high costs. Due to the limited resources and funding, the participants stressed that their organisation could not afford the huge investment. However, they highlighted that they can adopt simple analytics strategies, which cost little to implement. Moreover, the participants opined that data analytics strategies are too important that they must look for alternatives to fund the implementation. For example, one participant said that “to progress with time, or to remain relevant in how businesses operate in today’s landscape, it is something we have to work towards. That is one important reason to do it. We have to find other ways to look at funding issues”. Data management is a common issue noted among the large organisations due to the volume, velocity and variety of data generated daily. In contrast, it has not been raised as a challenge for the small, mediumsized enterprises in the literature review. However, all the participants raised data management as the most difficult challenge for their organisation. This is because their data are stored in various spreadsheets, and most may not be complete to perform effective data analytics. Despite the lack of data, the participants opined that perfect data will not exist, and the “organisation must live with imperfection”. Inspite of the challenges, the case study organisation has decided to push ahead with the implemantion by taking “baby steps” in their data analytics strategies. Firstly, this could avoid the huge investment needed

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to acquire sophisticated tools. Secondly, due to their small size, the business is fairly simple and hence they do not need advanced data analytics tools. The organisation agreed to start their initiatives with simple tools, such as Microsoft Excel. As one participant said that “data analytics does not need to be sexy”. The organisation has resolved to take “baby steps”, which is aligned with that proposed by Alibaba Cloud (2018), as their approach to embark on the data analytics journey. Although the lack of data has delayed the project, the case study organisation is making progress to a data analytics strategy that will strengthen its marketing capability. This can be attributable to the strong leadership support and positive culture to embrace changes. In turn, the leadership support and change culture are due to the awareness of data analytics and benefits. This information may be critical to the policymakers in their pursuit to push small, medium-sized enterprises in Singapore to adopt data analytics.

Five Recommendations for the Small, Medium-Sized Enterprises Based on the knowledge gained from the literature review and the implementation experience for the case study organisation, the following five recommendations are helpful to encourage more small, medium-sized organisations to embark on the data analytics journey.

Recommendation 1: Policymakers to Raise Awareness Studies have shown that there is a general lack of awareness at the leadership level leading to incorrect perceptions about data analytics strategies, which are commonly perceived as being designed for large organisations and are expensive. The experience at the case study organisation demonstrates that once the leaders understand the importance of data analytics,

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they will lead their teams to overcome other obstacles to make things happen. There is a need for policymakers, government, and associations to promote data analytics strategies’ benefits. They can organise seminars, conferences, and courses to promote such awareness. In addition, they can make toolkits to guide the launching of data analytics strategies and to dispel the ideas that they are expensive.

Recommendation 2: Leaders to Make Proactive Exploration As discussed in the historical development of data analytics section, the strategies are not new. However, with the advancements in technologies and tools, the costs of analysing data have been reduced recently. In the case study organisation, the leaders constantly attend conferences, seminars, and discussions with thought leaders to equip themselves with the latest knowledge. Leaders in small, medium-sized enterprises must demonstrate their leadership by exploring new ideas, concepts, and ways of doing things. They can start by attending conferences, seminars, or other discussion forums to pick up new knowledge about data analytics. Once they understand the trends, they will become more confident in leading their organisations into the fields of data analytics.

Recommendation 3: Make a Business Case for Data Analytics Strategies Studies have highlighted that the purpose of embarking on data analytics strategies must be articulated. The case study organisation has performed a SWOT analysis to identify areas where data analytics can plug the gaps. The purposes are communicated to all the staff, and those involved in the interviews have undoubtedly stated their support. It has also been shown that the levels of staff resistance are lower when employees have a sense of ownership. In the case study organisation, the employees are part of the SWOT analytics where the purposes of data

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analytics are identified. Many of them participated in the data analytics implementation project. Small, medium-sized enterprises must link their data analytics strategies to their business objectives. They should involve their employees to instill a sense of belonging and openly communicate the purposes to employees to reduce staff resistance.

Recommendation 4: Start Small The launching of data analytics strategies need not be too ambitious. Various authors have recommended a stage approach by starting small, or pilot at certain units, gain experience before wide-scale implementation. The case study organisation has decided to take “baby steps” by piloting their data analytics in the marketing effort. They have also decided to use simple tools to let their employees gain confidence before exploring more complex ones. Small, medium-sized enterprises should adopt the same approach by identifying the “low hanging fruits” to pilot the strategies where benefits can be seen quickly. This will give employees the morale booster.

Recommendation 5: Identify Data Required at the Onset The data analytics project at the case study organisation was delayed due to the lack of data to perform meaningful analytics. The issues were noted during the stage to develop analytics tools. The extent of the delay could be minimised if the lack of data was identified earlier and alternative tactics could be adopted to modify the analytics approaches. Therefore, once the purpose of the data analytics has been ascertained, small, medium-sized enterprises should identify the data required to perform the analytics and check whether the data are available. As it may involve system and process changes to acquire the necessary data, it cannot be achieved quickly. Therefore, this step cannot be ignored at the onset of the project.

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Research Limitations and Recommendations for Future Research Due to the lack of prior research, exploratory research is needed to ascertain the challenges that small, medium-sized enterprises in Singapore face. As such, this piece of research is performed using one case study organisation in Singapore, with qualitative approach, which is suitable for explorative research. Although these geographical and methodological limitations do not hinder the findings from being generalised, users should note the cultural differences when they adopt the recommendations. Future research can use the findings from this piece of work as a reference and use small, medium-sized organisations outside Singapore to explore new knowledge. The study can adopt different methodologies, such as quantitative or mixed approaches. Future research could also involve small, medium-sized enterprises with other profiles, e.g., those where the leaders and employees are not well versed with data analytics. The authors believed that different challenges would be discovered. As data analytics is a fast-evolving field, longitudinal research can also be performed to observe the impact of data analytics as technologies evolve.

Conclusions This chapter has started by reviewing the trends and benefits that data analytics can bring. After highlighting the slow adoption of data analytics among the small, medium-sized enterprises, especially in Singapore, a case study approach is used to analyse the underlying challenges and barriers to adopt data analytics strategies. Some of the challenges noted in the research work differ from those in the literature review, which use organisations with different profiles and geographical locations. Based on the findings of this research work, five recommendations are provided

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in this chapter to guide small, medium-sized enterprises to launch data analytics strategies successfully. The authors have ended the chapter by identifying a few areas for future research to discover new knowledge in data analytics. With the completion of this piece of research work, the authors are confident that small, medium-sized enterprises, which harness technologies to implement data analytics strategies and use the insights to innovate, e.g., create new business models to service customers better, will stand a higher chance to beat their competitors.

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8 How Can Luxury Fashion Brands Create a Multisensory Environment Online to Improve Customer Experience? Laura Stancescu, Lillian Clark, and Carolina Redolfi

Introduction The Covid-19 pandemic has pushed luxury brands into the digital space at a faster rate than ever before (Bain & Company 2021). With major city centres shutting down, online penetration has seen enormous growth, the equivalent of 6 years in only 8 months. The stable, continuously growing market has been shaken up and recovery to precrisis numbers can be seen only by brands taking major decisions in terms of distribution channels, positioning and future outlook (BOF & McKinsey 2020). L. Stancescu · L. Clark · C. Redolfi (B) Northumbria University, London, UK e-mail: [email protected] L. Clark e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_8

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The physical retail of fashion has been on a downtrend for years and the post-pandemic era will likely bring even more permanent store closures (BOF & McKinsey 2020; McKinsey & Company 2020). E-commerce is now a complimentary part of flagship stores, but it is predicted to be the other way around in the future, with online shopping being the main channel and brick-and-mortar stores bringing an additional experience (BoF 2020). Similarly, Okonkwo (2007) believes that e-retail has become an essential complementary channel for offline retail activities of the global fashion industry, including luxury fashion, with either the shopping experience starting online and ending in store, or beginning in the store and finishing with an online purchase. Studies conducted by Bain estimate that out of all global luxury purchases made in 2021, around 85% of them were digitally influenced (Bain & Company 2021). Creating a synergy between online and offline will establish a constant relationship with the customers, being present at every step of their buying journey (Kapferer 2014). As the Covid-19 pandemic has accelerated the shift of the brands into digital space by approximately five years, some of them were early on the trend, adopting Augmented Reality (AR) technology, or Virtual Reality (VR), for a store-like shopping experience from the comfort of the customer’s home (Harvard Business Review 2020). Dior, Versace and Ralph Lauren are some of the brands that have already implemented VR e-commerce technology, attempting to reduce losses from the closure of the stores due to government restrictions (Glossy 2020). It is argued that interactive experiences play a significant role in maintaining customer loyalty both offline and online and that the human touch in particular remains a necessity when it comes to purchasing luxury goods (Bain & Company 2021). Luxury fashion consumers demand a higher level of digital interaction and a more sophisticated way to engage their senses, with a stronger emphasis on touch. The digital space may lack human interaction, but high-end brands are already trying to integrate artificial intelligence and experimenting with virtual try-ons with the help of augmented reality. The improvement of customer experience online is a priority for luxury brands in the near future, with the integration of personalisation, gamification and a larger focus on social shopping and online reviews (BOF & McKinsey 2020).

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According to Kapferer (2014) and Okonkwo (2010), digital goes beyond the website of a brand, being an opportunity to translate the rich brand heritage into content that gives the customers a deep understanding, makes them relate to the brand’s values and allows the brand to express the individuality of its intangibles. It is difficult to entirely transfer the in-store experience to a virtual store, as retailers cannot fully replicate the ambiance (Okonkwo 2007). Unlike in brick-andmortar stores, when it comes to the digital space, luxury brands cease to differentiate from non-luxury ones (Kapferer 2014). Nothing ruins the ‘luxury dream’ more than e-commerce sites presenting like a row of postal stamps their collection. (Kapferer 2014, p. 725)

Not long ago, the sale of luxury goods online was a subject of constant debate as a result of the negative association of the Internet and lack of exclusivity and prestige (Okonkwo 2007; Geerts 2013). While Kluge and Fassnacht (2015) have found that online accessibility of luxury brands does not reduce desirability or exclusivity from a customer’s perspective, Kapferer and Bastien (2012) argue that the online presence of luxury brands should limit to communication, rather than using it as a sales channel. However, recent data reveals that the digitalisation of luxury is no longer an option, but a necessity (Forbes 2020). McKinsey (2018) predicts that by 2025, approximately 20% of all global luxury sales will be done online. Luxury brands are no longer asking the question “Do we have to be online?” and are now focussing on answering “How do we fully take advantage of the digital opportunities?”. The research aim is to find out how luxury fashion brands can improve customer experience online. Thus, the main research objectives of this paper emerge on three levels: the first one that provides background, the second one meant for in-depth research work and the third one being the point to the concluding activity. The first five objectives belong to the first level, the sixth one represents the second level and the last two objectives represent the third level: The following section contains the literature review that will critically analyse relevant existent research, theories, studies and frameworks that will help build the foundation for this paper. This part will satisfy the

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first level of objectives, meaning the first five objectives of this paper. In section “Luxury Experience” the key elements of luxury customer experience will be identified (one of them being the sensorial component) and the impact of the human senses on customer shopping experience and behaviour will be outlined in section “Multisensory Marketing”. Section “Multisensory Experience in the Online Space” will determine how the previous findings in terms of multisensory inputs can be adapted for the online space, based on existent literature and research and section “Theoretical Framework” includes the theoretical framework and hypotheses. Section “What Are Luxury Fashion Brands Implementing at the Moment?” will explore what is being applied at the moment of research by five luxury fashion brands: Gucci, Chanel, Hermès, Dior and Louis Vuitton. The third section comprises the research methodology. This research will be built starting from a positivist philosophy, taking a deductive approach. The method used will be a survey, the data being statistically analysed in SPSS. Section “Data Collection Method” contains the quantitative analysis conducted with the data obtained in the previous part. The sixth objective of this research paper will be satisfied in this section and in the following one, as seven null hypotheses will be tested, analysing the relationships between the variables identified in the literature review and collected in this section. In the fifth section, there is the discussion on the results found in the previous section. Each result will be discussed and the implications, thus finalising the sixth objective. The last part constitutes the conclusion of this research paper. This part also satisfies the seventh objective, where a strategy for luxury fashion brands will be developed to improve shopping experience online, based on the findings from the previous sections. The limitations, together with recommendations for future research will constitute the final objective that will be fulfilled.

Luxury Experience Purchasing a product is not the only reason luxury consumers engage in the shopping activity, but also to live a holistic experience (Blazquez and

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Boardman 2019). The customer’s involvement at a rational, sensorial, emotional, physical and spiritual level triggered by a set of interactions between the customer and the brand, product or environment represents the customer experience (LaSalle and Britton 2003). Luxury has evolved in the last decade from displaying wealth, to focussing on the luxury experience (Bauer et al. 2011). Holmqvist et al. (2020) put forward an alternative concept of luxury experience as a hedonic escape from the stress of the daily routine. The customer experience can be evaluated by comparing their expectations and the stimuli they encounter at the different touchpoints of their purchase. Similarly, Godovykh and Tasci (2020) define customer experience as the sum of affective, cognitive, sensory and conative responses on a range between positive and negative that are induced by the stimuli that the customer comes across pre, during and post the purchase journey. The dimensions of customer experience identified by Gentile et al. (2007) are: Component

Explanation

Sensorial component

The stimulation of the human senses to arouse aesthetical pleasure, overall satisfaction, sense of beauty, engagement and excitement Which is used to cater to the customer’s affective involvement by engaging their feelings, emotions and moods Generating customer involvement in terms of creativity, problem-solving and challenging assumptions the usability of a product in all stages of its life cycle constitutes the practical component of the customer experience The customer experience element that derives from the system of values and beliefs that the brand and the customer share Engages the customer’s social context, their relationship with other people and with their ideal self. This element can be connected to the lifestyle component when it comes to inducing a sense of belonging of the customer within a certain social class

Emotional component

Cognitive component

Pragmatic component

Lifestyle component

Relational component

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In-store atmospheric variables are sensorial stimuli that lead to behavioural responses (Turley and Milliman 2000). A few years later, Fiore and Kim (2007) also validate the value of sensory experiences within the retail setting and marketing techniques. The experiment conducted by Ballantine et al. (2015) has concluded that congruent atmospheric cues, such as visuals, temperature, music, employee characteristics, etc. create in-store comfort for female shoppers with hedonic motivation to purchase in the fashion industry. Furthermore, comfort is more likely to result in prolonged shopping sessions and increased purchase probability for this segment of customers. The physical store needs to create a cognitive and emotional scenario through sensory attributes, customer involvement and experiential design (Spena et al. 2012). However, luxury consumers exhibit trust towards different aspects when it comes to online and offline shopping, such as customer reviews, as opposed to the physical environment. The risk-averse online shoppers tend to value more the ability to touch, feel and see the product in person (Liu and Burns 2013).

Multisensory Marketing Krishna (2011) defines sensory marketing as marketing that engages a customer’s senses and impacts their behaviour. She also states that much of the research focusses on the impact of a sense, rather than multisensory inputs. Realising how powerful the responses to nonconscious stimuli are, recent research is also centred on the concept of embodied cognition. The term refers to how consumer’s decisions are determined through bodily sensations, without them being consciously aware (Harvard Business Review 2015). The capacity of people imagining sensations is called sensory imagery and it has various applications in many domains, including marketing (Krishna 2013). A consumer might see an advertisement for a sweater and is able to imagine the softness of the texture, from a purely visual cue. Krishna explains how cross-sensory experiences are possible and

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consumers might not even realise they are actually living a multisensorial experience. Helmefalk and Hultén (2017) have found that congruent auditory and olfactory cues significantly improve customers’ emotions and purchase behaviour in a dominantly visual environment, rather than adding other visual cues. A recent study conducted by Mood Media has concluded that in a multisensorial in-store environment, sales increase, consumers place more and higher-priced products in their shopping baskets, time spent shopping rises, while in a non-sensory atmosphere, consumers feel less comfortable (Mood Media 2019). Moreover, in a scented environment, shoppers feel more emotionally elevated and when they are given the chance to touch and interact with the products, they are more energised. When it comes to the digital space, visual sensory is the most used to interact with the customers.

Sight Probably the most relevant sense when it comes to fashion, sight is engaged through image, style, colour, vibrancy, shapes and look (McKay 2020). When it comes to consumer behaviour, vision is the most researched sense up to date (Krishna 2009). Psychology and marketing research reveal that sight is the dominant sense (Krishna 2011). According to Creusen and Schoormans (2005), the customer evaluates six categories of visual cues related to the product’s design and packaging: aesthetic part, symbolic part, functionality, ergonomics, attentiondrawing qualities and ease of categorisation. The most predominant reason for choosing a product is the aesthetic part, with overall roundedness, colour, size and specific details playing a significant role in a product’s attractiveness. Often intertwined with the aesthetic aspect, the symbolic role is also an important element in choosing a product. Visual sensory signatures can be vast, including logos, shapes, colours, patterns, designs, pictures, etc. Apart from these, sight is the enabler for activities associated with the other senses, such as deciding what one wants to touch, taste or smell (Krishna 2013).

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With the consumer’s attention span becoming shorter, brands are facing a challenge online trying to capture it in the first few seconds (Marketing Magazine 2020). Bold colours, videos and website design are no longer sufficient in this competitive environment, so brands need to integrate technologies such as virtual catwalks, try-ons and mass customisation.

Hearing When it comes to fashion, hearing is engaged through product design, such as the sound of the heels when walking or a clasp that clicks on an expensive bag. These stimuli evoke feelings such as satisfaction or empowerment (McKay 2020). Ambient sound, such as music, affects consumer mood, time spent in the location, money spending and the perception of time spent (Krishna 2011). If the ambiance music is slow, customer generally spend more time browsing, without even realising (Krishna 2013). Sound is also a powerful tool that generates positive customer feedback (Petit et al. 2019). However, congruence is vital. Congruence between auditory stimuli and a brand, environment or product has been proven to have positive effects on consumer behaviour, such as satisfaction or product evaluations (Krishna 2009). The type of music and the volume that a brand plays in store also have different effects on the target groups they wish to attract or keep away. Consequently, contemporary louder music will attract teenagers and keep parents and older people away, while classical music or opera will have the opposite effect (Krishna 2013). Krishna (2013) defines the association of a certain sound to a specific brand as sonic branding and they can be, but are not limited to, slogans, jingles, words, noises, sounds or phrases that sometimes complement each other. Sonic branding accomplishes different goals, such as brand recognition or arousing certain feelings related to a brand or product. However, when using human voice as sonic branding, marketers need to pay attention to what they wish to transmit. Who speaks, what they say and the rate of speech affects how the message is perceived. Male voices

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tend to express authority and power, while female voices may transmit a playful nature. A faster rate of speech will make the speaker sound more knowledgeable, but a slower rate will make the listener retain more of the information.

Touch The sense of touch comes as complementary to sight when it comes to fashion and choosing the clothing a customer wants to purchase. After sight approves the interest in a piece of clothing, most people feel the material for a confirmation of the quality of the product (McKay 2020). Spence and Gallace (2011) also stress the importance of touch when making a buying decision, especially for products that come in direct contact with the skin. Peck and Childers (2003a) have developed a measurement scale for a consumer’s need for touch (NFT), to be able to work with the motivation component. The scale consists of 12 statements that the respondents give a rating between −3 (strongly disagree) and +3 (strongly agree). The researchers distinguish two underlying factors of NFT, instrumental (with the goal being evaluating and ultimately purchasing the product) and autotelic factor (seeking enjoyment, fun, sensory stimulation or arousal, the goal not being necessarily to purchase). Building on previous literature and research, Krishna (2009) expands on motivation, identifying four reasons for touch in consumer behaviour. The first three can be grouped as instrumental touch (as a means to an end) and the last one is purely hedonic touch (as an end in itself ): • Touch to purchase: the primary objective is to buy the product, without any other information being purposely obtained; • Touch to obtain non-haptic information of the product: the aim is to obtain visual, olfactory, auditory or gustatory information; • Touch to obtain haptic information of the product: the main goal is to extract material properties, such as texture, weight, temperature or hardness; • Hedonic touch: the aim is to explore for fun or just for the sensory experience, without evaluating or making buying decisions.

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The term “haptics” or “haptic sensing” was first introduced by Révész (1950) and it represents people’s ability to identify the properties of an object through active touch (Jones 2018). Haptic technology is used in the present to refer to technology that virtually imitates the senses of touch and motion. Peck and Childers (2003b) have found that evaluating objects through touch results in higher confidence and lower frustration in high-NFT customers, but there is no change in low-NFT’s confidence level. What is more, a written description of the product does not compensate for the inability to touch when it comes to customers high in NFT, but a visual cue can play an important part. Even though engaging the sense of touch is difficult online, luxury brands could experiment with direct touch effects, like scrunching a material by pinching the screen, to give the impression of interacting with the product (Marketing Magazine 2020).

Smell Clothing can carry smells such as the material or the dye, but smell can also be activated through sight, such as a floral design on a dress (McKay 2020). Rimkute et al. (2016) have identified multiple types of effects that the sense of smell has on people, such as cognitive, affective, memory, behavioural responses and attitudes and perceptions: • Cognitive responses: Customer’s attention can be captured through smell if the product carries a distinctive scent (Krishna et al. 2010). If the customer is not familiar with a brand, a pleasant-scented ambiance will make them increase the processing effort and the amount of time spent evaluating the new brand (Morrin and Ratneshwar 2000). • Affective responses: Although it is generally assumed that smell can affect the consumer’s mood, there is scarce empirical evidence that supports this claim (Krishna 2009). Morrin and Ratneshwar (2000) also claim that consumer mood and arousal are not affected by the ambiance scent.

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• Memory: Krishna et al. (2010) and Lindstrom (2005) affirm that scented products recall unaided memory and it is very likely to persist with time, for at least two weeks after exposure. But as far as memory is concerned, product scent is more effective than ambient scent. However, the ambient scent does impact memory when it comes to unfamiliar brands (Morrin and Ratneshwar 2000). Smell is the most influential sense, creating associations almost instantly and being the most memorable one (Gobé 2001). • Behavioural responses: Ambiance scent significantly influences approach behaviours, mainly time and money spent when combined with music (hearing) (Morrison et al. 2011). Similarly, Parsons (2009) states that scent increases spending in the store and impacts customer purchasing decisions. Vinitzky and Mazursky (2011) have reached the same conclusion both in the offline and online space. It is said that smell is the most persuasive sense (Lindstrom 2005). • Attitudes and perceptions: Madzharov et al. (2015) differentiate the environment scents by induced temperature, warm scents and cool scents. The researchers discovered that a warm-scented ambiance gives the perception of a higher level of social density, increasing the preference for premium brands and products. On the other hand, cool-scented environments induced opposite effects. The presence of either ambient smell or smell directly infused in products improves consumer’s attitudes, especially when it is congruent with other sensory cues (Krishna 2009). Morrison et al. (2011) also found that overall satisfaction, pleasure and emotions are enhanced when olfactory cues are used, but recent results contradict the findings, claiming that scent is not an atmospheric cue of great significance (Blazquez and Boardman 2019). When it comes to online, there is a good opportunity for brands to use scented packaging across a range of products and to build a personalised brand smell (Marketing Magazine 2020).

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Taste Functionally unrelated to the fashion industry, the gustatory sense is sometimes tangentially appealed through a café, bar, restaurant, etc. within the store (Lund 2015). However, Kotler (1973) mentions that other senses (mainly sight) can activate remembered tastes. Krishna (2009) explains how taste is barely effective on its own, requiring the other human senses in order to be accurately perceived. Besides the five main tastes that people are able to distinguish (sweet, salty, sour, bitter and umami), the input of the other four senses is required for a complete taste understanding. To be able to comprehend how certain foods taste like, people require the aid of smell (how the food smells), sight (colour and aesthetics), touch (temperature, texture and fattiness) and hearing (for example, the sound of crisps) (Herz 2007).

Multisensory Experience in the Online Space Exactly how the ambiance of the physical store has a significant impact on the customer’s behaviour, the online space has such as much influence through web atmospherics (Manganari et al. 2009). Web atmospherics include all elements that combine to create the online store atmosphere, such as images, products, descriptions, sounds, instructions, support, etc. (Kim et al. 2007). The web atmospherics quality has a positive impact on how effective online retailing is (Eroglu et al. 2001) and on consumer behaviour when shopping (Manganari et al. 2009). Labrecque (2020) and Krishna (2011) state that consumers favour a multisensory environment that stimulates and increases their cognitive and affective responses. Empirical studies show that the customer’s attitude towards a luxury brand is enhanced if the relationship with the website is stronger (Kim et al. 2015). However, when it comes to the online space, only one sense at once has been studied, in isolation from the others (Mishra et al. 2020). With the emerging technologies, brands now have the opportunity to tailor a virtual world for their customers, evoking positive emotions (Lee and Lin 2005). Okonkwo (2010) places emphasis on luxurious

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webmosphere, introducing the term luxemosphere, which is conceptually a framework that encompasses the five dimensions of human senses: sight, hearing, touch, taste and smell. She reasons that luxury is perceived through the pleasure and indulgence of senses, with goods and services more grandiose than necessary. Conceptualising Online Luxury Experience (OLX), Klaus (2020) determines 3 categories of luxury consumers, based on the frequency of using the online channel: purists, opportunists and e-lux customers. Thus, the purists are the customers that prefer the traditional luxury retail, resorting to the online channel as a last option due to lack of time, location or other restrictions. The opportunists appreciate a combination of both offline and online, depending on the context and purpose of purchase. Lastly, the third segment comprises e-lux customers, the opposite of purists, and they mainly prefer shopping online, avoiding in-store experiences. These customers state that they have had bad interactions with staff, other customers or both in the past and they now only decide to visit the store to determine which material or size fits their needs. Shopify (2020), one of the most popular e-commerce platforms, has found that 3D models of the product and AR technology allow online shoppers to virtually “try” the products before they purchase, resulting in significantly increasing conversion rates on product pages and time spent. What is more, compared to traditional photography, a 3D model of a product can be used to generate photorealistic images, colour variations, lifestyle shots, augmented reality experiences, virtual reality experiences and animations, permitting customer to fully experience the products. Similarly, Li et al. (2001) state that 3D images create a mental simulation of product interaction, by engaging the senses of sight and touch. Additionally, a 3D product with a rotating function allows the customers to experience it from different angles, increasing the quantity of perceived information and having a positive effect on attitude and consumer behaviour (Park et al. 2008). Only a few luxury brands have already experimented AR technology, through Snapchat filters. Gucci has created a filter that lets people virtually try on their sneakers as seen in Fig. 8.1. Researches affirm that although the visual dimension is dominant when it comes to online shopping, the audio dimension (music, sounds),

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Fig. 8.1 Gucci AR Snapchat filter

touch (on-screen commands), smell and taste could potentially lead to an unforgettable holistic experience (Hwang et al. 2020). Krishna (2009) states that the sense of touch is not feasible for online shopping and marketers should rely on the sense of sight. Spence and Gallace (2011) recognise as well that it has now become even more of a challenge to allow the customer to feel the product when shopping online, making the purchase decision based solely on visual attributes and price. Similarly, Citrin et al. (2003) warn that consumers with a higher preference for the tactile input are less likely to purchase goods online. However, even though the sensation of touch cannot be reproduced online, the consumers already physically interact with various interfaces, such as mice and touch screens (Petit et al. 2019). Thus, zooming, rotating, pinching, screen vibrations might compensate for the lack of touch for the high-NFT shoppers, as seen in Fig. 8.2. Consumer’s preference can be manipulated through haptic interfaces, meaning that in order for consumers to choose a hedonic option rather than a utilitarian one, they need to shop using a direct touch interface (touchscreen), not a non-touch one (mouse) (Shen et al. 2016). Luxury

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Fig. 8.2 Multi-gesture interface on touchscreen device

fashion brands sell products that have high haptic importance, such as clothing, handbags, footwear, etc. For this industry, shopping through a touchscreen gives a significantly higher sense of ownership than using a touchpad or a mouse (Brasel and Gips 2014). The sensory internet refers to brands now having the opportunity to create content that includes not only visual but also aural stimulation (Lipscombe 2020). ASMR (autonomous sensory meridian response), “brain tingles” and sonic logos are now ways to connect with customers online. Some of the luxury brands that have experimented with the sensory internet are Coach and Gucci. Within the campaign “See it, feel it, hear it: authentic American leather craftsmanship”, Coach posted a video of a leather handbag being made, showing hands stroking and feeling the leather and the sounds of the material being cut, stamped and the click of the Coach bag being shut. Gucci also posted on Instagram (2019) an ASMR video of a pair of sneakers being touched, scratched, tapped and stroked, stimulating the visual and aural senses to a high level. While on television it is generally expected for the consumer to have the sound on, when it comes to browsing the Internet, many people will have the sound disabled, especially for advertisements (Krishna 2013). This creates a problem for the brands that wish to use the auditory sense to create a holistic experience. When it comes to smell, Royet et al. (2013) mention that the existence of odour mental imagery is still debated among researchers and whether visual images can invoke the sense of smell.

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Table 8.1 Sensory cues summary Sense

Means/cues

Sight

Images, videos, logos Colours, shapes, size 3D interactive view Virtual try-ons (AR) Headphones Loud speakers ASMR content Ambiance music Direct touch effects (zooming, pinching) Vibrotactile interface Haptic-enabling technologies Through perceptual re-enactments Through perceptual re-enactments

Hearing

Touch

Smell Taste

It becomes clear by now that luxury fashion brands have had limited possibilities to engage consumer’s senses online. However, with new technologies and creativity, brands are able not to replicate the human senses, but to compensate through various methods. These means or cues, which have been discussed above, are summarised in Table 8.1:

Theoretical Framework The literature review conducted is summarised below in a visual representation. Within the online environment created by the luxury brand’s website, the consumer will be engaged through added sensorial inputs. Even though at first sight might look that these sensorial cues are isolated, they are actually interconnected, as per sensorial imagery explained in this part. One sense can give the impression of another and vice versa, thus using a sense, such as sight, to evoke another, such as touch. These multisensory cues that are meant to influence the customer, have a strong impact of consumer behaviour, as related in section “Multisensory Experience in the Online Space”. With a significant influence in the consumer behaviour, such as improved satisfaction, better reviews, more time spent, more money spent, etc., the customer experience is implicitly improved. All in all, Fig. 8.3 is the conceptual model of this

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Fig. 8.3 Conceptual model

research paper, that will serve as a foundation of the furthering of this research. The literature review part of this research enabled the development of the following hypotheses that will be transformed into null hypotheses for statistical analysis in section “Data Analysis and Results”: Hypothesis 1 Age category is associated with choice of channel (offline, online or both). Hypothesis 2 There is a significant relationship between the importance of seeing how the product fits and impact of added visual cues (AR and avatar) on shopping experience online. Hypothesis 3 There is a significant relationship between the importance of touching the product and the impact of haptic add-ons (close-ups, 3D and 360 rotation, zoom) on shopping experience online. Hypothesis 4 There is a significant relationship between likelihood of using a virtual store and the impact that added visual cues have on shopping experience.

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Hypothesis 5 The importance of seeing how the product fits and the importance the consumers place on sight impact the effect of added visual cues on shopping experience. Hypothesis 6 The need for touch and the importance the consumers place on touch impact the effect of added tactile cues on shopping experience. Hypothesis 7 The importance the consumers place on hearing impacts the effect of added auditory cues on shopping experience.

What Are Luxury Fashion Brands Implementing at the Moment? According to Luxe Digital (2021), the most popular luxury fashion brands online are Gucci, Chanel, Hermès, Dior and Louis Vuitton. Their websites were analysed to see how they create a superior luxury shopping experience, in regards to engaging the human senses. Table 8.2 summarises what these top luxury fashion brands are implementing in their online stores in terms of creating a holistic multisensory experience for their customers:

Research Methodology This research was conducted through a positivist approach, as real and objective data will be quantified and statistically analysed. A deductive approach was used to achieve the objectives of this research and find the answers to the research questions. Thus, based on the literature review, hypotheses will be developed and tested, giving confirmations of rejecting them. To be able to statistically analyse data, a survey was used, with a structured questionnaire. The questionnaire was designed using the variables identified in the literature review part that summarises what top luxury fashion brands are implementing at the moment of the research.

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Table 8.2 Top brands summary Sense

Gucci

Chanel

Hermès

Dior

Louis Vuitton

Sight

Gucci Garden (VR), close-ups, 360˚ rotation

Poor zoom quality, AR try-on only for make-up

Average zoom option

High-quality zoom option, close-ups

Hearing

Ambiance music, congruent sounds in Gucci Garden

–

Touch

360˚ rotation, mental imagery of touch through close-ups – Gucci Osteria

Films and audio statements about certain products –

Good zoom option, AR try-on only for make-up –

–

–

Mental imagery of touch through close-ups

– –

– –

– –

– –

Smell Taste

–

Data Collection Method The most suitable way to collect the data needed for the quantitative analysis was conducting a survey. The data used for this research was obtained through primary data sources, collected from the participants that have answered the questionnaire created. Google Forms was used to create the survey, the questions being designed to be answered in the form of multiple choice, linear scale and multiple-choice grid. There have been used non-probability sampling methods, more specifically, convenience sampling and voluntary response sampling. Nonetheless, all the respondents belong to the target group of people that have shopped for luxury fashion within the past 12 months, both men and women, aged 18–44, located in Europe, America and Asia. The survey was conducted online. The majority of the participants were found through acquaintances and networking, being voluntary responses from

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the target audience. Most of the respondents are from Europe. The sample is relevant to the general population, due to the difference in background, culture, education and many other factors of the participants. The number of reached people was between 150 and –170, with the final sample size of the respondents being 103. The response rate calculated is approximately 65%. The data was collected from the responses to the questionnaire. The variables taken into consideration were age group, preferred channel of shopping, opinion on shopping experience for online versus in store, opinion on shopping experience online for luxury versus non-luxury brands, the importance of each human sense when shopping online for luxury fashion, the need for touch, the need to see how the product fits, the likelihood of using a virtual store and the impact on shopping experience online when visual, auditory and tactile inputs are added.

Data Analysis Methods Based on the information obtained from a sample, a choice is made between a null hypothesis and its alternative, the process being called hypothesis testing (Lane et al. 2003). The data obtained from the survey will be analysed using statistics and statistical tests with the help of the SPSS software. Afterwards, after running the relevant tests, the results will indicate which null hypotheses can be rejected and which cannot be rejected. Thus, the results were introduced in Excel and translated into values that could be understood by statistical software. The questions were developed and transformed into variables, these being assigned values.

Data Analysis and Results Generating a frequency table of the gender, it can be noticed that 34% of the respondents were men and 66% were women (Table 8.3). As one of the questions was about finding out how important is for the customers to have the senses engaged when shopping for luxury

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Table 8.3 Gender frequency Valid

Male Female Total

Frequency

%

Valid %

Cumulative %

35 68 103

34.0 66.0 100.0

34.0 66.0 100.0

34.0 100.0

Table 8.4 Statistics of senses

N

Valid missing

Mean Median Mode Std. deviation Skewness Std. error of skewness Kurtosis Std. error of kurtosis Minimum Maximum

Sight

Hearing

Touch

Smell

Taste

103 0

103 0

103 0

103 0

103 0

3.5922 4.0000 5.00 1.46486 −0.634 0.238

2.9612 3.0000 1.00 1.45469 −0.107 0.238

4.3495 5.0000 5.00 0.99714 −1.601 0.238

2.7087 3.0000 3.00 1.17683 0.147 0.238

2.3495 2.0000 1.00 1.41912 0.595 0.238

2.169 0.472

−0.828 0.472

−0.973 0.472

−1.351 0.472

−0.978 0.472

1.00 5.00

1.00 5.00

1.00 5.00

1.00 5.00

1.00 5.00

fashion online, the respondents have ranked each sense on a scale from 1 to 5, with 1 being not important at all and 5 extremely important. Thus, Table 8.4 summarises the score given by the sample. Considering both the average and the median, it can be noticed that it is the most important for customers to have their sight engaged, followed by touch, smell, hearing and, lastly, taste. With a mean of 4.35, sight appears to be extremely important when shopping online. The sense of touch has scored 3.59 (between “somewhat important” and “important”), meaning it is the second most desired sense to be engaged. Table 8.5 represents the summary of each statement included in the “added sensory cues” questions, which were ranked from 1 to 5, 1 being completely disagree and 5 being completely agree. It can be observed that with medians of 4, the respondents tend to agree that added visual

Valid

Mean Median Mode Std. deviation Skewness Std. error of skewness Kurtosis Std. error of kurtosis Minimum Maximum

N

−1.333 0.238

1.757 0.472

1.00 5.00

0.327 0.472

1.00 5.00

4.1748 4.0000 5.00 0.97445

103

Visual 2

−0.991 0.238

3.9806 4.0000 5.00 1.10241

103

Visual 1

1.00 5.00

−0.763 0.472

−0.574 0.472 1.00 5.00

0.052 0.238

2.7670 3.0000 3.00 1.20622

103

Auditory 2

−0.210 0.238

3.1456 3.0000 3.00 1.13248

103

Auditory 1

Table 8.5 Summary table added sensorial inputs

1.00 5.00

−1.087 0.472

−0.158 0.238

2.9320 3.0000 4.00 1.27773

103

Auditory 3

1.00 5.00

−0.858 0.472

0.415 0.238

2.3398 2.0000 1.00 1.21714

103

Auditory 4

1.00 5.00

0.755 0.472

−1.066 0.238

4.0000 4.0000 5.00 1.05719

103

Tactile 1

1.00 5.00

1.061 0.472

−1.149 0.238

4.2621 4.0000 5.00 0.88518

103

Tactile 2

1.00 5.00

1.234 0.472

−1.323 0.238

4.3495 5.0000 5.00 0.90433

103

Tactile 3

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cues, such as realistic avatar and AR try-on would improve their shopping experience. When it comes to added auditory cues, the respondents seem to be neutral or disagree with the statements, with medians of 3, 3, 3 and 2. As far as the tactile cues (haptic imagery) are concerned, customers appear to agree and even strongly agree to the statements, with medians of 4, 4 and 5. Close-ups pictures, 3D and 360 rotation and quality zooming make them more likely to purchase the product and increase their shopping experience. Null hypothesis 1 There is no significant association between age category and choice of channel (offline, online or both). A test was conducted to establish if there is any association between age and the predominant channel used to shop for luxury fashion (Table 8.6). Within this sample group, all age groups predominantly shop both online and in store equally. According to the Table 8.7, with a significance of 0.592, it cannot be said that there is an association between age group and the predominant Table 8.6 Age * channel crosstabulation Channel Age

18–24

25–34

35–44

Total

Count % within age % within channel % of total Count % within age % within channel % of total Count % within age % within channel % of total Count % within age % within channel % of total

Total

In store

Online

Both

24 30.4 80.0 23.3 5 23.8 16.7 4.9 1 33.3 3.3 1.0 30 29.1 100.0 29.1

25 31.6 83.3 24.3 5 23.8 16.7 4.9 0 0.0 0.0 0.0 30 29.1 100.0 29.1

30 38.0 69.8 29.1 11 52.4 25.6 10.7 2 66.7 4.7 1.9 43 41.7 100.0 41.7

79 100.0 76.7 76.7 21 100.0 20.4 20.4 3 100.0 2.9 2.9 103 100.0 100.0 100.0

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Table 8.7 Chi-square test age * channel

Pearson Chi-square Likelihood ratio Linear-by-linear association N of valid cases

Value

df

Asymptotic significance (2-sided)

2.799a 3.590 1.163 103

4 4 1

0.592 0.464 0.281

a3

cells (33.3%) have expected count less than 5. The minimum expected count is 0.87

Table 8.8 Relationship between the importance of seeing how the product fits and the impact of added visual cues Correlations Need for try on

Added visual

a Correlation

Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N

Need for try on

Added visual

1

0.213a 0.030 103 1

103 0.213a 0.030 103

103

is significant at the 0.05 level (2-tailed)

channel to shop. The null hypothesis 1, cannot be rejected, meaning there is no significant association between age category and choice of channel (offline, online or both). Null hypothesis 2 There is no significant relationship between the importance of seeing how the product fits and impact of added visual cues (AR and avatar) on shopping experience online. The second hypothesis of the study is to assess if the importance customers place on seeing how the product fits influence the perceived level of shopping experience when visual cues are added. Table 8.8 shows the correlation between the two variables, noted as “need for try on” and “added visual”. From the Table 8.8, it can be summarised that the need for try on has a slight relationship with the added visual variable. The R coefficient value between the importance placed on seeing how the product fits (need

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for try on) and the shopping experience based on added visual inputs is 21.3%, this depicting a direct relationship (the higher the importance placed on seeing how the product fits, the more increased shopping experience when visual cues are added). With a significance of 0.03, the correlation between the two variables proves to be significant for the entire population, thus being able to reject null hypothesis 2. It can be affirmed that there is a significant relationship between the importance of seeing how the product fits and the impact of added visual cues (AR and avatar) on shopping experience online. Null hypothesis 3 There is no significant relationship between the importance of touching the product and the impact of haptic add-ons (close-ups, 3D and 360 rotation, zoom) on shopping experience online. It can be summarised that the need for touch has little or no relationship with the shopping experience given by added tactile cues. The R coefficient value between NFT and added tactile is 4.4%. The null hypothesis 3 cannot be rejected, as the significance is 0.661, significantly larger than the acceptable cut-off point of 0.05. Thus, it can be affirmed that there is no significant relationship between the importance of touching the product and the impact of haptic add-ons on shopping experience online. Null hypothesis 4 There is no significant relationship between likelihood of using a virtual store and the impact that added visual cues have on shopping experience. In order to analyse the relationship between the likelihood of using a virtual store to shop online and the shopping experience perceived when visual cues are added, a bivariate correlation test was conducted. It can be summarised that the likelihood of using a virtual store has a slight relationship with the added visual variable. This is interpreted as the higher the likelihood of using a virtual store, the more increased shopping experience when visual cues are added. With a significance of 0.032, the correlation between the two variables is significant for the larger population. Null hypothesis 4 can be rejected, meaning that there

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is a significant relationship between the likelihood of using a virtual store and impact of added visual cues (AR and avatar) on shopping experience online. Null hypothesis 5 The importance of seeing how the product fits and the importance the consumers place on sight have no significant impact on the effect of added visual cues on shopping experience. A linear regression test has been conducted and the adjusted R Square is 0.12, which means that the model can predict the impact on customer experience with a degree of only 12%. With Sig being 0.001 < 0.05, the model is significant, meaning that the independent variables reliably predict the dependent variable. However, this is an overall significance of the model and does not depict if each independent variable accurately predicts the level of customer experience when visual cues are added. Table 8.9 breaks down each independent coefficient and the significance of their ability to predict the dependent variable. Seeing that the Sig of need for try on is higher than 0.05, this variable is not fit to predict the independent variable. However, the variable that is statistically significant and can predict the added visual variable is sight (the importance the customers place on sight when shopping online). The formula of added visual predicted = 1.631 + 0.23 * need for try on + 0.308 * sight, according to the coefficients seen in the Table 8.9: Table 8.9 Coefficients of regression model Coefficientsa Model

1

(Constant) Need for try on Sight

a Dependent

Unstandardised coefficients

Standardised coefficients

B

Std. error

Beta

1.631 0.230

0.829 0.174

0.308

0.095

variable: added visual

t

Sig

0.128

1.968 1.327

0.052 0.188

0.314

3.259

0.002

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Thus, we can reject the null hypothesis 5, but we adjusted by keeping only the sight variable as independent variable and predictor: The importance the consumers place on sight has a significant impact on the effect of added visual cues on shopping experience. Null hypothesis 6 The need for touch and the importance the consumers place on touch have no significant impact on the effect of added tactile cues on shopping experience. Similar to hypothesis 5, to understand how shopping experience can be improved this time through added tactile cues, this hypothesis aims to find out if the need for touch and the importance of touch when it comes to online shopping impact the effect of added tactile and haptic cues on the customer experience. A linear regression test has been conducted and with the adjusted R Square being −1.2%, this model is weak. Moreover, with a Sig of 0.67, way larger than the acceptable cut-off p-value of 0.05, the model cannot reliably predict the independent variable. Taking each variable into consideration, the Sig is higher than 0.05, thus neither of the two independent variables can predict the dependent one. Null hypothesis 6 cannot be rejected (Table 8.10). Null hypothesis 7 The importance the consumers place on hearing has no significant impact on the effect of added auditory cues on shopping experience. Table 8.10 Coefficients of regression model hypothesis 6 Coefficientsa Model

1

(Constant) NFT Touch

a Dependent

Unstandardised coefficients

Standardised coefficients

B

Std. error

Beta

3.898 0.033 0.044

0.546 0.120 0.056

0.028 0.080

variable: added tactile

t

Sig

7.146 0.274 0.782

0.000 0.785 0.436

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Table 8.11 Coefficients of regression model hypothesis 7 Coefficientsa Model

1

(Constant) Hearing

a Dependent

Unstandardised coefficients

Standardised coefficients

B

Std. error

Beta

2.185 0.226

0.212 0.072

0.298

t

Sig

10.287 3.136

0.000 0.002

variable: added auditory

In an attempt to predict the dependent variable of customer experience when auditory input is added, the independent variable of importance placed on sight was used. The model summary below shows that the model is weak, being able to predict with a level of 8%, as the R Square is 0.08. Even though the model is weak, it is statistically significant, with Sig being 0.002. The formula of added auditory predicted = 2.185 + 0.226 * hearing, according to the coefficients seen in Table 8.11. Thus, we can reject the null hypothesis 7, affirming that the importance the consumers place on hearing has significant impact on the effect of added auditory cues on shopping experience.

Discussion of the Results The sample of 103 respondents from the target audience of luxury fashion customers has been given a questionnaire and the results of the data obtained has been statistically analysed in the previous section. The respondents were 34% men and 66% women. The respondents were divided by the channel of shopping, some of them shopping predominantly online, others predominantly in store and the rest both online and in store equally. However, being asked if they find the shopping experience online as luxurious as in store, the majority of the respondents stated that the in-store experience is better than online. This finding may show that even though the customers shop online for luxury fashion, they still find the in-store experience to be superior. This means that luxury brands

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should focus on providing an improved shopping experience online, as the customers seem to use this channel and are not as satisfied as they would be shopping in physical stores. When asked if they find the shopping experience on the luxury brands’ websites the same as non-luxury brands, most of the respondents answered that the experience is either the same or the luxury brands offer a superior experience to non-luxury ones. However, luxury fashion brands should be able to differentiate from non-luxury, especially at a time when premium and other non-luxury brands are starting to gain traction among customers. The senses engaged when shopping online were ranked by the respondents by importance and are, in order, sight, touch, smell, hearing and taste. Sight and touch are the two most important senses that the customers want to have engaged, which is in congruence with the literature review. Smell seems to take the third place, being somewhat important for the respondents. While it is sense that is definitely engaged in physical stores, brands using fragrance for the shopping environment, in the online space the sense of smell cannot be easily and accessibly replicated yet. As discussed in the literature review, it would be difficult to invoke the sense of smell through other senses, especially online, and this is why luxury fashion brands should focus on the senses that they can engage. The null hypothesis 1 couldn’t be rejected, meaning that there is no significant association between age category and choice of channel. Therefore, customers in the age groups of 18–24 and 25–34 (as the majority of the sample belonged to these two age groups) shop for luxury fashion online, offline or both, regardless of age. The first null hypothesis rejected was hypothesis 2, leading to the conclusion that there is a significant relationship between the importance of seeing how the product fits and impact of added visual cues (AR and avatar) on shopping experience online. It is known that in stores, customers are able to try on the products that they like, see how they fit them and purchase if pleased. However, when shopping online, customers are not able to see how the product would fit them, how the colours would go with their skin tone and how the products would go together with other items to form an outfit. The result of this test shows

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that the higher importance customers place on this aspect of seeing how a product fits, the better they would consider the shopping experience online if visual cues are added. The two visual cues in question are having a realistic avatar of themselves where they can combine items and see how they go together and having an AR filter that enables them to try on clothes/shoes/bags and see how they look on them. What the latest means is using the mobile phone camera (for example) and pointing it at their feet, they can see shoes as augmented reality and be able to move to different angles and still look realistic. The same tool can be used for clothes or handbags. Null hypothesis 3 couldn’t be rejected, as findings say that there is no significant relationship between the importance of touching the product and the impact of haptic add-ons on shopping experience online. However, even if there is no relationship between these two variables, it cannot be ignored that the results show that most customers have a high and very high NFT. The NFT when shopping for luxury is not related to the impact added tactile cues have on the shopping experience online. Most of the respondents agreed that added tactile cues (through direct touch or haptic imagery) improves their experience online. The results show that the customers are more likely to purchase a product if there are close-up photos of the material so that they can imagine how it feels to touch it. Customers appear to be more likely to purchase a product if a 3D model is available and they can rotate it, interact with it and see it from various angles. What is more, customers also tend to zoom on the images of the products, and this is why brands should make sure that the quality of the picture provided is high. Thus, the tactile inputs that will be proposed for the strategy will be close-up picture, 3D photos and quality zoom. While it may seem that these cues are visual, they actually invoke the perception of touch, as related in the literature review. The null hypothesis 4 was rejected, results showing that there is a significant relationship between the likelihood of using a virtual store and impact of added visual cues (AR and avatar) on shopping experience online. Hence, the higher the likelihood of using a virtual store,

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the more increased shopping experience when visual cues are added. Most of the respondents were open to using a virtual store for shopping, meaning that they were also impressed by the added visual cues which were discussed above. Another null hypothesis that was rejected was the fifth null hypothesis. The importance the consumers place on sight has a significant impact on the effect of added visual cues on shopping experience. This finding makes it able to predict how the shopping experience will be perceived when the visual cues are added based on the importance customers place on having the sense of sight engaged. As most respondents affirmed that sight is of extreme importance, also being the most important of the senses based on this research, customers find the shopping experience online to be better when visual cues are added. However, to have a stronger model to predict, other variables should be considered and tested. In an attempt to try and predict the customer experience when tactile cues are added, null hypothesis 6 was tested but could not be rejected. The need for touch and the importance the consumers place on touch have no significant impact on the effect of added tactile cues on shopping experience. Other variables should be found and tested to see if they can be used as predictors. The last null hypothesis that could be rejected was hypothesis 7. It can be said that the importance the consumers place on hearing has significant impact on the effect of added auditory cues on shopping experience. However, even though literature review has concluded that hearing (as in ambiance music and sounds) impact consumer behaviour and increase customer experience, the self-reported results show that customers do not place high importance on this sense when shopping online. The discrepancy in results may come either from the difference in channel (in store versus online) or from the fact that the literature review brought findings from field experiments (customers were oblivious of having their auditory sense engaged) and this research brought self-reported results.

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Final Considerations The objectives of the research paper that were stated in the beginning were on three levels: establishing the background, in-depth research and the concluding points. The first six objectives have been achieved, the final two being satisfied one in this part and the other one in the following one, as recommendations for future research. The Literature Review section has satisfied the first level of objectives, meaning the first five objectives. Luxury consumer behaviour has been discussed, concluding that customers shop for luxury fashion to live a holistic experience, this being increased when in a multisensory environment. The dimensions of customer experience have been identified, one of them being the sensorial component. The term atmospherics has been introduced and the importance of it for a better customer experience was discussed. The third objective fulfilled was outlining the impact of each human sense on customer shopping experience and consumer behaviour, and how each sense impacts mood, time and money spent, improve product reviews, memory, perceived time, etc. The next objective was satisfied by exploring how multisensory inputs can be adapted for the online space, namely sight through images, videos, 3D interactive view, virtual try-ons (AR), hearing through ASMR audio content, ambiance music, touch using direct touch effects (zoom, pinching), haptic imagery and smell and taste through perceptual re-enactments. The last objective that belongs to the first level was fulfilled by exploring and reviewing what luxury fashion brands implement at the time of the research, in terms of close-ups, zoom quality, VR, AR, 3D, 360 rotation, sounds, music, haptic images, etc. The second level was comprised of the sixth objective, satisfied through hypotheses testing. Seven null hypotheses have been tested, from which five could not be rejected and four have been rejected. These are valuable findings for understanding how the multisensory environments are perceived from the customer’s perspective. All findings and results have been discussed in section “Discussion of the Results”. This part contains the fulfilment of the seventh objective which belongs to the third level. This objective is developing a proposed strategy for luxury fashion brands to implement in order to influence consumer

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behaviour and increase customer shopping experience through added multisensory cues on their website. Last but not least, the final objective is yet to be satisfied in the following part, comprising recommendations for future research. As a result of the literature review and the results and findings that have emerged from this research, the proposed strategy for luxury brands to create a multisensory experience online in order to increase the customer experience is as follows: • The website will have added visual cues: besides the standard ones (colours and shapes), that seem to already work, as customers find the shopping experience on luxury brands’ websites better than nonluxury ones, the websites can integrate a realistic avatar that each individual can customise. This avatar will be able to either be created by each customer, according to their height, measurements and skin tone, or automatically created with the use of AI, by scanning the body of the customer and replicating it virtually. This implemented tool will allow customers to see how the product would fit them, without having to actually try it on. Additionally, customers will have the possibility to add different items and combine them into an outfit. Thus, instead of seeing a blouse on a model, a pair of pants on another model from a different picture and a handbag by itself, customers will be able to add all these items and see how this created outfit will look directly on their avatar. The second proposed visual input is an AR filter, which doesn’t require the creation of an avatar. Instead, through camera, customers will be able to see directly on themselves the products in the form of augmented reality. • The website will have added haptic images that invoke the tactile sense: the product pictures will contain one very high-quality closeup of the product’s material. Products such as shoes and handbags will have a 3D model that can be rotated 360, so customers are able to see it from all different angles. As customers zoom on the product pictures, brands have to make sure that they provide this function with high definition, so the image does not become blurry or unclear.

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• Lastly, the website will have a virtual experience of their flagship store, so customers can enjoy the most exquisite location from whenever they are in the world. This virtual store can include multisensory inputs, including sound if the website visitors choose to activate this option. Auditory inputs have received divided opinions, but overall, customers do not place a high importance on the sense of hearing when it comes to online shopping and the lack of auditory cues does not decrease their shopping experience. That is why this strategy will not include the engagement of hearing. All in all, this research paper has satisfied the objectives stated, bringing new information and state-of-the-art research to light. It has been discovered that more and more people shop online for luxury fashion and the customer experience does not raise to the in-store one. The dimension of sensorial inputs has been analysed in-depth and the findings show that customers are actually eager to have their senses engaged (especially visual and tactile) and are open to using new technology, such as AR and VR. The proposed model to create a multisensory experience online can be easily implemented, as the technology already exists and is used. The most relevant aspects have been added to the model, as hearing, smell and taste are either not important enough for the customers or not possible to replicate.

Limitations and Recommendations for Future Research The findings emerged from this research paper are definitely relevant for this topic and wish to be of help for future research on this domain. There is still limited research and literature for how multisensory experiences can be implemented online, especially for the luxury fashion industry. While the findings of this research paper are helpful for brands, extended research is recommended. First of all, some limitations come from the fact that all the data used comes from self-reported answers, the respondents answering to the best of their ability. An evaluation of the customer experience when the multisensory inputs are added and

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perceived unconsciously would be more accurate and relevant. In this manner, if this study found out that auditory inputs do not have a big impact on customer experience (as reported by the respondents), maybe when directly testing it and measuring the change in behaviour will give other results. Recommendations for future research include more studies on how the olfactory sense can be invoked through odour imagery, as the results of this study show that actually customers do find the sense of smell important to have engaged when shopping online. While brands cannot replicate the smell online, more studies on how it can bring this sense through perceptual re-enacted would be needed. This research lacked the resources for field testing in regards to this matter and the literature is scarce and contradictory. Another recommendation is to do an experiment using the proposed strategy, to confirm or infirm the results of this study. Other variables that will emerge from the change in behaviour, such as time spent, money spent and mood should be taken into consideration to fully understand if the multisensory experience has such a big impact on customer experience online as in store.

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Part III Digital Transformation in Cyber Business Village, Privacy, Cybersecurity Consciousness and Entrepreneurship Business Models in Different Sectors

9 Video Camera in the Ambient Assisted Living System—Health Versus Privacy David Josef Herzog

Introduction The smart home is a home-based system of systems, which consists of monitoring sensors, connected together as a net to the analytical and automated appliances, with local and distant control of indoor management and environment. The smart home concept encompasses several utilitarian dimensions: in-house automated systems with control and monitoring; communication; health monitoring; entertainment (Pal et al. 2018). Smart homes can be integrated with a smart IoT environment and permanent ubiquitous health monitoring under the aegis of Artificial Intelligence (Amin et al. 2019). The medical aspects of AAL are divided into the supervision and monitoring part and support part. There are numerous AAL healthcare support systems, which can be subdivided into several groups in accordance with the needs of patients, D. J. Herzog (B) University Fernando Pessoa, Porto, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_9

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who have systemic impairments, or its narrow type, built for patients with a singular pathology. Comparative analysis tends to be more technical than medical (Memon et al. 2014). For a similar reason, important medical parameters are often treated together with less important ones. From the healthcare point of view, home-based patients need to be monitored with a focus on the critical parameters. The most important parameters are vital signs, such as heart rate, blood pressure, breath rate, body temperature. The top causes of death include heart diseases, vascular damage, e.g. stroke and respiratory diseases (Vos et al. 2020). Most of the health conditions can be observed with help of different sensors, which give necessary information immediately. They help to control the condition of the person, health dynamic, follow up of the medication and treatment procedures, subject to biological compatibility (Patel et al. 2012). In the case of Mild Cognitive Impairment, besides possible somatic dysfunctions, moderate memory, cognitive and psychiatric impairments create additional requirements for the AAL system (Blackman et al. 2016).

Medical Data Registration in AAL Vital signs and behavioural patterns can be registered with help of sensors. The data is collected via sensors’ network and transmitted with help of middleware to the analytical tools. An alert is set on in the case of an emergency. The long-term problems can be followed up and general condition and level of representational independence evaluated.

Vital Signs Heart Rate Heart function is central to wellbeing. The resting heart rate of a healthy adult person is normally regular and has 60–80 beats per minute. It can vary with physical and emotional load, medication intake or underlying medical conditions. Heartbeat rate, regularity, volume, peripheral signs

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of blood perfusion give important information about the health status. Ischemic heart disease, arrhythmia, heart valve pathologies, vascular diseases are diagnosed and controlled with permanent checks of these characteristics. The heart rate can be measured by non-invasive Smart Wearable Sensing devices (SWS). Sensors or SWS are placed on the heart area, major arteries, peripheral arteries. They are pulse metres, electrocardiogram (ECG) sensors or SWSs, echo cardiac sensors. Blood perfusion in different body parts can be assessed by electroplethysmography and photoplethysmography techniques (Pantelopoulos and Bourbakis 2009). ECG, besides the heart rate and its rhythmicity, gives information about potential changes in the myocardial integrity and cardiac conduction condition. In some cases, sensors are seamlessly implemented as a part of smart shirts with textile-integrated non-invasive magnetic sensors (Teichmann et al. 2014). They often are worn as wristbands or similar wearable items. There are ways to assess heart rate distantly, for example with the help of Doppler radar (Otake et al. 2021).

Breath Rate Respiratory diseases are the next major factor of pathology and death. The breathing rate of a healthy adult person is normally relatively regular, with 16–20 cycles of inhalations and exhalations per minute. The BR can be measured by wearable on-body sensors, wearable seamless shirtintegrated sensors, wearable breath analysis sensors (Mitchell et al. 2010). The last ones can measure exhaled CO2 to evaluate breath effectiveness. Sensors can be used to control exhaled acetone to control glucose metabolism for patients with diabetes (Righettoni et al. 2012). Blood oxygenation is often measured by peripheral photoplethysmography and can be combined with pulse rhythm measurement and tissue perfusion level monitoring. Exist different wearable types of sensors for permanent use, designed as earbuds, finger rings or wristwatches (Tamura et al. 2014). There are also methods of distant respiratory rate monitoring with help of infrared Doppler sensors by the Kinect (Procházka et al. 2016).

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Blood Pressure The normal arterial blood pressure of a healthy adult person is 110– 140 mmHg systolic and 70–90 mmHg diastolic. The BP directly reflects myocardial function, heart valves integrity and functionality and indirectly neuro-humoral heart rate, vascular tonus and blood volume regulation. The blood pressure can be measured by wearable pressure sensors, placed on the skin above the underlying subcutaneous artery. Usual places are: wrists, biceps, ankles. The measurement can be done with help of an inflated cuff, cuff-less pressure sensors, cutaneous tension sensors, photoplethysmographic sensors, measurement of pulse wave transit time, by combining two sensors along with the blood flow (Yilmaz et al. 2010). Some researchers propose ultrasound sensors (Weber et al. 2013). Invasive methods are used to measure blood pressure in the main blood vessels to control their integrity (Lee et al. 2019). Sometimes other bodily liquids require pressure measurements.

Body Temperature Surface Body Temperature (BT) of healthy adult person usually homeostatically fixed around 36.6 °C if measured on the skin or in the oral cavity. It is an important parameter of metabolism. Core body temperature is higher and achieves a level of 38 °C. Temperature is measured through the contact body wearable sensors or distantly, with help of infrared sensors. Wearables are designed in different forms as bracelets, watches, jewellery, smart clothes. Non-contact infrared sensors are used less often for body temperature measurement. However, systems based on the temperature detection proposed for the indirect cardiac rate measurement (Garbey et al. 2007) or breath rate measurement (André et al. 2009).

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Physical Activity The normal gait as a physical process is divided into several phases, which repeat cyclically. The gait cycle usually is comprised of eight phases. It can be structured as two big sequential phases for the right and left leg, with stance taking 62% of the time and swing 38%. Each phase is then subdivided into four stages. One of the most important parameters is walking speed. In the metastudy, speed measurements are checked in 40 studies on more than 23,000 adults in different countries (Bohannon and Andrews 2011). Normal walking speed is around 1.2–1.4 m/s, while pathological is supposed to be lower than 0.6 m/s (Fritz and Lusardi 2009). Abnormal walking may reflect musculoskeletal pathology, neurological dysfunction, skin pathology or more general abnormality. Some researchers propose walking speed to be the sixth vital sign (the fifth is Body Mass Index, BMI). There are numerous ways to measure convenience in-home walking speed. Stationary sensors are based on a Doppler effect or electromagnetic tracking system, wide area pressure sensors, furniture pressure sensors, video and audio sensors. Wearable inertial sensors include accelerometers, gyroscopes, electromyographic sensors, pressure sensors, goniometers (Tao et al. 2012). Accelerometers can be used to measure acceleration-deceleration and start/stop time, because they may change in some pathological conditions. More complex activities than walking are also routinely registered in most AAL systems. Utilities usage (Fell et al. 2017) or mounted sensors, signalling about refrigerator usage, doors and windows operation, other activities are usually monitored with help of different sensors and ontological models ´ (Augustyniak and Slusarczyk 2018; Ihianle et al. 2018). Walking speed can be predicting sign for the future health condition (Purser et al. 2005), as well as Activities of Daily Living and Instrumental Activities of Daily Living (ADL and IADL) (Salguero et al. 2018; Snyder et al. 2018). ADL is estimated by the level of independence with: bathing, dressing, toileting, transferring, continence, feeding.

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AAL for MCI and Dementia Patients Diagnostic Aspects Standard intelligence is generally reflected by IQ. The normal IQ is 85– 115 (100 ± 15), ±1 SD. MCI is diagnosed, when a permanent general IQ decline from a previously normal level is lower than 85 and higher than 70. Patients with dementia have stable IQ lower than 70. There are multiple methods of intelligence tests, various types of intelligence and diagnostic are non-trivial, but for simplicity IQ level is relevant enough, with more than 50% cases of MCI and dementia constituted by Alzheimer Disease (AD). There are many more causes for MCI and dementia: pseudobulbar affect, Parkinson’s disease, frontotemporal lobar degeneration, Lewy body disease, vascular diseases, traumatic brain injury, substance/medication use, HIV infection, prion diseases, Huntington’s disease (Sadock 2020). Dementia as a condition has specific modes of behaviour. Patients have problems memorising necessary information and have difficulties performing everyday tasks. Reminders have to be more persistent, patient and avoid provoking a negative emotional reaction. Unfinished tasks, like open doors, gas stoves, the water supply may have adverse results. Misuse of objects creates danger for patients, their close relatives, carers, neighbours and visitors. Wandering without a clear objective, especially in an environment with obstacles, stairs, windows without protection is potentially harmful to dementia patients.

ADL While MCI can be transitory between normal cognition and dementia, this pathology may stay for years and condition, in some cases, can improve. MCI is not easily diagnosed. ADL of patients with MCI is found to be lower than that of healthy old people. It can be connected to general activity during the day and to walking speed as well (Hayes et al. 2008). Comparative analysis of data sets also shows the difference in IADL. It can help to detect cognitive decline early (Riboni et al. 2015). Special service-oriented application (SOA) AAL platform “DemaWare”

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is created to address part of these issues, but partially based on obtrusive camera wearing for complex activity recognition (Stavropoulos et al. 2014). In the European AD automated diagnostic project, Dem@Care patients’ movement data is collected from the wearable ankle-mounted accelerometer. Additional data adaptation by creating more day and week time domains improves automated diagnostic (Bian et al. 2018). Memory is one of the functions, which often suffers profoundly in dementia and MCI. It creates multiple problems, especially with repetitive tasks. Some AAL components are built to compensate for the loss of the function. There are attempts to create systems (HERMES) with the ability to remind about daily tasks, free time use (Costa et al. 2009). The addition of smart objects, smart pillboxes, electronic calendars, smart white goods with reminders creates a better environment for patients with memory loss.

Spatial Movement The connection between ADL tasks and cognitive impairment is well known and often reported (Pereira et al. 2008). Moreover, a strong positive correlation between quantitative gait characteristics and dementia is found in several studies. Mostly affected are step velocity and step length. A number of daily bounds (sessions, rounds) negatively correlates with cognitive status (Mc Ardle et al. 2018). In some studies proposed prediction of the mental status change, based on the walking features, as speed, angular velocity and balance (Mulas et al. 2020). Other researchers found only a spatial correlation between gait and cognition for healthy old people (Valkanova et al. 2018). However, in a major longitudinal study of 2938 mentally healthy participants, of which 2233 participants were reassessed and 226 developed dementia. Future decline correlated with walking speed. It is also proposed that diminished mental processing speed plays a crucial role in lower walking speed. One standard deviation in walking speed shows a potential increase in the possibility of future dementia (Welmer et al. 2014).

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Sleep Abnormalities in MCI While sleep deficit or disturbed rest often has negative impact on mental abilities, there are signs of the influence of cognitive dysfunction or conditions, leading to it, on the rest/activity patterns and sleep architecture. Sleep and wake pattern is often disturbed in MCI patients (Djonlagic et al. 2019). Ability to register patient activities in AAL during day and night are clearly demonstrated (Taillard et al. 2019). These findings can be supported on the level of EEG registration. These pathological changes can be predictive in the case of MCI and correlate with deterioration. Specific signs during non-REM sleep phase show future MCI in ageing patients (Taillard et al. 2019).

Mental Health AAL Applications Monitoring of mental health cases in the AAL system can be divided into two big groups. One deals with psychiatric emergencies, such as suicide, psychotic events, major depression, alcoholic or drug-induced events. In every such case, the patient is potentially dangerous for himself through self-harm or self-neglect or can be dangerous to other people. The other type is long-term supervision, which can deal with emergencies, but mainly intended to be diagnostic and supportive in the case of a chronic condition with potential for physical and mental deterioration. Behaviour detection is based on the complex events analysis, activity time ratio, daily activity rates, complex event processing (CEP) and pattern recognition against existing pre-collected sets (Veronese et al. 2018). Other systems are focused on the RFID of GPS objects usage (Hodges et al. 2010). For emergency cases, different prediction models are based on the sensors combinations and behavioural data sets (Alam et al. 2016). Other systems propose a connection with previous patients’ records for better diagnostics (Alam et al. 2015). Identification and prediction of abnormal behaviour with the help of neural networks (NN) are proposed by another team (Lotfi et al. 2012).

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Video Observation in AAL There is a number of observation methods in the AAL. Video cameras are one of the frequently chosen types of sensors. While they are very useful for communication, operating cameras for monitoring in AAL is supposed to be invasive and raise concerns about patients’ privacy. There are intermediate solutions when the image is reproduced as an abstract imitation. However, this method of representation is relevant to other positioning systems as well. In addition, video stream demands higher requirements for the data transfer and, subsequently, higher energy consumption. Several types of video observation are suggested. The usual RGB or RGB-D sensors are part of several AAL projects. Infrared and thermal cameras are another type. Optical sensors of different types are also utilised in AAL. All types are used for fall prediction and report, general well-being and medical observation, ADL measurement, communication and abuse prevention. The cameras can be static, movable, wearable, used as a group or in combination with other sensors (Sanchez-Comas et al. 2020). As multisensor ubiquitous system can be costly and laborious to implement, video cameras represent reasonable price and effectiveness. The wearable camera, despite some specific advantages, e.g. demonstrating the personal view and reflecting ADL and spatial activity, is restricted by lower compliance, certain level of user inconvenience and battery life limitations (Cardinaux et al. 2011). Privacy concerns are also high (Arning and Ziefle 2015). Laser-based optical systems can be utilized for positioning or fallreport. However, there are some difficulties to adopt security technologies for the AAL needs.

AAL Evaluation Methods Existing and planned AAL systems have to be evaluated, validated and tested. Several methods are used for surveys and assessments. There are also theoretical methods, modelling in silico and practice, prototyping,

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live-in lab experiments and dry runs. After the start of the practical use data from the system and stakeholders is routinely collected and assessed with help of analytical tools.

Conceptual Stage Health, WHO Quality of Life, WHOQOL survey and questionnaires are used for patients (Queirós et al. 2017). Healthcare specialists and caregivers formulate medical and social requirements and then the information is presented to technical specialists for conceptual validation, reference design and prototyping. The opinion of the social institutes and caretakers is also taken into account. At this stage, initial questionnaires are presented to stakeholders. The choice of every element in the system is based on the cross-section of requirements, from the skeleton to the user interface in later stages. Architecture is more influenced by technical standards.

Model An initial phase demands the formulation of functional requirements, based on stakeholders needs. ADL, IADL with help of Prototype is envisaged with help of modelling, scenarios creation, personas and simulations. Data flow evaluation and model quality control are used, with analysis of acquisition, transmission and usage (Kara et al. 2017). In the first stage, sensors are chosen and calibrated. In the second stage ways and periodicity of transmission are analysed, as well as security. In the third stage storage, backup and potential recovery are envisaged. Personas are used and portraits of potential users are generated. Father, more narrowly focused questionnaires can be utilized together with expert reviews.

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Prototype In the next stage, the prototype is tested for usability, functionality and interoperability by special tools or in living labs (Colomer et al. 2014). Experiments and questionnaires are instruments on this stage, as well as reviews (Salvi et al. 2015). While technical and instrumental measures are more objective and based on external metrics, questionnaires tend to be more subjective instruments and require different instruments of analysis. Both approaches have to be balanced in every case. For example, in living labs, there are different approaches to the information presented for the actors or patients. Some can be informed about testing, and others are instructed afterwards. In every case, the nature of questionnaires and surveys may differ.

Impact Assessment Wider impact assessments include social, financial, industrial and political impacts, as demonstrated in the “Learnings from the 2019 and 2020 AAL Impact Assessment Final report” by http://www.aal-europe.eu and Technopolis group. There are three main types of frameworks of impact evaluation: Re-EIM, MAST and UTAUT (Østensen et al. 2014). Re-EIM is an acronym of “Reach”, “Effectiveness”, “Adoption”, “Implementation”. Reach speaks about the type and size of focus groups, inclusion and exclusion criteria. Effectiveness measures all effects, including positive and negative impacts. Adoption calculates the number of stakeholders, who adopted the scheme or system. Implementation registers social, financial, administrative and other costs. Maintenance is a measure of long-term adoption, level of institutionalisation or routine practice change. MAST is a Model for Assessment of Telemedicine. It is a multidomain approach to healthcare IT systems, which include Ambient Assisted Living. It is divided into three stages: a preliminary assessment, multidisciplinary assessment and transferability assessment. At every stage multifaceted analysis of social, administrative, financial, ethical and other aspects is done. Safety, effectiveness, maturity, possibility to be practically adapted are surveyed. The UTAUT stays for the

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Unified Theory of Acceptance and Use of Technology. It is a model framework, which consists of four elements: performance expectancy, effort 31 expectancy, social influence and facilitating factors (Venkatesh et al. 2012). There are also price value, hedonic motivation and habits in the extended model. The behavioural intention in the model is also influenced by age, gender and experience. All factors lead to user behaviour. Every model can be used separately, partially or in full, with extensions, in combination with other models or provide elements for a specially constructed framework.

Questionnaires One of the widely employed methods is a questionnaire. Economical and policy institutions create a significant impact on the AAL provisions. At the same time stakeholders: technical service providers, medical service providers, e.g. institutions and workers, end-users, such as patients and family carers, are the most important immediate players in the field of AAL. Current, prospective and retrospective assessment of stakeholder opinion through the survey is an important tool, addressing various aspects of the AAL. The level of acceptance, satisfaction, informed opinion or professional view is significant in the design and exploitation of AAL systems. Questionnaires can be subjective report tools but include objective information e.g. technical or biomedical parametric qualitative and quantitative data for comparison (Bethlehem 2009). Objective information can be obtained by other means than questionnaire. Psycho-social factors, such as subjective acceptance, readiness to learn new technologies or to be involved in services with extensive AAL components are also important. Results can be presented as qualitative data, but scaled questions and frequency tables allow formal non-parametric statistical analysis.

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Questionnaire Framework The general framework depends on the questionnaire objectives and weights, attributed to certain metrics and variables. It is planned on the stages of conceptualization and questionnaire design (Brancato et al. 2006). Extensive literature review leads to the general understanding of the necessity, goals and the type of respondents the survey is targeting. While the concept influences every part of the questionnaire and every change in it, the nature of the expected category of interviewees is quite clearly split between the general population sample and the expert group. It affects the length of the survey and cognitive load, required from the respondents. The design depends on questionnaire structure, complexity, types of questions, wording, instructions, types of feedback and ways of administration. When the questionnaire is completed, it is tested, reevaluated, adjusted and implemented for data collection.

Types of Questionnaires There are several types of questionnaires (Saris and Gallhofer 2014). They depend on the research goal, focus group, type of questions, length and depth. Questions can be more qualitative or quantitative, open and closed, dichotomous, with simple dual answer options, or multiple options, factual or opinion-based. Scaled questions of several Likert types are also often used to measure level or degree. Complex questions can be designed with internal subquestions and mixed options. Butteries of questions and specific batches can be arranged in sections or be spread randomly. Questionnaires are used in a direct interview, by mail, phone, online application, mobile app. The obtained information is often analysed with the help of statistical instruments. The separate complex research class is formed by multifaceted surveys, designed with axes for several stakeholders. AAL4ALL project (Cunha et al. 2013), run as an interdisciplinary, academic and industrial scheme. This project, for example, is created with a goal to answer questions about applicability, affordability and necessity to provide AAL as part of the

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communal healthcare program. It includes three major groups of respondents: patients as end-users, informal caregivers and healthcare and social care providers. Another complex approach is to present the same type of questions to different stakeholders in iterations, known as Delphi Survey, named after a well-known historical oracle. Questions are iteratively updated by answers and re-presented to the “oracle” panel of experts (Spitalewsky et al. 2013). Results are scaled, which helps to rank importance inside of the questions’ groups.

Questionnaire: Statistical Analysis Reliability Several well-established tests are applied for the examination of internal consistency and reliability of the questionnaires. Split-half methods of different complexity are usually employed. Tabled results of the Likert scale responses undergone specific procedures. Cronbach’s Alpha (tau equivalent), Revelle’s beta, McDonald’s omega, Guttman’s lambda are described below. Test–retest reliability is checked by Cohen’s kappa (Sideridis et al. 2018).

Cronbach’s Alpha Tau-equivalent or Cronbach’s alpha is a measure of covariance between elements of the questions group. This parameter counts “dimensions” of the questionnaire and their interrelation with the help of the covariance matrix. Every respondent result is compared with the entire count of each observation. The higher number of “dimensions” and a stronger correlation between them gives higher results for alpha. Cronbach’s alpha results are considered valid in the range of 0.8–0.9, with variations up and down. Alpha below 0.5–0.65 is considered to be a sign of low reliability, while higher than 0.9 shows redundancy or a high number of “dimensions”. Nc α = v+(N −1)c , where N is set power, c is average covariance for every element, v is average variance. Kuder–Richardson Formula 20

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  n (KR-20) (n−1) × 1 − (x×y) is a variant of alpha for binary items v in dichotomous questions, where n is a size of the sample, v is variability, x is the proportion of respondents answering positively, y—the proportion of respondents, answering negatively. KR-21 is used for questions with  a close rate for  questions, where m is mean count for the test: m×(n−m) n . (n−1) × 1 − (n×v)

Revelle’s β Beta is minimum or lowest split-half type test estimate of internal reliability. c −c ×c p , p βx1 = d p1 1−cd p22 1 2 , where c is correlation/covariance, p 1 and p1 , p2

p 2 are predictors and d is dependable variable. Beta is supposed to be more conservative estimator, than alpha—the later has tendency to “overshoot”.

McDonald’s Omega Omega as a parameter is similar to alpha. Confirmatory Factor Analysis (CFA) of factor F for n variables X n are connected by load l n and influenced by the error e n .  2 ln ω =  2  2 σe n ln + The additional level of factor analysis is added. The covariance matrix of results is obliquely rotated and then so-called Schmid-Leiman or S-L second transform   is Tperformed. 2 , where C Cm ≈ F SS + Dm m is square p × p correlation 2 is n × n matrix, F is factors matrix n × n, S is n × p matrix, Dm diagonal matrix. F is transformed to create F m second-order correlation matrix.

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McDonald’s omega is supposed to be a more reliable coefficient than Cronbach’s alpha. Levels of 0.7–0.95 show reliability of the results.

Guttman’s λ2 Coefficient lambda is similar to alpha and tau-equivalent of reliability. It comes in several grades, starting from lambda 1. The difference is that for alpha is used more random algorithm, while lambda has a lower level of randomness. Covariance between sums of items and average variances are included into the formula:   2    n× Ci , j Ci λ2 = 1 − C x + , where Ci2 , j is covariance C2 x

between results. Lambda can be employed for more complex tasks. Lambda is usually higher than Cronbach’s alpha. Values for reliable test are 0.8–0.95.

Correlation: Non-Parametric Methods There is a significant difference between parametric and non-parametric analysis. Usual statistical analysis is based on mean, variance, standard deviation, analysis of probability distribution and ANOVA, analysis of variance. Parametric methods often consider continuous data. Non-parametric data does not have a usual tendency for normal distribution and often discrete ranks. Nominal data is presented by nominal categories, while ordered data is also scaled. In non-parametric methods, most important measurements are mode, median, quartiles and interquartile range (IQR) (Sprent and Smeeton 2016). Sets of data can be compared between each other to trace independent or common sources of results (Kvam and Vidakovic 2007).

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Mann–Whitney-Wilcoxon Test Mann–Whitney-Wilcoxon Test checks the equality of two ordinal sets of data. Sets can be of unequal size. MWW test calculates “unbiased” U parameter. It checks the equality of distribution and the supposed inde pendence of sets. U = N x N y + N x (Nx2+1) − Rx where N x is set X, N y is set Y and R is the sum of ranks. Precision of the test is lower with significant difference between sets there is a possibility for type II error in this case.

Kruskal–Wallis Test K-W test or one-way rank analysis of variance (ANOVA), calculates H parameter to test mutual dependency of data sets. K-W test is designed for two or more sets. The size of data sets can be unequal, because the calculation does not involve paired comparison. 2 12 m R x H = n(n+1) x=1 n x − 3(n + 1), where n is certain data set power, m is number of groups, Rx is rank of x and x is number of the data set. Two or more samples are compared. Big differences between sets can cause type I error, giving false positive results.

Spearman’s Rho Spearman’s Rho correlation coefficient is a rank analogue of Pearson coefficient. When Pearson coefficient is applied for continuous data, Spearman’s Rho can be used for non-parametric ordinal data. Two sets of the same size, for example, answers on two questions, are compared pairwise.  2 6× ( R x −R y ) where n is number of results. ρ =1− 2 n (n +1)

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Research Method Medical and social requirements for the AAL are formulated on the conceptual stage. There are several ways to find answers, theoretical and practical. Any route gives only partial vision. The needs of caretakers and healthcare stakeholders are collected by questionnaires and expert suggestions. The process can be iterative, mixed and include detailed recommendations. The best approach is to try to encompass all these raised problems in one research to weight and compare information between subquestions. In the conditions of limited research complex questionnaire for healthcare stakeholders is the easiest way to obtain necessary preliminary answers. Web-based questionnaire is easy to deliver worldwide. In current research Google Forms-based questionnaire was used.

Reliability Questionnaire was tested on several runs before wide implementation. Reliability of the questionnaire is checked in Jasp 0.14.0.0.—for scaled questions. Responses on 50 questions have McDonald’s = 0.899, Cronbach’s = 0.911, Guttman’s 2 = 0.921. ω α λ The highest values in the questionnaire are: for Cronbach’s is 0.920; for McDonald’s is α ω 0.934; for Guttman’s 2 is 0.932 (Table). Values above 0.9 may reflect (a) redundancy of the λ test—there are specially added questions in some dimensions to recheck values of the responses (b) multidimensionality of the test. 15 questions with opposite scales and negative results in the table were excluded from analysis. In this section is presented simple analysis and comparative description analysis between 76 sections and questions without group results comparison. In some cases Spearman’s Rho pairwise correlation test is performed.

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Focus Group The Ambient Assisted Living system design has to be based on the opinion of the main stakeholders: healthcare professionals, technical stakeholders, administrative stakeholders and patients. Every opinion group is important, and the opinion has to be assessed appropriately. Healthcare professionals represent a specific cross-section of society with a skilled understanding of patient’s needs in specific conditions. Years of focused training and practice give a wealth of information about the needs and problems of home-based patients. Still, there is a range of possible opinions, dictated by the professional view, personal experience and wide scope of technical, social and organisational knowledge. This study is based on a complex questionnaire. The questionnaire is presented to the healthcare workers, mainly medical doctors. In order to achieve the best possible combination, heterogeneous groups of medical professionals from different countries are included. In order to obtain as much and as wide information as possible and to keep the sample big enough despite complexity of the questionnaire all specialists with finished medical education or clinical psychology diploma were considered. The respondents were reached via web of personal contacts and with help of social media. The main reason was to eliminate subjective element of self-report about the profession and professional experience. More than three hundred medical specialists were contacted in the USA, Canada, UK, Netherlands, Germany, Switzerland, Sweden, Greece, Israel, Armenia, Ukraine, Belarus and Russian Federation and asked to answer the questionnaire. Around 120 agreed to participate, of whom 60 answered all questions. Those who did not finish the questionnaire named several reasons for it: unknown topic, the length and complex nature of the questionnaire, heavy workload and shortage of time because of the COVID-19 pandemic. Country name was removed from the questionnaire for reason of required anonymity. However, there was no informally registered difference in the approach of specialists, depending on the country of practice or residence. Age and gender were collected for statistical necessities.

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Table 9.1 Age and gender structure Age Gender: F Gender: M

Number

Minimal-Maximal

Mean, years

Median

Mode

60 29 31

21–63 21–60 42–63

49.9 49.0 50.7

50 50 50

50 50 49

Age and Gender Structure Age and gender were collected for statistical necessities. The age is from 21 to 63, with average age 49.9 years (Table 9.1).

Medical Profession There are 60 respondents. Of those who answered, there are 41 medical doctors, 10 nurses, 4 paramedics, 2 dental medicine doctors, 3 clinical psychologists. Some information is available about medical doctors’ specialisation. Limitations arose from wide options of the question about the medical profession, so some doctors did not mention their specialisation. The additional matter is a possibility to have more than one profession and report only one, often the most recent. Physician, MD—14. Psychiatrist, narcologist—9. Neurologist—3. Geriatric consultant—2. ONT consultant—1. Gynaecologist—2. Surgeon—1. Family doctor— 1. Anaesthesiologist—4. Haematologist—1. Paediatrician—1. Dentist, DMD—2. Urologist—1. Traumatologist, Orthopaedist—1. There was no option to learn nurses’ specialisations. The age distribution by professional groups is presented Table 9.2.

Health Versus Privacy: Results Data is obtained from answers to multiple-choice questions, matrix questions and scaled item-by-item questions. Healthcare and IT experience is projected on questions about medical aspects of technology implementation. Results are assessed with the help of descriptive and analytical statistics. Google Forms provide not only an easy way for questionnaire implementation but also a preliminary analytical structure. Numerical

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Table 9.2 Age distribution for professional groups Age Valid Missing Mean Median Modea Variance Range Minimum Maximum a More

DMD

MD

Psychologist

Nurse

Paramedic

2 0 49.500 49.500 48.000 4.500 3.000 48.000 51.000

41 0 50.951 50.000 49.000 37.298 32.000 31.000 63.000

3 0 48.667 52.000 40.000 57.333 14.000 40.000 54.000

10 0 45.900 49.500 50.000 100.100 35.000 21.000 56.000

4 0 50.250 50.500 47.000 6.250 6.000 47.000 53.000

than one mode exists, only the first is reported

data and percentages are presented for scales and frequency tables, graphs and histograms provide visual information. All data can be extracted as an XML file. Excel and analytical software help to analyse data. JASP package is used for data analysis. This chapter are provided only descriptive statistics for statements about video camera in AAL. All group comparative analysis is not shown.

Descriptive Statistics Sensors in AAL, Answers’ Frequency Table Table 9.3 with more than one possible answer per question per option. Discussion: There is a tendency is recognition of the prominent role of video cameras in the AAL system—for security (68%) and for abuse prevention (75%). At the same time the invasive nature of video registration is acknowledged. 48% of the respondents think that video sensors can be switched on only at the time of emergency. Still, wearable sensors are perceived as more invasive (47%) than video cameras (33%). 72% of the interviewees marked video camera as second best for communication. Microphones received 82% for the communicative purposes. Microphones are recognised as second best for the security and abuse prevention.

Most important sensors in AAL The best combination of sensors in AAL These sensors are not necessary for AAL These sensors are too invasive to be switched on 24 hours a day/7 days a week These sensors can be switched on only in emergency These sensors can be used for communication These sensors are most informative These sensors are least informative These sensors are important for security These sensors can help to prevent abuse

35 58.3% 34 56.7% 14 23.3% 14 23.3%

21 35.0% 49 81.7% 21 35.0% 16 26.7% 29 48.3% 30 50.0%

29 48.3%

43 71.7%

27 45.0% 8 13.3% 41 68.3%

45 75.0%

Microphone

38 63.3% 38 63.3% 18 30.0% 20 33.3%

Video camera

Table 9.3 Sensors in AAL, answers’ frequency table

14 23.3%

28 46.7% 13 21.7% 23 38.3%

6 10.0%

15 25.0%

38 63.3% 41 68.3% 8 13.3% 12 20.0%

Infrared positioning sensor

9 15.0%

28 46.7% 12 20.0% 11 18.3%

4 6.7%

14 23.3%

33 55.0% 35 58.3% 10 16.7% 11 18.3%

Mechanical pressure sensors

17 28.3%

44 73.3% 8 13.3% 19 31.7%

7 11.7%

10 16.7%

49 81.7% 48 80.0% 4 6.7% 28 46.7%

Wearable sensors

7 11.7%

10 16.7% 33 55.0% 11 18.3%

3 5.0%

21 35.0%

14 23.3% 18 30.0% 35 58.3% 6 10.0%

Temperature, air sensors

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72% of the respondents believe that video camera is the best sensor in the AAL system for the MCI patients. 68% support the importance of the microphones in the system. Preference of the video camera as one of the most important sensors, even if it can be switched on 24 hours 7 days a week, raises question about privacy of the patient.

Sensors in the Ambient Assisted Living System for Patients with Mild Cognitive Impairment: Statements and Results (A) “The video camera is the best sensor in the AAL system for patients with Mild Cognitive Impairment”. (B) “Microphones are very important in the AAL system for patients with Mild Cognitive Impairment”. (C) “The video camera and microphones are too invasive to be used 24 hours a day/7 days a week as AAL sensors for patients with Mild Cognitive Impairment”. (D) “Video cameras and microphones can be used in AAL only for emergency”. (E) “Video camera and microphone can be used in AAL for patients with Mild Cognitive Impairment for communication only” (Table 9.4). Discussion: Vitally important sensors have to be part of AAL for MCI patients according to most of the opinions. This includes wearable sensors, door and windows’ sensors, smart water and gas leak sensors (These statements are not shown here). However, there is less consensus about invasive ones’, such as video camera and microphone or sensors, controlling positioning and gestures and smart electricity sensors. While nearly two thirds of respondents generally support use of all these sensors, there is a disagreement. Support for video camera and microphone use is quite significant. There is a vision of necessity to use it not only for communication, even though it might be switched on 24/7.

43; 41; 39; 38; 35;

(A) (B) (C) (D) (E)

71.7% 68,3%, 65.0% 63.3% 58.3%

Likert 6–10

Statement 26; 27; 26; 25; 23;

43.3% 45.0% 43.3% 41.7% 38.3%

Likert 8–10 9; 15.0% 8; 13.3% 11; 18.3% 11; 18.3% 6; 10.0%

Likert 10 6.6 6.6 6.5 6 6

Mean 7 7 7 7 6

Median

7 8 10 10 1

Mode

Table 9.4 Descriptive statistics by statements, “Sensors in the AAL for patients with MCI”

5; 7; 9 5; 7; 8 4; 5; 7 3; 7; 9 3.5; 6; 8.5

Quartiles

4 3 4.5 6 5

IQR

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Privacy of the Patient with Mild Cognitive Impairment in a Home Equipped with the Ambient Assisted Living System: Statements and Results (A) “Patient’s health is more important than privacy issues in AAL”. (B) “Privacy is more important than the patient’s health in AAL”. (C) “Privacy issues in AAL can harm the mental health of patients with Mild Cognitive Impairment”. (D) “Patients with paranoid thoughts are not advised to live in a home with the AAL system”. (E) “Emotionally sensitive patients are not advised to live in a home with the AAL system”. (F) “AAL system is not more invasive than traditional healthcare” (Table 9.5). Discussion: There is a tendency to put health problems before privacy problems, even though a significant part of respondents disagree with this less balanced, by their opinion, view. There is also no consensus about the danger of AAL systems for emotionally sensitive patients or those with paranoid thoughts. At the same time more than half of the respondents see AAL system more invasive, than traditional healthcare system. There is correlation between answers to questions (A) and (G). Spearman’s Rho r s = 0.34036. P-value is 0.00779. There is also correlation between answers to questions (E) and (G). Spearman’s Rho r s = − 0.25529. P-value is 0.049.

Conclusion The decision about the necessity for inclusion of video sensors into the general AAL and AAL for MCI patients design depends at the same time on the healthcare needs and technical solutions and feasibility. For medical reporting, communication, abuse control and security video cameras are supposed to be suitable by 72%—they believe it is the best sensor. Microphones are supported by 68%. Still, 65% of the respondents agree that video cameras and microphones are too invasive

41; 13; 35; 36; 32; 21; 36;

(A) (B) (C) (D) (E) (F) (G)

68.3% 21.7% 58.3% 60.0% 53.3% 35.0% 60.0%

Likert 6–10

Statement 27; 45.0% 7; 11.7% 26; 43.3% 18; 30.0% 15; 25.0% 9; 15.0% 20; 33.3%

Likert 8–10 15; 25.0% 3; 5.0% 14; 23.3% 5; 8.3% 3; 5.0% 2; 3.3% 6; 10.0%

Likert 10 7 4.1 6.5 6 5.7 4.8 6

Mean 7 3.5 7 6 6 5 6

Median 10 3 10 8 5 5 8

Mode

5; 7; 9.5 2; 3.5; 5 5; 7; 9 5; 6; 8 5; 6; 7.5 3; 5; 7 4.5; 6; 8

Quartiles

Table 9.5 Descriptive statistics by statements, “Privacy of the patients with MCI in homes with AAL” IQR 4.5 3 4 3 2.5 4 3.5

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to be switched on 24/7, and 63% think they can be turned on in the case of an emergency. Only 58% agree with the statement that video cameras have to be used for communication only. The solution to the privacy problem can be technological. The use of another type of sensors, ways of the information presented and switching on only in the case of emergency are practical ways to lower the intrusive nature of observation and to improve patient’s privacy. Infrared motion registration is seen as important by 77% of the respondents. 72% think gesture recognition is necessary for the AAL system for MCI patients. 72% accept the necessity of pressure sensors, mounted on the furniture for positioning. The complex manner of information collection gives an opportunity to compensate for the less obtrusive way of non-permanent video camera use.

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10 Mobile Applications in Urban Ecotourism: Promoting Digitization and Competitive Differentiation Ana Filipa Silva Cardoso, Bruno Barbosa Sousa, and Ana Cristina Gomes da Cunha

Introduction In the literature, there are still very few studies addressing urban ecotourism, compared to other types of tourism. Moreover, as mobile devices and digital technologies are being developed at a very quick pace, there have been changes on people’s touristic experiences and demands that need to be analyzed. The aim of this article is to showcase a compilation of projects developed worldwide concerning mobile and digital technologies for urban ecotourism, and also try to understand how these new approaches can provide a better experience for tourists, pinpointing A. F. S. Cardoso (B) · A. C. G. da Cunha University of Minho, Braga, Portugal e-mail: [email protected] B. B. Sousa Polytechnic Institute of Cávado and Ave (IPCA), Barcelos, Portugal

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_10

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what has been learned until now and what can be improved. Technological advances in urban ecotourism are of interest for society, bringing new ways to explore or enjoy urban green spaces and changing how people complete their vacations or short breaks (even their contact with nature, for those that reside in cities), and, at the same time, fomenting awareness for environmental issues and sustainability. With this work, it is also intended to highlight the multidimensional value of urban green spaces, in parallel with the increasing demand for those spaces by society, and to provide some insights concerning the definition of Urban Ecotourism, as well as its benefits and coupled significance in sustainable development.

Urban Green Spaces Across a city it is possible to find smaller or larger green spots, for example, in the streets, such as trees in an avenue or a garden in a square, usually owned by the municipalities; in commercial establishments, companies or residential domains, meaning they are privately owned; or even community gardens, managed by a community, with or without municipal support. Besides differing in extent, urban green spaces may also differ in the density and diversity of vegetation (Madureira et al. 2018). The entire aggregate of green spots, following the examples above or being structural elements of buildings, such as green roofs or green balconies, constitutes the urban green infrastructure (Cardoso et al. 2017). There are also urban blue spaces, including lakes, canals, rivers, coastal areas and marine areas. These are places preferred by many due to the unique landscape they provide, meaning they are aesthetically very attractive and also play an important role in well-being (Madureira et al. 2018). Blue elements can also be found in green spaces, which is actually a characteristic widely appreciated by visitors (Madureira et al. 2018). Attention given to urban green spaces was relatively low a few years back, due to short budgets and more emphasis on man-made and built components (Chiesura 2004), but that has been changing. Many researchers have focussed their studies on the impacts of these spaces, providing a variety of information that highlights the benefits of urban green infrastructure (revised by Cardoso et al. 2017). Following this

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academic research, different organizations, from more anonymous to well-known entities, as the Institute for European Environmental Policy, the Nature Conservancy and the World Health Organization, provide accessible reports that incorporate proven benefits (Institute for European Environmental Policy 2016; The Nature Conservancy 2016; World Health Organization 2016). The Food and Agriculture Organization of the United Nations showcases stories worldwide of how green infrastructure contributes for the sustainable development of cities (FAO 2018) and the International Union for Conservation of Nature has designated urban protected areas, recognizing the importance of green areas in cities and the need for a general awareness towards conservation of nature (IUCN 2021). Existence of green areas, in particular of those accessible to the general public, is crucial for the quality of life in the cities, providing a contact with nature and promoting outdoor activities, as well as introducing facilities for relaxation, sports and others, which is associated to a better lifestyle (Madureira et al. 2018). In fact, direct or indirect contact with nature is believed to have positive effects on restoration of attention and on increasing workplace productivity. Social support derived from activities taking place in urban green spaces can prevent fluctuations in neuroendocrine, cardiovascular and immune functions (Jennings and Bamkole 2019). In Fig. 10.1, the recognized benefits of urban green spaces arranged by environmental, economic and socio-cultural domains are summarized. Health, as a sub-domain of socio-cultural benefits, is included because green spaces provide relevant positive healthy effects on individuals and on the general urban population (Jennings and Bamkole 2019). Connections interlinking benefits from different domains intend to highlight how some benefits impact on other domain’s, potentiating overall benefits. In addition to understand the impacts of urban green spaces, researchers have been studying what drives people to visit. Until now, the main raised motivations concern relaxation, nature appreciation and practice of physical exercise, even though there are significant differences among the studies, especially varying from location to location (Madureira et al. 2018). Also, it was shown that, besides the natural

Fig. 10.1 Benefits of urban green spaces arranged by environmental (green), economic (yellow) and socio-cultural (light blue) domains, as well as health (dark blue) as a socio-cultural subdomain. Connections show how a benefit from a domain can have an impact on other domains (using respective colours)

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aspects, people care about cleanliness, safety, facilities provided and social use of these spaces (Madureira et al. 2018). Therefore, there is a need to maintain the quality of the green space in itself and its functionalities, in order to guarantee a good experience for visitors and the attachment provided by these environmental and socio-cultural aspects. In these studies, there are diverse parameters used to assess information, including motives for visiting, duration of visits, areas used and interaction with established information-provider media, along with characterization of individuals and groups, use of facilities, interaction with other visitors and presence in activities (Avila and Rosa 2018; Jennings and Bamkole 2019; Madureira et al. 2018). Some methods consist of in situ observations/real-time data gathering (by using mobile applications), focus groups, interviews or surveys/questionnaires (Madureira et al. 2018). It is important that these approaches become more standardized, making it plausible to compare data reported worldwide. In this way, it will be possible to facilitate and improve decisionmaking for managers of green spaces, such as in the design of the space and facilities, in marketing strategies and in the organization of activities, as well as to provide a background for the development of new spaces that highly attract and engage residents and tourists (Avila and Rosa 2018; Jennings and Bamkole 2019). Therefore, in addition to more standardized approaches among researchers, there should be available a network between researchers and managers of green spaces for transference of knowledge, experiences and concerns in an immediate and simple mode. For example, factors that visitors consider relevant are access to urban green spaces (transportation options, signaling and road conditions) and availability of sidewalks and shaded areas (Avila and Rosa 2018; Jennings and Bamkole 2019). If a local study reveals that one of these factors is low rated by visitors, solutions can be rapidly developed for enhancement, leading to visitors’ satisfaction, and, perhaps, more time spent in the space. To begin attracting more residents and tourists, on top of studying how interactions can progress with use of mobile devices and digital technologies, it is possible to develop experimental visits and register feedback. This should be done in partnership with managers of urban green spaces and municipalities, due to permits and possible need of

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insurance. In parallel, understanding what specific reasons motivate residents to visit local green spaces can support local decision-making towards making spaces differentiated from each other (Madureira et al. 2018). Besides, local analyses are considered valuable as it has been observed that local context has major influence on peoples’ preferences about green spaces (Madureira et al. 2018). For example, spaces that provide more leisure activities are perceived differently than those possessing more cultural elements. These characteristics can distinguish spaces and attract different audiences with varied reasons for visiting (Avila and Rosa 2018). Furthermore, maintenance is an important issue, often overlooked, regarding management of green spaces, and includes having qualified staff who is able to run inventories of living species, cultural productions, existing facilities and activities, among other responsibilities. All this data is intended to pass on to visitors, so it should be as accurate and updated as possible. Moreover, caring for these aspects is important to appeal aesthetically to people, as well as allowing them to feel welcome (for example, absence of littering and vandalism are positive aspects). In fact, in the study conducted by Madureira et al. (2018), 62– 67% of people who answered to an inquiry prefer an investing in quality of green spaces. Along with increasing natural and aesthetical attractiveness, correct governance of spaces may prompt awareness regarding natural and cultural values, something that, as will be later presented, is very important for urban ecotourism. Therefore, setting international standards and criteria for conservation of cities’ ecological facets, including green spaces and street arborization, can promote preservation and innovation, eventually boosted and assisted by technology.

Urban Ecotourism Similarity to The International Ecotourism Society (TIES 2015), the World Tourism Organization (UNWTO) defines Ecotourism as follows: “a type of nature-based tourism activity in which the visitor’s essential motivation is to observe, learn, discover, experience and appreciate

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biological and cultural diversity with a responsible attitude to protect the integrity of the ecosystem and enhance the well-being of the local community. Ecotourism increases awareness towards the conservation of biodiversity, natural environment and cultural assets, both among locals and the visitors, and requires special management processes to minimize the negative impact on the ecosystem” (UNWTO 2019). Also, the UNWTO characterizes Urban/City Tourism as “a type of tourism activity which takes place in an urban space with its inherent attributes characterized by non-agricultural based economy such as administration, manufacturing, trade and services and by being nodal points of transport. Urban/city destinations offer a broad and heterogeneous range of cultural, architectural, technological, social and natural experiences and products for leisure and business” (UNWTO 2019). Researchers have approached the definition of Urban Ecotourism from various perspectives, but no description is provided by the UNWTO. Available work focussed on defining Urban Ecotourism includes articles that use other “ecological” related terms, such as “green”, “sustainable” or even “nature” (Santos and Silva 2017). However, these related expressions may have different meanings and can be used to describe distinct aspects of tourism, causing misinterpretations. While “green tourism” is currently associated with “sustainable tourism”, even though its original use was connected to environmental-friendly measures taken by tourist accommodations (Rainforest Alliance 2016), “nature tourism” relates to nature-based tourism activities, which can be included in many types of tourism (UNWTO 2019). “Sustainable” tourism refers to sustainable development of all types of tourism. It is a trending expression referring to long-term viability of environmental, economic and socio-cultural aspects in tourism while providing a satisfactory and consciousness experience to the tourist (UNWTO—Sustainable Development). There is also some discussion as to the inclusion of the term Ecotourism in Urban Ecotourism, concerning that, in cities, there are no natural areas, but only areas, modified by humans, possessing a certain degree of naturalness (Santos and Silva 2017). However, alongside different aspects in management, this is the major difference that Urban Ecotourism represents to Ecotourism. The alteration of location provides unique beneficial characteristics to Urban Ecotourism that will be presented here.

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Urban Ecotourism encompasses the intersection between Ecotourism and Urban/City Tourism (Radomska and Kolotylo 2019). It takes place in an urban area, which holds nodal points of transport and available infrastructures for a high number of tourists all year-round, directing towards natural experiences—nature-based activities—that provide nature conservation and protection values, as well as cultural significance, for locals and travellers (Kaae et al. 2019; Radomska and Kolotylo 2019). Appreciation of and disposition to learn and discover more about green spaces inspires environmental concern (Kaae et al. 2019; Radomska and Kolotylo 2019) and, indirectly, benefits residents in many terms, such as health, economy and security (Santos and Silva 2017). In this context, there are visibly many benefits of urban ecotourism, as well as strengths and opportunities associated with cities’ infrastructures, which encourage and pay off the effort to implement urban ecotourism as a pivotal strategy for sustainable development of tourism in urban areas, and, consequently, provide solutions for more sustainable cities (Fig. 10.2).

Fig. 10.2 Major opportunities/strengths (highlighted in dark blue) and resultant benefits (highlighted in light blue) of urban ecotourism, along with their integration concerning sustainable development (in green)

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Urban ecotourism can pressure cities’ touristic accommodations and businesses to minimize their environmental impact. Examples are restaurants with an urban garden, such as Frame, in Paris (France), which benefits from local production and even keeps beehives, taking part in conservation of bees (Frame 2020). Other cases are “zero-emission” hotels, such as the Hotel Milano Scala, in Milan (Italy), which provides online measures that they apply to decrease carbon dioxide emissions and more environmental-friendly actions (Milano Scala 2021). Besides, the ecotourism movement in urban areas may also influence cities’ design in relation to the green infrastructure (Madureira et al. 2018). For instance, green spaces have been emerging in grey zones that no longer have a use. This is illustrated with the “Promenade du Paillon” in Nice (France), which replaced an old bus station and a parking lot (Ville de Nice 2020). Another example is Port Sunlight River Park in Wirral (England) (Land Trust 2021), which was previously a landfill, as well as the Fresh Kills Park, in New York (USA) (Fresh Kills Park 2021). Finally, urban ecotourism can promote cities’ financial investment in natural and cultural preservation, especially considering caption of ecotourists to cities. A bordering case regarding this possible investment took place in Oamaru (New Zealand), where community and governmental structures took action that enabled the creation of the Oamaru Blue Penguin Colony, a former rock quarry that nowadays contributes for protection, research and conservation (Oamaru Blue Penguin Colony 2021).

Contribution of Mobile and Digital Technologies for the Development of Urban Green Spaces and Urban Ecotourism Mobile devices, including mobile phones and tablets, hold many important features, such as internet connection (through mobile data/wireless networks), Global Positioning System (GPS) and Quick Response Code (QR Code) readability, improving user interaction. In addition, they allow the download and update of applications for multiple purposes,

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which are designed relying on the previously mentioned features. Internet connection, GPS and QR Code readability are very useful for a tourist during a journey. QR Codes can, among other uses, be incorporated in signaling, like interpretation panels, facilitating self-guided visits. An advantage of implementing QR Code technology is that information will only be visible after scanning, allowing the visitor to first appreciate the scenery and create an emotional connection (Baptista and Moreira 2017). Many mobile applications available are games. Games during visits may intensify motivation for exploring, providing fun and entertainment, while using rewards (can include vouchers for physical products and experiences) and challenges. Therefore, games improve visitors’ experience, contributing for acquisition of information and encouraging interaction and engagement, at the same time they increase value of the attraction from which they are created about. Uses of gamification may even include directing visitors towards less popular attractions, giving them higher recognition and facilitating flux of visitors (Shen et al. 2020). Leading tourists to certain locations can be achieved by displaying brand advertisement and promotion. Visits can incorporate either a hunting-game or a challenge-game form. While the first relates to finding virtual clues, treasures or objects/completing checklists, the last is about completing tasks (including quizzes or missions). In this way, Shen et al. (2020) associate hunting-game visits to a more casual, exciting and popular experience, while challenge-game visits can be more didactic, ludic and memorable, due to “a sense of achievement” when tasks are completed. In order for a gamified application to succeed and create a better experience for visitors, it is important to understand who is the target audience for this type of application and what are the motivations to use them. In their work, Shen et al. (2020) describe motives like following a storyline, experiencing role-playing, receiving instant travel information/tips, using interactive maps or even experiencing Augmented Reality (AR). Making use of physical markers, such as already existent signaling, it is then possible to apply AR, increasing user engagement. This technology allows for input of computer graphics into real scenarios, having great potential for use, including in tourism. A good example relates to

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digital recreation and exhibition of the first works of historical artefacts found in museums (Özkul and Kumlu 2019) or literary tourism (Anjo et al. 2021). AR has proved to captivate attention and excitement and to provide a richer experience. Moreover, AR can have a very significant impact on marketing strategies (Özkul and Kumlu 2019). However, it is important to understand the visitor journey in order to best design and apply this technology, knowing where to place it and how to construct it in a way that provides meaning to users, considering their receptiveness to learn. Optimization of touchpoints of AR during the visitors’ journey leads to further valorization of the experience by users (Han et al. 2019). Considering how mobile devices can make use of the generality of digital technologies, either incorporating them (such as GPS) or by allowing their perceptibility (QR Code/AR), mobile phones/tablets are powerful tools for tourism and, in particular, urban ecotourism, encouraging people to spend more time in urban green spaces. There is the possibility of making self-guided and flexible visits, of promoting visibility of the spaces through social media or of being involved in activities/practices taking place in green spaces, for example. In fact, a study concerning the Gardens at new waterfront of Thessaloniki (Greece) identified that, from the total of people that answered an inquiry, almost 80% used their smartphones to take photos, around 40% to get information about the place and also close to 40% to communicate with friends. Besides, it was assessed that people would enjoy having Wi-Fi hot spots, even though the majority did not consider Wi-Fi access a main reason to go to these Gardens (Ruchinskaya et al. 2019). Local people return frequently to these Gardens and they are portrayed as memorable places, although being considered under-designed and failing to adapt to people’s needs and contemporary lifestyle (Ruchinskaya et al. 2019). Using digital technologies provides new perspectives and stimulates social integration/cohesion, increasing attachment to green spaces and leading to enhancement of their value as attractive and alive places (Ruchinskaya et al. 2019). Nevertheless, it is important to consider the implications that these technologies have in live interaction with the place (Han et al. 2019). First of all, designing the application must consider the time visitors spend looking at the screen and performing tasks on the device. Planning the visitor journey allows for testing balance

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between live interaction with the place and using digital technologies. Attention falls also on possible malfunctions, which create the opposite effect on visitors than what is intentioned (Han et al. 2019). Some examples are accuracy of GPS signal (may be overridden with manual introduction of location) and low quality of cameras or low illumination screens for AR. Instead of an improved experience, generating more connection and amazement, if there are errors, visitors will lose motivation and gain frustration, which will harm the journey and, consequently, feedback and sharing of the experience. Finally, mobile applications using digital technologies, especially games, rely on mobile devices’ multimedia features. Combining static media, such as texts, photos or graphics, with dynamic media, like video, audio or animation, allows the creation of one or multiple narratives. These storylines provide the context of the attractions and guidance of the visit, allowing visitors to appreciate the attraction upon one’s own interest and pace, learning in a self-motivated and self-guided way (Han et al. 2019).

Storytelling Urban green spaces can provide more static biological/taxonomic information concerning flora/fauna, as well as more dynamic ecological one, such as seasonal changes, interactions between species and responses to the environment. Furthermore, there are usually present sculptures and other artistic productions, along with facilities for recreational activities, on top of the landscape. Also, green spaces, in addition to their cultural elements, are or can be associated with a historical legacy related to their construction, surroundings, significance and toponomy. All this sort of information can be used to create different narratives to interact with visitors. Storytelling highlights each space’s characteristics, distinguishing one from the others. Contents may be personalized, allowing a more comprehensive and immersive experience (Cardoso et al. 2017). Nevertheless, attracting tourists to visit these places is a primary concern. As so, cities urge to develop their own urban ecotourism storytelling or, at least, to construct proper networks regarding all their green spaces, on which are visible the anticipating narratives and displayed information

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of convenience, such as location (pinpointing green spaces in city maps), schedules and existing facilities. Tinkering with usual touristic information about the city, arranging it to tell a story concerning its nature-based attractions, might even constitute a very unique urban identity that results in interest from tourists to visit. Botanical gardens, for example, are visited by more than 250 million people each year worldwide, more than half general public (remaining visitors are school public), and it is estimated that 80% of the general public prefers self-guided visits. Some surveys/studies show that motives for visit are attending special events or for pleasure, either being outdoors, admiring plants’ aesthetics and rare qualities, enjoying the sceneries and ambience or noticing design and landscaping of gardens (Gaio-Oliveira et al. 2017). Therefore, providing a successful application depends too on good storytelling. This conveys an attractive manner for informal education, which may impact especially young people towards more ecological habits (Baptista and Moreira 2017).

Mobile Projects Related with Urban Ecotourism There are a lot of mobile applications that provide (ludic) information concerning biodiversity, and in specific, plant taxonomy. This is the case of the gamified application Seek that uses image recognition to identify photos taken by users (iNaturalist 2021a). iNaturalist is the name of the project responsible for Seek, being a joint initiative by the California Academy of Sciences and the National Geographic Society for citizen science. The project has another application, named iNaturalist, which also allows identification of animals and plants but works as a community, gathering data for research (iNaturalist 2021b). In some natural parks, there are ecotourist applications that provide guided tours through walking, cycling, equestrian or automobile trails in protected areas and expose local products and services, which is the example of the application NaturalPTrails, developed by the Portuguese Institute of Nature and Forests Conservation (ICNF 2018).

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Using a mobile application can also be beneficial in community gardens. The University of Dundee created a project called GROW Observatory. They gave individuals who own gardens a sensor to place in the garden’s soil and gather data concerning quality, moisture levels, light conditions and atmospheric temperature, as well as statistics related to weather conditions. Information regarding crops, plants and regenerative growing practices is available on a mobile application to help these individuals managing their gardens. Besides, it is possible to input data on the application to combine with the sensor’s data. The goal of this project is to contribute to soil conservation and sustainable food growth methods (GROW Observatory 2021). Concerning implementation of technological solutions for urban ecotourism, some botanical gardens make available online supported guided tours. However, there is a lack of specialized staff to develop communication strategies in most countries (Gaio-Oliveira et al. 2017). Nevertheless, around the world, there are different projects that apply or intend to apply mobile applications in urban green spaces. In the United Kingdom, the Bournemouth Parks Foundation conducted a project in which it was developed a speaking bench and markers in form of owls in order to provide storytelling of a chosen valley. They also have a website with available information on different projects and a map of parks in Bournemouth (Bournemouth Parks Foundation 2015). Other project in England, Netpark, allowed the creation of applications for artworks and stories at the Chalkwell Park. It is now incorporated as part of the visit to the park, available using mobile devices (Metal 2021). In Copenhagen (Denmark), there is the initiative Copenhagen Green, a website with information on Copenhagen’s 100 green spaces that enable activities for visitors, having the goal of increasing use of these places and of environmental and cultural values (Foreningen ByandNatur 2021). In Portugal, there are a few urban ecotourism projects. From viability studies (Cardoso et al. 2017), to applications for concrete gardens, such as iLugar Jardim Gulbenkian for Calouste Gulbenkian’s garden in Lisbon (iLugar 2021), and broader developed applications for entire regions, as Green Gardens—Azores for Açores (Green Gardens Azores 2021).

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In Prague (Czech Republic), there is an application to discover green urban spaces, having the ability to find the shortest route to get to the selected place. This application is called DoPˇrírody! , which means To the ˇ Nature! (Cábelka and Jakl 2017). In New York (USA), there is an application for The High Line gardens, enabling visitors to learn more about them. This space has a deep connection with the environment and the arts, organizing educational programmes and public events. It also provides community engagement through initiatives like “adopt a plant” or volunteering and supports community-based projects that work on solutions for the threat of pollinator extinction (The Highline 2021). A European initiative level is CyberParks. This initiative includes all public spaces, addressing how information/communication technologies can improve attractiveness and use of those spaces. CyberCardeto improves the experience for visitors of the Cardeto Park in Ancona (Italy). It was designed by the Engineering Faculty of Università Politecnica delle Marche and uses GPS and Bluetooth Low Energy technology. There are historical and botanical points of interest presenting contextual information and suggestions of paths. This application also tracks the behaviour of visitors in the park (CyberParks 2020a). The MONITORING TOOL CyberParks aims to support fieldwork on the interactions between people and the public urban spaces through use of mobile resources. Basically, there is a real-time tracking of peoples’ paths and time spent and a sort of survey at specified points of interest using AR. It collects data of spaces from Barcelona, Bilbao, Bristol, Lisbon, Ljubljana and Thessaloniki. The goal is to provide information for municipalities to fix existing problems, improve spaces and plan investments in long term (CyberParks 2020b). A parallel initiative in the United Kingdom, named Rethinking Parks, potentializes gathering and use of data to improve green spaces. These improvements may concern the need/best location for new facilities in a certain area, the implementation of data-driven maintenance schedules and routes or analysing the impact of visitors on biodiversity (Rethinking Parks 2021a). It sustains, for example, WiseParks, from the University

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of Nottingham and Nottingham City Council. This project uses WiFi sensing to obtain information such as visitors’ preferred schedules and areas where they spend more time during visits, which can have different durations. Therefore, it is possible to study visitor flows, leading to changes in management of the spaces, including strategic planning and cost-effective staffing and maintenance (Rethinking Parks 2021b). Another example is ParkLife, from the University of Edinburgh, with the goal of using data to increase engagement to the space from people who visit green spaces and from managers. It relies on using data from wildlife and people to promote awareness of the value of green spaces and nature (Rethinking Parks 2021c). Rethinking Parks also supports projects such as MyParkScotland that provides information on location, facilities and events from parks in Scotland and works as a platform for crowdfunding for those parks (MyParkScotland 2021). Overall, applications can be categorized as guiding tours (with increasing levels of interaction), informative points or data gathering instruments (useful for improvements). Available feedback from users is very positive, even though number of downloads is still low (which may be analysed at, for example, Google Play Store).

Boosts, Constraints and Future Steps Nowadays, environmental consciousness is growing, individually and collectively. So, for municipalities, the opportunities for urban ecotourism are rising. Investment and support from governments and municipalities, in partnership with private investors, are required to develop urban ecotourism further, specifically, with mobile devices and digital technology. This includes rethinking arborization in cities and green spaces, as well as considering visitors and technologies when designing new spaces, providing adequate infrastructure and proper management (Ruchinskaya et al. 2019; Santos and Silva 2017). Also, tourism managers, trying to diminishing negative impacts due to disordered tourism growth (Baptista and Moreira 2017), are becoming increasingly concerned about tourists’ active participation, intending

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to enrich peoples’ experiences. In this way, after their travel, tourists will have more knowledge, which relates to new information they have learned during their journey (Baptista and Moreira 2017). In this sense, technology can be very beneficial, as it has been shown in this article, and has a wide-range potential. However, few attractions are able to use technology to interact with tourists, providing active participation. The only expectation is for tourists to share publications on social media (Baptista and Moreira 2017). Besides, some studies concerning mobile applications for urban ecotourism are, unfortunately, not available in English (Miskovic et al. 2015). There is also a need for the assessment of how tourists decide which attractions to visit in a city and motives to choose natural attractions, which will contribute for tracing overall urban ecotourist profiles (Jennings and Bamkole 2019). One motive may even include experiencing local appropriation of nature and culture. As mobile and digital technologies presently offer some aid on how to attract tourists and provide them better experiences, future uses will possibly enhance public perception on urban green spaces and assist management teams in promotion of these spaces. This kind of initiatives can showcase municipalities in how they are committed to nature and environment and that the ones that fulfil these standards and criteria can be marketed as best destinations for urban ecotourism, attracting more business, employment, ecological thinking and fun. With this article, it is possible to obtain a general sense of main topics involving urban ecotourism and of current technological developments in this area, as well as important studies to follow. Based on the present compilation of benefits generated with urban ecotourism, there is available ground for public (specially municipalities) and private stakeholders to explore ways of actively investing in mobile solutions for urban ecotourism.

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References Anjo, A., Sousa, B., Santos, V., Dias, A. and Valeri, M. (2021). Lisbon as a literary tourism site: Essays of a digital map of Pessoa as a new trigger. Journal of Tourism, Heritage & Services Marketing, 7 (2), 58–67. Avila, M. and Rosa, C. (2018). Parque Estadual da Serra do Conduru: perfil, percepções e sugestões dos visitantes. Revista Brasileira de Ecoturismo, 11(3), 449–466. Baptista, L. and Moreira, J. (2017). Simbiose entre tecnologia móvel e patrimônio natural: uma proposta pedagógica. Revista Brasileira de Ecoturismo, 10 (2), 227–246. Bournemouth Parks Foundation (2015). Talk of the town: Stour Valley. Retrieved from: https://www.bournemouthparksfoundation.org.uk/our-pro jects/talk-of-the-town-stour-valley/. Accessed: 8 December, 2021. ˇ Cábelka, M. and Jakl, M. (2017). Dopˇrírody!—Geoinformation mobile application. AUC Geographica, 52(2), 249–257. Cardoso, A., Sousa, B. and Cunha, A. (2017). Technological entrepreneurship applied to Green Spaces and Ecotourism. European Journal of Applied Business and Management, Special Issue, 336–347, ISSN 2183–5594. Chiesura, A. (2004). The role of urban parks for the sustainable city. Landscape and Urban Planning, 68, 129–138. https://doi.org/10.1016/j.landurbplan. 2003.08.003. CyberParks (2020a). Cyber Cardeto: CyberParks can become senseable places. Retrieved from: http://cyberparks-project.eu/app/cyber-cardeto. Accessed: 8 December, 2021. CyberParks (2020b). Monitoing tool cyberparks. Retrieved from: http://cyberp arks-project.eu/app/monitoring-tool. Accessed: 8 December, 2021. European Commission (2016). The health and social benefits of nature and biodiversity protection (Report ENV.B.3/ETU/2014/0039). London/Brussels: Institute for European Environmental Policy. Food and Agriculture Organization (FAO) (2018). Forests and sustainable cities—Inspiring stories from around the world . Rome: FAO. Foreningen ByandNatur (2021). About Copenhagen green. Retrieved from: http://www.kobenhavnergron.dk/om/?lang=en. Accessed: 8 December, 2021. Frame (2020). Our garden. Retrieved from: https://www.framebrasserie.fr/en/ our-garden/. Accessed: 3 December, 2021.

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Fresh Kills Park (2021). The park. Retrieved from: https://freshkillspark.org/. Accessed: 8 December, 2021. Gaio-Oliveira, G., Delicado, A. and Martins-Loução, M. (2017). Botanic gardens as communicators of plant diversity and conservation. The Botanical Review, 83, 282–302. Green Garden Azores (2021). Home. Retrieved from: https://www.otacores. com/greenga/?lang=en. Accessed: 8 December, 2021. GROW Observatory (2021). About. Retrieved from: https://growobservatory. org/. Accessed: 8 December, 2021. Han, D., Weber, J., Bastiaansen, M., Mitas, O. and Lub, X. (2019). Virtual and augmented reality technologies to enhance the visitor experience in cultural tourism. In tom Dieck M. and T. Jung (eds.), Augmented reality and virtual reality. Cham: Springer, pp. 113–128. Institute for Nature Conservation and Forests (ICNF). (2018). NaturalPTrails app—The best routes in Portugal Natural. Visiting protected areas is now easier! Retrieved from: https://www.icnf.pt/noticias/appnaturalptrails. Accessed: 8 December, 2021. iLugar (2021). iLugar Jardim Gulbenkian. Retrieved from: https://ijardimgulbe nkian.wordpress.com/. Accessed: 8 December, 2021. iNaturalist (2021a). Seek. Retrieved from: https://www.inaturalist.org/pages/see k_app. Accessed: 8 December, 2021. iNaturalist (2021b). How it works. Retrieved from: https://www.inaturalist. org/. Accessed: 8 December, 2021. International Union for Conservation of Nature (IUCN). (2021). Urban protected and conserved areas. Retrieved from: https://theurbanimperative. org/urban-protected-areas. Accessed: 8 December, 2021. Jennings, V. and Bamkole, O. (2019). The relationship between social cohesion and urban green space: An avenue for health promotion. International Journal of Environmental Research and Public Health, 16 (3), 452. Kaae, B., Holm, J., Caspersen, O. and Gulsrud, N. (2019). Nature Park Amager—Examining the transition from urban wasteland to a rewilded ecotourism destination. Journal of Ecotourism, 18(4), 348–367. Land Trust (2021). North West. Retrieved from: https://thelandtrust.org.uk/spa ces/north-west/. Accessed: 8 December, 2021. Madureira, H., Nunes, F., Oliveira, J. and Madureira, T. (2018). Preferences for urban green space characteristics: A comparative study in three Portuguese cities. Environments, 5 (2), 23. Metal (2021). NetPark. Retrieved from: http://www.metalculture.com/projects/ netpark/. Accessed: 8 December, 2021.

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11 Improving Learning Experience and Privacy in Education Using the Power of Big Data and Artificial Intelligence Usman Javed Butt, Aristeidis Davelis, Maysam Abbod, Caleb Eghan, and Haiiel-Marie Agbo

Big Data Definition Big Data is a term mainly used to describe huge amounts of data or “data sets that are too large or complex to be dealt with by traditional data-processing application software” (Lareyre et al. 2020). Like any other emerging discipline, Big Data faces the issue of conceptual vagueness when it comes to pin-pointing its main concepts, origin, and purpose, while many researchers have attempted to study its origins and propose a definition. U. J. Butt (B) · M. Abbod Electronic and Computer Engineering, Brunel University, London, UK e-mail: [email protected] M. Abbod e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_11

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An online survey performed by Harris Interactive on 154 C-level global executives in 2012, demonstrated the diverse understanding of Big Data and its applications, as seen on Fig. 11.1. Similarly, many researchers attempted to define Big Data based on its characteristics. A survey by De Mauro et al. (2016) indicated that the various definitions of Big Data provided by researchers, could be classified into four categories, discussed below.

Fig. 11.1 2012)

Definitions of Big Data (online survey of 154 global executives in April

A. Davelis · C. Eghan · H.-M. Agbo Engineering & Environment, Northumbria University, London, UK e-mail: [email protected] C. Eghan e-mail: [email protected] H.-M. Agbo e-mail: [email protected]

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• Attributes of Data Big Data in this section is defined considering the attribute of the data it contains. One popular definition associated with this group comes from Beyer and Laney (2012), defining Big Data as “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making ”. This defines Big Data by focusing on three characteristics: Volume, Velocity , and Variety , generally called the “3 V’s”. Additional attributes observed by other researchers include Veracity, Value, Complexity, and Unstructuredness. • Technological Needs Big data can also be defined by focusing on the type of technology needed for their analysis, handling, and processing. A fitting example is the definition provided by Microsoft Research (2013), where Big Data is used as a term linked with the processing of massive sets of information by means of “serious computing power ”. This definition emphasises the technological requirements needed to handle Big Data due to their size and complexity. Also identifying Technological Needs linked with the processing of Big Data, are definitions by NIST (2015), highlighting the need for “a scalable architecture for efficient storage, manipulation and analysis” and Ward and Barker (2013) that mention storage and analysis techniques including “NoSQL, MapReduce and Machine Learning ”. • Thresholds The definitions in this group focus on the limitations that differentiate simple datasets and datasets considered “BIG ”. Dumbill (2013) considers Big Data as “data that exceeds the processing capacity of conventional database systems”, arguing that data too big, too fast, and unfit for standard database architectures, call for the adoption of different processing methods, namely Big Data. In the same vein, Fisher et al. (2012) argue that what is considered “BIG ” has grown according to Moore’s Law. In small datasets, it is easy

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to determine if the data is clean, the values are reasonable and results can the computed rapidly, which does not apply to “BIG ” datasets. • Social Impact Definitions in this group focus on the effect Big Data has or might have on society. Mayer-Schönberger and Cukier (2013) argue that Big Data can be identified by their increased ability to influence society and science. They note three key Big Data attributes; “More”, referring to their size, “Messy”, referring to the higher chance of errors, and “Correlation”, relating to the large potential for statistical analysis and underlying connections between elements of Big Data sets. Finally, De Mauro et al. (2016) propose a definition encompassing a variety of the aforementioned ones: “Big Data is the information asset characterized by such a high Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value”.

Characteristics Following the proposed definitions, it is critical to take a look at the various elements used to characterise and define Big Data. Figure 11.2 gives an overview of Big Data’s main characteristics. The five main characteristics, or “5V’s” associated with Big Data were identified as Volume, Velocity, Variety, Veracity and Value. Other researchers have identified various other characteristics moving up to 17V’s (Wook et al. 2021). • Volume Volume refers to the massive amount of data generated, gathered, and processed from any kind of source (Younas 2019). The volume of data is the most important feature of Big Data since it dictates the technological requirements and tools for their analysis, while presenting serious challenges in terms of extracting valuable knowledge out of vast sets of information.

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Big Data 5V characteristics

• Velocity Velocity refers to the speed at which data is generated, processed, and moved between different systems and devices (Younas 2019). With data being generated in huge quantities and speeds, it is critical for systems to be able to quickly respond to the massive amount of incoming data (Razavi et al. 2021). A few examples where data velocity is key are social media, online transactions, live transportation data, millions of internet search queries, etc. • Variety Variety refers to the structural heterogeneity in a dataset (Gandomi and Haider 2015). Companies interact with various data types such as structured data (data in relational databases and spreadsheets), semistructured data (i.e. XML, HTML documents), and unstructured data (i.e. video, images). Although variety is an important element of Big Data, it also represents one of its biggest challenges when it comes to data analysis.

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• Veracity Veracity refers to the trustworthiness, correctness, and accuracy of data (Yaqoob et al. 2016). With data being generated at incredible speeds and amounts, data accuracy becomes a fundamental factor. Similarly, consistency, trust, security, reliability, quality, governance, metadata management, privacy and legal concerns become key when using or processing data (Younas 2019; Bello-Orgaz et al. 2016). • Value Introduced by Oracle (2011), “Value” refers to the type of benefit that can be derived from the processing, analysis, and extraction of valuable information from Big Data (Younas 2019; Bello-Orgaz et al. 2016). For example, consumer data can vary from Personal, to Engagement, Behavioural and Attitudinal Data (Freedman 2020). Once collected, Data Analysis allows companies to refine marketing strategies, improve customer experience, or use targeted advertisement. This gives an understanding of why Big Data, once extracted, processed, and used appropriately, is one of the most valuable assets in modern economy (Szczepa´nski 2020).

Challenges of Big Data In this section, the various challenges faced during the use and implementation of Big Data are discussed, as shown in Fig. 11.3. • NoSQL Databases NoSQL is “a non-relational database that stores and accesses data using key-values” (TechTerms 2013), commonly used in storing Big Data. Although NoSQL provides advantages over standard relational databases, such as flexibility, cost-effectiveness, and scalability, it still faces challenges when it comes to the handling of large amounts of data. These include the lack of maturity, performance consistency, and the inability to effectively deal with analytics (Yaqoob et al. 2016).

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Challenges of Big Data

• Big Data Indexing Schemes Traditional data retrieval methods, using queries made with query languages such as SQL, are perfect to retrieve data stored in a distributed manner. However, due to size and complexity, the retrieval of data in Big Data using conventional methods is challenging. Several researchers tried to address this problem by proposing various algorithms and techniques. Chen and Zhuang (2013) introduce a dynamic insertion algorithmic model which improves query performance and the real-time effectiveness of data. Another method proposed uses k-Nearest Neighbour queries (Dhanabal and Chandramathi 2011), while an approach by Lu et al. (2013) allows users to index their own spatial data, providing drastic improvements on massive special datasets in terms of performance and scalability.

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• Visualisation Visualisation refers to the ability to represent data in the form of graphs. Due to the complexity, large size and dimension of Big Data, visualisation becomes challenging. Wang et al. (2015) argue that “Big Data visualisation tools have poor performances in scalability, functionalities, and response time”. Some of the challenges identified are Visual Noise, Information Loss, Large Image Perception, High Rate of Image Change, and High-Performance Requirements. To mitigate these, Wang et al. (2015) propose various solutions including hardware improvements, “understanding the data” by implementing domain expertise, setting up data governance to ensure clean data, using data clustering methods to improve visibility, and separating outliers from data. • Big Data Security and Privacy Issues In all emerging technologies security is a key parameter and Big Data is no exception. Ensuring security in a technology where data is generated at an incredible speed and amount is difficult. Three elements make this task challenging; the ability to identify malicious data in real-time, the ability to perform such a task while maintaining realtime processing demands, and finally, the need for security mechanisms capable of processing dynamic changes in Big Data. Yaqoob et al. (2016) suggest that existing security mechanisms should integrate Big Data characteristics such as data patterns and variation into their processes to address these issues.

Artificial Intelligence (AI) History of Artificial Intelligence Although there is no standardised definition for AI, most definitions agree that AI-enabled systems should be “systems capable of performing tasks that would require a human factor ” (Butt et al. 2021). Friedman

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et al. (2013) refer to AI as the “ability of human-made systems to mimic rudimentary human thought ”. One of the biggest historical events towards the establishment of AI was the development of “The Bombe” by Alan Turing in 1936, a codebreaking machine designed to decipher the Enigma code used by the German army in World War II. This incredible feat made Turing question the intelligence of machines; in his seminal article “Computing Machinery and Intelligence” (1950), he describes how “to create intelligent machines and in particular how to test their intelligence” (Haenlein and Kaplan 2019), and a way to test a “machine’s ability to think as a human would ” (Reynoso 2021). In 1955, John McCarthy proposed an “Artificial Intelligence” workshop, using the term for the first time (Russell and Norvig 2010). Later in 1956, Newell and Simon invented the “thinking machine” called the “Logic Theorist” capable of proving symbolic logic, considered to be the first running AI software (Gugerty 2006). In 1967 the computer “Mark 1 Perceptron” was built by Frank Rosenblatt based on Neural Networks, which was said to “learn through trial and error”, and in 1968 Marvin Minsky and Seymour Papert publish “Perceptrons”, a landmark book on the development of Neural Networks. In the 1980s algorithms using backpropagation to train themselves were introduced and widely used in AI applications. In 1997, IBM’s chess machine “Deep Blue” managed to “defeat thenreigning World Chess Champion Garry Kasparov in a six-game match” (Campbell et al. 2002). In 2015, the Chinese search giant “Baidu” claimed to have created a supercomputer able to “identify and categorize images with a higher rate of accuracy than the average human” using a Convolutional Neural Network, with Ren Wu (2015) arguing that it “set a new record for recognising images”. In 2016, Progressive Learning was introduced by Google’s DeepMind project, as a type of deep transfer learning technology that builds on “previous, related knowledge, similar to human learning capabilities” (Rusu et al. 2016).

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General Classification of Artificial Intelligence Following McCarthy, several definitions and classifications of AI were later proposed, and most can be sorted into two categories: “AI and AIEnabled” and “Technology-Oriented” as seen in Fig. 11.4.

AI and AI-Enabled In this category, classification is based on the capability to display human-like behaviour such as the ability to “think” and “feel”. Under this scope, machines can be classified into four subcategories: “Reactive”, “Limited-Memory”, “Theory of Mind”, and “Self-Aware” (Hassani et al. 2020). • Reactive Machines As the name suggests, Reactive Machines were the first AI-based systems capable of automatically reacting to a number of inputs or combinations. Their functionality did not involve any memory-based features resulting in limited capabilities, since previous actions or combinations couldn’t

Fig. 11.4

General classification/types of Artificial Intelligence

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be used to shape future decisions. Essentially, Reactive Machines lacked the ability to “learn” (Hassani et al. 2020). • Limited-Memory Machines Limited-Memory Machines (LMM) are machines capable of learning from available datasets and making decisions accordingly. LMM encompasses all of the Reactive Machine’s abilities and after this advancement in AI development various applications were built, including Chatbots, Virtual Assistants, self-driving vehicles et cetera (Hassani et al. 2020). • Theory of Mind Considered as the next level of AI development, Theory of Mind aims at developing AI systems that can comprehend the needs, emotions, beliefs, and thought processes of individuals they interact (Hassani et al. 2020). Davies (n.d.) argues that the Theory of Mind systems will be able to explain their actions and their consequences, infer objectives and understand the intents of similar systems. Despite the promising prospects provided by Theory of Mind, the majority of AI systems in this category are considered as concepts or still under development. • Self-Aware AI Self-Aware is the final stage of AI development, only referred to on a hypothetical basis for systems that have evolved to a stage of selfawareness. Joshi (2019) argues that “this type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own”. Although very promising and revolutionary, Self-Aware AI is also a concern for researchers and scientists since self-awareness implies self-preservation which could indirectly spell the end of humanity (Joshi 2019).

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Technology-Oriented The second category is the Technology-Oriented approach and consists of three main subcategories; Artificial Narrow, General, and Super Intelligence. • Artificial Narrow Intelligence (ANI) ANI systems are the most prevalent form of AI currently employed around the world. Also called “Weak AI”, ANI systems are limited to the range of pre-programmed tasks. The first application of AI technology in Education was the Intelligent Tutoring System (ITS) “SCHOLAR” designed “to support geography learning, and capable of generating interactive responses to student statements” (Carbonell 1970). This led to more complex and efficient AI Applications in Education, that facilitate the teaching and learning process ranging from ITS to robotic systems and chatbots. A good example of ANI applied in Education is the statistics tutor “Stat Lady” which required learners to solve a set of problems before assuming they have gained mastery of the topic (Shute 1995). Another example is the rule-based ITS “QUE” designed for learners to explore the difference between correct and wrong answers and “the intelligent system’s reasoning processes by asking “why not” and “what if ” questions” (Ouyang and Jiao 2021). Algorithms such as the Bayesian Network, Natural Language Processing and Markov Decision trees have been used in this context to analyse large data sets, based on interactions, aiming to improve the learning process through accurate predictions, data visualisation, and tailor tutoring (Ouyang and Jiao 2021). • Artificial General Intelligence (AGI) AGI, also called “Strong AI” implies the ability to “learn, perceive, understand and function completely like a human being ” (Hassani et al. 2020). In other words, AGI systems are capable of performing intelligent actions and handle unknown/unexpected situations without the need for any external support. Other key features agreed upon by the AGI community include the ability to achieve goals and carry out various tasks in different

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contexts, handle problems and situations in unexpected manners and generalise knowledge so as to transfer it from one task to another (Goertzel 2014). In education, AGI-enabled systems would essentially give control to the learner and only act as supporting tools focusing on the learning process. The ideology behind this is the belief that “learning occurs when a learner interacts with people, information, and technology in socially situated contexts” (Heffernan 2014; Liu and Matthews 2005; Vygotsky 1978; Ouyang and Jiao 2021). • Artificial Super Intelligence (ASI) As mentioned, AI’s primary goal is to emulate basic human thought. ASI however transcends this, is defined as “the capability of humanmade systems to surpass humans” (Yampolskiy and Duettmann 2019). Although many believe ASI is yet to be achieved, some researchers claim to have observed ASI already working “inconspicuously and tirelessly in our midst ” (Silver et al. 2016; Tang et al. 2018; Le et al. 2012). With researchers and companies invested in deploying AI-powered Adaptive Learning Systems (ALS), Yampolskiy and Duettmann (2019) claim that ALS might have started showing signs of ASI, since they have surpassed teachers by performing tasks such as continuous one-on-one tutoring, progress checks, remediation etc., regardless of time, location, and student special requirements. It is however important to mention that these examples are in no way standards to qualify ALS as ASI, but just ways in which AI has started outperforming humans.

Artificial Intelligence Major Branches As an innovative and ever-growing field, AI has various branches, subcategories, and domain applications. Figure 11.5 gives an overview of the major branches of AI.

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Fig. 11.5

Major branches of Artificial Intelligence

Expert Systems Considered the first successful model of AI, Expert Systems were developed in the 1970s as software capable of learning and imitating human decision-making. To that end, Expert Systems do not rely on conventional programming and use logical notations and reasoning via elements such as “if–then” rules. Their key features include responsiveness, reliability, transparency, and high performance.

Robotics This field of AI focuses on the design, development, and construction of robots, with the primary aim of helping humans conduct tasks considered monotonous, bulky, or laborious. Robots are mainly involved in the automobile industry for assembly, in space to move large objects, etc. Furthermore, researchers have embedded robots with Machine Learning

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technology to achieve social interaction and perform real-world tasks without the need for support.

Natural Language Processing (NLP) NLP is a subfield of AI and Machine Learning that aims to identify, break down, and classify language, so it can be analysed by machines. Popular applications of NLP are language translation, sentiment analysis, speech recognition, smart assistants (Siri, Alexa etc.), data analysis, and more.

Fuzzy Logic Fuzzy Logic is a branch of Machine Learning that “represents and modifies uncertain information by measuring the degree to which the hypothesis is correct ” (Tyagi 2021). In other words, it provides reasoning flexibility in cases of uncertainty, by using standard logic to determine if an idea is true, false, or partially true and partially false. Hernández and Hidalgo (2020) claim that “Fuzzy logic deals with the usefulness of imprecision and the relative importance of precision”.

Machine Learning Machine Learning is considered to be a subfield of Artificial Intelligence and Computer Science (Subasi 2020). Edgar and Manz (2017) state that Machine Learning is a “field of study that uses computational algorithms to turn empirical data into usable models”. In other words, Machine Learning algorithms use raw data from experiments, observations, or training data sets, learn from it, identify patterns and use it to make decisions with minimal human interaction. Figure 11.6 displays the relationship between AI, Machine Learning and Deep Learning. Machine Learning can be divided into four categories: “Supervised Learning”, “Unsupervised Learning”, “Semi-Supervised Learning”, and

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Fig. 11.6

Hierarchy between AI-ML-DL

“Reinforcement Learning”. Figure 11.7 demonstrates Machine Learning types and associated methods. In this section, the various types of Machine Learning are discussed to get a better understanding of its functions and areas of application. • Supervised Learning In Supervised Learning, the system’s algorithm acquires the ability to understand the connection and mapping between inputs (data) and outputs (labels) through extensive training, and then predict the output of new inputs (Liu and Wu 2012). The training stage utilises a prelabelled data sample which is critical for algorithms in this category to learn and predict, making this process “supervised”. The most common supervised tasks are “classification” and “regression”. Tasks such as “sentiment analysis” or “text classification” are good examples of supervised learning.

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Machine Learning types

• Unsupervised Learning Unsupervised Learning aims at identifying patterns in datasets and categorise data based on them (Choi et al. 2020). To that end, Unsupervised Learning algorithms model underlying patterns in data in order to learn more about its features (Sah 2020), and then sort data into categories based on similarities. Once new data is introduced, previously identified features help classify instances of the same category. The process is considered “unsupervised” since data patterns are not pre-indicated and left for the algorithm to determine and organise. Common tasks in Unsupervised Learning are clustering, density estimation, feature learning, dimensionality reduction, finding association rules, anomaly detection, etc. (Sarker 2021). • Semi-Supervised Learning As the name suggests, Semi-Supervised Learning is a method utilising both Supervised and Unsupervised Learning techniques. SemiSupervised Learning algorithms are useful for datasets containing both

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labelled and unlabelled data; data with similar features are clustered using Unsupervised Learning algorithms and labelled data is then used to label the unlabelled data (Sah 2020). • Reinforcement Learning Reinforcement Learning is the process that best imitates human learning, functioning on the principle of trial and error. Reinforcement Learning algorithms are designed to perform “a specific task where no single answer is correct, but an overall outcome is desired ” (Choi et al. 2020). In other words, the algorithm makes decisions to approximate a desirable outcome in a process of trial and error. This results in increasingly positive outcomes, since the algorithm receives feedback after each decision and optimises accordingly. This process accelerates learning since the algorithm learns from experience. As mentioned, Machine Learning functions with the help of various algorithms to analyse inputted data, learn from it and improve system performance. Although many algorithms are available, some are very common when it comes to Machine Learning, as seen in Fig. 11.8.

Fig. 11.8

Machine Learning algorithms

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Neural Networks and Deep Learning Neural Networks is a branch of AI that makes use of Neurology to improve the cognitive abilities of computer systems and machines. The first artificial neuron was used by “Perceptron”, introduced in 1958, in an attempt to replicate the human brain’s neural pathway (Iman et al. 2020). In Perceptron, artificial neurons were clustered into a single layer with the ability to detect linear patterns once data was introduced to it, by using a sigmoid function. Training was accomplished “by feedforwarding the data while backpropagating the labels to tune the weights to each node” (Iman et al. 2020), and this process gave birth to the first Artificial Neural Network (ANN) (Anderson and McNeill 1992). However, the inability to address nonlinearity was Perceptron’s major drawback. One of the most significant attempts to address this issue was the use of the Multi-Layer Perceptron (MLP) method, characterised by multiple layers of nodes that formed a Network Architecture (Fig. 11.9). In the early stages of ANN, all elements were fully connected, and each connection had a weight and node. Each node was linked to the next, each node contained an activation function and to determine the next node in a recursive loop, the value of the previous nodes was multiplied by the weight of the connection (Iman et al. 2020). The second

Fig. 11.9

Example of MLP network architecture (Iman et al. 2020)

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generation of ANN calculated error rates and back-propagated errors. This method had severe limitations such as sensitivity to noisy data, and various methods such as the “Restricted Boltzmann Machine” were proposed (Mathew et al. 2021) to overcome them. To function effectively, ANN uses various statistical techniques such as regression analysis, multiple regression, discriminant analysis, logistic regression et cetera (Paliwal and Kumar 2009). Despite multiple technological advances, ANN still faced many challenges such as the “limited number of available nodes in each MLP layer, combined with the limited number of layers” (Iman et al. 2020). Those challenges were later overcome through the use of more advanced functions such as sign, linear, tanh, ReLU, and leaky-ReLU (Xu et al. 2015), resulting in ANN with multiple hidden layers. Deep Neural Networks (DNN), also known as Deep Learning, was created as a way to overcome challenges faced in ANN, consisting of more than three neuron layers (IBM 2020). The development of Deep Learning was facilitated by the emergence of Big Data sets, which greatly improved the training process and consequently, the performance of Deep Learning algorithms. Popular architectures in Deep Learning include Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN), with the latter being the driving force behind Image Recognition. DNN architecture is set up so that layers are multi-connected, and every layer only receives a connection from previous layers and provides connections only to the next layer in the hidden section (Kowsari et al. 2019). Figure 11.10 provides a graphical representation of the structure of a standard DNN.

Big Data & AI Transforming Education Big Data & AI in Unison As noted, the dramatic advancement of AI technologies in recent years (Lu et al. 2018), is linked to three key developments. These include the constant improvement of computer processors and storage capacity

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Fig. 11.10 Standard, fully connected DNN (Kowsari et al. 2019)

(Drigas and Ioannidou 2012), advances in algorithmic approaches, and the mass availability and accumulation of Big Data, which as Holmes et al. (2019) observe has pushed AI into a period of renaissance. The tremendous amount of new data, data generation rates, and data complexity, make analysis by traditional algorithms and architectures challenging (Kersting and Meyer 2018). This accumulation of Big Data however, which as Duan et al. (2019) note would otherwise be of lesser significance to humans due to their inability to analyse and process them efficiently, enables AI to derive meaning and “make sense” of them through the utilisation of AI techniques (Guptaa et al. 2018). AI “thrives” on data, using it to become more accurate and reach its full potential (Pedro et al. 2019). As Surya (2015) states, AI’s capability to synthesise data and “learn” from it, makes AI and Big Data appear inseparable and “inextricably entwined”, allowing for risk control, better

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AI enabling key attributes of Big Data (O’Leary 2013)

Big Data Attribute

Contribution to Artificial Intelligence

Volume

Pattern recognition, learning, and delegation of difficult tasks to computer-based approaches Rapid computer-based decisions further enhancing decision-making in any organisational or societal context Capturing, structuring, and understanding of unstructured data sets

Velocity

Variety

utilisation of resources and enhanced decision making for the benefit of businesses and society (Kulakli and Osmanaj 2020) (Table 11.1). Specifically, regarding Artificial Intelligence in Education (AIED), large data sets from learning systems and educational contexts have incurred a rapid growth in Learning Analytics (Ocaña-Fernández et al. 2019), providing a data-oriented perspective and useful domain knowledge. Big Data has become a critical part of Intelligent Tutoring Systems (ITS), Learning Management Systems (LMS), Massive Online Open Course platforms, Computer-Assisted Learning (CAL) and game-based learning systems among others (Hwang et al. 2020). As Luckin et al. (2016) highlight, applications already in use in educational institutions incorporate AI and Educational Data Mining (EDM) to analyse student behaviour and support students, whereas many contemporary ITS use Machine Learning, ANN and self-training algorithms based on Big Data, to derive useful knowledge for tutors and learners. Additionally, the combination of AIED with sensors and the Internet of Things (IoT) has provided new ways to support learning and “leverage the big data from rich interactions with smart technologies to produce value for the teacher and classroom” (Timms 2016).

Applications of Artificial Intelligence in Education As Chassignol et al. (2018) mention, the emergence of ITS dates to the 1970s, with Jaime Carbonell’s assumption that computers can act as tutors apart from being tools. AIED has since evolved dramatically,

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Fig. 11.11 The roles of AIED (Hwang et al. 2020)

with the ultimate goal of making computers that can solve problems and achieve as well as humans (McCarthy 2004) being close to realisation. In fact, as LeCun et al. (1998) observe, the performance of intelligent systems will constantly improve due to the continuous advancements in computational power, training data, and our understanding of learning algorithms (Fig. 11.11). Hwang et al. (2020) note that the evolution of AIED introduced uses beyond intelligent tutoring, including intelligent tutees (systems that learn from interacting with humans, such as AI chat bots), intelligent learning tools (such as digital assistants) and policy-making advisors (techniques that help understand trends in educational contexts and guide the development of policies and laws). An inclusive range of AIED applications encountered in modern literature can be seen on Table 11.2. Additionally, the contribution of EDM and Learning Analytics in AIED is undeniable. These utilise statistics, Machine Learning and Data Mining to analyse tutor and learner data, detail learning habits, predict responses and student requirements for support, provide feedback and improve policy-making and curriculums (Pedro et al. 2019; Woolf et al. 2013).

Sample ITS Architecture Luckin et al. (2016) emphasise that knowledge about the world is represented in “models”, observing three distinct models at the core of AIED systems: the Pedagogical model , representing teaching methods, feedback

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Educational scenarios and applications of AIED

Educational scenarios

AI applications

Teaching and learning support

ITS educational cobots AI-powered teaching assistants AI-powered LMS AI-based chatbots digital assistants for lifelong learning game-based learning systems AI-supported reading multi-lingual analytics record of lifelong learning achievements educational data mining learning Analytics computer-assisted testing online proctoring systems automated writing evaluation image recognition/computer vision continuous assessment tools virtual reality (VR) augmented Reality (AR) face Recognition speech Recognition natural language processing collaborative learning tools IoT cognitive ability simulation discussion forum monitoring educational data mining learning analytics

Evaluation/Assessment

Smart classroom

Policy-making support Curriculum development

and assessment, the Learner model , representing “knowledge, difficulties and misconceptions” of individual students (Bull 2004) and the Domain model , representing what is being taught. A typical ITS architecture encompassing these models is presented in Fig. 11.12, including the Open Learner model , which as Bull (2004) analyses implies the openness and transparency of the Learner model to students and stakeholders.

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Fig. 11.12 Model-based ITS architecture (Holmes et al. 2019; Luckin et al. 2016)

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Evaluation of AIED The growing establishment of AIED is inevitably linked to major educational, technological and sociological impact, entailing numerous benefits as well as various challenges for tutors and students alike. In this section, an attempt is made to analyse the merits and flaws of an educational ecosystem facing transformation through the application of AI, Big Data and accompanying technologies.

AI Benefits to Education Business Model AIED encompasses undeniable benefits for all stakeholders in the educational system, driving research and development and guiding its rapid establishment (Fig. 11.13). • Enhancing Decision Making As Almohammadi et al. (2017a) note, AI can “manage the inherent uncertainty” in human decision-making. This can assist tutors by supporting decisions and data-driven work (Chen et al. 2020) while creating the potential for predictive decision-making (Pedro et al. 2019). Constant technological improvements also lead to near real-time analysis of inmemory data and large databases, speeding up decision-making (O’Leary 2013). It is important to note, however, that as Duan et al. (2019) highlight, AI performs well in structured and semi-structured decisions to the point of replacing human decision-makers, but should only be used as a support tool for unstructured decisions at strategic organisational levels (Edwards et al. 2000). • Reducing Admin Tasks Effective and targeted feedback, considered one of the most important responsibilities of tutors, can become a difficult task in large classrooms (Chassignol et al. 2018). AI can speed up time-consuming administrative

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Fig. 11.13 Benefits of AIED

tasks like reviewing student work and providing feedback (Chen et al. 2020), while grading tests and homework can be reduced to a simple task for AI assessment tools (Pedro et al. 2019). This potential, paired with the ability to provide bespoke and direct support whenever required, can reduce the number of administrative staff required to perform these functions (Popenici and Kerr 2017; Chen et al. 2020). Furthermore, as Luckin et al. (2016) highlight, freeing time off lengthy routine tasks allows tutors to “devote more of their energies to the creative and very human acts that provide the ingenuity and empathy needed to take learning to the next level ”. The effect of AIED on time

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management and the use of virtual teaching assistants can also enable the creation of a better professional environment for student guidance, personalised communication, and focus on students with learning difficulties (Pedro et al. 2019). Furthermore, AI can support teacher collaboration by allowing them to quickly find and share quality teaching resources (Luckin et al. 2016). • Improving Evaluation and Drop-Out Rates As Luan et al. (2020) observe, Big Data in combination with Learning Analytics and Machine Learning demonstrate high accuracy in predicting academic performance (Huang et al. 2020). This can help discover learning difficulties and address them early, as well as predict the possibility of dropping out and thus enhance student retention rates by generating early warnings (Chen et al. 2020). AIED can also help develop indicators of progress on multiple skills and capabilities, providing tutors with fine-grained, non-intrusive, and continuous built-in evaluation, concurrent to the learning process (Luckin et al. 2016). • Enhancing Performance and Self-Learning One of the most promising attributes of AIED, is the potential to enhance student performance and guide self-learning. As Ma (2017) discusses, the use of ITS has been shown to result in greater learning and achievement compared to other forms of tutoring, with the exception of small-group or one-to-one tutoring which is ideal but usually not feasible (Luckin et al. 2016). In fact, Schiff (2021) points out that ITS are quickly converging on the effectiveness of human tutors; multiple studies demonstrate at least equal results in driving student learning gains (VanLehn 2011), with students taught by ITS even outperforming human tutors in the vast majority of studies (Kulik and Fletcher 2016). Luckin et al. (2016) note that ITS can put the learner in control of their learning, assist in developing self-regulation and “scaffold” learning (bridge learning gaps and gradually reduce external aid). They can engage students in Socratic learning experiences involving dialog, discussion,

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and Q&A, and increase motivation and engagement. Especially in the context of collaborative learning, AI can aid student groups by closing social interaction skill gaps which are further exacerbated in online learning by the fact that participants rarely or never meet in person. In addition, AIED analysis can support tutors with information on student mental states by indicating confusion, boredom or frustration, helping them enhance student “emotional readiness” for learning. AIED can also help with career decisions by predicting career paths with the use of Big Data (Chen et al. 2020), while digital assistants can support life-long learning both inside and outside classrooms (Luckin et al. 2016; Woolf et al. 2013). • Customising Curriculums Chen et al. (2020) highlight that pattern discovery and predictive modelling aspects of EDM can extract hidden knowledge, enabling tutors to adjust, and improve curriculums. Luan et al. (2020) further note that this potential has been analysed in numerous studies attempting to enhance course planning and curriculum development, among tutor evaluation and support (Zawacki-Richter et al. 2019; Quadir et al. 2020). In addendum, the development of improved AI applications paves the way for new and versatile curricula, susceptible to accelerated adaptation (Ocaña-Fernández et al. 2019). • Personalising Tutoring Perhaps the strongest benefit of AIED, is the ability to provide personalised learning, adapt to differing rates of progress (Baker 2000), and classify students based on their unique learning style (Bajaj and Vidushi 2018). As Chen et al. (2020) state, Learning Analytics and AI-chatbots can be used to assess each student’s needs, level of understanding, and abilities, and tailor teaching methods. AI with the power of EDM can enable students to learn at their own pace based on their interests, and tutors to adjust teaching methods and learning plans based on each student. Personalisation can also extend beyond student ability and

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performance, to personal characteristics and behaviour patterns (Ciolacu et al. 2018; Luan et al. 2020). AI-enabled bespoke learning can help map strengths, weaknesses, and individual trajectories (Pedro et al. 2019), while considering differing goals and backgrounds to devise custom learning paths (Roll and Wylie 2016). Educational cobots can monitor learners through their use of Intelligent Learning Environments and flag those who need extra help, while keeping everyone engaged (Timms 2016). In total, as noted by Luckin et al. (2016), AIED can offer “flexible, inclusive and engaging ” learning, aiding students and tutors alike, with the example of an assistant robot tasked by the tutor to provide one-on-one tutoring to a subgroup of students lagging behind with a particular topic. This “unprecedented qualitative improvement ” at all levels of education allows to accurately adapt to diverse student needs, with support mechanisms available instantly and ad-hoc, regardless of the student’s time and space (Ocaña-Fernández et al. 2019). As Woolf et al. (2013) mention, this aspect of AIED, with the power of Machine Learning and Data Mining, also encompasses the potential for a “mentor for every learner ”. • Building Tutor Skills AIED generates the need for trained professionals who understand and shape the technological environment according to educational needs (Ocaña-Fernández et al. 2019). As Pedro et al. (2019) mention, this means that tutors should be taught skills to manage digital technologies and support students in their creative use, according to teacher Competency Frameworks (UNESCO 2011, 2019), to produce AI-enabled tutors. Ensuring the acquisition of these skills could be viewed as a challenge until tutor AI competencies are fully implemented, but once integrated they will ensure tutors are equipped with the knowledge and tools needed to lead a technologically transformed education. As noted by Roll and Wylie (2016), the teacher role is ultimately transforming from an all-knowing “sage on the stage”, to a “guide on the side” (King 1993), and to achieve this, teachers need to be able to become active collaborators in the AIED environment.

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• Providing Equal Opportunities An undeniable merit of AIED is inclusivity and the provision of equal opportunities. As Luckin et al. (2016) underline, AIED can fight illiteracy, address teacher shortages, and assist in countering the fact that students from rich backgrounds outperform students from poorer backgrounds, with direct benefits for developing countries. AIED aids lowincome or limited education parents, traditionally unable to support students learning at home, and combats social inequalities that are exacerbated by unequal access to new resources. As Pedro et al. (2019) state, AI can also ensure “equitable and inclusive access to education”, by providing equal learning opportunities to people with disabilities or special needs, marginalised communities, refugees, and those who cannot attend school. Continuity of education can be assured in periods of crisis or emergency, via AIED’s attributes of ubiquitous access. It is important to note however that to achieve this, AI needs to be designed in an “ethical, non-discriminatory, equitable, transparent and auditable manner ” (UNESCO 2019). This observation prefaces the need for a parallel analysis of the multiple challenges introduced by AIED, alongside the aforementioned benefits.

Challenges Apart from the benefits of AIED, multiple challenges that cannot be ignored are encountered and should be considered, in the context of AI adoption (Fig. 11.14). • Varying Student Backgrounds Foremost on the list of AIED challenges, comes the fact that no two students is the same in terms of background, environment and understanding. This further underlines the importance of Personalised Tutoring and Equal Opportunities discussed earlier, which should be regarded as a top priority for AI-enabled tutoring to function efficiently. Almohammadi et al. (2017b) observe that in e-learning environments, students could “interpret the same terms, words or methods differently

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Fig. 11.14 Challenges of AIED

depending on their background and their experiences, motivation, knowledge and future plans”. Consideration of different progress rates, strengths, and needs is also especially important for students with disabilities or learning difficulties, as noted in the Special Educational Needs (SEN) Code of Practice (Drigas and Ioannidou 2012). Adding to the issue’s complexity, different national and organisational cultures or personal and religious values can influence the adoption of AI (Duan et al. 2019). This shows that interactive learning environments should be embedded in cultural norms, as Roll and Wylie (2016) point

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out, especially considering the fact that most AIED research originates in developed countries, with least developed countries lagging behind. This results in “privileged researchers addressing privileged problems”, with some cultures and countries left out. As Ocaña-Fernández et al. (2019) also mention, many citizens of the “world village” are at disadvantage, unaware of the risks and possible effects of the emerging transformation in education. Schiff (2021) observes that cultural bias in developing curricula also extends to the point of national interests conflicting with individual or local interests, within a nation. This conflict can be magnified when lobbies are involved in AIED implementations, resulting in mass standardisation instead of individualisation. In summary, access to AI should promote equal high-quality education and learning opportunities regardless of cultural background, socioeconomic status, religion, gender, age, or disability, in order to avoid polarisation, bias or deepening the digital divide (UNESCO 2019). This is the only way to realise the promise of “AI for all ” and take advantage of the unfolding technological revolution (UNESCO 2021). • Black-box Effect Another major challenge in AIED is the fact that it will not be fully embedded unless its decisions are “readily comprehensible by teachers and other social actors in the educational system” (Baker 2000). The need for comprehension of processes however is not feasible in most AI techniques (such as Bayesian Networks, Neural Networks and Hidden Markov Models), where transparency and external observability is low by default, thus creating a “black-box” effect (Timms 2016) (Almohammadi et al. 2017a, 2017b; Davenport and Ronanki 2018; Duan et al. 2019). This black-box effect observed in many areas of AI, can benefit applications such as image recognition, as Perrotta and Selwyn (2020) discuss; in educational constructs, however, it can lead to “ignoring domain knowledge and structure in favour of massive data sets” (Khajah et al. 2016). Additionally, this lack of understanding of the causality between input data and final results can lead to issues of trust (Canales et al. 2020) or even regarding AI as unjust (Holmes et al. 2019).

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• Training AI For AI and Machine Learning to be effective, AI models need to be trained using training data sets (e.g. student interaction Big Data). Training DNNs however can be difficult and time-consuming (Nielsen 2015), and especially under the fluid and ever-changing context of elearning processes (Almohammadi et al. 2017a) this could lead to a constant need for re-training. The issue of inflexible AI training also introduces the “Frame Problem” (Lu et al. 2018), where AI is limited to a single type of problem and does not reflect the multidimensionality of the real world. A multi-frame AI, apart from faster training would also need nearly instant coordination of AI subsystems and data access processes, more likely to become feasible alongside future technological advances such as quantum computing. • Data Privacy and Regulation An important issue in AIED adoption, is also the need for revised educational policies and regulatory frameworks, addressing issues such as the ethical and transparent use of learner data (Pedro et al. 2019) (UNESCO 2019, 2021). The Big Data required to train AI raises concerns on student data privacy and security (Iafrate 2018) (Holmes et al. 2019), and virtually no legal background exists in terms of defining who is responsible for data misuse or other harm caused by AI decisions (Scherer 2015). Consequently, there is an imperative need for well-defined data stewardship (Canales et al. 2020), translating to data governance, protection, and access control, ensuring privacy and regulatory practices. Undoubtedly, an “optimal balance” between data collection and data protection should be researched by governments, in terms of AI policymaking (Luan et al. 2020). Ultimately, only a successful production of adequate local and global regulation mechanisms, protecting the population from bad practices, can enable AI to “become the catalyst for the most fertile changes in human history” (Ocaña-Fernández et al. 2019). Figure 11.15 further demonstrates regulatory problems of AI, as discussed by Scherer (2015).

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Fig. 11.15 The regulatory problems of AI (Scherer 2015)

• Replacing Human Resources Another concerning effect of the establishment of AIED, is the substitution of human tutors and pedagogists under the context of reducing costs (Chassignol et al. 2018). As noted by Luckin et al. (2016), this is viewed as one of the most important social challenges introduced by AI across various sectors, and generates responsibility for tutors to be involved and ensure that AIED delivers “the support that educators need – not the support that technologists or designers think they need ”. Although it is arguable that tutors are relatively “insulated” by the fact that social and emotional skills are hard to automate, the rapid technological advancement rates of the twenty-first century make the limits of AI impossible to predict (Schiff 2021). As Popenici and Kerr (2017) suggest, it is important for humans to identify problems and risks, question issues of privacy and control, and highlight the importance of creativity, serendipity, and unexpected paths in education. Furthermore, Ulloa-Cazarez (2020) emphasises that society should strive to implement “robot-proofing ” competencies, by taking advantage of human creativity and guarantying technological literacy and humanitarian values for all

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employees, in order for them to stay relevant in the face of accelerated automation. To this end UNESCO (2019) calls for policy makers and implementers to ensure that AI remains “human-controlled, centered on people (…) and in the service of people” and that it does not replace teachers with machines. • Interpersonal Skills The inclusion of interpersonal skills remains a challenge for AI-based curricula. This encompasses arts, literature, and other non-STEM subjects that introduce great complexity in assessment and evaluating competencies and learning outcomes (Chen et al. 2020). As Popenici and Kerr (2017) note, the detection of irony, sarcasm, or humour is usually reduced to algorithmic solutions counting punctuation marks, capitalisation, or key phrases (Tsur et al. 2010) and the real meaning behind words can be easily eluded. Schiff (2021) underlines that mathematics topics that rely on equations, symbols, and distinct rules, are easier to model computationally than history or poetry which demand reflection or criticism. Deriving meaning and context in these cases are far more complicated, posing a threat of biased development of AIED towards STEM content (Roll and Wylie 2016). Furthermore, the majority of cognitive state analysis methods only measure student attention, ignoring emotion and its role in learning (Xu et al. 2020). • Human Bias AI algorithms are currently built by humans. Consequently, human bias in development and testing is integrated into their processes (Canales et al. 2020). Additionally, the fact that AI training sets rely on data generated from human interactions, can lead to reinforcing this bias and perpetuating false data (Thomas 2018). An example of this is the Microsoft Tay chatbot, which replicated racial slurs it encountered online while training itself and was discontinued as a result (Hwang et al. 2020). This algorithmic bias might lead to AIED favouring or discriminating against groups based on gender, race, or religion, partly due to

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the inelastic nature of training sets derived from small subsets of users and not the actual population (Schiff 2021). • Implementation Barriers As with any new system considered for implementation, AIED faces the barrier of integration with existing systems and processes in education, potentially incurring significant costs (Davenport and Ronanki 2018). The integration of AIED also entails numerous other implementation barriers, such as the need for funding, teacher training in the use of AI, ongoing development and structural changes in schools and educational institutions (Schiff 2021). This multidimensional implementation landscape shows that the success of AIED does not only rely on the effectiveness and value of technology, but also on the preparedness of the educational system to accept it, an endeavour that can easily surpass the complexity of defining the AI system itself. • Commercialisation The involvement of “for-profit ” organisations in the development and establishment of AIED might encourage adoption, but ignore some of the aforementioned challenges or the ethical aspects of integration (Perrotta and Selwyn 2020). As highlighted by Schiff (2021), “as AIED moves out of the laboratory and into classrooms, corporate developers and large purchasers will have an increasing say”. This outcome could result in favouring profitability and labour-saving versions of AIED, disregarding the consensus of educators and researchers. As Luan et al. (2020) note, it is important to form a sustained partnership between the industry and academia, based on mutual benefit, with the active involvement of governments. This will lead to smart technologies that adapt to intended learning outcomes instead of what is “generally liked ” (Luckin et al. 2016) and profitable, focusing on pedagogical reasoning and humanistic perspectives instead of “productivityoriented solutionism” (Popenici and Kerr 2017).

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• Data Hygiene As discussed, Big Data enables AI and Machine Learning, but also raises various challenges, such as “data hygiene” and the cleanliness of data used for training sets (Perrota and Selwyn 2020). As O’Leary (2013) discusses, Big Data can also include “dirty data”, defined by the existence of errors, incompleteness, and imprecision. This imposes a need for data cleansing by AI or other algorithms in order to establish accurate context knowledge. Additionally, the heterogeneity and large volumes of data used by AI create concerns about their quality, stability, or consistency, resulting in the need for data “harmonisation” prior to usage (Kedra and Gossec 2020). The volume and variety of Big Data also calls for fast and efficient retrieval and management processes (Kulakli and Osmanaj 2020), with additional admin and technological overhead.

The Role of IoT As Ciolacu et al. (2018) observe, the AI-assisted “Education 4.0 ” can enhance Learning Analytics with multi-modal data via input from smart sensors and wearable devices. This sensor data allows for contextaware systems and new kinds of interactions between learners and their environment (Roll and Wylie 2016). Timms (2016) notes that the implementation of “smart classrooms” can result in higher cognitive abilities for AI, based on sensorimotor and proprioceptive (spatial positioning) sensor data. This data can allow educational robots to read and adjust to student psychological states, but also to point and gesture, assume “facial expressions” and enhance interaction with students, resulting in higher acceptance and trust in intelligent technologies. As Timms (2016) further states, it is evident by neuroscience research (Krach et al. 2008) that attention span, interest, and student engagement increases dramatically when ITS assume human-like form, and even further when these robotic instructors assume human-like characteristics like facial cues and personality

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traits. This assimilation is shown to be especially effective amongst younger students. Argumentatively, the integration of IoT in AIED can greatly enhance its effectiveness. It is also important to highlight, however, that the use of sensors and wearable devices to detect emotion or attention should be non-invasive and compliant with student privacy (Ciolacu et al. 2018).

Massive Online Open Courses Massive Online Open Courses (MOOCs) became known in 2008 and became popular rapidly (Almohammadi et al. 2017a). They are onlinedelivered courses, usually focusing on a specific subject and targeting a very large number of students at once (Ocaña-Fernández et al. 2019). MOOCs have various advantages including their global reach, flexibility, and provision of both synchronous and asynchronous learning, opportunities for career changes and lifelong learning, and the collection of useful research data (Pedro et al. 2019). They are also usually free without entry requirements, resulting in massive enrolment numbers (Popenici and Kerr 2017). MOOCs however have significant disadvantages, including huge drop-out rates (Woolf et al. 2013; Ruhalahti 2019; Schiff 2021), lack of personalisation, differentiation, and individualisation compared to AIED (Woolf et al. 2013; Schiff 2021) and evidence that shows success mainly among learners with high levels of knowledge and motivation (Woolf et al. 2013). Schiff (2021) also observes that MOOC participants generally tend to be well-educated and from wealthier countries (Christensen et al. 2013; Rivard 2013) countering the supposed benefit of global reach for less developed areas where access to education is most needed. Additionally, students in MOOCs cannot be encouraged and motivated at a level comparable to AIED, with little to offer in terms of socio-emotional engagement and support. In total, Schiff (2021) continues, students do not seem prepared “to abandon the traditional degree and educational experience over a series of online videos”.

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To summarise, MOOCs offer the invaluable merit of free education, but lack in terms of results, engagement, and personalisation. The potential of AI to empower their effectiveness definitely exists, remaining yet to be properly implemented.

AI Establishing Itself Expansion of AI in Education and Other Industries The use of AI has accelerated in a variety of businesses. According to Bughin et al. (2017), a survey conducted by McKinsey reveals that 47% of questioned enterprises integrated at least one AI feature into their operations, with industries like telecommunications, financial firms, and tech firms being pioneers of AI functionalities. In comparison to these businesses, educational institutions are slowly but steadily adopting AI. The relevant applications of AIED and other industries are yet to experience their greatest expansion. In recent times, educational institutions have adopted AI to automate service and task delivery and provide efficiency. Machine Learning in particular is projected to advance at a faster pace in the process of adopting AI into the education industry (Hall and Pesenti 2019). As discussed, this is due to the growing demand for fully automated grading systems and for personalised assignments targeted to individual students based on their strengths and weaknesses. According to a report presented by Guan et al. (2020) (Stefan and Kerr 2017), the increased emphasis on conversational learning is a prominent trend in AI in the education sector. Dhawan (2020) also establishes that interactive learning has become a critical part of education around the world, as the focus on digital content and online learning portals has increased. The surge in demand for AI-supported individualised education solutions is a major factor driving the global growth of AI in the education market (psmarketresearch 2020). Organisations such as Carnegie Learning Inc., Blackboard Inc., Jenzabar Inc., and Cognii Inc. are major frontiers championing the cause of AIED adoption (Intrado 2020).

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AI Industry Market Share and Estimated Growth The international educational market in the context of AI adaptation and other industries is extremely fragmented, with a significant number of global and regional participants. To expand their market share, major firms such as Carnegie Learning Inc, Microsoft Corporation, IBM Corporation, Pearson plc, Google LLC, and Nuance Communications Inc are focusing on product launches and collaborations (Patrick 2021). Due to corporate offices, schools, and education institutions increasingly focusing on exploiting e-learning portals in their settings, based on this, competition in the global AI in the education market is gradually increasing (Fig. 11.16). Considering the AIED market in 2019, learning platforms produced the most income as a service application (psmarketresearch 2020). This can be attributed to the increased desire for online and distance education courses, the growing trend of personalised learning via online portals, as well as a growing emphasis on the incorporation of nextgeneration technology in education (Teräs et al. 2020). A report presented by IDC, indicates that global revenues for the AI market, comprising of Software, Services, and Hardware, are estimated to reach a growth percentage of 16.4 within a year to $327.5 billion in 2021 (IDC 2021). Based on a comprehensive five-year annual growth rate (CAGR) of 17.5 per cent and total revenues gain of $554.3 billion, the market

Fig. 11.16 2020–2024 growth rates of (Left) AI Software Platforms/(Right) AI Application Development and Deployment

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Fig. 11.17 Global Business AI and IT AI service growth projection (x-axis: Billion $)

share is estimated to surpass $500 billion by 2024. Data gathered by psmarketresearch (2020), depict a 13% annual revenue growth rate in 2020 for the Services category of AI. AI Services are expected to reach 17.4% by 2021, outpacing the entire AI market. Revenues are predicted to reach $37.9 billion by 2024, with a projected five-year CAGR of 18.4 per cent (Fig. 11.17). Business and IT AI services account for nearly 80% of the total revenue generated by AI-enabled services. With 2024 projected to be exceptional, IT-oriented AI Services are expected to outperform both Business Services for AI and the entire AI Services market. Even though the COVID-19 pandemic slowed the global AI services market’s revenue increase, enterprise demand for AI capabilities to enhance business resiliency and boost human productivity grew by double digits in 2020, despite delays in other discretionary projects, according to IDC (2021).

The Exponential Growth of Data The amount of data collected and distributed by all agencies and industries have skyrocketed, affecting the decision-making process of organisation, service delivery, product development, and infrastructure

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acquisition. For many private and commercial organisations, AI, Big Data, and Machine Learning are becoming standard operating procedures. This is due to the ongoing increase of acquired data, particularly from products deployed in IoT ecosystems, the development of tools to manage and analyse them, and the greater understanding of potential business benefits (Fig. 11.18). The global Big Data market is expected to grow to $103 billion by 2027, more than doubling its current size with a 45% share (Mlitz 2021). Reflecting on the popularity of Big Data solutions, it is of no doubt that the technology possesses tremendous growth potential. A new technological approach is required to use edge devices, in computation, centralised storage and analysis, and Deep Learning approaches to expedite data processing at scale. Big Data analytics solutions, once again, show no signs of slowing down. By 2023, the market is predicted to reach $40.6 billion in revenue. Furthermore, technology will have a major impact on the global economy, with the industrial internet increasing global GDP by $10 to $15 trillion by 2030 (Evans and Annunziata 2012). Machinegenerated data is now growing at a rate ten times faster than traditional corporate data, with computer data growing at a 50-fold quicker rate than human data. Nevertheless, the general consensus is that total data volume will at least double every two years, with a 50-fold increase from 2010 to 2020 already being observed (Fig. 11.19).

Fig. 11.18 Global revenue from Big Data and Business Analytics, 2015 to 2022 (y-axis: Billion $)

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Fig. 11.19 Estimated Volume of data captured, captured, consumed, and created globally from 2010 to 2024 (x-axis: data volume in Zettabyte)

Conclusion Artificial Intelligence has been researched and applied for decades, but the recent evolution of computational resources and processing speeds, Machine Learning and Deep Neural Networks, have enabled it to reach new potentials. These advancements allow for the efficient analysis and utilisation of Big Data and vast amounts of information, to quickly derive patterns and draw meaningful conclusions, leading to a new world of applications. Harnessing this potential to empower Education has undeniable merits, and the eventual integration of AI in educational systems can be considered unavoidable. It is of paramount importance, however, to realise this progress towards AIED under a carefully examined framework, protecting sensitive data, clearly defining motives and means, and highlighting inclusion and the importance of human resources. Essentially, only a collaborative effort between both educators, regulators, and developers, will provide the greatest benefit for future generations and pave the road to the successful integration of AI in future Education.

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12 Digital Trends in Education: Marketing of the Online Teaching Trevor Gerhardtl, Anu Laitakari, Michael Rice, and Chandra Bhasham

Introduction During 2020, many universities scrambled to get their teaching online responding to the Covid-19 global pandemic (Cohen 2020). This was new for many Higher Educational Institutions (HEIs), their academic staff and students. Insightfully, Gerhardt (2020, p. 360) in a chapter on digital trends in Educational operations and marketing commented that T. Gerhardtl (B) University of London Birkbeck, London, UK e-mail: [email protected] A. Laitakari Kaplan Open Learning, Essex, UK M. Rice Pearson Business College in London, London, UK C. Bhasham University of West London, London, UK

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_12

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Information Communication Technology (ICT) was becoming increasingly mainstream requiring HEIs to be ‘e-ready’, including people, processes and technology. Not too soon afterwards, all HEIs were forced to be ‘e-ready’ due to the pandemic. This could be viewed from an innovative perspective as technology push or market pull as ‘innovations do not just come from scientific research but can be pulled by users in the external market’ (Johnson et al. 2014, p. 297). The online teaching mode was and is still new and requires an upskill and, in some cases, reskill by the academic sector (Sia and Abbas Adamu 2021). For example, in India many Higher Educational Institutions (HEIs) were not well equipped with institutionally supported technologies such as Moodle, Blackboard, Microsoft Teams and Zoom; and so, made it obligatory for teachers to use open-source online teaching platforms such as WhatsApp, YouTube, Skype, and Google hangout to deliver their online sessions (Joshi et al. 2020). This emergency-education mode—called triage or band-aid teaching by faculty forced teaching online suddenly (Deshmukh 2021), also called ‘crisis distance education’, ‘emergency remote teaching’ or ‘transitional emergency model’ (Varma and Jafri 2021). Most studies have addressed the problems faced by students during this process, ignoring the problems faced by teachers (Joshi et al. 2020) and the educational sector overall. This chapter is an exploratory investigation of university teachers’ experiences of teaching online (Sikes 2017) considering the following questions: • What digital trends emerged and what this may mean for future technological educational innovation i.e., what will be marketed? • Will online education remain as a new business model in HEIs i.e., will blended learning for example become the new educational marketing product and will this create strategic differentiation across the sector for institutions and staff? • What opportunities and challenges will this raise for academic staff if any, and will this similarly create new marketing products and services? The crisis requires a paradigm shift from content delivery to providing an interactive and engaging learning experience (Shearer et al. 2020; Gerhardt 2020; Rumaksari 2021). In this transition, teachers should rely

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on past experiences and their professional expertise to reconceptualize what learning might ‘look like’ in an online class context (Itow 2020). Prior to the pandemic, online education was an alternative competitor to traditional education although some institutions already had some form of e-learning. A survey of 82 institutions in India for example revealed that about 34% of them were already following online teaching in some form (Varma and Jafri 2021). The key benefits of online education include its reach to the masses with cost-effectiveness, anytime, anywhere access, that helps development in any phase of one’s life, recognition by employers for the certificates earned, combatting the rising costs of physically attending educational institutions and providing highest quality education from the best faculty (Pillai and Sivathanu 2020). This technological educational innovation of course means new products in existing markets, access to new markets with existing products using technology as highlighted by Gerhardt (2020) as a new trend and new products in new markets. Therefore, innovators potentially are creating whole new business models (Johnson et al. 2014). The transition of traditional institutions to these forms or modes of online innovative educational models, however, were resisted by some faculty due to a lack of time, technical competence and lack of technological support, computer self-efficacy, and because some course content could not be translated to an online format (Joshi et al. 2020) i.e., a lack of ‘e-readiness’ (King et al. 2016). Research by Rumaksari (2021) in Indonesia in schools revealed similarly that educators and the education sector were ill-equipped for the drastic shift to online teaching caused by Covid. Educators were reluctant to be content creators. Mehta and Aguilera (2020) concerningly recognize that a transition towards more technologically educational innovative blended and online contexts of teaching and learning, means institutions may be running the risk of reproducing, if not exacerbating, the educational inequities already structured into face-to-face contexts. The transition from traditional teaching to online modes is complex and must take into account students’ and lecturers’ digital literacy skills, abilities, disabilities as well as hardware and internet connection speeds. This may have an impact on the expectation that many EdTech unicorns may emerge. Rumaksari (2021) concurs, stating that although EdTech such as MOOC will

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create higher learning outcomes overall, governments readiness to implement such changes equitably are debatable. The key concepts will now be explored.

Defining the Concepts Teaching can take place in a real time, face-to-face format (synchronous) or at any time convenient to students using online resources (asynchronous), with blended learning using aspects of both. This research is about teaching modes and technological educational innovation that include any aspect of asynchronous activities. Terms used within this field of interest include Remote Leaning (RL) (Deshmukh 2021), Online Learning (OL) (Zuhairi et al. 2020), Distance Education or/and Online Learning (ODL) (Shearer et al. 2020), online-merge-offline (OMO) and the combination of online and offline (OnO) (Xiao et al. 2019), also known as Blended Learning (BL). Blended Learning (BL) is the most contemporary approach in education today, wherein multiple pedagogical tools are used, a practical approach to engage a classroom experientially with a large number of students, thus enhancing learning outside the traditional teaching environment (Nayar and Koul 2020). This provides an innovative educational opportunity using digital platforms to offer new educational products to new and existing markets. Asynchronous discussion boards have been used in online education for decades as they allow students to learn collaboratively and interactively (Li and Yu 2020). A learning environment of open education in the context of Web 2.0 is not only an OnO platform that expands access to all sorts of resources from offline to online (and vice versa) but also an interactive environment blurring the boundary between producers (e.g., traditional teachers), and consumers (e.g., traditional students) of content (Xiao et al. 2019). The introduction of online and distance learning (ODL) has created the possibility of flexible and boundaryless campuses and classrooms and has revolutionized the modes of learning by dissolving the canvas of site-bounded teaching–learning into open, flexible, and collaborating spaces (Amin and Mirza 2020). Below in Fig. 12.1 is a visualization of what Web 1.0 to Web 5.0 may look like:

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Web 0.0 to Web 5.0 (www.trendONE.de)

Small group lectures and discussion-focused seminars can be synchronous when well supported by various virtual platforms with the capacity for full-group exchange and breakout sessions such as Blackboard Collaborate. Conversely, larger lectures—particularly foundational courses relying on knowledge transfer without discussion—may be better suited to asynchronous learning via pre-recorded lectures using for example Panopto and virtual office hours for one-on-one clarifications (Deshmukh 2021). This Blended Learning approach was the common mode among most of the authors of this paper (see data discussion section).

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There is a very pivotal distinction that must be noted, the virtual experience attempted to recreate the classroom online, but not the campus (Deshmukh 2021). The latter will be further discussed later regarding the impact upon the building of an online learning community. As to pedagogy, Blended Learning (e.g., flipped classroom activities, synchronous online teaching, exploratory learning, personalized learning, and experiential teaching) can be greatly supported in the classroom by innovative educational technology and can allow teachers to use different teaching modes according to their course features and objectives set for students (Xiao et al. 2019). Due to new technologies making online learning possible, it seems to have converged the boundaries between distance and face-to-face learning modes to provide more flexible learning environments (Zuhairi et al. 2020). These innovative educational technologies are also referred to as EdTech and has seen the rise of EdTech unicorns. ‘As of 2 Nov 2021, there are now 32 EdTech Unicorns around the world who have collectively raised over $21B of total funding in the last decade and are now collectively valued at $95B+’ (HolonIQ, no date). Educational technology is this century’s driver of innovation, digitalizing the didactic process (Baggio 2019). As such, Baggio (2019, p. 7) states writing from a Tel Aviv University perspective, ‘introducing EdTech solutions in the classroom environment denotes a radical revolution in the teaching–learning environment.’

A Review of the Literature and Previous Research The historical and current reading of online distance initiatives suggest an investigation is complex in the pedagogy of online distance education as a product of the interplay among instructional design, technological tools, and learner/instructor characteristics, tailoring the design of the online experiences to the needs and demands of learners (Shearer et al. 2020). Market forces also disrupted education. Corporate universities emerged decades ago already, dissatisfied with unprepared graduates for the modern workplace, with Motorola University being one of the

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earliest and most extensive, established in 1979 with 100 sites in 24 countries (Shaw 2005). More recently, Google announced the next steps in their plan to disrupt the world of education, including the launch of new certificate programs, designed to help people bridge any skills gap and get qualifications in high-paying, high-growth job fields with no college degree necessary (Bariso 2021). The challenge and disruption in the ODL environment is not limited to providing technology infrastructure but also providing mentoring and guidance to use the digital resources for interaction with teachers and peers anywhere and anytime (Amin and Mirza 2020; King et al. 2016) and, common to disruptive innovators, are resources and capital (Johnson et al. 2014). This section will therefore first explain the key theories and models and then explore their impact on the institution, the teacher, and the student. Taking all of this into account, the future of Higher Education will be explored regarding the potential disruption of the industry as a result and what this may mean for marketing within Higher Education more generally.

Key Theories Online education requires the study of the intersection between technology, content, and instruction, three key recurring themes. The TPACK model can be used in this regard (Aguilar 2020). The understanding that emerges from interactions between Technology, Pedagogy and Content Knowledge (TPACK) implicitly acknowledges the need for educational literacy among all stakeholders (Aguilar 2020). Therefore, TPACK knowledge is a synthesis of what one needs to teach (content), how one should best teach it (pedagogy), and what technology can be brought to bear given the former two (technology knowledge) (Aguilar 2020). It is unreasonable to expect educators thrust into online education during a global pandemic to have high levels of TPACK knowledge, but increased TPACK knowledge should be the eventual goal (Aguilar 2020). This could lead to new marketed products and services. The implications of this will be considered later. Other theories related to online education are Moore’s (1983, 1993, 2013) theory of Transactional Distance (TD) and Garrison et al. (2000)

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theory/model of the Community of Inquiry (CoI) (cited in Shearer et al. 2020). The TD theory is about ideas of dialogue, structure, and autonomy, where dialogue is a unique segment of the educational conversation that leads to the construction of knowledge, structure is a measure of responsiveness to a learner in terms of meeting learning needs, and autonomy the degree that a student was dependent or independent (selfdirected and self-regulating). The interaction of these variables set the degree of TD for each learner within each learning situation (Shearer et al. 2020). Col is a model that describes the combination of presences that will help assure a good online experience encompassing the idea of Cognitive Presence (CP) related to the notion of the construction of knowledge online by the community of learners, Social Presence (SP)— the sense of being there even in a virtual environment, and Teaching Presence (TP)—the notion of facilitation and modelling of behaviours to move the learning experience forward within a structured online community of learners (Shearer et al. 2020; Pillai and Sivathanu 2020; Li and Yu 2020). TD is focused on the individual even when participating in a group distance education learning environment, while the CoI is focused on the community and the social aspect of learning within a community (Shearer et al. 2020), an important aspect often overlooked in the transition from traditional teaching modes to online modes. TD and Col represent the first two generations of theory related to online learning. Anderson (2016) created the 3rd generation known as Connectivism. The theory of Connectivism explains how learning happens in an online network community in the digital age (Pillai and Sivathanu 2020). A comparison between major learning theories and Connectivism can be seen below in Table 12.1: Connectivism pushes the notions of TD, structure, autonomy, and dialogue to be redefined. In the context of Connectivism, one could argue that learning is gaining the ability to locate the necessary information through networked connections and applying it at the correct time in the correct place. Further, it highlights the co-creation of content (a Web 4.0/5.0 characteristic), reflecting upon it, and meaningful distribution of the content and the reflections (Shearer et al. 2020). The learning/instructional design strategies are aimed to be

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Learning theories compared (Pillai and Sivathanu 2020)

Behaviorism

Cognitivism

Constructivism

Connectivism

It is largely unknown how learning happens in a person

Similar to computer processing, learning is considered as some inputs getting processed, and then saved in short-term and long-term memory Knowledge is represented by symbolic mental constructs and learning helps save them in memory

Learning is the knowledge creation attempts by learners from their experiences

Connectivism is when chaos, network, complexity and self-organizing theories come together in a technology-driven digital era

Learners actively try to relate meanings to the experiences on their own. That makes it more complex and noisy

New information and rapid changes in knowledge drive connectivism

The thinking in a person is encouraged in cognitivism

Knowledge is expanded when new meaning is added to what already is known

Networking with people and other resources help in the learning process

Learning helps bring out behavior change. Observed behavior change is important than internal changes Behavior is focused on specific stimuli and outcome

tailored to support (1) customized and personalized learning with ontime rich peer feedback, scaffolding, online mentoring and cognitive apprenticeship efforts, learners’ control, independence, and engagement, (2) collaborative learning with in-depth online discussions, social interaction, community building, co-construction of knowledge, developing collaborative and communication skills, collaboration and communication both within and between domains, and (3) deep learning with authentic and real-world tasks, critical and creative thinking, and active and meaningful knowledge and skill-building (Shearer et al. 2020). This emphasizes the need for digital skills.

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Teaching and Learning pedagogy are traditionally informed by Blooms Taxonomy. Bloom’s Taxonomy describes the conventional or basic cognitive skills but not the digital skills parallel to the various levels in the hierarchy of the cognitive domain, as just discussed. The Bloom’s Digital Taxonomy (BDT) provides a framework to teach and assess the teachers’ and students’ understanding and usage of associated digital tools in the academic and non-academic contexts (Amin and Mirza 2020). It uses ‘digital verbs’ to describe digital learning outcomes such as editing, creation, sharing, and interaction, core of many digital activities (Amin and Mirza 2020). Therefore, as online learning continues to evolve and be adopted in various forms, it is crucial that pedagogical approaches must move beyond the mere replication of the face-to-face experience. Figure 12.2 illustrates a comparison of the development of Bloom’s Taxonomies: These models and theories are important. Cohen (2020) states that in the transition during the pandemic to online educational modes, nothing of pedagogical value has happened. It is not simply about digital tools. Another online learning model put forward by Chen (2016) is the ICCEE model: identify, choose, create, engage, and evaluate. The model provides online educators with effective guidance and a checklist when designing online learning materials (Zuhairi et al. 2020). Having briefly reviewed key theories and models, the next section will explore what the implications are on institutions, teachers, and students and what this may mean regarding educational marketed products and services.

Higher Educational Institutions Pedagogical Reconsideration The exponential growth in online learning and the rush for HEIs to maintain market share and remain competitive resulted in the quality of online learning, from both a learning design and a teaching perspective, to require improvements (Cohen 2020; Panitsides and Karapistola 2020). Furthermore, the transition to online was viewed as a means to chase enrolments, use technology, and increase revenue (Shearer et al. 2020). This means an increase in marketing, more flexible degrees, digital

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skills training programmes for staff and students, digital teaching products and resources, and new organizations to deliver and support this growth.

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During Covid, the transition to remote teaching among teachers in Malaysia included challenges such as changing teaching styles to suit online teaching (45%), Internet connection (46%), including all students in the lesson (53%) and unfamiliarity with online teaching tools (32%) and in general, increasing student engagement (Adi Badiozaman et al. 2020). In India, teachers faced challenges such as a lack of technical infrastructure, namely, a laptop, internet, and microphones in their home to teach or conduct assessments online (Joshi et al. 2020). Many of these challenges are institutionally related. Students disliked turning on their cameras/mics suggesting they contributed less when online. This impacted upon the role of the teacher. Online education creates a change in the role of educators where the use of technology in teaching is less about the educator as a sage and more about a guide on the side (Cohen 2020). Teacher preparation time for online or blended environments are significantly increased due to the complexity of online and remote teaching according to Adi Badiozaman et al. (2020). There is a need for HEIs to embrace the changes brought about by Covid as a long-term response that will develop and improve over the next few years and should include better infrastructure, policies for quality improvement, accessibility standards and strategic plans for continuous access in the future (Adi Badiozaman et al. 2020). E-learning technology use is an inherently multidisciplinary task, placing a support from across the institution on the top of the change implementation agenda (Panitsides and Karapistola 2020). According to Martzoukou et al. (2020), universities have not yet developed a systematic approach to exploring and mapping the digital competences of students, embracing both technology-based and digital citizenship areas as a priority agenda. Supporting students for success in a culturally diverse educational setting is always problematic with numerous challenges, such as poor bandwidth, funding, adequate time, unpreparedness of staff, student unreadiness, difficulties with technical support, challenges with adapting content to an online mode of teaching and difficulties in evaluation and assessment (Coetzer and Mapulanga 2021). E-pedagogical integration of open educational resources will allow for a digitally inclusive educational environment and diminish the negative effects of the digital divide (Adi Badiozaman et al. 2020).

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Furthermore, environment as the third teacher (Deshmukh 2021) also needs to be considered. The environment is moving from a well-thought and designed physical space (campus and classroom) to one which is virtual. Good design would normally consider Naturalness (light, sound, temperature, air quality and links to nature), Individualization (ownership, flexibility and connection) and Stimulation (appropriate level of complexity and colour) (Deshmukh 2021). What does this look like online and how will the campus of the future incorporate the ebbs and flows of habitation (Deshmukh 2021)?

Repositioning the Roles of Educators By default, as already discussed, online education has contributed to the changing role of educators (Cohen 2020), transferred into more pressure on academic staff to transform the ways education is delivered (Panitsides and Karapistola 2020). Sia and Abbas Adamu (2021) report that in Malaysia this caused extreme mental stress for staff who felt untrained and unprepared due to a lack of infrastructure. Applying higher-order thinking skills in the current digital world requires sufficient knowledge of the digital tools and expertise to use these (Amin and Mirza 2020). Despite teachers/trainers often not being synchronously present, rather than this being considered as lessening the role teacher’s role it is best seen as a repositioning (Cohen 2020). The transition online raised challenges for the teacher including a lack of training, a lack of awareness and interest in online pedagogy, less attendance by students online, a lack of personal touch and a lack of interaction (Joshi et al. 2020). These challenges often focus on affective processes such as feelings and attitudes, demanding additional skills of those teaching in online environments (King et al. 2016). Research in e-learning adoption in Brazil by staff reported by Panitsides and Karapistola (2020), indicated that attitude (perceived usefulness, ease of use, compatibility, and relative advantage), subjective norms (external influence and student– instructor interaction), and behavioural control (level of interactivity and resource facilitating conditions) collectively influence intention to use e-learning, which in turn determines actual behaviour. Furthermore,

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there was a significant difference between full-time (professors) and part-time (tutors) faculty, with professors being more likely to possess favourable attitudes and greater behavioural control towards e-learning adoption. Gerhardt (2020), in addressing digital trends, highlighted the importance of self-efficacy. The teacher’s role as a content delivery expert is reduced in ubiquitous learning environments [such as online learning environments], and so the need for pedagogical skills in effective facilitation has increased in importance (Cohen 2020). It is a prerequisite for teachers (and students) to have the knowledge and proficiency of using digital tools for positive academic outcomes (Amin and Mirza 2020). E-literacy, digital literacy, ICT literacy and multiliteracies are defined as the awareness, skills, understandings, and reflective approaches necessary for an individual to operate comfortably in information-rich and IT-enabled environments (Morris and Brading 2007). Another term is digital fluency meaning the ability to leverage digital tools and platforms to communicate critically, design creatively, make informed decisions, and solve wicked problems while anticipating new ones (Martzoukou et al. 2020). Digital literacy is made up of eight interrelated components or dimensions, namely, functional skills, creativity, critical thinking and evaluation, cultural and social understanding, collaboration, the ability to find and select information, effective communication and e-safety (Adi Badiozaman et al. 2020). This indicates four levels of expertise, namely, limited, novice, proficient, and expert (Adi Badiozaman et al. 2020) or according to Martzoukou et al. (2020), novice, basic, intermediate, and advanced expert. However, there is a plethora of debates and different opinions with respect to what individuals (teachers and students) should master and accomplish in order to become sufficiently ‘digital’ (Martzoukou et al. 2020). Online learning raises issues of privacy, access, equity and disengagement i.e., ‘Zoom fatigue’ from ‘Zoom-school’ (Deshmukh 2021). Well-designed online learning should be designed in consideration of the role of the teacher/trainer in an interdependent manner, not as an afterthought (Cohen 2020). Research among teachers in Malaysia revealed that the teachers reported only moderate confidence in their online teaching, while teaching hours and preparation were reported to

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have increased (Adi Badiozaman et al. 2020). Teachers become facilitators, negotiators, and guides in an online context (Cohen 2020). As such, very few teachers receive systematic support or instruction on how to design high-quality, interactive online learning experiences (Adi Badiozaman et al. 2020) which is why collective learning design incorporating instructional designers and other pedagogical experts, as discussed previously, becomes an important aspect of educational institutional approach.

University Students Today, students as lifelong learners need to continuously adapt to technological changes and professional requirements (Zuhairi et al. 2020). The transition online due to the Covid-19 pandemic has sharpened social focus on the need for digital skills (Martzoukou et al. 2020). Online learning design needs to assume that learners are self-directed, and to this end learning material should be organized, scaffolded, and clustered accordingly so that learning can occur with or without the teacher/trainer’s presence (Cohen 2020). However, the rapid pivot to remote learning due to Covid-19 has exposed and exacerbated the inequities across all levels in the education institutions globally (Adi Badiozaman et al. 2020). Many students faced anxiety and panic owing to the numerous implications for courses, assignments, seminars, and thesis defences, not to mention the lack of self-discipline, suitable learning materials, or good learning environments while self-isolated at home (Izumi et al. 2021). From a learner-centred teaching perspective, learning online should not require students to wait around for a teacher to show up for learning to occur (Cohen 2020). Adi Badiozaman et al. (2020) highlights a range of issues from the students’ readiness and access for remote learning, to the digital divide in teachers’ digital literacy. Cohen (2020) argues that student engagement is enhanced when students have more control of their learning. The key issues of constructing appropriate learning environments are not only tech-rich or tech-enabled classrooms, but also learners’ OnO access to necessary digital resources, receiving relevant

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learning guidance and suggestions at the right time, and interacting with teachers and peers anytime anywhere (Xiao et al. 2019). Essential to Learner-Centered Teaching (LCT) is the sharing of power between students and the teacher/trainer and as such students are empowered to make decisions about when they learn, how they learn, where they learn, with whom they learn and on some occasions what they learn and how they are assessed. When teachers have all the control, student motivation to learn decreases and learners may become despondent and overly dependent, lacking self-efficacy, particularly when studying online. However, in Malaysia as a case in point, students are in remote locations, where technology has not been fully embedded in the education system and where Internet availability is limited or non-existent (Adi Badiozaman et al. 2020). Cohen (2020) suggests e-learners are highly autonomous and have control of their own learning process. They are motivated, selfcontrolled, and self-monitored. They learn through engaging in deep discussions and interactions with their peers and instructors and solve real-life challenging problems with critical thinking and meta-cognitive processes. Therefore, students transitioning to an online format need to be challenged to step out of their comfort zone in a psychologically safe online environment, requiring a more sophisticated and strategic onboarding experience (Shearer et al. 2020). Student retention in distance learning is comparatively poorer than that in the face-to-face mode of learning (Zuhairi et al. 2020) which places a Blended Learning mode in an advantageous position incorporating both modes. Educators recognize that learning is a social act and that a multiplicity of formal networks and informal interactions occur frequently and naturally across campus spaces, supporting learning and fostering cognitive development, collegiality, and general well-being (Deshmukh 2021). What does this look like in a virtual space? Shearer et al. (2020) suggest (1) co-construction and co-delivery of the learning process, and (2) collaborative and connected learning, harnessing the potential of in-between spaces and extending the learning and social continuum (Deshmukh 2021). An additional benefit of interacting with others

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online is that interaction fosters social presence i.e., learners’ perception of having contact with ‘real’ people. Social Presence (SP), in turn, is positively associated with satisfaction and retention in online courses (Strauß and Rummel 2020). This is possible through activities such as asking thought-provoking questions that require learners to verbalize their thoughts (thinking aloud). This allows the other group members to observe reasoning processes and internalize them by imitation (modelling of cognition—CP). During argumentation, learners need to explain and clarify their ideas by using examples to illustrate concepts (elaboration) and during argumentation, learners also need to reconcile cognitive discrepancies that arise (Strauß and Rummel 2020). Itow (2020) suggests the following to enhance collaboration: informal peer discussions; reflections on expert- and novice-generated artifacts; social annotations of resources; and responses to and application of individualized feedback.

Technological Innovation and Disruption Online education is not new. The Open University, the University of Essex Online, and the MOOC have been around for quite a while now. MOOC has been considered as ‘disruptive’ by many researchers (Pillai and Sivathanu 2020). Massive Open Online Courses (MOOC) use Open Course Ware (OCW) to provide an online, communitybased, easy to assess and instructor–student interaction platform to OCW which when established, meant free, reusable and easily accessible content without any fees (Pillai and Sivathanu 2020). Transitioning to online learning during the Covid crisis, it was evident that the need for innovative solutions to optimize educational endeavours was required (Adi Badiozaman et al. 2020), learning from and adapting based on online modes already in existence. Reviewing the top 10 EdTech Unicorns (HolonIQ, no date) (Fig. 12.3) reveals that only one is aimed at university markets and that most EdTech Unicorns are situated in emerging markets. Emeritus in

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Fig. 12.3

Top 10 EdTech Unicorns (HolonIQ, no date)

India aims to deliver ‘Partnerships with top universities to make worldclass education accessible globally’. It is evident that there is a possibility of disrupting partnerships with co-creation collaboration innovation. Technology, as the driving force of educational processes, has transformed the teaching–learning landscape to a whole different level. It has also changed the way students and teachers think, perform, interact and process information (Amin and Mirza 2020). Quality online learning design shifts the focus from direct instruction to learning affordances and self-direction, enhanced through technology and aided by careful learning design (King et al. 2016). As such there are a wide variety of learning activities, including those facilitated by the teacher or trainer, such as an organized debate, a case study, an interactive demonstration, a role-play, or an organized synchronous discussion (Cohen 2020). The relationship between technology and pedagogy has shifted from a more technocentric approach to a more pedagogically deterministic approach, which transformed the role of web technologies from a delivery tool, for the content, to one focused on connected and social communities encouraging participatory ‘models of education’ (Shearer et al. 2020). Gerhardt (2020) discussed the emerging tools in their chapter regarding digital trends. This chapter focuses now on pedagogy, especially as the

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digital natives make the majority of those in Higher Education. Baggio (2019) furthermore illustrates that any technological innovation should follow the SAMR Model theorized by Puentedura (2010) which stands for Substitution, Augmentation, Modification, and Redefinition as steps of implementation. ODL has increased the opportunities to adapt innovative and creative ways for the engagement of millennials (Gen Y) (Amin and Mirza 2020), considered the connected generation. The present generation of learners belongs to Generation Z (Gen Z)—defined as individuals born between 1996 and 2012, who are highly technology-driven (Nayar and Koul 2020), compared to those teaching them in the transition to online formats (Gen X and Boomers), a serious challenge for many academics whose own formative educational experiences and professional orientations were shaped under different circumstances (Panitsides and Karapistola 2020). This creates disruption and tension among the generation in the way they have traditionally learned (Deshmukh 2021) as seen in Fig. 12.4: It is vital for the teachers to understand that the students would not be using these digital tools for professional and academic purposes as they use them primarily for social and collaborating purposes (Amin

Fig. 12.4

Generational disruption (Deshmukh 2021, p. 144)

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and Mirza 2020). However, Greenhow and Chapman (2020) found that teaching with social media in K-12 education fostered active earning, community building, and civic participation. Self-directed learners should be able to manage their own learning via text, audio, and podcasts, graphics, animation, video as well as interactive scenario-based learning developed with the aid of for example an authoring tool such as Articulate Storyline 360 V or Adobe Captivate (Cohen 2020). Digital storytelling can also be channelled through social media platforms such as Instagram, Flicker, or VoiceThread (Greenhow and Chapman 2020). Traditional classroom activities confine teaching to a fixed place and are implemented by teachers facing the same student groups in a classroom. In open education, with the emergence of smart classrooms (e.g., live classroom and broad-casting), schools can use a live-broadcasting classroom for multi-campus teaching, urban and rural teaching, give full play to the role of outstanding teachers, enable the share of outstanding teaching resources and solve the problem of unbalanced resource distribution and unreasonable allocation (Xiao et al. 2019). Disruption in the way space is defined (Deshmukh 2021) can be seen in Fig. 12.5: In the era of information and computer technology, OMO classrooms can be expected to cover most necessary equipment and facilities, software, learning analysis, and teaching evaluation services, OnO platforms, and data integration mechanisms (Xiao et al. 2019). Other applications in an online format include Moodle cloud, automation and artificial

Fig. 12.5

Disruption of space (Deshmukh 2021, p. 144)

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intelligence (AI), Google Classroom, MS Teams, CISCO WEBex and Zoom, Learning Management Systems (LMS) (Adi Badiozaman et al. 2020, Blackboard collaborate, Canvas, Edmodo LMS (Sia and Abbas Adamu 2021) and collaborative e-whiteboards (Deshmukh 2021). LMS has seen significant growth in use due to the sudden shift online as a result of Covid. Research by Gerhardt and Mackenzie-Philps (2018) of a small group of students revealed just how little engagement with the LMS took place (prior to Covid). Moodle as an LMS allows educators to directly implement a script by using the conditional activity and activity completion features used to enhance and guide collaboration (Strauß and Rummel 2020). Amin and Mirza (2020) found in their study that students and teachers of ODL must be proficient in advance searching, using e-tools for collaboration and data processing on computers in online mode and recommended that teachers use digital tools in conjunction with the traditional pedagogies to make learning experiences more meaningful and constructive. Helpful recommendations include ‘Use what you have’, i.e., be aware of current contracts in place for learning management systems or e-learning software; ‘Build your own’, i.e., understand the needs of your community and build tools that best address them; ‘Go for the free stuff ’, e.g., use social media platforms with existing community organizing features; ‘Patch elements together’, i.e., find technologies that can work with one another, like RSS feeds and blogs; ‘Get a commercial platform’, e.g., purchase learning management systems; ‘Build on an enterprise platform’, i.e., use software applications that are integrated with one another; and ‘Use open-source tools’, e.g., embed documents or maps by using tools like Google drive (Aguilar 2020, p. 303). Teaching online: (i) encourage contacts between students and faculty; (ii) develop cooperation among students; (iii) use active learning techniques; (iv) give prompt feedback; (v) emphasize time on task; (vi) communicate high expectations; and (vii) respect diverse talents and ways (Panitsides and Karapistola 2020). In the UK, JISC has now developed the ‘Digital Capability Discovery Tool’ which offers an empowering

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first step for staff and students to reflect on their digital capabilities (Martzoukou et al. 2020). Considering the impact of technological educational innovations in relation to integrating learning with the world of work i.e., work-integrated learning (WIL), the key themes and disruptors that emerge are interdisciplinary innovation, working with SMEs and startups, adjusting curriculum, building capacity, configuring systems and processes and enhancing flexibility (Kay et al. 2022), masterclasses, design thinking workshops, bootcamps, accelerators and prototyping (Smith et al. 2022) leading to what is called e-WIL (entrepreneurial work integrated learning). A new emphasis on entrepreneurialism, now considered vital to economic growth, is beginning to dominate, innovating teaching about, for and through entrepreneurship (Eisenstein and Raz 2021). The latter two modes being innovative and disruptive leading to self-directed E-WIL and more collaborative co-creation models. Chalmers et al. (2020) examine the impact of artificial intelligence (AI) in new venture processes considering the potential opportunities to enhance entrepreneurial activities. Their conceptual framework maps these impacts fusing strands from entrepreneurship, economics, and digital theory. The main potential with AI is to leverage large data sets to provide predictions and AI-augmented search activities to aid decision-making processes (Agrawal et al. 2018). In education, one of the most successful examples is Watson Discovery by IBM, an intelligent virtual agent which can be trained to provide conversational responses to users. The agent requires information population and training to provide accurate answers to questions, but the AI application is capable of self-improvement through usage. The more data it has, the higher the accuracy rate is (IBM 2020). Watson, which has its origins in machine learning and natural language processing, has been adopted by several universities as a first point of contact. York University, Canada, uses Watson to power Savy, their virtual assistant (YorkU 2020). Populated with relevant content it recommends resources, reminds students of upcoming deadlines, provides campus directions, and connects students to people and places.

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The potential for education 4.0, the fourth revolution may seem limitless with AI, especially a basis for intelligent chatbots and other support services. There are however ethical challenges that cannot be ignored when considering AI-powered applications for education. Unlike direct consumer products, where AI-driven applications are used to drive and potentially change consumer behaviour (Davenport et al. 2020), the basic premise of education as a means to make us realize what it is to be a human being (Foshay 1991), can be at odds with the perceived inhumanity of AI. This relates not just to building the AI application and the underlying infrastructure, the machine learning algorithms and neural networks with ethical principles in mind but also to using AIpowered systems ethically which requires robust ethical frameworks to be in place (Chalmers et al. 2020). In 2016, it was reported that a Georgia tech professor had used an AI teaching assistant all semester unbeknownst to the students (Larson 2016). The assistant, named Jill, which was based on IBM Watson was trained on a vast dataset to handle basic enquiries. The oversight of not informing the students that Jill was not a real human does not seem morally sensitive and poses questions about trust and potential misuse of AI applications. The review of the literature has identified technological educational innovation disruption to three key themes, namely, the institution (structure), the technology, and the users (teachers and students). It has highlighted the possibilities of HEIs differentiating in the online educational market based on different kinds of provision, adopting digital technologies to enable that provision, and providing training and support to those using and providing these provisions. This paves the way for new products and services to emerge in present and new markers.

Methodology There is a long tradition of biographical and autobiographical work in Marketing (Tadajewski and Jones 2017). Four narrative vignettes (Downs 2016) represent the experience of academics from HEIs across the Southeast of England. From these narratives/sketches, common threads and points of divergence will be drawn out (Tadajewski and Jones

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2017). Life history research explores the meaning of stories in their wider historical, social, political, cultural and geographical contexts (Downs 2016). The co-productive involvement of all authors in the process will potentially offer insights into and contribute to, the complex and everevolving relationships and differences between social, structural and cultural locations and the identities and agency that tellers and hearers accord themselves (Sikes 2017), understanding how personal troubles become public issues (Downs 2016), a sociological imagination which will enable authors to understand the larger historical context in terms of its meaning for the inner life and external career (Downs 2016). By using vignettes from their research and experiences in online teaching and learning, they offer ways to integrate into their core pedagogy teaching online (Mehta and Aguilera 2020). Each author will therefore have a self-portrait, as self-portraits help us to understand the authors’ contributions to marketing thought (Tadajewski and Jones 2017). Being a novice online educator does not necessarily predetermine one’s attitude towards e-learning, which largely depends on his/her professional biography (Panitsides and Karapistola 2020) which is why self-portraits are included. As part of the nod to the prerequisite for every student and teacher to have the knowledge and proficiency of using digital tools for positive academic outcomes (Amin and Mirza 2020), digital data collection methods were purposely used in this research. Similar to Mehta and Aguilera (2020), step 1 involved designing ‘Flipgrid-Introduction’, using a popular platform called Flipgrid—a relatively easy to use video-recording educational tool modelled after popular social media platforms, such as Snapchat and Instagram—to answer the following questions: • Your name, the institution where you work/teach and in what capacity • Your age and experience in higher education and expertise in digital skills • Your experience teaching online during Covid (for or against?) • Examples of the digital tools used

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• Your views on the future of HE i.e., how much of what we have been doing during Covid will continue, should/shouldn’t continue? • How your future as an academic may be impacted? • Anything else you think would be worth adding Step two involved a debate using Microsoft Teams. Once all authors viewed each other’s ‘Flipgrid-Introduction’, a time was scheduled to debate these views. This was recorded and a transcript produced. All authors then agreed on the key codes and themes. In accordance with the stages of thematic analysis, the recording and transcription was reviewed several times to gain data familiarization, then coded, themes produced, and meaning integrated and interpreted (Brown and Scaife 2019; Robson 2011). Thematical analysis is a generic approach giving labels (codes) to chunks labelling them as examples of a particular ‘thing’ (Robson 2011, p. 469). It is useful in examining ‘the perspectives of different research participants, highlighting similarities and differences, and generating unanticipated insights’ (Nowell et al. 2017, p. 2). Based on these themes, the final step involved creating a list of advantages and disadvantages of a potential new online Higher Educational mode using Padlet.

Data Findings Self-Portraits See Table 12.2.

Collaborative Debate See Table 12.3.

Synthesis of Ideas See Table 12.4.

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Summary of self-portraits A lecturer with experience teaching across a number of modes. In favour of ‘online growth’ due to allowing greater access and flexibility but recognizes digital literacy challenges. Key challenge is developing a sense of community online. However, ‘I fear I may not keep up with the fast changing pace of digital tech and younger competition’ Learning technology manager for an online university. ‘I am usually the admin, not the front-end user’. Emphasizes the uniqueness of the different modes. In favour of ‘online growth’ but recognizes the implications of a more dispersed educational sector as a result A lecturer with experience that demarcates two groups which are more aligned with specific modes i.e., younger students prefer traditional synchronous modes while mature working students may prefer the flexibility of an online option. However, their own preference is to maintain a physical presence within a classroom space. ‘I can see how tutors might for example take a ‘bad’ online session more to heart than a ‘bad’ F2F session’ HE tutor and international partner manager that takes a more holistic analysis of the pandemic experience and feels strongly that the structure was ill-equipped to support the demand of the technology requirements impacting upon the social and psychological well-being of all involved

Data Discussions Reflections on Step 1: Flipgrid Introductions A key challenge raised by tutors in an online environment is the inability to see a student, ‘to read the room’ (MR) due to students not turning on video cameras. Interestingly, one of the collaborators expressed a similar view when using Flipgrid, ‘I didn’t find the FlipGrid format comfortable as it pushed me into the limelight, so I chose to record only audio (without video) which made it easier for me to articulate myself. I also struggled with the spontaneity of the platform. I like to plan, and it did not allow for very well’ (AL). One collaborator (CB) did not use the platform and sent their ‘introduction’ by email. This initial starting point already links to the discussions regarding self-efficacy in using digital tools. Perhaps responses would be different if TG had not designed the data collection tools himself, choosing tools he was familiar with,

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Thematic analysis based on teams debate Links to literature

Codes

Themes

Support and training; pedagogy; terminology; jargon; mechanics; technological tools; different and difficult; users (age, working, apprenticeships); Blooms; recordings; communications; Teams; Web 5.0

Technical

Sia and Abbas Adamu (2021) Bloom’s Digital Taxonomy Cohen (2020) Adi Badiozaman et al. (2020) Amin and Mirza (2020)

Control; paranoia; community; straightforward but not easy; holistic; off the shelf but not tailor-made; online environment vs campus-based environments (not converting); access + more students; the media; disruptive; flexibility; in-house or contracts; distinctive provision; revenue streams

Structural

Deshmukh (2021) Joshi et al. (2020) TPACK model Connectivism Martzoukou et al. (2020)

Key quotes ‘…just because it’s straightforward, is does not mean that it’s going to be easy’ (AL) ‘But who said that every student understands and appreciates the technical side of it’ (CB) ‘…freelancers and and and full-time staff were none of us are particularly tech savvy’ (MR) ‘…the emphasis falls on us as tutors to create their community [digitally]’ (TG) ‘I think for universities to stay competitive, they’re going to be looking at other revenue streams and online learning is one of the kind of’ (AL) ‘The pandemic is differently disrupted things’ (TG) ‘…[HEIs] quite literally picked up off the shelf [online versions]. ‘Nothing as such made me believe that this was tailor made’ (CB) ‘[HEIs] finding a fit very quickly and also a bit of a bit of paranoia’ (MR) (continued)

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(continued)

Codes

Themes

Community mindset; presence; confidence; role of tutor; feedback and support; digital literacy and growth; experience and ability; student-centred; forced; expectations; motivation; EQ and resilience; self-efficacy; empathy

Social

Table 12.4

Links to literature Shearer et al. (2020) Amin and Mirza (2020) Transactional Distance Community of Inquiry (CP/SP/TP) Li and Yu (2020)

Key quotes ‘…so there was a mixture of having to manage my own sense of frustration and disappointment, but managed the students as well’ (TG) ‘…[students] demanding a fuller university experience’ (AL) ‘online is not easier, I found it difficult’ (MR) ‘I think in this journey of digitalization, we’ve completely ignored the most important person, that is, the student’ (CB)

Advantages and disadvantages

Advantages

Disadvantages

Great for digital natives

Platforms evolve and change and trying to keep up with everything is impossible Our experience of online classrooms when students don’t turn camera or audio on was shared and highlighted as a major drawback of online Lack of digital skills among students and tutors and thus issues of equity

Digital skills will allow competitive advantage for tutors and institutions

Digital skills will allow wider pedagogical diversity and expertise for a tutor and institution Increase access Highly flexible

Infrastructure differences such as hardware, wifi, etc Confidence and self-efficacy required and impacted

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‘Having designed the methodology, I obviously was familiar with the tools’ (TG).

Reflections on Step 2: Thematic Analysis The Teams debate was coded, and similar to the review of the literature, also seemed to focus on similar themes such as the institution (e-readiness and consequent structure), the technology, and the end-users (teachers and students). Support and training to use the technology both by teachers and students were discussed at length. The challenges raised echo the challenges found among teachers in Malaysia (Adi Badiozaman et al. 2020), India, Israel, and Indonesia. AL, an online technology manager, emphasized how important the design of pedagogy was in effective online teaching. This resonated with CB who felt most HEIs simply adopted an ‘off-the-shelf ’ version without taking into account pedagogical implications. These views resonate with Cohen (2020) and Panitsides and Karapistola (2020). MR felt that the online terminology and jargon was difficult to follow, and this caused many to simply continue as always replicating much of the traditional mode online. All authors agreed that the mechanics of online technological tools required familiarity, confidence and self-efficacy. “I can see how tutors might for example take a ‘bad’ online session more to heart than a bad F2F session” (MR). It was acknowledged that online was different and difficult. Authors did debate the digital efficacy users suggesting that there are differences based on age, working students and degree apprenticeships resonating with the generational disruption of learning by Deshmukh (2021). Half the authors had not come across the BDT and none of the authors were taught or exposed to this when doing their HE certificate in HE. The lack of training for teachers in digital skills is confirmed by Martzoukou et al. (2020). Helpful digital tools mentioned were recordings of either lectures (asynchronous) using Panopto or live sessions (synchronous) using Blackboard collaborate, zoom, or WebEx; and communication tools such as Teams, Whatsapp and Zoom. The future development of

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technology such as Web 5.0 was mentioned in the debate by TG and the self-generating knowledge potential of AI potentially means the role of the tutor will become more of a facilitator making learning a more collaborative, co-creating process. The data collection methods for this research duplicated in some sense the co-creation process: ‘I did enjoy the collective approach and collaboration through the platforms’ (AL). All authors agreed that among traditional HEIs the transition to online modes raised challenges of control and consequently paranoia. Most authors agreed the challenge of replicating the campus to create an online community was difficult. Although digital appears straightforward, the transition was not easy. In the transition, most HEIs, it was felt among authors, lacked a holistic approach to what was being offered, focusing only on the digital replication of the classroom, and not the campus (Deshmukh 2021). As such any ‘off the shelf ’ model was adopted as a short-term solution ignoring the student experience as a source for a tailor-made response. The temporary solution approach was recognized in the clear pedagogy associated with an online environment vs campus-based environments i.e., it is not about not converting from one to another, exasperated by the media’s misunderstanding as well. However, HEIs did take advantage of the transition to access more students and markets post-pandemic. All agreed online education offers maximum flexibility to students. AL suggested the future of HE may see an increase of in-house or contracted provision to enable online education provision. More students and new markets are valuable revenue streams, a trend already identified by Gerhardt (2020) pre-pandemic. From an educational perspective it is about developing and delivering technology-supported, technology-facilitated, technology-blended, and technology-based innovation (Bayerlein et al. 2022). All authors agreed that learning is a social activity and therefore that having a community mindset was crucial. Presence as discussed in the TD theory by Shearer et al. (2020) was prominent in the debate. Another crucial discussion was the role of the lecturer, their confidence, digital literacy EQ, and resilience. These are common themes discussed already

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by Cohen (2020), Adi Badiozaman et al. (2020), Panitsides and Karapistola (2020), and Martzoukou et al. (2020). All these authors, echoed by the authors in the debate, emphasize the importance of training and support. This means new products but also potentially new providers of a service.

Reflections on Step 3: The Future of HE The review of literature and the analysis of four tutors in Higher Education reveal and confirm further disruption in learning (in general) and how this learning is facilitated by Higher Educational Institutions will occur. CB did not complete the final padlet reflections. ‘Given the nature of the topic, it was good to use the collaborative tools we did, rather than just usual emails’ (MR). Regarding the future structure: ‘Online learning, learning platforms, digital tools and pedagogy are here to stay and will be employed more and more in future years’ (AL). HEIs will begin to market themselves not just in what they offer regardings courses but also in how they offer the learning within those courses with an increasing digital emphasis. ‘Universities and other educational institutions are going to try and search their identity in the new digitally enabled educational space’ (AL). Those who are able to replicate not only the classroom in an online context but also the campus will begin to differentiate themselves within the digital educational market. With an increased focus on digital delivery (Blended or distinctively online), digital literacy of those who deliver the learning will be a pivotal requirement. ‘Our experience of online classrooms when students don’t turn camera or audio on was shared and highlighted as a major drawback of online’ (MR). ‘Digital literacy among academic staff is something that should be encouraged and embraced’ (AL). Job profiles and requirements will differentiate between those with digital skills and differentiate between those with digital skills based on how advanced they are in those skills. This means that for some HEIs the only viable option will be to subcontract these requirements to dedicated digital educational providers, creating a new subset of providers within the HEI sphere. To

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meet the growing demand of digital skills, new training programmes and training providers will emerge. With the increase of the digital industry such as data mining, IoTs and AI, more courses related to the skills are and will continue to be developed. This means there is a disruption in pedagogy that is foreseeable. ‘Pedagogy should drive the digital tools or online learning, solutions need to be tailored to the subject, programme, student group, tutor, and not dictated by management’ (AL). However, with Web 5.0 and selfgenerating learning and co-creating becoming more common, pedagogy will be changed. This provides opportunities for new courses and new tools to facilitate this new self-generating and co-producing knowledge to emerge. Newly marketed products will emerge.

What Digital Trends Emerged? Flipgrid: ‘…it’s much easier to get a proper sense of what our contributors were conveying through audio/ video, i.e., Flipgrid was very useful’ (MR). ‘I find Flipgrid useful and used it to create community (introductions) during the pandemic with students’ (TG). ‘I didn’t find the Flipgrid format comfortable’ (AL). Padlet: ‘I have used Padlet extensively and find it a useful tool to brainstorm and create connections and mind maps. It is easy to create rich multimedia content collaboratively with a group in real time which makes it a fantastic tool for group projects’ (AL). ‘Padlet - I am less familiar with -but sure I will see the benefits when all contributions are entered in’ (MR). ‘We originally were going to use Kaila, but I could not find it (never used it before) and so switched to Padlet which I again have used in the past to create community by having students introduce themselves on it’ (TG). Google Docs: ‘Google Docs has been useful for me, I used it fairly extensively when studying for my masters’ (AL). ‘I find Google Drive excellent in terms of simple edits and latest versions’ (MR).

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Microsoft Teams: ‘Teams, where we conducted the debate, I have mixed feelings about. Mostly it works ok but the terminology and some actions do not seem intuitive. The best thing about it is the ability to provide a live transcript’ (AL). Other digital tools used during the pandemic mentioned by the authors include, Blackboard collaborate, WebEx, Mentimeter, YouTube, Trello, and Zoom. Will online education remain as a new business model in HEIs? ‘I think for universities to stay competitive; they’re going to be looking at other revenue streams and online learning is one of the kind of ’ (AL). Blended Learning as a mode, all felt, would remain as it is a resource and mode which saves time and money e.g., pre-recorded lectures mean replacing synchronous lectures. Some HEIs will become distinctive in being 100% face-to-face, others blended while others may provide online versions of what they traditionally offered face-to-face e.g., University of London announced 12 new online courses from 2021. The latter of course means more students, increasing revenue. What opportunities and challenges will this raise for academic staff if any? Increases in online provision will mean a demarcation in the student profile and a demarcation among lecturers in terms of their digital abilities. ‘…we / I spent a lot of time considering the e-question as to how to better enable ‘communities of practice online’ (CB). Will some students exit these online courses with better digital literacy? As MR reminded us, ‘…how, for example, can we get students to be as chatty in the formal educational online environment as they are in social settings e.g., WhatsApp etc.’. Not all digital nomads know how to use the digital tools for learning. ‘In a market where subject discipline and publications are no longer the only differentiation among lecturers, digital skills will allow competitive advantage and wider pedagogical diversity and expertise for a tutor’ (TG).

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Conclusion: The Future of Higher Education The physical classrooms are losing their dominance as the only place of learning as technology-mediated learning delivered via online education methods is becoming commonplace (Pillai and Sivathanu 2020). Across the world, education paradigms are shifting to include, among other developments, more flexible approaches, online learning, blended learning and collaborative models of learning (Devlin and McKay 2016; Panitsides and Karapistola 2020). Blended learning models could be mainstream and take over as the norm in future learning and education. (Izumi et al. 2021). There are major shifts regarding the future of jobs due to industry 4.0, a new phase in the industrial revolution that focuses heavily on interconnectivity, automation, machine learning and real-time data which inevitably will impact Higher Education, a potential Education 4.0 era (Nayar and Koul 2020). The next term (terms?) is not about making a binary Either–Or a selection of teaching modalities, but rather conceiving, beta-testing, and refining creative solutions for Both–And Learning experiences (Deshmukh 2021). Faculty envision an eclectic pedagogical approach for the future ideal online learning experience, which merges three pedagogical views: personalized/adaptive learning experience, transformative learning experience, and collaborative, constructive, and connected learning experience (Shearer et al. 2020). Trends from credentialing to lifelong learning, from just-in-time education to public–private partnerships for the delivery of job-oriented skills, have been gaining momentum over the past decade (Deshmukh 2021). Four categories have emerged: personalization of content, personalization of assessment, adaptive learning process, and adaptive/personalized infrastructure (Shearer et al. 2020). These changes could imply more individualized learner-centric education and personal journeys, potentially conferring greater adaptability for pedagogy, staffing and operations, diversity and inclusion and thereby build greater resilience for institutions (Deshmukh 2021). It is suggested by Xiao et al. (2019) that future learning will centre on an intelligent learning environment and therefore Pedagogy-Space-Technology (PST). See Fig. 12.6:

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OMO classroom framework based on PST (Xiao et al. 2019)

This means differentiation in teaching skills and competency in the industry market. To illustrate this view, an adapted technological educational innovation (TEI) strategy for appropriate curriculum development and co-creation by tutor and student is suggested as seen in Fig. 12.7:

Fig. 12.7 Technological educational innovation strategy (Adapted from Amin and Mirza 2020)

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Fig. 12.8 Technological educational innovation model (B adapted from A— Goold et al. [1994])

To address the potential of new markets and new products and services, Fig. 12.8 illustrates the potential of a new TEI business model adapted from the model of synergy in the corporate parenting model: What the adapted business model suggests is that some traditional institutions will ‘experiment’ by starting new technological educational innovations such as the University of London launching fully online courses or creating new start-ups through partnerships, mergers, and acquisitions such as Emeritus as a subset of their existing provision. Disruptors using platform technology will start from outside the traditional models such as MOOC and continue to innovate even further by creating more technological education innovations. All these trends will bolster personalized learning, peer-to-peer learning, virtual tutoring, and independent research, potentially redefining internationalization and even going beyond brand-defined institutions (Deshmukh 2021). Reimagining campuses will include a series of agile, multifunctional spaces with robust, scalable, flexible, techenabled infrastructure which can be refashioned owing to sequential or disruptive changes (Deshmukh 2021). Lifelong learning has been a crucial part of the development of open universities that allow flexibility

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to accommodate working students beyond the traditional college-age population and this trend will continue and diversify further (Zuhairi et al. 2020). As such new marketing trends in this educational digital world concerning products and services will emerge related to the institution, the technology, and the users (student and lecturer).

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Chalmers, D., MacKenzie, N.G. and Carter, S. (2020). Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution. Entrepreneurship Theory and Practice, 45 (5), 1028–1053. Available from: https://journals.sagepub.com/doi/full/10.1177/ 1042258720934581. Coetzer, L. and Mapulanga, P. (2021). Delivering Equitable Distance Library Services to Off-Campus Users at the University of the Free State in South Africa. Global Knowledge, Memory and Communication, 70 (1/2), 94–113. https://doi.org/10.1108/GKMC-11-2019-0145. Cohen, J.A. (2020). A Fit for Purpose Pedagogy: Online Learning Designing and Teaching. Development and Learning in Organisations: An International Journal. https://doi.org/10.1108/DLO-08-2020-0174. Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747019-00696-0. Deshmukh, J. (2021). Speculations on the Post-Pandemic University Campus—A Global Inquiry. Archnet-IJAR, 15 (1), 131–147. https://doi. org/10.1108/ARCH-10-2020-0245. Devlin, M. and McKay, J. (2016). Teaching Students Using Technology: Facilitating Success for Students from Low Socioeconomic Status Backgrounds in Australian Universities. Australasian Journal of Educational Technology, 32 (1). https://doi.org/10.14742/ajet.2053. Downs, Y. (2016). Testing the Potential of Auto/Biographical Life History. Qualitative Research Journal, 16 (4), 362–373. Eisenstein, A. and Raz, N. (2021). Entrepreneurial Work-Integrated Learning. In: T. Gerhardt and P. Annon (Eds), Applications of Work Integrated Learning Among Gen Z and Y Students (119–136). IGI Global. Emeritus. (No Date). https://emeritus.org/. Foshay, A.W. (1991). The Curriculum Matrix: Transcendence and Mathematics. Journal of Curriculum and Supervision, 6 (4), 277–293. Gerhardt, T. (2020). Digital Trends in Education Operations and Marketing. In: S.S. Dadwal (Ed), Innovations in Technology and Marketing for the Connected Consumer (356–373). IGI Global. Gerhardt, T. and Mackenzie-Philps, L. (2018). The Challenges and Opportunities of ICT in WIL: A Case Study Among Priests Exploring the Correlation and Trajectory Between Effective WIL and ICT Pervasiveness. Higher Education, Skills and Work-Based Learning, 8 (4), 408–421. https://doi.org/10. 1108/HESWBL-07-2017-0045.

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Goold, M., Campbell, A. and Alexander, M. (1994). How Corporate Parents Add Value to the Stand-Alone Performance of their Businesses. Business Strategy Review, 5 (4), 33–55. Greenhow, C. and Chapman, A. (2020). Social Distancing Meet Social Media: Digital Tools for Connecting Students, Teachers, and Citizens in an Emergency. Information and Learning Sciences, 121 (5/6), 341–352. https://doi. org/10.1108/ILS-04-2020-0134. HolonIQ. (2021). Global EdTech Unicorns. https://www.holoniq.com/edtechunicorns/. IBM. (2020). Watson Assistant Improves Intent Detection Accuracy, Leads Against AI Vendors Cited in Published Study. IBM . Available from: https:// www.ibm.com/blogs/watson/2020/12/watson-assistant-improves-intent-det ection-accuracy-leads-against-ai-vendors-cited-in-published-study/. Itow, R.C. (2020). Fostering Valuable Learning Experiences by Transforming Current Teaching Practices: Practical Pedagogical Approaches from Online Practitioners. Information and Learning Sciences, 121 (5/6), 443–452. https://doi.org/10.1108/ILS-04-2020-0106. Izumi, T., Sukhwani, V., Surjan, A. and Shaw, R. (2021). Managing and Responding to Pandemics in Higher Educational Institutions: Initial Learning from COVID-19. International Journal of Disaster Resilience in the Built Environment, 12 (1), 51–66. https://doi.org/10.1108/IJDRBE-062020-0054. Johnson, G., Whittington, R., Scholes, K., Angwin, D. and Regner, P. (2014). Exploring Strategy. Pearson. Joshi, A., Vinay, M. and Bhaskar, P. (2020). Impact of Coronavirus Pandemic on the Indian Education Sector: Perspectives of Teachers on Online Teaching and Assessments. Interactive Technology and Smart Education, ahead-of-print. https://doi.org/10.1108/ITSE-06-2020-0087. Kay, J., Ferns, S.J., Russell, L., Smith, J. and Younger, A. (2022). Innovation in Work-Integrated Learning. In: S.J. Ferns, A.D. Rowe and K.E. Zegwaard (Eds), Advances in Research, Theory and Practice in Work-Integrated Learning (133–144). Routledge. King, M.R.N., Rothberg, S.J., Dawson, R.J., and Batmaz, F. (2016). Bridging the Edtech Evidence Gap. Journal of Systems and Information Technology, 18 (1), 18–40. https://doi.org/10.1108/JSIT-06-2015-0059. Larson, S. (2016) College Students Didn’t Realize Their Professor’s Assistant Was Actually an AI Bot. Daily Dot. Available from: https://www.dailydot. com/debug/watson-teaching-assistant-robot/.

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Li, X. and Yu, Y. (2020). Characteristics of Asynchronous Online Discussions in a Graduate Course: An Exploratory Study. Information and Learning Sciences, 121 (7/8), 599–609. https://doi.org/10.1108/ILS-04-2020-0120. Martzoukou, K., Fulton, C., Kostagiolas, P. and Lavranos, C. (2020). A Study of Higher Education Students’ Self-Perceived Digital Competences for Learning and Everyday Life Online Participation. Journal of Documentation, 76 (6), 1413–1458. https://doi.org/10.1108/JD-03-2020-0041. Mehta, R. and Aguilera, E. (2020). A Critical Approach to Humanizing Pedagogies in Online Teaching and Learning. International Journal of Information and Learning Technology, 37 (3), 109–120. https://doi.org/10.1108/IJILT10-2019-0099. Morris, A. and Brading, H. (2007). E-Literacy and the Grey Digital Divide: A Review with Recommendations. Department of Information Science, Loughborough University. http://jil.lboro.ac.uk/ojs/index.php/JIL/article/ view/RA-V1-I3-2007-2 (accessed 22/08/2021). Nayar, B. and Koul, S. (2020). Blended Learning in Higher Education: A Transition to Experiential Classrooms. International Journal of Educational Management, 34 (9), 1357–1374. https://doi.org/10.1108/IJEM-08-20190295. Panitsides, E.A. and Karapistola, A. (2020). Enhancing the Role of the Hellenic Open University as a Lifelong Learning Organisation: Crossing the Rubicon into the E-Learning Era. In: E. Sengupta, P. Blessinger, and M.S. Makhanya (Eds), International Perspectives on the Role of Technology in Humanizing Higher Education: Innovations in Higher Education Teaching and Learning (Vol. 33, pp. 89–104). Emerald Publishing Limited. https://doi.org/10. 1108/S2055-364120200000033006. Pillai, R. and Sivathanu, B. (2020). An Empirical Study on the Online Learning Experience of MOOCs: Indian Students’ Perspective. International Journal of Educational Management, 34 (3), 586–609. https://doi.org/10. 1108/IJEM-01-2019-0025. Puentedura, R. (2010). SAMR and TPCK: Intro to Advanced Practice. Robson, C. (2011). Real World Research (2nd ed.). Blackwell Publishers. Rumaksari, A.N. (2021). Digital Learning: The Threat of EdTech Firm to School Amid a Covid-19’s Pandemic. Scholaria: Journal Pendidikan dan Kebudayaan, 11 (1), 30–36. Shaw, S. (2005). The Corporate University: Global or Local Phenomenon? Journal of European Industrial Training, 29 (1), 21–39. https://doi.org/10. 1108/03090590510576190.

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Shearer, R.L., Aldemir, T., Hitchcock, J., Resig, J., Driver, J. and Kohler, M. (2020). What Students Want: A Vision of a Future Online Learning Experience Grounded in Distance Education Theory. American Journal of Distance Education, 34 (1), 36–52. https://doi.org/10.1080/08923647. 2019.1706019. Sia, J.K.-M. and Abbas Adamu, A. (2021). Facing the Unknown: Pandemic and Higher Education in Malaysia. Asian Education and Development Studies, 10 (2), 263–275. https://doi.org/10.1108/AEDS-05-2020-0114. Sikes, P. (2017). And Then He Threatened to Kill Himself: Nightmare Viva Stories as Opportunities for Learning. Qualitative Research Journal , 17 (4), 230–242. Smith, J., Russell, L., Bliemel, M. Donnet, T., Elkington, R. and Larken, I. (2022). Developing University Learners’ Enterprise Capabilities through Entrepreneurial Work-Integrated Learning. In: S.J. Ferns, A.D. Rowe, and K.E. Zegwaard (Eds), Advances in Research, Theory and Practice in Work-Integrated Learning (145–156). Routledge. Strauß, S. and Rummel, N. (2020). Promoting Interaction in Online Distance Education: Designing, Implementing and Supporting Collaborative Learning. Information and Learning Sciences, 121 (5/6), 251–260. https://doi.org/10.1108/ILS-04-2020-0090. Tadajewski, M. and Jones, B. (2017). Modern Pioneers in Marketing: Autobiographical Sketches by Leading Scholars. Journal of Historical Research in Marketing, 9 (2), 118–112. Varma, A. and Jafri, M.S. (2021). COVID-19 Responsive Teaching of Undergraduate Architecture Programs in India: Learnings for PostPandemic Education. Archnet-IJAR, 15 (1), 189–202. https://doi.org/10. 1108/ARCH-10-2020-0234. Xiao, J., Sun-Lin, H.-Z. and Cheng, H.-C. (2019). A Framework of OnlineMerge-Offline (OMO) Classroom for Open Education: A Preliminary Study. Asian Association of Open Universities Journal, 14 (2), 134–146. https://doi.org/10.1108/AAOUJ-08-2019-0033. YorkU. (2020). Meet SAVY: Student Virtual Assistant Launches with New Name. York University. Available from: https://yfile.news.yorku.ca/2020/09/ 09/meet-savy-student-virtual-assistant-launches-with-new-name/. Zuhairi, A., Hsueh, A.C.T. and Chiang, I.-C.N. (2020). Empowering Lifelong Learning Through Open Universities in Taiwan and Indonesia. Asian Association of Open Universities Journal, 15 (2), 167–188. https://doi.org/ 10.1108/AAOUJ-12-2019-0059.

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Additional Reading Advance HE. (N.D.). Online Teaching. https://www.advance-he.ac.uk/knowle dge-hub/online-teaching. Advance HE. (2020). The Online Student Experience: More Than Learning Online. https://www.advance-he.ac.uk/news-and-views/online-student-exp erience-more-learning-online. Chen, L. (2016). A Model for Effective Online Instructional Design. Literacy Information and Computer Education Journal (LICEJ), 6 (2), 2303–2308. KPMG. (N.D.). The Future of Higher Education in a Disruptive World. https://home.kpmg/xx/en/home/industries/government-public-sector/edu cation/the-future-of-higher-education-in-a-disruptive-world.html. Nowell, L.S., Norris, J.M., White, D.E. and Moules, N.J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods. December 2017. https://doi.org/10.1177/160940 6917733847. TrendOne. (N.D.). www.trendONE.de.

13 The Emergence of Technopreneurship for Sustainable and Ethical Economic Growth: Theory, Research and Practice Dinusha Maduwanthi Rathnayake and Teresa Roca

Introduction to Entrepreneurship The main aim of this chapter is to provide an inclusive discussion about the value of studying entrepreneurship and technology for both, higher education students and education policymakers. Entrepreneurship has gained considerable attention in the business field due to its substantial benefits to national economies. Schumpeter (1942) defined “entrepreneurship” as more than just starting a new business, as it involves introducing revolutionary changes in business methods and practices. Schumpeter seminal work also postulated the theoretical D. M. Rathnayake Asia Pacific Institute of Information and Technology, Colombo, Sri Lanka e-mail: [email protected] T. Roca (B) Northumbria University Newcastle, Newcastle upon Tyne, UK e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8_13

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construct of “creative destruction”, portraying entrepreneurs as essential dynamic agents of change and disrupting conventional business methods by using their sheer creativity and persistence. Wider research clearly supports the value of entrepreneurship in our contemporary highly technological “volatile, uncertain, complex and ambiguous” (VUCA) world (Bennett and Lemoine 2014; Saleh and Watson 2017). Exploration, amalgamation and utilisation of new technologies have become one of the key challenges but also of opportunities for entrepreneurship, and no industry is considered to be unaffected by its impacts. Creative destructions combined with novel technologies (e.g. blockchain, Internet of Things and Artificial Intelligence) have boosted entrepreneurial activities and cultivated innovations which act as critical pillars of growth in modern economies. Further, the Covid-19 global pandemic has taken a huge toll on lives and economies globally, leading the business world into faster technological transformation primarily through digital consumption, business digitalisation, fintech, automation and localisation. These challenging transformations call for problem-solving through mindful innovation and fast responses, which tap into what has been construed as the essence of entrepreneurs. Research has consistently identified entrepreneurs as having the ability to analyse market realities, identify and fill gaps creatively and innovatively. Therefore, in the battle against the effects of the pandemic and the rise of new market realities, entrepreneurship emerges as an essential lever to the building of new economies. However, from the emergent lessons of the pandemic, it is becoming clear that a consideration of the ethical and sustainable aspects of entrepreneurial activities is essential, when pursuing financial success by tapping into the potential of ever-increasing technology developments. Hence, the need for techno-entrepreneurial education taking the above into account is increasingly acknowledged among higher education authorities worldwide and by governments who have a key responsibility for investment in entrepreneurship education programmes (Ratten and Jones 2021; Ahmed et al. 2020; Krishnamurthy 2020). Acquiring technological and entrepreneurial self-efficacy underpins the entrepreneurial intentions and needs of students in higher education. Thus, the embedding of entrepreneurial education in universities is acknowledged as strengthening regional innovation systems and economic growth through

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knowledge transfer activities (Aldianto et al. 2018). Ultimately, it is argued that entrepreneurship empowered by technology has the ability to actively contribute to the redevelopment of societies and economies. The term “entrepreneurship” has started to gain the global interest since the early 1980s with the economy’s focus on small and medium businesses as a resolution for unemployment (Hardie et al. 2020; Pepin 2018; Jones and Iredale 2014). Entrepreneurship is mostly being defined as a process, a state-of-being or a resource, thus considered as a multifaceted phenomenon (Ahmad and Hoffmann 2008). The scholarly views of entrepreneurship have evolved over time. The initial attempts can be coined from the studies of Schumpeter—one of the greatest economists in the twentieth century who introduced the concept of entrepreneurship. Schumpeterian view highlighted the role of innovation in entrepreneurship, seeing entrepreneurs as not only changing agents and innovators, but also planners of production. Schumpeter (1911) claimed that under five conditions of newness entrepreneurship can occur, being: new production, new methods, new markets, new materials and new enterprises. By him, entrepreneurial process creates the economic development of a country or region by generating new opportunities. Kirzner (1973), however, argued that entrepreneurs should be seen as individuals who capture and respond to opportunities rather than creating them. Inline with Kirzner (1973), Gries and Naudé (2011) highlighted that entrepreneurs are the individuals who exploit favourable positive opportunities in the market by forming or developing new businesses. A popular and a quasi-general accepted definition was offered by Shane and Venkataraman (2000) advising that: “Entrepreneurship is a process that involves the discovery, evaluation, and exploitation of opportunities to introduce new products, services, processes, ways of organizing, or markets” (p. 219). Thereby, it is accepted that entrepreneurship is an innovative and creative process which generates and exploit business opportunities. Risk tolerance of entrepreneurs is the most studied personality characteristic in the field of entrepreneurship, and it is widely accepted that risk and entrepreneurship are interconnected, inextinguishable, yin and

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yang. Many studies have claimed that individuals with low risk aversion are more likely to become entrepreneurs (Caliendo et al. 2009, 2010; Fairlie and Holleran 2012). Hence, it implies that entrepreneurship and risk aversion are oxymorons, whereby entrepreneurs show the willingness to bear risks. Even though entrepreneurs are keen on taking risks, according to Harvard Business Review (HBR 2018), 75% of entrepreneurial ventures fail within 10 years. Carter (2019) has outlined eleven common reasons for failures of entrepreneurs, such as; lack of capital, poor understating about the market, unrealistic goals, trying to become a jack-of-all-trades, lack of vision, weak marketing, poor planning, reluctance to accept constructive criticisms, not delegating, lack of soft skills and burnout. Entrepreneurs often face unforeseen business challenges of different kinds, which requires particular personality characteristics, such as risk tolerance and readiness to take risks for entrepreneur. Ultimately, there is the need to distinguishing entrepreneurial venture and small business since both play a major role in economic development. Entrepreneurial ventures focus on strategic objectives including market share, target market, market development and market position, whereas small businesses focus on profit targets, sales and survival (Amolo and Migiro 2017). That is, entrepreneurial ventures are involved in innovating, unlike small businesses which thrive on establishing products and services. Nevertheless, entrepreneurs are not only limited to innovations, they also impact on completion, productivity and structural change there fore considered crucial in overall economic performance (Ed 2016; Kritikos 2015).

Entrepreneurship and the Economy Schumpeter’s view (1911) emphasised the importance of the entrepreneur process in the economy development of a region or a country. This relationship has been widely reviewed by researchers (Kritikos 2014; Litau 2018; Malecki 2018; Ranjan 2019; Stephens and Partridge, 2011; Sautet, 2013). There is some consensus that human creativity and entrepreneurship need to be combined in a profitable

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way in order to become the ultimate factors on the development of the economy. Entrepreneurship activities aid to reduce unemployment, which is an issue of considerable importance particularly in underdeveloped countries (Sudha 2015). Furthermore, entrepreneurship affects poverty and destitution considered a greater income generation contributing to create higher living standards and with the capability to boosting the quality of the life of underprivileged in society. Entrepreneurship impacts the economy as it aids to create, or innovate new businesses to meet the demands of people and the markets (Sudha 2015; Kritikos 2014; Litau 2018; Malecki 2018; Stephens and Partridge 2011). In an ever increasing global world entrepreneurial new businesses or enterprises, through exportaction of goods or services to adjoining regions strengthen the economies by boosting incomes. Moreover, by the high reliance the new technologies and innovative ways of creating products or delivering services they impact on the country’s economic development innovative uses (Ranjan, 2019). Despite many positives are evident, it is argued that under certain circumstances the macroeconomic impact of entrepreneurship could be negative. Entrepreneurs ought to consider what to produce, in what quantities and how to produce and for whom, whilst considering the environmental impact of economic growth (e.g. by increasing consumption of non-renewable resources, or by generating higher levels of pollution, or by contributing to global warming or to the potential loss of environmental habitats, Neumann 2021). Therefore the relationship between the entrepreneurship benefits to economic growth is complex and multifaceted (see further the empirical studies carried out by Fritsch and Mueller (2008); Andersson and Noseleit (2011); and Carree and Thurik (2008) amongst other. Yet, overall Entrepreneurship is acknowledged in research and practice as having a critical role and solutions for many economic affairs.

Technology in the Business World The official origin of technology-based companies can be traced with the launch of World Wide Web in 1989, which is known as a dotcom boom, whereas rampant investments into technology stocks took place. Since

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access to the World Wide Web became more popular across the world, in the mid-to-late 1990s, the world attention turned to internet-based firms seen as the future of business. This named ‘irrational exuberance’ lead to extreme market conjectures and many hasty “fad” investments resulting in the end of the dotcom boom in the early 2000s. With the dotcom bubble burst, tumbling the stocks of many tech firms esulting that, many popular online firms declared bankruptcy such as Pets.co., Boo.com, Webvan and eToys. However, many other companies which struggled and survived have now become giants, remarkably Amazon, Microsoft, eBay and Cisco. Since the dotcom bubble crash, technology has developed in many ways. According to World Economic Forum (2021), in 2000, less than 7% of the the world population was online, whereas at present over half the global population has access to the internet. Meanwhile, the usage of mobile phones has extensively increased during last two decades. That is, in the early 2000s there were around 740 million mobile phone users worldwide whilst at present this number has exceeded 8 billion, which indicates that currently there are more mobile phones than people in the world. More significantly, a new report published by PwC (2021) highlights that the demand for internet connectivity was driven to new heights in 2020 by the global pandemic, whereas global data consumption increased more than 30% in 2020 compared to the previous year. In the early 2000s, a chain of web 2.0 technologies such as E-Commerce, EBusiness, E-Learning, cloud computing and social media platforms were introduced. Now there are numerous internet-based technologies that have countless applications in many contexts, such as business, science, engineering and healthcare. A few of these relevant technologies for the business world are namely Internet of Things (IoT), Big Data Analytics and Blockchain.

Internet of Things The IoT has brought a paradigm shift in the technological era, as well as to business world by creating a global network of machines and devices which have accelerated the development of digital innovation

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in enterprises. IoT is slithering into the human life and business activities through embedding day-to-day objects with sensors and control systems. Ng and Wakenshaw (2017) have defined IoT “as a system of uniquely identifiable and connected constituents (termed as Internetconnected constituents) capable of virtual representation and virtual accessibility leading to an Internet-like structure for remote locating, sensing, and/or operating the constituents with real-time data/information flows between them, thus resulting in the system as a whole being able to be augmented to achieve a greater variety of outcomes in a dynamic and agile manner ” (p. 6). It is widely accepted that enterprises that adopt technologies especially IoT into their business models have higher opportunities for innovation and to become more competitive (Krotov 2017); Langley et al. 2021). Likewise, IoT has become vital to improve existing processes and daily routines as it enables businesses digitalisation (Krotov 2017). Meanwhile, different physical devices can be connected to the internet with the use of IoT and it allows the continuous data exchange over the internet. Accordingly, by collecting and analysing these datasets, enterprises are able to boost their efforts to understand and forecast customer behaviours (Lo and Campos 2018). However, it is important to note that analysing such vast amount of datasets with traditional data-processing applications is not possible giving rise to the concept “Big Data”, which will be discussed in the next section. Enterprises can make more effective decisions by leveraging the data collected and analysed through IoT and Big Data. Amazon Go is one of the best examples that can be given which thrives on using IoT technology by partially automating the in-store retailing experience. Recently, extending the concept of IoT, Internet of Everything (IoE) has been introduced, whereas IoE provides connectivity not only among things but also among people, data and processes (Yang et al. 2017). Accordingly, IoE is defined as “a network of connections between smart things, people, processes, and data with real-time data/information flows between them” (Langley et al. 2021, p. 853). Various connection and internet-based paradigms such as IoT, Internet of People (IoP) and Industrial Internet (II) come under the IoE umbrella (Yang et al. 2017). IoE generates unparalleled prospects for individuals, enterprises and communities to understand greater usefulness of networked connections

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among data, people, processes and physical objectives (Krotov 2017). However, despite the new value propositions and business opportunities it can offer entrepreneurs and established companies, IoE also brings considerable threats to organisations, such as interoperability among systems, coping with stagnant industry partners, liability issues, pathdependent processes, security and privacy concerns and loss of control (Krotov 2017; Langley et al. 2021). Hence, it is crucial for organisations to consider and understand the degree to which smart things will transform existing business models. That is, disruptive innovations once they mature they can completely change the businesses landscapes (e.g. making existing investments and competences of market leaders obsolete, whilst creating opportunities for smaller startups).

Big Data and Big Data Analytics As previously mentioned, processing large datasets using traditional applications is not possible anymore, hence the concepts Big Data and Big Data Analytics (BDA) emerged and have received substantial attention in the technology world. Big Data can be introduced as large datasets with varied and complex structures, which are difficult to store, analyse and visualise for further results or processes (Lansley and Longley 2016). Nowadays, the total amount of capturing, creating, copying and using of data is rapidly increasing. Therefore, it is forecasted that the volume of data created globally will grow more than 180 zettabytes by 2025 (Statista 2021). Meanwhile, BDA is the process of collecting, organising, analysing and visualising constantly evolving data (Marjani et al. 2017). BDA enables to examine large datasets with various data types, to expose hidden patterns, unseen market trends, correlations, consumer behaviours and other vital business information to understand the current state of the business, past performance, customer experience and to predict future trends both internal and external (Rehman et al. 2019). Due to the vast benefits, of having informed decision-making contemporary businesses show a tendency towards adopting BDA. According to Big Data and AI Executive Survey—2021, conducted by NewVantage Partners (2021), 99% of Fortune 1000 companies—1000

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largest American companies maintained by Fortune magazine report active investment in BDA and AI. This implies that investments in BDA and AI have become nearly universal and a fruitful arena for entrepreneurial innovation in tandem with the ever increasing technological developments. It is projected that BDA market will develop with a rapid annual growth rate nearly 30%, whereas its revenue will reach over 68 billion USD by 2025 which used to be around 15 billion USD in 2019 (Statista 2021).

Blockchain Technology As of now, the area of loT applications has become extensive that it has scoped from transportation, digital supply chain, environment monitoring, food processing, security and surveillance, manufacturing, and healthcare amongst other (Wang et al. 2021). In the contemporary digital era day-to-day enormous amounts of data are being transmitted to centralised systems, private sector and cloud to name a few. These systems contain software that makes possible to perform multiplex computation like algorithms to process and scrutinise huge amounts of IoT data simultaneously. When engaged in the operations of platforms like cloud, makes IoT-based services easily available and accessible online, thus providing people with the need for countless security protections. Therefore, nowadays, developing business models based on IoT face numerous challenges and difficulties, since it has also created issues of privacy and reliability. Depending on centralised systems to handle confidential and sensitive data of individuals has generated high privacy risks, which has led to unauthorised access or illegal discloses of data and/or data selling (Leinonen 2016; Banafa 2017; Liang 2017). This in turn has made it difficult to trust services given by IoT devices, since at times organisations are not capable of controlling the software and hardware employed by such devices (Novo, 2018; Agrawal et al. 2018). As example IoT devices regularly face vulnerabilities due to security whereas unsecured IoT devices provide an easy target for cyber-criminals to exploit the weak security protection to hack them into launching Distributed Denial of Service attacks. Yet, using blockchain to store

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IoT data would add another layer of security that hackers would need to bypass in order to get access to the network (Deloitte 2022). These concerns have increasingly highlighted the need for more secure systems. When considering the factors that contribute to the growth of IoT applications it clearly emerges that a distribution of trusted technology is pivotal, but also ensuring business scalability, security and privacy (Panarello et al. 2018). Modern Blockchain technology seems to address these needs and is increasingly being recognised as reliable and suitable to use with IoT environments (Banafa 2017; Liang et al. 2017). Blockchain has been seen as the principal technology which supports cryptocurrency such as Bitcoin (Queiroz and Wamba 2019). In the study of Viriyasitavat and Hoonsopon (2019) blockchain has been defined as “A technology that enables immutability and integrity of data in which record of transactions made in a system are maintained across several distributed nodes that are linked in a peer-to-peer network” (p. 33). This definition postulates that Blockchain technology is reliable by its design. More specifically, a Blockchain has a record of digital events (Yamin 2019a), which is a distributed ledger technology administrated by a consensus mechanism and safeguarded by cryptography (Yamin 2019b). Asymmetric cryptography and distributed consensus algorithms are used to ensure ledger consistency and user security in the Blockchain, whlist it removes the requirement of a lawyer to certify an event, or within the banking industry for example, of a bank statement to verify funds availability (Yamin 2019b). Blockchain technology is usually associated with cryptocurrencies, however, its applications have been enlarging in various areas such as finance, law enforcement, education, healthcare, supply chains and manufacturing (Queiroz and Wamba 2019). Therefore, the secure use of cryptocurrencies is enabled by the Blockchain technology is a significant technological development that is increasingly been successfully transferred to other business areas increasing confidence with the IoT and AI applications and innovations (Queiroz and Wamba 2019; Akter et al. 2020; Gligor et al. 2021). Blockchain in-built characteristics such as consistency, decentralisation, transparency, accountability, persistency equality and auditability, make its development and transferability vital to the innovation of businesses in our contemporary digital era, (Zheng et al. 2017; Al-Saqafa and Seidler 2017).

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Technology in the Business World In the world of business where opportunities are developing every second, technology plays a significant role. As previously identified Blockchain technology, which has enabled data and process security has emerged as paramount in the global world of business. The collection of data at an accelerated level has directly impacted the growth of today’s market. Blockchain has the ability to coordinate in transparent data sharing, perfecting the business processes, turning down the operating costs and advancing in collaborative efficiency and effectiveness (Yamin 2019b). Meanwhile, this has created new ways of green production as well as examining, observing and storing data, related with population and environmental degradation (Saberi et al. 2019). The blockchain system assists in analysing actual green or low carbon data, which in turn enables the possibility of timely decision-making. All these enhancements provide opportunities and pave the path for business advancement, innovation and adaptive modifications in supply chain that contribute to sustainable development (Bai et al. 2020). Huckle et al. (2016) have described scenarios about how IoT and Blockchain Technology have helped to contribute to effective distributed applications. For example, considering energy trading as the first application sector to electronically and automatically transfer data; IoT sanctioned autonomous machine-to-machine (M2M) trading between commercial machines. Using blockchain technology this trade has shaped into various forms like grids, energy harvesting networks, electricity trading and vehicle to get rid network (Huckle et al. 2016). Li et al. (2018) suggested that consortium blockchain securely distributed energy trading in the industrial internet of things (IIoT). As a result of in-depth concerns on the transaction confirmation delays, a specific credit-based payment scheme has been invented to aid in fast trading, which has resulted in frequent peer-to-peer (P2P) energy trading via energy-coin loans. Nevertheless, “time delay” is still perceived as a major obstacle and drawback since the system relies on proof-of-work (POW) as a general agreement. Sikorski et al. (2017) have suggested another solution for the use of blockchain when it comes to electricity trading. Blockchain Technology

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is normally used to ease M2M interactions to automatically interchange information of electricity and cost of offering. Among several techniques introduced, the method of locking and atomic exchange are assimilated to maintain the consistency of trading located between energy and cryptocurrency blockchains. However, security analysis and other social factors have not yet been assessed properly and explanation of how IoT devices transfer information is lacking. As mentioned earlier, IoT devices are able to collect and scrutinise relevant data at an unparallel level of granularity. All the data collected using IoT are of much significance and can be used later for further remuneration. However, in practice third-party involvement in exchanging paid information has become an unavoidable fact, which has led to generate extra expenses, and security and privacy problems. Meanwhile, this has also slowed down the efficiency of the process while hampering the extension of IoT applications. Due to these growing concerns, ChainAnchor (Hardjono and Smith, 2016); a blockchain-based architecture has been constructed for authorising IoT devices into a cloud ecosystem. This structure assists device owners to be remunerated for selling device sensor-data to service providers yet acting as a safeguard for the anonymity of the owner, however, this systematic approach is still arduous, since its protocol requires multiplex asymmetric key factors related to management (Viriyasitavat et al. 2019). Therefore, it is not compatible for many IoT devices, which do not have adequate resources. The reliability of IoT data depends on a broker that leads to inevitable problems related to centralisation to address those Zhang and Wen (2017) developed an advanced architecture for IoT-based E-business model using Bitcoin, which expanded the scope of authorisation. The IoT E-business model however, differs from conventional E-business model since the main focus of many conventional E-business models is with financial features, infrastructure management, innovations and customer relationships. The important aspect to emphasise is that the IoT E-business model is a broader concept, which is linked with Blockchain technology as well. In fact, Zhang and Wen (2017) study developed this new business model with the use of IoTcoin, which is designed along with Blockchain technology to provide authorisation for the transaction of IoT commodities.

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Nonetheless, even after all of the technological developments used, the accuracy of transactions made and the security of the communications between these two systems; ChainAnchor and IoTcoin they have not still met the proficiency and security levels expected (Viriyasitavat et al. 2019). The use of Blockchain technology has opened the gateways for shared services to be completely automated, which is the most significant factor of service relationships among people, organisations and applications, however, its developments and wider applications are still in its infancy (Viriyasitavat et al. 2019). Currently, researchers and practitioners show a great interest in developing new business models which involve higher efficiency and effectiveness by establishing trust without trusted third parties, however, how these innovations will translate into new business entrepreneurial business opportunities is still unknown and a challenge for entrepreneurs and technology developments.

Entrepreneurship and Technology Nascent Entrepreneurs and Technology The concept nascent entrepreneurs came to debate receiving a considerable attention, owing to the Panel Study of Entrepreneurial Dynamics (PSED) research program (Curtin and Reynolds 2018) and Global Entrepreneurship Monitor (Harding et al. 2008). A nascent entrepreneur is defined as an individual who is currently setting up a new business, expecting to be an owner or part-owner, and who has been actively engaged with to start of the new business for the past 12 months and has already commenced the start-up, however, without a positive monthly cashflow to cover expenses for more than three months (Reynolds 2000; Reynolds et al. 2004). One of the most significant economic activities in any society that strive for economic growth is new business formation and nascent entrepreneurs actively engage with new business creation (Wagner 2006). Hence, nascent entrepreneurs have become key players in developing economies and generating revenue.

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Entrepreneurial self-efficacy is a key antecedent and determinant of nascent entrepreneurship behaviours (Mwiya et al. 2019). Individuals who deem that they possess entrepreneurial competencies such as being strategic, organised and dedicated to new enterprises are more likely to become nascent entrepreneurs (González-López et al. 2020; Bayon et al. 2015). Moreover, research has identified that success and development of nascent entrepreneurs are contributed by certain factors: entrepreneurial experiences (Watson and McGowan 2019), pre-venture assistance programmes (Lanivich et al. 2021) and core self-evaluations (Lanivich et al. 2021). Further, social cognitive theory has been suggested as a valuable theoretical framework to explain entrepreneurial characteristics (Hmieleski and Baron 2009). Bandura (1977), theory emphasise the influence of internal and external social reinforcement on a person. Social cognitive theory posits that past experiences, actions of significant others and environmental factors impact on beliefs, expectations and reinforcements determining whether individuals will exhibit specific behaviours and provide explanations for the reasons of such behaviours (Bandura 1977). It claims individuals tend to observe and imitate role models altering their behaviours and beliefs accordingly, which over time lead to develop self-efficacy (Bandura 1997). Hence, motives for nascent entrepreneurs can be made sense through the lenses of social cognitive theory (e.g. research identified that some individuals who plan to become entrepreneurs show a tendency to study the life stories of successful entrepreneurs and follow them) (Belchior & Lyons, 2021; Lanivich et al. 2021; Brändle et al. 2018). However, wide research also has shown that nascent entrepreneurs often terminate their journey even before they start it, due to fear of failure (Davidsson and Gordon 2016; Khan et al. 2014; Cacciotti and Hayton 2015; Morgan and Sisak 2016; Kollmann et al. 2017). Therefore, developing strategies to overcome fear of failure is highlighted as pivotal for nascent entrepreneurs.

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Technology Entrepreneurship–Technopreneurship Technology entrepreneurship/ technopreneurship is considereds one of the more effective forms of entrepreneurship due to its higher contribution towards commercialisation of innovations, competitive position and economic development (Àcs et al. 2014; Plummer and Àcs 2014). This concept is also called e-entrepreneurship, digital entrepreneurship technopreneurism or cyber-entrepreneurship (Davidson and Vaast 2010; Hafezieh et al. 2011; Ngoasong 2015). Even though technology entrepreneurship has become popular in the twenty-first century, its origins can be traced from the twentieth century as well. The first gathering to exchange findings and observations on the topic happened in October 1970, which was the first symposium on technology entrepreneurship held at Purdue University (Bailetti 2012). In 1995, Jones-Evans defined technology entrepreneurship as an establishment of a new technology venture, whilet Nichols and Armstrong (2003) claimed that technology entrepreneurship is involved in organising, managing and assuming the risk of a technology business, or enterprise. Bailetti (2012) provided a more inclusive definition wherein, “Technology entrepreneurship is an investment in a project that assembles and deploys specialised individuals and heterogeneous assets that are intricately related to advances in scientific and technological knowledge for the purpose of creating and capturing value for a firm” (p. 9). According to this definition, technology entrepreneurship involves creating and capturing value for an organisation through projects which amalgamate, specialised individuals and heterogeneous assets to adopt and produce technology. Moreover, Bailetti (2012) claimed that technology entrepreneurship relies on scientific and technical knowledge to fulfil the ultimate outcomes of creating and capturing value. A more recent definition of this concept can be found in the study of Kordel and Wolniak (2021), who argued that, technological entrepreneurship is the “specific configuration of strategy and organisational structure around the core of new technologies” (p. 3). In this definition organisational configuration refers to the capability of dynamically allocating resources within a business model to respond to unpredictable changes in the environment.

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Technology entrepreneurs or technopreneurs consider technology as a fundamental element of the value proposition (Oestreicher-Singer and Zalmanson 2013). Hence, the chnopreneurs go beyond than just using technologies since they believe that adopting new technologies increases productivity, which drives their economic growth (Krammer 2015). Further, the widespread of technological organisations in a region leads to regional development and economical success (Du and Meng 2020; Mantaeva et al. 2021). Within the contemporary business world of the digital era, access to information regarding new technologies and opportunities is ever-increasing (Elia et al. 2020). Technopreneurs however, are highly depend on information to reduce uncertainty and manage knowledge flow (Shane and Venkataraman 2003). Therefore, information availability and accessibility plays a vital role in technology entrepreneurship (Vrontis et al. 2017; Wellalage et al. 2021). Research has revealed that availability of information impacts on a country’s rate of technopreneurship, whereas greater information availability boosts technopreneurship (Yeganegi et al. 2021). Additionally, many researchers have claimed that social media is a key source in generating rich information with unprecedented multifaceted insights, driving innovations (Bhimani et al. 2019; de Zubielqui and Jones 2020). By and large, there is some consensus that information availability is vital for technopreneurs, however, there is disagreement over what and how information accessibility may actually hinder the growth of technopreneurs (e.g.social media and AI misuse for personal branding) (Motoyama et al. 2018; Cantwell and Salmon, 2018; Yeganegi et al. 2021). Nevertheless, it is emphasised that the use and ever-increasing development of new digital technologies have changed the fundamental nature of entrepreneurship and innovations—how to deal with growing uncertainty inspires radical thinking an technopreneurs creative endeavours (Nambisan 2016; Nambisan et al. 2017).

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Technological Entrepreneurship Ecosystem/Digital Entrepreneurship Ecosystem First of all, it is important to understand the meaning of the concept “ecosystem”. An ecosystem refers to an interacting system consisting of all the living and non-living things in a particular area (Weathers et al. 2021). Roots of this concept can be found in biology and ecology, however, frequently appears in different socioeconomic domains as well. Various notions linked with ecosystem are identified in the business management literature, such as industrial ecosystem, business ecosystem, innovation ecosystem, entrepreneurship ecosystem and digital business ecosystem (Pilinkien˙e and Maˇciulis 2014). The industrial ecosystem was firstly presented by Frosch and Gallopoulos (1989). They argued that individual manufacturing processes should be converted into a more integrated model: an industrial ecosystem. In an industrial ecosystem, “the consumption of energy and materials is optimized, waste generation is minimized, and the effluents of one process- whether they are spent catalysts from petroleum refining, fly and bottom ash from electric-power generation or discarded plastic containers from consumer products-serve as the raw material for another process” (Frosch and Gallopoulos 1989, p. 144). Hence, according to them, it is suggested that an industrial ecosystem focuses on sustainable development followed by environmental protection, which could operate for manufacturers and consumers alike as a solution of waste management for example (Galateanu and Avasilcai 2014). The business ecosystem metaphor, originally highlighted by Moore (1993) emphasises the need to view an organisation “not as a member of a single industry but as part of a business ecosystem that crosses a variety of industries” (p. 76). The Business ecosystem highlight the significance of inter-organisational relationships (Jacobides et al. 2018) and its value proposition strongly depends on a set of complementary contributions—accepting that value cannot be delivered by one organisation alone (Jacobides et al. 2018; Talmar et al. 2020). The rise and growth of business ecosystems have created a completely new world for managers who need to learn new ways of competing (Adner 2017).

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The concept of business ecosystems has open the way for new competitive landscapes (Jacobides et al. 2018) where powerful organisations might lose power, whilst (Gueler and Schneider 2019 unknown, or unexpected powerful organisations might emerge (Gueler and Schneider 2019, 2021). However, it appears that in order to become powerful business ecosystems, require the possession of sustainable and valuable resources. The Innovation ecosystem concept origin can also be traced from the business ecosystem literature to Moore (1993). When he first used the concept of business ecosystem he stated that organisations “coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations” (Moore 1993, p. 76). Here he, highlighted the significance of inter-organisational relationships as well as how innovations contribute to the development of new organisational capabilities. Since then, numerous definitions of innovations ecosystems have been presented by different scholars culminating in the contemporary definition by, Klimas and Czakon (2021) who suggest that innovations ecosystem are usefully defined as: a “cooperation environment surrounding the innovation activities of its co-evolving actors, organised across co-innovation processes, and resulting in co-creation of new value delivered through innovation”. According to them innovation ecosystems condense the innovation processes run by different involved parties, thus, it is not limited to one co-innovation process or processes conducted by one central organisation or party. It corroborates that both business and innovations ecosystems are related and importantly highlight the value of inter-organisational relationships and collaboration. However, it has been argued that innovation ecosystem definitions and practices prominently highlight collaboration and the need for complementarity of inter-organisitional actions and processes, yet does not provide enough focus on competition processess, substitutes and artefacts (Granstrand and Holgersson 2019; Gomes et al. 2018; Granstrand and Holgersson 2019). This seems to be a fair argument since we ought to consider that although collaboration is vital with business innovation, inevitable competition does exist in the business world and be discerning of when

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it may not be practical or advisable to have joint endeavours with competitors. Further, entrepreneurial ecosystems have become a popular topic in the field of entrepreneurship. According to Spigel (2017), “entrepreneurial ecosystems are combinations of social, political, economic, and cultural elements within a region that support the development and growth of innovative start-ups and encourage nascent entrepreneurs and other actors to take the risks of starting, funding, and otherwise assisting high-risk ventures” (p. 50). The main aim of entrepreneurial ecosystems is the interactive activities related to resource allocation, developing networks and creating opportunities among entrepreneurial and other actors to establish a broader ecosystem (Chen et al. 2020). Consequently, entrepreneurial ecosystems are considered sources of employment, innovation and economic development. There are 12 common elements identified by Chen et al. (2020), which are vital to sustain and support entrepreneurial ecosystems in regions, namely; “government policy (e.g., policy support, tax incentives), culture, human capital, financial capital, entrepreneurship organisations, education, infrastructure, economic clusters, networks, support services, early customers, and leadership” (Chen et al. 2020, p. 6). Technological entrepreneurship ecosystem is a relatively new concept, which has emerged upon the foundation of the various ecosystem concepts described above, such as: innovation ecosystem, business ecosystems and entrepreneurial ecosystems. Based on a study conducted by Maysami and Elyasi in 2020 adopting a grounded theory approach, the technological entrepreneurship ecosystem is portrayed consisting of four main domains: environmental conditions, entities, functional areas and technological entrepreneurship agents (see Table 13.1 below).

Digital Transformation and Entrepreneurship The rise of a miscellaneous set of new and powerful digital technologies, digital devices, digital infrastructures and digital platforms has transformed both entrepreneurship and innovations in noteworthy ways—termed as digital transformation (Nambisan 2017; Nambisan

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Four main domains in technological entrepreneurship ecosystem

Main domain

Description

Environmental conditions

Covers the context and external factors such as Technological, Economic, Political and legal, General social and cultural conditions and Natural and geographical conditions These are mostly national issues that can affect the Technological entrepreneurship ecosystem and also being affected by it Entities and their actions form and affect the functions of ecosystems. Each one of these entities should perform special roles and also possess specific features in order to have a healthy ecosystem: Governmental entities, Education and research entities, Support entities, Commercial entities, Promotion entities and liaisons, Investment entities, Basic social entities and People Core internal functions and essential resources for the ultimate performance of the ecosystem Two types of realms are identified (1) role-based realms that cover the key necessary activities of the ecosystem, (2) resource-based realms that present the required resources for the ecosystem functioning Support creation and growth of tech-entrepreneurial firms The realms are: Support services, Knowledge, technology, equipment and materials, Accreditation and control services, Education and training, Research, Financial sources, Policies and regulations, Linking services, Promotion, Talent, Culture and Market

Entities

Functional realms

(continued)

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Table 13.1

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(continued)

Main domain

Description

Main technological entrepreneurship agents

There are three key agents namely: Technological entrepreneurs, initial entrepreneurial teams and Technology-based entrepreneurial firms

et al. 2017). Digital transformation indicates the significance of radically transforming businesses in the emerging digital world to become successful (McAfee and Brynjolfsson 2017; Venkatraman 2017). Meanwhile, digitalisation of innovations and entrepreneurship has brought substantial benefits such as amplified regional and national entrepreneurial activities, increased productivity and wider economic and social gains (Burtch et al. 2018; Kenney and Zysman 2016). Moreover, new work structures have emerged due to digital infrastructures, which are reshaping industrial boundaries and improving national and regional economic growth (Malone 2018). Digital transformation and the fourth industrial revolution (also known as Industry 4.0) have been fuelled by digital technology (European Commission 2017), due to its unparalleled convergence of communication, between computers, digital devices and humans (Bryniolfsson and McAfee 2014; Tapscott 2014). According to Nambisan (2016), in the context of entrepreneurship, digital technologies appear in the form of three distinctive but related elements: digital artefacts, digital infrastructures and digital platforms. Digital artefacts refer to digital application components, or media content, which are part of new products, or services and it provide certain values or features to end-users (Kallinikos et al. 2013). Such digital artefacts have unleashed numerous opportunities for entrepreneurs in different fields (Elia et al. 2020). A digital infrastructure is defined as “digital technology tools and systems (e.g., cloud computing, data analytics, online communities, social media, 3D printing, digital makerspaces, etc.) that offer communication, collaboration, and/or computing capabilities to support innovation and entrepreneurship” (Nambisan 2016, p. 4). E.g. Mobile telecom and digital communication suites, including apps and enterprise portals,

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platforms, systems, and software. Digital platforms represent common and shared sets of services and architectures that serve to host complementary offerings, including digital artefacts (Nambisan 2016). For example Apple’s iOS platform facilitates apps to run on iPhones. Digital platforms have empowered industry transformation whilst creating new foundations for industry leadership and ecosystem innovation (Gawer and Cusumano 2014). Digital technologies bring multifaceted roles (e.g. facilitator and mediator), and possibilities to create innovation (e.g. a whole new or partially business model) to entrepreneurs, (Steininger 2018). Digital entrepreneurship is considered as the juncture linking entrepreneurship and digital technologies. Accordingly, creation of new economic activities enabled by digital technologies is named digital entrepreneurship (von Briel et al. 2021). The term “new economic activities” refer to any entrepreneurial pursuit of opportunity including the creation of new ventures, transformation of existing businesses or intrapreneurial endeavours (Shepherd et al. 2019). Digital entrepreneurship enables the acquisition, exchange and transfer of knowledge, whilst engendering new ways of doing businesses (Geissinger et al. 2019).

Entrepreneurial Opportunities and New Technology Digital technologies facilitate entrepreneurial endeavours in various ways. Entrepreneurial activities engage with creating and commercialising new value propositions that are enabled by digital technology (von Briel et al. 2021). For instance, there is a new booming wave of digital hardware ventures due to the improvements in digital technologies for prototyping, developing and commercialising digital hardware (von Briel et al. 2018). Consequently, physical prototyping has become faster and cheaper for entrepreneurs, with the emergence of low-cost platforms for electronics development such as; 3D printers, Arduino and Raspberry Pie. Moreover, some digital platforms such as Airbnb and Uber connect multifaceted demands and highly personalised offerings. Meanwhile, crowdfunding has also become easier with digital platforms such as; IndieGoGo or Kickstarter. The diffusion of digital technologies have

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created very successful entrepreneurs such as Elon Musk (Tesla, SpaceX), Jeff Bezos (Amazon), Bill Gates (Microsoft) and Mark Zuckerberg (Facebook) who have become the world’s richest people (figuring on Forbes World’s Billionaires List in 2021).

Sustainability in Technology Entrepreneurship Sustainable development is a key concept in the contemporary world, which has been becoming increasingly prominent since the 1980s. A special attention to sustainable development was given by professionals, academics and scientists after the publication of the report “Our common future” by Brundtland Commission—known as World Commission on Environment and Development (WCED). This report highlighted the over usage of Earth’s resources, and alerted the world that danger that future generations may not have enough resources in the future. The WCED (1987) defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (p. 49). This emphasis has been continuing ever since and operationalised with the United Nations 17 Sustainable Development Goals, under “The 2030 Agenda for Sustainable Development” (United Nations 2016). It reflects a focus on human basic needs such as; food, air, water and shelter, which are fulfilled by the environment. Nevertheless, since the 1980s, the environmental balance has been becoming more fragile due to the expansion of human footprint, which has been increased with industrial and technological advancements. In spite of industrialisation and technology progresses being vital for the development of society and economic growth, it is causing environmental challenges such as; climate change, deforestation and marine pollution. Hence, the need for a greater environmental focus has risen. Yet, research suggests that there is a deprivation of manufacturing industries with eco-efficiency (Ezici et al. 2020) and it is claimed by many that industries per se are a threat to the environment (Khan et al. 2019). Scholars and scientists from different disciplines (environmental studies, economics, ethics and entrepreneurship) deem that even though

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industries may create environmental problems and threats, they also play a chief role in addressing these threats and solving them effectively (Hörisch et al. 2017). Accordingly, the contribution of small and medium enterprises (SMES) and of entrepreneurs towards resolving environmental challenges and treats through sustainable innovation has gained rising attention (Meyskens and Carsrud 2013; Hörisch et al. 2017). We have seen before that financial value creation has been the main motive of traditional entrepreneurship (Schumpeter 1934). Due to the growing awareness of social and environmental issues globally, the central motive of entrepreneurship has shifted to integrating social and environmental focus together with fundamental business activities. Meanwhile, research studies have identified different forms of entrepreneurship, which surpass the aim of profit maximisation and which are gaining increasing consideration in our contemporary worlds (McMullen and Warnick 2016; Muñoz and Cohen 2017; York 2018). According to Horisch (2016), entrepreneurship is crucial in nurturing sustainable development as it has the ability to develop operations and processes that are innovative and importantly sustainable. For many, entrepreneurship has been considered as a key answer for environmental and social challenges (Muñoz and Cohen 2018). It has been argued and evidenced that through entrepreneurial actions and adaptive and responsible use of technology the impact of climate change can be neutralised, ecosystems preserved, deforestation minimised, natural water supply protected, agricultural practices improved and biodiversity sustained (Cohen and Winn 2007; Dean and McMullen 2007; George et al. 2021; Niehoff 2022; Anand et al. 2021). Furthermore, education, socioeconomic standards, self-reliance, physical health and productivity can also be enhanced through entrepreneurial activities whilst contributing to reducing poverty and inequality (Wheeler et al. 2005). These environmental and social gains are highlighted by the United Nations (2016) under “The 2030 Agenda for Sustainable Development”, representing the sustainable development goals to be achieved by 2030. Hence, it reflects that the United Nations itself highlights the requirement of entrepreneurship to be combined with environmental, social, political and technical aspects to work collaboratively to address

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environmental challenges and treats whilst promoting innovation and sustainable economic growth. Hart and Milstein (1999) made the initial attempts to bring the concepts of entrepreneurship and sustainable development together. They emphasised that sustainable development is a crucial opportunity for innovators and entrepreneurs. Consequently, the notion sustainable entrepreneurship has been developed which combines the traditional entrepreneurship with environment and society (Cohen and Winn 2007; Gibbs 2006), this has been named as sustainoentrepeneurship (Aghelie et al. 2016). As per, Shepherd and Patzelt (2011), “sustainable entrepreneurship is focused on the preservation of nature, life support, and community in the pursuit of perceived opportunities to bring into existence future products, processes, and services for gain, where gain is broadly construed to include economic and non-economic gains to individuals, the economy, and society” (p. 156). This definition implies that sustainable entrepreneurship engages with a broader spectrum than traditional entrepreneurship, and focusing on environmental, social and economic aspects. Hence, it reflects that there is a close relationship between sustainable entrepreneurship and the concept triple-bottom-line (TBL). Therefore, it may be argued that the conventional profit maximisation paradigm (financial motive) has been challenged by TBL paradox theory (Walker et al. 2020), which claims that there should be three driving components for a sustainable business: financial, environmental and social motives (Attig and Cleary 2015; Gelhard and von Delft 2016; Glavas and Mish 2015; McWilliams et al. 2016). Consequently, the concept of environmental entrepreneurship— referred to “ecopreneurship” emerged and its first presence can be observed in 1990s. Environmental entrepreneurship is defined as the process of applying entrepreneurship to create and develop businesses that answer to environmental issues or function sustainably (Gregori et al. 2021). This form of entrepreneurs are driven by values and beliefs and their business efforts do not only focus on profits, but also on concerns with environmental justice and to protect nature (Schuyler 1998; Kirkwood and Walton 2010). Environmental entrepreneurs exhibit their ecology-oriented values through their entrepreneurial

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endeavours (York and Venkataraman 2010), whereas they create ecofriendly businesses combined with profit orientation, which develop a greener business world (Thompson et al. 2011). They notably create a hybrid identity by blending commercial and environmental aspects together (York et al. 2016). It is suggested that, this type of entrepreneurship has the capability to change business dimensions while providing new opportunities to business who practice it to be ahead of the competition (Kumar and Kiran 2017). Taking the triple bottom line framework into account, “ecopreneurs” can be seen as a part of “sustainable entrepreneurship” (Patzelt and Shepherd 2011). The concept of green entrepreneurship is also related to environmental and sustainable entrepreneurship, which also emerged due to the growing concerns with environmental issues. Green Entrepreneurship is the process of developing new products and technology to address environmental issues (York and Venkataraman 2010). This is a new category of entrepreneurship that emphasises environmental stewardship. It combines a strong entrepreneurial spirit with an appreciation for sustainability and other environmental movements. Green entrepreneurship links with green markets and green innovations. The application of market orientation to achieve greater environmental and societal benefit is known as “green market orientation”. Green marketing initiatives are put in place by firms to help reduce the environmental impact of their operations and the goods offered (Ahmadzadeh et al. 2017). Green innovation focuses on the development of environmentally friendly products and methods that can meet regulatory criteria to safeguard the environment (Chen et al. 2006). Green innovation and green marketing are associated with better corporate success (Hasan and Ali 2015). Many aspects of the venture creation process, such as the necessity to innovate, to take risks and to coordinate resources (Schumpeter 1934), will be common to social and commercial entrepreneurs. In this respect, the two occupations may draw on a similar entrepreneurial talent pool. However, while social and commercial entrepreneurship both create value, they differ in the primary objective of the activity. Social entrepreneurs identify opportunities arising from “neglected problems in society involving positive externalities”, which are neither incorporated into the market nor addressed by the government (Santos 2012,

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p. 342). By realising those opportunities, social entrepreneurs create “social welfare” (Mair and Marti 2006; Zahra et al. 2009), while taking the financial viability of their venture as a constraint. In contrast, commercial entrepreneurs maximise “private welfare” by creating value while capturing the residual for themselves (Santos 2012). Because the goals and the way in which value is created differs for social and commercial entrepreneurs, they may need to rely upon different skills and abilities; implying that the two types of entrepreneur may not be drawn from exactly the same pool of talent. Social entrepreneurship focuses on resolving societal problems and driving social change (Dees 1998; Williams and Shepherd 2016; Zahra and Wright 2016). In social entrepreneurship settings, the social entrepreneur is often confronted with a paradox: he or she must align organisational values with existing community values to mobilise support for change while simultaneously stimulating the re-evaluation of these very values to trigger progressive, beneficial societal change (Mair et al. 2016; Maurer et al. 2011; Seelos et al. 2011; Venkataraman 2004). Although scholars acknowledge the role and importance of values in the change process, few studies pay attention to processes of managing and actively negotiating values during social change efforts (Alvord et al. 2004; Kraatz et al. 2010). Sustainable entrepreneurs are assessed to play a primary role, to sustainable development through the execution of financially feasible and unconventional business models that produces positive social and environmental impact (Bocken et al. 2014; Hahn et al. 2018; Muñoz and Cohen 2017). Nevertheless, sustainable entrepreneurs are undergoing critical challenges as their businesses need to amalgamate environmental, social and commercial logics, which frequently disunite concerning their values, practices and objectives (Laasch 2018). Generating environmental and social values can emerge severe disparity with the “Logic of the commercial markets” that prioritise financial benefits resulting in the cause of stress for entrepreneurs (De Clercq and Voronov 2011; Gregori et al. 2019; York et al. 2016). If all these appearing tensions are unable to be responsible for the design of the business model, it could directly result in unstableness of the business and could also

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hamper environmental and social value creation (Davies and Chambers 2018). Therefore, examining how entrepreneurs induce and align numerous methodologies of value inside their business models is a significant and immediate but not an adequately researched area of inquiry (Hahn et al. 2018; Terán-Yepez et al. 2020). Contemporary investigations have embraced the idea that digital technologies can aid in tackling the obstacles faced by sustainable entrepreneurs (George et al. 2020; Parida and Wincent 2019). This presupposition set up the transformative capacity of digitalisation that change the nature of the entrepreneurship (Nambisan 2017; Yoo et al. 2012) and alter the approach to problems related to sustainability (Seele and Lock 2017; Stuermer et al. 2017). Particularly, digital technologies provide many chances for novel practices that open the gateway for new possibilities for entrepreneurs, ultimately assisting in the development of fresh business models (Hinings et al. 2018; Holzmann et al. 2017; Nambisan et al. 2017). Thus, there are capable synergies between entrepreneurs’ endeavours’ for sustainable growth and digitalisation that have not yet being traversed into depth. With this being said, it is important to reflect on institutional logic theoretical perspectives on sustainable business models (Gregori et al. 2019; Hahn et al. 2018). Institutional logics refer to institutionalised sets of values, beliefs and practices that orient, enable and/or constrain action (Thornton et al. 2012) based on this point of view, to theorise on sustainable business models, which are posited as constitutes of various components such as value proposition, value creation and value capture that could bring environmental, social and commercial institutional logic (Laasch 2018, 2019; Ocasio and Radoynovska 2016). Moreover, Gregori and Holzmann (2020) proclaim that digitalisation involves an upcoming digital logic that has well-defined relationship with sustainable logics. Gregori and Holzmann further (2020) convey that digital technologies assist the advancement of value propositions that mingle environmental, social and economic value. In addition, digital technologies impart distinctive constellations for value creation elements enabling for practices of community development, co-creation and comprehensive stakeholder unification. Utilisation of digital technologies can also direct to multivariate value capture as it allows impact complementarities, the scalability of socioenvironmental value and value spillover. The

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special attributes of digital technologies generate digital affordances that create brand new prospects with regard to a particular user or use context that could be lifted by entrepreneurs (Autio et al. 2018; Nambisan et al. 2019). The procedure of digital transformation reveals new institutional arrangements, leading the way for new values, practices and structures that may affect formed rules and generate contesting present-day logic constellations (Hinings et al. 2018). The arrangements consist, for instance, normally accepted and customisable digital modules such as ERP systems or standard-setting digital infrastructure that assembles the interconnection between product platforms and blockchain technology. These extremely manipulative innovations do impact on the business models. Some scholars insist that the digital affordances that come with digital infrastructure and modules widen the choices and design novel footpaths for creating, delivering and capturing value (Holzmann et al. 2017). The transmutation of economic activities guides to progressive unconventional business models (e.g. Nambisan et al. 2017) that require particular organisational capabilities to be comprehended victoriously.

Change, Innovations and Entrepreneurship Through Technology Change is unavoidable in our ever increasing complex, volatile and uncertain worlds observed. Organisational change tends to focus on the actions which alter major component in an organisation, such as; culture, structure, technologies or infrastructure or internal processes (Stobierski, 2020). Generally, it is expected that at some point, every organisation undergoes a transition or change in order to survive; however, organisational change is not always easy to get adapted to. There are several factors that make organisational change necessary, which can be categorised as mainly external factors and internal factors. A multitude of inter-related internal and external factors make organisational change complex. Innovations are highly linked with organisational change. According to Peter Drucker (2002), innovation is the specific function of entrepreneurship, whether in an existing business, a public service

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institution or a new venture started by a lone individual. He further mentioned, that entrepreneurs either create new wealth-producing resources, or endow existing resources with enhanced potential for creating wealth. Entrepreneurship is vital when discussing innovations. Risk-taking and innovativeness are the entrepreneur mindset significant characteristics, and which connected with entrepreneurial activities drive the implementation of new knowledge pursuits contributing to successful organisational change (Han and Park 2017; Estrin et al. 2019). In the context of globalisation and digital era, innovations amalgamated with technology have taken special attention. It is widely accepted that there is a strong connection between technological advancements, innovations and entrepreneurship (Fowosire et al. 2017). The new forms of technology innovations refer to new or improved products, or process whose technological characteristics are significantly different from before. These technology innovations have brought substantial benefits in this digital era, while fostering more and more technology innovations and therefore are useful to highlight here. The MIT Technology Review (2021) has listed the Ten Breakthrough Technologies, which have inspired numerous technology innovations in 2021, namely: the Messenger RNA vaccines, GPT-3, TikTok recommendation algorithms, Lithium-metal batteries, Data trusts, Green Hydrogen, Digital Contact Tracing, Hyper-accurate positioning, Remote everything and Multi-skilled AI.

Entrepreneurial Education and Technology Entrepreneurial Universities It is assumed that universities play a conspicuous role in the science and technology-based economic development. Research has revealed that increased influence of external environment factors such as government, community and industry have changed the role of university education and research (Cunningham et al. 2018, 2019; Fuller et al. 2019). Meanwhile, due to the application of concepts such as enterprise

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and entrepreneurship in university context, the notion “entrepreneurial universities” has evolved (Cunningham et al. 2019; Jones and Patton 2020). Beside the two traditional missions of universities: education and research, Cunningham et al. (2019) have introduced a third mission to entrepreneurial universities, which includes economic and social development via entrepreneurial activities. Scholars have emphasised several benefits of entrepreneurial universities. This type of universities embraces a diversified strategic orientation, which guides multiple stakeholders to achieve goals and their long-term survival through value creation and hybrid business models involving entrepreneurial and innovative activities (Abdelkafi et al. 2018; Madichie and Gbadamosi 2017). Moreover, entrepreneurial universities tend to adopt an international approach, which enables developing strategic partnership with agents who have a resilient presence and recognition across the globe in terms of entrepreneurial innovation (Minola et al. 2016). Therefore, entrepreneurial universities are seen as able to attract and retaining entrepreneurial talents, whilst being able to take advantage of balanced and expanded funds to ensure continuing investments in entrepreneurial and innovative initiatives (Cattaneo et al. 2015). Entrepreneurial universities as seen as promoting regional change, which benefits entire regions through regional interaction and economies in general (Guerrero et al. 2018). Scholars (Klofsten et al. 2019; Phillips 2018) have emphasised that the model of entrepreneurial university is a multifaceted phenomenon, which amalgamates different decision-making levels, academic conducts, research values, organisational and sub-organiational cultures. Hence, it has become essential to understand the business model of entrepreneurial universities (Soliman et al. 2019). First, it is noteworthy to mention that both, enterprise and entrepreneurship frameworks are integrated within the concept “entrepreneurial university”, which has made it difficult to define the term “entrepreneurial university” and its business model (Jones and Patton 2020). Although the concept of a “threering entrepreneurial university” is still fairly untouched in literature, the importance of each ring in determining a business model is heavily discussed (Siegel and Wright 2015). While teaching and research represent the first two rings, considered as the traditional missions of

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universities, entrepreneurship activities are the third ring that creates a bridge to transfer knowledge and commercialise between university and industry (Audretsch and Belitski 2021). Three-ring entrepreneurial university is a new business model, which needs a stronger congruence between knowledge capital and entrepreneurial capital (Audretsch and Belitski 2021). Knowledge capital includes applied research, inventions, academic publications and investments in research and development while entrepreneurial capital consists of identifying market opportunities (Audretsch and Belitski 2021). Some scholars have broadly distinguished the characteristics of an entrepreneurial university compared to Humboldt University, which are (1) possessing both knowledge capital and entrepreneurial capital (Audretsch 2014) and (2) the presence of key three levels such as individual level, organisational level and the ecosystem level (Guerrero et al. 2015). Individual level engages with skills and competences while organisational level involves with infrastructure and processes and ecosystem level refers to the entrepreneurial ecosystem with its stakeholders. In all these levels, knowledge creation is a significant constituent. However, if knowledge creation is not involved in knowledge transformation and commercialisation, it is not possible for universities to be competitive or bring substantial effects to stakeholders (Perkmann et al. 2011). In order to make the knowledge transformation and commercialisation successful, there needs to be a resilient congruence between knowledge and entrepreneurial capital (Audretsch and Belitski 2021). The significance of knowledge commercialisation can be highlighted with the use of knowledge spillover theory of entrepreneurship and endogenous economic growth theory. Knowledge is seen as inherently leaky since it tends to spill over in the form of spinout, however according to knowledge spillover theory, individuals tend to start entrepreneurial firms because they have access to knowledge spillovers (Acs et al. 2013). Further, the endogenous economic growth theory proposed by Romer (1986) emphasises that economic growth is driven by the internal factors of the economy itself and not by the external determinants. Therefore, , it is highlighted that universities invest in new knowledge and as suggested in knowledge spillover theory, the universities generated knowledge spills over into their regions, generating regional

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economic growth. This, is clearly explained with the assumption of endogenous economic growth theory purporting that these knowledge spinouts supports the entrepreneurial ecosystem along with innovation and regional economic growth (Acs et al. 2013). However, universities may use knowledge filters, which can be obstructions to investments in knowledge, and knowledge commercialisation. Thus, it is suggested as pivotal to facilitate the knowledge spillover of entrepreneurship by reducing knowledge filters. Consequently, recent research shows that many universities have initiated engaging with entrepreneurial communities and educational ecosystem stakeholders in order to accelerate the knowledge spillover of entrepreneurship (Miller et al. 2018a, b). From this examination, it is noticeable that the key role of the entrepreneurial university (EU) exceeds the traditional role of creating knowledge via teaching and research, instead the EU aims at knowledge transformation and commercialisation (Baglieri et al. 2018; Audretsch and Belitski 2021). Even though this notion of entrepreneurial university has gained a considerable attention during the last decade, its origin can be traced in 1990s, with the introduction of the concept triple helix model developed by Etzkowitz (1983). Triple helix is a dynamic interaction between three spheres—university, government and industry which presents a spiral model of innovation where the interaction between the three helices is the key to encourage innovations, social development and economic growth in a knowledge-based economy (Etzkowitz and Zhou 2017; Mêgnigbêto 2018). This model is based on knowledge creation, however with communication and technological innovations as central aspects in this process, which directly links the concept of entrepreneurial universities with triple helix model presented above. With this model each helix has a particular role—university creates new knowledge and technology, industry engages with production and government facilitates the regulations and stability of effective interaction (Van Horne and Dutot 2017). However, some studies have emphasised the inadequacy of only three helixes for long-term growth, hence, a fourth helix- society and public (Quadruple Helix model) was suggested by Carayannis and Campbell (2013) and it is becoming widely accepted

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that all four helixes are significant spheres in the innovations system (Schütz et al. 2019; Paredes-Frigolett 2016; Malik et al. 2021). Another relevant concept related to entrepreneurial universities and Triple and Quadruple helix model, is the Smart Specialisation Strategy (S3) which was introduced by the European Commission in 2012, with the main aim of facing major economic challenges whilst strengthening innovations in the Europe region. This is a place-based innovation policy concept which supports regional innovations through the “entrepreneurial discovery process” (EDP) (European Union 2020). EDP is a bottom-up approach where quadruple helix actors with entrepreneurial knowledge are involved in; thus, quadruple helix actors are central to S3 policy concept. Initially, in 2012, they focussed on designing and implementing a national/regional research and innovation strategy for smart specialisation (RIS3) to be achieved in 2020. Accordingly, five important agendas consisted in RIS3 (European Commission 2012, p. 8): 1. Focus policy support and investments on key national/regional priorities, challenges and needs for knowledge-based development, including ICT-related measures; 2. Build on each country’s/region’s strengths, competitive advantages and potential for excellence; 3. support technological as well as practice-based innovation and aim to stimulate private sector investment; 4. Get stakeholders fully involved and encourage innovation and experimentation; 5. Being evidence-based and include sound monitoring and evaluation systems. The main three priorities of RIS3 approach highlighted by European Commission (EU) in 2012 was smart, sustainable and inclusive growth.

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This indicates that almost a decade ago, the EU emphasised the significance of digital technologies and sustainability. However, the implementation and achievement of priorities of RIS3 especially the ones related to smart and sustainable growth are questionable. A recent study conducted by the European Investment Bank (EIB) shows that industries in Europe currently fall behind compared to the United States in terms of adopting digital technologies, especially within the construction sector and in utilising the IoT (EIB 2020). Moreover, new reflection report published by the European Commission regarding the attainment of the key Sustainable Development Goals (SDGs) in Europe has claimed that the Covid-19 pandemic halted progress towards achieving the SDG of Europe, since the average SDG Index of Europe has declined in 2020 (OECD 2021). This indicates that, Europe has failed in terms of protecting oceans, conserving marine resources and having sustainable consumption and production patterns, and that the sustainability priority is not being fully achieved. Currently, the European Union is elaborating S3 for 2021–2027, to face these and new challenges posed by COVID-19 pandemic. While focusing on research and innovation in the regional economic systems, the new S3 policy emphasises the essential role of international and interregional co-operation in innovation (OECD 2021). Hence, the S3 concept is being shifted from the regional focus to both regional and international focus, where the engagement of quadruple helix actors in the international context is vital.

Entrepreneurial Academics and Academic Entrepreneurs An increased attention towards the role of universities as main channels of regional economic development has been given with the emergence of knowledge economy (Guerrero et al. 2016b). Universities have become the key actors in the regional and national innovation systems, due to the significance of knowledge sharing which is the main source of new idea generations and inventions (Guerrero et al. 2016a). Accordingly, universities are becoming more entrepreneurial in offerings (Miller

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et al. 2018a). In order to respond to the changing requirements of the industry and society, many progressive universities have started reassessing their fundamental activities along with research capabilities (Fitzgerald and Cunningham 2015). These actions are related to the concepts of entrepreneurial universities, third mission of universities and the use of quadruple helix model, discussed previously. Conventional duties of academic used to be teaching, engaging with research publications and administration tasks. However, with the emergence of adopting the concepts of entrepreneurial universities, third mission and quadruple helix model, new roles were added to academics’ duties. These include, becoming more entrepreneurial, involving with technology, commercialisation activities, knowledge transfer, and winning research funding (Miller et al. 2016). Universities are responsible to encourage the academics to engage in entrepreneurial activities linking with external stakeholders to accomplish their aims of becoming “entrepreneurial universities” (Alexander et al. 2015). The literature, emphasised two distinct roles—academic entrepreneur and entrepreneurial academic. Academic entrepreneurs mainly engage with formal commercialisation activities to achieve the entrepreneurial configuration of universities (Rasmussen et al. 2015; Miller et al. 2018a, b). Such activities are namely, patents, licenses, research collaborations with industry, idea spin-offs into new ventures and providing entrepreneurial education to highly skilled individuals and business incubators (Siegel and Wright 2015; Miller et al. 2018a, b). In contrast, entrepreneurial academics are more associated with collaboration, networking, engagement, knowledge transfer activities, joint journal publications, joint supervision of research students, graduate and student placement, secondments, executive education, collaborative research and contracted research and consultancy (Perkmannet al. 2013; Miller et al. 2018a, b). They involve with activities, which generally have a wider scope than publishing research studies, that is activities that are more valued by the industry rather than in developing patents (Perkmann et al. 2013). After reviewing several studies on the academic entrepreneur and entrepreneurial academic, Miller et al. (2018a, b) have summarised the

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activities of both these types of academics, which are presented in Table 13.2 below. Importantly, they highlight that both types of academics are required in contemporary universities as both contribute to the success of entrepreneurial universities. Entrepreneurs have been able to exploit attractive innovation opportunities, due to the development of digital technologies. Thus, it is advised that Schumpeter’s (1912) notion of creative destruction Table 13.2

Entrepreneurial academics vs academic entrepreneurs

Softer, more informal, relational, partnering-style engagement utilised by ENTREPRENEURIAL ACADEMICS Networking—groups of professionals and/or academics come together and meet face-to-face under a banner of common interest or subject discipline Joint Conference—audience of company employees and academics. Speakers are taken from both groups Joint Journal Publications—academics and professionals develop a paper together into professional journals Joint Supervision—academics and industrialists come together to supervise a piece of research Student Placements / Graduate Employment—transfer of a graduate into a company partner Secondment—member of staff is present for a period of time in another organisation Executive Education—commercial partners keep their professional knowledge up to date with new developments delivered by academics Collaborative Research—commercial and academic partners agree to work together to discover new knowledge or to propose solutions solving a problem

Harder, more formal, transactional, contracting-style engagement utilised by ACADEMIC ENTREPRENEURS Contract Research and Consultancy—a company has a problem and wishes for either: a “known” solution to be applied to their problem (consultancy); an unknown solution to be researched and then presented to the company Shared Facilities—a university and a commercial partner join together to invest in the development and operation of a facility or piece of equipment Joint Ventures—rely on a set of legal agreements that ties a company partner and an academic with a common purpose without creating a new legal entity Patents and Licenses—a particular piece of knowledge or know–how is protected by either an academic partner or a commercial partner Spin-outs—University personnel join together with commercial partners to create a company

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provoked by innovative entrepreneurs is more relevant today than ever. The rapid acceleration of digital transformation has brought huge impacts academics, with the emergence of the concept of digital academic entrepreneurship (Rippa and Secundo 2019). The traditional mission of commercialising academic research has been overhauled by the digital academic entrepreneurship model, which joins digitalisation into the university ecosystem. Digital academic entrepreneurship requires applying new digital technologies within the academic entrepreneurship context in order to create digital entrepreneurial ventures, enable digital spin-offs and alumni start-ups and innovation development with a broader spectrum, which goes beyond regions into the global world (Rippa and Secundo 2019). Therefore, this new form of academics tend to deal with more stakeholders through new digital technologies to identify entrepreneurial opportunities, while accelerating the entrepreneurial process in the Universities Ecosystems. The main aim of adopting digital technologies by academic is to advance knowledge and technology transfer processes, which open up digital spin-offs, digital alumni start-ups, regional development and glocal (both local and global) innovations. According to Rippa and Secundo (2019), digital academic entrepreneurship requires academics to be involved with some key digital technologies such as Massive Open Online Courses (MOOCs), Social Media, Fab-Lab, 3D printing, IoT, Big Data, Analytics, Digital and cloud platforms—since knowledge and technology transfer are central to digital academic entrepreneurship. For example, Coursera and edX are popular MOOCs platforms which provide free entrepreneurial courses for students to have a vibrant knowledge from a wide range of supporters and content. Moreover, social media has been shown to improve learners’ course participation, engagement, motivation, achievement and completion of MOOCs (Ripiye 2016). Alumni start-ups and spin-offs supported by digital technologies are new trends with digital academic entrepreneurship. Further, digital fabrication laboratories also known as Fab-Labs provide various types of manufacturing equipment to students who have entrepreneurial intentions, where they can carry out different types of projects within the Fab-Lab to improve their practical knowledge as well as nurture their passion towards entrepreneurship. Many universities have already implemented the concept Fab-Lab, for

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example: Fab Lab Coventry, MIT Fablab and Stanford Fab lab. Business simulations or virtually simulated enterprises also come under digital academic entrepreneurship, where students are provided with a virtual business environment to make decisions and run businesses successfully. Rippa and Secundo (2019) suggest that this new form of academic entrepreneurship should consist of social media environments, virtual learning, 3D virtual labs, Fab-Labs and digital accelerators. This brings a wider social and democratic value to universities, which may counter the current trend towards pure economic value generated from the commercialisation of universities and research—becoming more able to respond to the values of the new generations (Johnson & Sveen, 2020; Hinduan et al. 2020).

COVID-19 Pandemic and Technology Entrepreneurship Entrepreneurs and business owners are highly impacted by macroeconomic factors that cause a halt in economic expansion such as the Covid-19 world pandemic. When the global pandemic (Covid-19) started, revenues of firms were reduced creating negative cashflows, especially due to lockdowns and travel restrictions. Meanwhile, the pandemic threatened the rise of innovations since business start-ups faced difficulties to access capital to invest and generate lower revenues. According to the latest edition of the World Bank Entrepreneurship Database in 2020 (reporting on the findings of a longitudinal study including 170 economies examining entrepreneurial activity of startups around the world), the number of newly registered firms (i.e. limited liability companies (LLCs)) dropped in 58% economies when compared to 2019 (Meunier et al. 2022). This study concluded that with the pandemic most economies experienced a decrease in New Business creation, however, with a higher impact on developing countries. That is, Europe and Central Asia experienced the biggest drop in business entry in 2020 (i.e. the number of newly registered firms dropped in 78%; followed by the Middle East and North Africa (75%) and Caribbean (72%)). Overall, the lowest level of entrepreneurship

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was with Sub-Saharan Africa and South Asia whereby less than 1 new LLC per 1,000 adults was created in 2020. Further, it was highlighted that with the Covid-19 crisis, digital technology became more important than ever for new business creation. The continuity of businesses during this crisis was supported by digital technology, and/or highincome economies who had the resources to support businesses and individuals with the necessary technological developments. Therefore, economies with online company registration processes and/or highincome economies were able to ensure the continuity of new business creations even during the pandemic restrictions (Meunier et al. 2022). Wide research has shown that in crisis situations, entrepreneurs play a vital role in improving product and service quality while advancing new technologies (e.g. Santos et al. 2019). Entrepreneurs are accredited with positive energy, vibrancy and dynamism which contributes to the competitiveness of economies (Santos et al. 2019). Entrepreneurs foresee opportunities in the marketplaces whilst being risk takers. In crisis contexts such as Covid-19, it is suggested the value to utilise entrepreneurship as a crisis management strategy (Ratten, 2021). With the globalisation, mobility between countries has been increased. Yet, during the global pandemic situation, mobility between counties was highly restricted due to travel restrictions and border closures, which brought numerous negative consequences to worlds’ economies (e.g. pause in business registration, slower economic activity). Nonetheless, importantly from the research examined, it emerged that entrepreneurial strategies and policies can be used as levers to reinvigorate economic growth (Ratten, 2021). Further, COVID-19 pandemic has become the major contemporary challenge which has impacted on humans’ lives as well as business. Most importantly, we have seen above that due to the pandemic conditions, entrepreneurship form of engagement with business and society have changed, also creating new opportunities (Meunier et al. 2022; and Ratten 2020. Many organisations are moving into digital entities with remote work and web-based teams as a result of the global pandemic (Naudé 2020; Barbulescu et al. 2021). Accordingly, entrepreneurships in such conditions shift towards digital entrepreneurship. Technology/digital entrepreneurship already existed before Covid-19, yet, the pandemic accelerated the rampant increase of

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digital entrepreneurship, which for some the entrepreneurs mean the transit towards a complete digital environment (Tang and Li 2021). Due to the growing digital wave during the pandemic, overabundance of opportunities for aspiring entrepreneurs to enter the market have been created (Brem et al. 2021). Further, organisations who invested in digital technology before the pandemic fared better during the pandemic than ones who did not transform for digital technology (Zahra 2021). Therefore, indicating that today the survival of many businesses relies highly upon their digital capabilities (Datta and Nwankpa 2021). As a result, governments are also motivating the private sector to embrace this trend, whilst moving themselves towards digital innovations and embracing new technologies to develop new ecosystems and support the environment (Bai et al. 2021). Creating novel and innovative solutions is essential for the growth of organisations as well as for the sustainability of national economies (Gajdzik and Grzybowska 2012; Saniuk et al. 2020). However, in industrialised countries a growing concern is the amount of waste generated (Shvetsova and Lee 2020). This is a circular economy whereby optimal solutions for macro cancers require political, economic, social, technological and environmental co-ordination focusing on the healthier usage of natural resources, whilst also focusing on fostering innovations and new job creation. Around the world it was seen that even though Covid-19 has brought severe negative impact, there are some positive outcomes and lessons learned as well. For example, the pandemic has positively affected the zero-waste approach in European Union countries, leading to the development of policies which incorporate the conservation of natural resources through responsible consumption, production, recovery and reuse without releasing to land, water or air in a harmful way (Sarkodie and Owusu 2020).

Conclusions Entrepreneurship is vital for many reasons, from promoting social and environmental change, to diminishing poverty to driving innovation and economic growth. Entrepreneurs are often considered national assets

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to be cultivated, motivated, and compensated to the greatest possible extent. Over the last four decades, technology entrepreneurship has become an increasingly important global phenomenon essential for growth, differentiation and competitive advantage at the firm, regional, national and international levels. The digital technology entrepreneurship ecosystem is a relatively new concept, which is taking considerable attention in the technology world. Even though this is a new concept, its roots can be found in the concepts such as industrial ecosystem, business ecosystem, innovation ecosystem and entrepreneurship ecosystem. Several agents are elemental parts in digital technology entrepreneurship ecosystem, whereby universities play a fundamental role. Universities by becoming entrepreneurial universities have moved forward from their traditional missions, focusing on education and research to start committing to a third mission, which is engaging with the community. Accordingly, entrepreneurial education has become vital in the higher education industry, whilst highlighting the significance of entrepreneurial academics in entrepreneurial universities. The concept of entrepreneurial university is directly related to the quadrant helix model where the innovation ecosystem in a region consists of four major helix namely, entrepreneurial universities, industry, government and society. Traditional views of entrepreneurship mainly focused on economic benefits yet, due to the social and environmental challenges we face, a substantial attention is now being devoted towards sustainable entrepreneurship. Indicative are the highlighted United Nations Sustainable Development Goals. Technology entrepreneurship may be considered as a strategic development process, which is even more significant within the context of uncertainty and unpredictable change management. In crisis times, such as during the COVID-19 pandemic, BREXIT in the UK, and the current Russia-Ukraine war, strategic thinking is not easy for individuals, organisations, and countries leaders and influencers such as I-WHO, NATO, Greenpeace, Friends of the Earth, Global Footprint Network. Flexible organisational structures and distributed leadership occurring around the most innovative technologies are key organisational capabilities to be deployed in uncertain and turbulent times. The current crisis have highlighted the importance of sustainable entrepreneurial practices which benefit from the use and development of technology. Even

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though, the Pandemic is seen to be detrimental to the whole world, some positive aspects that emerged are the raised awareness of positive impact on the natural environment due the the reduction of the carbon footprint for example and where sustainable innovations have been increased in organisations.

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Ted Talks Introduction to entrepreneurship - How to Know If You’re Meant to Be An Entrepreneur- https://youtu.be/gFY BqZnFQ6w - The Power of an Entrepreneurial Mindset- https://youtu.be/Ihs4VFZWwn4 Technology entrepreneurship- technopreneurship - Build The Future: Youth in Tech Entrepreneurship- https://youtu.be/6x-GB7 hqAp4 Technology in the business world - A future worth getting excited about https://www.ted.com/talks/elon_musk_ a_future_worth_getting_excited_about COVID-19 pandemic and technology entrepreneurship - We can make COVID-19 the last pandemic https://www.ted.com/talks/bill_g ates_we_can_make_covid_19_the_last_pandemic

Index

0–9

3PL 154, 155, 159, 160, 182–185, 192, 195 4PL 153–155, 157–160, 185, 195 5G technology 19, 23, 24, 36, 161

A

AAL systems 317, 318, 321, 324, 325, 328, 337, 339, 341, 343 Activities of Daily Living (ADL) 321–323, 325, 326 AI growth 410–413 AI History 378 Airbnb 71, 488 Amazon 5, 72–74, 252, 472, 473, 489 Ambient Assisted Living (AAL) 317, 318, 322–325, 327–329, 334, 335, 337–339, 341

Analytical Models 258 Appropriate technology 11, 12 AR advertising 48, 51, 55, 56 AR education and training 58 AR gaming 52, 53, 59 AR online retailing 53, 59 AR tourism 52, 56, 58 Artificial intelligence (AI) 8, 19–21, 26, 36, 59, 68, 116, 135, 207, 210, 214, 215, 221, 256, 276, 307, 317, 378–385, 389–394, 396–408, 410, 411, 414, 445, 446, 454, 456, 474, 475, 496 Artificial Intelligence in Education (AIED) 392–394, 396–407, 409–411, 414 Artificial Narrow Intelligence (ANI) 382 Augmented reality (AR) 48–55, 57, 58, 60, 61, 276, 287, 293,

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Singh Dadwal et al. (eds.), Integrated Business Models in the Digital Age, https://doi.org/10.1007/978-3-030-97877-8

537

538

Index

297–300, 303, 304, 306–308, 358–360, 363, 394 Aviva 253

B

Bangladesh 4, 48, 49, 55–61 Big data 68, 104, 155, 195, 208–212, 214, 218, 221, 222, 226, 228, 231, 236, 239–241, 259, 371–378, 390–392, 396, 398, 399, 404, 408, 413, 414, 472–474, 504 Bitcoin 88–90, 92, 96, 97, 100–102, 133, 476, 478 Blended learning (BL) 426, 428–430, 440, 457, 458 Blockchain 6, 8, 88–94, 96–137, 163, 165, 174, 193, 472, 475–479, 495 Blockchain implementation 100, 112, 118, 119, 123–127, 129, 130, 132, 135 Blockchain types 92, 93, 129 Brilliant Earth 117 Business integration 18 Business model(s) 4–6, 10–12, 17, 18, 21, 27, 31, 36, 54, 209, 210, 231, 240, 241, 251–254, 256, 269, 396, 426, 427, 457, 460, 473–475, 478, 479, 481, 488, 493–495, 497, 498 Business operations 5, 8, 9, 21, 36, 241, 261 Business strategy 51, 68–70, 76, 78, 79, 219, 239, 253

C

CISCO 445, 472 Cloud computing 22, 155, 160, 161, 174, 193, 195, 207, 231, 472 Co-creation 432, 442, 446, 454, 459, 484, 494 Communication technologies 78, 85 Competitive advantage 7, 12, 48, 49, 51, 112, 133, 152, 154, 156, 188, 208, 210, 217, 222, 224, 231, 237–239, 241, 251–255, 259, 452, 457, 500, 508 Consensus protocol 92, 98 Consumer 8, 18, 47, 48, 50, 51, 53, 55, 56, 59, 71, 76, 105, 117, 208, 210, 212, 214, 216, 222, 224, 226, 227, 234, 235, 240, 241, 252, 254, 276, 278, 280–290, 300–302, 305, 306, 376, 428, 447, 474, 483 Continuous improvement 159, 171, 173 Contract management systems (CMS) 219 Contracts 68, 71, 72, 85, 91, 94, 100, 154, 157, 164, 187, 218–220, 225, 445, 451, 454, 502 Conventional business models 252, 493, 495 Covid 19 5, 17, 55, 58, 59, 67–70, 72, 73, 75, 76, 80, 84, 154, 195, 255, 264, 275, 276, 335, 412, 425, 439, 501, 506–508 Cryptocurrency 88, 90–93, 96, 97, 99, 102, 133, 134, 476, 478

Index

Customer 4, 6–9, 13, 17, 19, 34, 47, 49, 51, 52, 55, 59, 74, 76, 77, 104, 106, 109, 110, 116, 117, 151, 152, 154, 156, 158–162, 167, 168, 173, 174, 182, 184, 187, 189, 194, 210, 212, 220, 223, 226, 227, 239, 252–254, 257, 269, 276–290, 292, 294, 295, 297, 298, 300, 302–309, 473, 478, 484, 485 Customer experience 47, 227, 276–279, 290, 300–302, 305–309, 376 Cybercrime 29, 32–37, 112 CyberParks 363 Cybersecurity 4, 14–19, 22, 27–32, 34, 36, 134, 220

539

Digitalization 58, 68, 73, 74, 160, 252, 254, 255, 277, 452, 473, 487, 494, 504 Digital literacy 427, 438, 439, 450, 452, 454, 455, 457 Digital spaces 275–277, 281 Digital technology 78, 364, 482, 487, 488, 507 Digital tools 85, 434, 437, 438, 443, 445, 448, 450, 453, 455–457 Digital transformation 6, 10, 239, 485, 487, 495, 504 Disruption 68, 69, 71, 73, 154, 162, 174, 188, 189, 219, 220, 431, 441, 443, 444, 447, 453, 455, 456 Distributed ledger 92, 94, 95, 97, 99, 102, 115, 134, 476

D

Data 207, 208, 210–217, 219, 220, 222, 224–232, 235–237, 239, 240 Data analytics 162, 193, 207, 208, 210–212, 214, 219, 228, 231, 234, 239, 251–269 Data compliance 13, 14 Data security 13, 15, 16, 34, 173, 221 Data systems 208, 211, 212, 214 Data tracing x Deep learning 21, 385, 389, 390, 413, 433 Demand forecasting 223, 227 DHL 118, 161, 167, 168, 195 Diagnostic 208, 213, 233, 260, 322–324 Digital business models 252 Digital education 455

E

E-business 472, 478 E-commerce 5, 9, 59, 276, 277, 287, 472 Economic growth 11, 446, 479, 482, 487, 489, 498, 499 Ecosystem 3, 92, 97, 102, 126, 129, 130, 161, 164, 355, 396, 413, 478, 483–486, 488, 490, 498, 499, 504, 507, 508 Ecotourism 349, 350, 354, 355, 357 Education 5, 48, 51, 52, 58, 60, 81, 213, 294, 335, 361, 382, 383, 396, 400, 401, 403, 405, 407, 409–411, 414, 426–428, 430–432, 436, 437, 439–442, 444, 446–448, 454, 458, 460, 476, 485, 486, 490, 496, 497, 502, 508

540

Index

Education 4.0 408, 447, 458 Effective communication 8, 438 E-learning 401, 404, 411, 427, 436–438, 445, 448, 472 Emerging technologies 4, 5, 7, 8, 17–21, 36, 48, 111, 286, 378 Entrepreneurs 5, 6, 10–13, 18, 36, 49, 52, 77, 208, 211, 469, 470, 479, 480, 482, 485, 487–496, 501, 502, 504, 507 Entrepreneurship 4–6, 10, 11, 36, 240, 446, 467, 469–471, 479–483, 485–499, 504–508 Ethereum 91, 92, 100–102, 115

F

Facebook 4–6, 9, 14, 80, 209, 226, 489 Flipgrid 448, 450, 456 Freelancers 69, 72, 73, 79, 81–83, 451 Future research 49, 60, 61, 195, 268, 269, 278, 306–309 Fuzzy logic 385

G

General Data Protection Regulation (GDPR) 14, 30, 33 Gig economy 68–82, 84, 85 Gig workers 69, 71, 72, 74, 75, 78, 79, 81, 84 Globalization 12, 154, 168, 219, 226, 496 Global Positioning System (GPS) 155, 165, 223, 224, 324, 357–360, 363

Google 5, 6, 14, 26, 52, 60, 293, 334, 336, 364, 411, 426, 431, 445, 456 H

Healthcare sector 52 Higher Educational Institutions (HEIs) 425, 426, 434, 436, 447, 451, 453–455, 457 I

IBM 5, 6, 26, 108, 114, 115, 163, 379, 390, 411, 446, 447 Industry 4.0 technologies 151, 165, 195, 221, 487 Information and communication technology (ICT) 5, 8, 9, 18, 32, 36, 55, 160, 426, 438, 500 Innovations 11–13, 18, 27, 126–128, 208, 219, 354, 426, 430, 442, 446, 454, 469, 470, 472, 478, 481–485, 487, 488, 492, 495–497, 499–501, 503, 504, 507–509 Internet of Things (IoT) 19, 21, 22, 36, 104, 107–109, 113, 116, 129, 135, 161, 162, 165, 195, 207, 220, 221, 317, 392, 394, 408, 409, 413, 456, 472, 473, 475–478, 501, 504 Inventory 9, 75, 129, 160, 166, 174, 176, 187, 191–193, 208, 219, 222, 227, 234, 354 L

Lead Logistics Provider (LLP) 105, 157, 185

Index

Learning Management Systems (LMS) 392, 445 Learning theories 432, 433 Limited-Memory Machines (LMM) 381 Logistics 103–105, 107, 108, 116, 118, 119, 127, 135, 137, 152, 153, 156, 158–161, 163–168, 173, 174, 181–186, 188, 189, 194, 208, 222, 223, 225, 226, 239, 240, 254 Logistics model 152, 156, 166, 168 Logistics service providers (LSPs) 151–154, 156–163, 166, 168, 169, 171–174, 176, 181–195 Luxury 276–280, 287, 288, 290, 292–294, 297, 302–304, 306, 308 Luxury brand 275–277, 284, 286, 287, 289, 290, 292, 302, 303, 307 Luxury experience 278, 279

M

Machine learning (ML) 164, 207, 213–216, 258, 384–388, 392, 393, 398, 400, 404, 408, 410, 413, 414, 446, 447, 458 Massive Online Open Course (MOOC) 392, 409, 410, 427, 441, 460, 504 Microsoft 6, 26, 254, 257, 406, 411, 472, 489 Microsoft Teams 426, 449, 457 Mild Cognitive Impairment (MCI) 318, 322–324, 339, 341, 343 Miners 90, 92, 96–99

541

Mobile 9, 23, 24, 49, 51, 53, 56–59, 77, 212, 224, 304, 329, 349, 353, 357–365, 472 Moodle 426, 444, 445 Multisensory marketing 280

N

Natural Language Processing (NLP) 382, 385, 394, 446 Neural networks (NN) 324, 379, 389, 403, 447

O

Online 9, 15, 17, 27, 31, 33–35, 49, 54, 59, 60, 78, 119, 135, 185, 227, 252, 275–278, 280, 282, 284–290, 292–295, 297–309, 329, 357, 362, 372, 375, 406, 409, 411, 425–428, 430–434, 436, 437, 439–441, 443–445, 447–455, 457, 458, 460, 472, 475 Online communities 432, 454 Online learning 399, 410, 428, 430, 432, 434, 438, 439, 441, 442, 451, 455–458 Online retailing technology 51, 59, 286 Online teaching 426, 427, 430, 436, 438, 448, 453 Organisation 5, 7, 12, 13, 17, 27, 29, 75, 77–79, 93, 94, 104–107, 114, 119, 121, 122, 124, 126–128, 130, 131, 134, 152–154, 159, 162, 166–169, 171, 172, 175, 176, 180–183, 186–194, 208–211, 214,

542

Index

216–218, 221, 223, 226–228, 230–232, 234, 235, 237, 239–241, 251–257, 259–268, 351, 407, 410, 412, 413, 435, 474, 475, 479, 481–485, 495, 498, 503, 506–508

151, 152, 159, 169, 173, 175, 176, 186, 188, 189, 191, 192, 207, 210, 211, 217, 219, 223–225, 227, 240, 253, 255, 257, 391, 403, 405, 427, 469, 470, 475, 481, 485, 492 Robotics 19, 20, 22, 23, 36, 48, 87, 215, 384

P

Padlet 449, 455, 456 Patient 110, 317–319, 322–328, 330, 335, 339, 341, 343 Personalising tutoring 399 Predictive analytics 208, 209, 213–215, 217, 218, 220–223, 225, 228, 230–237, 239, 240 Predictive modelling 216, 239, 260, 399 Privacy 13, 14, 19, 21, 24, 25, 30, 32, 33, 37, 88, 101, 104, 107, 111, 112, 119, 130, 209, 227, 325, 339, 341, 343, 376, 404, 405, 409, 438, 474–476, 478 Procurement 6, 75, 77, 166, 168, 179, 180, 218–220, 240 Promotional marketing 9 Proof-of-Stake (PoS) 99, 101, 129

Q

QR Code 357–359 Quantum computing 19, 25–27, 36, 112, 404

R

Regulating cyberattacks 27 Risks 8, 12, 19, 20, 72, 104, 109, 110, 121, 122, 125–127, 130,

S

Search engine optimization (SEO) 9 Sensors 22, 107, 108, 116, 135, 161, 220, 221, 317–321, 324–326, 337–339, 341, 343, 362, 392, 408, 409, 473 Service characteristics 151, 152, 156, 159, 160, 194 Small and Medium-Sized Enterprises (SMEs) 256, 257, 261, 446 Smart contracts 91, 96, 100, 101, 105, 107, 109, 115, 131 Smart home 317 Smart logistics 165, 224 Social media 9, 48, 68, 70, 80, 82, 87, 220, 226, 227, 335, 359, 365, 375, 444, 482, 504, 505 Social media marketing 9 Social media platforms 69, 70, 80–82, 84, 226, 444, 445, 448, 472 Spatial movement 323 Storytelling 360–362, 444 Strategy 15, 29, 49, 55, 70, 76, 77, 79, 112, 118, 120, 171, 208, 235, 237, 257, 262–265, 278, 304, 306–309, 356, 459, 481, 500

Index

Student 52, 58, 71, 75, 383, 392–394, 396–402, 404, 406, 408–410, 425–428, 430–432, 434–448, 450–457, 459, 461, 502, 504, 505 Supervised learning 385, 386 Supplier evaluation scorecard 171 Supplier Selection and Evaluation (SSE) 151, 169 Supply chain 67, 68, 71, 72, 79, 103, 104, 106–109, 111, 112, 114, 117–119, 130, 131, 151, 153, 154, 159, 160, 164, 165, 167–169, 176, 179, 183, 188, 189, 195, 208–211, 215, 217–222, 226, 227, 232, 234, 237, 240, 241, 475–477 Supply chain management 21, 34, 75, 76, 107, 108, 186, 194, 195, 209, 211, 217, 222, 227, 228 Surge pricing 252 Sustainability 104, 126, 129, 192, 219, 231, 350, 489, 492, 494, 501 Sustainable development 12, 117, 350, 351, 355, 356, 477, 483, 489–491, 493

T

Teaching and learning pedagogy 434 Technological innovation 6, 21, 441, 443, 499 Technological tools 78, 430, 451, 453 Technopreneurship 481 Traceability 104, 105, 109, 117

543

Transactions 6, 12, 47, 88–98, 100–102, 104–106, 111, 112, 123, 134, 173, 179, 224, 226, 375, 476–479 Transport and logistic industries 114 Twitter 9, 80–82, 226

U

Uber 14, 71, 72, 74, 75, 81, 82, 84, 252, 253, 488 Unicorn 210, 241, 427, 430, 441, 442 University 23, 425, 426, 430, 436, 441, 442, 446, 450–452, 455, 457, 460, 496–505, 508 Urban ecotourism 349, 350, 354–357, 359–362, 364, 365 Urban green spaces 350, 351, 353, 357, 359, 360, 362, 365

V

Value 212, 259, 373, 376 Value chain 152, 153 Value creation 18, 26, 61, 209, 217, 490, 494, 497 Variety 211, 236, 259, 264, 374, 375, 392, 408 Velocity 212, 236, 259, 264, 373–375, 392 Venture 8, 154, 446, 470, 481, 493, 496, 504 Video camera 325, 337–339, 341, 343, 450 Vital signs 318, 321 Volume 211, 236, 259, 264, 374, 392, 408, 413, 474

544

Index

W

Walmart 115, 116 Warehouse 154, 187, 224, 226 Warehouse management systems (WMS) 112, 122, 190, 225 WhatsApp 426, 453, 457

World Tourism Organization (UNWTO) 354, 355

Z

Zomato 80–82 Zoom 60, 426, 445, 453, 457