The Routledge Companion to Technology Management 2021059868, 2021059869, 9780367496135, 9780367550530, 9781003046899

Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
List of Boxes
List of Figures
List of Tables
About the Contributors
Introduction – The Routledge Companion to Technology Management
PART 1 Technology Management Background
1 Strategy of Critical Technology: A Case of Turkish Industry and Technology Policy
2 Exploring the Interplay between Digital Business Models and Dynamic Capability in the Pursuit of Competitiveness
3 Innovative Places and Regions: Implications for Technology Management
4 IT-Driven Service Innovation
PART 2 Technology Planning
5 Simultaneous Scheduling of Energy Demand and Supply in the Industrial Microgrid
6 Where We Are and Where We Want to Go: A Patent Analysis Approach Toward Strategic Technological Planning
7 Pioneer or Follower: Which Strategy to Choose?
8 Bubble Planning and the Mathematics of Consortia
PART 3 Technology Evaluation
9 An Evaluation Model for the Design of Virtual Reality Systems
10 An Evaluation Model of Smart Speaker Design
11 Methodological Frameworks for Opportunity Discovery in Innovation and Technology Management
12 Technology Assessment and Selection: An Assessment of the Autonomous Driving Technologies by Using Type-2 Fuzzy Sets and Systems
13 Multicriteria Assessment of a Technological Ecosystem: A Multi-Country Approach
14 Multi-Criteria Decision-Making Methods for Technology Selection
15 Software Assessment for Capacity Planning and Feasibility Check of the Master Production Schedule
16 Technology Assessment and Selection
PART 4 Technology Development and Transfer
17 Regional Innovative Intensity in an Emerging Economy: Analyzing China’s Provincial Regions
18 Understanding Components of Entrepreneurial Ecosystems
19 The Performance of Science and Technology Parks under Triple Helix Systems in Turkey
20 Technology and Entrepreneurship: A Systems and Simulation Approach
21 Transformation into New Drug Companies: The Perspective of Dynamic Capabilities
PART 5 Managing Technological Innovations
22 Coworking Ecosystems: Institutionalization of “Homes” for Innovation and Venturing
23 The New Imperatives of Defense Technological Innovation: Past, Present, and Future
24 Mapping Buyer-Supplier Relationships after the Open Innovation Perspective
25 Evolution of Collaborative Innovation Strategy for Global Manufacturing Firm Driven by Integrated Modularity Thinking
26 Open and Closed Innovation Strategies in Converging Environments: How the Internet of Things Blurs the Boundaries between ICT and Logistics
PART 6 Social Issues
27 Proposing an Application Model for Personnel Recruitment by Using a Multi-Criteria Decision-Making (MCDM) Approach: A Case of Blue-Collar Cashier Personnel Recruitment
28 Electronic Payment Technology: Developing a Taxonomy of Factors to Evaluate a Fraud Detection and Prevention System for the Airlines Industry
29 Engaging the Technology Management for Gender Equality
30 Creating a Networked Innovation Ecosystem for Industry 4.0
PART 7 Emerging Technologies and Emerging Regions
31 Smart Manufacturing: An Evaluation Model for Taiwan’s Innovation Applications
32 Data Mining As a Knowledge Extraction Tool and an Application on Decision Tree-Based Algorithms
33 Smart City: An Evaluation Model for Taiwan’s Social Innovation
34 Smart Transportation: An Evaluation Model for Taiwan’s Innovation Policy
35 Energy Efficiency in Smart Street Lighting System for ITU
Index

Citation preview

“The Routledge Companion to Technology Management provides a comprehensive approach to the field. Management of technology has evolved substantially over the past years and this companion gives the reader a multifaceted approach to the topic, covering its most recent developments. It can work both as a textbook for graduate courses as well as a guiding source for researchers and practitioners.” –Leonardo P. Santiago, Associate Professor, Department of Operations Management, Copenhagen Business School, Denmark “Technology plays a crucial role in shaping societies. This edited volume provides a global and comprehensive review of the challenges inherent to new and emerging technologies and their management. Through a compilation of informative cases, it offers research-based adaptable management frameworks to successfully anticipate, navigate and resolve those challenges. This book is a must-read for students, scholars, thought leaders and policy makers with an interest in technology management and its broader implications for people, organisations and societies.” –Anne-Laure Mention, Professor, Director of the Global Business Innovation Enabling Capability Platform, RMIT University, Australia

ROUTLEDGE COMPA NIONS IN BUSIN ESS, M A NAGEM ENT A N D M A R K ETING

Routledge Companions are prestige volumes which provide an overview of a research field or topic. Surveying the business disciplines, the books in this series incorporate both established and emerging research themes. Compiled and edited by an array of highly regarded scholars, these volumes also benefit from global teams of contributors reflecting disciplinary diversity. Individually, Routledge Companions in Business, Management and Marketing provide impactful one-stop-shop publications. Collectively, they represent a comprehensive learning and research resource for researchers, postgraduate students and practitioners. THE ROUTLEDGE COMPANION TO GLOBAL VALUE CHAINS Reinterpreting and Reimagining Megatrends in the World Economy Edited by Renu Agarwal, Christopher Bajada, Roy Green and Katrina Skellern THE ROUTLEDGE COMPANION TO MARKETING AND FEMINISM Edited by Pauline Maclaran, Lorna Stevens and Olga Kravets THE ROUTLEDGE COMPANION TO CORPORATE BRANDING Edited by Oriol Iglesias, Nicholas Ind and Majken Schultz THE ROUTLEDGE COMPANION TO KNOWLEDGE MANAGEMENT Edited by Jin Chen and Ikujiro Nonaka THE ROUTLEDGE COMPANION TO TECHNOLOGY MANAGEMENT Edited by Tugrul Daim, Marina Dabić and Yu-Shan Su

For more information about this series, please visit: www.routledge.com/RoutledgeCompanions-in-Business-Management-and-Marketing/book-series/RCBUS

THE ROUTLEDGE COMPANION TO TECHNOLOGY MANAGEMENT

Bringing together an international range of expertise, this comprehensive Companion to Technology Management is designed to facilitate the development of management frameworks adaptable for a wide range of organizations, as well as an overview of the development and integration of technology in advanced and emerging economies. Research-based and drawing on a range of practical tools and international cases, it covers the diverse spectrum of the challenges of technology management and how to approach them:

This Companion is an essential comprehensive source of new and emerging approaches for researchers and advanced students in engineering and technology management, as well as professionals seeking an authoritative global reference source. Dr. Tugrul Daim  is a Professor in the Department of Engineering and Technology Management. He is also the director of the Technology Management Doctoral program at Portland State University. Dr. Daim leads a research group on Technology Evaluations and Research Applications. He has published over 200 refereed journal papers, more than 20 special issues, and more than 20 books. He has made more than 200 conference presentations and given several keynote lectures. He is editor-in-chief of IEEE-TEM. He is ranked as the top researcher in Technology Roadmapping, Forecasting, and Management of Technology.

 Marina Dabić is a Full Tenured Professor at the Faculty of Economics and Business, University of Zagreb, the School of Economics, and the Business University of Ljubljana Slovenia. Prof. Dabić is an associate editor of leading journals such as Technological Forecasting and Social Change and Technology in Society, and department editor for IEEE-TEM. Additionally, she is a member of editorial boards in more than 30 journals. Her research focuses on innovation, technology transfer, management of technology, entrepreneurship international trade, and competitiveness. Yu-Shan Su is Distinguished Professor of Technology Management at the National Taiwan Normal University, Taipei, Taiwan.

THE ROUTLEDGE COMPANION TO TECHNOLOGY MANAGEMENT

Edited by Tugrul Daim, Marina Dabić and Yu-Shan Su

Cover image: Getty Images First published 2023 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 selection and editorial matter, Tugrul Daim, Marina Dabić and Yu-Shan Su; individual chapters, the contributors The right of Tugrul Daim, Marina Dabić and Yu-Shan Su to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Daim, Tugrul Unsal, 1967– editor. | Dabić, Marina, editor. | Su, Yu-Shan, editor. Title: The Routledge companion to technology management / edited by Tugrul Daim, Marina Dabić and Yu-Shan Su. Description: Abingdon, Oxon; New York, NY: Routledge, 2022. | Series: Routledge companions in business, management and marketing | Includes bibliographical references and index. Identifiers: LCCN 2021059868 (print) | LCCN 2021059869 (ebook) | ISBN 9780367496135 (hbk) | ISBN 9780367550530 (pbk) | ISBN 9781003046899 (ebk) Subjects: LCSH: Management—Technological innovations. | Business planning. Classification: LCC HD30.2 .R679 2022 (print) | LCC HD30.2 (ebook) | DDC 658.4/0380285—dc23/eng/20220121 LC record available at https://lccn.loc.gov/2021059868 LC ebook record available at https://lccn.loc.gov/2021059869 ISBN: 978-0-367-49613-5 (hbk) ISBN: 978-0-367-55053-0 (pbk) ISBN: 978-1-003-04689-9 (ebk) DOI: 10.4324/9781003046899 Typeset in Bembo by codeMantra

CONTENTS

List of Boxes List of Figures List of Tables About the Contributors

xii xiii xvi xix

Introduction – The Routledge Companion to Technology Management PART 1

Technology Management Background

1

17

1 Strategy of Critical Technology: A Case of Turkish Industry and Technology Policy Mert Cihan Tonkal and Tugrul Daim

19

2 Exploring the Interplay between Digital Business Models and Dynamic Capability in the Pursuit of Competitiveness Berk Kucukaltan

34

3 Innovative Places and Regions: Implications for Technology Management Scott W. Cunningham

47

4 IT-Driven Service Innovation Magdalena Marczewska and Marzenna Anna Weresa

59

vii

Contents PART 2

Technology Planning

73

5 Simultaneous Scheduling of Energy Demand and Supply in the Industrial Microgrid Zeynep Bektas, M. Özgür Kayalica, and Gülgün Kayakutlu 6 Where We Are and Where We Want to Go: A Patent Analysis Approach Toward Strategic Technological Planning Priyanka C. Bhatt, Vimal Kumar, Kuei-Kuei Lai, Yu-Hsin Chang, and Yu-Shan Su

75

90

7 Pioneer or Follower: Which Strategy to Choose? Aleksej Dreiling and Peter M. Bican

100

8 Bubble Planning and the Mathematics of Consortia Jonathan Bard, Boaz Golany, and Fred Phillips

116

PART 3

Technology Evaluation

125

9 An Evaluation Model for the Design of Virtual Reality Systems Yu-Shan Su and Wen-Hua Wu 10 An Evaluation Model of Smart Speaker Design Yu-Shan Su and Jung-Hsien Hsia

127 141

11 Methodological Frameworks for Opportunity Discovery in Innovation and Technology Management Cristian Mejia and Yuya Kajikawa

157

12 Technology Assessment and Selection: An Assessment of the Autonomous Driving Technologies by Using Type-2 Fuzzy Sets and Systems Koray Altun, Recep Kurt, Reyhan Ozcan Berber, Serkan Altuntas, and Turkay Dereli

172

13 Multicriteria Assessment of a Technological Ecosystem: A Multi-Country Approach Ronnie Figueiredo, João J.M. Ferreira, Helder Gomes Costa, and Arnab Basu

184

14 Multi-Criteria Decision-Making Methods for Technology Selection Zeynep Didem Unutmaz Durmu şoğlu and Alptekin Durmu şoğlu viii

199

Contents

15 Software Assessment for Capacity Planning and Feasibility Check of the Master Production Schedule Arash Yazdani and Tugrul Daim 16 Technology Assessment and Selection Nathasit Gerdsri and Pard Teekasap PART 4

209 248

Technology Development and Transfer

265

17 Regional Innovative Intensity in an Emerging Economy: Analyzing China’s Provincial Regions Kenneth G. Huang

267

18 Understanding Components of Entrepreneurial Ecosystems Haydar Yalcin, Tugrul Daim, Aynur Kirbac, and Marina Dabić 19 The Performance of Science and Technology Parks under Triple Helix Systems in Turkey Hülya Ünlü 20 Technology and Entrepreneurship: A Systems and Simulation Approach Leon Pretorius, Vida Davidavičien ė, and Jan Harm C. Pretorius 21 Transformation into New Drug Companies: The Perspective of Dynamic Capabilities Feng-Shang Wu, Chien-Hsin Wu, and Ta-Wei Tao PART 5

288

304 320

340

Managing Technological Innovations

357

22 Coworking Ecosystems: Institutionalization of “Homes” for Innovation and Venturing Yixin Qiu and Ricarda B. Bouncken

359

23 The New Imperatives of Defense Technological Innovation: Past, Present, and Future Bharat Rao, Bala Mulloth, and Adam Jay Harrison

373

24 Mapping Buyer-Supplier Relationships after the Open Innovation Perspective Antonello Cammarano, Francesca Michelino, and Mauro Caputo

386

ix

Contents

25 Evolution of Collaborative Innovation Strategy for Global Manufacturing Firm Driven by Integrated Modularity Thinking Haijun Wang and Chaohui Shu 26 Open and Closed Innovation Strategies in Converging Environments: How the Internet of Things Blurs the Boundaries between ICT and Logistics Nathalie Sick, Svetlana Okara, Stefanie Bröring, and Annika Wambsganss PART 6

399

415

Social Issues

431

27 Proposing an Application Model for Personnel Recruitment by Using a Multi-Criteria Decision-Making (MCDM) Approach: A Case of Blue-Collar Cashier Personnel Recruitment Sinan Yimaz Yuruten, Cagla Ozen, Fazli Yildirim, Tugrul Daim, and Amir Shaygan

433

28 Electronic Payment Technology: Developing a Taxonomy of Factors to Evaluate a Fraud Detection and Prevention System for the Airlines Industry Sultan A. ALGhamdi, Tugrul Daim, and Dirk Meissner

450

29 Engaging the Technology Management for Gender Equality Dilek Cetindamar and Sancheeta Pugalia

512

30 Creating a Networked Innovation Ecosystem for Industry 4.0 Fatma Nur Karaman Kabadurmus

529

PART 7

Emerging Technologies and Emerging Regions

541

31 Smart Manufacturing: An Evaluation Model for Taiwan’s Innovation Applications Yu-Shan Su and Yuan-Ting Lin

543

32 Data Mining As a Knowledge Extraction Tool and an Application on Decision Tree-Based Algorithms Fatma Önay Koçoğlu

561

33 Smart City: An Evaluation Model for Taiwan’s Social Innovation Yu-Shan Su and Yi-Zhen Cheng

x

574

Contents

34 Smart Transportation: An Evaluation Model for Taiwan’s Innovation Policy Yu-Shan Su and Shao-De Liu

593

35 Energy Efficiency in Smart Street Lighting System for ITU Eren Deliaslan, M. Özgür Kayalica, and Gülgün Kayakutlu

615

Index

635

xi

BOXES

4.1 Four archetypes of service innovation – the example of TripAdvisor 4.2 The IT as a service innovation driver – an example of healthcare services 4.3 Financial service innovation (Fintech) – new opportunities in business practice

xii

61 62 66

FIGURES

4.1 6.1 6.2 7.1 8.1

A conceptual framework of IT-driven service innovation management Patent analysis as an intermediary for market dominance Strategic planning to identify current position and build strategies for future Market entry matrix Bubble plan for the Management in Science & Technology (MST) School at Oregon Graduate Institute of Science and Technology 10.1 Research framework for an evaluation model of smart speaker designs 11.1 Generalization of opportunity discovery between two disjoint topics belonging to the same knowledge representation (usually a database of academic articles, patents, etc.) 11.2 A generalization of opportunity discovery framework by comparing the same topic across two different knowledge representations or databases (1) two knowledge representations of the same topic (2) relationships in quadrant “a” when similarity exists, or gaps being the quadrants “b” and “c” 12.1 A practical innovation intelligence process 12.2 A technology evaluation framework based on type-2 FIS 12.3 Validation of the generated keywords 12.4 Designed type-2 fuzzy membership functions (MFs) and the input values 15.1 Total aggregated monthly demand distribution 15.2 Due dates distribution (minimum 2 days; maximum 45 days) 15.3 Hierarchical decision model perspectives and sub-criteria with relative weights 15.4 Absolute weights of the sub-criteria 15.5 Value curve of the candidate software 15.6 Simulated manufacturing processes using Simio 15.7 Resource status of “SensorAssembly” lines with constant demand (left: least slack time; Right: least setup time) A15.1 HDM layers and connections A15.2 Optimization algorithm for the resource selection problem A15.3 Simulation of manufacturing processes and states of the system by Simio 16.1 Guideline for strategic determination to fill technological capability gaps xiii

65 93 94 110 117 148

161

164 174 175 177 179 231 231 233 235 236 238 238 245 246 247 251

Figures

16.2 Relative importance of each dimension for each scenario 16.3 Comparison of the technological capability areas between two scenarios ranked according to the size of weighted gap in each dimension 18.1 Researcher network 18.2 Organization network 18.3 Country network 18.4 Funding unit network 18.5 Funding organizations burst analysis 19.1 Distribution of technology development zones in NUTS (1) regions 20.1 Unemployment and entrepreneurship causal loop diagram 20.2 Unemployment and entrepreneurship system dynamics (SD) model 20.3 Extended unemployment entrepreneurship and technology causal loop diagram 20.4 Extended unemployment entrepreneurship technology system dynamics (SD) model 20.5 Entrepreneurship and unemployment simulated using the SD model and basic parameter set 20.6 Some nonlinear effect of entrepreneurship on E1 20.7 Entrepreneurship simulation with nonlinear effect of entrepreneurship on E1 20.8 Unemployment simulation comparison with technology interaction included 20.9 Technology simulation with technology interaction included 20.10 Technology change rate simulation with technology interaction included 20.11 New enterprise births as proxy for entrepreneurship; Spain and Ireland data analyzed from OECD (n.d.) 20.12 New enterprise births as proxy for entrepreneurship; Lithuania data analyzed from OECD (n.d.) 20.13 Entrepreneurship Index; data analyzed from OECD (n.d.) 20.14 Unemployment rate; Spain data analyzed from OECD (n.d.) 20.15 Unemployment rate; South Africa data analyzed from OECD (n.d.) 21.1 Research 24.1 Buyer-supplier relationships after the OI perspective 24.2 Buyer-supplier relationships and stages of evolution of the product architecture 24.3 Buyer-supplier relationships within the smartphone industry 24.4 Buyer-supplier relationships and partner selection criteria 25.1 Evolution of Haier’s collaborative innovation models under different modularity scenarios 25.2 Layout of the modular production lines of Haier’s double-tube washing machines 25.3 The “three-in-one” technical innovation system of Haier 26.1 Innovation strategy matrix based on Bröring (2010) 26.2 Top ten assignees by number of patents in IoT 26.3 Relevant actors positioned in the innovation strategy matrix 26.4 Collaboration partners for logistics companies to pursue a technology-based open innovation strategy 28.1 Gaps analysis diagram 28.2 HDM objectives, perspectives, criteria and desirability curves 29.1 The selection process 29.2 The distribution of articles by year xiv

258 260 292 296 298 299 301 317 325 326 327 328 329 329 330 331 331 332 333 333 334 334 335 344 390 391 393 394 403 405 406 418 420 423 424 495 497 518 519

Figures

30.1 31.1 31.2 31.3 31.4 31.5 31.6 33.1 33.2 33.3 33.4 33.5 33.6 33.7 34.1 34.2 34.3 34.4 34.5 34.6 34.7 35.1 35.2 35.3 35.4 35.5 35.6 35.7 35.8 35.9 35.10 35.11 35.12 35.13 35.14 35.15

Prevalence of different cooperation strategies, by country DEMATEL causal diagram of five constructs for smart manufacturing DEMATEL causal diagram of A “Smart design” DEMATEL causal diagram of B “Smart production” DEMATEL causal diagram of C “Smart factory” DEMATEL causal diagram of D “Smart services” DEMATEL causal diagram of E “IIoT management” DEMATEL causal diagram of six constructs for smart cities DEMATEL causal diagram of A “Smart infrastructure” DEMATEL causal diagram of B “Smart government” DEMATEL causal diagram of C “Smart environment” DEMATEL causal diagram of D “Smart living” DEMATEL causal diagram of E “Smart security” DEMATEL causal diagram of F “Smart mobility” DEMATEL causal diagram for the six dimensions for smart transportation DEMATEL causal diagram for A Connected and automated vehicles (CAVs) DEMATEL causal diagram for B Smart traffic safety DEMATEL causal diagram for C Transportation management system DEMATEL causal diagram for D Intelligent-based transportation technology DEMATEL causal diagram for E Transportation resources integration and sharing DEMATEL causal diagram for F Traffic data collection Smart lighting control system (Adopted from lighting.philips.com) Study framework Structure of LSTM cell Total energy consumption of ITU – Ayazağa Campus Model flowchart Trend, seasonality and residuals of data set Stationary and nonstationary dataset The ACF and PACF Model diagnostics Observed and one-step ahead forecast to validate predictions Residual dataset SARIMA model forecast Hybrid model forecast SARIMA and hybrid model forecast for continuous times Street lighting energy consumption forecast for both old scenario and smart scenario

xv

533 553 553 554 554 555 555 585 585 586 586 587 587 588 606 607 607 608 608 609 609 619 619 622 623 624 625 626 627 627 628 628 629 630 630 631

TABLES

5.1 5.2 5.3 5.4 5.5 5.6 5.7 8.1 8.2 8.3 8.4 10.1 10.2 10.3 11.1 11.2

Technical parameters and values Optimal solutions of Scenarios 1 and 2 Optimal solutions of Scenarios 3 and 4 Optimal solutions of Scenario 1 Optimal solutions of Scenario 2 Optimal solutions of Scenario 3 Optimal solutions of Scenario 4 Possible “Items of Exchange” in a consortium Linking resources to programs Linking programs to budget lines Overhead rates applied Five principal dimensions and 24 criteria for smart speaker designs Institutions and working years of the experts AHP analysis results Articles applying the LBD approach in ITM and other non-biomedical fields Articles finding relationships or gaps across different knowledge representations of the same topic 12.1 Calculated “hotness” values and their clusters 12.2 Obtained trendiness degrees of the key technology groups under consideration 13.1 The Likert-based scale adopted for collecting the evaluations 13.2 Twelve evaluators from three universities 13.3 The Likert-based categories 13.4 Categories and their boundaries 13.5 Alternatives (actors) sorted into the innovation categories A13.1 The collated alternative evaluator answers 15.1 Analysis of the current practices in planning and scheduling (ASP) 15.2 Hierarchical decision model (HDM) architecture 15.3 MTS and MTO characteristics and KPIs 15.4 Detailed scheduling decisions and optimization rules 15.5 Statistical final scores of the candidate software xvi

83 84 84 85 86 86 86 118 120 121 122 146 149 150 161 164 178 181 188 189 190 190 191 196 218 225 230 232 237

Tables

15.6 Scenario analysis and 16. 1 Comparison of technological capability dimension used in Bell and Pavitt’s model and the one used in this study 16.2 Broad description for each technological capability level 16.3 Technological capability index for Scenario A – close collaboration with China and India manufacturers 16.4 Technological capability index for Scenario B – technological leadership development for specific types of module sets 17.1 Variable definitions and sources 17.2 Descriptive statistics of variables (years 1999–2008) 17.3 Pairwise correlations of variables (years 1999–2008) 17.4 Fixed effects regression models to estimate the effects of key drivers of innovative intensity in China’s developed and developing provincial-level regions (years 1999–2008) 18.1 Researcher parameters 18.2 Organization parameters 18.3 Country parameters 18.4 Funding organization parameters 19.1 Variables’ description and sources 19.2 Descriptive statistics 19.3 Multivariate statistics and F approximations for independent variables (MANOVA) 19.4 Multivariate multiple regression model results A19.1 Univariate multiple regression model results A19.2 Multivariate multiple regression model results for regions 20.1 Some typical base case and extended SD model parameters 22.1 Data Materials 25.1 Implementation of Haier’s collaborative innovation strategy based on platform-based modularity 25.2 Comparison of collaborative innovation effects at Haier driven by modularity concepts 26.1 Search terms for patents in the area of IoT 28.1 HDM perspectives and criteria 29.1 Details about the process of systematic literature reviews 30.1 Summary statistics 30.2 The impact of network capability on radical innovations 31.1 Smart manufacturing: five constructs 31.2 Smart manufacturing: five constructs and 29 criteria 31.3 List of experts 31.4 Smart manufacturing: AHP overall weight and ranking 31.5 Smart manufacturing: DEMATEL overall weight and ranking 32.1 Confusion matrix 32.2 Hold-out (70%-30%) performance evaluation measure values for all algorithms 32.3 Hold-out (80%-20%) performance evaluation measure values for all algorithms 32.4 Five-fold cross validation performance evaluation measure values for all algorithms xvii

239 252 253 258 259 277 278 279

280 293 295 297 300 310 311 312 313 317 318 326 364 409 410 500 517 535 536 546 547 549 550 552 567 568 569 569

Tables

32.5 Ten-fold cross validation performance evaluation measure values for all algorithms 32.6 Summary of finding with best accuracy and F-scores 33.1 Smart city: social innovation theory 33.2 Smart city infrastructures in Europe and Asia 33.3 Smart city: Six constructs and 33 criteria 33.4 List of experts 33.5 Smart city: AHP weight and ranking 33.6 Smart city: DEMATEL results 34.1 Smart transportation innovation policies in Asia, America, and Europe 34.2 Smart transportation: six dimensions 34.3 Smart transportation: six dimensions and 32 criteria 34.4 List of experts 34.5 Smart transportation: AHP overall weight and ranking 34.6 Smart transportation: DEMATEL overall correlated weight and ranking 35.1 Armature types and numbers

xviii

570 570 576 578 581 582 583 584 596 597 600 604 605 610 618

CONTRIBUTORS

Sultan A. ALGhamdi is a Ph.D. candidate in Engineering and Technology Management at Portland State University, with B.Sc. and M.Sc. degrees in Management Information Systems. His research revolves mainly around e-payment, e-commerce, fraud software, and fintech. Sultan has experience in research, road mapping technologies, technology assessment, technology transformations, e-payment applications, fraud technologies, banking, technical documentation, and data report compilation. He is an organized, process-oriented professional, highly attentive to detail with a capacity to educate and guide others. Currently, he is working as a Professor at the University of Jeddah in the Business School, teaching the following subjects: IT Project Management, Business Ethics, Introduction to Management Information System, Entrepreneurship, Research Methods, Information Security, and Project Management. Sultan is working as Fraud Prevention Tech Manager at JPMorgan Chase Co., the fifth largest bank in the world and the largest bank in the US. In addition, Sultan used to work at Saudia Airlines and led and managed one of the critical projects within the e-commerce department, which is e-payment and fraud management. Sultan has a leadership aptitude and practical communication skills acquired via positions in e-commerce, e-payment, project management, employee supervision and training, program development, customer service, CRM management, and banking. Dr Koray Altun  is an Assistant Professor of Industrial Engineering at TU Bursa, Turkey. He holds Ph.D. and B.Sc. degrees in Industrial Engineering from Gaziantep University and Erciyes University, respectively. His recent research interests include innovation excellence model, systematic innovation and digital innovation. His ORCID ID is 0000-0003-0357-9495. Serkan Altuntas  is an Assoc. Prof. Dr at the Industrial Engineering Department of the Yıldız Technical University in Turkey. He received his B.Sc. degree in industrial engineering from Eskisehir Osmangazi University, Eskisehir, Turkey. He received his M.Sc. degree in industrial engineering from Dokuz Eylul University in Izmir, Turkey. He received his Ph.D. degree from the Department of Industrial Engineering at the University of Gaziantep. xix

Contributors

His research interests include facility layout, service systems and technology and innovation management. His ORCID ID is 0000-0003-4383-4710. Jonathan Bard is Professor of Operations Research & Industrial Engineering at the University of Texas at Austin. Dr Bard is a founding editor of IIE Transactions on Operations Engineering. He is a fellow of INFORMS and IIE, and a senior member of the IEEE. Arnab Basu is Assistant Professor of Physics in the Department of Basic Science and Humanities, Institute of Engineering & Management, Kolkata, India. He is one of the active members of the entrepreneurship drive of this Institute. He is the co-advisor of IEM – AIP Society of Physics Students’ chapter. He was INSPIRE Scholar and Fellow of Department of Science and Technology, Govt. of India. Zeynep Bektas received the B.Sc. degree in Mathematics Engineering, and the M.Sc. and Ph.D. degrees in Industrial Engineering from Istanbul Technical University in 2012, 2015 and 2021, respectively. She works as a research assistant in the Department of Industrial Engineering at Istanbul University – Cerrahpasa, Turkey. Priyanka C. Bhatt is currently based at Department of Information Management, Chaoyang University of Technology, Taiwan. She has more than five years of experience in computer and information science engineering. Her research interests include scientometrics, patent analysis and artificial neural networks. Her ORCID ID is 0000-0001-5638-6844. Peter M. Bican is a Full Professor at the Chair of Technology Management, Friedrich-Alexander-University Erlangen-Nuremberg, Germany. He holds a doctoral degree from the WHU-Otto Beisheim School of Management, Germany and was a visiting Research Fellow at the Kellogg School of Management of Northwestern University, USA. His research centres on the strategic management of technology and entrepreneurship. He has published in various renowned international journals like the California Management Review or the R&D Management Journal. Ricarda B. Bouncken is Chair Professor of Strategic Management and Organization at the University of Bayreuth, Germany. Her research centres on strategies and structures towards innovation and entrepreneurship, particularly between organizations and by fluid organization. She has published more than 200 articles. Stefanie Bröring is a Professor and Chair for Technology and Innovation Management in Agribusiness at the Institute for Food and Resource Economics at the University of Bonn in Germany. Her research interests are converging industries as well as the diffusion of technology-induced innovations across the food chain. Antonello Cammarano is a Researcher at the Department of Industrial Engineering of the University of Salerno. His research interests comprise innovation management, knowledge management, technology management, emerging technologies, blockchain, start-ups, patent data, supply chain management and open innovation. Mauro Caputo is Professor of Innovation and Technology Management at the Department of Industrial Engineering of the University of Salerno. He is the author and co-author of xx

Contributors

more than 100 scientific studies. His research interests enclose the areas of innovation management, supply chain management and operations management. Dr Dilek Cetindamar  is Professor of Contemporary Technology Management at University Technology Sydney, Australia. She has more than 220 publications, including nine books. She received the PICMET Fellow Award in 2019, the best book award from the International Association for Management of Technology in 2012 and an “encouragement award” from the Turkish Academy of Sciences in 2003. She is specialized in Entrepreneurship and Innovation & Technology Management. She has an experience of more than 20 years in designing, delivering and managing entrepreneurship programmes as well as management of accelerators. Yu-Hsin Chang is an Associate Professor in Department of Marketing & Logistics Management, Chaoyang University of Technology, Taichung, Taiwan. His major research area focuses on technology management, patent analysis and patent network analysis. His ORCID ID is 0000–0002–9689-8761. Yi-Zhen Cheng is from Taipei, Taiwan. He graduated from National Taiwan Normal University. His specialization is in business strategies planning and implementation. Scott W. Cunningham holds a Chair of Technology Policy in the School of Government and Public Policy. He delivers a research and teaching programme involving the comprehensive engineering of the urban environment. This involves the use of administrative as well as unconventional data sources to track the quality and sustainability of the urban experience. The work increasingly uses spatial and networked analyses, and considers factors such as housing, employment, environment, infrastructure, and public and private sector innovation. He is one of the editors-in-chief of Technological Forecasting and Social Change, a premier journal in the field of strategic technology management. Until 2019, Professor Cunningham worked at the Department of Multi-Actor Systems at the Delft University of Technology. There he engaged in a programme of research and teaching on the operation of socio-technical systems given the diverse needs, capabilities, and interests of system operators. The work encompassed diverse engineering systems including transport, logistics, telecommunications, and energy. The work embraced both human actors, with decision capability, and computational agents. The work was validated by research grants from the Dutch Science Foundation, the Next Generation Infrastructures Foundation, ProRail the national rail network provider, and the European FP7 and Horizon 2020 programmes. Dr Marina Dabic is Full Professor of Entrepreneurship and International Business at the University of Zagreb, Faculty of Economics & Business, Croatia and the University of Ljubljana, School of Economics and Business. Her research has appeared in a wide variety of journals including Journal of International Business Studies, Journal of World Business, Journal of Business Ethics, Small Business Economics, Journal of Business Research, IEEE-TEM, Technological Forecasting and Social Change, International Business Review, International Journal of Human Resource Management, and Technovation, among others. She is an Associate Editor of the Journal of Technological Forecasting and Social Change and Technology in Society and department editor for IEEE-TEM. Her research areas include entrepreneurship, international business, open innovation, innovation, knowledge management, management of technology, and the impact of xxi

Contributors

innovation on CEE countries. The book Entrepreneurial University in Innovation Seeking Countries Challenges and Opportunities co-authored by Dabić, Schwartz, and Gonzalez-Louero was published by MacMillan Palgrave (USA, 2016). For her research, she has been granted several fellowships: British ALIS, Erasmus+, Tempus, and Ministry of Culture of Taiwan. She is a Mildred Miller Fort Foundation Visiting Scholar in European Studies at Columbus State University, USA. She has been a grant holder of several EU projects like JP TEMPUS Project: Fostering Entrepreneurship in Higher Education- FoSentHE. Currently, she is WP leader at Horizon 2020 RISE OpenInnoTrain project, Interreg Wool projects, and Erasmus +KA220-VET - Cooperation partnerships in vocational education and training project Virtual Open Innovation Environment for SMEsVOIS. She is also the editor of eight books published by Springer and Routledge, and co-editor of Springer book series Applied Innovation and Technology Management. Dr Tugrul Daim is a Full Professor of Engineering and Technology Management and Associate Director of Research for National Academic Center of Excellence for Cybersecurity at Portland State University, Portland Oregon, USA. He is also affiliated with Chaoyang University of Technology in Taiwan as a Honorary Chair Professor. He is also the recipient of the prestigious Fulbright Award. Professor Tugrul U Daim leads a research group on Technology Evaluations and Research Applications. His group has had more than 25 Ph.D. graduates. Several rankings out his research group and output as one of the top in the field. His research group has been supported by National Science Foundation, National Security Agency, US Dept. of Defense, National Cooperative Highway Research Program, Energy Trust of Oregon, US Dept. of Energy, Bonneville Power Administration, Northwest Energy Efficiency Alliance, EPRI, Biotronik, Ford Motor Corporation, Intel Corporation, US Aid, Fullbright, Saudi Arabia Cultural Mission, Libyan Ministry of Education, CAPES Brazil, Maseeh Foundation, Oregon BEST, TUBITAK Turkey, Chinese Scholarship Council, FAPESP Brazil, Oregon Health and Science University, and University of Bremen. Professor Daim has published over 250 refereed journal papers, more than 20 special issues, and more than 20 books. He made more than 200 conference presentations. Professor Daim gave several keynote lectures at conferences, companies, universities, and research centers around the world including Iamot, Euromot, Samsung, Helmut Schmidt University, Kuhne Logistics University, Seoul National University, Bogazici University, Koc University, University of Gaziantep, Izmir Institute of Technology, University of Pretoria, Tampere University of Technology, STEPI, EPIC at UNCC, Cambridge University, National Taiwan University, Higher School of Economics in Moscow, Friedrich Alexander University, and Chinese Academy of Engineering and Office of Naval Research. Vida Davidavičienė  is Doctor of Social Science (Business and Administration). She is a Professor at Vilnius Gediminas Technical University. Her research interests are ICT development influence on society, business and economics. She has co-authored more than 100 research papers, nine monographs, and six textbooks. Eren Deliaslan is an Electrical Engineer. He has worked as a Data Analyst in the software industry. Then he moved to the energy sector by starting his master’s degree at ITU. He is working on the artificial intelligence applications in energy and energy storage technologies. Prof. Turkay Dereli is the Rector of Hasan Kalyoncu University in Turkey. He received his B.Sc. and M.Sc. degrees in Mechanical Engineering from Middle East Technical University xxii

Contributors

(METU) and Gaziantep University (GAUN) in 1992 and 1994, respectively. He earned his Ph.D. degree from GAUN in 1998. His current research interests include technology and innovation management, techno intelligence, CAD/CAM, quality planning, agile/responsive manufacturing, management science, operations management, informatics, applications of artificial intelligence, and soft computing (including fuzzy logic). His ORCID ID is 0000-0002-2130-5503. Aleksej Dreiling is a Consultant at McKinsey & Company in Germany. He holds a Master of Science degree in Management from the WHU-Otto Beisheim School of Management (Germany). His research focuses on innovation and strategy. Alptekin Durmuşoğlu is currently an Associate Professor at the Department of Industrial Engineering of the University of Gaziantep. He received his B.Sc. degree from the Department of Industrial Engineering of Çankaya University, Ankara, Turkey. Dr Durmuşoğlu holds M.Sc. and Ph.D. degrees in Industrial Engineering from Gaziantep University. He is a member of editorial board of several academic journals such as IEEE Transactions on Engineering Management and Technology in Society. Zeynep Didem Unutmaz Durmuşoğlu is Associate Professor of Industrial Engineering of Gaziantep University, Turkey. She received her B.Sc., M.Sc., and Ph.D. degrees at Industrial Engineering of Gaziantep University. She worked as a Research Assistant at the same department for six years. She has research interest in multi-criteria decision-making, medical modelling, technology analysis, simulation optimization, heuristic optimization, and negotiation. João J.M. Ferreira  is an Associate Professor at the University of Beira Interior (UBI), Portugal. Currently, he is the scientific coordinator of the UBI Research Unit for Business Sciences (NECE), Portugal. He has edited or co-edited several books on strategy, innovation, and entrepreneurship and published over 200 papers in premier international journals. Ronnie Figueiredo is an Assistant Professor and Director of Centre for Service Innovation at Universidade Europeia, Portugal. Currently, he is a researcher at NECE, University of Beira Interior (UBI), and Invited Professor at Nova School of Business and Economics (NOVA SBE) working on the NEXTLAND Project (Innovation and Earth Observation), H2020. He is the Chairman of the Spinner Innovation Centre, Europe. Dr. Nathasit Gerdsri is an educator, consultant, researcher, technologist, and entrepreneur with over 20 years of experience in technology and innovation management supporting the development of corporate strategy and government policy as well as the program development for management education. Dr. Gerdsri holds a Bachelor of Mechanical Engineering degree from Chulalongkorn University, Thailand, a dual Master in Engineering Management and Mechanical Engineering, and a Doctorate in Systems Science/Technology Management from Portland State University, USA. Dr. Gerdsri conducts research in the areas of technology planning and roadmapping, innovation management, R&D management, decision analysis, and project management. The outputs of his research works have been published in many leading journals including IEEE Transaction on Engineering Management, Journal of Technology Forecasting Social Change, Journal of Technology Analysis and Strategic Management, Engineering Management Review, Int’l Journal of Technology Intelligence and Planning, Int’l Journal of Technology, Policy and Management, Int’l xxiii

Contributors

Journal of Innovation and Technology Management, Asian Journal of Technology Innovation, Journal of Mathematical and Computer Modeling, and Journal of Engineering and Technology Management. Dr. Gerdsri is a co-author of the book Technology Assessment: Forecasting Future Adoption of Emerging Technologies (ISBN: 978-3-503-12675-0) and several book chapters on the topics related to strategic management of technology. Currently, Dr. Gerdsri serves as an Area Editor of the International Journal of Innovation and Technology Management (IJITM) as well as Editorial board member of IEEE Transactions on Engineering Management. Dr. Gerdsri is the Founder and Head of Technology Innovation Management and Education Labs (TIME Labs) where Thailand - National Technology Roadmaps get developed. He is also the Founder and CEO of TheStrategence - a startup that provides digital platform for management consultancy. He is a member of IEEE Society, the Honor Society of Phi Kappa Phi, Omega Rho International Honor Society which he is used to serve as the PSU Chapter President. He is also listed in Marquis Who’s Who in the World. Boaz Golany  is Vice President for External Relations and Professor in Industrial Engineering and Management at the Technion. He was awarded the Naor Prize of the Israeli Operations Research Society in 1982 and serves as Vice President of the Israeli OR Society. Helder Gomes Costa  is a Associate Professor at Universidade Federal Fluminense, Brazil. He is currently an assistant coordinator of PROMINP (PETROBRAS/ANP) and an appraiser of CAPES and CNPq. He coordinates the Research Group on Multicriteria Decision Support (CNPq/ UFF) and the project “Analysis of Decisions in Corporate Environments” (CNPq/ UFF). Jung-Hsien Hsia  currently works in Google Taiwan. His main works are technical programme management, data analysis, and machine learning technology application. His hobbies are film and drama appreciation, video games, and modern military research. He received his Master degree in Engineering from National Taiwan Normal University in 2019. Prof. Kenneth G. Huang is tenured Associate Professor of Innovation, Entrepreneurship and Technology Management at the National University of Singapore (NUS) in the Department of Industrial Systems Engineering and Management (ISEM), NUS College of Design and Engineering, and Department of Strategy and Policy, NUS Business School. He is the Academic Director of the Master of Science in Management of Technology and Innovation at NUS. He also serves on the advisory board of the Economist Intelligence Unit (EIU). He is the Senior Editor of Management and Organization Review and was a co-editor of Journal of Management Studies (for a special issue). He also serves on the editorial boards of Academy of Management Journal, Strategic Management Journal, and Journal of International Business Studies. His research focuses on innovation and technology management, entrepreneurship, intellectual property management and strategy, global strategy, science and technology policy, open science/ innovation, and institutional change particularly in emerging economies like China and ASEAN. His research is internationally influential and has been published (or accepted) in high-impact, leading journals such as Science, Academy of Management Journal, Strategic Management Journal, Organization Science, Journal of Management, Journal of International Business Studies, Research Policy, Nature Biotechnology, Proceedings of the National Academy of Sciences of the USA (PNAS), Industrial and Corporate Change, Journal of Management Studies, and Academy of Management Best Paper Proceedings. His research has also been recognized by several international research and xxiv

Contributors

best paper awards from the Academy of Management (AOM), Strategic Management Society (SMS), DRUID, Academy of International Business (AIB), and INFORMS. His work and commentaries have been featured in Reuters, CNBC, Economist Intelligence Unit, Business Times, MIT Technology Review, Foreign Affairs, China Daily, Asian Scientist, Straits Times, Lianhe Zaobao, ThinkChina, and Channel NewsAsia, among other media outlets. At NUS, he designs and teaches courses such as Innovation and Entrepreneurial Strategy, Technology Management Strategy, Intellectual Property Management, and Corporate Strategy in the EMBA program. Before joining NUS, he was Assistant Professor of Strategic Management (Innovation and Entrepreneurship) at Singapore Management University (SMU), Lee Kong Chian School of Business (LKCSB). He has also taught in the Sloan Fellows Program in Innovation and Global Leadership (MBA) at the Massachusetts Institute of Technology (MIT) Sloan School of Management. He is the recipient of several teaching awards such as the NUS Faculty Teaching Excellence Award 2019-2020 (winner), NUS Engineering Educator Award 2018-2019 (winner), university-wide NUS Annual Teaching Excellence Award 2018-2019 (nominee) and 2016-2017 (nominee), Faculty Teaching Award Honors List 2016-2017 (winner), and SMU LKCSB Dean’s Teaching Honor List (top 20 faculty members) every year from 2009 to 2015. He earned his Ph.D. in Technology Management and Policy from MIT. He also holds M.S. degree in Technology and Policy from MIT, and B.S. degree in Biomedical Engineering and Electrical Engineering from the Johns Hopkins University. Fatma Nur Karaman Kabadurmus is an Assistant Professor at Yasar University, Turkey, and holds a Ph.D. degree in Economics from Southern Illinois University-Carbondale. Her research interests are antecedents of innovation and governance in developing countries. Yuya Kajikawa is currently a Professor in the School of Environment and Society, Tokyo Institute of Technology. He is also a Professor with the Institute for Future Initiatives, University of Tokyo. His research interests include development of methodology for technology and innovation management, structuring knowledge, system design, and structuring of action to realize sustainable society. Dr Kajikawa serves as an Associate Editor of Technological Forecasting and Social Change, Frontiers in Research Metrics and Analytics, an Editor of Sustainability Science, and a member of editorial boards in other four international journals. Gülgün Kayakutlu received the B.Sc. and M.Sc. degrees in Industrial Engineering from Middle East Technical University in 1976 and 1980, respectively, and the Ph.D. degree in Industrial Engineering from Marmara University in 2004. She works as a Professor at the Energy Institute in Istanbul Technical University, Turkey. M. Özgür Kayalica received the B.Sc. degree in Economics from Gazi University in 1992, and M.Sc. and Ph.D. degrees in Economics from the University of Essex in 1996 and 2000, respectively. He works as a Professor at the Energy Institute in Istanbul Technical University, Turkey. Aynur Kirbac is currently serving as a Dr. Res. Assist. in Operations Management and Marketing department at Izmir Katip Celebi University. She earned her BSc. in Business Administration, her MSc in Management with merit degree and PhD. in Business Administration with honorary degree. She has been awarded special grants by the Scientific and Technological Research Council of Turkey (TUBITAK 2209-2214) to support her xxv

Contributors

undergraduate and graduate research. She has been also awarded a prestigious graduate degree scholarship for overseas universities from the Republic of Turkey Ministry of National Education for her Master’s degree. She was Visiting Scholar at the Department of Engineering and Technology Management of Portland State University’s Maseeh College of Engineering and Computer Science for her doctoral research in 2021. She has published several papers based on patent analysis, bibliometric analysis, and multi-criteria decision-making models. Her main interests are related to technology intelligence, technology roadmapping, technology assessment, technology forecasting, decision-making and R&D management. Fatma Önay Koçoğlu graduated from the Department of Mathematics of Istanbul University, Turkey and the Department of Industrial Engineering, Sakarya University, Turkey. She received her Ph.D. degree in Informatics from Istanbul University. Her research areas are data mining, machine learning and optimization. Dr Berk Kucukaltan completed his Ph.D. degree in Business Management at Brunel University London, with the Dean’s Prize for Innovation and Impact in Doctoral Research in the Brunel Business School. He is an Assistant Professor at Trakya University, Turkey and is also a Visiting Fellow at the School of Management, University of Bradford, UK. His research interests stretch across strategic management, performance management, strategic decision-making, logistics, innovation management and entrepreneurship subjects. In addition to his editorial board memberships in several journals listed in the Web of Science, he has a large number of publications in ABS-listed journals and in widely known conferences. Vimal Kumar is an Assistant Professor in the Department of Information Management at Chaoyang University of Technology, Taichung, Taiwan. His research interests are in the area of technological innovation and patent analysis, total quality management, manufacturing strategy, and supply chain management. His ORCID ID is 0000–0001–7179-3878. Dr Recep Kurt is the Director of Martur’s R&D Center in Bursa, Turkey and manages innovative projects supported by TUBITAK-related automotive seating systems. He graduated from the Department of Mechanical Engineering at Uludag University. He received his M.Sc. degree with his thesis on finite element analysis in acoustics and his Ph.D. degree with his study on composite material simulations from the same university. His ORCID ID is 0000-0002-3371-9818. Kuei-Kuei Lai is the Vice President and Professor a Department of Business Administration, Chaoyang University of Technology Taichung, Taiwan. His research interest focuses on management of technology in patent citation analysis, patent portfolio and patent family, and technological forecasting. His ORCID ID is 0000-0001-6049-1161. Yuan-Ting Lin  has graduated from National Taiwan Normal University. He works in Taichung, Taiwan. Shao-De Liu has graduated from National Taiwan Normal University. His specialization is in science and technology policy, and decision analysis. He is currently a manager of a hotel in Taichung, Taiwan.

xxvi

Contributors

Magdalena Marczewska, Ph.D., is an Assistant Professor at the Faculty of Management, University of Warsaw, Poland. Her research focuses on innovation and technology management, project management and sustainability. Dirk Meissner is Distinguishes Professor HSE University, Head of the Laboratory for Economics of Innovation at HSE and Academic Head of the international Master program “Governance Science, Technology and Innovation”. Dr. Meissner has 20 years experience in research and teaching technology and innovation management and policy. He has strong background in science, technology and innovation for policy making and industrial management with special focus on Foresight and roadmapping, science, technology and innovation policies, funding of research and priority setting. Prior to joining the HSE Dirk was responsible for technology and innovation policy at the presidential office of the Swiss Science and Technology Council. Dirk also has long experience in top level consulting to key decision makers in industry as a management consultant for technology and innovation management with Arthur D. Little. He is Associate Editor Journals Technology Forecasting and Social Change, IEEE Transactions on Engineering Management and Journal of Knowledge Management and Editor Springer book series ‘Science, Technology and Innovation Studies’. Cristian Mejia is an Assistant Professor at the Department of Innovation Science, Tokyo Institute of Technology, Tokyo, Japan. His research interests include quantitative science studies, the structure of knowledge, and methods for linking social issues to science and technology. Francesca Michelino is Associate Professor of Marketing and New Product Development at the Department of Industrial Engineering of the University of Salerno. She is a co-author of more than 70 scientific studies. Her research interests include supply chain management and innovation management. Bala Mulloth is an Assistant Professor at the Frank Batten School of Leadership and Public Policy at the University of Virginia. He is the co-author of Defense Technological Innovation: Issues and Challenges in an Era of Converging Technologies, published by Edward Elgar Publishing in 2020. Svetlana Okara is a Product Manager at Deutsche Post DHL in Bonn, Germany. Her area of work lies in digitalization of logistics processes and products. Dr Reyhan Ozcan Berber  is the Innovation Responsible of Martur’s R&D Center in Bursa, Turkey and works for innovative projects supported by TUBITAK-related automotive textiles and seating systems. She graduated from Textile Engineering Department at Ege University. She received her Ph.D. degree with a study on the Thermal Comfort of Automotive Textiles from Ege University. Her ORCID ID is 0000-0002-7167-3152. Dr. Cagla Ozen is the Head of the Department of Information Systems and Technologies, Yeditepe University, Turkey. She has completed her B.Sc. and M.A. degrees at Bogazici University (one of the best universities of Turkey) and her Ph.D. at Computer Science Department of University of York, UK. She has more than 40 publications that are cited by many academics, and one among them was awarded Outstanding Paper Award Winner at the

xxvii

Contributors

Literati Network Awards for Excellence 2011. Her research interests are human-computer interaction (HCI), user website experience, learning styles, cultural differences, personalization, user interface characteristics, technology adoption, Internet of Things (IoT), Society 5.0, disruptive technologies and digital transformation. Fred Phillips is a Professor at the University of New Mexico and at Tongji University. He is a winner of the Kondratieff Medal, which was awarded by the Russian Academy of Sciences. He is also Vice President of ACIEK, a Fellow of PICMET and Editor-in-Chief Emeritus of Technological Forecasting & Social Change. Jan Harm C. Pretorius  (M.Ing., M.Sc., D.Ing.) is currently Professor and HFaculty of Engineering and the Built Environment at the University of Johannesburg, South Africa. He has co-authored 240 research papers and supervised over 50 Ph.D. and 260 Master’s students. He is a senior member of the IEEE. Leon Pretorius (D.Eng., M.Sc. Eng., M.Sc. Math.) is an Emeritus Professor at the University of Pretoria, South Africa. He has supervised more than 200 Master, and 60 Ph.D. students and co-authored more than 290 research papers. He is an Honorary Fellow of SAIMechE, and a member of SAIIE, ASME, and IEEE. Sancheeta Pugalia is a Ph.D. student in the School of Information, Systems, and Modelling at University Technology Sydney, Australia. Before pursuing her Ph.D., she did her Master by research programme from the Indian Institute of Technology Madras and focussed her research in the area of entrepreneurship. She has also received world-renowned scholarships, including DAAD and JASSO scholarships for Germany and Japan, respectively. She has worked on multiple projects including the entrepreneurial ecosystem and escalated her research skills. Along with publishing her work in conferences and books, she is also an editor for the book titled Key Ideas in Business and Management: Innovation. Yixin Qiu  is a Ph.D. candidate at the Chair of Strategic Management and Organization at the University of Bayreuth in Germany. Her research agenda focusses on organizational innovation management. Specifically, she studies the changes derived from collaborative workspace and digitalization. Bharat Rao is Associate Professor of Technology Management and Innovation at the Tandon School of Engineering, New York University, where Adam Jay Harrison is a John R. Boyd National Security Research Fellow. Amir Shaygan  received his Ph.D. from the Department of Engineering and Technology Management at Portland State University. He has a B.Sc. and M.Sc. in Industrial Engineering, and is currently working as a System Application Analyst in the Population Health Team at Oregon Health and Science University’s Business Intelligence and Advanced Analytics. His dissertation “Technology Management Maturity Assessment in Health Research Centers” focuses on developing a multi-criteria model to assess technology management maturity and continuous learning in health research centers in university hospitals. Amir’s current research interests are multi-criteria decision-making models, learning health systems, continuous improvement in healthcare, health tech adoption, and health disparities. xxviii

Contributors

Chaohui Shu received her M.Sc. degree in Management from the Shenyang University of Technology, Shenyang, China, where she is currently pursuing a Ph.D. degree with the School of Management. Her research interests include technology innovation management, modularity, and innovation ecosystems. Nathalie Sick is a Senior Lecturer for Contemporary Technology Management at the University of Technology Sydney, Australia. Her research interests are industry convergence as well as knowledge and technology transfer in interdisciplinary collaborations. She obtained a Ph.D. degree in innovation management and holds diploma in Business Administration and Industrial Engineering. Dr. Yu-Shan Su*  is a Distinguished Professor at National Taiwan Normal University, Taipei, Taiwan. Her research interests are innovation management in the AI, healthcare, and high-tech industry. Dr. Su serves as an Associate Editor of Technological Forecasting and Social Change, a member of editorial board of IEEE Transactions on Engineering Management, and an Administrative Committee member of IEEE Technology and Engineering Management Society (2021–2022). She has published in Research Policy, IEEE Transactions on Engineering Management, Journal of Innovation & Knowledge, Technological Forecasting and Social Change, R&D Management, Computers in Human Behaviour, Journal of World Business, Asia Pacific Journal of Management, Management Decision, International Journal of Technology Management, Journal of Organizational Change Management, International Journal of Innovation and Technology Management, Tsinghua Business Review, and Sun Yet-Sen Management Review. Her new book Routledge Companion to Technology Management: Next Steps edited with Tugrul Daim and Marina Dabić is published by Routledge Taylor & Francis Group in 2021. Her book Innovation Management: Winning the Competitive Advantage in the Globe edited with Jin Chen and Gang Zheng was published by Bestwise Publisher in Chinese in 2015. Pard Teekasap is an Assistant Professor at Stamford International University. He got his DBA in International Business from Southern New Hampshire University, where he was introduced to and amazed by the system science. After finishing his DBA, he returned to Thailand and tried to spread the knowledge of system science in his hometown. He created a Facebook page, “System Thinking Thailand,” to share the concept and application of system science in daily issues. He had an opportunity to work in the modeling team for the Ministry of Public Health in Thailand to develop a COVID-19 model to predict the trend and analyze the impact of policies. His interest also expands to other areas of system science, including social network analysis and agent-based modeling. Mert Cihan Tonkal  was born in Turkey. He received Undergraduate Education in Industrial Engineering and International Relations and Graduate Education in Economics in Turkey. He also completed his other Master’s degree in Engineering and Technology Management and Graduate Education in Technology Entrepreneurship and Project Management in the United States. His PhD thesis is in the field of history of science and about national technology capability analysis based on patents of Turkey. He has been working at the Turkish Ministry of Industry and Technology for about 13 years. He acted as an expert in the Undersecretariat office and Deputy Minister office for a significant part of this period, and also worked as facilitator of R&D projects and technopark management at Ankara University. Within the scope of his duty, he worked on industrial zones, technoparks, and clustering policy and its legislative development. In this context, he xxix

Contributors

did research in Finland, Austria, Italy and Germany and wrote his ministry specialization thesis on current account deficit and national clustering policy. His major talents are creation of science, technology and clustering policy, institutional strategy development and financial planning, and strategic management of R&D. Especially; photography, trips to historical and natural beauties, documentaries and books about archeology and civilization constitute his personal interest. He is also a professional chess player. He is 38 years old and currently living in Turkey. Hülya Ünlü holds a Ph.D. in Banking and Finance from Yildirim Beyazit University in Ankara, Turkey. Currently, she is an Assistant Professor of Economics at Cankiri Karatekin University, Turkey. She has an M.Sc. degree in Financial Economics and Econometrics from the University of Essex in UK. Her research interests are innovation, finance and econometrics. Annika Wambsganss is a Doctoral Candidate in Innovation Management in the collaborative doctoral programme between the University of Technology Sydney (Australia) and the Technical University Berlin (Germany). Her research interest lies in converging industries and related strategic management processes. Haijun Wang received the Ph.D. degree in mechanical and automation from the Dalian University of Technology. From 2005 to 2011, he has worked with Haier Group. From 2012 to 2013, he was with Shenyang Yuanda Group. He was a Teacher with the Shenyang University of Technology. He is currently a Professor with the School of Management, Shenyang University of Technology, Shenyang, China. His research interests include product management, modularity, and intelligent computing. Marzenna Anna Weresa,  Ph.D., is Full Professor of Economics and Director of the World Economy Research Institute at the SGH Warsaw School of Economics (Poland). Her research focuses on innovation systems, technology transfer, FDI, international trade and competitiveness. Chien-Hsin Wu  is a Postdoctoral Researcher at the Graduate Institute of Technology, Innovation and Intellectual Management, National Chengchi University, Taipei, Taiwan. His research areas are innovation management, knowledge management and co-opetition. Wen-Hua Wu currently works as a Reliability Engineer in GTW Lab in Google Taiwan. He is a supervisor of Project Team. He mentors seven members to contribute reliabilities for Pixel Phone, Nest and Hearables products. He received his Master degree in Engineering from National Taiwan Normal University in 2019. He is qualified as Certificate of Reliability Engineer (CRE) by Chinese Society for Quality (CSQ) in 2020. He is qualified as Fire Prevention Manager (FPM) by China Productivity Center (CPC) in 2019. Feng-Shang Wu  is a Professor at the Graduate Institute of Technology, Innovation and Intellectual Management, National Chengchi University, Taipei, Taiwan. His teaching and research areas are innovation management, R&D management, technological forecasting and assessment, and science and technology policy.

xxx

Contributors

Dr. Haydar Yalcin is an Associate Professor at the Ege University, Division of Management Information Systems, Department of Business Administration, Faculty of Economics and Administrative Sciences. His research interests are knowledge management, data analytics, scientometrics, patent analysis, network visualization, engineering and technology management, and R&D management. He is using data mining applications for extracting usable patterns to direct science and technology policy. He performs scientometric analysis and patent analysis on emerging technology domains to prepare scenario-based technology management studies. He published many book chapters and articles in different national and international journals.

xxxi

INTRODUCTION – THE ROUTLEDGE COMPANION TO TECHNOLOGY MANAGEMENT

Knowledge embodied in technology becomes the spiritual pledge of one generation to another. The whole history of humankind has been consistently committed to technologies. Nations, people, and states were disappearing, but technology remained. Technology mounted up with humanity. In it crystallized education that shakes minds, and all passions, that inspire the heart. Through technology, a vast confession of the turbulent life of humankind is written. Technology itself presented a huge autobiography, which we could describe via world history. In turn, it affects the very development of technology which falls within Samuelson’s (1954) definition of public goods in that they are available for use by “all and that use by anyone economic actor does not reduce the amount available to others” (p. 377). A major driver for the expanding models for value creation is digitalization. The digital macro-shift affects all sectors. Old monopolies fall, and new winners like Apple, Facebook, Netflix, HBO, Amazon, Google, Tesla, and Rimac cars demonstrate enormous innovation capability and thus enormous power. They are symbols of innovators that “take it all” and transform one sector after the other. They analyze and understand consumers’ behavior, think of digital as the starting point, integrate and establish new digital technology, and build digital leadership. Research on technology management and digital technology has grown massively in the last years within various fields; thus, the Management of Technology can be viewed and investigated from many different perspectives. The Routledge Companion to Technology Management is divided into six parts: • • • • • • •

Technology Management Background Technology Planning Technology Evaluation Technology Development and Transfer Managing Technological Innovations Social Issues Emerging Technologies and Emerging Regions

Part 1 – Technology Management Background Technology is becoming the most important factor in economic, social, cultural, and civilizational development. It affects the relationships of people, economic entities, and countries. Since DOI: 10.4324/9781003046899-1

1

Introduction

prehistory, technology has been man’s companion, with the first and fundamental task: to make man’s life easier. Today, life is unthinkable without it, which was realized in 1777 by J. Beckmann: “technology is a comprehensive science of the intertwining of technology, economy and society.” A review of the literature reveals that there is no consensus on the concept of technology. The term “techno-logic” comes from combining the Greek words “techne” and “logos” used for “art” and “conversation,” respectively. The word “art” seems to be closely related to “technicus,” and the term “technikus” refers to the “skill, dexterity or power to perform certain actions acquired through experience, learning or observation. According to this, a person has the art of managing a business for his benefit.” However, in the most recent use, the word “technology” is translated differently by implicitly or explicitly including the notion of scientific knowledge. Technology is a special category of ingenuity and differs in content and meaning. There are several definitions of technology in terms of breadth of definition: from the general use of knowledge to the “science of industrial art.” Other interpretations include tools, machines, and intellectual property. Further interpretations are more inclined to the systematic use of scientific or other organized knowledge in practice. The main feature of most definitions is emphasizing one or more particular aspects of technology depending on the subject, method, or type. They differ in whether they include real or intangible assets or their systemic characteristics. In short, the technology is approached in three main streams: the simplest version considers that “technology” involves only “changes in handicrafts.” A more sophisticated approach adds work and management skills to physical subjects. The third approach considers “technology” a socio-technological phenomenon, which means that technology also encompasses cultural, social, and psychological processes and involves material and artificial progress. The expansion of high technology (microelectronics, robotics, flexible automation, biotechnology and genetics, lasers, optical fibers, cybernetics, aerospace industry, marine and submarine technology, production of new raw materials, satellites, etc.) radically changes production systems. It creates the material basics of the information society. Such a development of technology enables computer-integrated production and changed attitudes towards work and develops attractive creative forms of action that are generally called “high tech/high touch” (high technology/human communication). Part 1, Technology Management Background, sets forth some fundamental issues and consists of four chapters. In Chapter 1, Tonkal and Daim propose the “Strategy of Critical Technology: A Case of Turkish Industry and Technology Policy.” They argue that industry and technology policies play a huge role in emerging economics and discuss critical technologies as a tool for gaining national technological independence. In the past, the Turkish defense industry made significant achievements in its national technological independence by leveraging critical technologies. This chapter examines the experiences in critical technology in the USA and key enabling technology policies in the EU. Then it expounds on the Turkish 2023 Industry and Technology Strategy in depth. Furthermore, this chapter offers some implications on the findings of the Strategy, such as simple technology labeling, vertical to horizontal, capitalization of R&D, and potential capabilities. Finally, this chapter defines the directions for future studies and concludes on a solution for redefined technology level, the linkage between technologies and economic growth, and well-designed public teams. In Chapter 2, Kucukaltan explores the “Interplay between Digital Business Models and Dynamic Capability in the Pursuit of Competitiveness.” In the era of “Industry 4.0,” the process of digitalization is a catalyst for the transformation of business models towards digital business models. Digital business models are based on the use of related digital technologies, automation, and Internet-enabled services. This study emphasizes that adopting dynamic 2

Introduction

capability principles is vital in this change. Moreover, this chapter aims to contextualize the interplay between digital business models and dynamic capabilities by aligning the internal organizational factors with the external competitive environment. To achieve sustainable competitive advantage, this study suggests focusing more on the impact of digitalization on designed business models along with extant and emerging dynamic capabilities. In Chapter 3, Cunningham proposes the concept of “Innovative Places and Regions: Implications for Technology Management.” This chapter explores the growing evidence that cities are the nurseries for innovation. It summarizes innovation theories on agglomeration, spillovers, and externality to show the importance of attractive and well-designed places for economic competitiveness. Additionally, it explores four specific areas of concern: industrial policy, urban planning, infrastructure development, and regional innovation. This chapter concludes on strategies with a discussion of data-based, place-based, and technology-based strategies. In its conclusion of an agenda for technology management, this chapter emphasizes privacy, geospatial data, and further exploration of information and computing technology as a complement for place-based strategies. In Chapter 4, Marczewska and Weresa delineate “IT-Driven Service Innovation.” This chapter aims to identify mechanisms and models of IT-driven service innovation to show how information technology (IT) impacts innovation in services. It summarizes the nature of service innovation and IT as a driver of innovation in services. This chapter further proposes a conceptual framework for managing IT-driven service innovation. It consists of value creation, value delivery/proposition, and value capturing in its conceptual framework. The role of technology innovation in service ecosystems is also discussed in this chapter, and the question of how firm value is created, delivered, and captured in a market is examined. Examples of IT-driven service innovation are shown, such as innovation in tourism, health care services, and FinTech. This chapter concludes on managing IT-driven service innovation with the co-creation of customer engagement and the development of IT capabilities.

Part 2 – Technology Planning The growth of low treasure technology with relatively low investment costs opens new possibilities for advanced analyses of heterogeneous data. If small- and medium-sized businesses (SMBs) manage to implement advanced data analytics as a base of their management, it will create new fundaments for sustainable value creation (Farrukh and Holgado, 2020). Technology planning research has recently focused on using sensory data. Shoval and Ahas (2016) divide the research into three generations: (1) methodological dimensions and the potential of tracking data, (2) use of sensory data to discover new potentials in places or attractions, and (3) how sources of new data can challenge fundamental questions of consumer behavior. Rettberg (2020) claims that today’s research has a bias, with substantial informatic research focusing on the fast development and adoption of new technology. Therefore, this part will have chapters from leading authors in planning technology in technology-driven organizations including industrial firms, government labs, and universities. Part 2, Technology Planning, consists of four chapters and considers approaches and methodologies that should influence firms to reevaluate their existing practices in technology planning. In Chapter 5, Bektas, Kayalica, and Kayakutlu study “Simultaneous Scheduling of Energy Demand and Supply in the Industrial Microgrid.” Microgrids, disconnected from the main grids, are constructed to add value to the smart environment. Today, microgrids give 3

Introduction

the best efficiency in many industrial zones. Furthermore, an industrial zone can become a prosumer that generates self-sufficient energy. From a technology management perspective, balancing demand and supply based on the stochastic features of renewable energies has become a major research topic. This study proposes an energy management model and aims to examine the load and power schedules for an industrial microgrid. Furthermore, it aims to minimize the energy costs of the microgrid by scheduling the power supply and load demand simultaneously. The model incorporates mixed-integer nonlinear programming to provide 24-hour load and power scheduling for the industrial zone. In Chapter 6, Bhatt, Kumar, Lai, Chang, and Su propose “Where We Are and Where We Want to Go: A Patent Analysis Approach Toward Strategic Technological Planning.” Companies are now striving to gain technological dominance in order to keep their foothold in the market amongst their competitors. The technological dominance of a firm can be measured in various ways, such as R&D capabilities, core competencies, and the product innovation curve of the firm. This chapter aims to highlight various patent analysis methods to give a comprehensive view of strategic technological planning, such as main path analysis, social network analysis methods, and patent family analysis. By using patent analysis and forecasting techniques, firms can carry out the important task of assessing where they are at the present as well as how and where they want to move ahead in the competitive curve of the market. Moreover, this chapter contributes to understand the decision-making capabilities of firms based on their strategic technological planning. In Chapter 7, Dreiling and Bican propose “Pioneer or Follower: Which Strategy to Choose?” The pioneer and follower strategies are examined in this chapter as possible market entry strategies for companies. Both first-mover advantages and disadvantages are evaluated in this chapter. This chapter summarizes empirical and theoretical studies on the impacts of industry-, regional-, and product-based differences on market entry strategies. After reviewing the determinants of the market entry strategies, this chapter develops a market entry matrix. The matrix determines the appropriate market entry strategy by accounting for the consumer and industrial goods markets, as well as R&D, manufacturing, and marketing resources. This chapter elucidates that firms with superior R&D resources have a higher probability of using a strategy to enter the market as a pioneer while the firms with superior manufacturing and marketing resources have a higher probability of using a strategy to enter as a follower. In Chapter 8, Bard, Golany, and Phillips demonstrate “Bubble Planning and the Mathematics of Consortia.” This chapter portrays a bubbles-within-bubbles picture. In this picture, “bubble planning” shows the natural grouping of the constituencies, suppliers, partners, and other entities of an organization. The chapter formalizes the simple idea of “bubble planning” to design for R&D and economic development consortia. By setting the purposes and the objectives of consortia, optimization models are set forth for selecting consortium participants and budgeting the consortium in a way that satisfies all members. A final optimization model of overhead costs in university-based consortia is addressed. The results are especially applicable to cross-sectoral consortia. In conclusion, these ideas for consortia can be applied to open innovation, the sharing economy, and cross-functional teams within a single organization.

Part 3 – Technology Evaluation Technical progress is the most important source of economic growth. The basic characteristic of the modern world is accelerated by scientific and technological development, which increasingly affects the position of each country and the overall international economic 4

Introduction

and political relations. Scientific and technological development and the acceleration of that development can make the economy of any country permanently intensive. Technological change is partially irreversible and bilateral. First, once technology and accompanying skills and routines move forward, previous and simpler technologies are “forgotten.” To introduce new technology, a learning process, individual skills, and organizational practices are required. Second, technologies are not chosen deliberately from a wide range of possibilities but are determined locally. Technological development is often non-ergodic in achieving more than one result, but a path or trajectory is established at one point, and the previous alternative is included (Lavoie, Daim, and Carayannis, 2020). Organizations leverage technological evolutions to impose, force, and form their strategic goals for obtaining market dominance. Disruptive technologies have challenged organizations to direct and transform their technological infrastructure and operational processes newly based on the market dynamics. Blockchain is recognized as the disruptive technology of the decade, which has undertaken incumbents to reassess their business models (Bhatt et al., 2020). Part 3, Technology Evaluation, through eight chapters places concepts in the perspective. In Chapter 9, Su and Wu study “An Evaluation Model for the Design of VR Systems.” Virtual reality (VR) has sparked a new wave of revolution in many industries, e.g., gaming, films, sports, medicine, and education. This chapter integrates both innovation and product design theory to establish an evaluation model for VR system designs. Four dimensions and 23 criteria are established. The four dimensions are functional attributes, new product attributes, product styling, and product attractiveness. A questionnaire was developed and distributed to 19 renowned experts in Taiwan’s VR industry. The AHP was used to analyze the evaluation model to obtain the weight and rank of each dimension and each criterion of VR system designs. The results show that functional attributes is the first priority among the four dimensions. For functional attributes, the most important criterion is display technology. For new product attributes, the most important criterion is compatibility. For product styling, the most important criterion is product size. For product attractiveness, the most important criterion is semantic attraction. This chapter provides strategies for developing technology system designs in the future. In Chapter 10, Su and Hsia study “An Evaluation Model of Smart Speaker Design.” Smart speakers are growing quickly in the smart devices market today. This chapter incorporates Abernathy and Utterback’s (1978) model to discuss dominant design, product innovation, and process innovation. Five dimensions and 24 criteria were established to create an evaluation model for smart speakers’ design. The five dimensions are styling elements, styling imagery, attractiveness, added value, and functional attributes. The AHP was used to analyze the questionnaire. The results show that functional attributes is the first priority among the five dimensions. For styling elements, the most important criterion is the format of composition. For styling imagery, the most important criterion is the high-tech feel. For attractiveness, the most important criterion is semantic attraction. For added value, the most important criterion is pop culture. For functional attribute, the most important criterion is high-quality music playback. This chapter provides strategies for developing technology hardware designs in the future. In Chapter 11, Mejia and Kajikawa propose “Methodological Frameworks for Opportunity Discovery in Innovation and Technology Management.” Today researchers can analyze big data at high speeds. Computational methods, such as text mining, network analysis, and machine learning, are being applied. Three data-driven analytic frameworks are applied in a variety of scenarios in this chapter. The first is linking discovery when two different and 5

Introduction

disjoint topics are compared to find connecting terminologies. In the second, two knowledge representations of a single topic are compared to establish linking patterns or white spaces, e.g., when comparing overlaps in the mining of academic articles and patents. The third is mixed methods. This chapter discusses the implications of data-driven methods and speeding up the knowledge discovery process. In addition, this chapter contributes to technology foresight and technology forecasting for innovation and technology management. In Chapter 12, Altun, Kurt, Berber, Altuntas, and Dereli study “Technology Assessment and Selection: An Assessment of the Autonomous Driving Technologies by Using Type-2 Fuzzy Sets and Systems.” Autonomous driving technologies are driving the waves of automotive industry innovation. This study aims to analyze the smart technology process to access the innovation potential of autonomous driving technologies. A technology evaluation framework incorporating type-2 fuzzy sets and systems is analyzed in this process. Patent and publication data are used in the technology evaluation process. The four key technologies in autonomous driving (i.e., car navigation systems, path planning, environment perception, and car control) are assessed based on their technology trendiness evaluation. This chapter provides the results of assessing autonomous driving technologies for project and portfolio management as well as assessing emerging technologies. In Chapter 13, Figueiredo, Ferreira, Costa, and Basu explore “Multicriteria Assessment of a Technological Ecosystem: A Multi-Country Approach.” Multicriteria-based modeling is applied to identify and classify the key aspects and actors that influence innovative ecosystems in three countries (Portugal, Brazil, and India). The ELECTRE TRI ME algorithm is applied to determine the actors in three universities in Portugal, Brazil, and India. The results demonstrate how the actor who bears greater influence over the success of innovative ecosystems is the person for whom there is both expected incremental and disruptive innovation, even though this is reflected in different ways in the three countries. This chapter helps both practitioners and researchers to better understand which actors influence the innovation ecosystem. In Chapter 14, Unutmaz Durmuşoğlu and Durmuşoğlu study “Multi-Criteria DecisionMaking Methods for Technology Selection.” There are multiple factors affecting the technology selection. It is called a “multi-criteria decision-making (MCDM) problem.” Technology selection is a difficult decision. Several decisions are taken in each technology selection stage. The actual costs create irreversible risks to the survival of companies. It has been desirable to terminate ineffective technology investments as early as possible. There are different methodologies to qualify the selection of technologies, such as the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP). This chapter presents a concise review of MCDM methods for technology selection. In this chapter, we can learn from insights about the state of the art, the gaps, and their extensive use. In Chapter 15, Yazdani and Daim study “Software Assessment for Capacity Planning and Feasibility Check of the Master Production Schedule.” In supply chain management, deterministic approaches in production planning and scheduling allow for inaccuracies. Until today, inaccuracies that may exist in rough-cut capacity planning (RCCP), which is used for the master schedule, may result in financial losses. In this chapter, manufacturing system simulation (MSS) is used to simulate manufacturing complexities and risks. The hierarchical decision model (HDM) is used to assess the importance of perspectives and criteria surrounding the selection. The discrete element simulation (DES) software Simio is selected for simulation of manufacturing processes. A manufacturing model, based on make-to- order (MTO) production, is simulated to assess the ability of Simio in capacity planning and detailed scheduling optimization. The results show that the decisions of production planning 6

Introduction

and scheduling can be checked and optimized to manage production efficiency as well as production resources. In Chapter 16, Nathasit Gerdsri and Pard Teekasap explore “Technology Assessment and Selection.” This chapter introduces guidelines for firms to assess its technological capabilities. The guidelines for technological capability assessment with scenario analysis are developed to determine technology development strategy. The guidelines for strategic determination consist of seven steps to fill the technological capability gap. The first step is determining technological capability dimensions. The second is assessing the current level of technological capability. The third is developing strategic scenarios. The fourth is prioritizing the importance of each dimension according to scenario. The fifth is setting a desired level of technological capability. The sixth is measuring technological capability gaps. And the seventh is proposing a development direction to bridge the gaps. A case example is also included in this chapter to demonstrate the use of the guidelines.

Part 4 – Technology Development and Transfer In the last decades, machine vision marks a qualitative shift from technology being theoretical questions to being relevant in the everyday lives of ordinary people (Rettberg, 2020). For example, smartphones have the advanced capacity for image manipulation, social media use facial recognition to sort and filter visual content, and machine vision is increasingly used in gaming, narratives, and art (e.g., recognition algorithms, eye tracking, VR/AR). In terms of technological trends (e.g., the 2018 Digital Transformation Index), the following developments are considered relevant: (1) 5G will roll out to both rural and urban locations; (2) chatbots will improve, and some 40% of large businesses will have adopted it within 2019, making it one of the top digital transformation trends; (3) chatbots will connect clouds of private, public, and hybrid environments into the so-called multi-clouds; and (4) there will be a growing use of artificial intelligence (AI), especially by data-driven organizations to add more meaning into the business analysis. Technology transfer has been visible throughout human history. Scholars broadly identify and accept technology transfer as a fundamental part of research and development management. The topic has been researched for more than half of a century (Lavoie and Daim, 2020). The acquired and accumulated knowledge in one and from one experience is transferred to another. This interaction played a crucial role in building ancient civilizations. Nowadays, technology transfer is an important issue discussed at every meeting between developing and developed countries. Technology transfer is a key element in achieving international competitiveness, which firms need to survive in the global marketplace. Technology transfer involves the transfer of physical goods and the transfer of tacit knowledge. The latter is becoming more important and involves acquiring new skills and technical or organizational capabilities. Thus, the transfer of technology, knowledge, and experience is an inevitable consequence of the existing international division of labor demand. The fundamental presumption is that technology always consists of two elements: one that can be codified and one that cannot be easily codified and is well identified in the literature as tacit knowledge. The concept of research and development is traditionally associated with new knowledge, while development is recognized by applying the knowledge of scientists and engineers. These are two separate processes that form a whole. Very often, there is a lack of connection that seeks to connect individual segments of knowledge in the function of product or process development. That link should be applied research. 7

Introduction

The relationship between scientific research and technology is not linear. Instead, scientific research determinates technology through basic research - applied research - technology development - technology application - innovation - needs. This sequence connects science and technology with the market, where both science and technology retain their survival. Still, at the same time, the feedback effects of technology on science are strengthened. Through five chapters, Part 4, Technology Development and Transfer, sets issues on the importance of implementing integrated transfer technology approaches and developing main pillars in the ecosystem universities, society, and firms. In Chapter 17, Huang demonstrated “Regional Innovative Intensity in an Emerging Economy: Analyzing China’s Provincial Regions.” In this chapter, a conceptual framework that draws from and builds upon research on regional innovation systems is developed. This study integrates insights from localized knowledge spillovers and regional absorptive capacities to explain the differential roles played by key drivers of regional innovative intensity with different levels of development. More than 865,000 patents awarded to Chinese assignees and other key innovation-related measures from 31 provincial-level regions between 1999 and 2008 were analyzed. The results showed that the positive effects of an increasing concentration of scientists and engineers, technological specialization, R&D spending, and foreign direct investments on the intensity of innovative output production are more salient in developed (eastern) than developing (western) provincial regions in China. This study suggests there are policy implications for regional innovative capacity building in China’s transitional economy. In Chapter 18, Yalcin, Daim, Kirbac, and Dabić proposed “Understanding Components of Entrepreneurial Ecosystems.” In this chapter, bibliometrics and social network analysis methods were used to determine the basic components of entrepreneurial ecosystems. Regarding entrepreneurial ecosystems, this chapter provides relevant, credible, and influential information for nanoscience and nanotechnology in the health sciences domain. This chapter contributes an analysis of the academic data of technology entrepreneurial ecosystems. In Chapter 19, ÜNLÜ demonstrated “The Performance of Science and Technology Parks under Triple Helix Systems in Turkey.” This chapter investigates the effects of the Triple Helix concept on the performance of Turkish Science and Technology Parks (STPs). Science and Technology Parks, as a source of knowledge spillovers, are an important component of the regional innovation systems. This chapter summarizes the main features of STPs and the Triple Helix Policy framework in Turkey. The results show that STPs that are associated with a university, government, or industry have a greater level of employment growth. The chapter suggests supporting more diversified sectors and regions in the country since the development of the regional innovation system is strongly government driven in a top-down approach. In Chapter 20, Pretorius, Davidavičienė, and Pretorius demonstrated “Technology and Entrepreneurship: A Systems and Simulation Approach.” Technology, innovation, and entrepreneurship are contextualized as a system, and some systems theories are introduced. In this chapter, entrepreneurship is broadly defined to explore its possible relationship with technology and innovation. The dynamic aspects of technology are explored as part of this scenario. Some drivers of entrepreneurship were integrated, such as unemployment in the business and technology environment. A simulation approach and a system dynamics approach for the dynamics of entrepreneurship and technology were presented. Some business statistics from databases related to entrepreneurship were analyzed to show the cyclic nature of entrepreneurship. This chapter reflects on the dynamic nature of technology in relation to entrepreneurship and unemployment. This chapter contributes to the analysis of business data for technology and entrepreneurship. 8

Introduction

In Chapter 21, Wu, Wu, and Tao proposed “Transformation into New Drug Companies: The Perspective of Dynamic Capabilities.” From the perspective of dynamic capabilities, this chapter aimed to investigate the transformation process of local generic pharmaceutical firms of transforming its capabilities from developing generic drugs to developing new drugs. Two small, local and generic pharmaceutical firms in Taiwan were studied. In the end, lessons were derived from the conclusion. For developing new drugs, the small and local firms prefer to establish a new subsidiary company. To enter the new drug market, the firms would adopt a focused strategy and evaluate their key resources and capabilities. The firms would leverage some of their original customers to build international collaborative capabilities when developing new drugs. Lastly, intangible assets, knowledge management, and organizational coordination and integration are the critical resources of newly transformed companies. This chapter suggests several implications for technology transformation.

Part 5 – Managing Technological Innovations Understanding the growing influence of global factors on commercial exchange is one of the most important issues in managing technological innovation. Knowledge stems from people (Dabić et al., 2011; Dabić et al., 2016). It is increasingly the result of an open collaboration process involving multiple actors throughout the value chain (Bogers, Chesbrough, and Moedas, 2018). This phenomenon has been captured with the introduction of the Open Innovation paradigm (Chesbrough, 2003). The new construct is currently defined as the “use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough et al., 2006, p. 1). In line with open innovation, “knowledge openness” (Love, Roper and Bryson, 2011; Foray, 2004, p. 165) occurs in cases of “rapid disclosure of new knowledge, in which knowledge, both codified and practical, can circulate and be reproduced freely through learning for modification or improvement to, consequently, generate a positive-sum game.” Knowledge openness induces rapid and complete distribution of knowledge, better coordination between agents, and reduction of risk of knowledge duplication between research projects and functions (Foray, 2004, p. 166). The systems of knowledge openness can occur either within a country (i.e., open science) or within an organization (i.e., firm’s research networks) (Foray, 2004, pp. 183–184). Part 5, Managing Technological Innovations, sets forward in five chapters the issue of implementing the real-life situation in handling functionality of innovation with other interfaces and reinforced it with literature sources. In Chapter 22, Qiu and Bouncken demonstrated “Coworking Ecosystems: Institutionalization of ‘Homes’ for Innovation and Venturing.” This chapter developed the concept of coworking ecosystems, which are connected by the elements of coworking spaces, makerspaces, and ecosystems. More innovation, technology, and venturing are flourishing in the emerging coworking ecosystem. Within a coworking ecosystem, those linkages to other organizations grant their users and members high autonomy and provide opportunities for multiplex social exchanges within the space. This chapter conducted a qualitative study to examine the institutionalization of coworking ecosystems. This chapter proposed three levels of coworking ecosystems: micro-level (institutionalized socialization and connected resources), meso-level (community-focused coordination and industrial value co-creation), and macro-level processes (emerging ecosystems and increasing legitimacy of coworking). In Chapter 23, Rao, Mulloth, and Harrison proposed “The New Imperatives of Defense Technological Innovation: Past, Present, and Future.” Nowadays, global R&D has 9

Introduction

multiplied the speed and diversity of innovation. This chapter demonstrated how the defense sector conceives, develops, and absorbs new technologies. In the defense ecosystem, key players have been forced to manage open innovations to collaborate, co-develop, and spur entrepreneurial efforts. In this chapter, we specifically reviewed how the US Department of Defense has evolved its R&D paradigm to manage open innovations. This chapter identified a set of institutional competencies for open innovations: developing opportunities, championing innovations, resource leveraging, and location leveraging. Drawing from other open innovation approaches, a set of key capabilities can strengthen defense technological innovations to neutralize the threats of the future. In Chapter 24, Cammarano, Michelino, and Caputo studied “Mapping Buyer-Supplier Relationships after the Open Innovation Perspective.” The open innovation paradigm has emphasized the effects of suppliers’ innovation capabilities on innovation performance. From the open innovation perspective, this chapter classifies buyer-supplier relationships. Four open innovation practices were presented and two dimensions were considered: first, the supplied component integration mechanism, and second, the importance of the suppliers’ innovation capabilities. The suggested classification can be seen as a framework for managing open innovations in buyer-supplier relationships. The recourses for these four open innovation practices depend on the stage of evolution and maturity of the manufacturers’ product architecture. Using the smartphone industry as an example, it demonstrates that each stage fits with the open innovation practices. By drawing on the open innovation practices, this chapter provides additional implications related to criteria prioritization for specific supplier selection. In Chapter 25, Wang and Shu demonstrated the “Evolution of Collaborative Innovation Strategy for Global Manufacturing Firm Driven by Integrated Modularity Thinking.” How has the collaborative innovation strategy evolved in conjunction with modularity thinking? First, this chapter reviews the background of modularity in technological innovations of global manufacturing. Three evolutionary stages of Haier’s collaborative innovation strategy are shown: collaborative innovation driven by product modularity (2005–2009), collaborative innovation driven by organizational modularity (2010–2014), and collaborative innovation driven by platform-based modularity (2015–). In this chapter, the mechanisms of user coordination, resource coordination, and organizational coordination are combined to discuss the three types of collaborative innovation. The lesson learned from Haier corporation is as follows: in user demand-driven scenarios, more innovative commercial values at scale can be cultivated and incubated while managing collaborative innovations. In Chapter 26, Sick, Okara, Bröring, and Wambsganss explored “Open and Closed Innovation Strategies in Converging Environments: How the Internet of Things Blurs the Boundaries between ICT and Logistics.” The Internet of Things (IoT) and the resulting disruptive industrial changes can lead to industry convergence. In this converging environment, companies face technology- and market-related competency gaps, so managing the open innovation process becomes essential. Both logistics and information and communications technologies (ICT) are affected by IoT. This chapter identifies the main players and their respective competency gaps to derive open and closed innovation strategies based on patent analyses and case studies. In this chapter, the 2×2 innovation matrix is provided. For logistics companies, which face technological competency gaps, a technology-based open innovation strategy is recommended. For ICT companies, which face market competency gaps, a market-based open innovation strategy is recommended. Moreover, new players in online retail and start-ups are taking the opportunity to successfully intensify the competition in both sectors. We can obtain insights on managing open innovation activities and a broad network of the right partners to strengthen the innovation potential. 10

Introduction

Part 6 – Social Issues The most important economic issues responsible for the impact of innovation on society and employment can be summarized through direction and degree of technological progress production versus process innovation, radical versus increased technological change, direction of technological change called the technological tilt (slope) that explains whether capital or labor has been saved, degree of technological progress, marginal improvements or technological progress that manages potential reduction costs. Technological progress can be driven by government regulations (e.g., regulations designed to reduce air pollution). Current and expected customer preferences (quantity and dynamics of total demand, i.e., current and expected market size; elasticity of demand price for output; (this measure shows the change in output as a percentage of the price is reduced by 1%) elasticity of demand shows a change in demand as a percentage where revenue is reduced by 1%; the degree of complementarity of demand for new and existing products). The structure of the product market refers to the degree and nature of competitiveness and the existence of entry barriers. The exponential acceleration of technological growth and information generation is shaping the current system more complex than ever. Thus, competitive advantage is no longer achieved solely through strategic planning but by revealing an effective force of technical people who could efficiently manage and drive the complex technological system. Empirical evidence suggests that researchers who embrace a research commercialization approach, thereby pursuing monetization of their research outcomes via patents or licensing agreements, slow the diffusion and accumulation of academic knowledge as well as its dissemination in the public domain (Huang and Murray, 2009; Toole and Czarnitzki, 2010; Le Roy, and Chesbrough, 2018). Qualitative findings resulting from theoretically sophisticated models focus on the positive, negative, or ambiguous effects of innovation on employment. If there is an impact, the question is whether it is large or small. Impact can be studied at three levels: company, industry, or economy. Furthermore, there are differences between short-term and long-term impacts. Part 6, Social Issues, covered four chapters dealing with social issues. In Chapter 27, Yuruten, Ozen, Yildirim, Daim, and Shaygan studied “Proposing an Application Model for Personnel Recruitment by Using a MCDM Approach: A Case of Blue-Collar Cashier Personnel Recruitment.” Personnel selection is a complex process during recruitment since it is very difficult to select the right staff. This study proposes an application model for personnel recruitment by using a MCDM approach. The ANP method is used for criteria weighting, and the Elimination and Choice Translating Reality (ELECTRE) method is used for alternative rankings. A pilot study was conducted for personnel recruitment of blue-collar cashiers in a retail company in Turkey. The results of the application model were consistent with decision makers’ expectations. In Chapter 28, ALGhamdi, Daim, and Meissner conducted a study on “Electronic Payment Technology: Developing a Taxonomy of Factors to Evaluate a Fraud Detection and Prevention System for the Airlines Industry.” This chapter develops a taxonomy to evaluate the airlines’ readiness to adopt new fraud software as a new protection system. It also identifies gaps in the existing literature. The identified gaps are the high cost of implementing fraud prevention software, unreliable e-payment systems, and the lack of preparedness among the airline industry. To this end, the HDM was provided to eliminate the identified gaps. The HDM provides different perspectives and criteria. The five perspectives are the 11

Introduction

economic and financial perspective, technological perspective, legal perspective, security perspective, and organizational perspective. The HDM is used to seek the solutions that eliminate the gaps of electronic payment technology. In Chapter 29, Cetindamar and Pugalia conducted a study on “Engaging the Technology Management for Gender Equality.” This chapter aimed to explore how the technology management discipline could address Goal 5 of 17 Sustainable Development Goals of the United Nations (UN) 2030 Agenda. Goal 5 aims to reduce the gender disparity and bring equality in opportunities, employment, and decision-making, as well as leadership positions across all levels. A literature review was conducted on the top ten journals of the technology management field. Two main topics were presented: the first topic was the challenges of a glass ceiling faced by female managers across organizations. The second topic was female entrepreneurs in technology-based industries. The findings underlined the issue of non-existent gender equality in the area of technology management. This chapter actively calls researchers to develop data and knowledge related to women working in technology industries. In Chapter 30, Fatma Nur Karaman Kabadurmus proposed “Creating a Networked Innovation Ecosystem for Industry 4.0.” Industry 4.0 is expected to be the trigger for the next wave of innovation in the new era of automation, digitalization, and robotization. This chapter aimed to examine how the network capabilities of innovation ecosystems contribute to the development of radical innovations in this new era. Five aspects of innovation were explored: new functions, new materials, new technological components, new appearances, and newness to the firm. The results indicated that enterprises’ network capabilities are positively related to all five aspects of their innovation outcomes. Also, other firm-level characteristics such as size, export status, use of foreign technology, and adoption of information and communication technologies help firms to seize advanced technologies and future technological developments.

Part 7 – Emerging Technologies and Emerging Regions Catching up is not an easy and costless process because new technology is not equally and freely available to all the countries. The key elements in the catching-up process are technologies, innovations, and privatization instead of public education and public funding. Unfortunately, economy in transition suffers from the weak technology sector and insufficiently developed industry to understand the significance of R&D and education in global movements. We are faced not only with the problem of redefining the relations between industry and R&D/university sector (who pay for what) but with the problem of preserving existing R&D and educational potentials what could be devastated in the knowledge-based economy. The evolution towards a “knowledge-based economy” which promotes full deployment of new, exiting, and complementary knowledge has posed great challenges to the management of the innovation system within an organization. New forms of organizations are required to manage links that facilitate the transfer and exchange of knowledge between internal and external sources. Strategic alliances appear to be a central factor in the acquisition of external knowledge, especially in the science-based sectors. One of solutions is to straighten legislation and develop further principles of assistance of the states in the innovation process, compatible with principles and practice in the EU countries. A very important role is the role of the international co-operation. The pace of media 12

Introduction

innovation, the shortage of venture capital, and the laxity in regulatory initiatives have stimulated a new ownership phenomenon: the rise in the number of joint ventures among companies. Foreign industrial/strategic investors have mostly proven their competence and the performance of such companies is a way better in most cases than the performance of countries counterparts. Other possible solutions have been identified following areas of mutual co-operation with states and industrial sphere of the regional grant support programs, joining of some corporate R&D programs, participation in the joint science and research centers, providing access to information sources for industries/associations. The road can be successfully passed through by means of intensive international cooperation in R&D, transfer of knowledge and technology with global institutions and strategic partners, and, of course, by means of dramatically changed attitude of people and positive legislative framework in the countries themselves. Part 7, Emerging Technologies and Emerging Regions, provides readers with five chapters that raise important issues about emerging technologies and emerging regions. In Chapter 31, Su and Lin conducted a study on “Smart Manufacturing: An Evaluation Model for Taiwan’s Innovation Applications.” Smart manufacturing makes production more efficient and sustainable. An evaluation model for smart manufacturing was built upon five constructs and 29 criteria. The AHP and Decision-Making Trial and Evaluation Laboratory (DEMATEL) were used to analyze Taiwan’s data. The results showed that the Industrial Internet of Things (IIoT) is the first priority for developing smart manufacturing based upon the five constructs. For each of the five constructs of smart manufacturing: (1) IIoT: sensors are prioritized in hardware development; (2) smart production: developing advanced process control is prioritized; (3) smart factory: developing digital transformations is prioritized; (4) smart designs; (5) smart services. Smart design and smart service are the last concerns in smart manufacturing. In Chapter 32, Koçoğlu demonstrated “Data Mining as a Knowledge Extraction Tool and an Application on Decision Tree-Based Algorithms.” Big data mining can extract knowledge from the database and data stack. The performance of data mining models is affected by many factors, such as an imbalanced distribution of the class attribute field values in the data set. This chapter compares the change in performance of various decision tree-based algorithms used in the data mining process against data with balanced and imbalanced class distribution. C4.5, C5.0, C5.0 (boosted), Gini, and Random Forest algorithms were used to develop the classification model and under-sampling to balance the data set. To evaluate the model in real terms, it is important to eliminate the unbalanced class distribution. In Chapter 33, Su and Cheng conducted a study on “Smart City: An Evaluation Model for Taiwan’s Social Innovation.” The development of smart cities has been accelerated in many countries. In this chapter, an evaluation model for building smart cities with IoT was created upon six constructs and 33 criteria. The AHP and DEMATEL were used to analyze Taiwan’s data. Developing smart infrastructure is the prioritized strategy. The prioritized strategies for developing each of the six constructs of smart cities are as follows: (1) smart infrastructure: the availability of a cloud platform for multiple equipment; (2) smart government: establishing a dedicated organization for promoting a smart city; (3) smart environment: developing a smart city and an urban ecosystem; (4) smart living: a connected first aid system; (5) smart security: an emergency response rescue system integrated with the police; and (6) smart mobility: integrating ICT. In Chapter 34, Su and Liu studied “Smart Transportation: An Evaluation Model for Taiwan’s Innovation Policy.” Many countries are transforming their transport policies into smart 13

Introduction

transportation as mobile technologies are gradually becoming widely adopted. Six major dimensions and 32 criteria of an evaluation model for smart transportation innovation policies were defined. The AHP and DEMATEL were used to analyze Taiwan’s data. The results showed the ranked priority of the six major dimensions of smart transportation innovation policies: (1) smart traffic safety is considered the most important in the short term; (2) traffic data collection is considered the most important in the long term; (3) intelligence-based transportation technologies; (4) transportation management systems; (5) connected and automated vehicles (CAVs); and (6) transportation resource integration and sharing. In Chapter 35, Deliaslan, Kayalica, and Kayakutlu conducted a study on “Energy Efficiency in Smart Street Lighting Systems for ITU.” Smart lighting systems are one of solutions created for smart cities to reduce energy consumption and solve the issue of increased energy demand. This chapter provides a forecasting model for the smart street lighting system of the ITU campus. Standard street lighting fixtures are substituted with light-emitting diode (LED) fixtures that require less energy, and combined with a smart control system. Furthermore, this chapter develops a Seasonal Autoregressive Integrated Moving Average – Long Short-Term Memory (SARIMA-LSTM) hybrid model to estimate total energy consumption. The total energy consumption of two scenarios based on old and new systems was calculated. The results showed that the hybrid system gives better results and the smart lighting system achieves 18% more energy efficiency. With the ambition of finding innovative ways to engage new audiences, generate new business models, drive creative excellence in management of technology content, and provide easier access to management of technology collections and archives, especially in areas less accessible to technology management expertise and for less-resourceful audiences, we hope that the presented handbook was enough ambitious and innovative, and with its structure advancing the state of the art in various ways.

References Bhatt, P.C., Kumar, V., Lu, T.C., Cho, R.L.T., Lai, K.K. (2020). Rise and Rise of Blockchain: A Patent Statistics Approach to Identify the Underlying Technologies. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Communications in Computer and Information Science, vol 1178. Springer, Singapore. https://doi. org/10.1007/978-981-15-3380-8_40. Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2), 5–16. Caputo, A., Pizzi, S., Pellegrini, M.M., & Dabić, M. (2021). Digitalization and business models: Where are we going? A science map of the field. Journal of Business Research, 123, 489–501. Chesbrough, H. (2003). Open Innovation. Harvard Business School Publishing. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open Innovation: Researching a New Paradigm. Oxford University Press on Demand. Dabić, M., Ortiz-De-Urbina-Criado, M., & Romero-Martínez, A.M. (2011). Human resource management in entrepreneurial firms: A literature review. International Journal of Manpower, 32(1), 14–33. Dabić, M., Švarc, J., & González-Loureiro, M. (2016). Outlooks and conclusions on entrepreneurial universities in innovation-seeking countries. In Entrepreneurial Universities in Innovation-Seeking Countries. Palgrave Macmillan, New York. Foray, D. (2004). Economics of Knowledge. MIT Press. Farrukh, C., & Holgado, M. (2020). Integrating sustainable value thinking into technology forecasting: A configurable toolset for early stage technology assessment. Technological Forecasting and Social Change, 158, 120171. Huang, K.G., & Murray, F.E. (2009). Does patent strategy shape the long-run supply of public knowledge? Evidence from human genetics. Academy of Management Journal, 52(6), 1193–1221.

14

Introduction Lavoie, J.R., & Daim, T. (2020). Towards the assessment of technology transfer capabilities: An action research-enhanced HDM model. Technology in Society, 60, 101217. Lavoie, J.R., Daim, T., & Carayannis, E.G. (2020). Technology Transfer Evaluation: Driving Organizational Changes through a Hierarchical Scoring Model. IEEE Transactions on Engineering Management. Le Roy, F., & Chesbrough, H. (2018). Open coopetition. In Routledge Companion to Coopetition Strategies (pp. 420–430). Routledge. Rettberg, J. W. (2020). Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps. Humanities and Social Sciences Communications, 7(1), 1–13. Samuelson, P., (1954). The pure theory of public expenditure, Review of Economics and Statistics, ­36(November), 387–389. Shoval, N., & Ahas, R. (2016). The use of tracking technologies in tourism research: the first decade. Tourism Geographies, 18(5), 587–606. Toole, A.A., & Czarnitzki, D. (2010). Commercializing Science: Is there a university “brain drain” from academic entrepreneurship? Management Science, 56(9), 1599–1614. van de Vrande, V., de Jong, J.P.J., Vanhaverbeke, W., de Rochemont, M., (2009). Open innovation in SMEs: Trends, motives and management challenges. Technovation, 29(6–7), 423–437. Yalcin, H., & Daim, T. (2021). A scientometric review of technology capability research. Journal of Engineering and Technology Management, 62, 101658. Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology mining: Artificial intelligence in manufacturing. Technological Forecasting and Social Change, 171, 120971.

15

PART 1

Technology Management Background

1 STRATEGY OF CRITICAL TECHNOLOGY A Case of Turkish Industry and Technology Policy Mert Cihan Tonkal and Tugrul Daim 1.1 Introduction There has been struggle between government and among academics about the relationship between industry and government—laissez-faire versus industrial policy—and also about the underlying theories of the role of technological change in the economy for a long time (Popper and Wagner, 2003, p. 115). In between 1940 and 1960, many commercial technologies had “spun off” from defense technologies. In the 1970s and early 1980s, some technologies emerged as “dual use”— applicable for commercial or defense. Later on the defense sector began to adopt technologies “off-the-shelf ” from the firms that were making them as commercial goods. When the idea of “national security and competitiveness were interdependent” emerged as one of the principal argument to counter foreign technology via industrial policy in the USA, the concept was that there were some technologies and supporting science which were contributing to a nation’s competitive strength and national security together (Popper and Wagner, 2003, pp. 114–115). Technology-based trade seemed counter to the rules of Ricardo’s “comparative advantage” theory. For example, the economies of scale in producing aircraft and semiconductors limit the world market to a few efficient-scale producers which are located in a few centers of production. If there are some industries whose products are “strategic” in the sense that they contribute externalities to the economy in ways that commodities do not, their existence raised the question of whether these technologies should receive special treatment from the government or not. In the 1970s and 1980s, the focus was on individual “critical” technologies as inputs to production. These technologies were implicitly viewed as a set of discrete, well-defined and self-contained objects. This movement was characterized by the view that certain technologies possess characteristics that make them particularly important to national interests (Popper and Wagner, 2003, pp. 114–115). Parallel the ovulation of technology development story in the world, Turkey created her own tale after North Cyprus Operation in the 1970s. During this operation, the Turkish Army faced with some communication problems and was banned from usage of other countries oriented guns. That was a huge push for the state to generate the production of defense systems locally. A group of firms called ASELSAN, ROKETSAN, HAVELSAN, etc. and DOI: 10.4324/9781003046899-3

19

Mert Cihan Tonkal and Tugrul Daim

their providers were created and supported to fill the technology gap. The success was impressing. Localization in production rate was pushed from 20% to 70% roughly. After 1980, other industries in Turkey were on a way of foreign direct investment (FDI)-oriented learning via multinational companies and tried to be providers of global buyers. Although there were huge capabilities earned in exporting ability on clusters, imports were also boomed incredibly because of the structure of production chains and depending on raw material of energy to produce. Right now Turkey tries to reduce dependency on imports, catching global trends, reducing current deficit and improving economic growth. In this way, the share of the Turkish Ministry of Industry and Technology (MoIT) is trying to generate the tale of independence of defense industry in other commercial industries via supporting critical technologies. This study shows what critical technology is and what was made in the USA and EU for this approach as Literature Review (LR). Later it focuses on Turkish 2023 Strategy of Industry and Technology document and relevant regulations very deeply to understand what the game plan is. After that, it matches lessons learned from LR and major identifications of the Strategy to create implications, offers future studies and ends with a conclusion.

1.2 Literature Review The critical technology terminology is very limited. The reasons are given as follows: (1)  there is a huge taxonomy in this area with lots of adjectives such as crucial, strategic, essential and processor rather than critical; and (2) although some studies are mentioning critical technologies, only a few countries used the same terminology in their policies. Thus, I will talk about two major examples only.

1.2.1 The US Case: Critical Technologies During the 1970s and 1980s, the US policy makers and industrialists expressed concern that the nation was losing trade competitiveness to Japan and Germany (not Soviets) in some industries according to relevant trade data. Although this was a complex situation, one belief was that the government should take more direct action to support US industrial competitiveness. This could be by supporting “targeted” R&D for “Critical Technologies”. The other belief was that the government could not pick “winners” and “losers” in a rapidly changing and competitive world. The decisions about funding R&D in specific technology should be left to private side and trade competitiveness should be left to free market. At federal level, a Reagan administration official said “... $100 of trade in computer chips or potato chips, what does it matter, it’s still $100 in trade”. Many officials did not realize that the USA had entered a new era in global competitiveness (Popper and Wagner, 2003, p. 116) During the 1980s, foreigner technological capabilities were not a threat only for competitiveness of US industry, but also be a problem for national security. Although defense-oriented technologies were “targeted” ones and they received significant aid from the government, this sector was thought to be different from commercial technologies (Popper and Wagner, 2003, pp. 114–116). On Capitol Hill, a commission was dedicated to monitoring the performance of US industries in critical technologies, in the light of the lists being prepared biennially by the National Critical Technologies Panel (NCTP) and “critical technology” entered in policy making in Washington. In the 1990s the question was “what is a critical technology” and the tool for the answer was 4 National Critical Technologies Report (NCTR). The principal 20

Strategy of Critical Technology

purpose of the critical technologies is defined as “essential for the long-term national security and economic prosperity of USA” (Popper and Wagner, 2003, p. 117). The first NCTP identified 22 national critical technologies into six broad categories: materials, manufacturing, information and communications, biotechnology and life sciences, aeronautics and surface transportation, and energy and environment. Each category offered at least two more specific important technologies. For example, under the broad heading of “materials”, there were electronic and photonic materials, high-performance metals, etc. The panel indicates that discovery, development and deployment (3D) must be integrated and viewed as concurrent rather than sequential activities (NCPT, 1991, cited in Popper and Wagner, 2003, p. 121). The second report expanded the list by exploring national capabilities in nine “economically important technology-intensive industrial sectors”. It addressed a larger economic context the critical technologies identified in the first report. While the relationship between technologies and the national economy is intuitively obvious, it is difficult to quantify. The second panel recommends that a study on the economic role of technologies should be undertaken in a comprehensive and coherent assessment of how technologies produce economic growth (NCPT, 1993, cited in Popper and Wagner, 2003, p. 121). The third report contains an encyclopedic list of over 100 technologies in taxonomy of ever-more-specific categories. The list-making effort became a process with no effort made to determine whether one technology was more critical than another. It served to provide the definitive list of national critical technologies growth (NCPT, 1995, cited in Popper and Wagner, 2003, p. 121). The fourth report lists eight technology fields that are critical because they either have “cross-sector ubiquity” or appear at the interstices of various sectors. More usefully, the fourth report used an interviewing phase of chief technology officers and senior executives of companies that produce/use technology (Popper and Wagner, 2003, p. 121).

1.2.2 Missing Points of Policy According to Popper and Wagner (2003, pp. 121–128), the critical technologies reports have had little effect on US technology development policy. Some of the reasons are given as follows: 1 2 3

4

5

The government did not have a pioneering role and there was a desire to separate the functional roles of the government and the industry. There was no agency dedicated to critical technologies for R&D efforts at federal level. There was a temporary committee. An evaluation and implementation model was not put forward for later. Thus, continuity was not achieved in panels and applications for critical technologies. While NCTRs focused on critical technologies and production, wider policy origins should be viewed as a concern not only for international economic-technological competition but also for the “loss” of national assets and technological capabilities through the acquisition or consolidation of foreign assets. The answer to the question of what is critical technology actually remained ambiguous. For public policy, the source of critical quality in certain technologies (congenital, conditions of use or concerns about appropriate policy) was bypassed. 21

Mert Cihan Tonkal and Tugrul Daim

6

Unlike other countries that need to be officially coordinated or have limited resource pressure due to their circumstances, there was an abundance of resources in the USA. This is why strategic selection and implementation were resilient.

1.2.3 European Union Case: Key Enabling Technologies According to the EU the major societal challenges are fighting climate change, overcoming poverty, fostering social cohesion and improving resource and energy efficiency. key enabling technologies (KETs) are knowledge intensive and associated with high R&D intensity, rapid innovation cycles, high capital expenditure and highly skilled employment. They are multidisciplinary, cutting across many technology areas with a trend toward convergence and integration. Based on current global research and market trends, the following could be regarded as the most strategically relevant KETs to solve societal challenges (CoEC, 2009): 1 2

3

4

5

6 7

Nanotechnology, smart-nano and micro-devices and systems in vital fields such as health care, energy, environment and manufacturing. Micro- and nanoelectronics, including semiconductors, for intelligent control in sectors as diverse as automotive and transportation, aeronautics and space, smart industrial control systems for more efficient management of electricity generation, storage, transport and consumption through intelligent electrical grids and devices. Photonics dealing with light, encompassing its generation, detection and management, for the economic conversion of sunlight into electricity which is important for the production of renewable energy, and a variety of electronic components and equipment such as photodiodes, LEDs and lasers. Advanced materials offer major improvements in aerospace, transport, building and health care. They facilitate recycling, lowering the carbon footprint and energy demand as well as limiting the need for raw materials that are scarce in Europe. Biotechnology brings cleaner and sustainable process alternatives for industrial and agrifood operations. Progressive replacement of non-renewable materials currently used in various industries with renewable resources. Low-carbon technologies and application KETs, such as new materials for energy production, transportation and storage. Advanced manufacturing systems and modern robotics in capital-intensive industries with complex assembly methods such as aircraft which involves the whole spectrum.

Because of smaller share of high-tech industry and R&D intensity, there is a gap in the deployment of KETs between the EU and USA/Japan. Also, the EU is less effective in terms of commercialization and exploitation of KETs. Thus, public R&D efforts are undertaken; however, they do not sufficiently translate into economic and societal gains. Some of the reasons are given as follows (EU, 2016): 1 2 3

The EU does not effectively capitalize on its own R&D results and intellectual property rights (IPR) are not effectively protected. There is often no proactive strategy bringing together stakeholders to address public concerns or fears to avoid delays in introduction of new technologies in the EU. There is a shortage of skilled labor tailored to the multidisciplinary nature of key enabling technologies. 22

Strategy of Critical Technology

4 5

In the EU for KETs, the levels of venture capital funding and private investment are comparatively low. European research teams need to seek venture capital at too early a stage when the uncertainties are often still too high for both parties.

A long-term vision, better division of work and coordination are needed to improve conditions for industrial exploitation in the EU. Depending on the maturity level of KETs, a strong integration between experimental research, innovation and industrial exploitation is essential. An example is the need to carry out very costly pre-production “proof of concept” and test fabrication projects to ensure the adoption of KETs. Policy areas are given as follows: 1 2 3

4

There should be programs to ensure that the flow of innovation is maintained and that technology adoption is facilitated. Calls for proposals in the years ahead should be designed to assure the link between research output and industrial impact. Publicly supported programs should be reinforced to help key industries to maintain their long-term innovation plans for enabling technologies and thus ensure their competitiveness in the subsequent economic upturn. The EU needs a favorable environment for effective capitalization of research results in products/technologies that the EU wishes to master in.

1.2.4 Lessons Learned from Literature Review Simple Labeling: Both cases use only “critical or key enabling” adjectives simply. Both cases mention that just identification of critical technologies without a regulatory definition is insufficient. Vertical to Horizontal Technology: In the US case, through the four reports, the process was transformed by picking critical technologies to drawing technology fields that are critical in terms of “cross-sector ubiquity”. The reason of this evolvement, a need of a study, appeared on the economic role of technologies, especially how they produce economic growth. Capitalization of R&D: Even if R&D consumes huge investments, its future in market (transforming expense to an asset) is unclear. The EU case roughly states that for a better expense to asset transformation the link between research output and industrial impact must be seen better on critical tech projects especially on proof-of-concept phase and the levels of venture capital funding and private investment should be raised. Need for a Structure: Both cases indicate that the government involvement is a need via institution on continuity of the way of 3D steps of critical techs but both cases do not offer an ideal organization formula. Loss of Capabilities: The US case indicates that the concern should not be only for international economic-technological competition, but also should be the acquisition or consolidation of the national assets of technological capabilities through foreign rivals.

1.3 2023 Turkish Strategy of Industry and Technology 1.3.1 Vision According to the 2023 Strategy of Industry and Technology, Turkish industry and technology vision is “National Technology, Powerful Industry” (MoIT, 2019, p. 2). 23

Mert Cihan Tonkal and Tugrul Daim

In this context the Minister implies: ...time is getting Turkey to a higher league of most advanced economies in the world. The body of this new success story will be the industrial and technology policies that we will develop in the light of the National Technology Move. He considers the National Technology Move as the most important tool for the vision (MoIT, 2019, p. 3). Again, the Minister says “... developing critical technologies as nationally a necessity, not choice and this will let us provide competitive products and services in hightech areas to become shareholders in the value chain”. Also, the Minister adds, …we will implement policies that will increase our global competitiveness, ensure our economic and technological independence and make progress in critical technologies as a whole. Our success in the defense industry is a role model for other sectors of the industry as well. (MoIT, 2019, p. 3)

1.3.2 Content Document starts the fourth Industrial Revolution as reference. The major technologies involved in this revolution and the resources allocated by governments and companies to these technologies are summarized very briefly. The output of this contextual analysis is depicted as the main element of transformation in industry is the power of integration with technology (MoIT, 2019, pp. 8–10). According to the current estimates of the World Bank, the IMF and the OECD, the document states that there will be a downward acceleration in the growth rate of the world economy for the medium term after 2019. In parallel, it is stated that international direct investments tend to decrease in the last three years worldwide (MoIT, 2019, p. 11). According to growth prospects, Turkey was evaluated as above the world’s expectations. In industry and technology, international strategic cooperation should be revised in manufacturing and market would be an important step in gaining access to larger markets (MoIT, 2019, p. 12). For the assessment of Turkish industry, the industrial production index and the capacity utilization rate in the manufacturing industry were described as key indicators of monitoring the current situation and trends. It was stated that the recent decline in these two indicators was caused by sudden and temporary exchange rate fluctuations in 2018, which was followed by positive signs later (MoIT, 2019, p. 13).

1.3.3 National Technology Move National Technology Move would provide an increase in the strength of the competition, which will lead to economic and technological independence and expressed breakthrough in critical technologies. The example of these can be seen in defense industry technology development projects. These projects have increased the ability of domestic suppliers to produce technology, reduced costs and revealed the product development capacity in advanced technologies such as drone vehicles, missiles, radar systems and satellites. It is stated that the source of increasing the level of domestic production and the use of critical technologies in the defense industry is a strong national planning and coordination (MoIT, 2019, p. 17). This policy will be discussed in more detail later. 24

Strategy of Critical Technology

1.3.4 Qualifications of the National Technology Move Qualifications are (MoIT, 2019, p. 18):

1.3.5 Local Concept The production in a factory located in the country with the elements such as raw materials, semi-finished products and labor required for manufacturing from certain geography, even if it was established by foreign capital, is local. If it obtained the vast majority of production inputs from its domestic sources, its product is also domestic goods.

1.3.6 National Concept The concept of national means “owned by” a nation. Even though there are imported inputs in production, it is a national production that we conduct by making decisions in line with our own national interests, since its intellectual and industrial property rights, management, decision mechanisms and capital are national. It has brought the development of national technology products to a strategic level.

1.3.7 Originality Intellectual development in industry and technology, from the design to the emergence of the product or service, is to be demonstrated by its developers without being affected by similar or equivalent ones. Originality enables one to produce products and services that are not directly affected by their counterparts in strategic areas and designed according to the country’s own strategic needs.

1.3.8 Fundamentals of the National Technology Move A “stakeholder-oriented” approach to doing business among industrialists, entrepreneurs and technology-developing science and R&D people and public institutions will be expanded. Targets and activities will be measured regularly with data-based analysis; the results will be made available to the relevant stakeholders to monitor, and a natural audit mechanism will be established. Policies that closely follow the world and direct leading pioneers will be created. There will be long-term plans which are sensitive for short-term changes. For flexibility, one of the methods to be used is a pilot application in the areas with high uncertainty. According to the results obtained, further steps will be passed (MoIT, 2019, pp. 23–24).

1.3.9 Targets In order to realize the National Technology Move, 12 main targets have been determined in the field of Industry and Technology. These targets are given as follows (MoIT, 2019, pp. 26–30): • • •

increasing the ratio of the manufacturing industry in the Gross Domestic Product (GDP), increasing the added value of the industry per worker in the industry, increasing the export of the manufacturing industry, 25

Mert Cihan Tonkal and Tugrul Daim

• • • • • • • • •

increasing the share of medium-high and high technology products in manufacturing industry exports, increasing the ratio of R&D expenditures in GDP, improving the human resources in R&D, increasing the number of Turkish companies in EU R&D Scoreboard, increasing the number of skilled software developers, increasing the amount of angel and venture investments to be made on technology-based business models, preparing the way for (at least) one of Turkish company as a world leader in the development of products with market share or brand value on disruptive technologies, increasing the number of companies reaching one billion USD or above valuation from the enterprise level, providing MoIT as a unique point of support and service to industrialists, suppliers, entrepreneurs, research infrastructures and universities with a “stakeholder-oriented” approach.

1.3.10 Components of Industry and Technology Strategy In the document, there are five components that have been designed for the vision, principles, framework and objectives mentioned above. These are high technology and innovation, digital transformation and industrial moves, entrepreneurship, human capital and infrastructure (MoIT, 2019, p. 33).

1.3.11 Critical Technology Concept in 2023 Industry and Technology Strategy As explained above, the strategic document is built on the concept of critical technology. However, there is no direct section or definition that defines what critical technology is. The best expression of what is meant by critical technology in the document is “… critical technologies that will increase global competitiveness and ensure economic and technological independence…” The document refers to critical technologies in the defense industry. However, no definition is given again. However, defense industry technology development projects are shown as successful examples in deploying critical technologies (MoIT, 2019, p. 17): …Defense industry projects have increased the ability of domestic suppliers to produce technology, reduced costs and improve production capability in advanced technologies such as drone vehicles, missiles, radar systems and satellites. The gains from the defense industry technology development projects and improved local suppliers will be examples for other industries. Strong national planning and coordination play a significant role in the local production of critical technologies of defense industry. Thus, the level of domestic product usage increased from 20% to 68%. In civil industrial sectors, governance mechanisms at national level will be established. These mechanisms will be pioneered by the private sector, and sectorial road maps will be prepared by considering that it should be cost effective and competitive… statement is located. Apart from that, there is no clear definition or description of what is meant by critical technology. 26

Strategy of Critical Technology

1.3.12 Other Technological Phrases in the Strategy 1.3.12.1 Focus Areas TUBITAK (The Scientific and Technological Research Council of Turkey) determined 42 focus areas and ranked the scientific study volume and quality of universities in some areas (MoIT, 2019, p. 20) to facilitate the cooperation with universities and to attract FDI (MoIT, 2019, p. 21) and to catch/lead in world’s new pioneering areas in order to establish systems that will let sustainability of enterprises (MoIT, 2019, pp. 22–23). Focus areas are embedded systems, information security, nano- and micro-electromechanical systems, cloud computing, coal-solar energy, machine design and manufacturing, automotive materials technology, field crops, broadband technologies, electric-hybrid vehicle technologies, energy efficiency, food security, software technologies, integrated watershed management, data processing, purification technologies, vaccine, animal feed breeding, automotive battery technologies, hydrogen fuel cells, automotive new production manufacturing, mobile communication technologies, robotic-mechatronics technologies, horticulture, factory automation, biomaterials, biomedical equipment, pharmaceuticals, medical diagnostic kits, public health, food processing and production technologies, energy storage, electrical power conversion transmission and distribution, bioenergy, wind energy, photonics, display technologies, manufacturing technologies, chemistry (basic science) and chemical engineering (MoIT, 2019, p. 20).

1.3.12.2 Focus Technologies Focus technologies that have strategic value for national independence are determined by considering their economic effects and the needs of the sector. These are 5G and beyond connectivity technologies, artificial intelligence and machine learning, robotics and autonomy, internet of things, big data and data analytics, cyber security, blockchain and distributed notebook, additive manufacturing, super-performance computing, drone vehicles, space technologies, nanotechnology, biotechnology agriculture and energy technologies. It is stated that these technologies will create road maps for developing national/original products and services (MoIT, 2019, p. 35) and products will be determined in these focus technologies to put forward Turkey (MoIT, 2019, p. 38).

1.3.12.3 Disruptive Technologies To be successful, it is very difficult to overcome the strengths such as intellectual property, experience, optimized supply chain and manufacturing management for a new investment in traditional industries. However, new players enter the same level with the existing players in the market of innovations and innovative products in disruptive technologies. Therefore, for the countries that do not have brand products (such as Turkey), disruptive technology areas provide products and services which have opportunities to attempt to find a place on a global scale. This potential is being accepted highly strategic (MoIT, 2019, p. 41). According to the document, destructive technologies are 5G, super-performance computing, digital technologies, neuroscience technologies (MoIT, 2019, p. 34), artificial intelligence and machine learning, internet of things (IoT), big data and data analytics, nanotechnology, biotechnology, robotics, cloud computing and sensors (MoIT, 2019, p. 30). In Japan, destructive technology focuses especially on automotive intelligence and networked machinery technologies for the automotive industry and the investment fund 27

Mert Cihan Tonkal and Tugrul Daim

established by the Japanese group Softbank for the development of disruptive technologies which has reached 100 billion USD in the last decade (MoIT, 2019, p. 8). In the document, there is a direct target that has been set for disruptive technologies. At least 23 intelligent products/services will be created with world-leading market share or brand value (MoIT, 2019, p. 30). In line with this goal, in order to determine the National Technology Mobility focus areas and priorities; it has been decided to prepare a research and development competency map to demonstrate the level of companies, universities and research infrastructures in disruptive technologies (MoIT, 2019, p. 34). There will be collaborations with international companies for rapid access to disruptive technologies that are not yet ready and investment requires time such as artificial intelligence machine learning, robotics and IoT. In addition, it is stated that when it is a necessity, company or technology acquisition opportunities will be evaluated and appropriate supports will be presented (MoIT, 2019, p. 67).

1.3.12.4 Target Products in Focus Industries In the 11th Development Plan (which is the national highest framework plan of all plans), the focus sectors are chemistry and medicine, motor land vehicles, watercraft, rail systems, machinery, semiconductors, electrical-electronics, aerospace and space sectors (MoIT, 2019, p. 52). It is emphasized in the document that it is no longer possible to define sectoral strategies by distinction, since the industrial sectors are generally converged. For this reason, the strategies and policies will be prepared together with the sectorial ecosystems. For example, the automotive industry will think the mobility ecosystem of the industry together or the pharmaceutical industry will be handled together with the health and life sciences ecosystem. The target products in focus sectors (MoIT, 2019, p. 34) and their strategic materials in these sectors (MoIT, 2019, p. 38) will be defined from this perspective.

1.3.12.5 Deep Technology In the document, technologies such as artificial intelligence, blockchain, life sciences, advanced manufacturing and robotics, agricultural technologies and new food and clean technology are expressed as deep technology and it is stated that initiatives in these issues will be supported (MoIT, 2019, p. 64).

1.3.13 Method for Success of Industry and Technology Strategy The basic framework of Turkey’s Industry and Technology Strategy is as follows: identifying the existing infrastructure, creating sector strategies suitable for this infrastructure and drawing technology roadmaps to complement the sectorial strategy. With these three steps, for the rapid commercialization and market entry of products and services, human resources, governance and financing methods will be introduced (MoIT, 2019, p. 36). In addition, for strategic enterprise (MoIT, 2019, p. 43) where the capital accumulation and investment appetite of the sector are low there is a huge economic importance; as with defense and space technologies, the government will be able to take the initiative. In other cases the state will continue its regulatory and guiding role (MoIT, 2019, p. 37). The tactical tools of this method (tested in the defense industry strategy) are mentioned as follows: measuring level of technological readiness (MoIT, 2019, p. 41); identifying existing infrastructure and capabilities for any technology, sectorial road mapping; establishing the 28

Strategy of Critical Technology

holistic framework of the sector and its ecosystem (MoIT, 2019, p. 34), specific technology road mapping; sketching the innovation cycle from research to the commercialization of products and services (MoIT, 2019, p. 36).

1.4 Technological Definitions in Other National Legislation 1.4.1 “Priority Product” in Technology-Oriented Industrial Move Program Application Principles Communiqué The program relies on OECD’s definition of medium-high and high-tech sectors. The program is aimed at improving the production capacity of the country in critical priority products in these tech groups of Turkey. The program is an end-to-end governance and support model. In other words, investment incentives are provided by the MoIT itself and project supports powered by KOSGEB (Small and Medium Enterprises Development Organization)/TUBITAK. Each stage of technology development and product creation processes can be completed with relevant incentives and project-based grants during the life time of the product. Criteria for the evaluation of applications (Ministry of Industry and Technology, 2019) are provided as follows: • • • • • • • • • •

• •

•

meeting the current and future critical market needs of Turkey, ensuring supply security in products with insufficient production capacity, improving technology capacity in areas which have technology deficits, reducing import dependency in areas with foreign trade deficits, providing high added value, geing able to produce new generation technologies that are not in present, gaining national competitiveness in different sectors, accelerating technological transformation of sectors, being an innovative and R&D-based investment, being the production of high value-added processed products to be realized in sectors that negatively affect the current account balance and where there is a shortage of raw materials, being an integrated production that allows the utilizing of the raw material potential, carrying out by a company with a financial adequacy that can carry out its investment and operational activities with limited external financing and government support via with a high equity financing rate, having project stakeholder purchase commitment and producing the goods which have accessible and developing market potential.

1.4.2 “Priority Product” List Communiqué The aim of the list is to increase value-added production in Turkey. Products are being identified in the 11th Development Plan (indicated in above sections) and priority criteria are such as foreign trade data, global competitive conditions, local and global demand trends, domestic production competence and capacity, and contribution to sectoral and intersectoral technological development. In medium-high and high technology level sectors (According to EU codes these are Chemistry (NACE-Code 20), Pharmaceuticals, Medical and Dental Equipment Manufacturing (NACE Code 21, NACE Code 3250), Computer, Electronic and Optical (NACE code 26), Electrical Equipment (Products that are included in NACE code 27), 29

Mert Cihan Tonkal and Tugrul Daim

Machinery (NACE code 28), Transportation Vehicles (NACE Codes 29 and 30) or selected from other sector products that are critical for the development of these medium-high and high technology level ones are chosen (Ministry of Industry and Technology, 2019b).

1.4.3 “Strategic Product, Strategic Product Sectors” in Strategic Product Support Program Application Principles The purpose of the Strategic Product Support Program; contributing the reduction of the current account deficit by increasing the technological production capabilities of small and medium-sized enterprises with the support of strategic product investments, to ensure the localization of strategic products with high imports by using higher domestic inputs in production (KOSGEB, 2019). The method criteria are the foreign trade deficit of country, the added value to industry, the reduction of import dependency, etc. In 2019 the strategic products are the goods in the areas of the manufacture of chemicals and chemical products, basic pharmaceutical products and pharmaceutical products, other non-metallic mineral products (in cooperation with the defense industry), weapons and ammunition (ammo), computer-electronic and optical products, electrical, machinery and equipment (not classified ones), motor vehicles, trailers and semi-trailers, other types of transportation, and medical and dental tools and equipment (KOSGEB, 2019).

1.4.4 “Priority Technological Area” Science, Technology and Innovation Policies and Strategies Framework In 2019 Science, Technology and Innovation Policy Council of Turkey identified priority technology areas. This work gave more importance to technologies rather than sectors. The applicability and impact were major criteria to be chosen as a choice. Priority technologies were identified according to target major outcomes: (1) transforming the gains from the inputs and outputs of R&D and (2) innovation activities into socioeconomic effect and social benefit. The first is for reducing foreign dependency in critical sectors for the needs of the country, and the second is for forming long-term policies in niche areas that will provide regional and global comparative advantage. The impact size of the priority technology areas was evaluated in three sub-dimensions: economic impact, social impact and national security impact. For the applicability dimension, academic knowledge, private sector competence, qualified human resources, research infrastructures, patent accumulation, ease of access to finance and technological preparation level were evaluated. As a further step, technology roadmaps will be prepared in the prioritized technology fields. Priority technologies are advanced functional and energetic material technologies, motor technologies, biotechnological medicine, IoT, energy storage, robotics and mechatronics, artificial intelligence, big data, information security, broadband technologies and micro/ nano/opto-electronic technologies (STPIC, 2019).

1.5 Basic Findings from Strategy (and Others) 1 2

Turkey’s 2023 Industry and Technology Strategy states that technological independence is the major driven force of political and economic freedom. For this independence, the major consideration of policy of critical technologies (identification process and government support) is the same with defense industry. 30

Strategy of Critical Technology

3

4

5

As a tool the Strategy relies on the National Technology Move and its regulations that went in effect previously. This Move should lead creation of local, national and original technologies (like in defense industry). Starting from the fact that the technological independence in the defense industry has been greatly achieved, it has been emphasized that planning and implementation model in defense will spread into other industrial sectors. This tactic is described as the key to be successful. Although Strategy has a technical introduction of Industry 4.0, all goals are predominantly economic.

1.6 Implications Simple Labeling: It is easily being realized that there are lots of terminology words on the Strategy, KOSGEB, TUBITAK and 11th Development Plan such as disruptive, strategic, focus, priority and deep. Simple classification will generate common sense easily. Thus, “critical” word should be used as legal language (as Minister says). Although critical adjective is used in the document generally, there is no direct description of it. By using relevant interpretation, I created a definition: A critical technology (product) has to be discovered, developed and deployed as local, national and original to gain technology independence in manufacturing. Vertical to Horizontal Technology: Policy focus may be on product or technology according to importance. If the technology is “cross-sector ubiquity” like indicated in the US case, then critical technology should be preferred. If the product consists of important sub-technologies then critical product should be addressed. Whatever the identification is a technology or product, the most important point as indicated cases, a study must be done in terms of economic role of technologies, especially how they influence/improve economic growth. This is extremely important since most of the targets are identified in terms of economic indicators on the Strategy. The indicated study must constitute a linkage from a microeconomic policy to a macroeconomic result. According to the OECD, the level of technology in products mainly relies on R&D expenditures. Expenditures are being dominating by huge amounts of developed countries. Thus, future of technologies is positioning via gigantic multinational companies especially located in developed ones. However, Turkey is a developing country and an emerging economy. Her talents and needs are pretty different in terms of manufacturing capabilities rather than others. If the OECD calculation approach is implemented in only Turkish (or other developing countries) economic data (the study), high and medium-high techs would be different from OECD classifications and this may show real domestic capabilities for policy makers of Turkey. Although it was indicated that disruptive technology market conditions are pretty easier than traditional ones, and this technology should be targeted smoothly, recalculation of national R&D talents and domestically high and medium-high techs may also illustrate the state to invest on better products and its critical technologies as well. This implication is important for allocation of limited resources and creating national successes also in current industries. Capitalization of R&D: For defense industry, all the success in critical technologies relies on close connection of stakeholders. Different from other commercial industries, in this sector client (second largest army of NATO) and its product characteristics, producer (national companies) and provider (Presidency of Defence Industry) are well defined, which keep cash flow in safe. Furthermore, the success tale of Turkey has taken nearly 50 years. However, critical technologies and their economic result are uncertain in other industries 31

Mert Cihan Tonkal and Tugrul Daim

since the free market is complex and needs are rapidly changing. This is a warning that the same application may not provide the same result everywhere. Though The Move regulation forces the tech producers (who are asking incentives/supports) to make alliance agreement with a certain client firm (in the relevant industry) to guarantee the innovation’s existence and this enforcement seems a solution of the uncertainty of capitalization of R&D, the feedback of the last users can change product characteristics and the level of this change may lead to a deletion on proof of concept rather than configuration. Furthermore, creating a technology is completely different from selling it. To scientists/technologists, an innovation may be super genius, but if the market asks different needs, failure is predictable. That is why critical technology/product projects should be evaluated in terms of marketing as well by MoIT. Need for a Structure: In all cases and the Strategy document, policy governance was described the missing and the most important part of all implementations for critical technologies. For successful governance, a unique unit is a need. The MoIT has enough specialists (more than 500 people from different backgrounds such as various engineering, law, business and economy). These human resources are mainly being used for routine tasks. Most of them are young and willing to participate in action. Observing processes in firms, making relevant researches and analysis via academics and connecting the missing communication in triple helix will be their further carrier. Perceptibly, the teams with two to three specialists from different but relevant backgrounds would be assigned with a critical technology in different firms and a new division such as Division of National Technologies may provide these teams in MoIT. The teams can observe all the process during 3D. Also they could research relevant patents which are alive/not, finding potential international partners on the topic. Most of assistant specialist can write their expertise thesis according to their technology experience, gained skills about the implementation of the policy and problems that occur in that particular technology. Furthermore, these stuff can let knowledge diffusion inter- or intra-industries since they could work in different firms. However, MoIT directs firms to KOSGEB and TUBİTAK for financial credits/grants via Move Program. But most of firms do not have enough awareness or know how to prepare for the steps to gain. These teams can also illustrate partners and could be bridges between private and public sectors. Thus, MoIT could use good/bad examples and tacit industrial knowledge of these experts during policy and decision-making processes on the way of 3D. Loss of Capabilities: The US case implies that the “loss” of national assets and technological capabilities through the acquisition or consolidation of foreign assets as a danger. To keep “the loss” as national, there should be specific support tools to pass later stages of lifetime of critical tech firms quickly. Although The Move Program provides lots of incentives/grants/ credits mainly during a pre-market stage via KOSGEB/TUBITAK and angel investors/venture capitalists are getting common, also additional financial options should be designed specifically for these firms such as initial public offering and public marketing to cross Death Valley or The Chasm. Turkish Wealth Fund may let this opportunity via a semi-public partner mother company. Because these critical ones are cutting-edge ones and FDI will seek to invest in their stocks if they cannot take the control of them in early stages easily. Otherwise firms may not reach and pass through breakeven point (in units/Dollars) and become national “loss”.

1.7 Future Studies To complete this chapter, some of further studies are taxonomy and terminology search of “critical” technologies; sketching public team duties and framework while supporting 32

Strategy of Critical Technology

critical technologies in firms; using OECD methods in one particular country data and determine the domestic capability to show role of critical technologies in economic growth, semi-public financial model of initial public offering and public marketing to cross Death Valley and The Chasm for critical tech companies and lastly a way of prioritizing the critical technologies for governmental strategy.

1.8 Conclusion The future of technology involves deep uncertainty. The problem in strategy and planning is this uncertainty itself. Even if technology foresight and forecast lead, they cannot completely resolve this uncertainty. It is not how to reduce the uncertainty of the problem, but how we can structure ourselves and develop adaptive strategies to overcome that uncertainty (Popper and Wagner, 2003). The real answer is effective governance. The future relies on technology and national freedom is completely depending on technology independence. Critical technologies and relevant products should be identified precisely so that limited funds could be used in the most effective way to reduce uncertainties of emerging economies on 3D technology. To be more precise, redefined technology levels, the linkage between technologies and economic growth and well-designed public teams can be a solution.

References CoEC (Commission of European Communities). 2009. Preparing for our future: Developing a common strategy for key enabling technologies in the EU. https://eur-lex.europa.eu/LexUriServ/LexUriServ. do?uri=COM:2009:0512:FIN:EN:PDF Date of access 20 Jan. 2020. EU (European Commission). 2016. Study on EU positioning: An analysis of the international positioning of the EU using revealed comparative advantages and the control of key technologies final report. https:// op.europa.eu/en/publication-detail/-/publication/402a0201-c1cd-11e6-a6db-01aa75ed71a1/language-en Date of access: 20 Jan. 2020. KOSGEB (Small and Medium Enterprises Development Organization) (Turkey). 2019. Strategic product support program application principles. https://www.kosgeb.gov.tr/Content/Upload/Dosya/ strateji-urun/10.04.2019/Stratejik_U%CC%88ru%CC%88n_Destek_Program%C4%B1_Uygulama_Esaslar%C4%B1.pdf Date of access: 1. Feb. 2020. MoIT (Ministry of Industry and Technology). 2019. 2023 industry and technology strategy. https://www. sanayi.gov.tr/assets/pdf/SanayiStratejiBelgesi2023.pdf Date of access: 1 Feb. 2020. Ministry of Industry and Technology (Turkey). 2019a. Technology oriented industrial move program application principles communiqué. Government Gazette, 30892, 18 Sep. 2019. Ministry of Industry and Technology (Turkey). 2019b. Priority product lists communiqué. Government Gazette, 30892, 18 Sep. 2019. NCTP (National Critical Technologies Panel). 1991. Report of the National Critical Technologies Panel. US Government Printing Office: Washington, DC. NCTP (National Critical Technologies Panel). 1993. The Second Biennial Report of the National Critical Technologies Panel. US Government Printing Office: Washington, DC. NCTP (National Critical Technologies Panel). 1995. The National Critical Technologies Report. US Government Printing Office: Washington, DC. Popper, W. S., Wagner, C. (2003). Identifying critical technologies in the United States: A review of the federal effort. Article in Journal of Forecasting 22, pp. 113–128. www.interscience.wiley.com DOI: 10.1002/for.854 STIPC (Science, Technology and Innovation Policies Council). 2019. Priority technological areas. https:// www.tccb.gov.tr/en/ Date of access: 1 Feb. 2019.

33

2 EXPLORING THE INTERPLAY BETWEEN DIGITAL BUSINESS MODELS AND DYNAMIC CAPABILITY IN THE PURSUIT OF COMPETITIVENESS Berk Kucukaltan 2.1 Introduction So far, the business field has gone through four industrial revolutions. Among these, steam engines initiated the first industrial revolution while incorporating electric energy induced the second (Buhr, 2015) and information and communication technologies, coupled with personal computers and the internet, accelerated the third revolution (Barreto et al., 2017; Jovanović et al., 2018). Today, in the fourth industrial revolution, originated in Germany, diverse digital-based technologies, such as cyber-physical systems, artificial intelligence, big data, internet of things, internet of services, and cloud computing, are placed at the centre of different research. In general, emerging digital technologies in this revolution have removed the boundaries between the place of production and the place of consumption. Furthermore, new technologies have also paved the way for new practices to be applied in various fields. As a result of these progresses and potential opportunities, the fourth industrial revolution, widely known as “Industry 4.0”, has received particular attention by both academics and practitioners in the business area. Unlike any other phenomenon, digitalisation brought by Industry 4.0 has recently made worthwhile changes in our daily lives (Geringer, 2020) and digital technologies have started to reshape the competitive landscape. By virtue of this fact, digitally enhanced business models have received a rising relevance in a growingly digitalised society (Vendrell-Herrero et  al., 2018). Yet, despite this increasing interest, there is still a lack of understanding in the society regarding the concepts and proposed technologies of Industry 4.0 and therefore, there is a need for clarification of Industry 4.0-oriented advancements (Alcácer and Cruz-Machado, 2019). To advance this awareness, prior to sophisticated implementations, on the practical side, it is imperative to investigate the effect of changes stemmed from digital technologies on business models because, as digital transformation continues, organisations constantly seek for the ways of adapting, rethinking, and reshaping their business models in this everlasting journey (Muthuraman, 2020). In this sense, first, digital technologies offered by Industry 34

DOI: 10.4324/9781003046899-4

Digital Business Models & Dynamic Capability

4.0 need to be appropriately and extensively scrutinised by decision-makers in organisations and, then, applying a holistic strategic perspective becomes a requisite for the fit of approaches into the proposed digital business model innovations (Loonam et al., 2018). In other words, decision-makers need to concentrate on developing new practices, capabilities, and strategies rather than clearing off the raised questions about the advances of digital technologies (Björkdahl, 2020). Accordingly, adoption and management of digital technologies are seen as necessary conditions for competitiveness of organisations (Gerasimenko and Razumova, 2020) and, this being the case, as Teece (2017) pointed out, possessing strong dynamic capabilities plays a key role for helping organisations to obtain competitive advantage. Regarding the capabilities needed in digital business models, in practice, it is apparent that numerous organisations lack the capabilities of rigorously analysing and exploiting available data in the business environment (Björkdahl, 2020). Similarly, on the academic side, there are insufficient discussions on what skills and abilities are needed for these emerging technologies (Freitas Junior et al., 2016). That is to say, the literature on business model change towards digitalisation and adopting the dynamic capability approach is still in its infancy (Ansong and Boateng, 2019; Witschel et al., 2019) and, as such, reveals vagueness about the link between these both concepts. What is more is that, as Rachinger et al. (2019) emphasised, exploitation of technological opportunities in relation to digital business models in competitiveness is blurry, especially from a strategic viewpoint. Accordingly, this chapter sets out to explore the interplay between the concept of digital business model and the dynamic capability approach in the pursuit of competitiveness, by adopting the strategic management standpoint. In order to achieve this aim, the chapter is structured as follows. In Section 2, the theoretical overview on transition towards digital business models and the use of dynamic capability approach is presented while in Section 3, gaining strategic advantage in competitiveness is explained in accordance with the internal organisational and external environmental parameters. In Section 4, the current academic state of both concepts is discussed in the context of competitiveness and real-life examples are also provided to substantiate the discourses. Finally, the chapter is concluded with insights, academic and practical, and future research directions regarding the discussed subject matter.

2.2 Theoretical Overview 2.2.1 Transition from Traditional to Digital Business Models The concept of a business model, emerged initially within the entrepreneurship literature, is a forward-looking strategic tool that is usually described as a structure, architecture, or a frame (Xu and Koivumäki, 2019). In contrast to its definable sketch, there is no broadly accepted definition of the business model concept and definitions vary in the literature. Despite this variety, there are, nevertheless, several common points emphasised in different clarifications. For instance, according to Osterwalder and Pigneur (2010, p. 14), a business model describes “the rationale of how an organization creates, delivers, and captures value”. In a similar vein, Teece (2010, p. 191) expressed that a business model “describes the design or architecture of the value creation, delivery and capture mechanisms employed”. As can be deduced from these widely acknowledged definitions, there is a general consensus among academics and practitioners that a business model is a strategic tool for organisations to achieve and hold competitive advantage through value propositions. 35

Berk Kucukaltan

Fundamentally, following the introduction of information and communication technology in the third industrial revolution (Barreto et al., 2017), the fourth industrial revolution emerged as the combination of physical and network structures (Maslarić et al., 2016). In this latest revolution, the components of Industry 4.0 enable organisations to gain superior strategic advantages over others in their sectors. More specifically, information technologies developed during this revolution, especially the digitalisation of production, consumption, and manufacturing, have changed the conditions of business ecosystem by means of allowing faster as well as more efficient exchanges of knowledge and information. Yet, owing to fact that failing to follow these advances can make organisations decline and lose their competitiveness, today, organisations strive for redesigning their business models in line with recent changes and are in quest for implementing applicable digital technologies in their digitally driven business models. Although diverse technological developments have recently occurred in business models with a shift from traditional to digitally driven models ( Jerman et al. 2019), the case of nonuniformity in descriptions is also the same in the definition of a digital business model. In this regard, a vast range of studies linked digital business models with the evolution of e-business concept (Luz Martín-Peña et al., 2018). To exemplify these explanations, Weill and Vitale (2001, p. 34) characterised e-business models as “a description of the roles and relationships among a firm’s consumers, customers, allies, and suppliers that identifies the major flows of product, information, and money, and the major benefits to participants”. In parallel, the differentiating characteristics of digital business models were generally summarised by Remane et al. (2017) as follows: digital products and services can be reproduced with zero marginal cost and become more valuable as more users join, value is established in practice, the balance between various stakeholders is found on digital platforms. In addition to outlining these characteristics, the researchers mentioned that bikesharing, carsharing, ridesharing, and intermodal travelling types are among the examples of digital business models. Given these examples, it is evident that digital enterprises rely largely on the mindset of understanding the needs of their customers or users (especially for platform-based models) and aligning technologically driven external changes with their internal systems. Accordingly, for such enterprises, it can be pointed out that a technological change tends to trigger fundamental alterations in the way organisations are managed and revenues are generated (Veit et al., 2014). Therefore, digital enterprises are referred to as organisations that employ digital technologies as a competitive advantage in internal and external activities (Rouse, 2011). Drawing on the conceptualisation of digital enterprises, it is worthwhile to underline that the use of digital technologies is only prerequisites for organisations since finding new ways of organising internal resources and building capacity through sensing, shaping, and seizing opportunities are also imperative for the continuity of successful efforts (Björkdahl, 2020). Indeed, organisations nowadays see their opportunistic roles less in industries yet more in virtual platforms where customers and organisations collaborate to sustain products and services. This being the case, in order to add more values and to remain competitive in this new era, organisations should move beyond simply providing products and/or services to their customers and understand customer experiences via data-centric business models (Riemensperger and Falk, 2020). Only at that time, would designing a digital transformation strategy result in success. Generally speaking, transformation is a non-linear progress and for the design of a suitable strategy, decision-makers in organisations need to have a clear idea of both the current state of their internal organisations and the dynamic changes occurring in the external 36

Digital Business Models & Dynamic Capability

environment (Gerasimenko and Razumova, 2020). From beginning to end, the progress of development of digital business models is exemplified by Muthuraman (2020) in several phases, which are digital reality, digital ambition, digital potential, digital fit, and digital implementation. In this entire progress, the organisations that align their internal processes with external digital technologies hold a potential to leverage their strategic advantages in their industries (Loonam et al., 2018). In line with this, as Kraemer-Mbula et al. (2013) noted, organisations should respond to volatile changes and co-evolve their capabilities and roles by considering interdependencies of different actors in the business ecosystem. For this attempt, it is crucial to determine critical success factors and capabilities affecting business models. In this regard, Jerman et al. (2019) highlighted that leadership and management orientation becomes prominent since business models are alive platforms. Yet, despite these changes and transformative efforts, numerous organisations, especially the small- and medium-sized companies, lack comprehending new digital technologies and their influences on business models (Muthuraman, 2020). In this vagueness, the dynamic capability approach holds a great potential to depict the set of required skills in this change and to scientifically explain this need (Witschel et al., 2019). Therefore, in the next sub-section, the dynamic capability approach is explained. Afterwards, since the transition towards digital business models affects organisations internally and externally (Vendrell-Herrero et al., 2018), internal organisation and external environment are also mentioned in the following section.

2.2.2 The Dynamic Capability Approach The concept of dynamic capability dates back to early 1990s. As a concept, it was initially introduced by Teece and Pisano (1994) as the capacity of an organisation for transforming its knowledge into actions by considering internal and external parameters. In this term, the word “dynamic” refers both to the capacity to renew competencies and to the response to rapid technological change, while “capability” emphasises the adaptation of both internal and external organisational skills to the demands of an unstable environment (Teece et al., 1997). According to Teece (2017), capabilities, which are referred to as the activities that enable organisations to generate a particular outcome, can be ordinary and dynamic. In this regard, the researcher pointed out that ordinary capabilities are about the skills to manage current production plan whereas dynamic capabilities are inherently future-oriented and have an interrelationship with strategies. Likewise, Helfat and Raubitschek (2018) indicated that dynamic capabilities contrast with ordinary capabilities and in the subset of this notion, dynamism connotes volatility and capabilities, which are latent until called into use, represent developments over time. Thus, as its very name signifies, dynamic capabilities are those that tend to change (e.g. extended, modified) for addressing challenges and demands of the marketplace (Kraemer-Mbula et al., 2013) since it is innovation-based and distinguishable from operational and ordinal capabilities (Warner and Wäger, 2019). Theoretically, the concept of dynamic capability, which stems from sensing business opportunities, seizing them, and transforming current assets (tangible and intangible) and core competencies to responsive resources as well as complementary competencies, is based on the fact that if an organisation holds resources and competencies without dynamic capabilities, it then becomes competitive mainly in a short term (Augier and Teece, 2009). In these days, companies are generally able to sense and seize the potential of using digital technologies; however, most organisations are unclear about getting the results from the transformation (Fitzgerald et al., 2014; Brenner, 2018). In dealing with this limitation and for achieving sustainable competitive advantage, the dynamic capability approach advocates 37

Berk Kucukaltan

that organisations need hard-to-imitate capabilities and should reconfigure internal activities and structures with external changes. In other words, the rationales of this approach are to use internal and external firm-specific competencies for addressing the dynamic environment and to invest strategically in organisational learning and knowledge exchange in order to keep pace with changing technological market conditions (Teece et al., 1997). This being the case, while reshaping business models in line with emerging advancements, some particular capabilities such as managerial skills, employee abilities, R&D competencies, and visionary stance in leadership come to the fore more. Thus, it is evident that the pivotal role of adopting the dynamic capability approach is of utmost importance for competitiveness of organisations in the business domain. Consequently, it is explicit that today’s business ecosystem is highly dependent on technological capabilities ( Jovanović et al., 2018). Hence, given the recent technological changes in this digital era, the dynamic capability approach remains suitable for understanding what digitally driven business models offer (Witschel et al., 2019) and for achieving agility (Björkdahl, 2020) in volatile environments (Rachinger et al., 2019). Indeed, foreseeing changes, responding to advancements, adjusting business models interacted with capabilities, and improving competitive outcomes in the business environment are among the intrinsic activities of the dynamic capability concept (Teece, 2017). Therefore, the dynamic capability approach serves as one of the main legs of this chapter in connection with internal organisation structure and external environment, which lead to gaining strategic advantage in competitiveness.

2.3 Gaining Strategic Advantage in Competitiveness: Internal Organisation and External Environment The competitiveness of an organisation in a relevant market depends not only on its internal structure but also on external factors within its environment. From this point forth, due to the necessity of evaluating organisations from different angles when adopting a comprehensive approach, both the internal organisational structure and the external environment are discussed in detail in this section.

2.3.1 Internal Organisation An organisation’s competitive advantage cannot be properly assessed without an understanding of its internal structure, nature, and sources of advantage (Nagy et al., 2018). The nature of an organisation is related to the way it is arranged (Lin et al., 2018). In this arrangement, the resources, which complete organisational tasks (including individual, social, and organisational spectrums and capabilities), have recently risen to greater prominence owing to Industry 4.0, and are regarded as fundamental internal functions which lead to superior competencies. On the whole, all of these functions have an impact on an organisation’s competitive advantage and competitiveness (Hitt et al., 2016). In more detail, Lin et al. (2018) highlighted that the size of an organisation is another significant internal factor for influencing competitiveness since larger organisations tend to possess a larger range of incentives, resources, skills, and experiences in terms of technology adoption. In addition to the size, as Müller et al. (2018) emphasised, technological knowledge becomes another critical resource, especially in the case of Industry 4.0 implementation. Relying on the knowledge, strong organisational structure in accordance with visionary and transformational leadership styles is also of vital importance since it requires the presence of a senior management team that promotes both change management and collaborative as 38

Digital Business Models & Dynamic Capability

well as explorative work environment. In this respect, it is significant to note that successful management is especially imperative to produce greater efficiency and stronger performance in the context of Industry 4.0 era (Nagy et al., 2018). Accordingly, these discourses uncover the importance of human resources, commonly described as the “soft” side of an organisation (Piccarozzi et al., 2018), for the achievement of competitive success in the digital age. In sum, the internal structure of an organisation mainly consists of resources (tangible and intangible), capabilities, and core competencies that differentiate organisations from one another. Especially, the resources and capabilities that support innovation reside largely at organisational level (Helfat and Raubitschek, 2018). Given these, since all these internal components are interacted with each other and affect competitiveness, they need to be extensively and diligently considered by decision-makers in organisations.

2.3.2 External Environment In addition to the internal structure of an organisation, achieving competitive advantage also derives from the ability of comprehending and responding to external factors. A retrospective glance at the history of technology unveils that information and communications technologies (e.g. cloud, data analytics) and material technologies (e.g. sensors, new materials) have sparked off further technological developments and fuelled productivity and growth across industries (Brenner, 2018). More particularly, in today’s external environment, the internet allows suppliers and customers to form a digital ecosystem, where they can access all relevant information and data (Nagy et al., 2018). Yet, it is worth stating that pursuing recent internet-based developments and implementing digitalisation are not sufficient for organisations on their own. Coordination between different actors is also vital for the success of the entire ecosystem. In other words, each actor, despite their different roles, needs to implement these advances with other actors in a coordinated way, as coordination between various players can affect the performance of organisations and the efficiency of the network in which they operate. Therefore, it is clear that the scope of Industry 4.0 is moving beyond the confines of a single organisation, as it covers the supply chain network where various actors belong to. Given the fact that Industry 4.0 initially emerged in the leading triad of Japan, Germany, and the USA (Sioutis and Anagnostopoulos, 2016), organisations nowadays make a great effort to follow the technological and practical advancements, which emanate from these countries, in order to stay ahead of this compelling environment. To have the last laugh in this competition, organisations should pay much attention to the events that take place in different segments of the external environment. These segments can be summarised as follows (David and David, 2017; Hitt et al., 2016; Rothaermel, 2017; Wheelen et al., 2018): – – – – – – –

Economic segment Sociocultural segment Demographic segment Political/governmental segment Legal segment Technological segment Environmental/ecological segment

Overall, external segments are not strictly positioned in an ecosystem and can vary according to different industrial and organisational norms as well as structures. Accordingly, the 39

Berk Kucukaltan

proposed list of segments in this chapter could be extended or shortened for different contexts. However, it is worth noting that these are the exemplary critical segments that bring opportunities to organisations, when they decide to monitor the external environment and align their internal operations with multi-faceted external conditions and activities. Therefore, at this level of complexity, as Industry 4.0 is closely related both to the actors in the supply chain network and to the changes in the external environment, organisations should accurately read the events that occur in different strata of this network and arrange their relationships with different actors. To do so, they may need to regularly revisit their goals, tools, and business models, as well as their relationships with their stakeholders in order to remain competitive in the business environment.

2.4 Holistic Thinking on the Interplay between Digital Business Models and Dynamic Capabilities in Competitiveness 2.4.1 What Does the Literature Put Forward? Generally, in the intersected literature of digital business models and dynamic capability in competitiveness, various topics were tackled by previous researchers. For instance, regarding the business model structure in digitalisation, Muthuraman (2020) aimed at causally connecting the components of business models with the characteristics of digital technologies in order to puzzle out the impact of technology on digitally driven business models, where cloud computing, internet of things, machine learning, robotics, mobile technology, and big data are of critical importance. In the Brazilian context, Cezarino et al. (2019) explored the relationship between the concepts of Industry 4.0 and circular economy, through structuralism, in order to reveal the potentials and limitations of these concepts in application. In the smart manufacturing concept, Alcácer and Cruz-Machado (2019) initially drew attention to the guidance of the Reference Architecture Model Industrie 4.0 and introduced the key Industry 4.0 components for manufacturing systems and, then, indicated their final remarks based on extant discussions. In sum, these three studies show that the link between the dynamic capability approach and digital business models was largely neglected and the discourses remained rather technical. On the other hand, in several studies, the pivotal role of abilities and skills was emphasised through servitisation in relation to digital business models. Within this scope, VendrellHerrero et al. (2017) empirically analysed the intersection of servitisation and digital business models for upstream and downstream firms in supply chain and their analysis was conducted in the publishing industry. In another study, Luz Martín-Peña et al. (2018) conducted a systematic literature review on digital business models and pointed out that servitisation and digitalisation have a mutual influence on the transformation of digital business models. Later, Ansong and Boateng (2019) quantitatively explored business model archetypes and the types of assets, such as financial, physical, intangible, and human, in digital enterprises in Ghana. Overall, as can be seen from these three studies, there is a need of a closer look into these subject matters, especially by considering the interplay between digital business models and the dynamic capability approach in relation to the competitiveness concept. In regard to digitalisation for competitiveness, Geringer (2020) focused on national digital taxes in Europe, in connection with European law, and discussed the impacts of taxation on competition and competitiveness. However, their focus remained very narrow given these mentioned concepts. In a broader manner, Jovanović et al. (2018) set out to explore the correlations of the Digital Economy and Society Index with other global indices that 40

Digital Business Models & Dynamic Capability

measure sustainability components and to examine the relationships between Hofstede’s cultural dimensions and digital performance. However, despite the given emphasis on competitiveness in these two studies, the changes in business models and the role of dynamic capability were not highlighted by researchers. Concerning the need of considering capabilities in digital business models for achieving competitive advantage, several studies paved the way for a holistic approach consisting of these concepts. For instance, in the smart concept, Jerman et al. (2019) examined the changes of business models of an organisation, which is a smart factory, and identified the key factors affecting its business model by using both the semi-structured interview technique with managers of the case company and the content analysis. The key factors indicated by the researchers were as follows: top management and leadership orientations, motivation of employees, collective wisdom, creativity, and innovations. Moreover, their findings exhibited that machines will have a major change in production and employees will predominantly take part in creativity expressions. Brenner (2018) conceptually discussed transformative sustainable business models in light of the digital advancements by drawing on the resource-based view and dynamic capability approaches coupled with external and internal factors. Similarly, through a multiple case studies of leading German firms from different industries, Witschel et al. (2019) examined how and under what conditions companies develop and implement digital business models. Their empirical findings showed that there is a moderating role of the organisational context on the relationship between dynamic capabilities and business model change and there needs to be an alignment between strategy, organisational design, and suitable leadership mindset. They also concluded that companies need to adopt the dynamic capability approach for responding to changing external conditions. Despite these efforts of discussing the interplay between digital business models and the dynamic capability approach in the competitive environment, the link between these concepts still remained hazy and case-based. For the aim of addressing the unknown practices and advancing the extant discourses to a significant extent, a number of researchers focused on this hazy link. For instance, Warner and Wäger’s (2019) qualitative study, based on multiple case studies, explored several incumbent firms in terms of establishing a relationship between dynamic capabilities and digital transformation. To this end, they structured their data in four aspects, namely, digital seizing, digital sensing, digital transformation, and contextual factors. More specifically, by underlining the importance of capabilities in digital business model transformation and implementation, Björkdahl (2020) discussed the digitalisation efforts (e.g. difficulties and coping strategies) of various manufacturing firms through multiple data sources, including interviews, site visits, and archival records. Likewise, in the context of automative and media industries, Rachinger et al. (2019) qualitatively explored how digitalisation influences a company’s business model, and, in turn, business model innovation based on the adoption of dynamic capability approach. Guichardaz et al. (2019) used the concept of transactional capabilities to explore the shift towards digital business models and illustrated a case study of the Sony Music Entertainment (France) to discuss these concepts and structures. As seen from these studies, digital transformation of business models was associated with the adoption of dynamic capabilities in a general manner without demonstrating the capabilities that can be particularly needed in the digitalised business environment. Regarding the studies working on capability types that are needed in digital business models, Kraemer-Mbula et al. (2013) discussed the cybercrime and cybersecurity ecosystem, particularly from the financial viewpoint, through the value chains, the emerging dynamic capabilities, and the business models that arise from changes. In their paper, they 41

Berk Kucukaltan

underlined the importance of being careful about data security and of developing socialengineering skills and techniques as emergent capabilities (e.g. cyber capabilities) needed for digital business models. In another related study, built on Teece’s (2016) research, Helfat and Raubitschek (2018) provided a theoretical analysis that proposes three critical dynamic capabilities, which are innovation capabilities, environmental scanning and sensing capabilities, and integrative capabilities for ecosystem orchestration, and argued how digital platform leaders create and capture values. All in all, the reviewed studies in this section unfolded that the intersection of digital business models and the dynamic capability approach is currently a nascent area and still in need of further studies to clarify the haziness between these concepts. In the age of Industry 4.0, bridging this gap is especially crucial since digitalisation has an increasing impact on the business environment and the dynamic capability is a robust approach to explore both the current practices and the forward-looking expectations. In this regard, based on the structurally reviewed literature, it becomes apparent that digital business models need to be encompassed by existing critical and emerging capabilities (digital and non-digital). Especially, in today’s competitive business ecosystem, digital competencies are listed among the eight critical competencies by the European Parliament (Gerasimenko and Razumova, 2020) and this urged researchers to focus on the dynamic capability approach by complementing non-digital capabilities with digital ones. To sum up, the extant literature underlines the prominence of following capabilities: creativity, knowledge sharing, data and information security, integration with different stakeholders (e.g. customers, partners), agile and innovative thinking, cultural tendency, transactional and transformational capacity, leadership and management orientation, flexibility, awareness and motivation of employees, and balancing portfolios. Accordingly, in today’s complex and ever-changing business ecosystem with the progress in digitalisation, digitally driven business models tend to require considering both extant and emerging dynamic capabilities, particularly by advancing the existing critical capabilities. This is also a requisite for global competitiveness since the complementarity of non-digital and digital capabilities helps to build digital ecosystem, which ultimately leads to a positive network impact.

2.4.2 What Are the Practical Examples in the Business Environment? In recent years, organisations have been reconfiguring their business models in the face of a changing order of innovation. Most decision-makers in organisations (e.g. CEOs) today are engaged in understanding how the shift towards digital business models is affecting their activities and systems. Even though some organisations are still struggling to embed the innovation-oriented mindset, there are also successful enterprises that implement digital technologies. In this success, adopting the dynamic capability approach as a whole in accordance with internal and external parameters plays a critical role. For instance, in the taxi sector, Uber previously sensed and seized the opportunities and, ultimately, has achieved considerable success after proposing and developing its technology-based on-demand digital business model at the right time. On the other hand, H&M, which is a multinational clothing-retail company, sensed and seized the opportunities later than its competitors and currently puts much effort to regain the growing competitive advantage (Björkdahl, 2020). In the automative industry, as a response to the lack of capabilities for rigorously analysing available data, Volvo and Scania have turned into forming data science groups so that they have aimed at identifying previously unrecognised events and developing competencies in the event of digital transformation (Björkdahl, 2020). In the context of transformation, 42

Digital Business Models & Dynamic Capability

establishing a business model open to continuous changes is of vital importance. In this sense, Amazon can be presented as a good example since it integrates cloud systems into its e-commerce operations and offers cloud-based systems to other organisations (Teece, 2017). Following the Amazon case, it is common to come across that organisations in the digitalised business ecosystem can collaborate on one layer while competing on another layer, such as Apple’s iPad and Amazon’s Kindle (Warner and Wäger, 2019). Thus, it is apparent that traditional competitive system among organisations has tend to evolve into the multilayered architecture. Lastly, as a salient example, Apple has continued to release updates in its tools and systems since 1980s in order to keep up with external changes and retain its customers (Teece, 2017). Indeed, Apple understood earlier that information and interactions are the core assets to form the basis of a competitive advantage (Brenner, 2018). At present, Apple is said to be the world’s largest corporation based on its market capitalisation (David and David, 2017). It dominates the technology industry as a product innovator and has created entirely new mobile product categories (unlike its rivals, such as Samsung, Nokia, and Blackberry) (Hitt et al., 2016). Moreover, for each of these product categories, Apple carried out an incremental innovation strategy (Rothaermel, 2017). The strategy was described by its CEO (in 2014) as about being the best, and by Steve Jobs as being intended to integrate innovation in products, services, and business models. This makes it clear that innovation has been a core competency for Apple hinging upon strong resources (e.g. intellectual property, distinctive store design, knowledgeable and skilled employees) and capabilities (e.g. launching products with speed and success, on-site tech support). To conclude, these real-life projections indicate that resources, capabilities, and core competencies have an impact on the competitiveness of organisations. Therefore, organisations need to reshape their business models by possessing strong resources, including infrastructure availability, and dynamic capabilities, including extant and emerging capabilities (digital and non-digital). In addition to these internal parameters, they need to be vigilant about volatile changes in the external environment. After these prerequisites, organisations can then be ready for applying a holistic approach to achieve a sustainable competitive advantage.

2.5 Conclusions and Future Research Directions Subsequent to three industrial revolutions, the fourth industrial revolution, widely known as Industry 4.0, offers significant advancements to the business environment. In the new digital era, implementing emerging technologies and concurrently developing competencies along with dynamic capabilities can cause a strategic advantage in competitiveness of organisations. However, given the academic and practical progress in relation to the implementation of Industry 4.0 technologies and competitiveness, it is evident that the connection between digital business models and dynamic capabilities remains meagre. In this regard, although several researchers have already attempted to conceptually and technically emphasise the need of using the dynamic capability approach in digital business model transformation, the link between these two concepts has received very limited attention, and, as such, causes vagueness in the business domain. Accordingly, this chapter set out to explore the blurry interplay between digital business models and the dynamic capability approach, by adopting the strategic management standpoint, in the context of competitiveness. In order to achieve this research aim, the extant literature was initially reviewed and, as a result of the discussions made in relation to the reviewed studies, the present research has provided some insights into the intersected area of the subject matters. First, the current 43

Berk Kucukaltan

state analysis conducted by virtue of the structured literature review revealed that there is a research gap in the area of studying digital business models and the dynamic capability approach from the strategic viewpoint. From this point forth, it can be concluded that the outcomes of this research enrich the literature since existing small number of studies remain predominantly discursive, technical, or case-based. Second, both the mentioned outcomes and the needed capabilities summarised in this research shed some light on the intersected area and to advance, academically and practically, the clarity of the hazy link between two previously mentioned concepts. More particularly, in practice, it is worth to note that not all types of organisations operate in the same manner. For instance, the focus of product-oriented organisations (e.g. manufacturers) is different than service-oriented companies (e.g. 3PL providers). Similarly, new entrants differ from the organisations that already exist in a market. Thus, in addition to the outcomes, the internal parameters and the external segments discussed in this chapter can help organisations to extensively overview their positions in the pursuit of gaining strategic advantage in the fierce competitive business environment. Consequently, relying on these insights, the chapter provides a research agenda that also calls for further research in the focused area. In this sense, in future studies, digital and non-digital capabilities needed in digital business models can be empirically and conceptually examined by adopting the strategic management perspective for different contexts rather than providing a technical discussion or practising a case-based approach. By doing so, the main limitations of the present research, such as using secondary sources, will be overcome and the literature will be flourished.

References Alcácer, V. and Cruz-Machado, V., 2019. Scanning the industry 4.0: A literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal, 22(3), pp. 899–919. Ansong, E. and Boateng, R., 2019. Surviving in the digital era–business models of digital enterprises in a developing economy. Digital Policy, Regulation and Governance, 21(2), pp. 164–178. Augier, M. and Teece, D. J., 2009. Dynamic capabilities and the role of managers in business strategy and economic performance. Organization Science, 20(2), pp. 410–421. Barreto, L., Amaral, A. and Pereira, T., 2017. Industry 4.0 implications in logistics: An overview. Procedia Manufacturing, 13, pp. 1245–1252. Björkdahl, J., 2020. Strategies for digitalization in manufacturing firms. California Management Review, 62(4), pp. 17–36. Brenner, B., 2018. Transformative sustainable business models in the light of the digital imperative—A global business economics perspective. Sustainability, 10(12), 4428. Buhr, D., 2015. Social innovation policy for Industry 4.0. Friedrich-Ebert-Stiftung: Bonn, Division for Social and Economic Policies. Cezarino, L. O., Liboni, L. B., Stefanelli, N. O., Oliveira, B. G. and Stocco, L. C., 2019. Diving into emerging economies bottleneck: Industry 4.0 and implications for circular economy. Management Decision. (Ahead-of-print) David, F. R. and David, F. R., 2017. Strategic Management: A Competitive Advantage Approach, Concepts and Cases (Sixteenth ed.). Pearson: Essex. Fitzgerald, M., Kruschwitz, N., Bonnet, D. and Welch, M., 2014. Embracing digital technology: A new strategic imperative. MIT Sloan Management Review, 55(2), pp. 1–12. Freitas Junior, J. C., Maçada, A. C., Brinkhues, R. and Montesdioca, G., 2016. Digital capabilities as driver to digital business performance. Twenty-second Americas Conference on Information Systems (Digital Commerce-eBusiness and eCommerce, San Diego. Gerasimenko, V. V. and Razumova, T. O., 2020. Digital competencies in management: A way to superior competitiveness and resistance to changes. Serbian Journal of Management, 15(1), pp. 115–126.

44

Digital Business Models & Dynamic Capability Geringer, S., 2020. National digital taxes–Lessons from Europe. South African Journal of Accounting Research, pp. 1–19. Guichardaz, R., Bach, L. and Penin, J., 2019. Music industry intermediation in the digital era and the resilience of the Majors’ oligopoly: The role of transactional capability. Industry and Innovation, 26(7), pp. 843–869. Helfat, C. E. and Raubitschek, R. S., 2018. Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), pp. 1391–1399. Hitt, M. A., Ireland, R. D. and Hoskisson, R. E., 2016. Strategic management: Competitiveness & globalization: Concepts and Cases. 12e, SouthWestern Cencage Learning: Canada. Jerman, A., Erenda, I. and Bertoncelj, A., 2019. The influence of critical factors on business model at a smart factory: A case study. Business Systems Research Journal, 10(1), pp. 42–52. Jovanović, M., Dlačić, J. and Okanović, M., 2018. Digitalization and society’s sustainable development–Measures and implications. Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu, 36(2), pp. 905–928. Kraemer-Mbula, E., Tang, P. and Rush, H., 2013. The cybercrime ecosystem: Online innovation in the shadows? Technological Forecasting and Social Change, 80(3), pp. 541–555. Lin, D., Lee, C. K. M., Lau, H. and Yang, Y., 2018. Strategic response to Industry 4.0: An empirical investigation on the Chinese automotive industry. Industrial Management & Data Systems, 118(3), pp. 589–605. Loonam, J., Eaves, S., Kumar, V. and Parry, G., 2018. Towards digital transformation: Lessons learned from traditional organizations. Strategic Change, 27(2), pp. 101–109. Luz Martín-Peña, M., Díaz-Garrido, E. and Sánchez-López, J. M., 2018. The digitalization and servitization of manufacturing: A review on digital business models. Strategic Change, 27(2), pp. 91–99. Maslarić, M., Nikoličić, S. and Mirčetić, D., 2016. Logistics response to the industry 4.0: The physical Internet. Open Engineering, 6(1), pp. 511–517. Müller, J. M., Kiel, D. and Voigt, K. I., 2018. What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247. Muthuraman, S., 2020. Digital business models for sustainability. Gedrag & Organisatie Review, 33(2), pp. 1095–1102. Nagy, J., Oláh, J., Erdei, E., Máté, D. and Popp, J., 2018. The role and impact of Industry 4.0 and the Internet of things on the business strategy of the value chain—The case of Hungary. Sustainability, 10(10), 3491. Osterwalder, A. and Pigneur, Y., 2010. Business model generation: A handbook for visionaries, game changers, and challengers. John Wiley & Sons: New York. Piccarozzi, M., Aquilani, B. and Gatti, C. 2018. Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 3821. Rachinger, M., Rauter, R., Müller, C., Vorraber, W. and Schirgi, E., 2019. Digitalization and its influence on business model innovation. Journal of Manufacturing Technology Management, 30(8), pp. 1143–1160. Remane, G., Hanelt, A., Nickerson, R. C. and Kolbe, L. M., 2017. Discovering digital business models in traditional industries. Journal of Business Strategy, 38(2), pp. 41–51. Riemensperger, F. and Falk, S., 2020. How to capture the B2B platform opportunity. Electronic Markets, 30, pp. 61–63. Rothaermel, F. T., 2017. Strategic Management. 3e. McGraw-Hill Education: New York. Rouse, M., 2011. “Digital enterprise”, available at: https://searchcio.techtarget.com/definition/Digitalenterprise, Accessed: 14.08.2020. Sioutis, P. and Anagnostopoulos, K., 2016. Performance measurement of technology-production base of the firms: Ascertaining their strategic competitive advantage. Journal of the Knowledge Economy, 7(3), pp. 694–719. Teece, D. J., 2010. Business models, business strategy and innovation. Long Range Planning, 43(2–3), pp. 172–194. Teece, D. J., 2016. Profiting from innovation in the digital economy: Standards, complementary assets, and business models in the wireless world. Research Policy (forthcoming). Teece, D. J., 2017. Dynamic capabilities and (digital) platform lifecycles. In J. Furman, A. Gawer, B. S. Silverman, S. Stern (Eds.), Entrepreneurship, Innovation, and Platforms (Advances in Strategic Management). Emerald Publishing Limited: UK, 37, pp. 211–225.

45

Berk Kucukaltan Teece, D. J. and Pisano, G., 1994. The dynamics capabilities of firms: An introduction. Industrial and Corporate Change, 3(3), pp. 537–556. Teece, D. J., Pisano, G. and Shuen, A., 1997. Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), pp. 509–533. Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Kundisch, D., Leimeister, J. M, Loos, P. and Spann, M., 2014. Business models: An information systems research agenda. Business & Information Systems Engineering, 6(1), pp. 45–53. Vendrell-Herrero, F., Bustinza, O. F., Parry, G. and Georgantzis, N., 2017. Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60, pp. 69–81. Vendrell-Herrero, F., Parry, G., Bustinza, O. F. and Gomes, E., 2018. Digital business models: Taxonomy and future research avenues. Strategic Change, 27(2), pp. 87–90. Warner, K. S. and Wäger, M., 2019. Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), pp. 326–349. Weill, P. and Vitale, M., 2001. Place to Space: Migrating to eBusiness Models. Harvard Business Press: USA. Wheelen, T. L., Hunger, J. D., Hoffman, A. N. and Bamford, C. E., 2018. Strategic Management and Business Policy: Globalization, Innovation, and Sustainability. Pearson Education Limited: UK. Witschel, D., Döhla, A., Kaiser, M., Voigt, K. I. and Pfletschinger, T., 2019. Riding on the wave of digitization: Insights how and under what settings dynamic capabilities facilitate digital-driven business model change. Journal of Business Economics, 89, pp. 1023–1095. Xu, Y. and Koivumäki, T., 2019. Digital business model effectuation: An agile approach. Computers in Human Behavior, 95, pp. 307–314.

46

3 INNOVATIVE PLACES AND REGIONS Implications for Technology Management Scott W. Cunningham 3.1 Introduction Cities require effective engineering management and effective technology management. There are three reasons. Creating liveable, dense urban spaces is in itself a major engineering challenge. Cities are increasingly the home for the majority of the world’s population. Decisions made for and behalf of cities have a major impact on human welfare and the environment. Cities are the wellspring of new technologies. Anyone interested in employment and economic development will be primarily interested in cities and their regions. Anyone interested in new technologies will be correspondingly interested in cities since they are the premier source for generating economic and technological complexity. This is increasingly valued in the world economy. Engineering and technology managers will have a corresponding interest in cities. They will be interested in the needs and requirements of urban residents, since cities showcase many of the demanding requirements for the design, deployment and maintenance of complex infrastructures. Cities are also a primary source of the platform economy, and are propagating new trends in servitization. Many engineering and technology managers are themselves planners, managers and administrators for the world’s cities. They will be interested in decision-making for urban networks and the creation of resilient networked infrastructure. Urban managers will be concerned with the challenges and opportunities of the smart city, enabled by affordable sensors, cheap data, and ubiquitous sensing platforms. Technology managers are also interested in fostering new technology, including taking advantage of externalities and spillovers. This may also entail choosing high opportunity homes for research and development laboratories, and entrepot for marketing new technologies. There are many distinct issues therefore to address even in a survey chapter on urban technology management. The approach of the chapter is therefore as follows. The chapter begins with a broad theoretical overview of how and why cities create innovation and variety. It then turns to specific areas of urban and technological concerns. The final part of the chapter discusses place-based strategies.

DOI: 10.4324/9781003046899-5

47

Scott W. Cunningham

3.2  Innovation Theory There is a growing body of literature which describes cities as “innovation machines,” as “laboratories for innovation” or even as “social reactors” (Bettencourt et al. 2007, Batty et al. 2012, Florida et al. 2017). Cities foster diversity, and enable the recombination of new ideas. This in turn leads to innovation, employment and growth (Feldman and Audretsch 1999, Scott and Storper 2003, Glaeser et al. 2010, Duranton and Puga 2014). This literature or urban innovation is well-rooted in innovation theory (Marx 1867/2012, Marshall 1920). Schumpeter argues that the firm is the ultimate locus of new ideas, although he himself doubted whether small firm innovation or large firm innovation represented the most significant transformative force (Schumpeter 1934a, Schumpeter 1934b). Conversely, it is Jacobs (1969) who first rooted the locus of innovation primarily within the city rather than the firm. The exact nature of these agglomerative benefits has been subject to some debate. The classic Marshallian view is that cities permit greater specialization of labour. This is contrasted with the Jacobian view that it is a diverse base of skill which matters most. The empirical evidence has been largely supportive of Jacobs. A similar consideration concerns whether greater spillovers are created as a matter of scale, or of density. It appears that a mix of both scale and density is likely responsible for the resultant economic benefit. The political economy of urban innovation has been subject to considerable debate. Schumpeter credits an elite class of inventor and innovator with the wherewithal to unleash “creative destruction.” Newer theories argue that smaller, urban elite are responsible for driving economic and innovative growth (Cooke et al. 1997). These parties create the opportunities for growth by attracting funding and championing specific locales. Jacobs presents a much more pluralistic view, arguing that the variety and abundance of skills and services drives opportunity, rather than any one particular group or actor in specific. In fact, Jacobs was particularly sceptical about the entrenched leadership of her own city at the time. Florida’s ideas of the creative economy (Florida et al. 2017) may represent a balance between the elite theories of urban regime, and Jacobs own highly pluralistic account of the city. The processes which actually underlie urban agglomeration and knowledge spillover remain murky. It is possible that these are entirely human processes which are rooted in the evolutionary economic ideas of tacit knowledge and routines. Likewise the prescriptive recommendations for designing innovative cities remain obscure. A secondary literature argues that dense urban districts enable fortuitous interactions leading to enhanced creativity, innovation and growth. Prescriptions include creating urban districts that encourage mixing and chance encounters by the population. Likewise it seems clear that many cities are falling short because of a poor spatial match between employers and labour. Poor transport systems impose negative externalities on the city, and reduce the economic productivity of a region.

3.3  Specific Areas of Concern This section addresses four specific concerns of the innovative city, where engineering and technology management have a particular role to play. The first of these areas is industrial policy. Here technology management is needed to create a long-term vision for industrial growth. The second area of concern is urban planning and administration. Here technology managers better support decision-making with new data, new models and new urban dashboards. Both these areas are of international concern. The third of these aforementioned mentioned areas of concern is in the area of urban infrastructure. Here technology management provides capabilities in project management and risk management. The final area of 48

Innovative Places and Regions

concern is in the space of urban innovation. Here the disciplines of scientometrics and tech mining enable the matching of technological production with engineering expertise. This matching is to the benefit of companies and regions alike.

3.3.1  Industrial Policy Many nations are concerned with increasing economic performance and decreasing regional disparities through targeted policy efforts. There is a significant discrepancy in the capacity of regions to grow and sustain jobs and employment. Although regional specialization enables city regions to standout in a competitive international market, specialization also exposes city regions to significant downside once core industries diminish and decline. There are also significant structural differences between regions. The processes which enrich some regions may well drive away and diminish opportunities from surrounding regions. These differences persist and even grow over time, resulting in significant inequality for regions and inhabitants. Industrial policies necessitate a range of specialized skills in technology management. This includes specialized policy analysis, the development of capability building exercises and the creation of venues which support policy learning in cities and regions (Department for Transport 2019, House of Commons Library 2020b). These are all areas of continued growth and opportunity for practitioners of technology management.

3.3.2  Urban Planning and Participation The specific research question asks why there are substantial, geographical differences in the capability of urban regions to grow and sustain employment. Cities are called “social reactors” for a reason. Nonetheless, the structural elements of urban regions which encourage individual interaction warrant further investigation. A network of urban decision-makers, at multiple levels, can benefit from an urban facing approach to technology management. For example, technology managers can support urban decision-making. Supporting effective decision-making means working closely with regional planning associations. Participants in these associations include participants from the public as well as the private sector. Public sector participants include city councils and enterprise companies. Public sector cooperation requires cooperation across city regions as well. Private sector participants include real estate developers as well as the chamber of commerce. Cities learn policies from one another, particularly by copying cities which are larger or proximate (Shipan and Volden 2008). City governments are increasingly forming worldwide networks for cooperation and competition (Scott 2006). There are new actors in the governance system, as well as new technologies of governance (Swyngedouw 2005). The planning process benefits from a collaborative process (Innes and Booher 1999). Community-based participatory research can help to address environmental and socio-­ spatial injustices (Ponzini and Rossi 2010, Wallerstein and Duran 2010, Wolch et al. 2014). Software supporting increased urban efficiency can enable public discussion to better focus on questions of equity (Heath 2020). Inclusive growth requires its own unique processes of planning (Visvizi et al. 2018). The policy instruments available for enhancing growth are distributed across multiple actors and stakeholders (Cooke et al. 1997). Decision-support systems can enhance cooperation and coordination over complex decisions of land use and infrastructure investment. Technology management perspectives, such as roadmapping, can be used to benchmark local 49

Scott W. Cunningham

opportunities as well as to highlight specific interventions and growth opportunities. Proven patterns of growth and opportunity may then be overlaid on areas of urban interest. Roadmaps can be used for facilitating difficult discussions about equitable solutions for urban and economic growth by examining the distributional effects of regional plans. An explicit strategy to facilitate decision-making using group-support systems and stakeholder engagement meetings is also possible. Existing urban districts can thereby be categorized and templated, and over- and under-performing regions in technology and innovation can thereby be identified. The research will produce a decision-support system for planners using these templates where a range of local “what-if ” design scenarios can be proposed and tested. These digital planning efforts are part of an extended effort to create “smart cities.” Some of this data-driven work enables and supports activities at the small-scale activities and processes (Visvizi and Lytras 2018). Smart city and decision-support system may in the future be supported by open-source libraries in R and Python which can be customized and replicated by other cities.

3.3.3  Urban Infrastructure Technology managers can help address significant policy challenge through the empirical survey and analysis of infrastructural assets of cities and regions in their home nations and abroad. A significant part of the fixed factors of production that drives urban and regional capability includes installed infrastructure. Infrastructure increases the proximity of economic actors in the region. Enhanced proximity created through transport capacity enables more matches in the market for goods, services, skills and labour. A significant class of technology policies pursue economic growth through improved physical and environmental infrastructure. There are few research exemplars of this infrastructure-led approach, despite the clear theoretical need for such explanations (Ahlfeldt and Wendland 2013, Knowles and Ferbrache 2016, Goswami and Lall 2019, Proost and Thisse 2019, Heider and Siedentop 2020). This need presents a research gap that can be addressed by using emerging sources of geospatial data concerning land use and infrastructure networks (Porta et al. 2006, Barthelemy 2011, Florida et al. 2012, Blondel et al. 2015, Thakuriah et al. 2018, Boeing 2020, Duranton and Puga 2020). Evidence-based approaches may also help to address the apparent shortcomings of public sector allocation of resources and infrastructure (Glaeser 1998). These are all significant areas for involvement by technology management researchers and scholars. Despite the prerogative to invest in urban centres, not all infrastructural investments are equally effective. The policy processes that endow regions can and should be tested using extensive empirical evidence provided using big data approach. Plentiful existing data sources should be compiled and synthesized. Increasing resolution of data means that features at the block level of the city can be incorporated into simulation models or digital twins. These comprehensive inventories of urban assets can be used in a substantive manner for a variety of further analyses.

3.3.4  Urban Innovation If, as suggested by the literature, cities are a primary locus of innovative activity, much more attention is needed in identifying favourable patterns of growth and development. Some of the work in this area involves modelling technological emergence. The theoretical framework acknowledges there are multiple causes of economic growth and innovation in 50

Innovative Places and Regions

cities. The multiple causes include the available factors of production, spatial organization, industrial organization, human factors and policy inputs. These multiple causes are explored in the literature both separately and in tandem. This suggests the extensive challenge facing innovation analysts. Given the complexity of the problem much more work is needed in examining factors of production as well as in apprehending the spatial organization of the city. Nonetheless, elements of industrial organization cannot be neglected, since cities characterized by large firms and monopolies will perform very differently than a city composed of multiple small start-ups. Policy processes are a complicating factor. Policy processes may be conceived of as a mediating variable between urban structure and innovative performance. This spatial turn in innovation demands new styles of analysis. This research method is based on techniques first innovated in the field of image processing. There are a number of surprising and significant parallels between images and urban data stored in a raster. These techniques enable the robust recovery of urban patterns in the presence of noise. The potential for these techniques in urban geography and regional innovation remains underexplored. The underlying models are not causal, and nor do they need to be to identify the output and operation of complex urban processes. In particular a family of methods known as graphical modelling seems particularly valuable ( Jordan et al. 1999, Roweis and Ghahramani 1999, Kschischang et al. 2001, Koller and Friedman 2009, Grace et al. 2012, Nickel et al. 2016). Despite the innocuous name, the field of graphical modelling sits aside two major traditions of machine learning (Domingos 2015). One of the two traditions is connectionism (and therefore neural network related), and uses graph theory as a formalism (Barthelemy 2011, LeCun et al. 2015, Szegedy et al. 2015). The other tradition uses Bayesian reasoning and probabilistic programming, and therefore makes use of probability as a formalism (Ghahramani 2015, Carpenter et al. 2017). Graphs are directed or undirected in character; both formalisms have their merits for modelling and analysing systems. A highly customizable, undirected graph model known as a Markov random field seems increasingly applicable to innovation studies in the city (Cross and Jain 1983, Besag and Green 1993, Pal and Pal 1993, Roth and Black 2009). Markov random fields have been previously used as a method for reducing noise in data, downscaling the data to add credible detail and imputing missing data. Such models have even been used for the complete synthesis of new examples (Geman and Geman 1984, Besag 1986, Wang and Tang 2009, Li et al. 2016). This makes the approach highly suitable for urban data where data is noisy and complete, and where supporting human creativity is a requirement (Candy 1997, Wang and Nickerson 2017). These models have seen rich applications in image processing (Geman and Geman 1984, Besag 1986, Wang and Tang 2009) and yet remain comparatively novel in the fields of urban science and economic geography (Karantzalos 2015, Kuffer et al. 2016). Nonetheless, a model is used widely in policy-relevant research and spatial analysis ( Jin et al. 2005, Sain and Cressie 2007, Sain et al. 2011).

3.4  Place-Based Strategies This section has touched upon the importance of urban data in multiple domains. Urban data plays a role in planning infrastructure management, and in analysing urban innovation. The variety of data-smart city approaches are a testament to the increasing empirical base of the field. In the following and final section, two aspects of technology strategy are ­considered – a place-based approach and a technology-based approach. Prior to both, it is 51

Scott W. Cunningham

useful to further discuss urban and spatial data analysis, and its relationship to the practice of technology management. The section therefore discusses strategies in three distinct manners, including a strategy for the appropriate use of technological data.

3.4.1  Data-Driven Strategies Four sources of data are particularly interesting for innovation analysts up to the challenge of exploring the geography of new technologies. Open geographical may be used to characterize the local urban fabric. Critical elements of this fabric, including access to transport networks and features of urban land use, are of particular relevance. Specific land use elements of interest include the number and density of buildings, the presence of public services, and the presence of urban parks and green space. A company should be used to examine the sectoral structure, employment characteristics and industrial organization of local firms. Financial data regarding the purchase and investment can be used to reveal the opportunity value of the land. Finally – only after all of urban features are evaluated – can innovative activities be considered. Patents may be used as a proxy for innovative capability and routinized production. The data types selected for further analysis are well-founded in urban theory. Property values are a strong indicator of alternative and competing uses for urban space (Anas et al. 1998, Bettencourt 2013, Kim and Park 2019). Transport networks at the local and regional scales are crucial for urban mixing (Holl 2004, Bettencourt 2013). Urban green space is associated with high-value land, and an important component of desirable urban density and the operation of the urban nexus (Tyrvainen 1997, Tyrvainen and Miettinen 2000, Kong et al. 2007, Landry and Chakraborty 2009, Scott and Storper 2015). Patents are a critical component of understanding urban innovation (Aghion and Howitt 1992, Almeida and Kogut 1999). Sectoral diversity and density are critical components of urban agglomeration (Harrison et al. 1996, Duranton and Puga 2000, Duranton and Puga 2005). The density of local employment is structured by economic organization, and provides an indicator of the efficient operation of local labour markets (Ciccone and Hall 1996, Scott and Storper 2003, Goswami and Lall 2019). Together these variables include necessary covariates for the research, while creating a complete portrait of the urban fabric. Taken collectively these data sources enable a variety of place-based as well as technology-driven strategies. These strategies are discussed more fully below.

3.4.2  Place-Based Strategies A one-size-fits-all approach cannot meet all needs nor handle all urban regions (Mommaas 2004, Audretsch 2015). However, we believe that the organizational approach is now extensively explored, and newer and more comprehensive theoretical perspectives are required (Martin and Sunley 2003, Peck 2005). The research asks which patterns of urban micro-­ agglomeration and accessibility best foster economic growth, inward investment, sustained employment and new firm creation. There are at least two broad strategies for the systematic enhancement of competitiveness in the city (Duncan and Schnore 1959, Audretsch 2015). One of these strategies focuses on enhancing the organizational elements of the city, while the other strategy focuses on enhancing the infrastructure of the city. Example organizational tactics involve growing the creative economy of the city (Florida 2002, Asheim and Hansen 2009, Boschma and Fritsch 2009), and expanding the industrial base of the city (Porter 1998a, Porter 1998b, Porter 2000). Applying an infrastructure-based strategy 52

Innovative Places and Regions

requires the recognition of a fundamental trade-off made between land use and transport (Alonso 1964, Mills 1967, Bertraud 2018). Urban scale and urban density have a positive role to play in enabling urban agglomeration and increasing economic efficiency (Ciccone and Hall 1996, Anas et al. 1998, Armington and Acs 2002). Appropriate infrastructure tactics include the creation of compact urban living opportunities (Lund 2003), the development of local environmental amenities (Walmsley 2006), and the enhancement of transport options that increase connectivity and accessibility (Glaeser and Kohlhase 2004).

3.4.3  Technology-Driven Strategies New research is increasingly demonstrating how technologies are embedded in specific times and places. This is amply demonstrated by the history of science and technology. Nonetheless, modern analyses, using patents as a proxy, demonstrate the spatial and competitive character of new innovation. Patents of particular economic value, and of unusual similarity, are contested with the patent offices. Similar inventions are disproportionately located in the same urban regions. This holds regardless of the urban region. While different technologies are located in different places, the most similar, most contested technologies are located together (Ganguli et al. 2019). This suggests that innovative firms and individuals should adopt a conscious strategy to select regions conducive to their learning, growth and competitiveness. Some individuals are selecting cities with high concentration of urban amenities. Many of these amenities are environmental in character. Such locations are attractive to companies as well, since under spatial equilibrium, the costs of labour are likely to be higher here. This arises since individuals willingly forgo higher salaries to live in more salutary environments. In the United States particular attention is being applied to three large ­Californian cities where inadequate housing is available regardless of the price being paid. This lack of adequate housing results in an innovative deficit with measurable losses to the U.S. economy as a whole. In general a strategy of multicriteria evaluation is a good approach for ­technology-driven firms and individuals to select favourable regions. This approach enables important trade-offs to be made between various requirements for networking, access, markets, and costs in general. Another aspect of this technology-driven strategy should certainly be configurational in character. That is to say that specialized knowledge and specialized assets in science and technology depend on how such assets can be recombined into products, services, platforms and architectures (Arthur 2010). Technologies are neither fixed nor unitary in character. Those individuals best able to participate in the creation of variety are likely to be more successful in the economy as a whole. One particularly vivid decision tool is provided by the Atlas of Economic Complexity (Growth Lab 2020). This Atlas shows, albeit at the national level, areas which are particularly adept in producing products of high economic complexity. The Atlas examines patterns of sectoral growth. A fine-grained analyses may well be possible for recombining individual technologies, components or skills.

3.5  Discussion and Conclusion Machine learning approaches increasing raise concerns because of the unique characteristics of geospatial data (Atluri and Chun 2004), and also because the firms and enterprises indirectly surveyed could be affected. As a result, technology managers must be increasingly concerned with the curation of the data in their care, and the dangers of involuntary 53

Scott W. Cunningham

disclosure of personal or proprietary data. As a result, technology managers in this space of innovation management will be increasingly concerned with the use of probabilistic privacy techniques to ensure the anonymity of database entries (Agrawal and Srikant 2000, Chawla et al. 2005, Aggarwal and Yu 2008, Fung et al. 2010). The data presented will be probabilistic templates (Yin and Huang 2001, Nanda et al. 2002, Grimm et al. 2005) which result from the fusion of hundreds or thousands of spatial examples. Machine learning techniques raise significant concerns of unintentional disclosure. Such techniques have proven capabilities in imputing missing data, enhancing resolution and removing noise. Such techniques do require large datasets. Armed with such datasets these techniques excel at the isolation, identification and recombination of visual and spatial patterns. Such capabilities are a threat to the privacy of local citizens, firms and enterprises. Care must be ventured to maintain the privacy of the firms and enterprises contained in the data. When using these techniques only the generalized patterns of urban performance need be revealed, while specifics of firms and their spatial embedding will remain anonymous. During the writing of this chapter (much of 2020), the coronavirus pandemic is underway across many parts of the world. This pandemic has been consequential in many ways, and may lead to dramatic changes in technology management, as well as practices of urban management. The information and communication technology is long underway, yet the pandemic seems to have been an important focal point for new practices of teamwork and communication. Researchers and policy-makers are working ever harder to identify sources of productivity and growth enabled by communication technology. Remote and distributed work is increasingly prominent in both large and small companies. Despite these trends, the supposed death of the city seems dramatically overstated. The need for real people to convene in dense urban spaces to share tacit knowledge is unlikely to diminish in the near future. Communication technology is not a substitute for proximity. In fact, tantalizing new evidence suggests that communication technology is in fact a complement for dense and innovative regions. The more dense the region, the more often inhabitants make calls and reach out to other individuals in the region. This is largely in keeping with a long tradition of urban psychology that describes the rapid pace of life, and the information overload which characterizes urban inhabitants. Thus, information and communication technology will not flatten the city or its territory. This chapter describes a particular approach to understanding technology, an approach which enthrones cities and regions as the primary locus of innovation. This suggests important new areas for technology management researchers and practitioners, in areas as diverse as planning and infrastructure management, and the spatial analysis of innovation. Technology managers will increasingly be a part of smart cities, and will call upon specific sources of urban innovative data in pursuit of technology management practices.

References Aggarwal, C. C. and P. S. Yu (2008). Privacy-Preserving Data Mining: Models and Algorithms. Berlin, Springer. Aghion, P. and P. Howitt (1992). “A model of growth through creative destruction.” Econometrica 60(2): 323–351. Agrawal, R. and R. Srikant (2000). “Privacy-preserving data mining.” Sigmod Record 29(2): 439–450. Ahlfeldt, G. M. and N. Wendland (2013). “How polycentric is a monocentric city? Centers, spillovers and hysteresis.” Journal of Economic Geography 13(1): 53–83. Almeida, P. and B. Kogut (1999). “Localization of knowledge and the mobility of engineers in regional networks.” Management Science 45(7): 905–917.

54

Innovative Places and Regions Alonso, W. (1964). Location and Land Use: Toward a General Theory of Land Rent. London, Oxford University Press. Anas, A., R. Arnott and K. A. Small (1998). “Urban spatial structure.” Journal of Economic Literature 36(3): 1426–1464. Armington, C. and Z. J. Acs (2002). “The determinants of regional variation in new firm formation.” Regional Studies 36(1): 33–45. Arthur, W. B. (2010). The Nature of Technology: What It Is and How it Evolves. New York Penguin Books. Asheim, B. and H. K. Hansen (2009). “Knowledge bases, talents, and contexts: On the usefulness of the creative class approach in Sweden.” Economic Geography 85(4): 425–442. Atluri, V. and S. A. Chun (2004). “An authorization model for geospatial data.” IEEE Transactions on Dependable and Secure Computing 1(4): 238–254. Audretsch, D. B. (2015). Everything in its Place: Entrepreneurship and the Strategic Management of Cities, Regions, and States. Oxford, Oxford University Press. Barthelemy, M. (2011). “Spatial networks.” Physics Reports-Review Section of Physics Letters 499(1–3): 1–101. Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis and Y. Portugali (2012). “Smart cities of the future.” European Physical Journal-Special Topics 214(1): 481–518. Bertraud, A. (2018). Order without Design. Cambridge, MA, The MIT Press. Besag, J. (1986). “On the statistical-analysis of dirty pictures.” Journal of the Royal Statistical Society Series B-Methodological 48(3): 259–302. Besag, J. and P. J. Green (1993). “Spatial statistics and Bayesian computation.” Journal of the Royal Statistical Society Series B-Methodological 55(1): 25–37. Bettencourt, L. M. A. (2013). “The origins of scaling in cities.” Science 340(6139): 1438–1441. Bettencourt, L. M. A., J. Lobo, D. Helbing, C. Kuhnert and G. B. West (2007). “Growth, innovation, scaling, and the pace of life in cities.” Proceedings of the National Academy of Sciences of the United States of America 104(17): 7301–7306. Blondel, V. D., A. Decuyper and G. Krings (2015). “A survey of results on mobile phone datasets analysis.” EPJ Data Science 4(1): 55. Boeing, G. (2020). “A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood.” Environment and Planning B-Urban Analytics and City Science 47(4): 590–608. Boschma, R. A. and M. Fritsch (2009). “Creative class and regional growth: Empirical evidence from seven European countries.” Economic Geography 85(4): 391–423. Candy, L. (1997). “Computers and creativity support: Knowledge, visualisation and collaboration.” Knowledge-Based Systems 10(1): 3–13. Carpenter, B., A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, A. Riddell, J. Q. Guo, P. Li and A. Riddell (2017). “Stan: A probabilistic programming language.” Journal of Statistical Software 76(1): 1–29. Chawla, S., C. Dwork, F. McSherry, A. Smith and H. Wee (2005). Toward privacy in public databases. In J. Kilian (Ed.), Theory of Cryptography, Proceedings. Berlin, Springer-Verlag Berlin. 3378: 363–385. Ciccone, A. and R. E. Hall (1996). “Productivity and the density of economic activity.” American Economic Review 86(1): 54–70. Cooke, P., M. G. Uranga and G. Etxebarria (1997). “Regional innovation systems: Institutional and organisational dimensions.” Research Policy 26(4–5): 475–491. Cross, G. R. and A. K. Jain (1983). “Markov random field texture models.” IEEE Transactions on Pattern Analysis and Machine Intelligence 5(1): 25–39. Department for Transport. (2019). “Transforming Cities Fund: Tranche 2 supplementary guidance for shortlisted city regions.” from https://www.gov.uk/government/publications/apply-for-thetransforming-cities-fund/transforming-cities-fund-supplementary-guidance-for- shortlisted-cityregions-tranche-2. Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York, Basic Books. Duncan, O. D. and L. F. Schnore (1959). “Cultural, behavioral, and ecological perspectives in the study of social-organization.” American Journal of Sociology 65(2): 132–146.

55

Scott W. Cunningham Duranton, G. and D. Puga (2000). “Diversity and specialisation in cities: Why, where and when does it matter?” Urban Studies 37(3): 533–555. Duranton, G. and D. Puga (2005). “From sectoral to functional urban specialisation.” Journal of Urban Economics 57(2): 343–370. Duranton, G. and D. Puga (2014). The growth of cities. In P. Aghion and S. N. Durlauf (Eds.), Handbook of Economic Growth, Vols 2a and 2b. Amsterdam, Elsevier North Holland: 781–853. Duranton, G. and D. Puga (2020). “The economics of urban density.” Journal of Economic Perspectives 34(3): 3–26. Feldman, M. P. and D. B. Audretsch (1999). “Innovation in cities: Science-based diversity, specialization and localized competition.” European Economic Review 43(2): 409–429. Florida, R. (2002). The Rise of the Creative Class: And How It’s Transforming Work. New York, Basic Books. Florida, R., P. Adler and C. Mellander (2017). “The city as innovation machine.” Regional Studies 51(1): 86–96. Florida, R., C. Mellander and T. Gulden (2012). “Global metropolis: Assessing economic activity in urban centers based on nighttime satellite images.” Professional Geographer 64(2): 178–187. Fung, B. C. M., K. Wang, R. Chen and P. S. Yu (2010). “Privacy-preserving data publishing: A survey of recent developments.” ACM Computing Surveys 42(4): 53. Ganguli, I., J. Lin and N. Reynolds (2019). “The paper trail of knowledge spillovers: Evidence from patent interferences.” American Economic Journal: Applied Economics 12(2): 278–302. Geman, S. and D. Geman (1984). “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6): 721–741. Ghahramani, Z. (2015). “Probabilistic machine learning and artificial intelligence.” Nature 521(7553): 452–459. Glaeser, E. L. (1998). “Are cities dying?” Journal of Economic Perspectives 12(2): 139–160. Glaeser, E. L., W. R. Kerr and G. A. M. Ponzetto (2010). “Clusters of entrepreneurship.” Journal of Urban Economics 67(1): 150–168. Glaeser, E. L. and J. E. Kohlhase (2004). “Cities, regions and the decline of transport costs.” Papers in Regional Science 83(1): 197–228. Goswami, A. G. and S. V. Lall (2019). “Jobs and land use within cities: A survey of theory, evidence, and policy.” World Bank Research Observer 34(2): 198–238. Grace, J. B., D. R. Schoolmaster, G. R. Guntenspergen, A. M. Little, B. R. Mitchell, K. M. Miller and E. W. Schweiger (2012). “Guidelines for a graph-theoretic implementation of structural equation modeling.” Ecosphere 3(8): 44. Grimm, V., E. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H. H. Thulke, J. Weiner, T. Wiegand and D. L. DeAngelis (2005). “Pattern-oriented modeling of agent-based complex systems: Lessons from ecology.” Science 310(5750): 987–991. Growth Lab. (2020). “Atlas of economic complexity,” from https://atlas.cid.harvard.edu/. Harrison, B., M. R. Kelley and J. Gant (1996). “Innovative firm behavior and local milieu: Exploring the intersection of agglomeration, firm effects, and technological change.” Economic Geography 72(3): 233–258. Heath, J. (2020). The Machinery of Government: Public Administration and the Liberal State. Oxford, Oxford University Press. Heider, B. and S. Siedentop (2020). “Employment suburbanization in the 21st century: A comparison of German and US city regions.” Cities 104: 14. Holl, A. (2004). “Transport infrastructure, agglomeration economies, and firm birth: Empirical evidence from Portugal.” Journal of Regional Science 44(4): 693–712. House of Commons Library (2020b). City Deals. Innes, J. E. and D. E. Booher (1999). “Consensus building and complex adaptive systems - A framework for evaluating collaborative planning.” Journal of the American Planning Association 65(4): 412–423. Jacobs, J. (1969). The Economy of Cities. New York, Vintage. Jin, X. P., B. P. Carlin and S. Banerjee (2005). “Generalized hierarchical multivariate CAR models for areal data.” Biometrics 61(4): 950–961. Jordan, M. I., Z. Ghahramani, T. S. Jaakkola and L. K. Saul (1999). “An introduction to variational methods for graphical models.” Machine Learning 37(2): 183–233. Karantzalos, K. (2015). Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data. New York, Springer.

56

Innovative Places and Regions Kim, S. and S. Park (2019). “Are spending patterns of local government interdependent?: Strategic interactions of US local governments in California.” Lex Localis-Journal of Local Self-Government 17(1): 121–137. Knowles, R. D. and F. Ferbrache (2016). “Evaluation of wider economic impacts of light rail investment on cities.” Journal of Transport Geography 54: 430–439. Koller, D. and N. Friedman (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA, MIT Press. Kong, F. H., H. W. Yin and N. Nakagoshi (2007). “Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China.” Landscape and Urban Planning 79(3–4): 240–252. Kschischang, F. R., B. J. Frey and H. A. Loeliger (2001). “Factor graphs and the sum-product algorithm.” IEEE Transactions on Information Theory 47(2): 498–519. Kuffer, M., K. Pfeffer and R. Sliuzas (2016). “Slums from Space15 years of slum mapping using remote sensing.” Remote Sensing 8(6): 29. Landry, S. M. and J. Chakraborty (2009). “Street trees and equity: Evaluating the spatial distribution of an urban amenity.” Environment and Planning a-Economy and Space 41(11): 2651–2670. LeCun, Y., Y. Bengio and G. Hinton (2015). “Deep learning.” Nature 521(7553): 436–444. Li, C., M. Wand and IEEE (2016). Combining Markov random fields and convolutional neural networks for image synthesis. 2016 IEEE Conference on Computer Vision and Pattern Recognition. New York, IEEE: 2479–2486. Lund, H. (2003). “Testing the claims of new urbanism - Local access, pedestrian travel, and neighboring behaviors.” Journal of the American Planning Association 69(4): 414–429. Marshall, A. (1920). Industry and Trade. London, MacMillan. Martin, R. and P. Sunley (2003). “Deconstructing clusters: Chaotic concept or policy panacea?” Journal of Economic Geography 3(1): 5–35. Marx, K. (1867/2012). Das Kapital: A Critique of Political Economy. New York, Regnery. Mills, E. S. (1967). “An aggregative model of resource allocation in a metropolitan area.” American Economic Review 57(2): 197–210. Mommaas, H. (2004). “Cultural clusters and the post-industrial city: Towards the remapping of urban cultural policy.” Urban Studies 41(3): 507–532. Nanda, H., L. Davis, Inria and Inria (2002). Probabilistic Template Based Pedestrian Detection in Infrared Videos. New York, IEEE. Nickel, M., K. Murphy, V. Tresp and E. Gabrilovich (2016). “A review of relational machine learning for knowledge graphs.” Proceedings of the IEEE 104(1): 11–33. Pal, N. R. and S. K. Pal (1993). “A review on image segmentation techniques.” Pattern Recognition 26(9): 1277–1294. Peck, J. (2005). “Struggling with the creative class.” International Journal of Urban and Regional Research 29(4): 740–+. Ponzini, D. and U. Rossi (2010). “Becoming a creative city: The entrepreneurial mayor, network politics and the promise of an urban renaissance.” Urban Studies 47(5): 1037–1057. Porta, S., P. Crucitti and V. Latora (2006). “The network analysis of urban streets: A dual approach.” Physica a-Statistical Mechanics and Its Applications 369(2): 853–866. Porter, M. E. (1998a). “Clusters and the new economics of competition.” Harvard Business Review 76(6): 77–91. Porter, M. E. (1998b). The Competitive Advantage of Nations: With a New Introduction. New York, Free Press. Porter, M. E. (2000). “Location, competition, and economic development: Local clusters in a global economy.” Economic Development Quarterly 14(1): 15–34. Proost, S. and J. F. Thisse (2019). “What can be learned from spatial economics?” Journal of Economic Literature 57(3): 575–643. Roth, S. and M. J. Black (2009). “Fields of experts.” International Journal of Computer Vision 82(2): 205–229. Roweis, S. and Z. Ghahramani (1999). “A unifying review of linear gaussian models.” Neural Computation 11(2): 305–345. Sain, S. R. and N. Cressie (2007). “A spatial model for multivariate lattice data.” Journal of Econometrics 140(1): 226–259. Sain, S. R., D. Nychka and L. Mearns (2011). “Functional ANOVA and regional climate experiments: A statistical analysis of dynamic downscaling.” Environmetrics 22(6): 700–711.

57

Scott W. Cunningham Schumpeter, J. A. (1934a). Capitalism, Socialism, and Democracy. London, Allen & Unwin. Schumpeter, J. A. (1934b). The Theory of Economic Development: Aninquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Cambridge, MA, Harvard University Press. Scott, A. J. (2006). “Creative cities: Conceptual issues and policy questions.” Journal of Urban Affairs 28(1): 1–17. Scott, A. J. and M. Storper (2003). “Regions, globalization, development.” Regional Studies 37(6–7): 579–593. Scott, A. J. and M. Storper (2015). “The nature of cities: The scope and limits of urban theory.” International Journal of Urban and Regional Research 39(1): 1–15. Shipan, C. R. and C. Volden (2008). “The mechanisms of policy diffusion.” American Journal of Political Science 52(4): 840–857. Swyngedouw, E. (2005). “Governance innovation and the citizen: The Janus face of governancebeyond-the-state.” Urban Studies 42(11): 1991–2006. Szegedy, C., W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich and IEEE (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition. New York, IEEE: 1–9. Thakuriah, P., N. Tilahun and M. Zellner, Eds. (2018). Seeing Cities through Big Data: Research, Methods and Applications. Berlin, Springer. Tyrvainen, L. (1997). “The amenity value of the urban forest: An application of the hedonic pricing method.” Landscape and Urban Planning 37(3–4): 211–222. Tyrvainen, L. and A. Miettinen (2000). “Property prices and urban forest amenities.” Journal of Environmental Economics and Management 39(2): 205–223. Visvizi, A. and M. D. Lytras (2018). “Rescaling and refocusing smart cities research: From mega cities to smart villages.” Journal of Science and Technology Policy Management 9(2): 134–145. Visvizi, A., M. D. Lytras, E. Damiani and H. Mathkour (2018). “Policy making for smart cities: Innovation and social inclusive economic growth for sustainability.” Journal of Science and Technology Policy Management 9(2): 126–133. Wallerstein, N. and B. Duran (2010). “Community-based participatory research contributions to intervention research: The intersection of science and practice to improve health equity.” American Journal of Public Health 100: S40–S46. Walmsley, A. (2006). “Greenways: Multiplying and diversifying in the 21st century.” Landscape and Urban Planning 76(1–4): 252–290. Wang, K. and J. V. Nickerson (2017). “A literature review on individual creativity support systems.” Computers in Human Behavior 74: 139–151. Wang, X. G. and X. O. Tang (2009). “Face photo-sketch synthesis and recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence 31(11): 1955–1967. Wolch, J. R., J. Byrne and J. P. Newell (2014). “Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’.” Landscape and Urban Planning 125: 234–244. Yin, P. Y. and Y. B. Huang (2001). “Automating data extraction and identification on Chinese road maps.” Optical Engineering 40(5): 663–673.

58

4 IT-DRIVEN SERVICE INNOVATION Magdalena Marczewska and Marzenna Anna Weresa

4.1 Introduction In the era of digital revolution, the focus of innovation processes moves toward services, which become to a greater extent complementary to manufacturing or integrated with manufacturing. As servitization has become a new characteristic of innovation and the boundaries between services and manufacturing have been blurring, service innovation becomes increasingly important across industries. Therefore, there is a need to develop a framework that can be useful for both theory development and management practice. Over the years the nature of innovation changed as innovations are becoming strongly based on data and dependent on collaboration in new forms such as data sharing, crowdsourcing and partnerships. Faster innovation cycles involve experimentation and personalization (OECD, 2019, pp. 26–35). Innovation in services includes a huge intangible component and is strongly driven by information technology (IT) and digitalization (Plattfaut et al., 2015). Therefore, the nature of innovation in services differs from that in other sectors as IT creates a new techno-economic paradigm (Gallouj and Savona, 2010; Alarcóna, Aguilarb and Galánca, 2019; OECD, 2019). Furthermore, services become more research intensive, and more tradable than in the past. These changes also have an impact on innovation processes in the service sector and, in general, service innovations in other sectors. The aim of this chapter is to explain how IT impacts innovation in services and identify mechanisms and models of IT-driven service innovation. Furthermore, the chapter shows some examples of service innovations in order to explain how IT adoption influences their development. The chapter is structured as follows. The next section offers a literature review on the nature of innovations in services. It is followed by a section describing the driving role of IT in service innovation development. Then the next subsection focuses on managing IT-driven service innovation and presents a conceptual framework explaining the interrelationships between IT, organizational challenges and service innovation development. Theoretical and conceptual considerations are illustrated by some examples of innovations induced by information technology in services. The chapter ends with conclusions and implications for management practice.

DOI: 10.4324/9781003046899-6

59

Magdalena Marczewska and Marzenna Anna Weresa

4.2  The Nature of Service Innovation Service innovation is not a homogeneous research field, and the literature offers many different definitions and typologies related to this subject. Innovation in services is regarded as a multidimensional phenomenon. One possible approach is to define it using supply or demand perspective, which leads to distinguishing two terms referring to innovation in service sector: innovation in services and service innovation. The first term is related to a supply approach and means the innovative change in service companies or service activities performed in any sector. The latter looks at innovation from a demand perspective and refers to innovation introduced in companies and other organizations that use innovative services (Gago and Rubalcaba, 2007, p. 26; Barcet, 2010, p. 51; UNECE, 2011, p. 7). However, this seems to be a rather theoretical distinction, as many authors use these terms interchangeable. These two modes of innovation in services can be extended by adding another perspective, which conceptualizes innovation in services according to the nature of innovation and distinguishes the following approaches: • •

• •

the assimilation (technologist), which focuses on technology adoption and use; differentiation (demarcation), which highlights the specificities in services resulting in different forms of service innovation as well as specific organization of innovation processes in service industries; inversion, which looks at some service innovations as a source of change in other sectors of the economy; integrative, which synthesizes above-mentioned approaches assuming that goods and services become more and more integrated, and therefore, the same definition of innovation can be applied to both types of activity (Gallouj and Savona, 2009; Djellal1, Gallouj and Miles, 2013).

The integrative approach offers the functional perspective based on the assumption that what customers look for is satisfaction of their needs, no matter whether they use goods or services (Alarcóna, Aguilarb and Galánca, 2019, p. 230). Thus, service innovation is defined as a process (not a result), and in this context six models of service innovation based on their characteristics have been identified: radical, improvement, incremental, ad hoc, re-­ combinative, and formalization innovation (Gallouj and Savona, 2009). This typology can be useful to understand and explain traditional patterns of service innovation, but it seems that it does not fully cover future directions of changes related to the rapid development of digital technologies and their role in value co-creation by both service and manufacturing that become more and more integrated. As Ostrom et al. (2015, p. 131) observed, traditional understanding of service innovation has focused on new services or the service process, but in the rapidly changing world there is a need to go beyond this perspective and investigate how various types of innovations in services, manufacturing and digital domains interact (service product, service process and business model). Some recent service research goes beyond traditional output- and process-based service innovation models. Helkkula, Kowalkowski and Tronvoll (2018) proposed a typology of four theoretical types of service innovation distinguishing output-based, process-based, experiential and systemic service innovation, and provided examples from business practice of each of them. Traditional output-based and process-based service innovations, meaning respectively new service offerings and new service development, have been enriched by including the active

60

IT-Driven Service Innovation

role of customers in creation of these new services. The third – experiential – archetype of service innovation has been distinguished drawing on Rubalcaba et al. (2012) works, who analyzed how users experience improved value co-creation in service innovations. Thus, experiential service innovation focuses on improved customer value co-created through individual experiences (Helkkula, Kowalkowski and Tronvoll, 2018, p. 288). Systemic archetype of service innovation offers a broader holistic view related to service ecosystem, which links resources, actors and institutional arrangements (Edvardsson and Tronvoll, 2013). The scope of service innovation is extended by including the whole environment (regulations, norms, etc.) where innovations emerge with the focus on resource integration by various actors (Helkkula, Kowalkowski and Tronvoll, 2018, p. 289). Thus, a social component and value creation context have been added to the definition of service innovation. Although the four archetypes differ, they are interrelated, and in many cases service innovations emerge as a set of these four archetypes, which complement to each other (see Box 4.1). Kowalkowski and Witell (2020) examined different definitions and typologies of service innovation and came to the conclusion that multidimensional view on service innovation offered by these four archetypes may be useful to better understand this complex phenomenon. It has been recently extended by Heinone and Strandvik (2021), who made an attempt to reframe service innovation by adding to its definition disruptive contexts related to COVID-19 pandemic. As a result, the concept of imposed service innovation has been introduced. The authors found that the main features of this new solution induced by COVID-19 pandemic and implemented in services are its technology-driven nature, focus on social and health issues, and flexibility. The IT component can be added to this perspective as the current pandemic forced businesses and societies to digitize rapidly. To sum up the overview of various definitions of service innovation and its taxonomies, it can be concluded that the synthesis perspective unifying technological and social elements of service innovation allows describing this notion in a comprehensive way and seems to be the most appropriate for studying the IT role in inducing and shaping innovative solutions in services.

BOX 4.1 FOUR ARCHETYPES OF SERVICE INNOVATION – THE EXAMPLE OF TRIPADVISOR TripAdvisor Inc., an American online travel company founded in 2000 with headquarters in Needham (Massachusetts, USA), offers online free interactive website and mobile app ­(output-base type of service innovation), which contains user-generated content enabling to reduce transaction costs and risks (process-based service innovation). Information and highlights about various travel destinations, hotels, tourist attractions, etc., shared by travelers with potential new customers, allow improving customers’ experience about managing traveling (experiential innovation), while centralized online reservations of hotels, transportation, lodging, entertainments, sights and restaurants supported by offering access to other users’ reviews (systemic innovation) bring new value for customers. Source: Authors’ elaboration based on Helkkula, Kowalkowski and Tronvoll, 2018, p. 288.

61

Magdalena Marczewska and Marzenna Anna Weresa

4.3  IT as a Driver of Innovation in Services Innovation in services depends on acquisition of technologies or non-technological knowledge from outside sources through purchases of equipment or intellectual property, as well as collaboration, education and training. In a digital age innovation has been increasingly based on IT and data; innovation cycles are faster and more focused on personalization and experimentation (OECD, 2019, pp. 26–35). IT development and use is associated with growing digitalization, which is about connecting people, information and machines. From business practice perspective, IT should be a way to create value through the emergence of new business models. It also changes the way companies innovate and communicate with the market. The OECD (2013, p. 18) defines digitalization in business as value added, which can be generated by activities related to the Internet usage (e.g. network services, e-commerce) as well as the development of the Internet (e.g. manufacture of broadband equipment). In the context of service innovation, the question is about the role of IT in the process of creating and diffusing service innovations and the ways they impact value creation, value delivery and value capturing practices. Admitting the growing role of information technology in firms’ capabilities to innovate (Plattfaut et al., 2015; Storey et al., 2015; Benitez, Llorens and Baraojos, 2018), some scholars divide innovation drivers into two broad categories: information technologies and Internet in particular, which results in e-innovations and other factors embodied in people that bring p-innovations (Dotzel et al., 2013; Snyder et al., 2016). Gago and Rubalcaba (2007, p. 27) referring to multidimensional perspective of service innovation distinguished three different roles of information technologies in the innovation process: • • •

a driving force of services and processes related to ICT adoption; facilitator of remote provisions of services; agent for the use of innovative services.

This view has been confirmed by other studies concerning IT and digitalization. These studies admit that IT is a driving force of innovation in services and perceive a dual role of IT in innovation processes: an enabler and an initiator. These roles change activity of companies and organizations (Lusch and Nambisan, 2015; Plattfaut et al., 2015) and enable value creation (Vargo and Lusch, 2011). An example of innovation in healthcare service (Box 4.2) nicely illustrates these roles.

BOX 4.2  THE IT AS A SERVICE INNOVATION DRIVER – AN EXAMPLE OF HEALTHCARE SERVICES There are several ways where IT can drive innovation in healthcare services. It is extremely important in the time of huge challenges that healthcare systems have to face due to aging population, increasing percentage of patients with multiple chronic diseases, coupled with shortages in workforce supply and skills, as well as new threats like COVID-19. Artificial intelligence (AI), i.e. computing framework and algorithms that can perform tasks associated with human intelligence (speech recognition, visual perception, decision making, etc.) as an innovation

62

IT-Driven Service Innovation

driver in health services, has a profound potential. AI can be used in many areas of healthcare services from diagnostics and treatment to healthcare administration with the growing number of potential applications. AI performing medical service for patients instead of human medical staff means IT adoption and is a service innovation itself (performing an initiator’s role). At the same time it acts as an agent for the use of innovative services (being an enabler). The use of AI in health care allows us to reduce avoidable medical errors, decrease inequalities in health care and increase efficiency of healthcare services (OECD, 2017), and thus create value for companies and societies. The use of Internet and social media by healthcare organizations allows healthcare organizations to offer new remote customer service (direct-to-consumer or on-demand telemedicine) (Housen, 2013). In the current pandemic, it allows physicians and patients to communicate using webcam-enabled computers or smartphones and it offers efficient health screening and monitoring (Hollander and Carr, 2020). Telehealth also speeds up emergency response and supply of clinical services when local hospitals are unable to meet demand, at the same time decreasing the risk of communicable diseases which are transmitted by personal contact. Thus, IT acts as a facilitator of remote provisions of services (enabler) helping to create value for both people (patients and medical staff ) and organizations (e.g. hospitals, clinics, nursing homes). Source: Authors’ elaboration based on Housen, 2013; Lusch and Nambisan, 2015; Plattfaut et al., 2015; OECD, 2017; Hollander and Carr, 2020.

The growing role of IT in driving service innovation creates a need to develop a conceptual framework, which will explain the interrelationships between IT and service innovation from management’s perspective.

4.4  Managing IT-Driven Service Innovation – A Conceptual Framework IT-driven service innovations differ from traditional innovations. Because of their complexity, general innovation management models, which are also valid for service innovations (Bessant and Davies, 2007), do not fully capture all different dimensions that influence their development. Thus, many authors investigated innovation processes in services in order to propose alternative models and frameworks relevant to service innovations (e.g. Gallouj and Weinstein, 1997; Sundbo and Gallouj, 2000; Windrum and García-Goñi, 2008; Toivonen, 2010). Recently, an increasing number of companies develop their portfolios or improve their processes, thanks to IT-driven service innovations (Neubert, Dominguez and Ageron, 2011; Benitez, Llorens and Baraojos, 2018). However, new technologies do not guarantee competitiveness and market success. These, in order to be beneficial, need to be supported by an appropriate business model (Chesbrough and Spohrer, 2006; El Sawy and Pereira, 2013; Teece and Linden, 2017). Moreover, in many cases, additional organizational transformation supporting alignment and fit between corporate strategy and information technology/ information systems is necessary to gain from these new IT-related pathways of development (Bush, 2009; Benitez, Llorens and Baraojos, 2018). The Internet changed traditional understanding of “industries” that is now becoming outdated, as digitalization and networking drive and facilitate convergence of various formerly separate streams of activity such as banking, advertising, IT, broadcasting and insurance (Teece and Linden, 2017). One way for companies to utilize opportunities arising from these changes is to understand different 63

Magdalena Marczewska and Marzenna Anna Weresa

possible pathways of service innovation management, assess their organization’s capabilities, as well as plan and structure their efforts in continuous and systematic development of ITdriven service innovations. As described in the previous section, IT has different faces, including Internet of things, artificial intelligence, big data, remote monitoring, smart factory or cloud computing (Parida, Sjödin and Reim, 2019). Data (including big data and the data from embedded systems that monitor the physical world) as one of the IT-related drivers of service innovations facilitates to optimize processes, search for new customers, define new market segments and develop new business models. Schuritz and Satzger (2016) analyzed over 100 case studies and described the following five patterns of data-infused business model innovation (listed from most to least common): (1) data-infused value creation (most common), (2) data-infused value proposition via creation, (3) data-infused value capturing, (4) new data-infused business model and (5) data-infused value proposition via capturing. Lin et al. (2010) performed a business model analysis of telecardiology service managed by a hospital in Taiwan and their main findings connect innovative, IT-driven service success with a sustainable business model that offers mutual benefits for both users and providers. (An example of IT-driven innovations in healthcare presented in Box 4.2 explains benefits that IT can bring to both patients and healthcare organizations.) An empirical study by Kitsuta and Quadros (2017) focused on main innovation management practices used by Brazilian IT service companies and identified three innovation management models that are appropriate to service innovations: (1) projects separated from practice model, (2) rapid application model (compressed or ­ hinese experiential), (3) practice-driven model. Wang et al. (2019) based on evidence of C enterprises proposed a framework for service innovations composed of actor-to-actor networks, value proposition, resource integration, institutionalization process and IT, and argue that the latter one is an essential element, as it serves as a carrier of other elements and, most importantly, boosts and triggers service innovations. Tsou and Cheng (2018) proved that organizational agility supported by IT capability and organizational learning is key to enhance IT B2B service innovation. Sjödin et al. (2020) complement these conclusions arguing that companies should adopt agile co-creation process supplemented by micro-service innovation approach allowing greater speed and risk minimalization of innovation processes, thanks to short iterations combined with co-creative customers’ engagement. Kohtamäki et al. (2019) distinguished business models relevant to digital servitization (understood as transition toward smart product-service-software systems) based on the characteristics combination of the following three dimensions: solution customization (standardized, modular, customized), pricing (product-, agreement-, availability-, outcome-oriented pricing) and digitalization (monitoring, control, optimization or autonomous). These models are (1) product-oriented service provider (model of companies that provide products and add-on services), (2) industrializer (model of companies willing to provide modular but customized solutions), (3) customized integrated solution provider (model of companies providing integrated product-service solutions), (4) platform provider (model of companies connecting various providers and customers with the use of IT solutions) and (5) outcome provider (model of companies that sell value created by the solution, instead of the solution). A conceptual framework of IT-driven service innovation development can be outlined based on an analysis of business model components describing how companies create, deliver and capture value (value creation, value delivery/proposition, value capturing) (Osterwalder and Pigneur, 2010; Schön, 2012; Parida, Sjödin and Reim, 2019; Paiola and Gebauer, 2020). Such approach can be justified by a contemporary shift of companies’ sales focus from products to outcome-based services (Visnjic et al., 2017; Sjödin et al., 2020) and servitization 64

65

Source: Authors’ elaboration based on Lin et al., 2010; Dijkman et al., 2015; Cenamor, Rönnberg Sjödin and Parida, 2017; Dellermann, Fliaster and Kolloch, 2017; Kitsuta and Quadros, 2017; Hasselblatt et al., 2018; Helkkula, Kowalkowski and Tronvoll, 2018; ­Kotarba, 2018; Kuula, Haapasalo and Tolonen, 2018; Metallo et al., 2018; Tsou and Cheng, 2018; Parida, Sjödin and Reim, 2019; Rachinger et al., 2019; Wang et al., 2019; Sjödin et al., 2020.

Figure 4.1  A conceptual framework of IT-driven service innovation management

IT-Driven Service Innovation

Magdalena Marczewska and Marzenna Anna Weresa

(Baines et al., 2017; Tauqeer and Bang, 2018), including digital servitization (Kohtamäki et al., 2019; Sjödin et al., 2020; Gebauer et al., 2021). These days the world develops faster than ever before, and becomes strongly dependent on data, intersectoral collaborations and technologies, especially information technology (see Figure 4.1). Numerous linkages between innovativeness, activities of companies and their networks, fueled by information technologies and digitalization, enable their fast growth and development. However, identifying and implementing relevant technologies in organizational ecosystem is not enough to innovate and benefit from new value-­producing opportunities. Multi-level organizational transformation aimed at aligning corporate strategy to IT systems (e.g. related to adjusting organizational processes, introducing novel management systems, implementing different organizational structures or focusing on organizational learning) seems to be necessary to create, deliver and capture value related to innovations’ development in these challenging times. Companies need to be aware of arising possibilities and their capabilities in addressing challenges related to designing a desirable value of their offerings, delivering it through means at least addressing or even surpassing customer’s expectations (based on their knowledge and networks) and creating profit, thanks to their business abilities (e.g. by introducing micro-service innovation approach). To illustrate this conceptual framework, we use an example of FinTech (Box 4.3).

BOX 4.3 FINANCIAL SERVICE INNOVATION (FINTECH) – NEW OPPORTUNITIES IN BUSINESS PRACTICE Innovation in the financial sector known under the name of FinTech is driven by multiple information technologies, in particular application programming interfaces, artificial intelligence (AI), cloud computing, machine learning, distributed ledger technology, ­biometric-based identification, authentication, etc. FinTech brings changes in the way that financial institutions and their customers arrange payments, save, invest and perform other activities, i.e. do business. These activities include, but are not limited to, digital banking, capital markets’ investment insurance and wealth management (Ehrentraud et al., 2020). When employing multidimensional typology of service innovation and the role of IT in driving them explained in previous sections of this chapter, it can be observed that a variety of FinTech solutions can serve as nice examples of different service innovation types. Mobile banking is an output-based type of service innovation, with IT role as initiator of innovation processes. The use of cloud computing in processing financial data is a process-based service innovation, with IT acting as an enabler. Automated investment advice offered by AI usage brings customers new experience about managing their financial assets (experiential innovation, with enabling role of IT), while a centralized clearing and settlement system, which makes transition processes simpler and safer, is an example of systemic innovation, with both initiating and enabling roles of IT. As Boratyńska (2019) proved, digital technologies in financial services initiated new ways of value creation and value appropriation. How does it happen? An application of a conceptual

66

IT-Driven Service Innovation

framework to FinTech solutions introduced in Figure 4.1 allows us to identify and better understand three components important for managing IT-driven service innovation: value creation, value delivery and capturing value. Application programming interfaces facilitate access to customers’ payments and information about accounts in a secure way for mutual benefits of both users and providers (value creation). Artificial intelligence and machine learning applications offer automated digital­­a lgorithm-driven financial advice (robo-advice) based on clients’ information about their financial situation, their needs and their future goals, and can automatically invest their assets (value creation). It can also provide credit scoring and allow high-frequency trading (value delivery). Cloud computing is used for storage, processing and management of big data in an efficient and flexible way, thus adjusting operational processes in providing financial services (value delivery). The main function of biometric-based identification data in financial services is to conduct sound customer authentication in mobile banking applications, which reduces risk of using the digital banking system and helps in risk management (value creation and value capturing). Distributed ledger technology is employed to reduce settlement risk, increase efficiency and lower costs of financial operations (value capturing). Source: Authors’ elaboration based on literature cited in Figure 4.1 and Boratyńska, 2019; Ehrentraud et al., 2020.

4.5 Conclusion The chapter focuses on service innovation seen in the context of IT development and implementation. The analysis of previous studies allows us to gain deeper understanding of how IT can be integrated with services and influences the emergence of service innovation. There are two basic roles that IT plays in driving service innovations: initiator and/or enabler of new solutions (Lusch and Nambisan, 2015; Plattfaut et al., 2015). These lead to four different types of service innovations: output-based, process-based, experiential and systemic (Helkkula, Kowalkowski and Tronvoll, 2018). Adding to this perspective a lens of external shocks like COVID-19 pandemic, it can be observed that some innovations can be imposed by unforeseen disruptions in order to transform service offering and business model taking into account the constraints of resource availability and changes in environment (Heinonen and Strandvik, 2021). Synthesizing these approaches, it can be concluded that to describe ITdriven service innovation in a comprehensive way, both technological and social elements have to be integrated. This conclusion leads to the proposition of a framework for managing IT-driven service innovation, which is based on and inspired by the theories related to business models and contains three components centered at the new value, i.e. value creation, delivery and capturing. This triple-value-centered approach has some implications for managing IT-driven service innovations. First, the development processes of IT-driven service innovations seem to be more complex compared to other innovative solutions. Therefore, tacit knowledge from different disciplines needs to be properly integrated in order to create

67

Magdalena Marczewska and Marzenna Anna Weresa

mechanisms to manage the vast amounts of information and data and build synergies with existing systems. Such an integration is especially important when it comes to value creation and delivery, which need to be addressed simultaneously by companies’ activities. Second, human (social) factors have to be taken into account in value creation and delivery, and thus, these allow for better alignment with customer’s needs, capabilities of other network actors and possibilities they may create and provide. Finally, as services to high extent have tacit and heterogeneous nature, in creation and implementation of IT-driven service innovation, behavioral aspects related to inconsistency in human performance have to be taken into account. In managing IT-driven service innovation, the focus on developing and applying employees’ and customers’ competences as well as collaboration between all stakeholders seems to be important. Last, but not least, taking into account dynamically changing environment and access to new IT technologies, one should remember that managing IT-driven service innovation is an ongoing process, which needs to be continuously monitored and, if necessary, adjusted to benefit from new opportunities.

References Alarcóna, J.C., Aguilarb, R. and Galánca, J.L. (2019) ‘Determinants of innovation output in Spanish knowledge-intensive service firms: Stability analysis throughout the economic crisis of 2008’, Structural Change and Economic Dynamics, 49, pp. 228–244. doi: 10.1016/j.strueco.2018.10.006. Baines, T., Ziaee Bigdeli, A., Bustinza, O.F., Shi, V.G., Baldwin, J. and Ridgway, K. (2017) ‘Servitization: Revisiting the state-of-the-art and research priorities’, International Journal of Operations & Production Management, 37 (2), pp. 256–78. doi: 10.1108/IJOPM-06–2015–0312. Barcet, A. (2010) ‘Innovation in services: A new paradigm and innovation model’, in Gallouj, F. and Djellal, F. (eds) The Handbook of Innovation and Services: A Multi-disciplinary Perspective, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 49–67. Benitez, J., Llorens, J. and Baraojos, J. (2018) ‘How information technology influences opportunity exploration and exploitation firm’s capabilities’, Information & Management, 55, pp. 508–52. doi: 10.1016/j.im.2018.03.001. Bessant, J. and Davies, A. (2007) ‘Managing service innovation’, Occasional Paper No. 9: London: Innovation in Services, Department of Trade and Industry, pp. 61–96. Boratyńska, K. (2019) ‘Impact of digital transformation on value creation in Fintech services: An innovative approach’, Journal of Promotion Management, 25 (5), pp. 631–639. doi: 10.1080/ 10496491.2019.1585543. Bush, M., Lederer, A.L., Li, X., Palmisano, J. and Rao, S. (2009) ‘The alignment of information systems with organizational objectives and strategies in health care’, International Journal of Medical Informatics, 78, pp. 446–456. doi: 10.1016/j.ijmedinf.2009.02.004. Cenamor, J., Rönnberg Sjödin, D. and Parida, V. (2017) ‘Adopting a platform approach in servitization: Leveraging the value of digitalization’, International Journal of Production Economics, 192, 54–65. doi: 10.1016/j.ijpe.2016.12.033 Chesbrough, H. and Spohrer, J. (2006) ‘A research manifesto for services science’, Communications of the ACM, 49, pp. 35–40. doi: 10.1145/1139922.1139945. Dellermann, D., Fliaster, A. and Kolloch, M. (2017) ‘Innovation risk in digital business models: The German energy sector’, Journal of Business Strategy, 38 (5), pp. 35–43. doi: 10.1108/jbs-07–2016–0078. Dijkman, R.M., Sprenkels, B., Peeters, T. and Janssen, A. (2015) ‘Business models for the Internet of Things’, International Journal of Information Management, 35, pp. 672–678. doi: 10.1016/j. ijinfomgt.2015.07.008. Djellal, F. Gallouj, F. and Miles, J. (2013) ‘Two decades of research on innovation in services: Which place for public services?’ Structural Change and Economic Dynamics, 27, pp. 98–117. doi: 10.1016/j. strueco.2013.06.005. Dotzel, T., Shankar, V. and Berry, L.L. (2013) ‘Service innovativeness and firm value’, Journal of Marketing Research, 50 (2), pp. 259–276. doi: 10.1509/jmr.10.0426. Edvardsson, B. and Tronvoll, B. (2013), ‘A New Conceptualization of Service Innovation Grounded in SD Logic and Service Systems’, International Journal of Quality & Service Sciences, 5 (1), pp. 19–31.

68

IT-Driven Service Innovation Ehrentraud, J., Ocampo, D.G., Garzoni, L. and Piccolo, M. (2020) ‘Policy responses to Fintech: A cross-country overview’, FSI Insights on Policy Implementation No 23, Financial Stability Institute, Bank for International Settlements. El Sawy, O.A. and Pereira, F. (2013) Business Modelling in the Dynamic Digital Space: An Ecosystem Approach. Berlin: Springer. Gago, D. and Rubalcaba, L. (2007) ‘Innovation and ICT in service firms: Towards a multidimensional approach for impact assessment’, Journal of Evolutionary Economics, 17 (1), pp. 25–44. doi: 10.1007/ s00191-006-0030–8. Gallouj, F. and Djellal, F. (eds.) (2010) The Handbook of Innovation and Services: A Multi-disciplinary Perspective, Cheltenham, Northampton, MA: Edward Elgar Publishing. Gallouj, F. and Savona, M. (2009) ‘Innovation in services: A review of the debate and a research agenda’, Journal of Evolutionary Economics, 19, pp. 149–172. doi: 10.1007/s00191-008-0126-4. Gallouj, F. and Savona, M. (2010) ‘Towards a theory of innovation in services: A state of art’, in Gallouj, F. and Djellal, F. (eds.) The Handbook of Innovation and Services: A Multi-disciplinary Perspective, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 27–48. Gallouj, F. and Weinstein, O. (1997) ‘Innovation in services’, Research Policy, 26 (4–5), pp. 537–556. doi:10.1016/s0048–7333(97)00030-9. Gebauer, H., Paiola, M., Saccani, N. and Rapaccini, M. (2021) ‘Digital servitization: Crossing the perspectives of digitization and servitization’, Industrial Marketing Management, 93, pp. 382–388. doi: 10.1016/j.indmarman.2020.05.011. Hasselblatt, M., Huikkola, T., Kohtamäki, M. and Nickell, D. (2018) ‘Modeling manufacturer’s capabilities for the Internet of Things’, Journal of Business & Industrial Marketing, 33, pp. 822–836. doi: 10.1108/jbim-11–2015–0225. Heinonen, K. and Strandvik, T. (2021) ‘Reframing service innovation: COVID-19 as a catalyst for imposed service innovation’, Journal of Service Management, 32 (1), pp. 101–112. doi: 10.1108/ JOSM-05–2020–0161 Helkkula, A., Kowalkowski, C. and Tronvoll, B. (2018) ‘Archetypes of service innovation: Implications for value cocreation’, Journal of Service Research, 21 (3), pp. 284–301. doi: 10.1177/1094670517746776. Hollander, J.E. and Carr, B.G. (2020) ‘Virtually perfect? Telemedicine for Covid-19’, The New England Journal of Medicine, 382, pp. 1679–1681. doi: 10.1056/NEJMp2003539. Housen, M. (2013) ‘The use of social media in healthcare: Organizational, clinical, and patient perspectives’, Studies in Health Technology and Informatics, 183, pp. 244–248. doi: 10.3233/9781-61499-203-5–244. Howells, J. (2010) ‘Services and innovation and service innovation: New theoretical directions’, in Gallouj, F. and Djellal, F. (eds) The Handbook of Innovation and Services: A Multi-disciplinary Perspective, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 68–83. Kitsuta, C.M. and Quadros, R. (2017) ‘Service innovation management models: Planned, iterative and emergent innovations’, 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Technology Management for the Interconnected World, Proceedings, pp. 1–10. doi: 10.23919/PICMET.2017.8125480. Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H. and Baines, T. (2019) ‘Digital servitization business models in ecosystems: A theory of the firm’, Journal of Business Research, 104, pp. 380–392. doi: 10.1016/j.jbusres.2019.06.027. Kotarba, M. (2018) ‘Digital transformation of business models’, Foundations of Management, 10 (1), pp. 123–142. doi: 10.2478/fman-2018-0011. Kowalkowski, C. and Witell, L. (2020) ‘Typologies and frameworks in service innovation”, in Bridges, E. and Fowler, K. (eds) The Routledge Handbook of Service Research Insights and Ideas, Abingdon: Routledge. Kuula, S., Haapasalo, H. and Tolonen, A. (2018) ‘Cost-efficient co-creation of knowledge intensive business services’, Service Business, 12, pp. 779–808. doi: 10.1007/s11628-018-0380-y. Lin, S., Liu, J., Wei, J., Yin, W., Chen, H. and Chiu, W. (2010) ‘A business model analysis of telecardiology service’, Telemedicine and e-Health, 16 (10), pp. 1067–1073. doi: 10.1089/tmj.2010.0059. Lusch, R.F. and Nambisan, S. (2015) ‘Service innovation; A service-dominant logic perspective’, MIS Quarterly, 39 (1), pp. 155–175. doi: 10.25300/MISQ/2015/39.1.07. Metallo, C., Agrifoglio, R., Schiavone, F. and Mueller, J. (2018) ‘Understanding business model in the Internet of Things industry’, Technological Forecasting and Social Change, 136, pp. 298–306. doi: 10.1016/j.techfore.2018.01.020.

69

Magdalena Marczewska and Marzenna Anna Weresa Neubert, G., Dominguez, C. and Ageron, B. (2011) ‘Inter-organisational alignment to enhance information technology (IT) driven services innovation in a supply chain: The case of radio frequency identification (RFID)’, International Journal of Computer Integrated Manufacturing, 24 (11), pp. 1058–1073. doi: 10.1080/0951192X.2011.602363. OECD (2013) ‘Measuring the Internet economy: A contribution to the research agenda’, OECD Digital Economy Papers, 226, OECD Publishing, Paris. doi: 10.1787/5k43gjg6r8jf-en OECD (2017) Tackling Wasteful Spending on Health, Paris: OECD Publishing. doi: 10.1787/ 9789264266414-en. OECD (2019) Digital Innovation: Seizing Policy Opportunities, Paris: OECD Publishing. doi: 10.1787/ a298dc87-en. Osterwalder, A. and Pigneur, Y. (2010) Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, Hoboken, NJ: Wiley. Ostrom, A.L., Parasuraman, A., Bowen, D.E., Patrıcio, L. and Voss, C.A. (2015) ‘Service research priorities in a rapidly changing context’, Journal of Service Research, 18 (2), pp. 127–159. doi: 10.1177/1094670515576315. Paiola, M. and Gebauer, H. (2020) ‘Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing firms’, Industrial Marketing Management, 89, pp. 245–264. doi: 10.1016/j.indmarman.2020.03.009. Parida, V., Sjödin, D. and Reim, W. (2019) ‘Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises’, Sustainability, 11 (2), 391. doi: 10.3390/su11020391 Plattfaut, R., Niehaves, B., Voigt, M., Malsbender, A., Ortbach, K. and Poeppelbuss, J. (2015) ‘Service innovation performance and information technology: An empirical analysis from the dynamic capability perspective’, International Journal of Innovation Management, 19 (4), 1. doi: 10.1142/ S1363919615500383. Rachinger, M., Rauter, R., Müller, C., Vorraber, W. and Schrigi, E. (2019) ‘Digitalization and its influence on business model innovation’, Journal of Manufacturing Technology Management, 30 (8), pp. 1143–1160. doi: 10.1108/JMTM-01–2018–0020. Rubalcaba, L., Michel, S., Sundbo, J., Brown, S.W. and Reynoso, J. (2012) ‘Shaping, organizing, and rethinking service innovation: A multidimensional framework’, Journal of Service Management, 23 (5), pp. 696–715. doi: 10.1108/09564231211269847. Schön, O. (2012) ‘Business model modularity - a way to gain strategic flexibility?’, Controlling & Management, 56 (2), pp. 73–78. doi: 10.1365/S12176-012-0388-4. Schuritz, R. and Satzger, G. (2016) ‘Patterns of data-infused business model innovation’, 2016 IEEE 18th Conference on Business Informatics (CBI), Paris, 29 August–1 September 2016, pp. 133–142. doi: 10.1109/CBI.2016.23. Sjödin, D., Parida, V., Jovanovic, M. and Visnjic, I. (2020) ‘Value creation and value capture alignment in business model innovation: A process view on outcome-based business models’, Journal of Product Innovation Management, 37 (2), pp. 158–183. doi: 10.1111/jpim.12516. Sjödin, D., Parida, V., Kohtamäki, M. and Wincent, J. (2020) ‘An agile co-creation process for digital servitization: A micro-service innovation approach’, Journal of Business Research, 112, pp. 478–491. doi: 10.1016/j.jbusres.2020.01.009. Snyder, H., Lars, W., Gustafsson, A., Fombelle, P.W. and Kristensson, P. (2016) ‘Identifying categories of service innovation: A review and synthesis of the literature’, Journal of Business Research, 69 (7), pp. 2401–2408. doi: 10.1016/j.jbusres.2016.01.009. Storey, C., Cankurtaran, P., Papastathopoulou, P. and Hultink, E.J. (2015) ‘Success factors for service innovation: A meta-analysis’, Journal of Product Innovation Management, 33 (5), pp. 527–548. doi: 10.1111/jpim.12307. Sundbo, J. and Gallouj, F. (2000) ‘Innovation as a loosely coupled system in services’, International Journal of Services Technology and Management, 1 (1), pp. 15–36. doi: 10.1504/ijstm.2000.001565. Tauqeer, M.A. and Bang, K.E. (2018) ‘Servitization: A model for the transformation of products into services through a utility-driven approach’, Journal of Open Innovation: Technology, Market, and Complexity, 4 (4), pp. 1–20. doi: 10.3390/joitmc4040060. Teece, D.J. and Linden, G. (2017) ‘Business models, value capture, and the digital enterprise’, Journal of Organization Design, 6 (8), pp. 1–14. doi: https://doi.org/10.1186/s41469-017-0018-x

70

IT-Driven Service Innovation Toivonen, M. (2010) ‘Different types of innovation processes in services and their organisational implications’, in Gallouj, F. and Djellal, F. (eds) The Handbook of Innovation and Services: A Multidisciplinary Perspective, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 221–249. Tsou, H. and Cheng, C.C.J. (2018) ‘How to enhance IT B2B service innovation? An integrated view of organizational mechanisms’, Journal of Business and Industrial Marketing, 33 (7), pp. 984–1000. doi: 10.1108/JBIM-07–2017–0175. UNECE (2011) Promoting Innovation in the Services Sector. Review of Experiences and Policies, ECE/ CECI/9, New York and Geneva: United Nations, United Nations Economic Commission for Europe. Vargo, S.L. and Lusch, R.F. (2011) ‘It’s all B2B…and beyond: Toward a systems perspective of the market’, Industrial Marketing Management, 40 (2), pp. 181–187. doi: 10.1016/j.indmarman.2010.06.026. Visnjic, I., Jovanovic, M., Neely, A. and Engwall, M. (2017) ‘What brings the value to outcome-based contract providers? Value drivers in outcome business models’, International Journal of Production Economics, 192, pp. 169–181. doi: 10.1016/j.ijpe.2016.12.008. Wang, L., Zeng, J., Strohmaier, D., Salam, S. and Shahzad, K. (2019) ‘Toward a service-innovation ecosystem of enterprises in China’, Human Systems Management, 38 (3), pp. 279–295. doi: 10.3233/ HSM-180397. Windrum, P. and García-Goñi, M. (2008) ‘A neo-schumpeterian model of health services innovation’, Research Policy, 37 (4), pp. 649–672. doi: 10.1016/j.respol.2007.12.011.

71

PART 2

Technology Planning

5 SIMULTANEOUS SCHEDULING OF ENERGY DEMAND AND SUPPLY IN THE INDUSTRIAL MICROGRID Zeynep Bektas, M. Özgür Kayalica, and Gülgün Kayakutlu 5.1 Introduction The management of optimal energy generation and consumption of microgrids is one of the current problems in the field of energy. The stochastic nature of both load demand and renewable power sources makes it more difficult to establish a supply-demand balance, especially in small-scale systems such as microgrids (Boait et al. 2015). One of the main reasons why the optimal energy management (EM) problem comes to the fore with microgrids is the establishment of the power generation balance. The microgrids are mostly self-sufficient systems, although they are not built autonomously. The implementation of such a system depends on the abundance of local energy sources, the adequacy of the capacities of the power producers, and correct energy planning and management. The literature on “optimal EM in microgrids” approaches the problem mostly by either generation or consumption side. There are very few examples that consider both power generation and load demand as variables simultaneously. To the best of our knowledge, our study is one of the original examples in terms of simultaneous scheduling of both power generation and load consumption, both of which carry uncertainty. After determining the outline of the problem, a comprehensive literature review was made in the relevant fields to determine the parameters of the problem. The literature of the last ten years has been examined to settle the objectives to be optimized. Moreover, the literature will help to choose the power sources to be used in energy generation, the other components of the system, and the methods and approaches to be used in solving the problem. The most common type of purpose in EM studies in industrial microgrids is economic objectives. These include cost optimization, profit optimization, and price-oriented works. In many studies, it has been observed that linear or nonlinear programming is used to aim cost minimization (see Alharbi and Bhattacharya (2018), Kim et al. (2017), Koko et al. (2017)). Marzband et al. (2013) used mixed-integer nonlinear programming to minimize the cost of an autonomous microgrid. The use of meta-heuristic methods in cost minimization is also frequently encountered. Sharma et al. (2018), Yuan et al. (2019), and Leonori et al. (2020) are some of them. Another focus is on scheduling studies. While mostly either power generation or load consumption is tabulated, very few studies have aimed to schedule generation and consumption DOI: 10.4324/9781003046899-8

75

Zeynep Bektas et al.

simultaneously (see Amrollahi and Bathaee (2017), Cha et al. (2015), and Chen and Trif kovic (2018)). With stochastic programming, Misaghian et al. (2018) aimed to maximize revenue and optimize scheduling for all components in a main grid-connected microgrid, while minimizing exchanges with the main grid. Studies for energy supply/demand optimization are also common in the literature. This type of objective includes topics such as power generation optimization, load consumption optimization, loss minimization, and producers’ efficiency maximization (see Aghajani et al. (2017), Aktas et al. (2018), and Bandeiras et al. (2018)). Studies on system design are another group and cover issues such as the determination of parameters that will affect the generation capacity of power producers, optimal placement of microgrid elements, and technical regulations. Hakimi et al. (2020), Kumar V et al. (2020), and Bernardi et al. (2021) are some of the very recent studies. Environment-related objectives are the least common type of objective among studies in the literature. The reason for this is thought to be that these systems, which are mostly operated with renewable resources, do not need to address objectives such as carbon emission minimization (see Nazari-Heris et al. (2017) and Nouri et al. (2018)). According to literature reviews, it is possible to say that resources other than solar and wind are used relatively less in the studies in this field. Solar and wind energies are indispensable in microgrids. However, the diesel fuel option, which is rarely found in the literature and recommended by experts, is also advantageous. This study will address the problem of scheduling power supply and load demand in industrial microgrids, with an approach that simultaneously manages energy supply and demand. The problem will be explained in detail in the next chapter. Chapter 3 proposes the model for the solution to the problem. Chapter 5 provides the applications and results of this model. In the last chapter, we conclude.

5.2  Energy Management Problem of Industrial Microgrids The main purpose of the study is to operate a microgrid in an organized industrial zone (OIZ) with renewable resources. The problem of managing energy supply and demand simultaneously will be addressed. For this, it is desired to minimize energy expenses by making simultaneous load and power scheduling. The main components of the microgrid to be dealt with are determined as photovoltaic (PV) system, wind power system, diesel generator, main grid connection, and consumer demand. In addition to renewable resources, the reason for choosing a diesel generator instead of an energy storage system as a backup power source is the findings obtained as a result of both the expert opinions consulted and the literature reviews. According to expert opinions, considering the high costs of energy storage technologies in Turkey, it has been seen that using diesel generators in an industrial microgrid system to be designed in developing countries is the most effective solution. However, as a result of the literature review, it has been observed that the use of diesel generators is less in similar studies and the choice of this source will contribute to the originality of our study. In addition, considering the importance of uninterrupted power flow in an industrial microgrid, it becomes a necessity to be connected to the main grid in case natural resources are insufficient and diesel fuel cannot be supplied. The main grid connection will be used both to purchase power from the mains when needed and to sell power to the main grid in case of excess supply. For nature-sourced power generators, the power output is exogenous. Renewable resources cannot be used intermittently. For instance, the power output of a PV panel is calculated by a generation formula in which the solar radiation is a multiplier. Here, we do not have a chance to use a certain portion of solar radiation. Hence, sometimes power 76

Energy Demand and Supply

output may be more than enough subject to load demand and natural resources data. In such circumstances, selling power surplus to the main grid is advantageous in terms of both preventing waste and generating income for the system. The microgrid consists of only one OIZ on the consumer side and no electricity costs should be ignored while calculating the demand. Therefore, consumer demand consists of industrial companies, commercial enterprises, and administrative units in OIZ. The problem aims to balance the total hourly electricity demands of the consumers without going into the details of load consumption and to schedule the power generation and the power to be taken from the main grid simultaneously. At this point, it should be emphasized that the problem under discussion includes double-sided uncertainty. Both power generation and load consumption are stochastic and these two stochastic elements will be balanced. In the proposed structure, all outputs of the power generation side in the microgrid are pooled and then shared among the load demanding customers. It is planned to obtain an optimum power generation and load consumption chart by proposing an algorithm suitable for the requirements of the problem. When managing the electricity demand of the final consumers in the microgrid, it is necessary to determine the extent to which the load demand can be changed, that is, the intervals to be shifted. In our study, by applying an original load shifting method, the demands will be shifted within six-hour intervals so that the total demand of n consumers for six hours will remain constant. The reason for setting such a range is to give flexibility to both producer and consumer parties, both of which contain uncertainty. The assumptions of the problem are given as follows: • • • • • • •

Power losses due to the distance between electricity generation and consumption points are ignored (Marzband et al. 2016). Reactive power flow is ignored (Marzband et al. 2016). Power supply and load demand quantities are discrete variables. Optimization problem is constructed for the operation of the microgrid and does not include strategical optimizations. All electricity consumer units in the microgrid work seven days – 24 hours (Mahani et al. 2017). The parameters used in the model are deterministic throughout the planning period (Salahi and Jafari 2016). Energy generation and consumption technologies used in the microgrid will be accessible and usable at the same capacity throughout the planning period (Mahani et al. 2017).

5.3  An Energy Management Model Proposal Cost minimization, which is the mathematical aim of the problem, should be dealt with centrally, although it concerns each consumer. Energy supply is distributed to consumers through a power generation pool; so, the cost of this process can be handled on OIZ-based. For the solution to the problem, a model will be proposed that takes into account all consumers across OIZ and aims to minimize the total cost of the system. Mixed-integer nonlinear programming (MINLP) will be used for the modeling. Decision variables of the model are hourly actual demand of each consumer, hourly power outputs of generators or power supply channels, and a binary variable indicating whether a power source is used at a given time. It should be noted that the hourly power output of PV and wind systems are natural variables. From here, solar radiation, ambient temperature, and wind speed data will be provided from meteorology on which these variables depend. 77

Zeynep Bektas et al.

Hourly load demands of electricity consumers can be obtained before the model of each day is solved, as the day ahead scheduling is performed in the study, or the results of a separate demand forecast study can be used. All indices, variables, and parameters of the model are defined as follows: i = 1, 2, …, n: Electricity consumers in the OIZ j = 1, 2, 3, 4: Quarters of a day k = 1, 2, 3, 4, 5, 6: Hours of a quarter p = 1, 2, 3, 4, 5: Power flow channels (1: Diesel generator, 2: PV system, 3: Wind turbine, 4: Buying from the main grid, 5: Selling to the main grid) xi , j ,k: Amount of load ith consumer receives from the pool in the jth quarter, the kth hour of the day (kWh) y p, j ,k: Amount of power transferred from the pth channel to the pool in the jth quarter, the kth hour of the day (kWh) I p, j ,k : Binary variable indicates whether the pth power exchange is done in the jth quarter, the kth hour of the day Di , j: Total load demand of ith consumer in the jth quarter of the day (kWh) F j ,k : Fuel consumption of diesel generator in the jth quarter, the kth hour of the day (lt) a, b : Constant coefficients related to fuel consumption of diesel generator (lt/kW) P1nom : Nominal power of diesel generator (kW) P1min : Lower limit for the power output of diesel generator (kW) CF: Unit cost of fuel purchased for diesel generator ($/lt) e1: Carbon emission amount of the fuel consumed by the diesel generator (g/lt) N PV /NWT : Number of identical PV panels/wind turbines used ηPV /ηWT : Efficiency coefficient of PV system/wind turbine P2, j ,k: Power output of a PV panel in the jth quarter, the kth hour of the day (kW) SPV : Area of a PV panel (m 2) Φj ,k : Solar radiation in the panel area in the jth quarter, the kth hour of the day (kW/m 2) K PV : Temperature coefficient of the PV panel (1/°C) Ta , j ,k : Ambient temperature in the jth quarter, the kth hour of the day (°C) P2max : Standard upper limit for the power output of a PV panel (kW) C 2 : Unit generation cost of electricity generated by PV system ($/kWh) P3, j ,  k : Nominal power output of a wind turbine in the jth quarter, the kth hour of the day (kW) P3max : Standard upper limit for the power output of a turbine (kW) ω j ,k : Wind speed in the relevant region in the jth quarter, the kth hour of the day (m/s) v ci: Wind speed at which the wind turbine will be able to generate power (m/s) v co : Wind speed at which the wind turbine will stop working (m/s) v r : Nominal wind speed of the wind turbine (m/s) C 3 : Unit generation cost of electricity generated by a wind turbine ($/kWh) C 4, j ,k: Electricity cost bought from the main grid in the jth quarter, kth hour of the day ($/kWh) C 5, j ,k: Unit price of electricity sold to the main grid in the jth quarter, the kth hour of the day ($/kWh) P4max /P5max : Maximum amount of energy that can be bought from/sold to the main grid within an hour (kWh) e p : Carbon emission amount of electricity obtained from the pth channel (for p = 2, 3,4) (g/kWh) 78

Energy Demand and Supply

5.3.1  Power Sources of the Microgrid The hourly power output of the diesel generator is determined by the model for the hourly load requirement. Since the required amount will be drawn directly from the diesel generator, at this stage, the formulas of the working technique of the generator are not needed. However, a formula is needed to calculate the cost of fuel the generator will consume. The formula which is given by Equation 3.1 has some constant coefficients: a = 0.246 lt/kW,  b = 0.08415 lt/kW (Borhanazad et al. 2014). There are also technical lower and upper limits for the power output of the diesel generator (Choobineh and Mohagheghi 2016). These limits given in Equation 3.2 should also be considered as constraints in the model. F j ,k = a ⋅ y1, j ,k + b ⋅ P1nom ∀   j, k   (3.1) P1min ≤ y1, j ,k ≤ P1nom   ∀   j, k (3.2) In the model, the hourly power output of the PV system is calculated by the multiplication of the power output of one of the identical PV panels in the jth quarter, the kth hour of the day, and the number of these PV panels (Bogaraj and Kanakaraj 2016). After installation, it only depends on hourly solar radiation; so, its value cannot be interfered. The formula given in Equation 3.4 is used for the power output of PV panels (Choobineh and Mohagheghi 2016, Misaghian et al. 2018). Here, K PV is equal to −3.7 ⋅ 10 −3  1/ C (Borhanazad et al. 2014). The efficiency coefficient of panels is calculated as 0.1032 for the Marmara Region (Kalogirou 2013). In addition, there are standard upper limits for the power output of each PV panel which will be determined according to the selection of panel type (Marzband et al. 2013). Equation 3.5 will be used for the power output of the PV system. y 2, j ,k = N PV ⋅ P2, j ,k ∀   j, k (3.3)

(

)

P2, j ,k = ηPV ⋅  SPV . Φ j ,k ⋅   1 + K PV ⋅  Ta , j ,k − 25    ∀   j, k(3.4)  

(

)

(

)

η  . S  .  Φ ⋅ 1 + K  .  T − 25  , η  . S  .  Φ .  1 + K  .  T − 25    ≤ P max   PV j ,k  PV a , j ,k PV PV j ,k  PV a , j ,k 2    PV   P2, j ,k =  ∀   j, k max    P2 , ηPV  . SPV  .  Φ j ,k  .  1 + K PV  .  Ta, j ,k − 25  > P2max   

(

)

(3.5) The hourly power output of the wind turbines is also calculated by the multiplication of the nominal power output of a wind turbine in the jth quarter, the kth hour of the day, the number of identical wind turbines, and turbine efficiency coefficient. After installation, it only depends on hourly wind speed; so, its value cannot be interfered with as with the PV system. The hourly nominal power output of a wind turbine is calculated by a piecewise function which is given in Equation 3.7 (Amrollahi and Bathaee 2017). The turbine efficiency coefficient is calculated as 0.32 in our study (Barutcu 2019). y 3, j ,k = NWT ⋅ P3, j ,k ⋅ ηWT ∀   j, k (3.6) 79

Zeynep Bektas et al.

P3, j ,k

 0, ω j ,k ≤ v ci  or  ω j ,k ≥ v co   ω j ,k 3 − v ci 3  , v ci < ω j ,k ≤ v r ∀   j, k(3.7) =  P3max . 3 v r − v ci 3   P3max , v r < ω j ,k ≤ v co  

The hourly power exchange amounts with the main grid are determined by the model concerning hourly load requirement or surplus. At a certain time, it is only possible either to sell to or buy from the main grid. Therefore, these two decision variables cannot take the value at the same time. Power purchase and sales have to be limited to certain upper limit values under current regulations (Banaei and Rezaee 2018). For the price of the electricity bought from the main grid, a three-time tariff for the OIZ strategy will be used. For the price of the electricity sold to the main grid, system marginal prices by EXIST will be used. y4, j ,k ≤ P4max ∀   j, k (3.8) y5, j ,k ≤ P5max ∀   j, k (3.9)

5.3.2  Model Proposal The MINLP model to be developed includes all consumers of the OIZ as mentioned before. An independent model will be established for each quarter of the day (for every j = 1, 2, 3,4). The proposed model for a quarter of j is described and given in this section. In the model, the variables and parameters previously defined are purified from the index j. Indices: i = 1, …, n; p = 1, 2, 3,4,5; k = 1, 2, 3,4,5,6 Decision Variables: xi ,k : Amount of load ith consumer receives from the pool in the kth hour of the quarter (kWh) y p,k : Amount of power transferred from the pth channel to the pool in the kth hour of the quarter (kWh) I p,k : Binary variable indicates whether the pth power exchange is done in the kth hour of the quarter The complete mathematical model for a six-hour quarter is given as follows: Min   z =

∑

(

6

)

I ⋅ CF ⋅  a ⋅ y + b ⋅ P nom + I ⋅ C ⋅ y + I ⋅ C ⋅ y 1,k 1 2,k 2 2,k 3,k 3 3,k  1,k + I 4,k ⋅ C 4,k ⋅ y4,k − I 5,k ⋅ C 5,k ⋅ y5,k  k =1

(3.10)

Subject to, I 1,k + I 2,k + I 3,k + I 4,k = 1  ∀  k   (3.11)

(

)

I 5,k − I 1,k + I 2,k + I 3,k ≤ 0  ∀  k (3.12) I 4,k + I 5,k ≤ 1  ∀  k (3.13) 80

Energy Demand and Supply

I p,k ≤ M .y p,k   ∀   p, k (3.14) 1 ⋅ 8

∑ D  ≤∑ x i

i

∑ D − ∑ i

i

∑

n

i

6

i ,k

≤

1 ⋅ 5

∑ D ∀ k  (3.15) i

i

xi ,k  = 0  (3.16) 

k =1

xi ,k − I 1,k ⋅ y1,k + I 2,k ⋅ y 2,k + I 3,k ⋅ y 3,k + I 4,k ⋅ y4,k − I 5,k ⋅ y5,k    = 0  ∀  k (3.17)

i =1

I 1,k ⋅ P1min ≤ y1,k ≤ I 1,k ⋅ P1nom ∀  k (3.18) y 2,k = N PV ⋅ P2,k ∀  k (3.19) P2,k   ≤ P2max ∀  k(3.20) y 3,k = NWT ⋅ P3,k ⋅ ηWT ∀  k (3.21) P3,k   ≤ P3max ∀  k(3.22) y4,k ≤ P4max ∀  k (3.23) y5,k ≤ P5max ∀  k (3.24)

(

)

e1 ⋅ I 1,k ⋅ a ⋅ y1,k + b ⋅ P1nom + e 2 ⋅ I 2,k ⋅ y 2,k + e 3 ⋅ I 3,k ⋅ y 3,k + e 4 ⋅ I 4,k ⋅ y4,k   1 ≤  0.6 ⋅ e 4 ⋅  6

∑ D  ∀ k i

i

(3.25)

Some lower/upper limits for xi ,k s according to consumers’ electricity needs xi ,k   ≥ 0  ∀  i, k(3.26) y p,k ≥ 0  ∀   p, k (3.27) I p,k ∈{0,1}  ∀   p, k (3.28) The objective function of the proposed model is the minimization of total energy expenses of the microgrid for a quarter which is given in Equation 3.10. While formulating the objective function, each item that would cause energy expenses and incomes were taken into account. The power output of each source is multiplied by its unit cost and the binary variable that indicates whether that source is used at the time. Then, the multiplications are summed. Income to be obtained when power is sold to the main grid is formulated in the same way and added to the objective function by adding a minus. In the constraints, the first group is about relationships between decision variables. Since there is always load demand every hour, the inequality in Equation 3.11 guarantees that one power channel always supplies power every hour. Through the objective function and constraints, the most suitable power supply source for that specific hour will be determined by the model considering the different parameters of the power sources. Equation 3.12 expresses 81

Zeynep Bektas et al.

that when the power supply from the microgrid’s own sources is not provided, any power cannot be sold to the main grid. Equation 3.13 means that both buying from and selling to the main grid cannot be done at the same time. The constraint given in Equation 3.14 expresses the relationship between decision variables I p,k and y p,k . It prevents that when y p,k equals 0, I p,k equals 1 for any p and k. The constraints given in Equations 3.15 and 3.16 emphasize that the sum of hourly loads to be provided to consumers within a quarter will be constant, keeping the sum of the load demands for this quarter. For the model to share the loads more evenly in six hours, the actual demand of every hour is limited within a certain interval. The constraints are not written separately for each consumer, but their totals are used based on consumers. The reason for this is to prevent the number of consumers, which can reach very high values in different application examples, from making the solution of the model difficult. Since there is no battery in the system, the total energy inputs and outputs of each hour should be equal. Equation 3.17 expresses the power balance. The previously given technical formulas of the power supplies to be used are not included in the constraints in detail so that the model does not look complicated. Equations 3.18–3.24 express basic equations and limits of these. Equation 3.25 is the carbon emission constraint. According to the 2030 projections of the European Union, a 40% reduction is targeted in the emission amount of all greenhouse gases compared to 1990 (Url-1). Accordingly, the sum of the carbon emissions caused by our microgrid is limited from above with 60% of the carbon emission that would occur if the supply was completely supplied from the main grid under the same demand. Since the carbon emission amount for the diesel generator is determined by the amount of fuel consumed, the first term on the left side of the equation contains the fuel consumption formula instead of the energy output, unlike the others. The other constraints to be included in the MINLP model are some lower/upper limits for xi ,k s according to consumers’ electricity needs – which can be determined according to the case study – and sign restrictions for decision variables of the model are given by Equations 3.26–3.28.

5.4  Case Studies The data and parameters to be used for case studies were obtained from different sources according to their subjects. For the demand values of Di , j, a historical data set is used. Φ j ,k (hourly solar radiation data), Ta , j ,k (hourly ambient temperature data), and ω j ,k (hourly wind speed data) were obtained from meteorology. For case studies, the value of the parameter CF is $0.735 per liter (EMRA 2020). Under current market conditions, the value of C 2 is $0.0279 per kWh, and C 3 is $0.0272 per kWh (EIA 2019). C 4, j ,k is obtained from EMRA by taking into account the three-time tariffs (EMRA 2020). C 5, j ,k is obtained from EXIST considering the system marginal price (EXIST 2020). When calculating the parameter e1, taking into account the results of the estimates in the study of Jakhrani et al. (2012), the carbon emission per liter of fuel was determined as 51.906 g/lt. PV panels and wind turbines are renewable, environmentally friendly resources, and their carbon emissions are negligibly low during their operations. Therefore, values of e 2 and e 3 are accepted as 0 g/kWh (Ufluoğlu 2018). In the calculation of e 4, the approach in Ufluoğlu’s work is taken as an example. Accordingly, using the inputs of grid electricity generation in Turkey and the carbon emission amounts of each of these resources, the amount of carbon emission for the grid electricity is calculated as 59.90 g/kWh (Ufluoğlu 2018). 82

Energy Demand and Supply

P275-2 model diesel generator of FG Wilson brand was selected for use (Url-2). For the PV system, the Tiger Bifacial model of the JinkoSolar company was chosen (Url-3). For different scenarios in terms of wind systems, two different wind turbine models belonging to the VESTAS company were determined (Url-4). The parameter values to be used in the model are given in Table 5.1 in line with the preferences for renewable energy systems and exogenous parameters. In our study, different scenarios will be created according to the following criteria: •

•

When examined the European Union’s and Turkey’s energy projections, we could not find any optimistic scenario which predicts that energy demand will fall. In some countries (including Turkey), it is a widespread belief that energy demand will increase by 1.3% annually between the years 2005–2030 (Url-5). In this study, we will analyze the situation after two years, taking into account the current state of energy demand and an increase of 1.3%. To take the wind turbines into account with different capacities and features that can be preferred, the cases where two different types of wind turbines are used in the microgrid will be studied.

The above cases produce four different scenarios. In the scenarios, all parameters will be kept constant, except for the demand and the wind turbine model. Separate MINLP models will be installed and solved by changing the parameters for the scenarios produced. Each scenario will be run for the same quarter of the same day. Thus, we will have different situations that we will consider for a certain period. The scenarios are given as follows: Scenario 1: Using a V126-3.45 MW model wind turbine with realized energy demand for the implementation date Scenario 2: Using a V136-4.2 MW model wind turbine with realized energy demand for the implementation date Scenario 3: Using a V126-3.45 MW model wind turbine with an estimated value of current energy demand two years later (1.3% increase each year) Scenario 4: Using a V136-4.2 MW model wind turbine with an estimated value of the current energy demand in two years Table 5.1  Technical parameters and values Parameter

Value

P1nom P1min P2max SPV N PV P3max v ci v co vr NWT P4max P5max

1,100 kW 200 kW 0.475 kW 2.276 m 2 5,000 3,450 kW 3 m/s 22.5 m/s 20 m/s 2 2,500 kWh 2,500 kWh

83

4,200 kW 3 m/s 25 m/s 23 m/s

Zeynep Bektas et al.

5.4.1  Case Study 1: A Sunny Day In the study, three companies in İkitelli OIZ, Istanbul were discussed for implementation. To implement the four scenarios produced, first the third quarter of May 31, 2019, between 12:00 and 18:00, was selected and the consumption data of these hours were used as demand. For the applied companies, the constraints to be obtained for consumers are given in ­Equations 4.1–4.3. These are also included in the model. x1,k   ≥ 30  ∀  k (4.1) x 2,k   ≥ 35  ∀  k(4.2) x 3,k   ≥ 40  ∀  k (4.3) The four MINLP models that emerged for the scenarios produced were coded using the 31.1.1 version of the GAMS software. No errors occurred in terms of model components in codes, syntax errors were eliminated, and optimum results were obtained. Results of the four scenarios are given in Tables 5.2 and 5.3. When the selected date and time range is examined in terms of natural resources, it is seen that it is a time period rich in solar energy and poor in wind energy. Therefore, as can be Table 5.2  O  ptimal solutions of Scenarios 1 and 2 12:00–13:00 13:00–14:00 14:00–15:00 15:00–16:00 16:00–17:00 17:00–18:00 y1,k (kWh) y 2,k (kWh) y 3,k (kWh) y4,k (kWh) y5,k (kWh) x1,k (kWh) x 2,k (kWh) x 3,k (kWh) Total cost for six hours

– 739.8821 – – 110.8141 30 35 564.068 $4.033

– 833.2183 – – 623.5289 30 35 144.6894

– 847.7696 – – 638.0802 30 35 144.6894

– 700.5967 – – 490.9073 30 35 144.6894

– 596.3332 – – 386.6438 30 35 144.6894

– 457.8367 – – 248.1473 30 35 144.6894

 ptimal solutions of Scenarios 3 and 4 Table 5.3  O 12:00–13:00 13:00–14:00 14:00–15:00 15:00–16:00 16:00–17:00 17:00–18:00 y1,k (kWh) y 2,k (kWh) y 3,k (kWh)

– 739.8821 –

– 833.2183 –

– 847.7696 –

– 700.5967 –

– 596.3332 –

– 457.8367 –

y4,k (kWh) y5,k (kWh) x1,k (kWh) x 2,k (kWh) x 3,k (kWh) Total cost for six hours

– 94.3521 30 35 580.53 $6.064

– 618.0417 30 35 150.1766

– 632.5929 30 35 150.1766

– 485.4201 30 35 150.1766

– 381.1566 30 35 150.1766

– 242.6601 30 35 150.1766

84

Energy Demand and Supply

seen in the optimum results, the preferred energy source for every hour in all four scenarios was the PV system. According to the demand, the surplus electricity generated in the PV system was sold to the grid. Total electricity costs are low, as revenue is generated by selling to the grid. In addition, since the wind source is not consulted due to the meteorological characteristics of the time period, the effect of using different wind turbines, which is one of the two situations discussed while creating four scenarios, cannot be seen. This is why the results of scenarios 1 and 2 and scenarios 3 and 4 are the same. In the scenarios, although the wind generation outputs not shown here are different, this source was not preferred. Hence, the results were exactly the same. Under these conditions, the scenario that can be preferred in this application is Scenario 1 or 2 when evaluated in terms of cost minimization, which is the objective of the models.

5.4.2  Case Study 2: A Windy Day To better see the effects of the change in wind turbine selection, the four scenarios produced were also applied in a time period when the wind speed was high. For this application, the second quarter of January 31, 2019, between 06:00 and 12:00, has been selected. Other choices and assumptions are the same as in the previous application. Four new MINLP models arranged for the time period under consideration were coded using GAMS software. No errors occurred in terms of model components in codes, syntax errors were eliminated, and optimum results were obtained. Results of the four scenarios are given in Tables 5.4–5.7. When the date and time interval discussed in this application are examined in terms of natural resources, it is seen that it is rich in wind energy and poor in solar energy, unlike the previous one. Since the amount of solar radiation is relatively high in the last hour of the six-hour period, the model again chose the PV system in the sixth hours and turned to the wind energy system in the previous five hours. The renewable power system selected met the demand every hour, and there was no need for the option to purchase from the grid. According to the demand, the surplus electricity produced was also sold to the grid. For this reason, the total electricity costs have fallen. When the total costs of six hours are examined, it is seen that there is a profit. Of course, the main reason for this is that the application only takes three consumers into account, so the Table 5.4  O  ptimal solutions of Scenario 1 06:00–07:00 07:00–08:00 08:00–09:00 09:00–10:00 10:00–11:00 11:00–12:00 y1,k (kWh) y 2,k (kWh) y 3,k (kWh) y4,k (kWh) y5,k (kWh) x1,k (kWh) x 2,k (kWh) x 3,k (kWh) Total cost for six hours

– – 1084.8394 – 483.1299 30 35 536.7095 −$20.845

– – 927.1772 – 726.6073 30 35 135.5699

– – 965.0641 – 764.4942 30 35 135.5699

85

– – 908.6085 – 708.0386 30 35 135.5699

– – 600.9482 – 400.3783 30 35 135.5699

– 641.9449 – – 441.3751 30 35 135.5699

Zeynep Bektas et al. Table 5.5  Optimal solutions of Scenario 2 06:00–07:00

07:00–08:00

08:00–09:00 09:00–10:00 10:00–11:00 11:00–12:00

–

–

–

–

–

–

– 867.3587 – 265.6492 30

– 741.3035 – 540.7336 30

– 771.595 – 571.0252 30

– 726.4572 – 525.8874 30

– 480.4745 – 279.9046 30

641.9449 – – 441.3751 30

35 x 2,k (kWh) 536.7095 x 3,k (kWh) Total cost −$4.498 for six hours

35 135.5699

35 135.5699

35 135.5699

35 135.5699

35 135.5699

Y1,k (kWh) Y2,k (kWh) Y3,k (kWh) Y4,k (kWh) Y5,k (kWh) x1,k (kWh)

Table 5.6  Optimal solutions of Scenario 3 06:00–07:00 07:00–08:00 08:00–09:00 09:00–10:00 10:00–11:00 11:00–12:00 Y1,k (kWh) Y2,k (kWh) Y3,k  ( kWh)

– – 1084.8394

– – 927.1772

– – 965.0641

– – 908.6085

– – 600.9482

– 641.9449 –

Y4,k (kWh) Y5,k (kWh) x1,k (kWh) x 2,k (kWh) x 3,k (kWh) Total cost for six hours

– 467.3834 30 35 552.456 −$18.904

– 721.3586 30 35 140.8186

– 759.2455 30 35 140.8186

– 702.7899 30 35 140.8186

– 395.1296 30 35 140.8186

– 436.1263 30 35 140.8186

Table 5.7  Optimal solutions of Scenario 4 06:00–07:00 07:00–08:00 08:00–09:00 09:00–10:00 10:00–11:00 11:00–12:00 Y1,k (kWh) Y2,k (kWh) Y3,k (kWh) Y4,k (kWh) Y5,k (kWh) x1,k (kWh)

– – 867.3587 – 249.9027 30

35 x 2,k (kWh) 552.456 x 3,k (kWh) Total cost −$2.557 for six hours

– – 741.3035 – 535.4848 30

– – 771.595 – 565.7764 30

– – 726.4572 – 520.6386 30

– – 480.4745 – 274.6558 30

– 641.9449 – – 436.1263 30

35 140.8186

35 140.8186

35 140.8186

35 140.8186

35 140.8186

electricity demand is lower than the reality. Under these conditions, the effect of the differences of the four scenarios was better seen, and as a result of the combinations, four different generation-consumption charts could be obtained. The scenario that can be preferred in this application is Scenario 1 when evaluated in terms of cost minimization. 86

Energy Demand and Supply

Considering the implementation studies for the two separate quarters in terms of the general purpose of the study, it is gratifying to see that although small-scale applications have been made, the microgrid primarily turns to renewable resources to meet the energy demand. The problem observed in the solutions of the models is that the most load is always assigned to the end consumer, and the other consumers are assigned only the minimum load to meet their constraints.

5.5 Conclusion and Recommendations In our study, it is aimed to minimize the energy costs of the microgrid by scheduling the power supply and load demand simultaneously in an industrial microgrid. The details of the problem, and the components of the microgrid were determined in line with the gaps in the literature and expert opinions. In line with the problem addressed, different constraints such as load demand, power flow balance, carbon emission, and technical specifications of system components have been taken into account. The model proposed for the solution of the problem was established with the MINLP method, and two separate applications were made in a system with three consumers for four different scenarios produced according to the requirements of the problem. The results obtained were analyzed and interpreted. For future work we have several suggestions. A multi-purpose model can be established for a new purpose that will reduce the demand deviations of the consumer side. Also, the number of scenarios can be increased; applications can be extended to large-scale ones. Components of the microgrid on the generation side may vary depending on the application area.

References Aghajani, G. R., Shayanfar, H. A. and Shayeghi, H. 2017. Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 126:622–637. Aktas, A., Erhan, K., Ozdemir, S. and Ozdemir, E. 2018. Dynamic energy management for photovoltaic power system including hybrid energy storage in smart grid applications. Energy 162:72–82. Alharbi, H. and Bhattacharya, K. 2018. Stochastic optimal planning of battery energy storage systems for isolated microgrids. IEEE Transactions on Sustainable Energy 9/1:211–227. Amrollahi, M. H. and Bathaee, S. M. T. 2017. Techno-economic optimization of hybrid photovoltaic/ wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response. Applied Energy 202:66–77. Banaei, M. and Rezaee, B. 2018. Fuzzy scheduling of a non-isolated micro-grid with renewable resources. Renewable Energy 123:67–78. Bandeiras, F., Gomes, M., Coelho, P., Fernandes, J., Camacho, A. and Castilla, M. 2018. Microgrid architecture evaluation for small and medium size industries. International Journal of Emerging Electric Power Systems 19/2:1–11. Barutcu, B. 2019. Wind Energy and Conversion Technology. Slides of the course at Istanbul Technical University, Istanbul. Bernardi, E., Morato, M. M., Mendes, P. R. C., Normey-Rico, J. E. and Adam, E. J. 2021. Fault-tolerant energy management for an industrial microgrid: A compact optimization method. Electrical Power and Energy Systems 124:106342. Boait, P., Advani, V. and Gammon, R. 2015. Estimation of demand diversity and daily demand profile for off-grid electrification in developing countries. Energy for Sustainable Development 29:135–141. Bogaraj, T. and Kanakaraj, J. 2016. Intelligent energy management control for independent microgrid. Sadhana 41/7:755–769. Borhanazad, H., Mekhilef, S., Ganapathy, V. G., Modiri-Delshad, M. and Mirtaheri, A. 2014. Optimization of micro-grid system using MOPSO. Renewable Energy 71:295–306.

87

Zeynep Bektas et al. Cha, H. J., Won, D. J., Kim, S. H., Chung, I. Y. and Han, B. M. 2015. Multi-agent system-based microgrid operation strategy for demand response. Energies 8/12:14272–14286. Chen, Y. and Trif kovic, M. 2018. Optimal scheduling of a microgrid in a volatile electricity market environment: Portfolio optimization approach. Applied Energy 226:703–712. Choobineh, M. and Mohagheghi, S. 2016. A multi-objective optimization framework for energy and asset management in an industrial Microgrid. Journal of Cleaner Production 139:1326–1338. EIA (U.S. Energy Information Administration). 2019. https://www.eia.gov/. EMRA (Republic of Turkey Energy Market Regulatory Authority). 2020. https://www.epdk.gov.tr/. EXIST (Energy Exchange Istanbul). 2020. https://www.epias.com.tr/. Hakimi, S. M., Hasankhani, A., Shafie-khah, M. and Catalao, J. P. S. 2020. Demand response method for smart microgrids considering high renewable energies penetration. Sustainable Energy, Grids and Networks 21:100325. Jakhrani, A. Q., Othman, A. K., Rigit, A. R. H., Samo, S. R. and Kamboh, S. A. 2012. Estimation of carbon footprints from diesel generator emissions. International Conference in Green and Ubiquitous Technology, Bandung – Indonesia, 78–81. Kalogirou, S. A. 2013. Solar Energy Engineering – Processes and Systems. Academic Press, Cambridge, MA. Kim, H., Bae, J, Baek, S., Nam, D., Cho, H. and Chang, H. J. 2017. Comparative analysis between the government micro-grid plan and computer simulation results based on real data: The practical case for a South Korean Island. Sustainability 9:197–214. Koko, S. P., Kusakana, K. and Vermaak, H. J. 2017. Optimal energy management of a grid-connected micro-hydrokinetic with pumped hydro storage system. Journal of Energy Storage 14:8–15. Kumar V, A., Verma, A. and Talwar, R. 2020. Optimal techno-economic sizing of a multi-generation microgrid system with reduced dependency on grid for critical health-care, educational and industrial facilities. Energy 208:118248. Leonori, S., Paschero, M., Mascioli, F. M. F. and Rizzi, A. 2020. Optimization strategies for microgrid energy management systems by genetic algorithms. Applied Soft Computing Journal 86:105903. Mahani, K., Farzan, F. and Jafari, M. A. 2017. Network-aware approach for energy storage planning and control in the network with high penetration of renewables. Applied Energy 195:974–990. Marzband, M., Sumper, A., Dominguez-Garcia, J. L. and Gumara-Ferret, R. 2013. Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP. Energy Conversion and Management 76:314–322. Marzband, M., Yousefnejad, E., Sumper, A. and Dominguez-Garcia, J. L. 2016. Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Electrical Power and Energy Systems 75:265–274. Misaghian, M. S., Saffari, M., Kia, M., Heidari, A., Shafie-khah, M. and Catalao, J. P. S. 2018. Trilevel optimization of industrial microgrids considering renewable energy sources, combined heat and power units, thermal and electrical storage systems. Energy 161:396–411. Nazari-Heris, M., Abapour, S. and Mohammadi-Ivatloo, B. 2017. Optimal economic dispatch of FCCHP based heat and power micro-grids. Applied Thermal Engineering 114:756–769. Nouri, A., Khodaei, H., Darvishan, A., Sharifian, S. and Ghadimi, N. 2018. Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: An epsilon constraint method and fuzzy satisfying approach. Energy 159:121–133. Salahi, N. and Jafari, M. A. 2016. Energy-performance as a driver for optimal production planning. Applied Energy 174:88–100. Sharma, S., Bhattacharjee, S. and Bhattacharya, A. 2018. Probabilistic operation cost minimization of micro-grid. Energy 148:1116–1139. Ufluoğlu, E. E. 2018. Ana Sisteme Bağlı Bir Mikro Şebeke için Gün İçi Elektrik Piyasasına Dayalı Çizelgeleme. PhD diss., Istanbul Technical University. Url-1: 2030 Climate & Energy Framework. https://ec.europa.eu/clima/policies/strategies/ 2030_en (accessed June 28, 2020). Url-2: FG Wilson Diesel Generators P275-2. https://www.fgwilson.com/en_GB/products/new / fg-wilson/diesel-generators/medium-range-225–938-kva/227227255580579.html (accessed November 3, 2019).

88

Energy Demand and Supply Url-3: JinkoSolar. https://www.jinkosolar.com/en (accessed July 1, 2020). Url-4: Vestas. https://www.vestas.com/ (accessed July 1, 2020). Url-5: Electricity Generation Sector Report 2008. https://www.enerji.gov.tr/File/?path =ROOT%2f1%2f Documents%2fSekt%c3%b6r+Raporu%2fSektor_Raporu_EUAS.pdf (accessed July 2, 2020). Yuan, D., Lu, Z., Zhang, J. and Li, X. 2019. A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption. Electrical Power and Energy Systems 113:139–53.

89

6 WHERE WE ARE AND WHERE WE WANT TO GO A Patent Analysis Approach Toward Strategic Technological Planning Priyanka C. Bhatt, Vimal Kumar, Kuei-Kuei Lai, Yu-Hsin Chang, and Yu-Shan Su 6.1 Introduction The success of a firm, in the present economic landscape, is measured in terms of its innovation competencies (Soto-Acosta et al., 2018). Organizations need to be up and tight with the dual task of technological upgradation as well as organizational design and structure (Raisch & Birkinshaw, 2008). The major challenge for a firm and its management, however, is to recognize the right time and right kind of innovation opportunity. Such an identification process is often considered under research and development (R&D) capabilities of a firm, and innovation, therefore, proved to enhance the market dominance and achieve competitive advantage (Chang et al., 2012; Miller & Roth, 1994; Pearson & Thomas, 2002). Previous research identifies such process as critical but not a well-defined one (Geum & Kim, 2020). The patents are one of the significant and crucial outputs of innovation in an organization (Chang et al., 2012; Lee & Kim, 2017; Li et al., 2011). Previous research (Chen, 2011; Ernst, 1998; Hall et al., 2005) points out that there is a positive relationship between firms’ patenting and their performance. Furthermore, patents have a significant impact on securing an organization’s R&D outcomes. Patent-related information of a firm can be assessed to analyze their R&D strategies as well as the latency of their technical innovation (Brockhoff, 1991). Patent counts have a direct effect on the success of an organization, with R&D strategies, technological innovations, and organizational structures playing part of the intermediaries in the direct relationship. Previous research has focused on various aspects of analyzing technological innovation, both based on the technological novelty and advantages, as well as exploring patent-related information (Grimaldi et al., 2015; Hanel, 2006), such as patent counts, patent citations, patent families, and patent classifications (Abraham & Moitra, ­ osenkopf & 2001; Geum & Kim, 2020; Sungjoo Lee, 2013; Nylén & Holmström, 2015; R Nerkar, 2001; Wang & Huang, 2018). Apart from assessing firm’s own strategic planning and decision-making based on patent information, it also aids the firms to assess and analyze competitors’ technological strategies and capabilities, which further helps to chart out the business opportunities and market dominance strategies (Geum & Kim, 2020). However, momentous changes in technological landscape have compelled organizations to keep 90

DOI: 10.4324/9781003046899-9

Patent Analysis Approach

themselves abreast of the novel innovations and keep up to date with the current trends in technology. An organization having a strong core competence may sometimes fail to assess the industry trends and lead to decline of its knowledge investment. Furthermore, the mismatch between the core competence and the current trend puts a test for the firm’s survival. Therefore, firms need to address the question of identifying correct technology trends in the market. The process of identifying technology trends may be vast, such as using the knowledge infrastructure, absorbed in a firm from internal or external sources – thus creating a significant role of core capability, knowledge, and technological trends in success of an organization. Industries use scientific innovations as a crucial element for competitive advantage, and patents are considered as the output of the technological competence and the development of a firm. Patents are quantifiable components of an organization’s intellectual property aiding further in business strategies. Research and development revolutions give birth to the patents and new technological innovations. Technological capabilities and marketplace knowledge are considered a function of organizational strategy, structure, and systems (Popadiuk & Choo, 2006). This chapter aims to highlight the importance of patent analysis methods, for various technological and business strategies. The major goal of this chapter is twofold: first, it enables a researcher to identify such analysis methods, and identify the correct method for different set of goals. Second, it aids business researchers to identify the patent analysis process, which can be set to build technological planning goals for businesses, recognizing their current position and aiming to predict their future position in the technological landscape.

6.2  Patent Information World Intellectual Property Organization describes patents as an exclusive right granted for an invention, which is a product or a process that provides, in general, a new way of doing something, or offers a new technical solution to a problem. To get a patent, technical information about the invention must be disclosed to the public in a patent application. A single patent document contains a plethora of information related to the technology or invention it is based upon. In general, patent information is categorized into two categories, viz., “ex ante” and “ex post” patent information (Noh & Lee, 2020). Ex ante patent information includes information such as technology classification, patent backward citations, and inventor classification; and ex post information includes data such as patent forward citations and patent renewal information. Another classification of patent data can be categorized into structured and unstructured text in the patent document. The structured text includes discreet information such as patent application date, inventor details, company name, citation counts, patent classification data such as International Patent Classification (IPC) code, and reference count. However, the unstructured text includes the title of the patent document, abstract, and main document text including images, figures, tables, etc. Different patent information requires a different set of analysis techniques. It is crucial to understand what importance does a particular set of patent information holds for technological innovation (Schoenmakers & Duysters, 2010), product efficiency (Leifer et al., 2001), or the overall business strategy (Lai et al., 2006; Lo et al., 2020). Previous research emphasizes upon the use of technology classification data, backward citations, inventor classification, etc. for patent analysis, considering them sufficient enough for empirical investigation of technological 91

Priyanka C. Bhatt et al.

innovation and prediction (Fleming et al., 2007; Noh & Lee, 2020; Verhoeven et al., 2016). In general, a novel technological innovation is considered to be built upon the existing technological outcomes already available in the marketplace. Thus, a patent document has enough information about the previous technologies that it is built upon, and a thorough predictive analysis could lead a researcher to understand the existing technological strategy as well as future technological roadmap. Patents, therefore, represent the technical inventions of a company or a country as well, and are considered as a formal contract between the patent inventor and the government agency. Patent analysis has also been conducted to understand the relationship between technical dominance and commercial growth of an organization (Abraham & Moitra, 2001; Ha et al., 2020; Lai et al., 2020). Furthermore, from a business strategy point of view, predictive analysis holds a promising outcome for planning the technological strategies in a particular domain.

6.3  Patent Analysis Techniques: Their Influence on Innovation Strategy Patent analysis is considered to be beneficial in analyzing technological trend (Choi & Song, 2018; Kim et al., 2019), technology prediction and forecasting (Lee et al., 2018; Shin et al., 2012; Suominen et al., 2017), technology strategy planning (Clarke, 2018; Lai et al., 2007; Lai et al., 2020; Lo et al., 2020), technology novelty analysis (Kim & Shin, 2018; ­Moehrle & Caferoglu, 2019; Wada, 2018), technology road mapping (Sungjoo Lee, 2013; Yu & Daim, 2017), competitor’s technological strategies (de Pablos & Lytras, 2018; Lalitnorasate  & ­M iyazaki, 2014), patent quality (Fallatah, 2018; Lai et al., 2017), and so on. To enhance the core technological capability and standards to boost the R&D portfolios needs thorough monitoring based on both core and exterior factors. The self-positioning of internal technical competencies in a specific technological field signifies the core technological competency of a company (Lai et al., 2020). One of the proven methods for patent analysis is the analysis of patent citations, using main path analysis. This method helps identify the scientific and technological development in a particular domain, as well as highlights the crucial patent documents in the domain. Hummon and Dereian (1989) proposed the method of main path analysis (MPA), and Batagelj (2003) further enhanced the method by designing a feasible and efficient algorithm workable on voluminous datasets. The major characteristic of the studies employing MPA is to trace back the earliest development further paving way for the path of scientific innovation. In terms of innovation and business strategy, the strategic innovation process (Afuah, 2003) states that an organization has a goal. Further, to attain the goal, first it tries to understand the business environment for any present threats or opportunities. Once the threats or opportunities are identified and analyzed, organizations can decide the profit direction to follow, whether to be identified as a “supplier, manufacturer, complementary innovator, distributor, or customer”. After identifying the competencies, organization articulates various strategies, viz., business strategy, innovation strategy, and functional and globalization strategy. The blend of these strategies paves way for the overall success and market dominance of the firm. Previous research has highlighted the ways to assess the innovation strategies of a firm – such as annual technical reports and product turnover – and patent strategies – such as patent counts, patent citations, and patent renewal. (Abraham & Moitra, 2001; Antony & Preece, 2001; Geng, 2017; Hedman & Henningsson, 2015; Longoni & Cagliano, 2015; ­Sorescu et al., 2011). The ever-increasing technological landscape has also shaped the innovation dynamics in the context of integration and interoperability approaches (Ahmad et al., 2019; Chandrahas et al., 2011; 92

Patent Analysis Approach

Zhang et al., 2018). Identifying the changes in technological innovation of a firm now includes assessing all mentioned strategies equally keeping a multi-­dimensional view (Suominen et al., 2017; Q. Wang & von Tunzelmann, 2000). When formulating their technological innovation strategy, organizations need to focus on their R&D infrastructure to efficiently manifest their technological capabilities. Patents here can be helpful in recognizing an organization’s R&D portfolios as well as monitor their core competencies. Previous research performed MPA to identify the technical competencies using R&D portfolios for photovoltaic companies (Lai et al., 2017). MPA has been applied extensively in existing studies to explore a different range of strategies and aid businesses in developing their market strategies and core competencies. Citation-link network of MPA highlights a significant level of information about the knowledge diffusion. Xu et al. (2020) explore the recent emerging research topics using key-route MPA. MPA has also been used extensively to identify the technological evolution of particular technology, such as mobile payments (Kumar et al., 2020; M. Mariani & Dihardja, 2010; Mohorko et al., 2008), thin-film solar cells (Chang et al., 2019; Kumar, Chen, et al., 2018; Lai et al., 2020; Tabrizi & Pahlavan, 2020; Tseng et al., 2011), lithium-iron phosphate battery (Hung et al., 2014), blockchain (Bhatt et al., 2020; Clarke et al., 2020; Su et al., 2019), Internet of Things (Ahn, 2020; D. H. Kim et al., 2017), cloud computing (Adamuthe & Thampi, 2019), energy storage devices (Kumar et al., 2018), and bibliometric studies (Chang et al., 2017; Marzi et al., 2017; Sternitzke et al., 2008; H. Xu, 2020), to name a few. However, primary goals of performing MPA include identifying the knowledge flows (Bhupatiraju et al., 2012; Higham et al., 2017; Su et al., 2019), technological trajectories (Bindu et al., 2019; Ho et al., 2014; Kumar et al., 2018; Kumar et al., 2020; Lai et al., 2006; Liu et al., 2019), knowledge spillovers (Aldieri et al., 2020; Wang & Huang, 2018), knowledge diffusion (Huang et al., 2017; Morescalchi et al., 2015) and convergence (Kim & Lee, 2014; Park & Yoon, 2018) patterns, technological roles and positions (Chang et al., 2018; Su et al., 2017), etc. Identifying the technological opportunities at the right time using significant tools such as patent analysis techniques and forecasting methods can lead to the market dominance in an organization (Figure 6.1). While focusing on technological innovations, new entrants as well as incumbents have to tackle the complexities arising due to exponential technological upgrades (Nylén & Holmström, 2015). Addressing such complexities is not just an infrastructural task for the organizations, but a decision-making task as well, whether to implement the change to whole technological landscape of their organizational structure or to assess the needs where a complete or a radical change is required. These kinds of decisions can be made by organizations by analyzing or predicting the technological evolution path in future. Previous research has also used predictive patent analysis using artificial neural networks ( Jeeeun Kim & Lee, 2014) and machine learning (Lee et al., 2018; Liu et al., 2020;

Figure 6.1  Patent analysis as an intermediary for market dominance

93

Priyanka C. Bhatt et al.

Figure 6.2  Strategic planning to identify current position and build strategies for future

Zambetti et al., 2018) approaches. Predictive analysis of patent citations and patent statistics as a whole is used to identify the future technology trends including diffusion and convergence of the technology, also early identification of emerging technologies (Mariani et al., 2019). Patent analysis has been the most proactive technique to recognize the information and knowledge structure. The patent counts and patent diversity in a particular technology domain usually represent the technology position and development in the technology life cycle curve. Thus, organizations with more developed and more cited patents are considered as leaders in the marketplace and considered as innovative. Economic and technology assessment and forecasting future trends can be done on patent statistics. Patent data is considered as incontestable technological indicator (Porter & Cunningham, 2004). However, patent information has been considered to have both advantages and drawbacks with respect to it being considered only as an output indicator of innovation and not as the input indicator for R&D innovation. Patent citations are, however, considered as a crucial measure for firm’s innovative competency (Lai et al., 2020). So, to use patent analysis and forecasting techniques to analyze the current core capabilities, technological intelligence, market position, and innovation strategies, firms can identify their goals based on the competitors and their own position in the market (Figure 6.2) – whether firms want to lead the market in a particular technology domain, or be a follower of other incumbents, also, if the firm wants to leverage the technological advantage and build a disruptive innovation model to emerge significantly where both followers and leaders follow their invention. Furthermore, on the function of technological direction, an organization can leverage the patent citation networks to assess the technology evolution using trajectories, as well as conventional technologies to plan their R&D and innovation strategies.

6.4 Conclusion Patent analysis techniques have been used in various ranges of an organizational planning, viz., marketing, product development, information and knowledge distribution, legal departments, etc. (Lee, 2013). This leads to a significant role of patent analysis in R&D planning, such as arriving at the decision whether to patent an innovation or not, or to plan the patent strategy ahead, in order to capture the market ahead of time and build long-term profit strategy. Previous research highlights the significance of knowledge intensive companies based on their patent values considered as their knowledge resources (Lee et al., 2009). To analyze the patent and patent portfolios, organizations train certain personnel for the specific tasks. Most importantly, patents can be leveraged to gain the competitive advantage, highlighting 94

Patent Analysis Approach

the importance of patents in the process of value creation. Previous research suggests organizations to consider patents as their strategic tools to achieve their business goals (Tseng et al., 2011). Patent analysis encourages the innovation as well as research activities, which further addresses the links between knowledge flows within organizations (Grimaldi et al., 2015). Furthermore, innovative product can help organizations to become market leader among the competitors. The primary focus of patent analysis has been ranging from technological monitoring to identifying the prospective technological opportunities. Different patent analysis techniques have been used by organizations as well as researchers to achieve different business objectives. Where a research tries to further improvise the analysis process by providing novel methods for patent analysis, organizations leverage such techniques to further enhance their competition game. Corporate performance can thus be expected to progress based on assessment of patent-based innovation standards. This chapter highlighted the role of patent analysis in innovation strategies of organizations. Previous studies have tried to perform patent analysis subject to various goals in mind; however, this chapter gives a direction to researchers and organizations to assess the value of patent analysis in overall business strategies. Patent information plays a crucial part in assessing the competitor’s technological strength and weaknesses, to lay down a strategic plan for businesses to plan their strategic developments.

References Abraham, B. P., & Moitra, S. D. (2001). Innovation assessment through patent analysis. Technovation, 21(4), 245–252. Adamuthe, A. C., & Thampi, G. T. (2019). Technology forecasting: A case study of computational technologies. Technological Forecasting and Social Change, 143(November 2014), 181–189. Afuah, A. (2003). Innovation Management: Strategies, Implementation, and Profits. New York: Oxford University Press. Ahmad, S. S., Khan, S., & Kamal, M. A. (2019). What is blockchain technology and its significance in the current healthcare system? A brief insight. Current Pharmaceutical Design, 25(12), 1402–1408. Ahn, S. J. (2020). Three characteristics of technology competition by IoT-driven digitization. Technological Forecasting and Social Change, 157(May), 120062. Aldieri, L., Makkonen, T., & Paolo Vinci, C. (2020). Environmental knowledge spillovers and productivity: A patent analysis for large international firms in the energy, water and land resources fields. Resources Policy, 69(September), 101877. https://doi.org/10.1016/j.resourpol.2020.101877 Antony, J., & Preece, D. (2001). Understanding, managing and implementing quality. Understanding, Managing & Implementing Quality, 1. http://search.ebscohost.com/login.aspx?direct= true&db=bth&AN=17441543&site=ehost-live Batagelj, V. (2003). Efficient algorithms for citation network analysis. Preprint Series, 41. Bhatt, P. C., Kumar, V., Lu, T.-C. T.-C., Cho, R. L.-T. R. L.-T., & Lai, K. K. (2020). Rise and Rise of Blockchain: A Patent Statistics Approach to Identify the Underlying Technologies: Vol. 1178 CCIS (pp. 456–466). https://doi.org/10.1007/978-981-15-3380-8_40 Bhupatiraju, S., Nomaler, Ö., Triulzi, G., & Verspagen, B. (2012). Knowledge flows - Analyzing the core literature of innovation, entrepreneurship and science and technology studies. Research Policy, 41(7), 1205–1218. Bindu, N., Sankar, C. P., & Kumar, K. S. (2019). From conventional governance to e-democracy: Tracing the evolution of e-governance research trends using network analysis tools. Government Information Quarterly, 36(3), 385–399. Brockhoff, K. (1991). Competitor technology intelligence in German companies. Industrial Marketing Management, 20(2), 91–98. Chandrahas, P., Kumar, D., Karthik, R., Gonsalves, T., Jhunjhunwala, A., & Raina, G. (2011). Some design considerations for a mobile payment architecture. 2011 National Conference on Communications, NCC 2011. https://doi.org/10.1109/NCC.2011.5734782 Chang, C. L., Lai, K.-K., Chen, H. C., & Chang, H. J. (2018). A structural approach to identify the position and role of the litigation relation network of smartphone companies. 2017 International Conference

95

Priyanka C. Bhatt et al. on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017-Proceedings, 2018-Janua, 111–118. Chang, C. L., Lin, C. Y., Lai, K. K., & Chen, H. C. (2019). The role on inter-organizational knowledge flows of patent citation network: The case of thin-film solar cells. Proceedings-2019 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2019. Chang, K. C., Chen, D. Z., & Huang, M. H. (2012). The relationships between the patent performance and corporation performance. Journal of Informetrics, 6(1), 131–139. Chang, Y. W., Yang, H. W., & Huang, M. H. (2017). Interaction between science and technology in the field of fuel cells based on patent paper analysis. Electronic Library, 35(1), 152–166. Chen, Y. S. (2011). Using patent analysis to explore corporate growth. Scientometrics, 88(2), 433–448. Choi, D., & Song, B. (2018). Exploring technological trends in logistics: Topic modeling-based patent analysis. Sustainability, 10(8), 15–17. Clarke, N. S. (2018). The basics of patent searching. World Patent Information, 54, S4–S10. https://doi. org/10.1016/j.wpi.2017.02.006 Clarke, N. S., Jürgens, B., & Herrero-Solana, V. (2020). Blockchain patent landscaping: An expert based methodology and search query. World Patent Information, 61(October 2019). https://doi. org/10.1016/j.wpi.2020.101964 de Pablos, P. O., & Lytras, M. (2018). Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness. Sustainability (Switzerland), 10(6). https://doi. org/10.3390/su10062073 Ernst, H. (1998). Patent portfolios for strategic R&D planning. Journal of Engineering and Technology Management, 15(4), 279–308. Fallatah, M. I. (2018). Does value matter? An examination of the impact of knowledge value on firm performance and the moderating role of knowledge breadth. Journal of Knowledge Management, 22(3), 678–695. Fleming, L., Mingo, S., & Chen, D. (2007). Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52(3), 443–475. Geng, D. (2017). How technology is reshaping financial services: Essays on consumer behavior in card, channel and cryptocurrency services. http://ink.library.smu.edu.sg/etd_coll_all%0Ahttp://ink.library.smu. edu.sg/etd_coll_all Geum, Y., & Kim, M. (2020). How to identify promising chances for technological innovation: Keygraph-based patent analysis. Advanced Engineering Informatics, 46( July), 101155. Grimaldi, M., Cricelli, L., di Giovanni, M., & Rogo, F. (2015). The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning. Technological Forecasting and Social Change, 94(1), 286–302. Ha, J., Kim, Y., Kim, B. K., & Kim, M. (2020). Patent based technological-level evaluation of dental implants industry. ICIC Express Letters, Part B: Applications, 11(8), 751–759. Hall, B., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. RAND Journal of Economics, 36, 16–38. Hanel, P. (2006). Intellectual property rights business management practices: A survey of the literature. Technovation, 26(8), 895–931. Hedman, J., & Henningsson, S. (2015). The new normal: Market cooperation in the mobile payments ecosystem. Electronic Commerce Research and Applications, 14(5), 305–318. Higham, K. W., Governale, M., Jaffe, A. B., & Zülicke, U. (2017). Unraveling the dynamics of growth, aging and inflation for citations to scientific articles from specific research fields. Journal of Informetrics, 11(4), 1190–1200. Ho, M. H.-C., Lin, V. H., & Liu, J. S. (2014). Exploring knowledge diffusion among nations: A study of core technologies in fuel cells. Scientometrics, 100(1), 149–171. Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63. Hung, S. C., Liu, J. S., Lu, L. Y. Y., & Tseng, Y. C. (2014). Technological change in lithium iron phosphate battery: The key-route main path analysis. Scientometrics, 100(1), 97–120. Kim, D. H., Lee, H., & Kwak, J. (2017). Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network. Research Policy, 46(7), 1234–1254. Kim, Jeeeun, & Lee, S. (2014). Identifying new technology opportunities from technology convergence: Patent based Artificial Neural Network Analysis. ISPIM Conference Proceedings, June, 1–9.

96

Patent Analysis Approach Kim, Junmo, & Shin, J. (2018). Mapping extended technological trajectories: Integration of main path, derivative paths, and technology junctures. Scientometrics, 116(3), 1439–1459. Kim, K., Park, K., & Lee, S. (2019). Investigating technology opportunities: The use of SAOx analysis. Scientometrics, 118(1), 45–70. Kumar, V., Chen, H. C., Lin, C. Y., Lai, K. K., & Chang, Y. H. (2018). Technological evolution of thinfilm solar cells through main path analysis. ACM International Conference Proceeding Series, 160–164. Kumar, V., Lai, K.-K. K.-K., Chang, Y.-H., Bhatt, P. C., & Su, F.-P. F.-P. (2020). A structural analysis approach to identify technology innovation and evolution path: A case of m-payment technology ecosystem. Journal of Knowledge Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/ JKM-01-2020-0080 Kumar, V., Lai, K. K., Chang, Y. H., & Lin, C. Y. (2018). Mapping technological trajectories for energy storage device through patent citation network. 2018 9th International Conference on Awareness Science and Technology, ICAST 2018, 56–61. Lai, K.-K., Chen, H.-C., Chang, Y.-H., Kumar, V., & Bhatt, P. C. (2020). A structured MPA approach to explore technological core competence, knowledge flow, and technology development through social network patentometrics. Journal of Knowledge Management, 25(2), 402–432. Lai, K.-K., Lin, C. Y., Yang, M. C., Chang, Y. H., Yang, M. C., & Yang, W. G. (2017). A structured approach to explore technological competencies through R&D portfolio of photovoltaic companies by patent statistics. Scientometrics, 111(3), 1327–1351. Lai, K.-K., Su, F. P., Weng, C. S., Chen, C. L., & Lin, M. L. (2006). A study of co-opetition strategy from the patent analysis perspective: The case of manufacturers in the stent market. Portland International Conference on Management of Engineering and Technology, 5(c), 2141–2149. https://doi. org/10.1109/PICMET.2006.296803 Lai, K. K., Su, F. P., Weng, C. S., & Chen, C. L. (2007). Co-opetition strategy from the patent analysis perspective: The case of the stent market. International Journal of Innovation and Technology Management, 4(2), 137–153. https://doi.org/10.1142/S0219877007001028 Lalitnorasate, P., & Miyazaki, K. (2014). Core technological competence and knowledge accumulation in the functional food industry: An empirical study of Japanese food firms. PICMET 2014, 1072–1081. Lee, C., Kwon, O., Kim, M., & Kwon, D. (2018). Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change, 127, 291–303. Lee, Sanghoon, & Kim, W. (2017). The knowledge network dynamics in a mobile ecosystem: A patent citation analysis. Scientometrics, 111(2), 717–742. Lee, Sungjoo. (2013). Linking technology roadmapping to patent analysis. Technology Roadmapping for Strategy and Innovation: Charting the Route to Success, 445(2007), 267–284. Lee, Sungjoo, Yoon, B., Lee, C., & Park, J. (2009). Business planning based on technological capabilities: Patent analysis for technology-driven roadmapping. Technological Forecasting and Social Change, 76(6), 769–786. Leifer, R., O’Connor, G. C., & Rice, M. (2001). Implementing radical innovation in mature firms: The role of hubs. Academy of Management Perspectives, 15(3), 102–113. Li, F., Yan, S., & Chen, S. (2011). Research and design of network ordering tickets mobile payment protocol. Proceedings of the 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, 916–918. https://doi.org/10.1109/ICDMA.2011.226 Liu, B., Lai, M., Wu, J. L., Fu, C., & Binaykia, A. (2020). Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model. Multimedia Tools and Applications, 79(15–16), 10177–10197. https://doi.org/10.1007/s11042-019-7422-x Liu, J. S., Lu, L. Y. Y., & Ho, M. H. C. (2019). A few notes on main path analysis. Scientometrics, 119(1), 379–391. Lo, C. C., Cho, H. C., & Wang, P. W. (2020). Global R&D collaboration in the development of nanotechnology: The impact of R&D collaboration patterns on patent quality. Sustainability (Switzerland), 12(15). https://doi.org/10.3390/su12156055 Longoni, A., & Cagliano, R. (2015). Environmental and social sustainability priorities: Their integration in operations strategies. International Journal of Operations and Production Management, 35(2), 216–245. https://doi.org/10.1108/IJOPM-04-2013-0182 Mariani, M., & Dihardja, J. W. (2010). The viability of proximity mobile payment implementation using near field communication (NFC) technology in Indonesia. Knowledge Management and

97

Priyanka C. Bhatt et al. Innovation: A Business Competitive Edge Perspective - Proceedings of the 15th International Business Information Management Association Conference, IBIMA 2010, 1, 59–71. Mariani, M. S., Medo, M., & Lafond, F. (2019). Early identification of important patents: Design and validation of citation network metrics. Technological Forecasting and Social Change, 146( January 2018), 644–654. Marzi, G., Dabić, M., Daim, T., & Garces, E. (2017). Product and process innovation in manufacturing firms: A 30-year bibliometric analysis. Scientometrics, 113(2), 673–704. Miller, J. G., & Roth, A. V. (1994). A taxonomy of manufacturing strategies. Management Science, 40(3), 285–304. Moehrle, M. G., & Caferoglu, H. (2019). Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology. Technological Forecasting and Social Change, 146( July 2018), 776–784. Mohorko, J., Chowdhury, A., & Planinšič, P. (2008). Emulation of mobile payment system. Proceedings of IWSSIP 2008–15th International Conference on Systems, Signals and Image Processing, 21–24. https:// doi.org/10.1109/IWSSIP.2008.4604357 Morescalchi, A., Pammolli, F., Penner, O., Petersen, A. M., & Riccaboni, M. (2015). The evolution of networks of innovators within and across borders: Evidence from patent data. Research Policy, 44(3), 651–668. https://doi.org/10.1016/j.respol.2014.10.015 Noh, H., & Lee, S. (2020). What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives. Technological Forecasting and Social Change, 157(May), 120046. Nylén, D., & Holmström, J. (2015). Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Business Horizons, 58(1), 57–67. Park, I., & Yoon, B. (2018). Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network. Journal of Informetrics, 12(4), 1199–1222. Pearson, B., & Thomas, N. (2002). Chapter Six: Starting your own business. In Book of Me -- Life Coach Yourself to Success (pp. 101–119). Thorogood Publishing Ltd. http://search.ebscohost.com/login. aspx?direct=true&db=bth&AN=22365999&site=ehost-live Popadiuk, S., & Choo, C. W. (2006). Innovation and knowledge creation: How are these concepts related? International Journal of Information Management, 26(4), 302–312. Porter, A. L., & Cunningham, S. W. (2004). Tech Mining: Exploiting New Technologies for Competitive Advantage. New York: John Wiley & Sons, Inc. Raisch, S., & Birkinshaw, J. (2008). Organizational ambidexterity: Antecedents, outcomes, and moderators. Journal of Management, 34(3), 375–409. Rosenkopf, L., & Nerkar, A. (2001). Beyond local search: Boundary-spanning, exploration, and impact in the optical disk industry. Strategic Management Journal, 22(4), 287–306. Schoenmakers, W., & Duysters, G. (2010). The technological origins of radical inventions. Research Policy, 39(8), 1051–1059. Shin, Y. G., Parte, S. S., & Jang, D. S. (2012). R&D indicators of a firm as predictors for predicting firm performance. Information, 15(2), 577–596. Sorescu, A., Frambach, R. T., Singh, J., Rangaswamy, A., & Bridges, C. (2011). Innovations in retail business models. Journal of Retailing, 87(Suppl. 1), S3–S16. https://doi.org/10.1016/j. jretai.2011.04.005 Soto-Acosta, P., Popa, S., & Martinez-Conesa, I. (2018). Information technology, knowledge management and environmental dynamism as drivers of innovation ambidexterity: a study in SMEs. Journal of Knowledge Management, 22(4), 824–849. Sternitzke, C., Bartkowski, A., & Schramm, R. (2008). Visualizing patent statistics by means of social network analysis tools. World Patent Information, 30(2), 115–131. https://doi.org/10.1016/j. wpi.2007.08.003 Su, F. P., Chen, S. J., Chang, Y. H., & Lai, K.-K. (2019). Construct a three-stage analysis model of integrated main path analysis and patent family-exploring the development of blockchain. ACM International Conference Proceeding Series, 151–156. Su, F. P., Lai, K.-K., Yang, M. C., Lin, C. Y., Chang, Y. K., & Liu, C. C. (2017). Roles and positions of knowledge flow of companies in the Silicone Hydrogel contact lens industry. ICMET 2017, Proceedings, 1–8.

98

Patent Analysis Approach Suominen, A., Toivanen, H., & Seppänen, M. (2017). Firms’ knowledge profiles: Mapping patent data with unsupervised learning. Technological Forecasting and Social Change, 115, 131–142. Tabrizi, A. A., & Pahlavan, A. (2020). Efficiency improvement of a silicon-based thin-film solar cell using plasmonic silver nanoparticles and an antireflective layer. Optics Communications, 454(August 2019), 124437. Tseng, F.-M., Hsieh, C.-H., Peng, Y.-N., & Chu, Y.-W. (2011). Using patent data to analyze trends and the technological strategies of the amorphous silicon thin-film solar cell industry. Technological Forecasting and Social Change, 78(2), 332–345. Verhoeven, D., Bakker, J., & Veugelers, R. (2016). Measuring technological novelty with patent-based indicators. Research Policy, 45(3), 707–723. Wada, T. (2018). The choice of examiner patent citations for refusals: evidence from the trilateral offices. Scientometrics, 117(2), 825–843. https://doi.org/10.1007/s11192-018-2885-5 Wang, C. C., & Huang, M. H. (2018). Knowledge spillover among semiconductor foundries. PICMET 2018- Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings, 1–9. Wang, Q., & von Tunzelmann, N. (2000). Complexity and the functions of the firm: breadth and depth. Research Policy, 29(7–8), 805–818. Xu, H. (2020). Topic-linked innovation paths in science and technology. Journal of Informetrics, 14(2), 101014. https://doi.org/10.1016/j.joi.2020.101014 Xu, S., Hao, L., An, X., Pang, H., & Li, T. (2020). Review on emerging research topics with key-route main path analysis. Scientometrics, 122(1), 607–624. Ying Huang, Fujin Zhu, Ying Guo, Porter, A. L., Yi Zhang, Donghua Zhu, Huang, Y., Zhu, F., Guo, Y., Porter, A. L., Zhang, Y., & Zhu, D. (2017). Exploring technology evolution pathways to facilitate technology management: A study of dye-sensitized solar cells (DSSCs). 2016 Portland International Conference on Management of Engineering and Technology (PICMET), 764–776. Yu, C. J., & Daim, T. (2017). Benchmarking of technology roadmapping process in energy sector: A literature review. PICMET 2017, Proceedings, 1–10. Zambetti, M., Sala, R., Russo, D., Pezzotta, G., & Pinto, R. (2018). A patent review on machine learning techniques and applications: Depicting main players, relations and technology landscapes. Proceedings of the Summer School Francesco Turco, 2018, 115–128. Zhang, P., White, J., & Schmidt, D. (2018). Architectures and patterns for leveraging high-frequency, low-fidelity data in the healthcare domain. 6th IEEE International Conference on Healthcare Informatics, ICHI 2018, 463–464.

99

7 PIONEER OR FOLLOWER Which Strategy to Choose? Aleksej Dreiling and Peter M. Bican

7.1 Introduction Firms constantly endeavor to optimize their profitability and general market positioning. One major source for such activities might be product or service innovations with their ability to create competitive advantages for the respective firm (Bican et al., 2017; Lieberman & Montgomery, 1988; Riar et al., 2022). Innovations and superior competitive advantage are not only important in traditional firm settings, but, also and in particular, for family firms as they strive to create value for future generations (Cruz & Nordqvist, 2012; Riar & Kellermanns, 2020). However, regardless of firm type, after the actual innovation phase, one of the most important questions in strategy is how this innovation should enter the market. The literature suggests that firms can choose between two different strategies: the innovator and first mover on the one hand or the imitator and later entrant on the other hand. The first mover, or also referred to as pioneer, is the first firm that sells a product on a specific market, like Wrigley did in the bubble gum market or AstraZeneca (with its drug Prilosec) did for proton pump inhibitors (Conley et al., 2013; Robinson & Fornell, 1985). New markets are characterized by higher levels of uncertainty with regard to technological and commercial aspects (Hienerth & Riar, 2015; Lieberman & Montgomery, 1988). Consequently, the pioneer is facing both missing information concerning the optimal product attributes and a lack of essential demand indicators. Although the entry into a new market bears significant risks, it simultaneously offers the potential for significant benefits since the first firm that enters a market does not face any competition. Preceding market entrants can reduce uncertainties substantially because their activities create information that might be useful for other firms that consider entering the same market (Srinivasan, Lilien, & Rangaswamy, 2004). Each firm that follows the pioneer into an active market is described as a later entrant, which is a synonym for follower. The literature distinguishes between early followers and late entrants by considering the order of entry (Lambkin, 1988). Despite this possible differentiation, the main focus of this chapter is the general distinction between pioneers and followers as well as the implications of using one of these strategies for the market entry. While the existing literature provides a high variety of theoretical and empirical studies with mixed results, the problem of identifying the suitable market entry strategy for a specific firm is still unsolved. Therefore, this chapter creates an extensive review of previous papers and their 100

DOI: 10.4324/9781003046899-10

Pioneer or Follower

findings for the research field of market entry. The combination of previous studies reveals potential triggers and enables the introduction of a market entry matrix that offers the possibility of an initial and simplified strategy evaluation under certain dimensions. By unifying both contributions, this chapter might simplify managers’ decision making and expand the research field of market entry strategies. Hence, this chapter’s main contribution is the presentation of market entry possibilities for pioneers and followers, depending on the characteristics of consumer and industrial goods markets on the one hand and firms’ characteristics concerning research and development (R&D), manufacturing, and marketing resources on the other hand.

7.2  Literature Review The rationale of market entry strategies is described by an overview of advantages and disadvantages for pioneers and followers, based on the paper of Lieberman and Montgomery (1988).

7.2.1  Rationale of the Pioneer and Follower Strategy 7.2.1.1  Sources of First-Mover Advantages One source of first-mover advantages is technological leadership that can arise either from the learning curve through increasing experience with production or through research and development activities whose outcome depends on relevant expenditures (Guderian et al., 2021; Riar et al., 2021). Regarding competitive advantages through the learning curve, Mansfield (1985) emphasized that competitors are able to gain detailed information on product as well as process technology within a year in ten different industries. Focusing on the chemical product industry, Lieberman (1989) highlighted that inter-firm diffusion of technology enables late entry, despite the existence of strong learning curve effects. As mentioned above, technological leadership can arise through R&D as well (Bican et al., 2017; Guderian, 2019). Several papers outline the so-called patent races where all returns go to the winner of the race, who benefits from his early start in the patent race, and followers are not able to leapfrog ahead (Fudenberg, Gilbert, Stiglitz, & Tirole, 1983; Gilbert & Newbery, 1982; Guderian et al., 2021; Rheinganum, 1983). Another source of first-mover advantages lies in the preemption of scarce assets. Three different types of preemption can be distinguished: first, preemption of input factors occurs when the first mover gains economic rents by buying bounded mobile and non-mobile assets whose prices are set too low because the market has not fully developed yet. Second, preemption of geographic locations and product characteristics is also a possibility to gain competitive advantages. According to this spatial preemption, the first mover has the option to choose the most profitable market segment. Additionally, the relatively lower profitability of the still available market segments could deter the entry of later entrants. Third, investments in plants and equipment are the last strategy of preemption of scarce assets. Here, the first mover is able to deter the entry of competitors by investing heavily into infrastructure, signaling that they strive for high output levels, even if the competition should enter. Switching costs and buyer choice under uncertainty are other sources of first-mover advantage. Since the first mover is the first active market participant, initial customers have to interact with him. However, the literature shows mixed results. While Klemperer (1987) and Wernerfelt (1986, 1988) supported the theory that switching costs enhance the value of early market shares, Klemperer (1987) highlighted conditions under which a large market share 101

Aleksej Dreiling and Peter M. Bican

can make the firm vulnerable to late entrants. In addition to switching costs, buyer choice under uncertainty is strongly impacted by first movers. Carpenter and Nakamoto (1989) as well as Schmalensee (1982) supported the theory that the first mover is able to (1) gain the position as the standard product of the category in consumers’ minds and (2) extend his advantages based on this superior positioning.

7.2.1.2  Sources of First-Mover Disadvantages Advantages and disadvantages of first movers are antithetic to advantages and disadvantages of followers. Therefore, first-mover disadvantages can highlight the advantages of ­followers. Lieberman and Montgomery (1988) distinguish between four sources of disadvantages: (1) free-rider effects, (2) the resolution of technological and market uncertainty, (3) shifts in technology or customer needs, and (4) incumbent inertia. 1 Free-rider effects describe the opportunity for later entrants to benefit from the efforts of the first mover by building on the foundations of earlier market participants and, thus, offer quite similar services or products at significantly lower costs (Mansfield, Schwartz, & Wagner, 1981). Further, Guasch and Weiss (1980) showed that late-mover firms have lower acquisition costs for employees because these firms can benefit from the employee screening of early entrants. In addition, Schnaars (1986) highlighted how firms like IBM or Matsushita have grown successfully by exploiting assets in the field of customer reputation, marketing, and distribution created by first entrants. 2 Resolution of technological and market uncertainty is the risk reduction effect from the first mover’s market activity. Hence, when the first mover enters the market, the uncertainty from a technological as well as a market and customer perspective is high; first-mover activities provide first insights on technological issues and customer reactions. Therefore, followers have the possibility to consider this information during their decision processes and, thus, have a clear advantage compared to the first mover’s situation (Kerin, Varadarajan, & Peterson, 1992; Urban, Carter, Gaskin, & Mucha, 1986). The shift in technology or customer needs (3) and incumbent inertia (4) (Lieberman & Montgomery, 1988) can be combined and summed up in the term “incumbent inertia.” This combination is based on the close relation of both concepts. They describe changes in the market situation due to an insufficient reaction of the first mover to changing market requirements. A market situation, where the first mover is profitably positioned and competitors are not able to significantly harm the market share of the first mover’s product, can often be the starting point of incumbent inertia. Incumbent inertia can trigger a change in the market characteristics, which in turn can lead to an entirely new competitive landscape. Lieberman and Montgomery (1988) highlighted three root causes for incumbent inertia: commitment to a specific set of fixed assets, reluctance to cannibalize existing product lines, and organizational inflexibility.

7.2.2  Empirical Analyses on Market Entry Decisions 7.2.2.1  Overview of Pioneer and Follower Interdependence This section combines the theoretical concepts of first-mover advantages and disadvantages with empirical evidence and is the foundation for the subsequent analysis of the impact of 102

Pioneer or Follower

differences based on industry, product, and region. Here, prior literature has focused on absolute and comparative first-mover advantages. Bain (1956) accentuated that an early entry enables firms to establish serious barriers for followers that lead to competitive advantages for pioneers. This generalized method of creating a competitive advantage, by entering the market early, constitutes the absolute firstmover advantage. Further support for the absolute first-mover advantage is provided by Robinson and Fornell (1985). Based on the PIMS database, they argued that pioneers have a significant market share advantage compared to early followers. A subsequent paper of Robinson (1988), which focuses on the industrial goods industry and is a replication of Robinson and Fornell (1985), emphasized related results. Both empirical studies will be presented in more detail in the next section that highlights the impact of industry-based differences. Urban et al. (1986) conducted an empirical study of frequently purchased consumer goods with high relevance for the research field of first-mover decisions. They supported the existence of significant pioneer market share advantages (Robinson & Fornell, 1985) and extended their findings by highlighting the impact of positioning and advertising on market shares. As outlined before, Lieberman and Montgomery (1988) created a conceptual framework that consolidated the broad range of relevant first-mover characteristics. It contrasted the possible sources of advantages and disadvantages, emphasizing the underlying complexity. With regard to the variety of sources of first-mover disadvantages, they highlighted the necessity of questioning the existence of competitive advantages solely as a result of the order of market entry. Furthermore, Lieberman and Montgomery (1998) referred to their prior study from 1988 and extended the perspective on first-mover advantages with the resource-based view (RBV) of the firm. The RBV describes a viewpoint shift; instead of focusing on a specific market activity with the minimal resources, firms might focus on their available resources and determine the optimal market activities to optimize performance (Wernerfelt, 1984; Yang et al., 2020). This combination of first-mover advantages and the RBV leads to comparative first-mover advantages, which accentuates that competitive advantages are also dependent on market as well as firm characteristics and not only on entry timing. Hence, Lieberman and Montgomery (1998) supported the second research stream of comparative first-mover advantages and, thus, the ability of the follower strategy to be more attractive for certain firms. One of the starting points of the resource considerations for strategic planning was Abell (1978), outlining that it is necessary to use a dynamic analysis for strategic market planning. Hence, business decisions have to consider the market situation and the individual firm’s assets and capabilities. This results in limited periods when the market requirements and the firm’s competences are strategically compatible, creating a limited timeframe for strategic activities. Based on Abell’s (1978) theoretical concept of strategic windows, market situations exist that would significantly advance a first-mover decision for firms with a specific set of resources and other market situations would strengthen the follower strategy for specific firms. Boulding and Christen (2003) extended the research field of market entry strategies by raising the issue of long-term profitability advantages instead of the development of market shares. With a study of 363 and 858 business units from consumer goods and industrial goods markets respectively, the authors found contrasting results to previous findings. Pioneers have a long-term profit disadvantage in comparison with followers. Additionally, this profit disadvantage is even stronger in industrial goods markets than in consumer goods markets, which is in line with Robinson’s (1988) findings that pioneers’ market share position is weaker in industrial goods industries than in consumer goods industries. Subsequently, 103

Aleksej Dreiling and Peter M. Bican

an economic framework was replicated and Boulding and Christen (2003) examined the long-term impact of consumer demand advantages compared with cost disadvantages. This leads to the result that pioneers are confronted with a greater long-term cost disadvantage in both industries. However, they proceeded with an extended analysis to emphasize that pioneers have an initial profit advantage, which holds for 12–14 years before it turns into a disadvantage. Further, it was argued that customer learning, patents, and market positioning can enhance the period of profit advantage. With regard to earlier findings of this study, empirical evidence showed that the mentioned moderators have a stronger impact in consumer goods industries. For the consumer goods, this moderating effect is even able to transform the initial advantage into a sustainable profit advantage for pioneers. Further, Boulding and Christen (2003) found strong support for the assumption that the entry timing has to be considered endogenous, which corresponds with the findings of Moore, Boulding, and Goodstein (1991).

7.2.2.2  Impact of Industry-Based Differences on Market Entry Strategies Subsequent to the general overview of first-mover decisions in the literature, the following sections emphasize the possible impacts of differences between specific industries, product attributes, and regions on first-mover decisions. Focusing on mature consumer goods industries, Robinson and Fornell (1985) studied 371 strategic business units based on the PIMS database and highlighted that pioneers have significantly higher market shares compared to late entrants. Treating pioneering as an exogenous variable, their empirical analysis came to the result that pioneers are leading with an average market share of 29%, followed by early followers and late entrants with 17% and 12% respectively. Furthermore, the authors analyzed possible reasons for these differences in market shares by formulating nine hypotheses. Important drivers for market shares are product quality as well as the breadth of product lines. Pioneers tend to have higher values in both characteristics based on time and experience advantages due to their early entry. The hypotheses that the pioneer’s distribution advantages lead to higher market shares in low-priced and high-frequented consumer goods or that pioneer’s low prices increase its market share were only weakly supported by the empirical evidence. With regard to the impact of pioneer’s low prices, Robinson and Fornell (1985) outlined that instead of offering lower prices, pioneers often offer the same prices as later entrants but with a higher product quality. Further support can be found for the hypothesis that low purchase prices and frequency lead to higher market shares for the pioneer due to consumer information advantages. On the contrary, product change on a seasonal or annual basis causes lower market shares for the pioneer. Only very weak support is found for the hypothesis that pioneers have higher market shares in markets with an intense advertising landscape. Additionally, Robinson and Fornell (1985) found only partial support for market share effects of deterioration. However, the study of Robinson and Fornell (1985) also rejected two hypotheses completely. They described that pioneers are able to gain market shares through direct cost savings which would lead to a stronger marketing mix based on absolute cost advantages in the first instance and scale economy advantages in the second instance. Both hypotheses were rejected by the empirical data mainly driven by the fact that pioneers actually have an absolute cost disadvantage. In addition to these findings, the authors delineated that the advantage from product line breadth appears to be more sustainable and larger than the advantage from superior product quality. As outlined in the general overview, Urban et al. (1986) supported the first-mover advantage in terms of market share for equal product attributes and advertising spending in 104

Pioneer or Follower

frequently purchased consumer goods markets. Their studies imply that followers have the opportunity to outweigh the advantages of the order of market entry by using a superior product positioning and advertising strategy compared to the pioneer. In the following, it is analyzed whether these findings from the consumer goods industry also hold in other industries like industrial, pharmaceutical, and telecommunication goods. In this context, Robinson (1988) has produced insightful results, based on Robinson and Fornell (1985) with an identical methodology and database. The average pioneer market share of 29.3% mirrored the 29% market share of the consumer goods study, while the market shares of the early follower with 21.35% and the last entrant with 15.03% differed. An additional difference between the consumer and the industrial industry was identified in the purchase amount as a source of pioneer advantages. Low purchase amounts are beneficial for the pioneer in consumer goods markets, while higher purchase amounts are beneficial for the pioneer in industrial goods markets (Robinson, 1988). Furthermore, Robinson (1988) highlighted that the initial benefit of being the pioneer is weaker in industrial goods markets than in consumer goods markets, but this disadvantage is only temporary and fades over time. Parry and Bass (1989) trailed the analyses of Robinson and Fornell (1985) as well as Robinson (1988) to extend the previous findings by considering the impact of concentrated and non-concentrated industries on pioneers’ market share advantage over early followers. The characteristic of concentration is used to examine the argument that the successful introduction of market barriers increases the market concentration, which is leading to an increase of incumbent firm’s profitability (Karakaya & Stahl, 1989). Using the PIMS database, Parry and Bass (1989) analyzed 593 consumer goods and 1,287 industrial goods markets, and the empirical evidence confirmed their hypotheses that market share advantages from pioneering are stronger in concentrated industries. This result holds for consumer goods as well as industrial goods markets. Additionally, they accentuated that market share advantages are stronger in consumer goods than industrial goods markets with 6.92 and 4.2 share points respectively. Further, Parry and Bass (1989) outlined that pioneers tend to benefit from consumer uncertainty as well as easy access to information in consumer goods markets with low purchase amounts. A pioneer’s market share advantage is dependent on the industry as well as the purchase amount (Parry & Bass, 1989). In addition to the empirical studies in industrial goods and consumer goods industries (Robinson & Fornell, 1985; Urban et al., 1986; Robinson, 1988), Shankar, Carpenter, and Krishnamurthi (1998) conducted a market analysis in the pharmaceutical industry. Analyzing 13 brands in two product categories, they contributed to the research field of market entry strategies by distinguishing between innovative and non-innovative late entrants to elucidate the factors, which lead to the success of followers. Contrasting the hypothesis with the results, Shankar et al. (1998) found that innovative late entrants have the fastest growth followed by the pioneers. This finding is contrary to Kalyanaram and Urban (1992), who outlined that the pioneer has the slowest growth. The empirical evidence also highlighted that the diffusion of innovative late entrants decreases pioneer’s diffusion while the diffusion of non-innovative competitors has no impact. Further, the market potential of innovative late entrants is equal to or even higher than the market potential of the pioneer. Similar to the growth hypotheses, non-innovative late entrants are not able to reach the pioneer’s level in terms of market potential. The hypotheses’ categories four and five revealed further insights. While previous findings assumed equal marketing mix effectiveness among late entrants (Kalyanaram & Urban, 1992), Shankar et al. (1998) elucidated that the marketing mix of non-innovative late entrants is less effective. Innovative late entrants and pioneers showed the same effectiveness. Additionally, the empirical evidence underlined that the 105

Aleksej Dreiling and Peter M. Bican

growth of innovative late entrants decreases the effectiveness of the pioneer’s marketing spending, while the non-innovative late entrant’s growth has no impact. Finally, the empirical study supported the hypotheses that innovative late entrants have higher repeat purchase rates than pioneers, and that pioneers have higher rates than non-innovative late entrants. Hence, Shankar et al.’s (1998) study of the pharmaceutical industry provided empirical evidence that innovative followers are able to leapfrog the pioneer with a more attractive and effective market position.

7.2.2.3  Impact of Product- and Region-Based Differences on Market Entry Strategies Product characteristics are an additional dimension that can be found in the literature to analyze market entry strategies. In this category, network effects and innovativeness are of special relevance (e.g., Keidel et al., 2021). The technological development of the Internet and online services has caused significant changes in the entire supply chain, especially for the distribution of products. Thus, network effects have an increasing relevance for first-mover advantages. Srinivasan, Lilien, and ­Rangaswamy (2004) examined the effects of network externalities on pioneers’ survival with an empirical study of 45 consumer durables and office products. As outlined by Katz and Shapiro (1985), the value of products or services in markets with network effects depends not only on the respective quality but also on the number of already existing users. Thus, the user base is of major importance in the realm of communication or software products such as the Microsoft Office programs. Besides examining the network effects in the context of pioneer survival, they tested the moderating effects of the product’s radicalness, its technological intensity, pioneer’s size, and incumbency of pioneers. The radicalness describes the degree of newness of an emerging technology compared to the old one. Technological intensity accounts for the complexity of applied knowledge measured by the number of different knowledge sources. The authors found that a general increase of network externalities decreases a pioneer’s survival duration (Srinivasan et al., 2004). However, product radicalness and technological intensity have a positive relation to pioneer survival. Thus, amplifying these characteristics and combining them with network externalities would extend the pioneer’s survival duration. Furthermore, the authors outlined that network externalities also have a greater positive effect on larger firms. Consequently, Srinivasan et al. (2004) emphasized that, based on the empirical evidence, the disadvantages outweigh the advantages of network externalities and, thus, network externalities decrease the pioneer’s survival period. Additionally, Min, Kalwani, and Robinson (2006) accentuated the issue of innovative and non-innovative pioneering of new markets. They examined the survival rate of pioneers and followers who used “really new” or “incrementally new” products to enter the market (p. 15). To distinguish between the two degrees of innovation, the authors described really new products as products, which change the market structure and require customer learning because of the application of a new technology. In contrast, incrementally new products are focused on already existing market needs and technology. Hence, the key driver is the difference in market and technological uncertainty (Min et al., 2006). On the one hand, pioneering with really new products shows high levels of uncertainty, because the product is making its first appearance on the market. On the other hand, pioneering with incrementally new products shows lower levels of uncertainty, because it builds on an already existing market. Empirically, pioneers with incremental innovations exhibited a significantly higher 12year survival rate (61%) than pioneers with really new products (23%), while the 12-year 106

Pioneer or Follower

survival rates of early followers showed no significant difference for really new products (38%) and for incrementally new products (39%). The empirical evidence also highlighted early followers having clear survival rate advantages in really new product markets (38%) compared to pioneers (23%). However, pioneers outperform early followers in incrementally new product markets with a survival rate of 61% compared to 39% for early followers. Finally, they elucidated that firms, which pioneer really new product markets, are often the first to fail. In contrast, the pioneering of incrementally new product markets is more lucrative due to significantly higher 12-year survival rates compared to early followers (Min et al., 2006). Song, Di Benedetto, and Zhao (1999) contributed with their cross-national study of nine countries to international comparisons. Examining the manager’s perceptions of first-mover advantages and disadvantages, they analyzed manufacturing and service firms in Taiwan, Singapore, Hong Kong, Japan, China, the United States, the United Kingdom, Germany, and South Korea. Using a questionnaire and 2,419 responses of senior executives, the authors found that all executives of manufacturing firms agree that pioneers have market share and profitability advantages. Even though the responses outlined that the majority of managers in the service industry perceive market share advantages for the pioneer, there was no support for profitability advantages. Further, Song et al. (1999) emphasized that while mainly Western countries perceive a cost advantage for pioneers over later entrants, managers from the Asia-Pacific area do not support cost advantages for pioneers. In relation to this finding, they highlighted that the respective manager’s evaluation of cost advantages for their market entry decision has the same peculiarity as well. Thus, possible cost advantages are less important for Asia-Pacific managers but important for Western managers. However, the possible differentiation advantages of pioneering are of importance for managers in the Western as well as the Asia-Pacific area. These findings contributed to the research field by extending the view on diverging markets with the consideration of different culture-based perceptions. Thus, even similar market environments might cause different market entry strategies due to contrasting management perceptions of first-mover advantages and disadvantages.

7.2.3  Determinants of the Market Entry Strategy Adding to the discussion of absolute and comparative first-mover advantages, Robinson, Fornell, and Sullivan (1992) studied the intrinsic strengths of market pioneers and their impact on the probability for individual market entry strategies. Their study distinguished between three categories – the pioneer, early follower, as well as late entrants – and is based on 171 strategic business units. With 78% in industrial goods and 22% in consumer goods, the data sample provides the opportunity of a cross-sectional analysis. Comparing the hypotheses with the results of the multinomial logit model, the authors found that increasing research and development skills do not significantly impact the market entry strategy. Further, there is evidence that first entrants have lower manufacturing skills but there is no support for the hypothesis that increasing manufacturing skills strengthen early followers (Robinson et al., 1992). However, the empirical results supported that increasing shared manufacturing resources encourage early following. Moreover, Robinson et al. (1992) acknowledged that growing marketing skills advance late entrants. Further, the authors accentuated that entry via licensing and acquisition does not lead to pioneering, and parent’s cooperation size does not increase the probability of early following. Instead, they outlined that early followers use acquisition entry in most cases and that the parent’s cooperation size decreases the probability of late entry. Finally, the empirical evidence emphasized that growing financial skills 107

Aleksej Dreiling and Peter M. Bican

increase the probability of being a first entrant (Robinson et al., 1992). The authors did not formulate a hypothesis about financial skills, because the literature gave no indication for its impact. Generally, these results support the comparative first-mover advantage of Abell (1978) because it underlines the different skills and capabilities related to pioneering, early following, and late entrance. With their empirical study of the minicomputer and the PC market, Schoenecker and Cooper (1998) compared two markets and tested the ability of firm resources and organizational attributes to affect firm’s entry timing. Based on the empirical evidence, the authors elucidated that firms with greater R&D resources tend to enter early. Further, greater marketing resources in terms of a direct sales force also lead to early entry. An additional characteristic that tends to cause early entry is the increasing firm size. Contrary to the findings of Robinson et al. (1992), Schoenecker and Cooper (1998) found no significant relation between financial resources and early entry. They argued that a possible explanation is that only firms with sufficient financial resources are able to enter a new market in general. Hence, as also suggested by more recent research, all market entrants have to be in the position of strong financial resources (e.g., Hohl et al., 2021; Riar et al., 2021). From the market characteristics perspective, the increasing threat of new markets for firm’s established business is related to early entry. Further, the authors found support for their hypothesis that a systematic relationship between firms’ capabilities as well as resources and the entry timing only exist in industries with the general possibility of building first-mover advantages. Finally, they emphasized that the significance of the mentioned results is limited to the minicomputer industry, and hence, this highlights the possible differences of determinants across industries. Additional analysis of the determinants of the market entry decision was provided by García-Villaverde and Ruiz-Ortega (2007). With regard to the market characteristics, the authors analyzed the level of imitation, environmental dynamism, and environmental rivalry. Based on Miller and Friesen (1983) and Boyd, Dess, and Rasheed (1993), they measured environmental dynamism with “...a steady level of entry and exit in the industry...” and with “...changes in demand, competitors, and technological conditions...” (p. 103). They also analyzed managerial, marketing, and technological capabilities with regard to firm characteristics. Using a hierarchical regression analysis, García-Villaverde and Ruiz-Ortega (2007) found that only the imitation level and the environmental dynamism have a significant impact on the order of entry. Thereby, firms enter later into markets with a high imitation level and earlier into markets with a high environmental dynamism. The authors linked the attractiveness of dynamism with pioneers’ opportunity to shape the market developments in his favor. Further, the researchers showed that all three firm characteristics have positive signs but do not have the needed significance. The disregard of managerial, marketing, and technological capabilities led to the analysis with an extended model, which included interaction effects of firm capabilities and market characteristics. The authors found that combining the degree of imitation with managerial capabilities, environmental dynamism with technological capabilities, and environmental rivalry with marketing capabilities leads to an increased probability of pioneering (García-Villaverde & Ruiz-Ortega, 2007). Ruiz-Ortega and García-Villaverde (2008) extended their analysis of first-mover advantages by examining the impact of certain capabilities after market entry. With a focus on the service industry, they found that marketing capabilities have the strongest impact on pioneer’s performance. This finding highlights that pioneering firms have to shift their view of the importance of capabilities because several researches argued that firms with strong R&D capabilities enter the market first and marketing capabilities tend to increase the probability 108

Pioneer or Follower

of becoming a follower (Robinson et al., 1992; Schoenecker & Cooper, 1998). Furthermore, Ruiz-Ortega and García-Villaverde (2008) emphasized that early followers experience a positive performance effect with the use of a low-cost strategy, which might correspond with specific manufacturing skills. Hence, the support of manufacturing skills for early followers is in line with previous findings (Robinson et al., 1992). Support for determinants can also be found in other related research fields. Lee, Beamish, Lee, and Park (2009) studied the strategic choice of firms during an economic crisis and examined the impact of specific capabilities on export activities. Testing their hypotheses, the authors found that investments in R&D enable a higher flexibility, and thus, these firms are able to increase their exports. However, investments in marketing have the opposite impact on the firm’s flexibility and export activity. If national boundaries are neglected, exports might be considered an entry into a new market. Hence, Lee et al. (2009) supported the empirical findings of the market entry literature (Schoenecker & Cooper, 1998) that capabilities in research and development increase the probability of early entry and that enhanced marketing resources can lead to a follower strategy. In a recent study, Rodríguez-Pinto, Rodríguez-Escudero, and Gutiérrez-Cillán (2012) examined the market entry strategy as a mediator between firm resources and new product performance. Considering the market entry as endogenous and dependent on managerial, R&D, manufacturing, and marketing resources, specific combinations of firm resources and market entry strategy can have positive or negative impacts on new product performance. Rodríguez-Pinto et al. (2012) found that only managerial resources have a significant direct impact on the performance of new products. The empirical evidence showed no significant impact for the other three resources. However, the data highlighted that managerial and R&D resources have a positive significant impact on market entry; thus, these resources lead to pioneering. Additionally, they found a significant negative impact of manufacturing resources on the market entry; thus, manufacturing resources lead to the follower strategy. The influence of marketing resources was not significant.

7.3  The Market Entry Matrix A wide range of variables impacts market entry decisions. Prior literature does not provide comprehensive and unified means to facilitate firms’ decisions. This chapter aims to provide a matrix that offers an initial evaluation of suited market entry strategies, as presented in ­Figure 7.1. It summarizes, incorporates, and condenses the above findings from prior literature. The matrix above distinguishes between two dimensions: (1) industry and (2) firm capabilities. For example, a firm in the consumer goods industry with significant capabilities in R&D has a higher probability to enter the market as a pioneer. However, if the same firm has superior manufacturing capabilities instead of R&D, the firm has a higher probability to enter the market as a follower. The probabilities of market entry type for the industrial goods industry as well as for firms with superior marketing skills are highlighted as well. Moreover, the shading indicates probabilities of market entry: the shaded areas indicate a lower probability of market entry with the specific strategy, compared to non-shaded areas. The vertical industry dimension is based on the empirical studies presented in the previous section Impact of industry-based differences on market entry strategies. Several findings underlined that pioneers have significant market share advantages in consumer goods markets (Robinson & Fornell, 1985; Urban et al., 1986). Additionally, direct comparisons of both industries outlined that pioneers have weaker first-mover advantages in industrial goods 109

Industrial Goods

Pioneer

Follower

Follower

Consumer Goods

Aleksej Dreiling and Peter M. Bican

Pioneer

Follower

Follower

R&D

Manufacturing

Marketing

Figure 7.1  Market entry matrix Source: own illustration

markets (Robinson, 1988). Parry and Bass (1989) supported the attractiveness of consumer goods markets for pioneers with their consideration of market concentration. By including profitability, Boulding and Christen (2003) found stronger profitability disadvantages in industrial goods markets. Therefore, the matrix introduces the shaded areas to accentuate that the specific strategies are less favorable in these areas. However, firms do not always have the chance to change the industry in which they operate. Although the areas may be shaded, there are still advantages for the specific strategy. In summary, the matrix assumes that pioneer advantages are more significant in consumer goods and less significant in industrial goods industries. The horizontal dimension of firms’ resources builds on the empirical studies described in the previous section Determinants of the market entry strategy. There is substantial support for the assumption that greater degrees of R&D resources support the market entry as a pioneer. Contrarily, high levels of manufacturing and marketing resources provoke a market entry as a follower. Thereby, Schoenecker and Cooper (1998) found support for the positive relation between R&D resources and pioneering. Further, Helfat and Lieberman (2002) outlined that start-up companies tend to enter the market as pioneers because of their innovativeness. Thus, if R&D causes innovations, it also supports the relation between R&D and pioneering. With their extended model that includes interaction effects, García-Villaverde and Ruiz-Ortega (2007) elucidated that technological resources lead to pioneering in dynamic environments. Arguing that nowadays most industries have high dynamics based on the already mentioned definition of environmental dynamism (Miller & Friesen, 1983; Boyd et al., 1993), this constitutes at least partial support for the matrix’s assumption. Moreover, additional support for R&D resources is found by Lee et al. (2009) in the assumptions outlined in the previous section. Finally, Rodríguez-Pinto et al. (2012) accentuated that R&D resources often entail the market entry as a pioneer. Concerning the manufacturing resources, Robinson et al. (1992) claimed that although increasing manufacturing skills have no significant impact on followers, shared manufacturing resources do increase the probability of following, which thus supports the matrix’s assumption. Under the expectation that anticipated performance after the market entry impacts the initial probabilities of available market entry strategies, Ruiz-Ortega and García-Villaverde (2008) acknowledged additional support for firms with superior manufacturing capabilities to choose the follower strategy. Further, Rodríguez-Pinto et al. (2012) emphasized that manufacturing resources tend to cause market entry as followers. 110

Pioneer or Follower

With regard to the marketing resources, support for the assumption that superior marketing resources lead to a market entry as follower can be found in Robinson et al. (1992) and Lee et al. (2009). However, the generalized consideration of marketing resources is delicate because they can vary significantly and have differing effects on the market entry decision ­( Rodríguez-Pinto et al., 2012). As outlined by Rodríguez-Pinto et al. (2012), marketing resources might be a brand or a direct sales force. Regarding the variety of marketing resources and the empirical findings, this chapter assumes that the findings of Schoenecker and Cooper (1998) on direct sales force leading to pioneering are outweighed by the remaining literature of marketing capabilities’ impact. This was elaborated on by Lieberman and Montgomery (1988): “if one firm has unique R&D capabilities while the other has strong marketing skills, it is in the interest of the first firm to pioneer and the second firm to enter at a later date” (p. 52). If a firm has superior resources in more than one category, the empirical evidence and the derived matrix provide a slight indication how this impacts the market entry strategy. Shankar et al. (1998) outlined the influence of innovative and non-innovative followers on the pioneer’s market share and diffusion for instance. As accentuated earlier, innovative followers can leapfrog over the pioneer while non-innovative followers do not influence the pioneer’s market position. Thus, additional R&D resources do greatly advance the follower, who has superior marketing or manufacturing resources. Further, Ruiz-Ortega and García-Villaverde (2008) stated that marketing resources have the strongest impact on pioneer’s performance after market entry. Hence, enhanced marketing resources are beneficial for pioneers with superior R&D resources. In conclusion, empirical studies showed that additional resources are beneficial for both strategies, which seems rational but the optimal combination of two categories out of three is still unclear. However, this study assumes that the strongest category will determine the entry strategy. This might lead to the situation that a firm with superior R&D and less superior marketing resources outperforms another firm with only superior R&D resources but both firms would still choose the pioneer strategy. In cases of equal strength, market entry decisions might be determined by factors outside the target market such as spillover effects on other markets, motivation of individuals within the firm, or coincidence. Since empirical studies reveal mixed results in the category of financing resources, they are not included in the market entry matrix. On the one hand, empirical research in the market entry strategy literature considers all market entrants and the market entry, as outlined before, requires significant financial resources. Hence, Schoenecker and Cooper (1998) argued that only firms with sufficient financial resources contemplate an entry into new markets. These circumstances lead to a lack of significance for financial resources in impacting the decision making between the pioneer and follower strategy. On the other hand, superior financial capabilities enable and yield the opportunity to invest in R&D, manufacturing, and marketing resources. Thus, by relying on the included resources of the market entry matrix, a significant share of financial capabilities is represented.

7.4 Conclusion With the aim to enhance the existing literature on market entry strategies, this chapter contributes to an extensive literature review, which unifies major empirical and theoretical studies as well as categorizes these to accentuate distinctions with regard to different industries, regions, and product characteristics. The analysis showed, for instance, that pioneers have more significant advantages in consumer goods markets, that drivers of the pioneering decision differ between Western and Asian countries, and that pioneers as well as followers are 111

Aleksej Dreiling and Peter M. Bican

able to benefit from network externalities. By formulating specific dimensions, this chapter introduced the market entry matrix, supporting managers in a first evaluation of the market situation. Accounting for specific industry and firm resources, the matrix follows the RBV literature and provides six distinct areas, with two areas suggesting a pioneer strategy and four areas suggesting a follower strategy. This matrix condenses market entry considerations of firms and serves as a starting point for future research. Therefore, future research should be directed at enhancing the availability of comprehensive market entry evaluation models, also accounting for innovation and digitalization capabilities (Bican and Brem, 2020a).

7.4.1 Limitations and Further Research Areas The limited scope of this chapter requires accurately selected literature. Therefore, the literature review provides fundamental insights into the aspects of entry timing, instead of covering the entire available literature. Hence, for a global view on the research field of market entry strategies and for a detailed perspective on a specific subfield, additional literature has to be considered. Another aspect that restricts the literature review is the regional focus of existing studies. Due to the strong focus on the United States, a generalization for other geographical areas has to be considered with caution. Further, the market entry matrix focuses on the mentioned dimensions with a simplified perspective. In addition, it has to be recognized that the research field is characterized by a high complexity with a variety of variables that might change the suited market entry strategy in specific circumstances. As emphasized by Lieberman and Montgomery (1998), it would be beneficial for the research field of market entry strategies to connect the pioneer and follower strategies with long-term profitability. Measuring the impact of R&D and innovation on the respective strategies could reveal addition insights (Bican and Brem, 2020b). Further, the existing research on market entry determinants should be continued. With reference to the findings of the literature review, recent studies on market entry strategies with their effects on the market position would advance the understanding of the current market situation due to the constant economic development. In a detailed perspective, studies with a regional focus outside the United States might elucidate geographical distinctions and increase the accuracy of determined first-mover advantages.

7.4.2 Final Remarks This chapter found that firms with superior R&D resources tend to enter the consumer goods and industrial goods markets as pioneers. However, firms with superior manufacturing or marketing resources tend to enter both markets as followers. Despite the similar evaluation of the entry strategy for the individual markets, the entry of pioneers has a higher probability in consumer goods markets while followers have a higher probability of entry in industrial goods markets. These differences result from empirical findings that first-mover advantages (disadvantages) are greater (weaker) in consumer goods markets. This chapter contributes to the research field by providing an initial evaluation approach of market entry strategies based on the self-developed market entry matrix and an extensive literature review (Cordes et al., 2021).

References Abell, D. F. (1978). Strategic Windows. Journal of Marketing, 42(3), 21–26. Bain, J.S. (1956). Barriers to New Competition. Cambridge, MA: Harvard Business Press

112

Pioneer or Follower Bican, P. M., & Brem, A. (2020a). Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There a Sustainable “Digital”? Sustainability, 12(13), 5239. Bican, P. M., & Brem, A. (2020b). Managing Innovation Performance: Results from an Industry-Spanning Explorative Study on R&D Key Measures. Creativity and Innovation Management, 29(2), 268–291. Bican, P. M., Guderian, C. C., & Ringbeck, A. (2017). Managing Knowledge in Open Innovation Processes: An Intellectual Property Perspective. Journal of Knowledge Management, 21(6), 1384–1405. Boulding, W., & Christen, M. (2003). Sustainable Pioneering Advantage? Profit Implications of Market Entry Order. Marketing Science, 22(3), 371–392. Boyd, B. K., Dess, G. G., & Rasheed, A. M. a. (1993). Divergence between Archival and Perceptual Measures of the Environment: Causes and Consequences. The Academy of Management Review, 18(2), 204–226. Carpenter, G. S., & Nakamoto, K. (1989). Consumer Preference Formation and Pioneering Advantage. Journal of Marketing Research, 26, 285–298. Conley, J. G., Bican, P. M., & Ernst, H. (2013). Value Articulation: A Framework for the Strategic Management of Intellectual Property. California Management Review, 55(4), 102–120. Cordes, A., Guderian, C. C., & Riar, F. J. (2021). Exploring the Practice of Evaluation in Corporate Venturing, International Journal of Innovation and Technology Management, 18(05), 2150026. Fudenberg, D., Gilbert, R., Stiglitz, J., & Tirole, J. (1983). Preemption, Leapfrogging and Competition in Patent Races. European Economic Review, 22, 3–31. García-Villaverde, P. M., & Ruiz-Ortega, M. J. (2007). Determinants of Entry Timing Firm Capabilities and Environmental Conditions. Management Research, 5(2), 101–112. Gilbert, R. J., & Newbery, D. M. G. (1982). Preemptive Patenting and the Persistence of Monopoly. American Economic Review, 72(3), 514–526. Guasch, J. L., & Weiss, A. (1980). Adverse Selection by Markets and the Advantage of Being Late. The Quarterly Journal of Economics, 93(3), 453–466. Guderian, C. C. (2019). Identifying Emerging Technologies with Smart Patent Indicators: The Example of Smart Houses. International Journal of Innovation and Technology Management, 16(2), 1950040. Guderian, C. C., Bican, P. M., Riar, F. J., & Chattopadhyay, S. (2021). Innovation Management in Crisis: Patent Analytics as a Response to the Covid-19 Pandemic, R&D Management, 51(2), 223–239. Hienerth, C., & Riar, F. (2015). The Architecture of Evaluation Processes in Open Innovation Settings. Academy of Management Proceedings (Vol. 2015, No. 1, p. 18090). Briarcliff Manor, NY 10510: Academy of Management. Helfat, C. E., & Lieberman, M. B. (2002). The Birth of Capabilities: Market Entry and the Importance of Pre-history. Industrial and Corporate Change, 11(4), 725–760. Hohl, L., Bican, P. M., Guderian, C. C., & Riar, F. J. (2021). Gender Diversity Effects in Investors’ Financing Decisions. Journal of Entrepreneurship, 30(1), 134–152. Kalyanaram, G., & Urban, G. L. (1992). Dynamic Effects of the Order of Entry on Market Share, Trial Penetration, and Repeat Purchases for Frequently Purchased Consumer Goods. Marketing Science, 11(3), 235–250. Karakaya, F., & Stahl, M. J. (1989). Barriers to Entry and Market Entry Decisions in Consumer and Industrial Goods Markets. Journal of Marketing, 53(2), 80–91. Katz, M. L., & Shapiro, C. (1985). Network Externalities, Competition, and Compatibility. American Economic Review, 75(3), 424–440. Keidel, J. Bican P. M., & Riar, F. J. (2021). Influential Factors of Network Changes: Dynamic Network Ties and Sustainable Startup Embeddedness. Sustainability, 13(11), 6184. Kerin, R. A., Varadarajan, P. R., & Peterson, R. A. (1992). First-Mover Advantage: A Synthesis, Conceptual Framework, and Research Propositions. Journal of Marketing, 56, 33–52. Klemperer, P. (1987). Markets with Consumer Switching Costs. Rand Journal of Economics, 18(1), 375–394. Lambkin, M. (1988). Order of Entry and Performance in New Markets. Strategic Management Journal, 9, 127–140. Lee, S.-H., Beamish, P. W., Lee, H.-U., & Park, J.-H. (2009). Strategic Choice during Economic Crisis: Domestic Market Position, Organizational Capabilities and Export Flexibility. Journal of World Business, 44, 1–15. Lieberman, M. B. (1989). The Learning Curve, Technology Barriers to Entry, and Competitive Survival in the Chemical Processing Industries. Strategic Management Journal, 10, 431–447.

113

Aleksej Dreiling and Peter M. Bican Lieberman, M. B., & Montgomery, D. B. (1988). First-Mover Advantages. Strategic Management Journal, 9, 41–58. Lieberman, M. B., & Montgomery, D. B. (1998). First-Mover (Dis)Advantages: Retrospective and Link with the Resource-Based View. Strategic Management Journal, 19, 1111–1125. Mansfield, E. (1985). How Rapidly Does New Industrial Technology Leak Out? The Journal of Industrial Economics, 34(2), 217–223. Mansfield, E., Schwartz, M., & Wagner, S. (1981). Imitation Costs and Patents: An Empirical Study. The Economic Journal, 91(364), 907–918. Miller, D., & Friesen, P. H. (1983). Strategy-Making and Environment: The Third Link. Strategic Management Journal, 4(3), 221–235. Min, S., Kalwani, M. U., & Robinson, W. T. (2006). Market Pioneer and Early Follower Survival Risks: A Contingency Analysis of Really New Versus Incrementally New Product-Markets. Journal of Marketing, 70, 15–33. Moore, M. J., Boulding, W., & Goodstein, R. C. (1991). Pioneering and Market Share: Is Entry Time Endogenous and Does It Matter? Journal of Marketing Research, 28, 97–104. Parry, M., & Bass, F. M. (1989). When to Lead or Follow? It Depends. Marketing Letters, 1(3), 187–198. Rheinganum, J. F. (1983). Uncertain Innovation and the Persistence of Monopoly. American Economic Review, 73(4), 741–748. Riar, F. J., Bican P. M., Fischer, J. (2021). It Wasn’t Me: Entrepreneurial Failure Attribution and Learning from Failure. International Journal of Entrepreneurial Venturing, 13(2), 113–136. Riar, F. J., Hienerth, C., & Berg Jensen, M. (2021). Digital Due Diligence Activities and Goal Setting in Equity Crowdfunding: Exploring the Differences between Novice and Experienced Investors. International Journal of Entrepreneurial Venturing, 13(1), 1–26. Riar, F. J. & Kellermanns, F. W. (2021). Family Business. In Dana, L.P. (ed.), World Encyclopedia of Entrepreneurship. Cheltenham: Edward Elgar. Riar, F.J., Wiedeler, C., Kammerlander, N., Kellermanns, F. W. (2022). Venturing Motives and Venturing Types in Entrepreneurial Families: A Corporate Entrepreneurship Perspective. Entrepreneurship Theory and Practice, 46(1), 44–81. Robinson, W. T. (1988). Sources of Market Pioneer Advantages: The Case of Industrial Goods Industries. Journal of Marketing Research, 25, 87–94. Robinson, W. T., & Fornell, C. (1985). Sources of Market Pioneer Advantages in Consumer Goods Industries. Journal of Marketing Research, 22, 305–317. Robinson, W. T., Fornell, C., & Sullivan, M. (1992). Are Market Pioneers Intrinsically Stronger than Later Entrants? Strategic Management Journal, 13, 609–624. Rodríguez-Pinto, J., Rodríguez-Escudero, A. I., & Gutiérrez-Cillán, J. (2012). How Market Entry Order Mediates the Influence of Firm Resources on New Product Performance. Journal of Engineering and Technology Management, 29, 241–264. Ruiz-Ortega, M. J., & García-Villaverde, P. M. (2008). Capabilities and Competitive Tactics Influences on Performance: Implications of the Moment of Entry. Journal of Business Research, 61, 332–345. Schmalensee, R. (1982). Product Differentiation Advantages of Pioneering Brands. American Economic Review, 72(3), 349–365. Schnaars, S. P. (1986). When Entering Growth Markets, Are Pioneers Better Than Poachers? Business Horizons, 29(2), 27–36. Schoenecker, T. S., & Cooper, A. C. (1998). The Role of Firm Resources and Organizational Attributes in Determining Entry Timing: A Cross-Industry Study. Strategic Management Journal, 19, 1127–1143. Shankar, V., Carpenter, G. S., & Krishnamurthi, L. (1998). Late Mover Advantage: How Innovative Late Entrants Outsell Pioneers. Journal of Marketing Research, 35, 54–70. Song, X. M., Di Benedetto, C. A., & Zhao, Y. L. (1999). Pioneering Advantages in Manufacturing and Service Industries: Empirical Evidence from Nine Countries. Strategic Management Journal, 20, 811–836. Srinivasan, R., Lilien, G. L., & Rangaswamy, A. (2004). First in, First out? The Effects of Network Externalities on Pioneer Survival. Journal of Marketing, 68(1), 41–58. Urban, G. L., Carter, T., Gaskin, S., & Mucha, Z. (1986). Market Share Rewards to Pioneering Brands: An Empirical Analysis and Strategic Implications. Management Science, 32(6), 645–659. Wernerfelt, B. (1984). A Resource-based View of the Firm. Strategic Management Journal, 5(2), 171–180.

114

Pioneer or Follower Wernerfelt, B. (1986). A Special Case of Dynamic Pricing Policy. Management Science, 32(12), 1562–1566. Wernerfelt, B. (1988). General Equilibrium with Real Time Search in Labor and Product Markets. Journal of Political Economy, 96(4), 821–831. Yang, X., Riar, F. J., Kellermanns, F. W., Leitterstorf, M. P. (2020). The Role of Evolutionary Psychology in New Venture Success, Academy of Management Proceedings (Vol. 2020, No. 1, p. 14064). Briarcliff Manor, NY 10510: Academy of Management.

115

8 BUBBLE PLANNING AND THE MATHEMATICS OF CONSORTIA1 Jonathan Bard, Boaz Golany, and Fred Phillips

8.1 Introduction The chapter begins by briefly formalizing the simple idea of “bubble planning” in order to develop it as a key tool for designing consortia, and to illustrate the interaction of optimization with managerial practice. Following some definitions and examples of consortia and their purposes, optimization models are set forth for the purposes of selecting consortium members and budgeting the consortium in a way that satisfies all members. A final optimization model addresses the special problems of overhead costs in university-based consortia. The results are especially applicable to cross-sectoral consortia. We remark also on the applicability of these ideas to other teaming and sharing regimes in the Internet age.

8.2  Bubble Planning A bubble plan depicts the constituencies, suppliers, partners, and other entities comprising the business environment of an organization. A bubbles-within-bubbles picture (see ­Figure 8.1) shows the natural groupings of these entities. It is a “plan” in the sense of a map, not in the sense of a sequence of future actions, though it is used to generate action plans. It is an obvious and straightforward picture of an organization’s multiple stakeholders and potential stakeholders. From the point of view of this chapter, a bubble plan is useful for an organization that engages in many consortial projects. Such an organization will use the bubble plan to choose, from its list of stakeholders and potential stakeholders, a subset that is best suited to participate in a given consortium. Bubble planning, a tradition at the IC2 Institute of the University of Texas at Austin, was promulgated by the Institute’s founding director, Dr. George Kozmetsky. An early published example of bubble planning is Phillips (1978), the first IC2 research monograph. At Kozmetsky’s urging, that publication contained a matrix representation of the flows of resources and interests among the participants in an international, public-private arrangement for the financing and distribution of energy. The repeatedly demonstrated efficacy of Kozmetsky’s approach spurred the present authors to represent it in formal optimization models. These are developed below. 116

DOI: 10.4324/9781003046899-11

Bubble Planning & Mathematics of Consortia

Figure 8.1  B  ubble plan for the Management in Science & Technology (MST) School at Oregon Graduate Institute of Science and Technology

8.3  Purpose and Organization of Consortia We characterize a consortium as a special-purpose alliance of different organizations. • • •

•

•

The member organizations may be competitors. There are generally more than two members. The totality of what the members bring to and take from the consortium is multidimensional, i.e., more than just the exchange of goods for money. Items of exchange may include knowledge, access to markets, entre to political circles, conference facilities, staff resources, etc. (see Table 8.1). The member organizations may cross traditional sectoral (government, industry, university, press) lines, and indeed the consortium may be necessary because the task at hand arises from recent changes in the business/social/political environment and cannot be dealt with by a single sector. The consortium is expected to last for a period probably measured in years, and thus depends on good relationships among members, favorable public opinion, and/or favorable political climate.

Well-known examples of industrial consortia have included CAM-I, the non-profit computer-aided manufacturing consortium; MCC, the for-profit, private microelectronics R&D 117

Jonathan Bard et al.

consortium (Gibson and Rogers, 1994); and SEMATECH, the government-supported semiconductor manufacturing consortium (Young, 1994). Public-private consortia include HUD’s enterprise zones and NIST’s Advanced Technology Program (ATP) support for high-cost, high-risk technology development by groups of industrial companies. Universities become participants in, or instigators of, consortia as exemplified by the University of Texas-NASA Technology Commercialization Centers. In each of the cases just cited, the consortium represented an innovative organizational response to changing business and technological conditions. There are many other reasons for organizations, even competing organizations, to ally. These are explained admirably in Lynch (1993), and we will not go into further detail about them here. More recently, Internet platforms have given rise to swapping, sharing, and customer-driven innovation schemes that are also well described by the models we present here. However, we articulate the models in the language of alliances and consortia.

8.4  Selecting Consortium Participants A well-networked consortium designer will have a good sense of the pressing business needs of all stakeholders in his/her bubble plan, and also an idea of the surplus resources and special expertise of each stakeholder. The business needs, resources, and areas of expertise are the “items of exchange” listed in Table 8.1 (which is not exhaustive). The designer’s organization has a mission and needs of its own, which are the impetus for the launch of a consortium. The designer believes that his/her organization’s needs can be provided by a combination of the entities in the bubble chart. The first part of the design task, then, is to identify a subset of these entities (stakeholders) that (i) can provide, collectively, what the designer’s organization needs; and (ii) are motivated to do so, by reason of having their own needs satisfied in exchange. (The obvious additional requirement, that it be done in a way that leads to a financially viable consortium, is dealt with in the next section of the chapter.) This first part of the design task, expressed as a mathematical optimization, leads to a modified set-covering problem. This optimization identifies a possible exchange economy,2 without quantifying the amounts of the exchange items that will change hands. To establish notation, call each potential consortium member a “node.” The index (node) having subscript “o” represents the consortium. As it is the needs of the designer’s organization that spurs the formation of the consortium, then the node “o” also represents the designer’s organization. “Resources” are items of exchange, exemplified by the list in Table 8.1. Let xj = 1 if node j is to be included in the consortium; otherwise xj = 0. The xj are to be determined by the optimization. Input data are given as follows: aij = 1 if node j has resource i; 0 otherwise. bij = 1 if node j requires resource i; 0 otherwise. wj = difficulty, distance, or communication “cost” of including node j in the consortium. Table 8.1  Possible “Items of Exchange” in a consortium Knowledge

Access to markets

Entry to political circles

Conference facilities Equipment Prestige/name recognition

Staff resources Lab space Favorable location

Graduate students Customers/qualified leads Investment capital

118

Bubble Planning & Mathematics of Consortia

wj might represent how receptive to a proposal node j is perceived to be, the weight of favors to be called in in order to recruit node j, or any other measure of difficulty. In general, wj may be seen as the inverse of node j’s reputation or desirability as a consortium partner. All wj will be set equal to one unless there is notable a priori ease or difficulty in recruiting a particular node j. To select consortium members, minimize Σj wjxj subject to Σj aijxj ≥ bio and Σj∈k Σi bikaijxj ≥ 1 for every k xj = 0 or 1 for every j. Note that this optimization allows for exchanges that are not directly between recruited members and the original organization. For example, node 1 might have something node 2 needs while node 2 has an item node o needs and node o can fulfill a need of node 1.

8.5  Setting Objectives and Budgeting the Consortium Organizations are motivated to join the consortium, which from their point of view may be a new and unfamiliar way of doing business, by anticipating a greater-than-ordinary return on their investment. For this reason, the consortium designer must set objectives that provide the required return. The designer must be familiar with potential members’ business models, and be in a position to set an objective that each member j will receive a multiple nj of its usual return on its several kinds of resource investments. Experience indicates that nj should be between two and ten. For example, suppose a candidate member needs exposure to potential customers and to new technologies. This same candidate has a high-profile chief executive and a corporate jet that makes frequent trips to Washington, DC. The consortium plans to hold conferences and deal with funding agencies in Washington. Based on an estimate that the company usually gains ten qualified leads to customers and technology suppliers each time the CEO gives a speech or each time he/she flies to Washington, the consortium must be able to offer 20–100 qualified leads each time the company’s CEO speaks at a consortium event or allows a consortium staffer to occupy a seat on the company’s airplane. We move toward a finished consortium budget by distinguishing resources (“items of exchange”), goals, programs, and budget lines (expenditure categories). A three-stage process of allocations and optimizations leads to the final budget. 1 Set goals for the consortium. Each member j will contribute cij units of resource i to the consortium. The designer intends for each unit of resource i coming from member j to be transformed into resource k at a rate specified by an “objectives” matrix Rikj . This matrix has nonzero elements only where member j has resource i and other members need resource k. The quantities nj are implicit in the Rikj matrix, although the latter matrix is not a one-to-one transformation of input resources into output resources. Rather, the R matrix is a multivariate production function transforming the full set of inputs to a set of outputs.3 The total input resource i available to the consortium is I i = Σ j ⋅ c ij , and the consortium’s output of resource k, denoted by Ok , is Ok = Σ j Σ i ⋅ c ij Rikj . 119

Jonathan Bard et al. Table 8.2  L  inking resources to programs Resource programs

Money

Laboratories

Staff time Students

Conferences Publications Internships Total

y11 y12 y13 I1

y 21 y 22 y 23 I2

y 31 y 32 y 33 I3

y41 y42 y43 I4

The cij must respect the (perceived) budgets of members j, yet be sufficient to meet output goals. The consortium designer must be satisfied both with the total set of Ok as they relate to the designer’s mission, and with member o’s own ROI, represented by Σ i c io Riko . 2 Allocate input resources to programs. The next step in this multi-stage objective/budget setting process is to link resources to programs. Programs are consortium activities such as publications and conferences. Table 8.2 shows by example how cash, lab space, staff, and student resources are transformed into conference, publication, and internship programs. Let the programs be indexed by r, and let yir be the amount of (input) resource i allocated to program r. A matrix Pikr transforms inputs i into outputs k for each program r. As input levels cij are already fixed, we wish to find any solution of Σ r Σ i yir Pikr ≥ Ok and Σ r Σ i yir = I i for every i and k. If the program productivity coefficients Pikr are known to be fixed, then they may be regarded as constants in the above simultaneous equations. It is also possible to allow the Pikr to vary, making these nonlinear equations. In that case, one might stipulate ranges for the Pikr, Lrik ≤ Pikr ≤ H ikr with the upper range constituting a “stretch goal” for the consortium. The goal in solving these equations is to ensure consortium programs can produce the needed outputs. 3 Linking programs to budget lines. Now that resources have been linked to programs, it remains to make the result look like a conventional budget by tying activities to conventional institutional budget lines. Table 8.3 shows how this amounts to “budgeting” each program. To effect this transformation, we need an additional quantity ρil which takes on a value of one if resource i can be used in budget line l, and zero otherwise. For example, students and paid staff (as well as cash) can be used to put on a conference, but laboratory space is generally not a useful resource for a conference activity. We then introduce zril , the fraction of resource i allocated to line l for/from program r. Values of zilr must be set so that Σ l ρil yir zilr ≤ yir for every i and every r . 120

Bubble Planning & Mathematics of Consortia Table 8.3  Linking programs to budget lines Programs/budget lines

Conferences Publications

Internships

Total

Salaries/benefits Rent Equipment Supplies Utilities Travel Outside services Overhead/G&A Total

Σ i zil1 … … … … … … … Σ iy i1

Σ i zil3 … … … … … … … Σiy i3

Σ r Σ i zilr … … … … … … …

Σ i zil2 … … … … … … … Σiy i2

It is likely that certain minimums are known for particular budget lines, e.g., that a program manager must be hired at a certain salary, that a minimum square footage must be rented, that a certain number of trips must be taken, and that all these expenditures serve all the consortium’s programs jointly. These constraints are of the form Σ r Σ i ρil yir zilr ≥ Bl . A final step is to clarify cash requirements. The set of resources may be partitioned into cash and non-cash (“in-kind”) resources. If it is necessary to represent the total “dollar size” of the consortial project, or to document matching contributions to a grant, one must then estimate the dollar values of in-kind contributions. Note that the quantities in Table 8.3 and the constraints noted just above, sum over the index i and so, are not meaningful until this partitioning and/or “dollarizing” has been done. In an actual consortium the link between, say (to revisit the example that opened this section), speeches, plane seats, and customer leads may be very approximate, or even (in some cases) implicit and unspoken. But it is important to remember that a convincing case must be made to prospective members about the return on their resources. The new consortium must announce ambitious goals to the press, and some of these must be quantitative. And of course by the time a financial budget is set, all relevant considerations must be reduced to numbers.

8.6  Overhead Costs and University-Based Consortia What we referred to above as “budget lines” are called “Expenditure Categories” in Table 8.4, in order to clarify issues of overhead accounting. The organization hosting the consortium may demand reimbursement for the indirect costs (utilities, janitorial, landscaping, other institutional G&A) of locating the consortium activity within the institution. While the nominal rate for indirect cost recovery varies according to the source and according to the disposition of the funds, indirect costs are not actually assessed until the associated direct costs are expended. Thus, overhead (indirect cost) rates must be defined in matrix form as in Table 8.4. These rates depend on the following: •

For federal government grants, a standing agreement between the government and the institution; 121

Jonathan Bard et al. Table 8.4  O verhead rates applied Income categories/ expenditure categories Salaries and benefits    On-campus    Off-campus Scholarships Equipment Other direct expenses    On-campus    Off-campus

• • • •

Donations Service fees and in dollars memberships Grants

In-kind donations

0% 0% 0% 0%

20% 20% 0% 0%

61% 35% 0% 0%

0% 0% 0% 0%

0% 0%

20% 20%

61% 35%

0% 0%

The principal investigator’s skill or “clout” in negotiating lower rates with institutional administration; Non-governmental granting bodies’ policies about use of their funds for overhead recovery; Widespread university policies that scholarship expenditure and gift and unrestricted donation income are exempt from overhead recovery; and Other considerations such as the reduced actual indirect costs of off-site activities.

As allowable overhead recovery rates often do not allow the institution to recover its actual overhead costs, it is in the institutional administration’s interest to recover the maximum allowable overhead recovery from each project (in this case, each consortium). While the administration is also interested in advancing the institution’s mission, the consortium designer (“principal investigator,” in grant language) has a more immediate need to advance the mission by devoting the maximum allowable consortium funds to the direct costs of consortium activities. Different consortium members (which may include government agencies) contribute different kinds of income to the consortium. Represent the overhead recovery rates in Table 8.4 as Ωlm , where m indexes the “flavor” of the income, as shown by the column headings of the table. Let M lm be the amount of money of “flavor” m that is allocated to budget line l. The marginal sums ΣlM lm are fixed. Then, administration wishes to Maximize ΣlΣmM lm Ωlm subject to ΣlM lm Ωlm ≤ ΣlM lm and all M lm ≥ 0 and the consortium designer wishes to Minimize ΣlΣmM lm Ωlm subject to ΣlM lm Ωlm ≤ ΣlM lm and all M lm ≥ 0. This resembles the transportation/distribution problem of operations research with the exception that row sums are not fixed. However, additional constraints of the row sum and other kinds may be appended to acknowledge mutual concessions of the two parties, as this is not a strictly adversarial situation. 122

Bubble Planning & Mathematics of Consortia

8.7  Discussion and Further Applications A modified set-covering optimization has been set forth as a way of designing consortia. Following the solution of this problem, a budgeting process occurs. In this chapter, we have prefaced a standard and familiar two-stage budgeting process with a “zeroth” stage which sets “return on resource” objectives for every consortium member. The resulting three-stage budgeting process produces a budget that satisfies the mission and resource needs of the consortium designer; provides a superior ROI to the selected consortium members and opens doors for new ways for the members to do business; and may result in a social good such as more efficient use of tax dollars or more rapid commercialization of technology. A final, optional optimization formalizes the game between principal scientist and central administration which, in a university consortium, balances the need for mission-related expenditure against indirect cost recovery.

8.8  Qualitative Considerations In practice it is found that the most useful features of consortia – flexibility, initiative, thinking outside the box, and dealing with new conditions and new organizational structures – are not appealing to all the employees of all potential consortium members. The best participants are people who are competent and devoted to their home organizations, yet capable of stretching themselves to respond to a larger vision. Employees with narrower orientations are best left in place within member organizations, and out of contact with the consortium. Personnel seconded to the consortium should be drawn from among participating organizations’ most capable people. Consortium executive leadership, especially when the consortium’s mission is a matter of national security or competitiveness (Industry 4.0 manufacturing, quantum computing, etc.), must balance attention to the consortium’s mission against attention to the accompanying national politics. This is difficult, and partially explains the usual rapid turnover of industry consortium directors.

8.9 Applications The consortium design process described here places a heavy burden on the designer, as he/she must show insight into the business situations of all potential consortium members, understanding their incentives, personalities, and motivations. Yet, designers of several successful consortia have risen to this challenge. This research was presented to the Minister of Science and Technology of a middle-eastern country having a relatively limited research budget. He took its point that even if a nation is not on the leading edge technically, it can benefit from leading-edge research by being a proactive and high-performing networker, selectively joining and bringing value to international research consortia.

8.10  Extensions and Concluding Remarks It seems to us that this skill and the design process set forth in this chapter are also applicable to multi-function teams within a single organization. Such multi-function teams (matrix organizations) often fail due to the primary loyalty of team members to their line department affiliation. Team leaders fruitlessly attempt to change the attitudes of team members, 123

Jonathan Bard et al.

or load them with conflicting incentives. Our experience with consortia, reflected in the models of this chapter, shows that in cooperative endeavors, we should not try to change people. We can only design a cooperative organization (consortium, cross-functional team, etc.) that respects people’s existing incentives and motives. We can only choose people with aptitude, then introduce them to a superordinate consortial vision that encompasses (rather than opposes) their current motives and stretches (rather than changes) them. This is the management philosophy underlying the mathematical models of this chapter. Kozmetsky’s close colleagues A. Charnes and W.W. Cooper maintained that after operations researchers derived an optimization principle for an industrial process, it turned out that the industry manager had been operating at or close to optimum in the first place. The present research turns that principle around: not only did we suspect that Kozmetsky’s repeated consortium-building was close to optimal, but devising the optimization model for selecting consortium members spurred the creation of still additional new models, as put forth in this chapter. As corporate innovation becomes more “open” and networked, and as the World Wide Web and phone apps bring us ride-sharing, equipment-sharing, and other “sharing economy” facilities, we find these models equally applicable to finding the best multi-sided innovation and sharing groups.

Notes 1 This research was supported by a grant from the Nieman Foundation to Technion (Haifa) and to the University of Texas at Austin. 2 Some of these ideas were foreshadowed in Charnes and Cooper (1974), and also, in an empirical study of a single-sector (multiple government agencies) problem, in Danziger (1978). 3 This raises the possibility of using productivity measurement tools like Data Envelopment Analysis to evaluate the performance of consortia, or the performance of multiple programs within a single consortium.

References Charnes, A. and Cooper, W.W. (1974). An Extremal Principle of Accounting Balance of a Resource-Value Transfer Economy: Existence, Uniqueness, and Computation. Center for Cybernetic Studies report #185, and Rendiconti di Accademia Nazionale dei Lincei, April. Danziger, J.N. (1978). Making Budgets: Public Resource Allocation. Beverly Hills: Sage. Gibson, D.V. and Rogers, E.M. (1994). R&D Collaboration on Trial. Boston: Harvard Business School Press. Lynch, R.P. (1993). Business Alliances Guide. New York: John Wiley & Sons. Phillips, F. (1978). A Model for Public-Private Sector Distribution Planning for the U.S. Coal Industry. IC2 Institute Austin, Technical Series #1. Young, R. (1994). Silicon Sumo: U.S.-Japan Competition and Industrial Policy in the Semiconductor Equipment Industry. Austin: The IC2 Institute of the University of Texas at Austin.

124

PART 3

Technology Evaluation

9 AN EVALUATION MODEL FOR THE DESIGN OF VIRTUAL REALITY SYSTEMS Yu-Shan Su and Wen-Hua Wu

9.1 Introduction The development of the Internet has accelerated the development of virtual reality (VR) systems. VR offers great immersion, interaction, and imagination solutions. The “immersive experience” of VR uses realistic projections to make users believe that they are in another world. Every user can use a VR system to visit another world like shown in the movie Ready Player One. The development of the Internet and smartphones has created technological progress and eliminated national boundaries. Convenient Internet access provides everyone with a mobile device that has transformed mobile phones which had been only used for making calls. Users can use their mobile phones for send SMS and receive email. They can even use mobile phones for communicating on social media, playing games, and watching films. The rapid advancement of mobile phone applications has also increased technology updates of peripheral equipment. Google and many other software and hardware companies have incorporated VR and AR into mobile phone applications. HTC uses innovative virtual reality system products to expand business opportunities. Facebook acquired the celebrated VR company Oculus and actively pursues expansion in the VR market. VR systems have become increasingly widespread in recent years with rapid development of different products and applications in the high-end, medium-range, and low-end markets. VR browser applications for mobile phones are also being developed. The product that has received the most attention from players is the Google Cardboard. Game console manufacturers have followed suit. For instance, Sony has launched peripheral VR devices for its PlayStation console. The high-performance e-sports consoles and graphic cards derived from personal computers make full use of the effects of high-end VR systems for players to completely immerse themselves into virtual three-dimensional game settings. For instance, the precision of positioning in the HTC VIVE VR system has reduced latency to one thousandth of a second and it is used with more than 70 sensors. With this, the software developer can develop more complicated single-player or multi-player VR games. This research has compiled the evaluation model for the innovative design of new VR system product development. According to the opinions of the experts consulted in the questionnaire survey, we use the AHP methodology to analyze the weight and ranking DOI: 10.4324/9781003046899-13

127

Yu-Shan Su and Wen-Hua Wu

of design elements of the VR system to provide future strategies for VR system product design. The goals of this research include (1) establishment of the evaluation model for VR system product design and (2) use of the Analytic Hierarchy Process (AHP) to analyze the relative weights of multiple criteria to obtain the prioritized ranking of the innovative design concepts of the VR system.

9.2  Theoretical Background 9.2.1  Virtual Reality The current computer-generated reality models include VR, AR, mixed reality (MR), and other similar reality presentation methods. This research only explores the design of VR. The year 2016 was defined as the first year of the VR era. The concept of VR originated from the 1935 science fiction novel Pygmalion’s Spectacles written by Stanley G. Weinbaum. The book was the first work of science fiction that explores VR. The protagonist in the story is only required to put on glasses to be immersed in a world of sight, sound, taste, smell, and touch. VR can break down the boundaries of space and time and provide participants with a strong sense of “coexistence” so that they can engage in interactions based on mutual understanding. A VR system uses three-dimensional virtual scenes and additional visual information along with a combination of touch, hearing, smell, and other senses to facilitate interaction and understanding through postures and intents. VR allows participants to experience feelings and adventures beyond the real-world environment to form a sense of coexistence. The term “virtual reality” was first proposed for related tests and development in the MIT Media Lab in 1985. In 2009, Pranav Mistry of the MIT Media Lab published an experimental prototype that facilitated interactions between the physical and digital worlds and referred to it as “SixthSense”. Companies began to invest in the development and design. However, the cost of R&D equipment was too high and the technologies were inadequate at the time. For instance, the Sega VR-1 and Nintendo Virtual Boy produced in Japan did not attain commercial success. Today, VR users use computer technologies to simulate a three-dimensional space which is referred to as model construction. VR technologies today create highly simulated real 3D space, which gives participants the illusion of being in a real environment. In this 3D virtual space, users can traverse or interact through the corresponding controllers. Three key elements of virtual reality. Burdea (1993) suggested using functional attributes to define VR and suggested that a VR system must have three elements including immersion, interaction, and imagination, which are also referred to as “3I” in VR. 1

2

Immersion: First, the sense of a real environment must be created in a virtual environment. It can be created with the simulation and integration of smells, images, and sound so that the user can receive stimuli from the virtual world in the most intuitive manner. Interaction: Users use sensors, peripheral controllers, and auxiliary devices for interface operations to create communication media which provide users with feedback and facilitates interactions.

128

An Evaluation Model for the Design

3

Imagination: The most important thing is to integrate user imagination and VR technologies to construct a perfect virtual environment that meets requirements. We may be able to live in an ideal world of our own creation in the future and enjoy the control of this world as the creator.

Categories of virtual reality systems. Current VR system products can be divided into two categories—mobile and stand-alone VR systems. 1

Movable VR systems include mobile VR systems and stand-alone VR systems.



a  A mobile VR device refers to the placement of a smartphone into a VR box to use the mobile phone and related apps to experience VR scenarios. The service contents include viewing and using mobile phone sensors for simple operations, such as Google Cardboard and Daydream. b  A stand-alone VR device refers to one that operates independently without additional equipment. The effectiveness and cost are different based on the differences in components.



2

A stationary VR device refers to a head-mounted VR device with head-mounted display (HMD). It uses handles, gloves, and other peripheral controllers for VR computing through computer connections. Examples include HTC VIVE, Oculus Rift, and Sony PlayStation.

Applications of virtual reality systems. Innovation and applications are crucial for VR hardware equipment. The development of software applications forms the core value of VR products. Current VR offers seven types of applications: 1 2 3

4

5

6

Interactive games: As an example, multiple games have been launched for Sony PlayStation VR such as the top-ranking “The Inpatient”. Education and learning: As an example, zSpace uses VR to help students put on glasses and learn in front of a screen. Shopping: Immersive shopping with VR has been implemented for online shopping. As an example, Alibaba’s Buy+ launched an online virtual shopping experience on the VR Store for the Double 11 Shopping Festival in 2016. It partnered with HTC in hardware equipment and selected merchants including Macy’s, Target, Costco, and P&G. Virtual guide: As an example, museums in the United States use VR in combination with iPads or mobile phones. The successful display of VR works of Van Gogh allows visitors to appreciate the works of art as though they were truly there. Virtual viewing: With interconnected VR devices, users can engage in social networking events, chats, and dating with relatives, friends, or strangers in the virtual world. VR is a remote social networking environment that incorporates settings, characters, and sound. Facebook founder Mark Zuckerberg is now actively pursuing such functions and effects for the Facebook Messenger. Viewing videos: Viewers can put on 3D glasses to become a character in a movie which allows them to feel like they are really there. The 360-degree video contents recently developed create different horizons for TV shows, films, and sports broadcasting to provide the audience with a completely different experience.

129

Yu-Shan Su and Wen-Hua Wu

7

Medical treatment: Many healthcare institutions are testing immersive VR simulations to treat anxiety, post-traumatic stress disorder, pain management, neurological recovery, fear of flying, and claustrophobia.

Current VR applications are widely adopted in daily life and professional disciplines. This research therefore focuses on head-mounted VR devices. Comparison of virtual reality systems. The current high-end stationary VR device market is dominated by three companies—Sony, Oculus, and HTC (Inside, 2017). Sony shipped 1.7 million VR headsets in 2017 and became a leader in the market. It was followed by Oculus and HTC with 700,000 units and 500,000 units respectively (Topology Research Institute, 2018). Refer to Table 9.1 for product specifications.

9.2.1.1  HTC VIVE HTC VIVE is a head-mounted VR system jointly developed by HTC and Valve Corporation which was launched in April 2016. The design of the HTC VIVE uses “room-scale” technology to transform a room into a three-dimensional virtual world with sensors. This environment allows users to navigate themselves and move in a natural manner and use the hand-held controller that tracks movement to manipulate objects in a realistic manner. It offers experience with intricate communication interactions and immersive environments. HTC VIVE won 22 awards in CES 2016 including the CES “best tech of the show” award (Wikipedia, 2021a). HTC VIVE head-mounted VR device has strong advantages and was recognized as a benchmark product by companies.

9.2.1.2  Oculus Rift In the “Meant to be Seen” 3D Forum, Oculus founder Palmer Freeman Luckey displayed a new HMD. At the time, it was a relatively efficient HMD on the market that was also affordable for gamers. Oculus Rift is a type of head-mounted VR device. Oculus Rift was financed and developed by Oculus VR i 2012. The company was acquired by Facebook in March 2014 and the product was launched on March 28, 2016. Oculus Rift was the first consumer-oriented head-mounted VR device on the market and it was the “first professional VR headgear for PC”. In addition to gaming entertainment, Oculus Rift supports media, social networking, and industrial applications (Wikipedia, 2021b). Table 9.1  P  roduct specifications of head-mounted virtual reality systems Product

hTC VIVE

Oculus Rift

Sony PlayStation VR

Supplier Price Screen Resolution Refresh rate Field of view Platforms supported Content platform

hTC & Valve 799 USD Dual AMOLED 3.6ʺ 2160 × 1200 90 Hz 110° PC Steam VR, Viveport

Facebook & Oculus 599 USD Dual AMOLED 3.6ʺ 2160 × 1200 90 Hz 110° PC Steam VR, Oculus Home

Sony 399 USD OLED 5.7ʺ 1920 × 1080 120 Hz 100° PlayStation 4 PlayStation Store

Source: This research

130

An Evaluation Model for the Design

9.2.1.3  Sony Play Station VR Sony launched PlayStation VR in October 2016. It was a device designed by Sony specifically for the TV game console “PlayStation 4”. It supports multiple games for consumers to choose from and uses the gaming platform of its own TV game console to continuously increase sales (Wikipedia, 2021c). The head-mounted VR systems of these three companies are used as the main subjects of this research. We compared the key technologies used by the three major companies and results are shown in Table 9.2.

9.2.2  Innovation Theory Rogers (1995) defines innovation as an idea, practice, or project that is perceived as new by an individual or other unit of adoption. An innovation must have relative convenience, reliability, compatibility, complexity, and emotional value. The innovation diffusion theory proposed by Rogers (1962) divided adopters of innovation into five categories: innovators, early adopters, early majority, late majority, and laggards. Innovation diffusion includes five stages: awareness, interest, evaluation, trial, and adoption. Current stage of development of the VR market. Each VR company has its own market positioning and strategy. They are primarily focused on the promotion of VR products. The difficulties in promoting current VR products include high prices and requirements for connections with high-end PCs for operations. As a result, VR technologies are not sufficiently mature to attract a large following of consumers. Gartner (2017) research showed that VR technologies are currently in a period of recovery and are attracting a group of hardware manufacturers into the market. Gartner (2017) predicts that VR technologies will peak in two to five years. Based on current conditions, mass production is still a long way off and more time is needed to overcome difficulties. VR technologies that are not sufficiently mature have affected consumers’ decisions for purchasing products. Therefore, this research will include innovative technology applications into the evaluation model for the design of the VR system. This research regards the “virtual reality system” as an innovative product that changes past consumption formats and consumer habits. Therefore, the VR system meets definitions of an innovative high-tech product. If we wish to effectively extend the life cycle and expand Table 9.2  Comparison of technical positioning of virtual reality systems hTC VIVE

Oculus Rift

Sony PlayStation VR

Technology

Laser-scan positioning

Positioning precision Blockage resistance Light resistance Stability and durability Multiple-target positioning Scope of movement

Highest Strongest Good Weak Realizable without restrictions Large

Active infrared and nine-axis positioning Higher Stronger Good Strong Realizable but with few targets Small

Active visible optical positioning Poorer Poorer Poor Strong Realizable but with few targets Small

Source: This research

131

Yu-Shan Su and Wen-Hua Wu

the market of VR systems, we must consider the “new product attributes” and “customer orientation”. These two factors affect purchase intent and actual purchase and adoption.

9.2.3  Product Design Product design and functions are intrinsic attributes of product features (Bilkey & Nes, 1982). The most important reason for the existence of a product is its main function which satisfies human needs (Cane, 1969). Khalid and Helander (2004) suggested emotional requirements for product design including aesthetics requirements, functional requirements, and overall impression. If a new product can satisfy consumers’ basic and functional requirements, and can greatly satisfy consumer expectations, it would be greatly successful and appealing, which will in turn improve the adhesiveness of the brand and subsequent products (Chen et al., 2020; Su et al., 2018a; Su et al., 2018b). We established the four dimensions of the evaluation model for VR system design based on the theories of innovation and product design. They include functional attributes, new product attributes, product styling, and products attractiveness. Burdea (1993) suggested that product functional attributes must meet seven criteria in order to satisfy the 3I immersive experience for a VR system: spatial positioning technology, eyeball tracking technology, wireless transmission technology, acoustic technology, display technology, battery technology, and heat dissipation technology. Rogers (1983) proposed five criteria that determine people’s use of innovative products: relative advantage, compatibility, complexity, trialability, and observability. We have identified seven criteria that affect product styling: appearance and contour, dimensions, format of assembly, color, surface treatment, detailed features, and design of materials (e.g., Hekkert & Schifferstein, 2008). Baxter (1995) proposed four criteria for product attractiveness: existing knowledge attraction, semantic attraction, symbolic attraction, and basic visual attraction. We have established four principal dimensions and 23 criteria for VR system product designs as shown in Table 9.3 and Figure 9.1.

9.3  Research Methodology The questionnaire was distributed to 19 renowned experts of R&D managers, project managers, or top managers in the VR industry in Taiwan. The questionnaire survey was conducted from April 16, 2019 to May 15, 2019. The research distributed questionnaires in interviews. We explained the contents of the questionnaire face-to-face during interviews so that the interviewees fully understand the contents of the questionnaire and the method for providing answers to increase the validity of questionnaire contents. A total of 21 questionnaires were distributed and 21 questionnaires were recovered, of which 19 were valid questionnaires. The background information of the interviewed experts includes the gender, age, education, employer, job title, and number of years of service. Statistical results were given as follows: (1) there were 13 males and 6 females. (2) Nine aged under 30, 7 aged between 31 and 40, and 3 aged between 41 and 50. (3) In terms of education, 12 held a bachelor’s degree and 7 held a master’s degree. The employers and number of years of service of the experts interviewed in the questionnaire are shown in Table 9.4. The AHP we adopted in this study is a theory on decision-making proposed by Thomas L. Satty in 1971. The AHP is mainly used for addressing multi-criterion decision-making

132

An Evaluation Model for the Design Table 9.3  Dimensions and criteria for virtual reality system product design Dimension

Criterion

Criterion description

A: Functional attributes

A1: Spatial positioning technology

High-precision positioning technology allows zero latency and zero error in operations as well as simultaneous projection in virtual reality The technology is compatible with human eye movement and increases the rendering of details at focal points and reduces equipment hardware requirements to effectively avoid motion sickness in VR 5G and WiGig wireless technology applications make products wireless VR 3D audio technology applications effectively improve immersion High-resolution, refresh rate, and wide-angle display applications High-capacity battery and rapid charging technology applications Effectively extend the useful life of hardware products and reduce user discomfort High performance and multiple additional innovative features, regular online system updates, and connection with various peripheral devices Design and development of new products down the line and adoption of a cross-platform and system usage approach to increase the number of consumers Design of a convenient and intuitive humanmachine interface to quickly get users started Supply of new products for trial use and trade-in of old products for new products Launch of related applications and services with well-known cartoons and movies The observable contours of an object are unified as a whole and include styling principles (balance, proportion, rhythm, tempo, contrast, and harmony) and Gestalt principles (proximity, similarity, closure, continuation, and regularity) Define the quality of the contours of the shape to feel the presence of the object such as the actual external dimensions of the object The external assembly of the object or the arrangement of parts such as the headset display integrated with the dedicated headphones

A2: Eyeball tracking technology

A3: Wireless transmission technology A4: Acoustic technology A5: Display technology A6: Battery technology A7: Heat dissipation technology B: New product attributes

B1: Relative advantages

B2: Compatibility

B3: Complexity B4: Trialability B5: Observability C: Product styling

C1: Appearance and contour

C2: Dimensions

C3: Format of assembly

(Continued)

133

Yu-Shan Su and Wen-Hua Wu Dimension

Criterion

Criterion description

C4: Color

The hue, colorfulness, and brightness displayed The texture portrayed on the surface of the object such as hairline finish, mirror finish, or sandblasted finish Treatment of the detailed features of product shape such as edge wrapping or vertex wrapping and R angle processing or C angle processing Materials for forming the objects such as ABS plastic, silica gel, and metal Extend the product design style and operating interface to increase brand recognition and familiarity of consumers Unique product appearance that corresponds to user habits and use while meeting ergonomic requirements for wearing Product is infused with the design style of leading companies in the industry and provides exclusive customization services Product has attractive design and has a streamlined and high-tech feel

C5: Surface treatment

C6: Detailed features

C7: Selection of materials D: Product attractiveness

D1: Extension of existing knowledge D2: Semantic attraction

D3: Symbolic attraction

D4: Basic visual attraction Source: This research

problems under risky and uncertain conditions. It is particularly effective for prediction, planning, judgment, resource allocation, and investment portfolios. As the AHP offers a clear and simple theory and simple operation methods, and can account for the opinions of multiple experts and decision makers, it is used to obtain the relative weights between elements when analyzing decision-making problems. The decision maker can ultimately determine the strengths and weaknesses of feasible decisions (e.g., Su et al., 2016).

9.4 Results 9.4.1  AHP Analysis Results of the Four Principal Dimensions The ranking of weights of the four principal dimensions is provided in Table 9.5. The top two dimensions are functional attributes and new product attributes while product styling ranked last. The design and functional attributes are the most important attributes for VR system design. Functional attributes are embedded in the VR system and support innovative applications and consumer expectations and demands. The results therefore show the close relationships of a VR system with comprehensive functions and the integrity of functional attributes. Therefore, the functional attributes are the primary dimension for evaluation in VR system design. The product styling is the last dimension to be considered.

134

An Evaluation Model for the Design Spatial positioning technology

Eyeball tracking technology Wireless transmission technology

Functional attributes

Acoustic technology Display technology Battery technology Heat dissipation technology Relative advantages Compatibility

New product attributes

Complexity Trialability Observability

Virtual reality system product design

Appearance and contour Dimensions Format of assembly Product styling

Color Surface treatment Detailed features Selection of materials Extension of existing knowledge

Product attractiveness

Semantic attraction Symbolic attraction Basic visual attraction

Figure 9.1  Evaluation model for virtual reality system product design Source: This research

135

Yu-Shan Su and Wen-Hua Wu Table 9.4  Institutions and working years of interviewed experts Questionnaire

Institution

Position

Working years

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19

Project Management Department, hTC VIVE Project Management Department, hTC VIVE Design Department, hTC VIVE Design Department, hTC VIVE Logic Design Department, hTC VIVE Software Design Department, hTC VIVE Optical Design Department, hTC VIVE Reliability Testing Department, hTC VIVE Design Department, hTC VIVE Design Department, XR SPACE Software Design Department, XR SPACE Optical Design Department, XR SPACE Logic Design Department, XR SPACE Project Management Department, XR SPACE Project Management Department, XR SPACE Mechanism Design Department, XR SPACE Software Design Department, XR SPACE Optical Design Department, XR SPACE Logic Design Department, XR SPACE

Project Manager Project Manager Manager Chief Engineer Chief Engineer Chief Engineer Chief Engineer Senior Engineer Senior Engineer Manager Manager Manager Manager Game Planner Game Planner Engineer Engineer Engineer Engineer

19 16 11 11 10 8 5 5 2 2 2 2 2 2 2 2 2 2 2

Source: This research.

9.4.2  AHP Analysis Results of the 23 Criteria 9.4.2.1  Seven Criteria for Functional Attributes The ranking of functional attributes is provided in Table 9.5. The top two criteria are display technology and introduction of spatial positioning technology. Acoustic technology applications ranked last. Display technology is the design element prioritized for consideration. The high-resolution, refresh rate, and wide-angle display applications of the display technology provide consumers with the most direct sensory stimuli in a short time. Users are more likely to be “convinced” in the “immersive experience”. Spatial positioning technology ranked second in terms of weight in the VR system. Spatial positioning technology is an indispensable basic technology in the VR system. Ultra-high positioning precision in VR means excellent immersion which provides users with the joy of instantaneous interactions. The aim is to make the virtual environment convincing so that the user can enter a fantasy world of perfection and imagination. The introduction of acoustic technology applications is the last design element to be considered.

9.4.2.2  Five Criteria for New Product Attributes The ranking of new product attributes is provided in Table 9.5. The top two criteria are compatibility and relative advantages. The observability is the last design element to be considered. Compatibility is the first design element to be considered among new product attributes. Products can be used more efficiently with existing software and 136

An Evaluation Model for the Design Table 9.5  W  eight and ranking in the AHP analysis Dimension

Weight

Criterion

A: Functional attributes

0.275

A1: Spatial positioning technology A2: Eyeball tracking technology A3: Wireless transmission technology A4: Acoustic technology A5: Display technology A6: Battery technology A7: Heat dissipation technology B1: Relative advantages B2: Compatibility B3: Complexity B4: Trialability B5: Observability C1: Appearance and contour C2: Dimensions C3: Format of assembly C4: Color C5: Surface treatment C6: Detailed features C7: Selection of materials D1: Extension of existing knowledge D2: Semantic attraction D3: Symbolic attraction D4: Basic visual attraction

B: New product 0.256 attributes

C: Product styling

0.229

D: Product attractiveness

0.24

Weight

Rank

Overall weight Rank

0.179 0.097 0.154

2 6 4

0.049322 0.026771 0.04229

8 17 11

0.053 0.243 0.117 0.158 0.22 0.373 0.208 0.103 0.096 0.197 0.222 0.127 0.115 0.104 0.093 0.14 0.115

7 1 5 3 2 1 3 4 5 2 1 4 5 6 7 3 4

0.014458 0.066746 0.032085 0.043347 0.056464 0.095486 0.053291 0.026348 0.024692 0.045124 0.050930 0.029128 0.026421 0.023816 0.021391 0.032139 0.027688

23 3 14 10 5 2 6 19 20 9 7 15 18 21 22 13 16

0.468 0.16 0.256

1 3 2

0.112322 0.038423 0.061318

1 12 4

hardware devices to support vertical integration and cross-platform horizontal extension of applications. Relative advantages ranked second. The successful development of a VR system requires high-performance and innovative function design and applications for connecting peripheral devices. It also needs regular online system updates to increase the adhesiveness of existing users to facilitate continuous use and attract the attention of potential consumers. Observability is the last design element to be considered.

9.4.2.3  Seven Criteria for Product Styling The ranking of product styling is provided in Table 9.5. Dimensions and appearance and contour ranked first and second in terms of priority. Detailed features ranked last. Dimensions are the prioritized design elements product styling. The suitableness of the dimensions affects the quality of the appearance and contour, product weight, product device attractiveness, and user comfort when wearing the product. Appearance and contour ranked second. The appearance of the product can attract the attention of potential users. The detailed features are the last design element to be considered.

137

Yu-Shan Su and Wen-Hua Wu

9.4.2.4  Four Criteria for Product Attractiveness The ranking of product attractiveness is provided in Table 9.5. The top two criteria are semantic attraction and basic visual attraction. Extension of existing knowledge ranked last. Semantic attraction is the first consideration for design evaluation. Superior VR system product design can make a product more attractive to users if it is easy to use, meets ergonomic requirements, and has an attractive external design. Basic visual attraction ranked second. An attractive product encourages consumers to experience the product and accept and use the product quickly. The symbolic attraction is the last design element to be considered.

9.5 Discussions 9.5.1  Academic Contributions We propose two academic contributions in this study.

9.5.1.1  Establishment of the Evaluation Model for Virtual Reality System Product Design We established the evaluation model for VR system product design. We use the analysis in this study to learn about the dimensions in the evaluation model and the weights and ranking of criteria in the overall product design. Evaluations on the design of related innovative products in the future can also use the model to create suitable models.

9.5.1.2  Integration of Innovation and Product Theories for Using 3I as the Core Values for Designing a Virtual Reality System We have integrated innovation and product theories and adopted the three main elements of VR as its core values. Regardless of whether the future mainstream design is based on stationary or movable VR, it must first focus on immersion, interaction, and imagination.

9.5.2  Practical Contributions We propose two practical contributions in this study.

9.5.2.1  Functional Attributes Are the Main Design Factors This research identifies functional attributes as the first dimension to be considered for design. If the 3I functions of a VR system are comprehensive and meet user expectations, they can increase the overall penetration rate of VR systems. The improvements in display technology and spatial positioning technology can enhance the immersive experience, prevent motion sickness, and reduce user rejection. New product attributes are ranked second for product design. The compatibility of new and old products with peripheral objects, high-performance settings, and a grand unified platform for applications facilitates rapid development in the industry and lower R&D and production costs.

9.5.2.2  Virtual Reality System Product Design Strategy This research set forth the weights and priorities for all criteria of VR system product design. The future development of related technologies can concentrate R&D resources on key technologies to maximize effectiveness. 138

An Evaluation Model for the Design

9.5.2 Research Limitations We propose two research limitations in this study. 1

2

Dimensions in assumptions made through AHP are independent criteria and may become a weakness. We adopted the AHP method in this study. The AHP can integrate complicated evaluation dimensions and criteria into a clearly defined hierarchical structure, and identify the weight and ranking of the dimensions and criteria. However, dimensions in assumptions made through methodologies such as the AHP are independent criteria and may be a weakness. We recommend adopting the Hierarchical Decision Model (HDM) for future research. The expert interviewees in the study are confined to those in Taiwan. Due to geographical constraint, the experts interviewed in this study were limited to experts on VR systems in Taiwan. We recommend including experts from across the world in future research such as experts on Oculus and Sony PlayStation VR.

9.5.3 Directions for Future Research We propose two ideas for future research.

9.5.3.1 Use of the Decision-Making Tool, Hierarchical Decision Model (HDM), to Rank Alternative Solutions for Evaluation and Identify the Optimal Solution The HDM is a tool that helps decision makers quantify complex problems and incorporate quantitative and qualitative judgments into complex problems. Where an alternative solution for evaluation has been established, the HDM can be used for technical management to identify the alternative solution with the optimal technology for a specific environment (Lavoie & Daim, 2020).

9.5.3.2 For Exploration of the Consumers’ View of Virtual Reality System Product Design, We Recommend Including Consumer Views into Future Research Researchers can begin with a customer-oriented approach and evaluate the key factors in the purchase decision for VR system, and create a list of product design requirements from the consumers’ perspective.

References Baxter, M.R. (1995). Product Design: Practical Methods for the Systematic Development of New Products. Chapman & Hall. Bilkey, W.J., & Nes, E. (1982). Country-of-origin effects on product evaluation. Journal of International Business Studies, 13, 89–99. Burdea, G. (1993). Virtual reality systems and applications. Electro’93 International Conference, 164. Cain, W.D. (1969). Engineering Product Design. London: Design Centre Bookshop, 47–55. Chen J., Su, Y.S., de Jong, J.P.J., & von Hippel, E. (2020). Household sector innovation in China: Impacts of income and motivation. Research Policy, 49(4), 103931. Chris Anderson (Producer), & June Cohen (Director). (2009). Pranav Mistry: The Thrilling Potential of Sixth Sense Technology. India: TEDIndia. Gartner (2017). Top trends in the Gartner Hype Cycle for Emerging Technologies, 2017. Retrieved from https://www.gartner.com/

139

Yu-Shan Su and Wen-Hua Wu Hekkert, P., & Schifferstein, H.N.J. (2008). Introducing product experience. In H.N.J. Schifferstein & P. Hekkert (Eds.), Product Experience. Amsterdam: Elsevier. HTC Vive. (2021a). In Wikipedia. Retrieved from: https://zh.wikipedia.org/wiki/HTC_Vive INSIDE (2017, 8th February). HTC, Oculus, Sony PlayStation VR. Tencent Technology. (In Chinese). Retrieved from: https://www.inside.com.tw/2017/02/27/playstation-vr-is-doing-betterthan-expected-sony-says Khalid, H.M., & Helander, M.G. (2004). A framework for affective customer needs in product design. Theoretical Issues in Ergonomics Science, 5, 27–42. Lavoie, J.R., & Daim, T. (2020). Towards the assessment of technology transfer capabilities: An action research-enhanced HDM model. Technology in Society, 60, 101217. Oculus Rift. (2021b). In Wikipedia. Retrieved from: https://zh.wikipedia.org/wiki/Oculus_Rift Play Station VR. (2021c). In Wikipedia. Retrieved from: https://en.wikipedia.org/wiki/ PlayStation_VR Rogers, Everett M. (1962). Diffusion of Innovations. New York: Free Press. Rogers, Everett M. (1983). Diffusion of Innovations, 3rd ed. New York: Free Press. Rogers, Everett M. (1995). Diffusion of Innovations, 4th ed. New York: Free Press. Saaty, T.L. (1971). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 40, 9–10. Su, Y.S., Kajikawa, Y., Tsujimoto, M., & Chen, J. (2018a). Innovation ecosystems: Theory, evidence, practice, and implications. Technological Forecasting and Social Change, 136, 14–17. Su, Y.S., Lin, C.J., & Li, C.Y. (2016). An assessment of innovation policy in Taiwan’s electric vehicle industry. International Journal of Technology Management, 72(1/2/3), 210–229. Su, Y.S., Zheng, Z., & Chen, J. (2018b). A multi-platform collaboration innovation ecosystem: The case of China. Management Decision, 56(1), 125–142. Topology Institute (2018, 31th July). Oculus and HTC in China Market (In Chinese). Retrieved from: https://www.topology.com.tw/DataContent/release/469. Weinbaum, S.G. (1935). Pygmalion’s Spectacles. Retrieved from: https://www.gutenberg.org/ files/22893/22893-h/22893-h.htm.

140

10 AN EVALUATION MODEL OF SMART SPEAKER DESIGN Yu-Shan Su and Jung-Hsien Hsia

10.1 Introduction With the rapid development of the global smart speaker market, the use of smart speakers is no longer confined to smart home. On one hand, many companies such as Amazon are now integrating smart speakers vertically into other industries such as hotels, stores, hospitals, and other commercial institutions. On the other hand, suppliers are trying to create new appearances and functions for smart speakers so that they are more useful for multiple occasions to create new lifestyles and entertainment services. Amazon’s Alexa Echo series currently leads the smart speaker market in terms of market share. However, other companies (e.g., Google, Apple, Samsung, and Xiaomi) are leveraging their own advantages to quickly enter the market. Apple focuses on sound functions while Xiaomi offers competitive prices and Google Assistant provides powerful artificial intelligence functions. Although Amazon’s Alexa Echo now enjoys its head start in the market, the ultimate winner has yet to be decided. Canalys (2018) divides smart speakers based on the audio assistant platform and Amazon was ranked first in market share with 50%. It is followed by Google with approximately 30%. The remaining 20% is shared by Apple and other companies. What are the elements of the design quality of great smart speakers? At first glance, the important elements include high sound quality from the speakers, strong audio technologies, and an attractive design. These elements collectively form the consumer interface of the equipment and ultimately affect the interactions between you and the smart speakers. When we judge the quality of a product, we ultimately rely on its functional performance. For instance, how reliable is the smart speaker when it responds to your regular questions on weather, traffic conditions, or general factual questions? When you ask a question out loud, does the omniscient speaker respond immediately? On the other hand, it is also important to ensure that your speaker is supported by access to a vast database and reliable audio recognition functions. Despite the recent rapid development of the smart speaker market, in terms of design, there have been few evaluations on the product design elements of smart speakers’ research. Most of the research on smart devices and smart home appliances is focused on consumer adoption (Shin et al., 2018) and information security (Shuai et al., 2019). According to DOI: 10.4324/9781003046899-14

141

Yu-Shan Su and Jung-Hsien Hsia

research on smart speakers developed based on the technology acceptance model (Park et al., 2018), consumer perceptions of the functions, design, branding, and price are important factors that affect the perceived benefits and risk assessment of smart speakers. This research focuses on the creation of an evaluation method of the design elements of smart speakers. We use the Abernathy-Utterback Model (Utterback, 1994) to position the development phase of the product in the market and we use the consumer demand theory (Park et al., 1986) and product design theory (Khalid and Helander, 2004) to define the research framework. We hope that this research can help companies increase product development efficiency and quickly identify the dominant design that supports competition. The goals of this research include (1) creating the research framework for an evaluation method of smart speaker designs and (2) using the AHP to establish the relative weights and sequence of priorities for the dimensions that correspond to the criteria. We aim to create an evaluation method of the design elements of smart speakers that increases competitiveness on the market.

10.2  Theoretical Background A smart speaker is, to put it simply, a speaker that serves as the fundamental vessel for providing access to artificial intelligence and the Internet of Things to serve consumers. What is different about smart speakers is the interactivity. In addition, smart speakers are currently the only consumer electronics devices that use only natural language for interactions and operations and such applications are very innovative. The growing demand of smart home appliances is an important factor for the growth of the smart speaker market. Other important factors that affect market growth include the increase in consumer readiness for investing in new technologies and the increase in the use of smart devices by the younger generation. In addition, the development of partnerships between smart home companies and smart speakers’ companies has facilitated their integration. These are the current key factors for the adoption of smart speakers. Advancement in microphones, automatic audio recognition technologies, and natural language processing has helped smart speakers identify and process the user’s audio commands with the highest accuracy. Amazon’s virtual assistant Alexa supports Amazon Echo. Google’s virtual Google Assistant supports Google Home. Amazon and Google products currently lead the smart speaker market in terms of value and quantity. Other companies include Apple, Alibaba, and Sonos. In addition to the penetration rate of the Android system in smart devices, Google has an unimaginably large database supported by a strong search engine. Big data remains the most important infrastructure for artificial intelligence. Amazon leverages its e-commerce advantages to create unparalleled multimedia entertainment and shopping services. Apple relies on its stable operating system, excellent design, independent hardware ecosystem, and iTunes video and audio platform services. Canalys predicts that by 2019 the installed base of smart speakers will exceed 200 million and will reach 500 million by 2023, surpassing the number of wearable devices in use in 2018 and the number of tablet computers in use in 2021. The installed base of smart speakers is based on the sales of 119 million units as of the end of 2018 and an increase of 93.9 million units in 2019. The installed base is expected to reach 207.9 million by the end of 2019 (Hattie, 2018). The sales volume of new smart speakers grew by 82.4%. We therefore learned about the development stage of smart speakers from innovative activities based on the Abernathy-Utterback Model. We use the consumer demand theory and 142

Evaluation Model of Smart Speakers Design

product design theory to explore the development of audio assistants, product specifications, and current market conditions. We established five principal dimensions including styling elements, styling imagery, attractiveness, added value, and functional attributes to discuss the design of smart speakers.

10.2.1  A-U Model The Abernathy-Utterback Model (A-U Model) mainly explains the interactions between product innovation and process innovation through time (Abernathy and Utterback, 1978). The Abernathy-Utterback Model divides the dynamic model of innovation chronologically into the fluid phase, transitional phase, and specific phase (Utterback, 1994). The level of product innovation is highest in the initial fluid phase. Different products and technologies have their own opportunities as well as uncertainties for future development. Products and technologies are constantly innovated in order to stand out from the competition. The market is thus competitive and includes many players. However, the variety and speed of innovation rapidly decline after the appearance of the dominant design. When the market begins to hold expectations for the characteristics or format of a certain product, the core product technology development shifts to the gradual improvements of the characteristics or format of existing products. This will gradually reduce the power for positive innovation. Once users have expectations for the characteristics or format of the product, and form loyalty and preference for the product, companies will focus on tasks that directly affect product sales and expand market share to meet market demand instead of merely pursuing product innovation. The dominant design often falls short of the latest technologies and designs that win in the competition are often not the latest technologies in the industry. On the other hand, the dominant design does not necessarily have to be superior to competing designs. These competing designs are often precursors of innovative features. A dominant design is not an innovation but a combination of a set of features. It is a pioneer in other respects and it creates a benchmark for comparison with subsequent designs. Wade (1995) suggested that the dominant design does not always include the latest technologies and performance. For technical possibilities created by business decisions of suppliers, users, and competitors, the dominant design is a most satisfying design. Anderson and Tushman (1990) proposed a more specific criterion. They believe that the dominant design on the market shifts between competing designs as time progresses. It is possible for a design to gain temporary advantages before it is replaced by a competing design, and soon surpassed by another competing design. However, when a dominant design is created, a product design may account for more than 50% of the market share in the product category. Once it maintains its market share for four consecutive years, it is regarded as a dominant design. Once the dominant design is formed, it will create a trail for future technological advancements and change the foundations of competition in the industry (Anderson and Tushman, 1991). The creation of a dominant design is the establishment of a set of product design standards in the industry which specifies the required conditions for product performance. It represents what the current market participants believe to be the optimal form of product innovation. As such, innovators and competitors must follow the dominant design if they wish to attract consumer. In addition, the creation of a dominant design also marks the start of new product diffusion. Anderson and Tushman (1990) suggested that the creation of the dominant design is directly linked to the diffusion of new technologies and the precondition 143

Yu-Shan Su and Jung-Hsien Hsia

for mass adoption and production of new technologies. After the dominant design is formed, new products will be quickly and widely adopted on the market on a wide scale. With regard to the changes in process innovation, product innovation continuously occurs in the preliminary phase of innovation. As the dominant product trends have not been formed, the process and methodology adopted for producing products are less efficient. They generally involve the use of basic and general production equipment or tools by operators with higher skill levels. However, the speed of product innovation begins to decrease once the dominant design appears, while the speed of process innovation begins to increase. This occurs because the market has developed clear expectations and preferences for the features or format of the dominant product. Companies transfer the resources they dedicated to production innovation to develop process innovation. Dedicated production equipment or tools or new process or methodology is thus created so that more efficient production process and technologies can be used for mass production to lower costs and expand market scale. We can use a classic case study to observe the diffusion of innovative products and how a dominant design is formed. Android is an operating system for smart devices developed Google. It is the integration of updated versions of the Linux kernel and other open-source software and mainly used for mobile devices with touch screens such as smartphones and tablet computers. Nokia and BlackBerry held 70% of the market share of the operating system and hardware products of early smartphones. Google predicted that the third-generation mobile communication technology (3G) will create a strong wave of hardware production and software application business opportunities. Google created the Open Handset Alliance toward the end of 2007 with members including the producers and developers of consumer electronics products, telecommunications operators, and chip manufacturers. This development strategy has created an industry cluster with high levels of horizontal and vertical integration that offers greater competitive advantages compared to other companies that tried to do it alone. In less than four years, the expansion of 3G infrastructure and telecommunications operators’ services allowed the Android system pioneered by Google to take over 80% of the market share as smartphones become mainstream. Amazon is the pioneer in the smart speaker industry. It began selling its smart speaker “Echo” in 2014 and quickly obtained an overwhelming lead in the market. It used its e-commerce website to quickly increase the number of smart speakers in use. Soon, competitors such as Google, Apple, Microsoft, Tencent, and Alibaba joined the market. In addition to explosive development in the number of products, these companies initiated a series of intense competition on the market. According to global statistics in 2017 and 2018, more than 78 million smart speakers were shipped in 2018. Amazon ranked first with 24.2 million Echoes shipped while Google ranked second with 23.4 million smart speakers shipped. They were followed by Chinese suppliers Alibaba, Xiaomi, and Baidu (Canalys, 2019). Despite a head start, Amazon’s sales volume and market share were quickly taken over by other competitors. We believe that the short-term explosive growth of smart speakers resulted from the lower entry barriers for hardware. However, the development of the design elements of the product is still being diffused and the audio assistant is still being developed. There have been no obvious applications of core technologies. According to the A-U Model, both the hardware and software are considered to be in the fluid phase. For instance, companies have added high-quality sound products or specialized products for managing the smart home to their smart speaker product lineup. This shows how companies continue to package smart speakers in different formats as they attempt find the dominant design favored by the consumer market. 144

Evaluation Model of Smart Speakers Design

10.2.2  Product Design Product design is a process for creating or improving products for people’s use. The main elements for product design are functions, reliability, usefulness, appearance, and cost. Consumers’ main demand for the design is the satisfaction of their needs. In the product design phase, designers often have no way of knowing consumer preferences and reactions when products hit the market. Park, Jaworski, and Maclnnis (1986) suggested that consumer needs can be divided into three categories set forth below: (1) Functional needs: Needs of consumers to resolve external problems (e.g., solution of current problems, prevention of potential future problems, and conflict resolution). (2) Experiential needs: Needs for sensory pleasure, diversity, and cognitive stimulation. (3) Symbolic needs: Needs for social status and self-fulfillment (e.g., enhancement of personal image, display of social status, improving group relations, and self-identity). With regard to the conceptual needs for product design, Khalid and Helander (2004) proposed three main elements for designing a successful product: style, emotional value, and functions. We integrated the consumer demand theory of Park, Jaworski, and Maclnnis (1986); the three main elements of product design of Khalid and Helander (2004); and product design literature (Chen et al., 2020; Su et al., 2018a; Su et al., 2018b) to construct the five principal dimensions for the framework of this research. The five principal dimensions we developed contain a total of 24 criteria. (1) Styling elements include seven criteria (e.g., Blijlevens, Creusen, and Schoormans, 2009). (2) Styling imagery includes four criteria (e.g., Ma, Chuang, Chuen, 1998). (3) Attractiveness includes four criteria (e.g., Baxter, 1995; Schiffman, 2000). (4) Added value includes four criteria (e.g., Sheth, 1991; Zeithaml, 1988). (5) Functional attributes include five criteria (by this study). We created a research framework for an evaluation method of smart speaker designs with five principal dimensions and 24 criteria as shown in Table 10.1 and Figure 10.1.

10.3  Research Method The questionnaire for this research was distributed to 16 experts who have expertise in different areas, such as consumer electronics industry, R&D engineers, and project managers. The questionnaire was distributed from April 7, 2019 to May 15, 2019. We distributed questionnaires in interviews and explained the contents of the questionnaire face-to-face during interviews. The interview allowed the interviewees to fully understand the contents of the questionnaire and the method for providing answers to increase the validity of answers. A total of 18 questionnaires were distributed and 18 questionnaires were recovered, of which 16 were valid questionnaires. The background information of the interviewees of the questionnaire includes the gender, age, education, employer, job title, and number of years of service. The background information of expert interviewees in the study includes the following: (1) there were 14 males and 3 females. (2) Ten aged between 31 and 40, four aged between 41 and 50, and two aged over 51. (3) In terms of education, three held a bachelor’s degree, ten held a master’s degree, and three held a doctoral degree. The employers and number of years of service of the experts of the questionnaire are compiled in Table 10.2. The analysis in this study was conducted using the AHP. The AHP is a multi-criterion decision-making technique. It is particularly useful for processing complicated multi-criterion decision-making and can even be used to process non-structural problems. The AHP 145

Yu-Shan Su and Jung-Hsien Hsia Table 10.1  F  ive principal dimensions and 24 criteria for smart speaker designs Dimension

Criterion

Criterion description

A: Styling elements

A1: Appearance and contour A2: Surface treatment

Product style or shape

The process technology for creating an artificial layer on the surface of the product that is distinct from the mechanical, physical, and chemical properties of the product. Examples include coating, polishing, and heat treatment. A3: Dimensions The length, width, and height of the product. A4: Format of The angle, center of gravity, orientation, and position of the composition product created based on the points, lines, surfaces, and basic threedimensional elements. A5: Selection of Materials that form the exterior of the product such as metal, plastic, materials and wood. A6: Color Color of the product. A7: Detailed Examples include the trademark of the producer, presence of interactive features buttons, and areas distinct from other competitors’ products. B: Styling B1: Simplicity More streamlined and smooth product with no excessive image imagery elements. Simple layout design with more pure colors. B2: Prominence Clear and eye-catching product image. B3: Coordination The overall image and style of the product achieves perfect integration with the environment of use or placement. B4: High-tech Product image provides high-tech connotations. A high-tech feel feel implies futuristic technology which surpasses contemporary science and technology. It is the sum of humans’ future science and technology and the exemplification of a certain advanced technology. C: Attractiveness C1: Cognitive A design that can be recognized by consumers at a glance as a similar attraction product or improved design of a product they had used or liked. C2: Semantic If the product is new to the customer, it must provide a certain visual cue attraction to inspire confidence in the customer for viewing the product as easy to learn. Many product designs were developed based on the principle of “making it appear easy for the product to perform its functions”. This concept is referred to as product semantics. C3: Symbolic The customer’s confidence in the decision to purchase a product is attraction influenced by how the product reflects the consumer’s character or emotions. They are motivated by the impression of the product on others. C4: Basic visual Whether the visual aesthetics of the product encourages the customer attraction to make a purchase. D: Added value D1: Social status The product retains strong social influence and the consumer’s ownership of the product helps increase positive social perception. D2: Emotional The product inspires consumers’ emotional empathy. empathy D3: Scenario The product value is not easily affected by the external environment requirements or perceptions and can provide consumers with a positive and worryfree experience. D4: Pop culture The product leads popular trends in contemporary technology and inspires consumers’ curiosity for using the latest electronic equipment on the market.

146

Evaluation Model of Smart Speakers Design Dimension

Criterion

Criterion description

E: Functional attributes

E1: High-quality A smart speaker can play music like any other speakers without being music playback linked to a stereo or computer equipment. It uses the internal active electronic components to amplify sound signals. It must therefore be able to play pleasing and high-quality music like traditional speakers. E2: Internal and Smart speakers can use Wi-Fi, Bluetooth, and other wireless external network connection functions to download software updates and install connections or receive the latest information from the Internet (e.g., traffic and weather conditions). In indoor environments, users can use smartphones to connect and control smart speakers or smart devices. E3: Voice A smart speaker has one (or multiple) internal microphone(s) which recognition can receive audio instructions you make to the speaker and use a capabilities powerful audio assistant to execute the function you need or provide accurate information. E4: Function Companies continue to perform system upgrade or add more expansion speed functions to products so that the audio assistant can identify and use more third-party applications. Examples include music streaming software, taxi services, and even making restaurant reservations. E5: Connecting Ability to connect and support more smart home appliances or smart to other devices. electronic products

Source: This research

is mainly used for multi-criterion decision-making problems and it can provide outstanding results for planning, prediction, judgment, resource allocation, and investment portfolios. Kamal (2001) and Lipovetsky and Conklin (2002) proposed that the decision maker needs to set an overall goal for issues, and develop criteria and sub-criteria based on the different development status of the items. This is repeated until the final layer of the criteria and the pair-wise comparison scale from one to nine is used to weight the eigenvectors by the weights of the criteria. The AHP study is ultimately summed up and weighted to identify the overall priority sequence (e.g., Su et al., 2016).

10.4 Results 10.4.1  AHP Analysis Results of the Five Principal Dimensions Table 10.3 shows the consensus values given by the experts for the five principal dimensions provided in descending order below: “E functional attributes” (0.46376), “C attractiveness” (0.16336), “D added value” (0.15984), “B styling imagery” (0.113), and A “styling elements” 0.10004. The results are consistent with the consumer demand theory of Park et al. (1986) and the product design theory of Khalid and Helander (2004). The main goal of consumers’ purchase of products is to use product functions to resolve problems in daily life. According to the results, the first things to be considered for the design elements of smart speakers are the functional attributes. Only good functions can satisfy consumers’ most important and primary needs. Functional attributes are followed by products attractiveness, added value, products styling imagery, and styling elements. 147

Yu-Shan Su and Jung-Hsien Hsia Appearance and contour Surface treatment Dimensions Styling elements

Format of composition Selection of materials Color Detailed features Simplicity

Smart Speaker design

Styling imagery

Prominence Coordination High-tech feel Cognitve attraction

Attractiveness

Semantic attraction Symbolic attraction Basic style attraction Social status

Added value

Emotional empathy Scenario requirements Pop culture High-quality music playback Internal and external network connections

Functional attributes

Voice recognition capabilities Function expansion speed Connecting to other electronic products

Figure 10.1  Research framework for an evaluation model of smart speaker designs

10.4.2  AHP Analysis Results of the 24 Criteria 10.4.2.1  Five Criteria for Functional Attributes The results of the functional attributes’ dimension are shown in Table 10.3. In this dimension, “E1: high-quality music playback”, “E3: voice recognition capabilities”, and “E2: internal and external network connections” were ranked top three. Their weights exceeded 0.2 which means that these three attributes were considered more important than “E5: connecting to other electronic products” and “E4: function expansion speed” in expert evaluations. The product design process should not merely focus on the quality of music playback but must account for Internet connections and the accuracy of voice recognition in order to provide consumers with a good smart speaker solution. 148

Evaluation Model of Smart Speakers Design Table 10.2  I nstitutions and working years of the experts Working Years

Questionnaire

Institution

Position

 1

Project Management Department, Hardware Design, Google Data Analysis Department, Hardware Design, Google Project Management Department, Hardware Design, Google Project Management Department, Hardware Design, Google Data Analysis Department, Hardware Design, Google Data Analysis Department, Hardware Design, Google Project Management Department, Hardware Design, Google Mechanism Design Department, HTC Mechanism Design Department, HTC Mechanism Design Department, Vega Force International Mechanism Design Department, Pegatron Mechanism Design Department, Castec International System Solution Business Department, NEC Taiwan System Solution Project Management Department, CTCI Group Department of Aeronautics and Astronautics, National Cheng Kung University Department of Aeronautics and Astronautics, National Cheng Kung University

Manager

12

Manager Chief Engineer

8 11

Chief Engineer

15

Senior Engineer Senior Engineer Senior Engineer

5 4 6

Chief Engineer Chief Engineer Product Design Consultant Senior Engineer Senior Engineer Deputy Manager Project Manager

10 11 20

Professor

35

Teaching Assistant

11

 2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

5 6 20 2

Source: This research

10.4.2.2  Four Criteria for Attractiveness The results of the attractiveness dimension are shown in Table 10.3. The first-ranking criterion in the dimension was “C2: semantic attraction” which has a weight of more than 0.4. This means that to effectively induce consumers’ desire for making a purchase, the product must be very approachable. It must let consumers believe that the smart speaker is a simple and easy-to-use electronic product. “C4: basic visual attraction” also had a high weight and ranked second. It means that the product design must create a solid first impression to attract consumers. They are followed by “C3: symbolic attraction” and “C1: cognitive attraction”.

10.4.2.3  Four Criteria for Added Value The results of the added value dimension are shown in Table 10.3. The criteria “D4: pop culture” and “D2: emotional empathy” are weighted above 0.3. The experts believed that the most important added value for smart speakers is to provide consumers with an avant-garde and high-end user experience. The product may also need to have a strong cultural symbol. Emotional empathy refers to product design based on common memory or experience of a certain group of consumers that suits the consumers’ lifestyle. They are followed by “C3: symbolic attraction” and “C1: cognitive attraction”. 149

Yu-Shan Su and Jung-Hsien Hsia

10.4.2.4  Four Criteria for Styling Imagery The results of the styling imagery dimension are shown in Table 10.3. The rankings in the dimension are distinct. The “B4: high-tech feel” criterion received a weight of over 0.4. The experts clearly believed that the styling imagery of smart speakers must provide consumers with a strong visual experience with high-tech connotations to show that the innovative consumer electronic product is powered by advanced technologies. It is followed by “B3: coordination”. As smart speakers are smart home appliances, they must fit inside the user’s living environment when they are placed in a home environment. They must maintain or enhance a positive lifestyle.

10.4.2.5  Seven Criteria for Styling Elements The results of the styling imagery are shown in Table 10.3. The top design element of the exterior of the product was “A4: format of composition”. It shows that the external design of smart speakers must remain true to the essential element of speakers. It is therefore important Table 10.3  A HP analysis results Dimension

Weight

Criterion

A: Styling elements

0.10004

B: Styling imagery

0.113

A1: Appearance and contour A2: Surface treatment A3: Dimensions A4: Format of composition A5: Selection of materials A6: Color A7: Detailed features B1: Simplicity B2: Prominence B3: Coordination B4: High-tech feel C1: Cognitive attraction C2: Semantic attraction C3: Symbolic attraction C4: Basic visual attraction D1: Social status D2: Emotional empathy D3: Influence of settings D4: Pop culture E1: High-quality music playback E2: Internal and external network connections E3: Voice recognition capabilities E4: Function expansion speed E5: Connecting to other electronic products

C: 0.16336 Attractiveness

D: Added value

0.15984

E: Functional 0.46376 attributes

Weight

150

Rank

Overall weight Overall rank

0.08825 0.18771 0.08853 0.19222 0.16665 0.13981 0.13684 0.14124 0.13404 0.32076 0.40396 0.07772 0.4563 0.134 0.33198 0.09673 0.3086 0.28357 0.3111 0.24288

7 2 6 1 3 4 5 3 4 2 1 4 1 3 2 4 2 3 1 1

0.00883 0.01878 0.00886 0.01923 0.01667 0.01399 0.01369 0.01596 0.01515 0.03624 0.04565 0.0127 0.07454 0.02189 0.05423 0.01546 0.04933 0.04533 0.04973 0.11264

24 15 23 14 16 20 21 17 19 12 10 22 5 13 7 18 9 11 8 1

0.20592

3

0.0955

3

0.21351

2

0.09902

2

0.15604 0.18164

5 4

0.07237 0.08424

6 4

for smart speakers to provide a good audio experience. The second-ranking element was “A2: surface treatment”. A good surface treatment helps improve the texture of the product and can, to a suitable extent, transform the visual characteristics of the original materials, providing basic materials with a wider range of sensory experience. The third-ranking element was “A5: selection of materials”. The selection of materials affects the cost of procurement and processing. More importantly, the use of materials directly influences the value of the product in the minds of consumers. The biased view that metal is expensive and plastic is cheap also has a significant impact on product design. The criteria that come after the selection of materials include “A6: color of the product”, “A7: detailed features”, and “A3: dimensions”.

10.5 Discussions 10.5.1  Academic Contributions 10.5.1.1  Establishment of a Model for Smart Speaker Product Design The design of smart speakers falls within the realm of industrial design. According to the results, the innovative technologies and art design styles of the audio assistant affect each other. They must be harmonized in order to create opportunities for designing products that can compete on the market. This interviews and extensive literature review in this research created an evaluation model for the design elements of smart speakers. We used this model for research and analysis to learn about the weights and ranking of decision-making dimensions and criteria to identify the optimal combination of design elements.

10.5.1.2  Verification of the Connection between the Consumer Demand Theory and the Product Design Theory The design elements were compiled from the consumer demand theory of Park, Jaworski, and Maclnnis (1986) and the hierarchy of user needs proposed by Jordan (1999). Level 1 functionality requirements correspond to the functional requirements in the product design elements. The functional attributes in this research verified the priority given to this item. Level 2 symbolic requirements correspond to the emotional value of the product design elements. The rankings of attractiveness and added value dimensions in this research meet the description in the theory and are based on the functionality. Level 3 experience-based requirements emphasize the importance of product aesthetics and design. The results of research on styling imagery and styling elements in this study also support the high-level positioning of these requirements in the theory.

10.5.1.3  Use of the A-U Model to Identify the Timing for Product Development in the Market Through the A-U Model theory and actual case studies, we learned that the formation of a dominant design of an innovative product is a phenomenon involving the innovative integration of multiple technologies into a new product within a certain period. The results are expressed based on the interactions between technologies and the market. According to the innovation experience of many products or industries, the key factors for the formulation of the dominant design are directly related to whether the product can retain a significant 151

Yu-Shan Su and Jung-Hsien Hsia

market share for an extended period of time. For smart speakers, it has only been four years since Amazon began selling its Echo before more and more companies entered market competition. The drastic changes of the market share of the product of each company show that the dominant design of smart speaker has not yet been established.

10.5.1.4  Use of the Analytic Hierarchy Process (AHP) to Determine the Ranking and Weight of the Design Dimensions and Criteria of Smart Speakers This study uses the AHP to establish a decision-making model and identify the relative weight and ranking of the dimensions and criteria for the design of smart speakers. The data on the weight of all dimensions and criteria have passed the consistency test and are thus deemed as credible and reliable.

10.5.2  Practical Contributions 10.5.2.1  Confirmation of the Priority of Functional Attributes in Smart Speaker Development We identified functional attributes as the highest priority among the five principal dimensions through the AHP. If a company has limited resources, investing more resources in software research and development of smart speakers will create optimal benefits. In terms of the ranking of criteria for evaluating functional attributes, the top choice is the high-quality music playback. However, the most important function of smart speakers remains audio assistant applications. The audio assistant is the technical key for development in this industry. Not all companies have the capacity for the research and development of artificial intelligence and there remain significant issues and obstacles in the research and development of the audio assistant. Most users only use a few functions of the smart speaker and they hardly ever want to learn more audio instructions. In addition, consumers often feel frustrated in interactions with smart speakers. This weakness is the same as that of artificial intelligence. Before the audio assistant can become more efficient, it is perhaps possible to integrate it with a certain panel for better expression and execution of commands. If the company does not have the capacity to develop the audio assistant, it can choose to work with suppliers that develop the audio assistant to integrate such functions into their speakers, and provide consumers with a diverse range of services from the company.

10.5.2.2  Identification of Possible Packaging and Marketing Strategies for Smart Speakers In addition to functional attributes, the emotional value of the product must be focused on lowering usage barriers and leveraging cross-industry collaboration for product packaging and promotion. Although it is clear that the high-tech feel and format of composition are indispensable for the appearance, excessive focus on the visual experience may not be acceptable for most consumers on the market. After all, products are marketed and sold globally and companies must account for the variation in the level of education as well as traditional and cultural backgrounds in different countries. The design of products must be suitable in order to meet the objectives of most consumer groups. The only way to increase the competitiveness of the dominant design is to increase the consumer base of the market. 152

Evaluation Model of Smart Speakers Design

10.5.2.3  Relationship between the Dominant Design of Smart Speakers and Entry Barriers The most important part of a smart speaker is the audio assistant that helps users improve their lives and perform other applications. However, not all companies have the capacity for the research and development of an audio assistant. The basis for the development of an audio assistant is determined by whether a company has a continuous stream of data and whether it retains the advanced software development capacity to improve the functions. This is an entry barrier into the competitive market for companies that do not have the key technology. Therefore, the entry barrier is a key factor that affects the market share. As the hardware entry barrier of smart speakers is low, and there is a lot of room for improving the audio assistant, the audio assistant companies have continuously made friendly licensing offers to hardware manufacturers who do not have audio assistants. They hope to increase the market share of their audio assistants and indirectly increase the potential value of their smart speakers. We believe that audio assistants have created a strong positive cycle in the market that will facilitate smooth development. Therefore, companies that have not yet entered or taken part in the competition in the smart speaker market can enhance their own advantages in different sectors by providing diversified services, thereby influencing and leading the dominant design of smart speakers. The Apple iPhone is a well-known success story. Apple’s iPhone had entered the market earlier than Google’s Android smartphones. Apple’s software and hardware design has always influenced the development of the overall smartphone industry and it has always retained up to 60–80% of the profits of the entire market (Pathak, 2018). This is a strategy for participating in market competition that warrants closer examination.

10.5.2.4  Results of the Research for Developing Other Smart Home Appliances The development of smart home appliances is closely related to that of smart speakers. As artificial intelligence will gradually improve, more and more smart home appliances will be equipped with an audio assistant. This will create management issues and information security risks that must be resolved. In the added value dimension of this research, the criterion for the influence of settings included data leak factors. The verification of the smart speaker framework in this research shows that a study of the design elements of smart home appliances or smart devices may be appropriate for more in-depth research in the future.

10.5.3  Research Limitations This research is limited by the literature review and expert questionnaires. We propose three research limitations here.

10.5.3.1  Enhanced Research Data Volume and Additional Analyses of Individual Groups in the Future Research The AHP methodology has been widely adopted but the number and selection of experts and academics constitute a primary limitation. Too many or too few candidates or bias in the candidate validation standards could affect the consistency of analysis results. Therefore, the 153

Yu-Shan Su and Jung-Hsien Hsia

issue of selecting experts and academics will be crucial for future research. We recommend, for future research, grouping the big data collected on the platform, and implementing cross analysis based on such groups and research results to explore the different weights of the design criteria for different groups.

10.5.3.2  Weaknesses of the AHP Research Methodology Although the AHP is one of the main methodologies for multiple criteria decision-making, one of the reasons for its use is its transparency and simplicity that renders the research results convincing to a certain degree, and it is suitable for researchers in most disciplines. However, the dimensions in assumptions made through a research methodology such as the AHP are independent criteria which cannot account for the correlation of the overall criteria. Independent adoption of the Analytic Network Process (ANP) can be used to calculate related weights and make up for the deficiency of accommodating independent conditions between the dimensions and criteria of AHP assumptions.

10.5.3.3  Global Experts Needed for Future Research This research invited top experts from different fields in Taiwan to take part in the study. Companies engaged in the research and development of audio assistant are mostly European or American companies. The United States and China are markets that have seen the highest level of expansion. Therefore, a study involving R&D experts from around the world will be more valuable for research.

10.5.4  Directions for Future Research 10.5.4.1  In-Depth Research on Individual Dimensions of Smart Speaker Design All dimensions and criteria have passed the consistency certification after AHP analysis. However, the scope of research on the design elements in this study was too broad. We recommend reducing the scope of future research and focus on individual dimensions. The development of the smart speaker market has just begun and there remains plenty of room for discussions on the functions and external design.

10.5.4.2  The Hierarchical Decision Model (HDM) Can Be Adopted for Future Research We recommend adopting the Hierarchical Decision Model (HDM) for future research to attain more comprehensive analysis results as the dimensions and criteria of the research framework can cover the subject of the research and be simplified into an acceptable quantity.

10.5.4.3  Smart Speaker Consumer Research We recommend using the Technology Acceptance Model (TAM) for consumer surveys in future research. The TAM can be used to explain and predict user acceptance of innovative products and the behavioral intentions model. Questionnaire surveys can be implemented on a large number of consumers in future research to explore consumer ideas and needs.

154

Evaluation Model of Smart Speakers Design

10.5.4.4 Results of the Research Can Be Used for the Design of Other Innovative Products The conclusion of this research has established a benchmark evaluation model of smart device product design. In this research, we explained the importance of product design and the functions, emotional values, and external elements of consumer demand. We hope that more researchers can use the results of the research for the research on other innovative products.

References Abernathy, W. J., & Utterback, J. M. (1978). Patterns of innovation in technology. Technology Review, 80, N97, 40–47. Anderson, P., & Tushman, M. L. (1990). Technological discontinuities and dominant designs: A cyclical model of technological change. Administrative Science Quarterly, 604–633. DOI:10.2307/2393511 Anderson, P., & Tushman, M. L. (1991). Managing through cycles of technological change. Research Technology Management, 34(3). DOI: 10.1080/08956308.1991.11670739. Baxter, M. R. (1995). Product Design: Practical Methods for the Systematic Development of New Products. Chapman & Hall. Blijlevens, J., Creusen, M. E., & Schoormans, J. P. (2009). How consumers perceive product appearance: The identification of three product appearance attributes. International Journal of Design, 3(3). Canalys (2018). Smart speakers to hit 100 million installed units by end of 2018. Canalys. Retrieved from https://www.gsmarena.com/canalys_smart_speakers_reach_100_million_installed_units_2018news-32139.php Canalys Newsroom (2019). Smart speaker market booms in 2018, driven by Google, Alibaba and Xiaomi. Canalys. Retrieved from https://www.canalys.com/newsroom/smart-speaker-market-booms-in2018-driven-by-google-alibaba-and-xiaomi Chen J., Su, Y. S., de Jong, J. P. J., & von Hippel, E. (2020). Household sector innovation in China: Impacts of income and motivation. Research Policy, 49(4), 103931. Hattie, H. (2018). Global smart speaker shipments grew 187% year on year in Q2 2018, with China the fastest-growing market. Canalys. Retrieved from https://www.canalys.com/newsroom/globalsmart-speaker-shipments-grew-187-year-on-year-in-q2-2018-with-china-the-fastest-growingmarket Jordan, P. W. (1999). Pleasure with products: Human factors for body, mind and soul. Human Factors in Product Design: Current Practice and Future Trends, 206–217. Kamal, M. (2001). Application of the AHP in project management. International Journal of Project Management, 19, 19–27. Khalid, H. M., & Helander, M. G. (2004). A framework for affective customer needs in product design. Theoretical Issues in Ergonomics Science, 5(1), 27–42. Lipovetsky, S., & Michael, C. W. (2002). Robust estimation of priorities in the AHP. European Journal of Operational Research, 137(1), 110–122. Ma, Yung-Chuan & Chuang, Ming-Chuen (1998). A study on the relationship between product image and product form of microelectronic products. China-Japan-Korea Design Symposium, 801–806. Park, C. W., Jaworski, B. J., & Maclnnis, D. J. (1986). Strategic brand concept-image management. Journal of Marketing, 50(10), 135–145. Park, K., Kwak, C., Lee, J., & Ahn, J. H. (2018). The effect of platform characteristics on the adoption of smart speakers: Empirical evidence in South Korea. Telematics and Informatics, 35(8), 2118–2132. Pathak, T. (2018). iPhone X Drove Apple’s “Revenue Super Cycle”. Counterpoint Technology Market Research. Retrieved from https://www.counterpointresearch.com/iphone-x-drove-apples-revenuesuper-cycle/ Schiffman, L. (2000). Consumer Behavior. Englewood Cliffs, NJ: Prentice-Hall, Inc. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170. Shin, J., Park, Y., & Lee, D. (2018). Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technological Forecasting and Social Change, 134, 246–253.

155

Yu-Shan Su and Jung-Hsien Hsia Shuai, M., Yu, N., Wang, H., & Xiong, L. (2019). Anonymous authentication scheme for smart home environment with provable security. Computers & Security, 86. DOI: 10.1016/j.cose.2019.06.002 Su, Y. S., Kajikawa, Y., Tsujimoto, M., & Chen, J. (2018a). Innovation ecosystems: Theory, evidence, practice, and implications. Technological Forecasting and Social Change, 136, 14–17. Su, Y. S., Lin, C. J., & Li, C. Y. (2016). An assessment of innovation policy in Taiwan’s electric vehicle industry. International Journal of Technology Management, 72(1/2/3), 210–229. Su, Y. S., Zheng, Z., & Chen, J. (2018b). A multi-platform collaboration innovation ecosystem: The case of China. Management Decision, 56(1), 125–142. Utterback, J. M. (1994). Mastering the Dynamics of Innovation. Cambridge, MA: Harvard Business School Press. Wade, J. (1995). Dynamics of organizational communities and technological bandwagons: An empirical investigation of community evolution in the microprocessor market. Strategic Management Journal, 16(5), 111–133. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

156

11 METHODOLOGICAL FRAMEWORKS FOR OPPORTUNITY DISCOVERY IN INNOVATION AND TECHNOLOGY MANAGEMENT Cristian Mejia and Yuya Kajikawa 11.1 Introduction The second framework refers to the identification of gaps or relationships across different knowledge representations of the same topic. Like finding new developments in solar cell research that have not yet been exploited by the industry may result in potential commercialization opportunities (Shibata et al., 2010a). While depending on the domain being compared these frameworks may help to identify emerging technologies previously discussed in the public domain, before being refined as academic research or inventions (Mejia and Kajikawa, 2019). A final framework refers to the linkage of problem and solution topics across different domains. These frameworks are a high-level representation of computational methods used by ITM researchers in the process of opportunity discovery. Although text and citation mining has reached some level of maturity when analyzing single topics, the process of discovery between two distinct domains is still nascent, and research opportunities abound concerning tool development, evaluation methods, theoretical definitions of topics, and data accessibility. Innovation and Technology Management (ITM) scholars face the pressing need of bringing sense to the large number of technological developments taking place around the globe. Those developments are being documented in the form of academic research, patents, industry reports, etc. The sheer volume of data containing new knowledge is beyond the grasp and understanding of any expert, as expertise itself requires deep specialization in a narrow field, making the potential overlook of new but related developments in other fields quite apparent. Fortunately, computational methods are being developed to navigate such big data. Methods used in ITM can be said to have reached some level of maturity, and are applied to different types of data, or for answering specific research questions, usually targeting a single domain (Antons et al., 2020). For instance, those methods have helped in identifying emerging technologies (Rotolo et al., 2015), assist in the creation of technology roadmaps (Kostoff and Schaller, 2001), and inform in foresight (Kayser and Blind, 2017). One wellspread usage is the analysis of scientific and industry fields by mining academic articles and patents, where computer-assisted approaches identify narrow subtopics allowing comprehensive overviews and reducing bias (Boyack et al., 2005). DOI: 10.4324/9781003046899-15

157

Cristian Mejia and Yuya Kajikawa

Few researchers have attempted to leverage those methods to connect distant and disjoint topics or concepts for opportunity discovery and hypothesis creation. When done, these methods are assistive in nature. It is the researcher who ultimately formulates hypotheses after the exhaustive navigation of the outputs generated by the tools. The hypotheses generated are based on observed relationships or gaps between the pair of topics under study. The development and improvement of these tools facilitate to have an impact in the research productivity and innovation, in that they reduce the time and increase the likelihood of finding linkages between previously disjoint topics (Smalheiser, 2012). Examples of these methods are the systematic discovery of linkages between developments in academia and industry for finding common players and establishing synergies. Gaps between the two can also be useful as they may represent industrial development opportunities of technologies being developed in academia (Shibata et al., 2010a). Another type of discovery deals with the systematic pathfinding between two distinct concepts in a shared knowledge domain – a method well exploited in biomedicine for finding potential treatments of a disease by text mining a large corpus of medical literature (Sebastian et al., 2017). The objective of this chapter is to present an overview of that particular use of computational methods. Research and methods discussed in the following sections apply a combination of text mining and other approaches to automate the process of finding linkages or gaps between two target literatures. We have divided them into three categories: • • •

Research establishing connections between two distinct topics or concepts within the same knowledge domain (e.g. technologies and social issues in academic literature) Research establishing connections between the same topic across distinct knowledge domains (e.g. Internet of Things in academia and industry) Mixed methods.

We expect to contribute by offering a high-level representation of opportunity discovery methodologies while discussing their use cases and current trends. We also discuss avenues for future development. This chapter is structured as follows. In the next section, we overview computational methods commonly used for analyzing a large corpus of knowledge, from citation networks to text mining and others. Then, we focus on a set of articles applying computational methods to find relationships or gaps between disjoint corpora, by attempting to either link different topics or link the same topics across different domains. We then discuss the implication of those methods for technology management to finally point to future avenues of improvement.

11.2  Related Literature In ITM the application of computer-assisted methodologies for analyzing science and technology information is oftentimes called “tech mining” (Porter and Cunningham, 2005). This term comprises a plurality of methodologies for analyzing structured and unstructured data like academic articles and patents. These methods also vary in their sophistication, from simple statistical yearly trends of publications to complex document classification systems applying machine learning. Among the tech mining methods, citation networks and text mining are notable in the process of opportunity discovery. Citation networks exploit the property of papers and patents of having a list of references pointing back to previous research or inventions from where new knowledge is built. A 158

Methodological Frameworks

citation network is built by connecting a set of documents to their cited references, or citing documents, and then connecting those to their subsequent references forming a network of documents. Citation networks can be created by following different approaches, namely direct citations (de Solla Price, 1965), bibliographic coupling (Kessler, 1963), and co-citations (Small, 1973). Properties of citation networks are well understood in the ITM community. They are used particularly in the context of science mapping and emerging technology detection – for instance, to analyze a large field like sustainability research (Kajikawa et al., 2017), or even snapshots of the whole scientific literature (Boyack et al., 2005). Topics represented by citation networks can be decomposed into granular subtopics by applying algorithms rooted in network theory (Lancichinetti and Fortunato, 2009). Text mining, on the other hand, attempts to find patterns based on text. Although the methods are different, ITM scholars apply text mining for the same purposes they do for citation networks – for instance, to decompose large fields of knowledge into granular ones (Antons et al., 2020; Griffiths and Steyvers, 2004). Typical text mining methods include keyword analysis and networks of keywords (Leydesdorff and Welbers, 2011), and also others based on probabilistic models like topic model (Blei et al., 2010). The process of systematic discovery has benefitted from both methodologies. Citation networks and text mining can be applied to a large amount of data and be split on demand into the granularity aimed by the researcher. Given that these methods are as comprehensive as the data fed into them, the process of discovering nascent or previously overlook subtopics becomes trivial if the method is executed properly. However, these methods are usually applied on a single topic, from where subtopics can be said to be discovered. Opportunity discovery is not restricted to the boundaries of a single field or data type. For that reason, another set of methodologies have been developed for attempting to discover relationships across topics previously believed to be unrelated. In particular, literature-based discovery (LBD) methods are used for this concrete task (Sebastian et al., 2017). LBD refers to a variety of computational assistive methods for finding associations between existing knowledge (Swanson and Smalheiser, 1997). However, LBD has so far been confined to biomedicine research, due to its valuable contributions in finding linkages between diseases and potential treatments (Swanson, 1986). Additionally, while they can be generalized, in practice LBD methods tend to be applied within the same knowledge domain, usually academic articles. Generalized frameworks for opportunity discovery, which are both topic and data agnostic, are still missing in the ITM literature. In the following section, we summarize and generalize the progress done so far by ITM scholars in leveraging the above-mentioned methods for three case scenarios of opportunity discovery.

11.3  Opportunity Discovery Frameworks in ITM In this chapter, we aim to study research articles that have attempted a linkage between concepts or domains with the aid of computational methods. As noted by Smallheisser this kind of automated linkage or computer-aided discovery is still largely unnamed, as method names like “literature-based discovery” are not well spread in the research community, despite some researchers using them without acknowledging them (Smalheiser, 2012). Hence, a broad search strategy is necessary to pull related articles from bibliographic databases because a simple keyword search will not suffice. We adopted the following strategy. First, we searched for a seed set of academic articles that contain the keywords “literature-based discovery” OR (“text mining” AND “citation analysis”) in their titles, abstracts, 159

Cristian Mejia and Yuya Kajikawa

or keywords and that belong to the fields of business and management. This set was screened to retain only those articles that develop a methodology for automated linkage of two text corpora, or that discuss a case study applying such a method. We then extracted the cited articles from their list of references (backward citations), and the citing articles (forward citations) provided by the bibliographic database. This is done because we expect to find related articles that have applied similar methods observed in the seed set even though those articles could use a different name for their methodologies. We then replicated the afore-mentioned screening retaining only methodological articles and case studies applying a computational linkage. While, in most cases, the screening can be done by inspecting only the abstract, when hesitant, the method section of the articles was read. Articles belonging to the biomedicine domain were neglected from our list because LBD methods are known in that domain, and other articles have already summarized those methodological approaches (Henry and McInnes, 2017). Our search strategy was conducted in the Web of Science and Scopus bibliographic databases to ensure the most coverage. We included research articles, conference proceedings, and book chapters written in English. The data extraction was performed on October 01 of 2020. By applying the search strategy, we retained 34 articles. Although they all use a generalizable text mining approach to bridge unconnected text corpora, they can be classified into any of three different approaches, which in this chapter we regard as the opportunity discovery frameworks. The first one corresponds to the classic approach of LBD, where two disjoint concepts existing in the same knowledge representation are bridged. The second bridges the same concept present in two different knowledge representations. And the third one is mixed methods. Articles in each category are further discussed in the following sections.

11.3.1  From Problem Topic to Solution Topic within the Same Knowledge Representation Twelve out of 34 articles applied a kind of automated linkage between two entities, concepts, or topics that are disjoint but belonging to the same knowledge representation. In this context, we refer as a knowledge representation to the database or data source where those entities, concepts, or topics exist. For instance, it is not uncommon in tech mining studies to refer to “academia” as the knowledge captured by academic articles, or “industry” the one captured by patents. The methods in these articles, whether acknowledged or not, refer to a form of the classic LBD approach. Figure 11.1 represents a generalized method for finding connections between two topics as observed in ITM research. In this framework, researchers choose a problem topic A and an expected solution topic C. Documents or text contents representing topics A and C come from the same database. Then, both topics are segmented into subtopics (also called “clusters” or “communities”) by using text mining or citation mining methods. The segmentation is performed to narrow down the problem and solution spaces and achieve more granularity. Bridging terms between clusters of topics A and C are extracted by applying another text mining method. Commonly, a similarity score is also assigned to each pair of clusters. Finally, those pairs of clusters with high similarity are further analyzed by an expert aiming to establish plausible connections and develop new hypotheses that will require extra studies to validate. Table 11.1 shows the articles applying the LBD methodology in ITM and other non-biomedical fields including the topics being connected, the type of discovery, and the type of database used.

160

Methodological Frameworks

Figure 11.1  G  eneralization of opportunity discovery between two disjoint topics belonging to the same knowledge representation (usually a database of academic articles, patents, etc.)

Table 11.1  A rticles applying the LBD approach in ITM and other non-biomedical fields N Paper

Topic A

Topic C

LBD

Data

1997 2002 2006 2006 2008 2012

Author C NA Entity C Concept C NA NA

Closed Open Closed Closed Open Open

Academic articles Web pages Government reports Academic articles Academic articles Academic articles

Complex Networks Gerontology

Closed Academic articles

  7 (Fujita, 2012)

2012

Author A Genetic algorithm Entity A Concept A Water purification Agricultural Economics Sustainability

  8 (Ittipanuvat et al., 2014)   9 (Kibwami and Tutesigensi, 2014) 10 ( Jha and Jin, 2016) 11 (Takano and Kajikawa, 2018) 12 (Mejia and Kajikawa, 2020a)

2014

Robotics

2014

Carbon emissions (Uganda) Entity A Internet of Things

 1  2  3  4  5  6

(Cory, 1997) (Gordon et al., 2002) ( Jin and Srihari, 2006) (Fox et al., 2006) (Kostoff et al., 2008) (Huang et al., 2012)

Year

2016 2018 2020

Carbon emissions (UK) Entity C WEHAB literature Internet of Things Poverty Alleviation

161

Closed Academic articles Closed Academic articles Closed Ontology Closed Academic articles Closed Academic articles

Cristian Mejia and Yuya Kajikawa

The earliest article belongs to the humanities, where the author applied the LBD method as introduced by Swanson (1986) to explore the possibility of connecting how an author could influence the thought of another. Concretely, Cory studied how modern poet Robert Frost was influenced by ancient Greek philosopher Carneades through the study of linkages of ideas in a humanities article database (Cory, 1997). This study did not apply a text mining method. Rather, by applying a series of search strategies the sets of articles to be studied for both extremes was narrowed down, and the linkage was largely left to the interpretation of those involved in the study. Although this paper does not belong to the ITM, it is usually cited as an example of the few applications of LBD in non-medical fields (Hui and Lau, 2019). By 2002, Gordon et al. (2002) noticed the potential generalization of LBD and experimented by applying directly in the World Wide Web using an open discovery approach. They applied it to concepts like genetic algorithms and concluded that there is a great variation of the effort needed to reach a new hypothesis depending on the starting concept. Later LBD will be applied in the field of counterterrorism to find a linkage between two entities in a government report database. The entities varied from personal names, target places, weapons, etc. – for instance, making it possible to establish connections between the names “Osama Bin Laden” and “George Bush”, where a set of intermediary terms related to those entities is extracted as output. This study aimed to build a predictive model for identifying potential targets of terrorist attacks. The study will later be improved in 2016 (Jha and Jin, 2016). The rest of the articles study linkage between distinct topics in academic literature. In a generic approach, Fox et al. (2006) took the ideas of LBD to develop a visualization tool that helps in the discovery process through a visual user interface. But it is until 2008 where we find the first instance of LBD properly attributed to ITM with the work of Kostoff et al. exploring the possibilities of open LBD to find potential technologies that would help on the task of water purification (Kostoff et al., 2008). Similarly, Huang et al. (2012) would use the method for agricultural economics. After those, we only found instances of closed discovery type – for instance, a methodological article for extracting intermediate vocabulary between the fields of sustainability science and complex networks to assess how the latter can help policymakers in setting agendas that promote sustainable practices (Fujita, 2012). Finally, in a different approach, Kibwami et al. (2014) applied this framework to discover best practices by studying the differences between research on green building architecture performed by researchers in Uganda compared to those in the UK. Subsequent studies applying these methods started to stress the necessity of linking technologies to social issues. Ittipanuvat et al. (2014) explored opportunities in the field of robotics to help solving issues in the field of gerontology. This paper is also one of the first on dealing with a relatively larger volume of data, comparing and analyzing 11,700 articles about robotics to 22,864 on gerontology. To navigate through that volume of data the approach shown in Figure 11.1 was used. This enabled the authors to shortlist ten specific robotic technologies (e.g. laparoscopic surgery) to 13 specific problems among the elderly (e.g. prostate cancer). Finally, two articles attempted to establish linkages between the Internet of Things (IoT) and related technologies to broader social issues for instance, by linking the IoT literature to several specific concerns related to water, energy, health, agriculture, and biodiversity, also known as the WEHAB framework proposed by the World Summit on Sustainable Development in 2002 (Takano and Kajikawa, 2018); or by linking the IoT to the Sustainable Development Goals proposed by the United Nations (Mejia and Kajikawa, 2020a). 162

Methodological Frameworks

11.3.2  Gaps and Relationships across Different Knowledge Representations of the Same Topic Twenty-one out of 34 articles correspond to this second opportunity discovery framework. In these articles, a single topic is selected for study, and commonalities or differences are extracted from two different knowledge representations. Figure 11.2 is a generalization of the methodological approaches followed by these articles. Topic A is designated for study. Then, related documents are extracted from two different databases that act as a proxy of the knowledge covered in two distinct domains. Academic articles corresponding to academia, patents to industry, and news or social network data to social attention are the most common representations. Topic A in each knowledge domain is subdivided into specific subtopics by applying a text mining method, or citation mining when possible. This is followed by a matching step between clusters of each representation by applying a semantic similarity score. Those pairs of clusters with high similarity are regarded as an established knowledge agreement between the two domains, whereas a low or zero similarity can be regarded as a gap. Both relationships and gaps play an important role in the discovery process of ITM researchers. Relationships found using these methods signal shared knowledge from where synergies could be established – for instance, to find collaborations between industry and academic researchers working on the same topics, or universities looking for industry collaborators for their spinoffs. On the other hand, gaps could signal commercialization opportunities for academic achievements still not patented in the industry. Or, looking from the other side, academics could find research opportunities by finding topics in industry or society that are yet to be seen in academic literature. Table 11.2 lists articles attempting to develop methods within this discovery framework or case studies applying such a framework. The earliest example is from Shibata et al. (2010); hence, there is a little more of a decade for this type of method being applied. While this framework has been applied to a variety of topics, energy-related research appears to be the most common target, with solar cells being the most frequent. It is important to point out that the articles listed in Table 11.2 refer to studies where systematic and computationally assisted methods have been applied to link both knowledge representations. Academic articles in ITM that simultaneously analyze two or more types of documents are abundant. For instance, Mikova et al. (2019) analyzed papers, patents, news, and other data on green energy for the purpose of technology monitoring. However, in that type of research, the associations across the different knowledge representations are made by the researchers by inspecting the output generated by the applied methods. The research discussed in this section, however, attempts to reach similar conclusions in an automated manner. From Table 11.2 we could infer that any combination of two distinct databases could make a case for this framework to work. However, so far, the combination of papers and patents is the most used by ITM researchers with 16 articles under that category, followed by three linking papers and news, and only two covering other types of data. Most of the articles linking papers and patents of a given topic show a consistent methodology well aligned to that represented in Figure 11.2. These articles replicate the method developed by Shibata et al. (2010a) with incremental improvements in the methods – for instance by applying different clustering algorithms based on citations (Mejia and Kajikawa, 2020b) or text mining (Ranaei et al., 2017); or by applying sophisticated similarity matching 163

Cristian Mejia and Yuya Kajikawa

Figure 11.2  A  generalization of opportunity discovery framework by comparing the same topic across two different knowledge representations or databases: (1) two knowledge representations of the same topic and (2) relationships in quadrant “a” when similarity exists, or gaps being quadrants “b” and “c”

Table 11.2  A rticles finding relationships or gaps across different knowledge representations of the same topic N Article  1  2  3  4  5  6  7  8  9 10

(Shibata et al., 2010a) (Shibata et al., 2010b) (Nakamura et al., 2010) (Shibata et al., 2011) (Ogawa and Kajikawa, 2014) (Wang et al., 2015) (Visessonchok et al., 2016) (Mejia and Kajikawa, 2017) (Ranaei et al., 2017) (Kayser, 2017)

11 (Mejia and Kajikawa, 2019) 12 (Takano and Kajikawa, 2019) 13 (Xu et al., 2019) 14 (Sasaki et al., 2019) 15 ( Jeong et al., 2019) 16 (Li et al., 2019a) 17 (Li et al., 2019b) 18 (Yamada et al., 2019) 19 ( Jianhua et al., 2019) 20 (Mejia and Kajikawa, 2020b) 21 (Shen et al., 2020)

Year

Topic

Linking

2010 2010 2010 2011 2014

Solar cells Secondary batteries Aerospace engineering Solar cells Electrolyte fuel cells

Papers – Patents Papers – Patents Papers – Patents Papers – Patents Papers – Patents

2015 Microalgal biofuels 2016 Green buildings 2017 Robotics

Papers – Patents Papers – Patents Papers – News

2017 Pharmaceutical Industry 2017 Cloud computing; artificial photosynthesis; vegan diet 2019 Robotics

Papers – Patents Papers – News

2019 Internet of Things

Papers – Patents

2019 2019 2019 2019 2019 2019 2019 2020

Papers – Patents Papers – Patents Patents – Trademarks Patents – Twitter Papers – Patents Papers – Patents Papers – Patents Papers – Patents

The CHEMDNER corpus Natural Language Processing Organic light-emitting diode (OLED) Perovskite solar cells Perovskite solar cells Artificial Intelligence Internet of Things Energy storage technologies

2020 Smart Health Monitoring

164

Papers – News

Papers – Patents

Methodological Frameworks

methods between clusters: for instance by comparing full sentences instead of keywords (Sasaki et al., 2019). Besides the methodological approach, these articles have in common the aim of understanding relations and gaps between academia and industry in the context of technology foresight. Articles linking papers and news pursue a different objective. This is due to the less understanding of the value of news data for ITM. Hence, these articles are explorative, trying to justify the usage of news media for technology road mapping and forecasting. For instance, academic articles and news on robotics are linked to explore how the sentiment and social attention observed in technology news affects the outcomes of academic publishing on the same topics. Peaks of elevated social attention seem to signal a larger production of academic articles on the same topic in the coming years (Mejia and Kajikawa, 2017). Similarly, technological topics that are discussed with a positive tone in newspapers result in a larger number of academic publications on the same topics in a later period suggesting the possibility of using news data as a potential tool for technological forecasting (Mejia and Kajikawa, 2019). News data has also been found to measure the diffusion of technological terms into the public discourse (Kayser, 2017). Finally, other types of data linkages include relating patent and trademark data for developing and informing research and business strategies by identifying gaps in intellectual property rights to be filled by new technologies or products ( Jeong et al., 2019). The linkage of patents and social network data like Twitter has shown to be helpful to observe changes in the expectations and interest of experts and professional “influencer” users of Twitter, and in general, to check the response and expectations of those interested in a particular technology like the perovskite solar cells (Li et al., 2019b).

11.3.3  Mixed Methods The only article dealing with the linkage of problem and solution topics where both belong to different knowledge representations was found by linking the field of robotics in academic literature to news about Japan. In this context, robotics is presented as a potential solution space, while the news about Japan in an international newspaper is clustered to find granular subtopics that may represent specific socio-economic issues. This case study followed a methodology like that of Figure 11.2 and subtopics like elderly care and nuclear spills were automatically identified and linked to social robotics and robotics for hazardous environments respectively (Mejia and Kajikawa, 2016).

11.4 Discussion Three methodological frameworks are being applied by ITM scholars to assist them in the discovery process. In a generalized manner, the frameworks attempt to find relationships or gaps between a pair of topics. When looking for relationships, these computer-assisted methods help ITM scholars navigating a large amount of data, and spot potential connections that could have been overlooked by experts – either because potential applications lie beyond the original and intended fields where a technology was developed or because the field itself has grown large enough for an expert to be aware of current developments in other sub-fields. Finding linkages of a technology as discussed in different domains like industry, academia, or public discourse helps in technology forecasting and diffusion (Kayser, 2017; Mejia and Kajikawa, 2019). Additionally, shared knowledge across domains could help to identify potential collaborators or monitoring competitors’ activities. 165

Cristian Mejia and Yuya Kajikawa

When looking for gaps, ITM scholars find “white spaces” where new developments in one domain can be applied in another. This has been proved to be useful in finding commercialization opportunities when academic research is not found as patented inventions, or by identifying social issues where few or no technologies are available as potential solutions. The first opportunity discovery framework discussed in this chapter is the generalization of the LBD methods but applied to ITM and other non-biomedical fields. Although LBD methodologies are often cited by the articles in this framework, there are some differences when compared to the more spread usage of LBD in biomedicine. Some notable differences are as follows. •

•

•

First, in LBD, the above-described process corresponds to what is called a “closed” discovery, where the starting and ending topics are known or selected a priori by the researcher. “Open” discovery methods also exist, and they start either with a problem or solution concept and expansion techniques are used until reaching to their plausible counterpart. In ITM the closed approach seems to be the preferred one. Bridging terms, also called “b-terms”, are regarded as the output of the method. In biomedicine, those b-terms are usually the ones analyzed by experts for hypotheses generation, while in ITM the study of the b-terms seems to be of less interest. Instead, the pairs of clusters with more connections are the ones being targeted by the experts. And those pairs are interpreted broadly, without necessarily revising the b-terms that connect them. Finally, LBD as observed in biomedicine does not necessarily apply a segmentation step (e.g. clustering) to bridge topics A and C. However, the clustering approach seems to be common in ITM.

The reasons for these differences may lay in that in biomedicine the usage of well-established ontologies like the MESH terms helps to spot bridging terms with concrete definitions (Smalheiser, 2012). Social science and humanities fields lacking those consensual ontologies seem to prefer the clustering approach, where the topics are divided into sub-topics which being still broad allow freedom to the experts to interpret the potential connections between two fields. The second framework deals with discovery across domains. The apparent large interest of ITM scholars in applying this discovery framework on the intersection of academia and industry can be argued to be related to data accessibility. While academic articles and patent data are already digitally distributed by bibliographic data providers or patent offices, in a format that is ready for data mining, the same is not true for other types of data. Few news article providers exist that deliver international news data ready for data mining (e.g. Factiva, LexisNexis). Government and industry reports are still found in less data mining-friendly formats like PDFs. And social network data, if available, require some level of programming skills to be gathered, because social media platforms usually offer such data through Application Programming Interfaces (APIs), rather than more accessible spreadsheet-like formats. Finally, ITM scholars applying these frameworks in recent years are interested in linking technologies and social issues. Technological developments, in the academic domain, are expected to exert some sort of social impact from the very moment of the idea creation (Gibbons, 1999). Researchers already have a problem in mind to solve when they develop something new, but nothing stops new developments to also help to solve other problems in other fields. ITM scholars applying the frameworks discussed in this chapter have an implicit assumption that the knowledge has grown so rapidly that it is not expected anymore that 166

Methodological Frameworks

new technological developments will discuss and cover all potential applications in the documents they are described. Some assistance is needed to find new opportunities for already existing knowledge.

11.5  Future Directions Despite that tech mining has reached some level of maturity, computational assistive methods for opportunity discovery in ITM are still nascent. Paths for methodological improvement are discussed in this section. •

•

•

Developing new tools for opportunity discovery. While an array of software and programming utilities exist to perform tech mining analysis, either by citation or by text mining, few of them address the problem of discovery across domains. Generic bibliometric tools like VosViewer (Van Eck and Waltman, 2010) or Bibliometrix (Aria and Cuccurullo, 2017) go as far as decomposing a topic into subtopics by employing a variety of algorithms. However, the matching of disjoint topics is still left to the skills of the researcher who will need to find other ways to extract bridging terms and attach similarity scores. For LBD we find tools like ARROWSMITH (Smalheiser et al., 2009), FACTA (Tsuruoka et al., 2008), and others (Weeber et al., 2005) that automate the process of finding relationships. Most of these tools are dependent or fine-tuned for LBD in biomedicine, and useful for a single knowledge representation (i.e. academic articles). Hence, new tools that are field-independent and that are data agnostic are still necessary. Solve the problem of evaluating the outputs of automated discovery. Currently, there is no evaluation metric for any of the frameworks discussed in this chapter. This is a shared issue with LBD methods (Smalheiser, 2012). ITM researchers so far have used expert opinions to validate the results of the matching (Shibata et al., 2010a), which can be costly, time-consuming, and prone to bias. Another technique that ITM researchers have attempted to reach robust outputs is by comparing multiple similarity metrics and choosing the results that are consistent across the metrics (Ittipanuvat et al., 2014). While this type of validation probes the robustness of the method, there is no indication that the relations or gaps found will lead to an actual practical outcome. So far, discovery opportunity frameworks only serve as guidance from where researchers, practitioners, and policymakers could draw ideas for better-informed decision-making. Define topic granularity. When two topics are compared, ITM scholars perform an optional clustering step to subdivide larger topics into specific ones. This is done to help to navigate the large amount of data being studied, or to compensate for the lack of an ontology of arbitrary topics in the social sciences and humanities. While the practice of clustering larger topics for opportunity discovery in ITM seems to be a staple, it is still not clear what should be the scope or to what level of granularity a topic should be segmented to reach satisfactory results. From a text mining perspective, clusters aggregating a large number of documents will consequently have larger vocabularies to compare against. This potentially hampers the discovery process if the cluster signals a false-positive relationship just due to a large vocabulary. To overcome this, some researchers proposed to use the clustering step recursively (Mejia and Kajikawa, 2020b). When recursively clustering citation networks, the level of specificity can be set given the structural properties of the network (Fortunato and Barthélemy, 2007). In the case of text mining-based clustering, commonly used methods include K-means and topic models (Blei et al., 2010). These methods offer the possibility of setting up a predefined 167

Cristian Mejia and Yuya Kajikawa

•

number of subtopics for the main topic to be split into. However, both the selection of the number of subtopics (Chang et al., 2009) and reproducibility issues of probabilistic methods are being subject to criticism (Hecking and Leydesdorff, 2018) – hence, the need for more research on topic detection and clustering. Finally, more work needs to be done concerning alternative databases, like news articles, or government reports – from the accessibility to that type of data, to the acceptability and valid use cases in ITM. News articles, reports, and other gray literature can contain biased information or unverified data. Methods to overcome those issues are missing from ITM research.

11.6 Concluding Remarks This chapter discussed three discovery frameworks applied by ITM researchers for opportunity discovery. The first one attempts to find relationships from a problem topic to a solution topic where both exist in the same knowledge representation. This framework corresponds to a kind of LBD where the discovery is observed in pairs of granular subtopics sharing a relatively high semantic similarity – for instance by linking academic literature of robotics and gerontology, to find specific robotic technologies like laparoscopy being linked to prostate cancer (Ittipanuvat et al., 2014). This type of linkage allows experts in the field to uncover potential solutions by using already existing technologies to solve known problems.

References Antons, D., Grünwald, E., Cichy, P., Salge, T.O., 2020. The application of text mining methods in innovation research: Current state, evolution patterns, and development priorities. R&D Manag. 50, 329–351. https://doi.org/10.1111/radm.12408 Aria, M., Cuccurullo, C., 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 11. https://doi.org/10.1016/j.joi.2017.08.007 Blei, D., Carin, L., Dunson, D., 2010. Probabilistic topic models. IEEE Signal Process. Mag. 27, 55–65. https://doi.org/10.1109/MSP.2010.938079 Boyack, K.W., Klavans, R., Borner, K., 2005. Mapping the backbone of science. Scientometrics 64, 351–374. Chang, J., Gerrish, S., Wang, C., Blei, D., 2009. Reading tea leaves: How humans interpret topic models, in: Advances in Neural Information Processing Systems 22, 288–296. https://doi.org/10.1.1.100.1089 Cory, K.A., 1997. Discovering hidden analogies in an online humanities database. Comput. Hum. 31, 1–12. https://doi.org/10.1023/A:1000422220677 de Solla Price, D.J., 1965. Networks of scientific papers. Science (80-.). 149, 510. Fortunato, S., Barthélemy, M., 2007. Resolution limit in community detection. Proc. Natl. Acad. Sci. U. S. A. 104, 36–41. https://doi.org/10.1073/pnas.0605965104 Fox, E.A., Neves, F. Das, Yu, X., Shen, R., Kim, S., Fan, W., 2006. Exploring the computing literature with visualization and stepping stones & pathways. Commun. ACM 49, 52–58. https://doi. org/10.1145/1121949.1121982 Fujita, K., 2012. Finding linkage between sustainability science and technologies based on citation network analysis, in: Proceedings-2012 5th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2012. https://doi.org/10.1109/SOCA.2012.6449422 Gibbons, M., 1999. Science’s new social contract with society. Nature 402, C81–4. https://doi. org/10.1038/35011576 Gordon, M., Lindsay, R.K., Fan, W., 2002. Literature-based discovery on the world wide web. ACM Trans. Internet Technol. 2, 261–275. https://doi.org/10.1145/604596.604597 Griffiths, T.L., Steyvers, M., 2004. Finding scientific topics. Proc. Natl. Acad. Sci. U. S. A. 101 Suppl, 5228–5235. https://doi.org/10.1073/pnas.0307752101 Hecking, T., Leydesdorff, L., 2018. Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps. Henry, S., McInnes, B.T., 2017. Literature based discovery: Models, methods, and trends. J. Biomed. Inform. https://doi.org/10.1016/j.jbi.2017.08.011

168

Methodological Frameworks Huang, S., He, L., Yang, B., Zhang, M., 2012. A compound correlation model for disjoint literature-based knowledge discovery. Aslib Proc. New Inf. Perspect. 64, 423–436. https://doi. org/10.1108/00012531211244770 Hui, W., Lau, W.K., 2019. Application of literature-based discovery in nonmedical disciplines: A survey, in: ACM International Conference Proceeding Series. Association for Computing Machinery, New York, pp. 7–11. https://doi.org/10.1145/3366650.3366660 Ittipanuvat, V., Fujita, K., Sakata, I., Kajikawa, Y., 2014. Finding linkage between technology and social issue: A literature based discovery approach. J. Eng. Technol. Manag. - JET-M 32, 160–184. https://doi.org/10.1016/j.jengtecman.2013.05.006 Jeong, Y., Park, I., Yoon, B., 2019. Identifying emerging research and business development areas based on topic modeling and visualization with intellectual property right data. Technol. Forecast. Soc. Change 146, 655–672. https://doi.org/10.1016/j.techfore.2018.05.010 Jha, K., Jin, W., 2016. Mining hidden knowledge from the counterterrorism dataset using graphbased approach, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 310–317. https://doi. org/10.1007/978-3-319-41754-7_29 Jin, W., Srihari, R.K., 2006. Knowledge discovery across documents through concept chain queries, in: Proceedings - IEEE International Conference on Data Mining, ICDM. Institute of Electrical and Electronics Engineers Inc., pp. 448–452. https://doi.org/10.1109/icdmw.2006.105 Jianhua, L., Song, P., Yafei, L., Zhaohua, J., 2019. Technology opportunity analysis of internet of things from the perspective of “technology-market,” in: 17th International Conference on Scientometrics and Informetrics, ISSI 2019- Proceedings. pp. 125–139. Kajikawa, Y., Saito, O., Takeuchi, K., 2017. Academic landscape of 10 years of sustainability science. Sustain. Sci. 12, 869–873. https://doi.org/10.1007/s11625-017-0477-6 Kayser, V., 2017. Comparing public and scientific discourse in the context of innovation systems. Technol. Forecast. Soc. Change 115, 348–357. https://doi.org/10.1016/j.techfore.2016.08.005 Kayser, V., Blind, K., 2017. Extending the knowledge base of foresight: The contribution of text mining. Technol. Forecast. Soc. Change 116, 208–215. https://doi.org/10.1016/J.TECHFORE.2016.10.017 Kessler, M., 1963. Bibliographic coupling between scientific papers. Am. Doc. 14, 10–25. Kibwami, N., Tutesigensi, A., 2014. Using the literature based discovery research method in a context of built environment research. undefined. Kostoff, R.N., Schaller, R.R., 2001. Science and technology roadmaps. IEEE Trans. Eng. Manag. 48, 132–143. https://doi.org/10.1109/17.922473 Kostoff, R.N., Solka, J.L., Rushenberg, R.L., Wyatt, J., 2008. Literature-related discovery (LRD): Water purification. Technol. Forecast. Soc. Change 75, 256–275. https://doi.org/10.1016/j. techfore.2007.11.009 Lancichinetti, A., Fortunato, S., 2009. Community detection algorithms: A comparative analysis. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 80, 1–11. https://doi.org/10.1103/PhysRevE. 80.056117 Leydesdorff, L., Welbers, K., 2011. The semantic mapping of words and co-words in contexts. J. Informetr. 5, 469–475. https://doi.org/10.1016/j.joi.2011.01.008 Li, X., Xie, Q., Daim, T., Huang, L., 2019a. Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology. Technol. Forecast. Soc. Change 146, 432–449. https://doi.org/10.1016/j.techfore.2019.01.012 Li, X., Xie, Q., Jiang, J., Zhou, Y., Huang, L., 2019b. Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology. Technol. Forecast. Soc. Change 146, 687–705. https://doi.org/10.1016/j. techfore.2018.06.004 Mejia, C., Kajikawa, Y., 2020a. A Network Approach for Mapping and Classifying Shared Terminologies Between Disparate Literatures in the Social Sciences. Springer, Cham, pp. 30–40. https://doi. org/10.1007/978-3-030-60470-7_4 Mejia, C., Kajikawa, Y., 2020b. Emerging topics in energy storage based on a large-scale analysis of academic articles and patents. Appl. Energy 263, 114625. https://doi.org/10.1016/j. apenergy.2020.114625 Mejia, C., Kajikawa, Y., 2019. Technology news and their linkage to production of knowledge in robotics research. Technol. Forecast. Soc. Change 143, 114–124. https://doi.org/10.1016/j. techfore.2019.03.016

169

Cristian Mejia and Yuya Kajikawa Mejia, C., Kajikawa, Y., 2017. Assessing the sentiment of social expectations of robotic technologies, in: 2017 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, Portland, US, pp. 1–7. https://doi.org/10.23919/PICMET.2017.8125441 Mejia, C., Kajikawa, Y., 2016. The field of social robotics as means of technology selection to address country specific social issues, in: 2016 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, Hawaii, US, pp. 2913–2921. https://doi.org/10.1109/ PICMET.2016.7806653 Mikova, N., Sokolova, A., 2019. Comparing data sources for identifying technology trends. Technol. Anal. Strateg. Manag. 31, 1353–1367. https://doi.org/10.1080/09537325.2019.1614157 Nakamura, H., Sasaki, H., Shibata, N., Kajikawa, Y., Sakata, I., Suzuki, S., 2010. Science and technology map analysis of a multi-disciplinary field-Case study of aerospace engineering, in: 2010 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, pp. 1901–1905. https://doi.org/10.1109/IEEM.2010.5674621 Ogawa, T., Kajikawa, Y., 2014. Assessing the industrial opportunity of academic research with patent relatedness: A case study on polymer electrolyte fuel cells. Technol. Forecast. Soc. Change 90, 469–475. https://doi.org/10.1016/j.techfore.2014.04.002 Porter, A.L., Cunningham, S.W., 2005. Tech mining: Exploiting new technologies for competitive advantage. https://doi.org/10.1002/0471698466 Ranaei, S., Suominen, A., Dedehayir, O., 2017. A topic model analysis of science and technology linkages: A case study in pharmaceutical industry, in: 2017 IEEE Technology & Engineering Management Conference (TEMSCON). IEEE, pp. 49–54. https://doi.org/10.1109/TEMSCON.2017.7998353 Rotolo, D., Hicks, D., Martin, B.R., 2015. What is an emerging technology? SPRU Work. Pap. Ser. 2015–06, 1–40. https://doi.org/10.1016/j.respol.2015.06.006 Sasaki, H., Yamamoto, S., Agchbayar, A., Enkhbayasgalan, N., Sakata, I., 2019. Inter-domain linking of problems in science and technology through a bibliometric approach, in: 2019 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, pp. 1–9. https://doi. org/10.23919/PICMET.2019.8893965 Sebastian, Y., Siew, E.-G., Orimaye, S.O., 2017. Emerging approaches in literature-based discovery: Techniques and performance review. Knowl. Eng. Rev. 32, e12. https://doi.org/10.1017/ S0269888917000042 Shen, Y.-C., Wang, M.-Y., Yang, Y.-C., 2020. Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology. Technol. Forecast. Soc. Change 160, 120225. https://doi.org/10.1016/j.techfore.2020.120225 Shibata, N., Kajikawa, Y., Sakata, I., 2011. Detecting potential technological fronts by comparing scientific papers and patents. Foresight 13, 51–60. https://doi.org/10.1108/14636681111170211 Shibata, N., Kajikawa, Y., Sakata, I., 2010a. Extracting the commercialization gap between science and technology - Case study of a solar cell. Technol. Forecast. Soc. Change 77, 1147–1155. https://doi. org/10.1016/j.techfore.2010.03.008 Shibata, N., Kajikawa, Y., Sakata, I., 2010b. Opportunity discovery by assessing the gap between science and technology case study of secondary batteries. Manag. Innov. Technol. (ICMIT), 2010 IEEE Int. Conf. 788–793. https://doi.org/10.1109/ICMIT.2010.5492794 Smalheiser, N.R., 2012. Literature-based discovery: Beyond the ABCs. J. Am. Soc. Inf. Sci. Technol. 63, 218–224. https://doi.org/10.1002/asi.21599 Smalheiser, N.R., Torvik, V.I., Zhou, W., 2009. Arrowsmith two-node search interface: A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE. Comput. Methods Programs Biomed. 94, 190–197. https://doi.org/10.1016/j.cmpb.2008.12.006 Small, H., 1973. Co-citation in the scientific Literature: A new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24, 265–269. https://doi.org/10.1002/asi.4630240406 Swanson, D.R., 1986. Fish oil, Raynaud’s Syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30, 7–18. https://doi.org/10.1353/pbm.1986.0087 Swanson, D.R., Smalheiser, N.R., 1997. An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artif. Intell. 91, 183–203. https://doi.org/10.1016/ S0004-3702(97)00008-8 Takano, Y., Kajikawa, Y., 2019. Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents. Technol. Forecast. Soc. Change 138, 45–68. https://doi.org/10.1016/j.techfore.2018.08.008

170

Methodological Frameworks Takano, Y., Kajikawa, Y., 2018. Advanced methods: Opportunities and potential of the internet of things for solving social issues, in: Innovation Discovery: Network Analysis of Research and Invention Activity for Technology Management. World Scientific Publishing Co. Pte. Ltd., pp. 531–558. https:// doi.org/10.1142/9781786344069_0019 Tsuruoka, Y., Tsujii, J., Ananiadou, S., 2008. FACTA: A text search engine for finding associated biomedical concepts. Bioinformatics 24, 2559–2560. https://doi.org/10.1093/bioinformatics/btn469 Van Eck, N.J., Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3 Visessonchok, T., Sugiyama, M., Sasaki, H., Sakata, I., 2016. Detection and introduction of emerging technologies for green buildings in Thailand. Int. J. Energy Technol. Policy 12, 2–19. https://doi. org/10.1504/IJETP.2016.074489 Wang, M.-Y., Fang, S.-C., Chang, Y.-H., 2015. Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels. Technol. Forecast. Soc. Change 92, 182–195. https://doi.org/10.1016/j.techfore.2014.07.008 Weeber, M., Kors, J.A., Mons, B., 2005. Online tools to support literature-based discovery in the life sciences. Brief. Bioinform. https://doi.org/10.1093/bib/6.3.277 Xu, S., Zhai, D., Wang, F., An, X., Pang, H., Sun, Y., 2019. A novel method for topic linkages between scientific publications and patents. J. Assoc. Inf. Sci. Technol. 70, 1026–1042. https://doi. org/10.1002/asi.24175 Yamada, C., Takemura, R., Fukushima, T., Ouchi, N., 2019. Investigating problems of research and development of artificial intelligence technology in Japan, in: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 750–754. https://doi.org/10.1109/ IEEM44572.2019.8978618

171

12 TECHNOLOGY ASSESSMENT AND SELECTION An Assessment of the Autonomous Driving Technologies by Using Type-2 Fuzzy Sets and Systems Koray Altun, Recep Kurt, Reyhan Ozcan Berber, Serkan Altuntas, and Turkay Dereli 12.1 Introduction Technology is a critical driving force of development for knowledge-based economies (Ryu and Byeon, 2011). It is extremely important to evaluate technologies in order to catch high technological developments for knowledge-based economies. Technology evaluation is the process of evaluating alternative technologies according to criteria determined by decision-makers. Science and technology form the basis of knowledge-based economies (Ryu and Byeon, 2011). Scientific studies and technological inventions are published as publications and patents. Therefore, patents and publications are seen as the most objective data sources in technology evaluation processes and are used in the evaluation of technologies in the literature. Many technology evaluation models have been proposed in academic research so far (Noh et al., 2018). Besides, most of the studies published on technology evaluation in the literature have proposed different framework structures that guide organizations when facing emerging technological changes (Ho, 2011). On the other hand, fuzzy logic is rarely used in technology evaluation literature. The use of the fuzzy logic-based approach in the technology evaluation process enables a robust technology evaluation process to be carried out. In this study, fuzzy logic is performed based on patents and publications data. Conducting a robust technology evaluation process helps interpret the evaluated technology related to trendiness levels. Critical gaps in the technology development process can be identified through a robust technology evaluation process. Additionally, the use of fuzzy logic for technology evaluation allows decision-makers to easily assess candidate technologies for technology opportunity discovery under uncertainty. This means that fuzzy logic-based technology evaluation helps investors to focus on the most significant technology fields and identify technology opportunities in real-life business environments. Autonomous driving, which is predominantly technology-driven progress, has been the recent disruptive innovation wave in the automotive industry. Identifying research 172

DOI: 10.4324/9781003046899-16

Technology Assessment and Selection

and innovation gaps in this area is getting crucial for staying competitive. This study aims to provide a data-driven concise understanding of these gaps by performing a technology evaluation process proposed by Dereli and Altun (2013a and 2013b). At the beginning of this technology evaluation process, patent data and publication data related to autonomous driving technologies are collected from the patent database and web of science/knowledge (WoS/K). Then, the type-2 fuzzy inference system is utilized to match publication and patent data. “Trendiness” of candidate technologies in the field of autonomous driving technology is found by using a type-2 fuzzy inference system. Knowing the “trendiness” of candidate technologies can help key players in the autonomous driving technology sector to construct a technology roadmap reflecting real life. The rest of this study is organized as follows: a review on the innovation potential assessment is given in Section 12.2. How to evaluate the trendiness is discussed in Section 12.3. Autonomous driving technologies are discussed in Section 12.4. Trendiness evaluation of the autonomous driving technologies is provided in Section 12.5. Concluding remarks are given in the final section.

12.2  Assessing the Innovation Potential of Technologies Aligning innovation projects to the strategy is an important objective for high-tech firms in general. For this reason, assessment of the innovation potential of candidate technologies (listed in technology roadmaps) is essential in most costly and complex acquisition efforts. Corresponding investment and/or development decisions need to consider that innovation is a risky and expensive endeavor, which results in low success rates. Dereli and Altun (2013a) propose an innovation intelligence process for the assessment of candidate technologies according to their innovation potentials. This process aims to achieve a robust assessment of the innovation potential of technologies and it composes three main aspects of the innovation potential regarding the maturity and market orientation of technologies under consideration. They are commercial potential, imitation potential, and technology-trendiness level. While the commercial potential and imitation potential correspond to the market condition, the degree of trendiness is required to infer about the technology maturity. These assessment aspects may have implicit correlations in practice. Although obtaining a precise assessment result for each of these aspects is possible, obtaining a final combined list may not be reliable. Arman et al. (2009) propose a simple prioritizing method for a similar situation. A practical grouping approach is proposed to consolidate different assessment results instead of ranking them precisely. The innovation intelligence process (namely, QIIP – quick innovation intelligence process) proposed by Dereli and Altun (2013a) employs this fusion approach to obtain an assessment result of candidate technologies according to their innovation potentials. This grouping and prioritizing approach is illustrated in Figure 12.1. When the top five candidate technologies are considered, Class A includes the technologies listed in the top five in every list. Class B is for the technologies listed in the top five of any two of these lists. The remaining technologies listed in the top five are a member of Class C. Technologies that are not listed in the top five in any list are the member of Class D. As is discussed in Klein et al. (2015), identifying the best ideas in huge volumes of potentially valuable solutions for the problems can be expensive and time-consuming. It is especially becoming a common issue in collections of open innovation platforms including high variation and a huge volume of ideas/suggestions. The innovation intelligence process 173

Koray Altun et al. Imitation potential

Commercial potential

C5 C9

B

C

C C2

C6

C4

A

B C3

B C7

Class A = {C4,C6}

C

Class B = {C3,C5,C7}

C10

Class C = {C2,C9,C10} Class D = {C1,C8} Ci

: Candidate technology i

Trendiness

Figure 12.1  A practical innovation intelligence process

as a filtering mechanism is very fruitful to eliminate bad and/or irrelevant ideas of these collections in the early stage of the idea management process. This innovation intelligence process makes use of some marketing intelligence information derived from marketing indicators/determinants and technology intelligence information derived from patent and publication data. A data-fusion framework that is based on interval type-2 fuzzy sets and systems (IT2FSSs) supports this technology intelligence phase to evaluate the trendiness degree of candidate technologies.

12.3  Technology Trendiness Evaluation Technology trendiness evaluation is one of the most important aspects of the innovation intelligence process. It is especially crucial to direct concentrations to the most rewarding technology areas in the early phase of idea management. This trendiness evaluation is based on relevant patent and publication data. An appropriate data-fusion methodology is essential to match different data sources. Some of the previous studies in the literature address the matching patent and publication data through different data-fusion methodologies. For example, Daim et al. (2006) propose a system dynamics approach as a data-fusion methodology. Similarly, Arman et al. (2009) propose a data-fusion methodology based on the consensus indicated by the three different ranking lists obtained from publications, patents, and experts’ opinions. Fuzzy logic is also one of the most popular and effective data-fusion methodologies enabling logical inferences. Although it has found so many applications in various fields, its use in technology evaluation for matching patent and publication data is limited. A framework proposed by Dereli and Altun (2013b) is based on its updated, interval type-2 version. This study uses this framework to evaluate autonomous driving technologies. Figure 12.2 illustrates how this framework operates. This framework uses patent data retrieved from the online database of the patent offices and publication data retrieved from Web of Science/Knowledge (WoS/K). A set of keywords is required to connect the patents and their related publications. This framework calculates hotness values indicating the 174

Technology Assessment and Selection

Hotness Keywords

Publication Data

k-means clustering

Patent Data

Type-2 MFs (patents)

Rule Base

Trendiness degree

Defuzzification

Type-2 MFs (publications)

Inference

Type-reducer (KM algorithm)

Hotness

Figure 12.2  A technology evaluation framework based on type-2 FIS

growth rate of technologies. This framework is based on the type-2 fuzzy sets to handle the uncertainty of this fuzzy term, “hotness.” The k-means clustering developed by MacQueen (1967) finds centroids of the clusters: low, medium, and high. Triangular membership functions are specified by using these centroids and the values limiting these clusters. A fuzzy rule base is required to indicate the relationship between patent and publication data. The inference process incorporating the Karnik-Mendel algorithm (Mendel and Wu, 2010) determines the trendiness degrees. For further details on the framework, see Dereli and Altun (2013b).

12.4  Autonomous Driving Technologies Autonomous cars are the latest innovation wave in the automotive industry. The traditional roles of the driver have been evolving through the advances in automated driving technologies and systems. These cars (in Level 4 and Level 5) can sense their surroundings and navigate themselves, autonomously, without a need for a human driver’s intervention. According to Singh (2015) and Maashoff (2017), 94% of road accidents are caused by human errors, and 1.2 million people die every year in the world because of these accidents. It is clear that some roles of human drivers in the dynamic driving tasks need to be changed or removed (through automation) to reduce these human errors. Autonomous driving solutions remove or change these human roles in DDT. According to Yurtsever et al. (2020) and Montgomery et al. (2018), if widespread deployment of these autonomous cars can be realized, annual social benefits are projected to reach $800 billion by 2050 through congestion mitigation, road casualty reduction, decreased energy consumption, and increased productivity caused by the reallocation of driving time. Yurtsever et al. (2020) discuss the potential social impact and benefits of the widespread usage of autonomous driving systems. Preventing traffic accidents, mitigating traffic congestions, and reducing emissions are the problems that can be solved through autonomous driving. Reallocation of driving time, and transporting the mobility impaired are the arising opportunities. Consuming mobility as service and the logistics revolution are the new trends foreseen by Yurtsever et al. (2020). Because of these social impacts and benefits, advances in autonomous driving technologies have been the main concern of automotive technology and innovation management. Ross (2014) discusses the technological development and main milestones of autonomous driving, starting from the modern cruise control in 1948 (see this study to have a closer look at the history of autonomous driving). Autonomous cars are intelligent cars employing advanced computer science, pattern recognition, and advanced control technologies. They are classified into six levels as to be from function-specific automation – driver assistance (Level 1) to full self-driving automation (Level 5). In this classification, while Level 0 stands for no automation in the dynamic driving 175

Koray Altun et al.

task, human attention is not needed in Level 4 and Level 5. While human driver performs most of the driving task in Level 2, human intervention is needed when there is a failure in automated dynamic driving tasks in Level 3 automation. The standard SAE J3016 2018 provides taxonomy and definitions for terms related to autonomous driving. There exists very extensive literature on the topic of autonomous driving. Some reviews have been undertaken to explore this phenomenon as well. Rosenzweig and Bartl (2015) provide a review and analysis of literature on autonomous driving. Besides, Yurtsever et al. (2020) and Zhao et al. (2018) address the emerging and key technologies enabling autonomous driving. Zhao et al. (2018) state that the technology of autonomous driving represents the level of scientific research and industrial strength of a country. Correspondingly, the key technologies of autonomous driving have a strategic priority for countries and their automotive industries. For this reason, technology roadmaps and technology development plans of each actor in the automotive industry cannot ignore these technologies. In order to stay competitive in the automotive industry, actors need to adjust their positions according to this innovation wave. They need to monitor these technologies to specify/filter related (open) innovation ideas/suggestions. In this way, a priority tag can be given for each of these ideas in the portfolio. Keyword difficulty is a metric used by search engine optimization (SEO) professionals when performing keyword research. Some keywords make it difficult to rank web pages in search engines. When the keyword “autonomous driving” is analyzed according to its keyword difficulty in the KWfinder (kwfinder.com) platform, it provides a list of keywords of alternative expressions for “autonomous driving.” In this study, we, therefore, use this list and the following query to make a more comprehensive search. Autonomous driving – TS = “autonomous driving” OR “self-driving car” OR “driverless car” OR “autonomous vehicle” OR “autopilot car” OR “automated driving” OR “advanced driver assistance system” OR “unmanned driving” OR “unmanned car” Zhao et al. (2018) classify the key technologies enabling the function realization of autonomous driving into four groups: (i) environment perception, (ii) car navigation, (iii) path planning, and (iv) car control. In this study, we use the innovation intelligence process to assess these autonomous driving key technologies. Through this assessment, we get the trendiness degrees of each key technology group. Similar keyword difficulty analysis can be performed to obtain a list of keywords of alternative expressions and/or related keywords for each of these key technology groups. Some expert opinions are also required to create a valid list. In this way, we list the following keywords for each key technology group. Car navigation system – TS = “car navigation system” OR “geographic information system” OR “global positioning system” OR “gps” OR “glonass” OR “galileo” OR “location system” OR “digital map” OR “electronic map” OR “hd map” OR “inertial navigation system” OR “ins” OR “gyroscope sensor” OR “accelerometer” OR “satellite based system” OR “span technology” Path planning – TS = “path planning” OR “map matching” OR “heuristic algorithm” OR “snapnet” OR “hmm algorithm” OR “dijkstra algorithm” OR “bellman-ford algorithm” OR “Floyd algorithm”

176

Technology Assessment and Selection

Environment perception – TS = “environment perception” OR “laser navigation” OR “visual navigation” OR “radar navigation” OR “laser sensor” OR “laser radar” OR “radar sensor” OR “multi sensor” OR “traffic sign” OR “distance detection” OR “slam” OR “simultaneous localization and mapping” OR “machine vision” Car control – TS = “car control” OR “vehicle control” OR “anti-lock braking” OR “anti-slip” OR “electronic stability” OR “sensotronic brake” OR “brake force distribution” OR “auxiliary brake” OR “supplementary restraint” OR “radar anti-collision” OR “automatic transmission” OR “continuously variable transmission” OR “cruise control” OR “control suspension” OR “power steering” OR “pid algorithm” OR “pid control” OR “cerebellar model articulation controller” Having performed this keyword generation analysis, we can reach the corresponding patent count data and publication count data. While the patent count data is collected from the European Patent Office (EPO) database, the publication count data is collected from the Web of Science/Knowledge (WoS/K) database. An overview of the obtained results for the period from 2008 to 2018 is as the following (see Figure 12.3). Utilizing the keywords of key technology groups, we reach to 85% of autonomous driving-related patents. Although it has a persuading performance rate on patent data, it does not seem so much persuading in publications because we grasp 31% of autonomous driving-related publications by using these keyword lists. However, it should be noted here that these keywords just represent the key technologies of autonomous driving. While patents are the most reliable indicators for technology, publications are indicators of the status of science. Technology is not the binding goal for developing science (as in pure applied research). For this reason, the rate for the publications can be acceptable to infer the science orientation of these technologies.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Car control Environment perception Path planning Car navigation system Not reached by using the keywords

Patent count data 4930 3492 879 2830

Publication count data 370 355 327 344

2187

3048

Figure 12.3  Validation of the generated keywords

177

Koray Altun et al.

12.5  Trendiness Evaluation of the Autonomous Driving Technologies Obtaining the keywords of autonomous driving technologies enables us to perform a trendiness evaluation. Arman et al. (2009) use an index called “hotness” to measure trendiness. It is simply a rate of appearances in the last three years to the last ten years. The term “hotness” has a vague definition as well. Therefore, three types of hotness values are used in the framework of Dereli and Altun (2013b) to cope with this vagueness and to perform a robust evaluation. This framework employs the type-2 fuzzy sets to handle this uncertainty. Three different hotness values are calculated by using the data of patents and publications obtained from the keyword lists of the key technologies. While hotness#1 (h#1) is a rate of the last two-year appearances to its last ten years, hotness#2 (h#2) and hotness#3 (h#3) are for the last three-year appearances and the last four-year appearances, respectively. Membership functions of the fuzzy system are formed by using these hotness values. The k-means clustering technique is executed to classify these calculated hotness values into three clusters as low, medium, and high. Table 12.1 shows the calculated “hotness” values and their clusters. The values limiting these clusters define the fuzzy triangular membership functions corresponding to low, medium, and high for the patent and publication data. Figure 12.4 illustrates the type-2 fuzzy sets designed by considering these clusters. The input processing phase of the fuzzy system is completed when defined the limits of each membership function (MF). The average hotness values of each key technology group (as seen in Table 12.1) are the input parameters of this fuzzy inference system. After completing the input processing phase by developing the fuzzy MFs, a fuzzy rule base is required to construct a bridge between these different data sources. This fuzzy rule base is a data-fusion tool matching patents and publication data, and therefore being experienced on the system behavior is not necessary when compared to the fuzzy systems constructed as fuzzy control mechanisms. A balanced consequence distribution between patent and publication data is assumed to generate a fuzzy rule base (see Dereli and Altun, 2013b for the list of these rules). The framework under consideration uses these average hotness values and their firing intervals of the fuzzy rules to perform an inference on the trendiness. This inference process is executed as the following. Table 12.1  Calculated “hotness” values and their clusters Patent data h#1

h#2

Publication data h#3

Avg.

h#1

h#2

h#3

Avg.

Car navigation system Path planning Environment perception Car control

0.669 0.768 0.827 0.755

0.380

0.497

0.616 0.498

0.618 0.729 0.804 0.717 0.664 0.785 0.851 0.766

0.366 0.388

0.507 0.549

0.623 0.499 0.684 0.540

0.694 0.802 0.860 0.785

0.448

0.583

0.705 0.579

K-means clustering

h#1

h#1

h#2

h#3

Cluster#1

0.694 0.768 0.827

0.384

0.497

0.616

Cluster#2

0.618 0.729 0.804

0.367

0.507

0.623

Cluster#3

0.666 0.794 0.856

0.448

0.566

0.695

h#2

h#3

178

Technology Assessment and Selection Fuzzy MFs for the publication data

Fuzzy MFs for the patent data

Limits of the MFs Low-U: [0 0 0.804 1] Low-L: [0 0 0.619 1] Medim-U: [0 0.667 0.828 1 1] Medium-L: [0 0.72 1 0.85] High-U: [0.694 1 1 1] High-L: [0.856 1 1 1]

Limits of the MFs Low-U: [0 0 0.616 1] Low-L: [0 0 0.367 1] Medim-U: [0 0.385 0.624 1 1] Medium-L: [0 0.5 1 0.8] High-U: [0.449 1 1 1] High-L: [0.695 1 1 1]

Input values (avrg hotness) Car navigation system: 0,755 Path planning: 0,717 Environment perception: 0,766 Car control: 0,785

Input values (avrg hotness) Car navigation system: 0,498 Path planning: 0,499 Environment perception: 0,540 Car control: 0,579

Figure 12.4  Designed type-2 fuzzy membership functions (MFs) and the input values.

The rule base includes N (in our case it is 9) rules as given below: Rule (n ): If x1 is X 1n and x 2 is X 2n then y is Y n , n = 1, 2,…, N , where X 1n are the MFs which are generated from patent data and X 2n are the MFs which are generated from publication data. x1 and x 2 are the average hotness values of the key autonomous driving technologies, respectively. Y n values are intervals (=  y n , y n  ) representing the trendiness degree.

( )

( )

Compute the membership of x1 on each X 1n ,  µ X n x1 , µ X n x1  , n = 1, 2,…, N .  1  1

( )

( )

Compute the membership of x 2 on each X 2n ,  µ X n x 2 , µ X n x 2  , n = 1, 2,…, N .  2  2

(

)

Compute the firing interval of the nth rule, F n x1, x 2 , through the following equation:

(

)

( )

( )

( )

( )

F n x1, x 2 =  µ X n x1 × µ X n x 2 , µ X n x1 × µ X n x 2  ≡  f n , f n  , n = 1, 2,…, N .  2 1 2  1 These type-2 fuzzy sets transform into their type-1 counterparts in the type-reduction process. This framework prefers to use the center of sets (Ycos) type reducer expressed as the following equation: N

∑f y

n n

Ycos ( x ) =

f n ∈F n ( x ) yn ∈Y n

n =1 N

∑f

=  yl , yr 

n

n =1

179

Koray Altun et al.

where yl and yr are the endpoints of the interval set. These points are expressed in the following equations, respectively. L

yl =

∑f

N

∑f

n

∑f +∑ f

n

n =1

y +

n

n

L

n

n =1

n = L +1

R

yr =

∑ n =1

yn

n = L +1 N

N

f ny n +

∑fy n

n

n = R +1 N

R

∑f +∑ f n

n =1

n

n = R +1

where switch points L and R are specified by y L ≤ yl ≤ y L + 1 and y R ≤ yr ≤ y R + 1, respectively. This framework is based on the Karnik-Mendel (KM) algorithm (Mendel and Wu, 2010) and the type reduction process finds the switches of interval sets by executing this algorithm. KM algorithm for computing yl : Step1: yn values are sorted in increasing order. Step2: The weights F n ( x ) are matched with their respective yn values. fn+ fn Step3: f n are initialized through f n = and then y is computed as; 2 N

y=

∑y

n

fn

n =1 N

∑f

n

n =1

Step4: Switch point k (1 ≤ k ≤ N − 1) is found as y k ≤ y ≤ y k + 1.  f n,n ≤ k  are set and then y′ is computed as follows: Step5: f n =  n  f , n > k N

y′ =

∑y

n

fn

n =1 N

∑f

n

n =1

Step6: Check if y ′ = y . If yes, stop and set yl = y and L = k . If no, go to Step7. Step7: Set y = y′ and go to Step4.

180

Technology Assessment and Selection

KM algorithm for computing y r : Step1: yn values are sorted in increasing order. Step2: The weights F n ( x ) are matched with their respective yn values. fn+ fn and then y is computed as follows: Step3: f n are initialized through f n = 2 N

y=

∑y

n

fn

n =1 N

∑f

n

n =1

Step4: Switch point k (1 ≤ k ≤ N − 1) is found as y k ≤ y ≤ y k + 1.  f n,n ≤ k  are set and then y′ is computed as follows: Step5: f n =  n  f , n > k N

y′ =

∑y

n

fn

n =1 N

∑f

n

n =1

Step6: Check if y′ = y . If yes, stop and set yr = y and R = k . If no, go to Step7. Step7: Set y = y ′ and go to Step4. By executing the Karnik-Mendel algorithm, we obtain the switching points of the interval set. The following equation provides the defuzzified outputs corresponding to the trendiness degrees: y=

yl + y r 2

In our case, there are four key technology groups under consideration. When this framework is executed, we obtain the following trendiness degrees for these technologies as outputs of this interval type-2 fuzzy system (Table 12.2).

Table 12.2   Obtained trendiness degrees of the key technology groups under consideration

Car navigation system Path planning Environment perception Car control

Average hotness (patent data)

Average hotness (publication data)

Trendiness degree (output)

0.755 0.717 0.766 0.785

0.498 0.499 0.540 0.579

0.503 0.488 0.521 0.542

181

Koray Altun et al.

12.6 Concluding Remarks In line with the findings of this study, we report that the “car control” technologies have the highest trendiness degree (as it is 0.542) when compared to other key autonomous driving technologies. In this study, the following keyword list represents the related topics of car control technologies: Car control – TS = “car control” OR “vehicle control” OR “anti-lock braking” OR “anti-slip” OR “electronic stability” OR “sensotronic brake” OR “brake force distribution” OR “auxiliary brake” OR “supplementary restraint” OR “radar anti-collision” OR “automatic transmission” OR “continuously variable transmission” OR “cruise control” OR “control suspension” OR “power steering” OR “pid algorithm” OR “pid control” OR “cerebellar model articulation controller” Car control is one of the high-level challenges of autonomous driving. Electronic control unit (ECU) and communication bus are the two main parts of a typical car control platform. While the ECU is associated with the control algorithms, the communication bus performs communication between the ECU and corresponding mechanical parts. These keywords mainly imply car speed and direction control. From these keywords and the discussions provided by Zhao et al. (2018), we can state that the following topics should be addressed to prioritize car control technologies: car anti-lock braking system, car drive anti-slip system, car stability system, sensotronic brake control, brake force distribution, auxiliary brake system, supplementary restraint system, radar anti-collision system, automatic transmission system, cruise control, electronic control suspension, electric power steering system, and so on. From these results, on the one hand, it is clear that any idea/suggestion regarding car control should have a priority in the portfolio. While assessing corresponding (open) idea/ suggestion collections, these trendiness degrees should be taken into account as an important dimension of the assessment process. However, idea generation workshops may be organized by considering the topics related to car controls in autonomous driving. As discussed in the second section of this chapter, trendiness evaluation is just one dimension of the innovation potential evaluation. To make a robust evaluation for autonomous driving technologies, their commercial potentials and imitation potentials need to be considered as well. Furthermore, the importance of features of the autonomous driving experience may also change when considered cultural differences. Sauer et al. (2020) have provided some pieces of evidence for these cultural differences from China, Germany, and the United States. &&Considering concepts of driver well-being in the context of autonomous driving seems also necessary (Sauer et al., 2019). Therefore, customer profiles and cultural differences may also be taken into account during these assessments. Future work can fill this research gap and evaluate the innovation potential of autonomous driving technologies, exclusively.

References Arman, H., Hodgson, A., Gindy, N. (2009). Technologies watch exercise: Foresight approach enhanced with scientific publications and patents analysis. International Journal of Technology Intelligence and Planning, 5, 305–321. Daim, T.U., Rueada, G., Martin, H., Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting & Social Change, 73, 981–1012. Dereli, T., Altun, K. (2013a). A novel approach for assessment of candidate technologies with respect to their innovation potentials: Quick innovation intelligence process. Expert Systems with Applications, 40, 881–891.

182

Technology Assessment and Selection Dereli, T., Altun, K. (2013b). Technology evaluation through the use of interval type-2 fuzzy sets and systems. Computers & Industrial Engineering, 65 (4), 624–633. Ho, J.C. (2011). Technology evaluation in Taiwan's technology industries: Strategies, trajectories, and innovations. Technological Forecasting & Social Change, 78 (8), 1379–1388. Klein, M., Garcia, A.C.B. (2015). High-speed idea filtering with the bag of lemons. Decision Support Systems, 78, 39–50. Maashoff, A. (2017). Improving the experience of a world in motion. Innovative Seating Congress, Dusseldorf, Germany. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. Berkeley: University of California Press. Mendel, J.M., Wu, D. (2010). Perceptual computing: Aiding people in making subjective judgments. IEEE Press Series on Computational Intelligence. Montgomery, W.D., Mudge, R., Groshen, E.L., Helper, S., MacDuffie, J.P., Carson, C. (2018). America’s workforce and the self-driving future: Realizing productivity gains and spurring economic growth. Securing America’s Future Energy, Washington DC, USA, Tech. Rep. Noh, H., Seob, J.H., Yoo, H.S., Lee, S. (2018). How to improve a technology evaluation model: A data-driven approach. Technovation, 72, 1–12. Rosenzweig, J., Bartl, M. (2015). A review and analysis of literature on autonomous driving. E-Journal Making-of Innovation, 1–57. Ross, P.E. (2014). Robot, you can drive my car: Autonomous driving will push humans into the passenger seat. IEEE Spectrum, 51 (6), 60–90. Ryu, J., Byeon, S.C. (2011). Technology level evaluation methodology based on the technology growth curve. Technological Forecasting & Social Change, 78 (6), 1049–1059. Sauer, V., Mertens, A., Groß, S., Heitland, J., Nitsch, V. (2020). Designing automated vehicle interiors for different cultures: Evidence from China, Germany, and the United States. Ergonomics in Design. https://doi.org/10.1177/1064804620966158 Sauer, V., Mertens, A., Groß, S., Heitland, J., Nitsch, V. (2019). Exploring the concept of passenger well-being in the context of automated driving. International Journal of Human Factors and Ergonomics, 6 (3), 227–248. Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Washington, DC, Tech. Rep. DOT HS 812 115. Yurtsever, E., Lambert, J., Carballo, A., Takeda, K. (2020). A survey of autonomous driving: Common practices and emerging technologies. IEEE Access, 8, 58443–58469. https://doi.org/10.1109/ ACCESS.2020.2983149 Zhao, J., Liang, B., Chen, Q. (2018). The key technology toward the self-driving car. International Journal of Intelligent Unmanned Systems, 6 (1), 2–20.

183

13 MULTICRITERIA ASSESSMENT OF A TECHNOLOGICAL ECOSYSTEM A Multi-Country Approach Ronnie Figueiredo, João J. M. Ferreira, Helder Gomes Costa, and Arnab Basu 13.1  Introduction Over the past two decades, the term ‘ecosystem’ has become a focus of great interest in strategic management literature. Definitions of the ecosystem concept characterize an interdependent, cooperating, and competing network of partners and a structured community that plays a critical role in determining the co-creation of value and the co-production of knowledge (Vargo, Wieland and Akaka, 2015). Recent conceptualizations of entrepreneurial ecosystems (EEs), broadly defined as a community comprising actors and their production and service activities, trace their roots to ecological biology, regional development, or other closely related fields approaching systems, networks, and actors (Milwood and Maxwell, 2020). There is a growing consensus among academic scholars, policymakers, and industry practitioners alike that the present and future secret of business survival and prosperity lies in strategic partnering and co-opting successfully rather than outright competition (Carayannis and Alexander, 1999). Technologies develop within innovation ecosystems. Bringing new technologies and services to markets ranks as an essential driver of economic prosperity and domestic employment growth (Schuelke-Leech, 2018). Thus, increasingly complex technological constellations of organizations have emerged in the form of innovation ecosystems in which actors mutually interact to create, deliver, and appropriate value (Walrave et al., 2018). However, the technology ecosystem perspective considers only the dimension of supply-side technology forces for innovations (Liu, Kauffman and Ma, 2015). Such systems nevertheless comprise the social, economic, and political contexts in which these technologies evolve. Individually, these technological developments may either be disruptive or generate only marginal impacts (Schuelke-Leech, 2018). However, our knowledge of university ecosystems’ impacts on student innovative outputs remains scarce (Bock et al., 2020). Similarly, Kilkki, Mäntylä, Karhu, Hämmäinen, and Ailisto (2018) convey how much of the existing literature focuses on disruptive innovation 184

DOI: 10.4324/9781003046899-17

A Multi-Country Approach

at the level of an individual technology or a single firm while often delving deep into the specific characteristics of the particular case under analysis. This general focus on innovation as creating an idea or an artifact has both dominated and limited our understanding of its diffusion (Vargo, Akaka and Wieland, 2020). Furthermore, previous work in this field demonstrates how actors’ ‘internal’ alignment plays a critical role in the value creation ongoing in any innovation ecosystem. However, the literature has broadly overlooked how any innovation ecosystem’s success also depends on its ‘external’ viability as determined by the broader socio-technical environment (Walrave et al., 2018). In this context, enterprises emerge as vital systems which interact internally and externally with other organizational environments such as universities, other firms, and government within an entrepreneurial society (Carayannis and Alexander, 1999). Thus, there is a clear need for innovation and innovative systems with the prevailing interest reflected in the range of research on this field as identified by Oliveira et al. (2018) and the results reported by De Carvalho Pereira, Costa, and Pereira (2019). This research’s main objective involves applying multicriteria-based modeling to identify and classify the core aspects and the actors responsible for influencing the innovation ecosystems of three countries (Portugal, Brazil, and India), in accordance with their respective impacts on such systems. The research structure is thus the following: Section 13.2 presents a literature review on innovation ecosystems. Section 13.3 outlines the research methodology, based on the Multicriteria Decision Modeling, before Section 13.4 discusses the research results and implications. Finally, Section 13.5 identifies the limitations of the study and sets out some proposals for future research.

13.2  Background The first decade of this century saw a series of studies on innovation systems with largescale, well-organized, and comprehensive surveys undertaken by international organizations (Lakitan, 2013). Nowadays, faced by continually changing economies, every organization requires preparation to embrace all the ongoing technological innovations and become good competitors in global marketplaces through their connections with their ecosystems (Ramona and Alexandra, 2020). According to Fox, Griffy-Brown, and Dabić (2020), research into technologies situated in biosocial systems reports a wide variety of cultural, philosophical, political, and economic dimensions. Furthermore, entrepreneurship policies have tended to substitute science policies and replace the internal logic and dynamics of scientific research with the needs of entrepreneurs who often lack any major interest in science (Švarc, Dabić and Daim, 2020). Indeed, ecosystems have become a matter of concern for business leaders. Recent conceptualizations of EEs, broadly defined as communities comprising actors and their production and service activities, trace their roots to ecological biology, regional development, or closely related fields approaching systems, networks, and actors (Milwood and Maxwell, 2020). There is broad acceptance of how launching companies and the decision-making thereby involved cannot stem from centralized processes as generally happens in contemporary business environments (De Carvalho, da Hora and Fernandes, 2021). As the present research focuses on evaluating and ascertaining the relevance and importance of these criteria, another point that needs underlining in multicriteria decision-making 185

Ronnie Figueiredo et al.

environments is that the level of criteria relevance may vary as a function of the degree of organizational maturity (Méxas, Quelhas and Costa, 2012). Research results demonstrate how Brazilian university innovation systems’ maturity levels have not yet fully attained an advanced stage of maturity (Pereira, Costa and Pereira, 2017; De Carvalho Pereira, Costa and Pereira, 2019). According to the perspective of Stone (2015), innovations result from problem-solving mechanisms based on processes of accumulating and combining existing knowledge. In general, firms with higher acquisition and transformation dimensions within the scope of their absorptive capacities can enhance and replenish their knowledge management practices, which in return drive higher innovation outputs (Dabić, Vlačić, Ramanathan and Egri, 2020). However, problem-solving necessarily requires applying decision-making models. Therefore, making decisions becomes a structured process based on the prevailing model. Parreiras, Kokshenev, Carvalho, Willer, Dellezzopolles Jr., Nacif Jr., and Santana (2019) convey how the Multiattribute Value Theory, considered within the framework of a Value-Focused Thinking approach, provides a basis for constructing multicriteria decision-making models. However, there are other methodologies for the study of decision-making with the Analytic Hierarchy Process (AHP) Application method also serving to depict decision-making processes. Ecosystems are most clearly recognizable when making innovations happen. However, to achieve this outcome, leaders should not always target achievements or success but instead concentrate on nurturing ecosystems able to evolve the existing potential and explore stateof-the-art ideas, experimentation, and analysis, with both internal and external collaboration, and naturally accepting failures along the way (Gomes, Facin, Salerno and Ikenami, 2018; Liu and Stephens, 2019). Hence, the ‘Innovation Ecosystem’ concept arose as achieving any sustained outcome depends on both the capabilities and potential of numerous and interconnected factors, spanning government and corporate sectors, civil society, institutions, investors, entrepreneurs, etc., to combine and work both simultaneously and efficiently (Essa, Abd Elaziz and Elsheikh, 2020). The ‘Innovation Ecosystem’ concept has developed gradually over recent years and, correspondingly, looks set to remain a topic of research interest into the future (Liu and Stephens, 2019; Wagner et al., 2019). Some ecosystems consider not the additional variables but rather the critical inputs that need integrating into the valuation process to assess whether the innovation under development contains the potential to return positive impacts (Dando and Lebmeier, 2020). In turn, another approach to ecosystems considers the digital infrastructure prevailing, thus the set of digital technology tools and systems that provide communication, collaboration, and computing capability (Elia, Margherita and Passiante, 2020). Within this stream, studying the way to provide open access to ecosystem data (Reggi and Dawes, 2016) underlines the relevance of transparency to ecosystems’ governance and success. As already stated, there are various factors or contributors to successfully implement innovation ecosystems, and let us now briefly discuss the role of several different contributors (Imerman and Fabozzi, 2020). Governments may play crucial roles in stimulating innovation through not only implementing favorable policies and statutory systems but also encouraging the development of start-ups and collaborative research, angel and venture capital, and private equity firms (Radnejad et al., 2020). 186

A Multi-Country Approach

Private organizations nowadays receive widespread recognition alongside governments, research and development agencies, and civil societies for their ability to handle large-scale socio-economic issues that represent significant constraints toward the development of comprehensive business environments (Carayannis, Meissner and Edelkina, 2017). Private Equity Firms oversee the money committed by different funds, whether from pensions and other institutional sources or individual investors, and typically focus on investing in established companies and even often toward those on the decline but able to return revenues through well-organized operations (Aksenova et al., 2019) Research Institutions play essential roles in innovation by creating and diffusing the knowledge that produces future entrepreneurs (Kauffman, Liu and Ma, 2015). Development Agencies support very early-stage innovators to stimulate innovation, thereby strengthening innovation ecosystems through connectivity and boosting ecosystem actors’ capacity (Liu and Stephens, 2019). Effective innovation ecosystems require a diverse and large group of Professionals deploying miscellaneous skills in conjunction with a strong resolution, enthusiasm, dedication, the courage to take risks, and the ability to overcome the inevitable and significant challenges and limitations (Tai, Xiao and Tang, 2020). Civil Society Organizations or NGOs also play a vital role in highlighting various concerns to governments, policymakers and encourage political involvement through the transparency and availability of information (Groesser, 2014). Many Start-ups hold the potential to pioneer new ideas and solutions that major organizations may perhaps otherwise ignore or disregard and correspondingly introduce sustained creativity and genuine competition into their respective ecosystems (Yin, Ming and Zhang, 2020). Market Facilitators and Intermediaries assume major responsibility for establishing innovation policies by interlinking several different entities within innovation ecosystems through exchanging and transferring resources, including knowledge, technology, etc. (Phoomirat et al., 2020). In addition, Incubators and accelerators, Angel Investors, and Venture Capitalists also generate effective impacts on innovation ecosystems (Milwood and Maxwell, 2020). Although innovation ecosystem environments and characteristics vary depending on the prevailing socio-economic conditions, innovation, demand, strategy, funding, and other factors as presented (Faraj et al., 2016), there are ecosystems, such as the Internet of Things (IoT), that deploy central management with greater openness and lower change management costs (Zdravković et al., 2018).

13.3  Methodology 13.3.1  Data The research involved composing a sample from Brazil, Portugal, and India, then surveyed through recourse to a Google questionnaire available at https://forms.gle/CFnDNVryZFkBjBAh9. In the questionnaire’s first section, we inquire about the kind of innovation system (incremental, radical, or disruptive) and flow (open or closed) respondents have in mind when answering the survey. All respondent evaluators choose ‘Open flow’ for the innovative system. In the second section, we asked the sample to set out their feelings regarding the influence of the members listed in A over the drivers that appear in F according to a Likert-based scale (Table 13.1). The numerical scale adopted by this table is derived from Nepomuceno and Costa (2015). The Appendix details the data collected from the sample. 187

Ronnie Figueiredo et al. Table 13.1  T  he Likert-based scale adopted for collecting the evaluations Linguistic meaning

Numerical value

The actor has a very positive influence on the driver The actor has a positive influence on the driver The actor has no influence on the driver The actor has a negative influence on the driver The actor has a very negative influence on the driver

 2  1  0 −2 −1

When the evaluator did not understand or when the evaluator was not sure how to answer the question, he/she had the choice of leaving the answer as ‘blank’ – in such cases an ‘NR’, a non-numerical value, would be assigned to the answer. Source: Author’s creation based on Costa et al. (2007)

13.3.2  Method In this study, we apply the ELECTRE TRI ME (Costa, de Oliveira Nepomuceno and Pereira, 2020) algorithm to handle the problems inherent to evaluating innovation. Developing the innovation ecosystem assessment model required undertaking certain steps as detailed below: a b c d e f g

To define the set of A (faculties or universities) that provide the study focal point. To define the criteria for the F set. To define the E set of evaluators. To define the C set of categories or classes to which the components of A are then assigned. To define the scale for application in the evaluations. To collect the data. To apply the ELECTRE TRI ME sorting algorithm to the collected data: i To define the B set of profiles or boundaries of each C category. ii To apply equation (1) to calculate the concordance degree c j (a, bh), with the assertion that an a  ∈ A enters into category C h ∈C .  g j ( a ) −   g j (b ) +   p j c j (a, bh ) = 1,  if   g j ( a ) ≥ g j (b ) − q j   ,   pj − qj  if   g j (b ) − p j ≤   g j ( a ) <   g j (b ) − q j  0,  if   g j ( a ) <   g j (b ) − p j   

(1)

As reported in Costa, de Oliveira Nepomuceno, and Pereira (2020): – bh depicts the inferior boundary of a category C h , – g j ( a ) ,   g j bh are, respectively, the grade or performance of a  ∈ A in the criterion j and the value of bh  in the same criterion, – p j  and q j are, respectively, the preference threshold in the criterion j.

( )



(h) To analyze the results from step h. 188

A Multi-Country Approach

To define the A set of factors that constitute the focus of this study, we composed a set of actors or A alternatives: A = {People; Integrated Management; Actor Support; Investor Support; Mentors Support; Infrastructure; Time; Connection with Ecosystems; Transparent Objectives & Partner Network } We selected the members of A because they represent actors that influence university innovation features, as concluded from the bibliographical review. To define the E set of evaluators, the sample (Table 13.2) contained 12 evaluators from three universities (four evaluators per university). To define the F criteria set, all E evaluators have applied the same criteria, so that: F = FE1 ∪ FE2 ∪ FE3 ∪ FE4 ∪ FE5 ∪ FE6 ∪ FE7 ∪ FE8 ∪ FE9 ∪ FE10 ∪ FE11 ∪ FE12 where FE1 = FE2 = FE3 = FE4 = FE5 = FE6 = FE7 = FE8 = FE9 = FE10 = FE11 = FE12 = {TRL1: basic principles observed, TRL2: technology concept formulated, TRL3: experimental proof of concept, TRL4: lab validated technology, TRL5: technology validated in the relevant environment (industrially relevant environment in the case of key enabling technologies), TRL6: technology demonstrated in an appropriate environment (industrially relevant environment in the case of key enabling technologies), TRL7: system prototype demonstration in an operational environment, TRL8: complete and qualified system, and TRL9: the system is proven in an operational environment (competitive manufacturing in the case of key enabling technologies or space)}. Defining the set of categories or classes for assigning the A components allows for comparison through applying the same C set of categories. Based on the Likert scale, this classified the actors into the categories shown in Table 13.3. Table 13.2   Twelve evaluators from three universities University*

Evaluator code Role

Type of innovation

uA

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12

Incremental Incremental Incremental Disruptive Incremental Radical Incremental Incremental Incremental Disruptive Incremental Incremental

uB

uC

Vice-Rector Professor and Dean of Faculty Professor and Head of Innovation Lab Managing Director Professor Professor Professor Researcher Professor and Head of Innovation Lab Professor and Member of Innovation Lab Professor and Head of the Business School Professor and Head of the Engineering Program

*uA: a University in Portugal; uB: a University in India; uC: a University in Brazil. Source: Author’s creation

189

Ronnie Figueiredo et al. Table 13.3  The Likert-based categories Code

Linguistic meaning

A B C D E

Very positive influence on the driver Positive influence on the driver No influence on the driver Negative influence on the driver Very negative influence on the driver

Source: Author’s creation

13.4  Results and Discussion After applying the ELECTRE TRI ME sorting algorithm to the collected data, we applied the set of categories and the scales to evaluate performance (see Tables 13.2 and 13.4) and with the set of boundaries defined as results for all evaluators (Table 13.4). The ELECTRE method applied to data collected based on the Likert scale renders the values of pj and qj as both equal to zero through applying the equation to calculate the concordance degree (Costa and Duarte, 2019). After defining the boundaries set out in Table 13.1, the ELECTRE TRI ME algorithm served to sort the alternatives/actors into the innovation categories (Table 13.5). The results demonstrate how the actor holding greater influence over innovative ecosystems’ success is People for both expected incremental and disruptive innovation, although this reflects different ways in the three countries. Most of the sample considered innovative incremental systems when answering the survey. Only one respondent conceived radical innovation, and two others answered the survey focusing on innovative incremental systems. From the results of sorting, we would highlight the following aspects. People constitute the most relevant factor when taking into account answers from Brazil and Portugal but emerge with a lower level of importance in India’s results. This situation may stem from the similarities prevailing between Brazil and Portugal’s national cultures, which differ substantially from Indian culture – which displays strong influences of the British colonization that diverges from Portuguese and Brazilian culture in terms of people in organizational environments. This difference in results concurs with (Faraj et al., 2016): the environment and characteristics of innovation ecosystems vary in accordance with the Table 13.4  Categories a nd their boundaries Boundaries Category

Code

Lower

Upper

Very positive influence on the driver Positive influence on the driver No influence on the driver Negative influence on the driver Very negative influence on the driver

A B C D E

1.5 0.5 −0.5 −1.5 −∞

+∞ 1.5 0.5 −0.5 −1.5

Source: Author’s creation based on (Costa et al., 2007)

190

A Multi-Country Approach Table 13.5  Alternatives (actors) sorted into the innovation categories Portugal

India

Brazil

Alternatives (Actors)

Inc.

Dis.

Inc.

Rad.

Inc.

Dis.

ALL

People Integrated management Actor support Investor support Support from mentors Infrastructure Time Connection with ecosystems Transparent objectives Partner network

A C B C B B B C B B

A B B B B B B B A B

B B C B A B B B B B

C B C B B B B B B b

A B B B B B B B B B

A B B B B B B B B B

A B B B B B B B B B

Inc: Incremental; Dis: Disruptive; Rad: Radical.

prevailing socio-economic conditions, innovation, demand, strategy, funding, and other factors. On the other hand, the highest level of importance attributed to Brazilian and Portuguese respondents’ People factor highlights professionals’ relevance in building an effective innovation ecosystem (Tai, Xiao and Tang, 2020). The Brazil answers indicate that all the criteria attain at least ‘High importance’, which emphasizes how the importance of criteria represents a function of the performance or the organization’s degree of maturity according to such criteria (Méxas, Quelhas and Costa, 2012). Therefore, this would expect high importance attributed to all the criteria in these answers as the Brazilian university innovation systems have not fully obtained an advanced maturity stage (Pereira, Costa and Pereira, 2017; De Carvalho Pereira, Costa and Pereira, 2019). The difference that appears when comparing the India answers for radical and incremental innovation systems draws attention to the following point: from the radical viewpoint, none of the criteria gain ‘Very High’ relevance, revealing an autonomous situation; and, on the other hand, from the viewpoint of incremental innovation systems, the relevance of the ‘support of mentors’ is ‘Very High’, which also links to the maturity level of the innovative ecosystems as well as corroborating the findings of Carvalho Pereira, Costa, and Pereira (2019b). On analyzing the results from Portugal, the following points especially stand out: i

i

The respondent considering a disruptive innovative system perceives the ‘Transparent objectives’ as a factor of ‘Very High’ importance, which is supported by Reggi and Dawes (2016), who emphasize the need for transparency to ensure successful ecosystems. The answers from Portugal portray how the relevance of criteria emerges as at least more relevant in disruptive innovation situations when taking into account the entire set of criteria. One can also note that the ‘Integrated Management’, ‘Investor Support’, and ‘Connection with Ecosystems’ criteria received less relevance in the case of incremental innovative ecosystems than in radical, innovative ecosystems, which reinforces the hypothesis that the level of relevance varies as a function of the maturity level of the innovation ecosystem (Carvalho et al., 2019b). 191

Ronnie Figueiredo et al.

13.5  Theoretical and Practical Implications The study undertaken returns certain academic implications supporting the objective – a­ pplying multicriteria-based modeling to identify and classify the main aspects or actors influencing innovation ecosystems in three countries (Portugal, Brazil, and India). Applying the ELECTRE TRI ME algorithm (Costa et al., 2020) approach determines the actors that compose the set. The initial structure addresses the different universities: in Portugal, India, and Brazil. The approach focuses on the comparative results in terms of the aspects or actors that influence innovative ecosystems. Other research studies adopt different approaches in terms of technological ecosystems, such as the perspective that innovations result from a problem-solving mechanism based on accumulating and combining existing elements of knowledge. However, to resolve this problem, we should apply a decision-making model. The research model presented fulfilled the necessary criteria for the identified results and clarified the understanding of decision-making in university technological ecosystems. Specifically, the factors adopted in this study (People, Integrated Management, Actor Support, Investor Support, Support from Mentors, Infrastructure, Time, Connection with Ecosystems, Transparent Objectives, and Partner Network) present an adequate impetus for the technological ecosystems in three different countries. This study also provides substantial information on the different factors in effect at three different universities to identify and classify the main aspects and actors influencing innovation ecosystems. This reflects on the understanding of how these essential actors positively shape the future insertion of a university (college) in a technological innovation ecosystem while highlighting their eventual contributions. There is a significant growth in the number of studies of technological innovation ecosystems highlighting the roles and contributions of actors in the construction and discussion of innovation. This trend understands how innovation results from a problem-solving mechanism based on a process of knowledge accumulation. Such innovations bring new technologies and services to the market and stand out as essential drivers of economic development and the generation of domestic jobs (Stone, 2015; Schuelke-Leech, 2018; Walrave et al., 2018; Parreiras et al., 2019).

13.6  Conclusion This research’s main objective involved applying a multicriteria model to identify and classify the facets and actors that influence innovation ecosystems in accordance with their impact on such systems and, to this end, analyzing three universities in Portugal, Brazil, and India. Lecturers, researchers, and university leaders made up the respective sample of respondents. Cross-analysis (comparison) demonstrates how the most influential actor on innovation ecosystems’ success is People – as detailed by answers from Brazil, Portugal, and India for both incremental and disruptive innovation systems. The exception was India’s answer that classified people as not influencing innovative ecosystem results about an expected radical innovation system. We believe that this modeling and results produced can enable practitioners and researchers to understand better the actors that influence the innovation ecosystem. This research displays certain limitations that require consideration in structuring future research studies. The first category of limitations relates to the methodology. First, this study’s sample includes specialized professors and innovation leaders in Portugal, India, 192

A Multi-Country Approach

and Brazil. Another issue stems from the non-inclusion of a wider range of alternatives for evaluation regarding technological innovation ecosystems. A third limitation encapsulates respondent mental and cultural models as they belong to three different countries. Future research studies might focus on a sample of academics from different specialist fields or a sample of employees who work with the central theme, technological innovation ecosystems. Second, this chapter employed a quantitative multicriteria method to examine three approaches in terms of decision-making outcomes. Consequently, future studies may apply a qualitative approach to determining how psychological and cultural aspects shape and influence the decisions taken in terms of a university’s innovation ecosystem. Another issue arises from the online survey format, which structured interviews might replace. In addition, although the study did not depend on a large sample, it would be interesting to replicate the survey with a sample from various countries targeting specific and similar universities.

References Aksenova, G., Kiviniemi, A., Kocaturk, T. and Lejeune, A. (2019) ‘From Finnish AEC knowledge ecosystem to business ecosystem: Lessons learned from the national deployment of BIM,’ Construction Management and Economics, 37(6), pp. 317–335. doi: 10.1080/01446193.2018.1481985. Bock, C., Dilmetz, D., Selznick, B. S., Zhang, L. and Mayhew, M. J. (2020) ‘How the university ecosystem shapes the innovation capacities of undergraduate students–evidence from Germany,’ Industry and Innovation, pp. 1–36. doi: 10.1080/13662716.2020.1784710. Carayannis, E. G. and Alexander, J. (1999) ‘Winning by co-opeting in strategic government-university-industry R & D partnerships: The power of complex, dynamic knowledge networks,’ Journal of Technology Transfer, 24(2–3), pp. 197–210. doi: 10.1023/A:1007855422405. Carayannis, E. G., Meissner, D. and Edelkina, A. (2017) ‘Targeted innovation policy and practice intelligence (TIP2E): Concepts and implications for theory, policy and practice,’ Journal of Technology Transfer, 42(3), pp. 460–484. doi: 10.1007/s10961-015-9433-8. Costa, H. G., Mansur, A. F. U., Freitas, A. L. P. and Carvalho, R. A. D. (2007) ‘ELECTRE TRI aplicado a avaliação da satisfação de consumidores,’ Production, 17(2), pp. 230–245. doi: 10.1590/ s0103-65132007000200002. Costa, H. G., de Oliveira Nepomuceno, L. D. and Pereira, V. (2020) ‘A proposal for avoiding compensatory effects while using ELECTRE TRI with multiple,’ Advances in Intelligent Systems and Computing, pp. 470–480. doi: 10.1007/978-3-030-14347-3_46. Dabić, M., Vlacic, E., Ramanathan, U. and Egri, C. P. (2020) ‘Evolving absorptive capacity: The mediating role of systematic knowledge management,’ IEEE Transactions on Engineering Management, 67(3), pp. 783–793. doi: 10.1109/TEM.2019.2893133. doi: 10.1109/TEM.2019.2893133. Dando, J. and Lebmeier, M. (2020) ‘A novel valuation model for medical intervention development based on progressive dynamic changes that integrates Health Technology Assessment outcomes with early-stage innovation and indication-specific clinical success rates,’ Journal of Innovation and Entrepreneurship, 9(1), pp. 1–28. doi: 10.1186/s13731-019-0111-1. De Carvalho Pereira, F., Costa, H. G. and Pereira, V. (2019) ‘Stimulating and inhibiting factors of patent filing with Brazilian universities,’ International Journal of Entrepreneurship and Innovation Management, 23(3), pp. 261–280. doi: 10.1504/IJEIM.2019.099844. De Carvalho, R. A., da Hora, H. and Fernandes, R. (2021) ‘A process for designing innovative mechatronic products,’ International Journal of Production Economics, 231( July 2019), p. 107887. doi: 10.1016/j.ijpe.2020.107887. De Vasconcelos Gomes, L. A., Facin, A. L. F., Salerno, M. S. and Ikenami, R. K. (2018) ‘Unpacking the innovation ecosystem construct: Evolution, gaps and trends,’ Technological Forecasting and Social Change, 136, pp. 30–48. doi: 10.1016/j.techfore.2016.11.009. Elia, G., Margherita, A. and Passiante, G. (2020) ‘Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process,’ Technological Forecasting and Social Change, 150(October 2019), p. 119791. doi: 10.1016/j.techfore.2019.119791.

193

Ronnie Figueiredo et al. Essa, F. A., Abd Elaziz, M. and Elsheikh, A. H. (2020) ‘Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization,’ Process Safety and Environmental Protection, 144, pp. 322–329. doi: 10.1016/j.psep.2020.07.044. Faraj, S., von Krogh, G., Monteiro, E. and Lakhani, K. R. (2016) ‘Special section introduction—Online community as space for knowledge flows,’ Information Systems Research, 27(4), pp. 668–684. doi: 10.1287/isre.2016.0682. Fox, S., Griffy-Brown, C. and Dabić, M. (2020) ‘From socio-technical systems to biosocial technical systems: New themes and new guidance for the field of technology in society,’ Technology in Society, 62, p. 101291. doi: 10.1016/j.techsoc.2020.101291 Groesser, S. N. (2014) ‘Co-evolution of legal and voluntary standards: Development of energy efficiency in Swiss residential building codes,’ Technological Forecasting and Social Change, 87, pp. 1–16. doi: 10.1016/j.techfore.2014.05.014. Imerman, M. B. and Fabozzi, F. J. (2020) ‘Cashing in on innovation: A taxonomy of FinTech,’ Journal of Asset Management, 21(3), pp. 167–177. doi: 10.1057/s41260-020-00163-4. Kauffman, R. J., Liu, J. and Ma, D. (2015) ‘Innovations in financial IS and technology ecosystems: High-frequency trading in the equity market,’ Technological Forecasting and Social Change, 99, pp. 339–354. doi: 10.1016/j.techfore.2014.12.001. Kilkki, K., Mäntylä, M., Karhu, K., Hämmäinen, H. and Ailisto, H. (2018) ‘A disruption framework,’ Technological Forecasting and Social Change, 129, pp. 275–284. doi: 10.1016/j.techfore.2017.09.034. Lakitan, B. (2013) ‘Connecting all the dots: Identifying the “actor level” challenges in establishing effective innovation system in Indonesia,’ Technology in Society, 35(1), pp. 41–54. doi: 10.1016/j. techsoc.2013.03.002. Liu, J., Kauffman, R. J. and Ma, D. (2015)’ Competition, cooperation, and regulation: Understanding the evolution of the mobile payments technology ecosystem,’ Electronic Commerce Research and Applications, 14(5), pp. 372–391. doi: 10.1016/j.elerap.2015.03.003. Liu, Z. and Stephens, V. (2019) ‘Exploring innovation ecosystem from the perspective of sustainability: Towards a conceptual framework,’ Journal of Open Innovation: Technology, Market, and Complexity, 5(3). doi: 10.3390/joitmc5030048. Méxas, M. P., Quelhas, O. L. G. and Costa, H. G. (2012) ‘Prioritization criteria for enterprise resource planning systems selection for civil construction companies: A multicriteria approach,’ Canadian Journal of Civil Engineering, 39(8), pp. 855–866. doi: 10.1139/L2012-071. Milwood, P. A. and Maxwell, A. (2020) ‘A boundary objects view of Entrepreneurial Ecosystems in tourism,’ Journal of Hospitality and Tourism Management, 44(August), pp. 243–252. doi: 10.1016/j. jhtm.2020.06.008. Nepomuceno, L. D. O. and Costa, H. G. (2015) ‘Analyzing perceptions about the influence of a master course over the professional skills of its alumni: A multicriteria approach,’ Pesquisa Operacional, 35(1), pp. 187–211. doi: 10.1590/0101-7438.2015.035.01.0187. Oliveira, A. S., de Barros, M. D., de Carvalho Pereira, F., Gomes, C. F. S. and da Costa, H. G. (2018) ‘Prospective scenarios: A literature review on the Scopus database,’ Futures, 100, pp. 20–33. doi: 10.1016/j.futures.2018.03.005. Parreiras, R. O., Kokshenev, I., Carvalho, M. O. M., Willer, A. C. M., Dellezzopolles Jr., C. F., Nacif Jr., D. B. and Santana, J. A. (2019) ‘A flexible multicriteria decision-making methodology to support the strategic management of science, technology and innovation research funding programs,’ European Journal of Operational Research, 272(2), pp. 725–739. doi: 10.1016/j. ejor.2018.06.050. Pereira, F. de C., Costa, H. G. and Pereira, V. (2017) ‘Patent filings versus articles published: A review of the literature in the context of Multicriteria Decision Aid,’ World Patent Information, pp. 17–26. doi: 10.1016/j.wpi.2017.07.003. Phoomirat, R., Disyatat, N. R., Park, T. Y., Lee, D. K. and Dumrongrojwatthana, P. (2020) ‘Rapid assessment checklist for green roof ecosystem services in Bangkok, Thailand,’ Ecological Processes, 9, pp. 1–17.doi: 10.1186/s13717-020-00222-z. Radnejad, A. B., Osiyevskyy, O. and Vredenburg, H. (2020) ‘Barriers to radical process innovation: A case of environmental technology in the oil industry,’ Journal of Strategy and Management. doi: 10.1109/EMR.2019.2931653. Ramona, T. and Alexandra, B. (2020) ‘Knowledge retention within small and medium-sized enterprises,’ Studies in Business and Economics, 14(3), pp. 231–238. doi: 10.2478/sbe-2019-0056.

194

A Multi-Country Approach Reggi, L. and Dawes, S. (2016) ‘Open government data ecosystems: Linking transparency for innovation with transparency for participation and accountability,’ in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 74–86. doi: 10.1007/978-3-319-44421-5_6. Schuelke-Leech, B. A. (2018) ‘A model for understanding the orders of magnitude of disruptive technologies,’ Technological Forecasting and Social Change, 129(September 2017), pp. 261–274. doi: 10.1016/j.techfore.2017.09.033. Švarc, J., Dabić, M. and Daim, T. U. (2020) ‘A new innovation paradigm: European cohesion policy and the retreat of public science in countries in Europe’s scientific periphery,’ Thunderbird International Business Review, 62(5), pp. 531–547. doi: 10.1002/tie.22166 Tai, X., Xiao, W. and Tang, Y. (2020) ‘A quantitative assessment of vulnerability using social-economic-natural compound ecosystem framework in coal mining cities,’ Journal of Cleaner Production, 258, p. 120969. doi: 10.1016/j.jclepro.2020.120969. Vargo, S. L., Akaka, M. A. and Wieland, H. (2020) ‘Rethinking the process of diffusion in innovation: A service-ecosystems and institutional perspective,’ Journal of Business Research, 116, pp. 526–534. doi: 10.1016/j.jbusres.2020.01.038. Vargo, S. L., Wieland, H. and Akaka, M. A. (2015) ‘Innovation through institutionalization: A service ecosystems perspective,’ Industrial Marketing Management. Elsevier Inc., 44, pp. 63–72. doi: 10.1016/j.indmarman.2014.10.008. Wagner, M., Schaltegger, S., Hansen, E. G. and Fichter, K. (2019) ‘University-linked programmes for sustainable entrepreneurship and regional development: How and with what impact?’ Small Business Economics, pp. 1–18.doi: 10.1007/s11187-019-00280-4. Walrave, B., Talmar, M., Podoynitsyna, K. S., Romme, A. G. and Verbong, G. (2018) ‘A multi-level perspective on innovation ecosystems for path-breaking innovation,’ Technological Forecasting and Social Change, 136(December 2016), pp. 103–113. doi: 10.1016/j.techfore.2017.04.011. Yin, D., Ming, X. and Zhang, X. (2020) ‘Sustainable and smart product innovation ecosystem: An integrative status review and future perspectives,’ Journal of Cleaner Production, 274, p. 123005. doi: 10.1016/j.jclepro.2020.123005. Zdravković, M., Zdravković, J., Aubry, A., Moalla, N., Guédria, W. and Sarraipa, J. (2018) ‘Domain framework for implementation of open IoT ecosystems,’ International Journal of Production Research, 56(7), pp. 2552–2569. doi: 10.1080/00207543.2017.1385870.

195

Ronnie Figueiredo et al. Table A13.1  The collated alternative evaluator answers Alternatives* Criteria

Evaluators

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

TRL1

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7

2 2 2 2 1 0 2 0 2 2 2 2 2 2 2 2 1 1 2 0 2 2 2 2 0 2 2 2 2 2 2

1 NR 0 1 1 1 2 1 2 1 0 2 2 1 1 2 1 2 2 1 1 1 0 2 0 0 0 1 1 1 1

1 2 0 1 2 0 0 0 1 1 1 2 2 2 1 1 2 1 0 0 1 1 1 2 0 1 0 1 1 1 1

1 NR 0 1 2 1 2 1 1 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 0 1 0 1 1 1 1

1 1 1 1 2 1 2 2 2 1 1 1 2 2 1 2 1 2 1 2 2 1 1 1 0 0 1 1 2 2 2

2 2 1 1 1 2 2 1 2 2 1 1 2 2 1 1 1 2 2 2 2 2 2 1 0 2 1 2 1 1 1

2 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 2 1 1 1 0 1 1 1 1 1 1

2 2 0 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 2 1 2 2 1 0 0 1 1 1 1 1

1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 1 1 1 2 2 2 2 0 2 1 2 1 1 1

1 NR 1 0 1 2 1 0 1 2 2 2 2 2 2 1 1 2 2 1 2 2 2 2 0 0 1 1 2 2 2

E8

2

1

1

1

2

1

1

1

1

2

E9

2

1

1

1

2

1

1

1

1

2

TRL2

TRL3

TRL4

E10

2

1

1

1

2

1

1

1

1

2

E11 E12 E1 E2 E3 E4 E5 E6

2 2 1 2 2 2 2 0

1 1 1 1 1 2 1 2

1 1 1 1 2 2 2 1

1 1 1 1 2 2 2 1

2 2 1 1 2 2 2 1

1 1 1 2 2 2 2 2

1 1 1 2 1 1 1 2

1 1 1 2 1 1 1 2

1 1 1 2 2 2 1 2

2 2 1 2 1 1 1 2

196

A Multi-Country Approach Alternatives* Criteria

TRL5

TRL6

TRL7

TRL8

Evaluators

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2

2 1 2 2 2 2 1 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 0 2 1 2 2 2 2 2 2

1 1 2 2 2 2 1 0 0 1 2 2 1 1 1 2 0 1 1 2 2 2 2 2 1 2 1 2 1 2 2 0 0 1 1 1 1 1 1 2 1 1 1 2

−1 2 1 1 2 2 1 1 1 1 2 1 −1 1 1 2 1 2 1 2 2 2 2 1 −1 2 1 2 1 2 2 1 0 1 1 1 −1 1 1 1 1 2 1 2

1 2 1 2 2 2 1 0 1 1 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 0 0 1 2 1 1 1 1 1 1 2 1 2

1 2 1 2 1 1 1 0 1 1 2 1 2 2 1 2 1 1 1 2 1 1 2 2 2 2 2 2 1 1 2 1 1 1 2 1 1 2 1 2 1 1 1 2

2 2 2 2 2 1 1 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 1 2 2

2 2 2 2 1 1 1 1 1 1 1 2 2 2 1 2 1 1 1 2 1 2 1 2 2 2 1 2 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 2

2 2 2 2 2 1 1 0 0 1 1 2 2 1 1 2 0 0 1 2 1 1 1 2 2 1 1 2 1 1 2 0 0 1 1 1 1 2 2 2 1 1 2 1

1 2 1 2 1 2 1 2 1 2 1 1 1 2 1 2 1 1 1 2 2 2 1 1 1 1 2 2 1 2 1 2 2 2 1 1 1 1 1 2 1 2 1 2

1 1 2 2 2 2 1 0 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 2 1 2 2 2 1 2 1 1 0 1 1 0 1 2 1 1 2 2 1 1 (Continued)

197

Ronnie Figueiredo et al. Alternatives* Criteria

TRL9

Evaluators

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12

2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 0 2 2 2 2 2 2

2 2 2 1 2 2 1 2 2 2 1 1 2 2 1 0 1 2 2 1 2 2

2 2 2 0 0 1 1 1 1 2 1 1 0 2 2 0 −1 1 1 1 1 2

1 2 2 1 2 2 1 1 1 1 2 0 2 1 2 1 1 2 1 1 1 2

1 1 2 1 2 2 1 1 1 1 1 0 1 1 2 1 1 2 1 1 2 1

1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1

1 1 2 1 2 2 2 2 1 2 1 0 1 2 1 0 2 2 1 1 2 2

1 1 1 1 2 2 2 2 1 1 2 0 2 1 1 1 2 1 2 1 2 2

2 2 1 1 1 1 1 2 1 2 1 2 2 2 1 1 1 1 1 1 2 2

2 1 2 1 1 2 2 2 2 2 2 0 2 1 1 0 1 1 1 1 1 2

* A1 = People; A2 = Integrated Management; A3 = Support of Actors; A4 = Investor Support; A5 = Mentors Support; A6 = Infrastructure; A7 = Time; A8 = Connection with Ecosystems; A9 = Transparent Objectives; A10 = Partner Network

198

14 MULTI-CRITERIA ­ DECISION-MAKING METHODS FOR TECHNOLOGY SELECTION Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu

14.1 Introduction The role of technology and its extensions in today’s businesses and societies have been continuously increased and thereby it has been vital to governments, enterprises and individuals. Within this context, a special study area, technology management, has been initialized to manage the multi-dimensional and challenging “technology” phenomenon. “Technology management” has been extremely important than ever while technology options increase along with the wide range of variety of customer demands. Various approaches in technology management literature indicate a commitment to manage technology systematically from both strategic and operational perspectives (Linn et al., 2000). Although there have been serious efforts to develop a consensus on a systematic approach for technology management, attempts have resulted with failure while there are considerable conflicts among the approaches (Linn et al., 2000). On the other hand, the need for it never decreased and adversely it has steadily increased. It is remarkable that “technology management” does not intend merely to fulfil the managerial functions for specific technologies for a particular domain, but also develops the strategies, by considering the resource constraints, possible future of the markets and socio-economic environment (Linn et al., 2000). One of the most challenging decision-making issues has been “technology selection” in “technology management” domain. Dussauge et al. (1992) define “technology selection” as the “identification and selection of new or additional technologies which the firm seeks to master”. As stated by Gregory (1995) “identification phase” covers detection of the alternatives while “selection phases” cover an action to decide among the alternatives. Technology selection is a difficult decision since there is a considerable increase in the options and complexity of the available technologies (Farzipoor Saen, 2006; Ma et al., 2013; Shehabuddeen et al., 2006; Shen et al., 2010, 2011; Tang et al., 2014; Yu and Lee, 2013). There are multiple factors affecting selection decision. Numerous economic, social and industrial factors influence the decision-making of technology selection (Ma and Hung, 2015; Shen et al., 2011). Although there are some pieces of empirical evidence providing insight into several of those factors (Chan and Kaufman, 2010), it is extensively handled as

DOI: 10.4324/9781003046899-18

199

Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu

a multi-criteria decision-making (MCDM) problem that is structured according to the requirements of the firms. In addition to those difficulties, involvement of stakeholders in technology selection plays an essential role (Xia et al., 2017), while their participation in the entire decision process is needed (Sloane et al., 2003). This brings the use of MCDM methods in the context of group decision-making (GDM). GDM approaches are implemented for aggregating opinions of experts or stakeholders. This chapter presents a review of the studies using MCDM models to deal with “technology selection” problem. This review intends to identify the current state of art and the gaps in the research domain. As a conclusion, we will list our critics for current approaches and clarify the areas requiring additional research. The review does not cover studies focusing on material/component/process/investment selection.

14.2  Technology Selection Decision Competitive business environment is surrounded by technological advances which have several important impacts on the way of operating a business. These improvements in technologies are expected to contribute to increasing productivity and the specialization of production and products. Furthermore, they have been helpful to develop solutions that are minimizing risks against the work accidents. Thereby, making superior decisions for technology selection creates several benefits for enterprises and the countries. In this respect, the proper technology selection is expected to provide some of the benefits listed below. • • • • • • • • • • • • •

help the companies/countries to identify the alternatives help companies/countries select the optimal/or near optimal promising emerging technologies help companies/countries to develop competitive advantages provides a list of criteria with respect to various economic, social and environmental aspects considers different priorities (environmental, legislative, etc.) specific to companies/ countries identifies local and global investment opportunities ranks technology alternatives with respect to their strengths and weaknesses identifies the relationships between decision factors identifies the cost, benefit and risk of corresponding alternatives identifies technology fields for research and development (R&D) focus for the policy makers identifies opportunities to hybridizing (integrating) technologies provides guidelines for other companies/countries through empirical findings provides a systematic and analytical approach to create a final decision where multiple stakeholders have roles on the decision.

14.2.1  Role of Decision Makers Goal-oriented and analytical approaches for getting the best possible benefits from technology selection should be at the heart of decision-making. However, decision-making is a complicated process which is heavily affected by the decision makers. Many evaluation methods employ experts, administrators and researchers to understand the phenomena 200

MCDM Methods for Technology Selection

under interest. As indicated in several studies, decision-making is the combination of experience, beliefs and perceptions of the employed experts as an aspect (Haupt, 2016), and decision support tools, techniques and theories as another aspect. Therefore, putting a road map for the proper mixture of these combinations is very crucial to systemize decision-making. It is known fact that most of the decision makers may not have sufficient information regarding the new technologies on the target, and thereby they may be unwilling to deal with the possible required efforts and the corresponding problems. Moreover, since there are no earlier references for most of the new technologies, decision makers perceive high risks. They may also insist on the traditional methods used for technology selection. In addition to these facts, the information environment surrounding technology has been increasingly complex since it requires correct retrieval and interpretation of the different information. The expected role of decision makers along with technology selection is to screen and deliver related information to the top managers as a way of providing reliable decision support. Each of the real technology implementation cases for a specific domain also tests the existing decision support by providing a measure for the performance. Thereby a new knowledge and experience for participants is gained for producing right decisions for future cases. Therefore, lately considerable attention has been given to the models of knowledge management and their significance on decision-making (Choo, 1996). Providentially, in the field of technology selection, decision support has been a favourable subject and several different studies have been performed in this respect.

14.3  Multi-Criteria Decision-Making (MCDM) Methods in Technology Selection Selection of technologies is known to be one of the most puzzling decision-making concerns while the number of technological options increases with their complex contents (Torkkeli and Tuominen, 2002). A technology selection problem also has multiple concerns like meeting the customer expectations, optimizing the cost/benefit ratio and maximizing the performance. MCDM methods have been a widely used approach for such difficult and multi-optional problems. MCDM approaches provide several frameworks for decision-making to deal with the multiple objectives if performance of the decision highly depends on the system characteristics, the decision makers and the problem itself (Agrell, 1995). There are various advantages of using MCDM approaches for technology selection problems. One of the strengths of the MCDM is its quantitative structure which can be mathematically assessed. MCDMs mostly provide a systematic approach. This means that results can be easily validated by repeating the procedure described in the approach. One other main advantage of MCDM is the opportunity of evaluating existing or potential alternatives by simultaneously applying multiple conflicting criteria (Goulart Coelho et al., 2017). Several researchers introduced different varieties of MCDM approaches to solve different technology selection problems. Therefore, the selection of the MCDM approach is not an easy task. Each approach in the literature has a large number of variants, and their applicability depends on data availability and the purpose of the selection. On the other hand, mostly, the essential steps of the MCDM approaches have been common. The majority of them use pairwise comparison as the weighting method. Scalability of the pairwise comparison can be seen as a disadvantage of MCDMs. In this regard, some researchers prefer to employ fuzzy pairwise comparison to solve this problem. 201

Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu

14.3.1  Use of MCDMs in Manufacturing System Selection Technology selection decision has been strategic decision while it is recognized as a major decision area within manufacturing strategy (Farooq and O’Brien, 2009). An MCDM for a computerized manufacturing control system selection was presented by Ghandforoush et al. (1985). They have used quantitative factors like cost and qualitative factors such as vendor reputation. The evaluation model was a modified version of a model developed by Brown and Gibson (1972). Stam and Kuula (1991) reported a two-phase decision approach which integrates AHP with multi-objective mathematical programming considering both qualitative and quantitative criteria for flexible manufacturing system (FMS) selection. Datta et al. (1992) preferred to use the AHP for the justification of expenditures made for manufacturing systems which requires high investments. Mohanty and Venkataraman (1993) have also implemented the AHP for automated manufacturing systems (AMS) selection. Their study was accompanied by a case for verification purposes. In other study, Bozdağ et al. (2003), four different fuzzy multi-attribute group decision-making methods were utilized including the fuzzy AHP for a selection among computer integrated manufacturing system alternatives. Jaganathan et al. (2007) presented an integrated fuzzy AHP approach to assist the selection and evaluation of manufacturing technologies by considering intangible attributes and uncertainty. The study performed by Shang and Sueyoshi (1995) evaluated FMS alternatives by considering the benefits as tangible and intangible ones. The presented approach integrated AHP, simulation and an accounting procedure to identify the significant outputs/inputs of FMS alternatives. Subsequently, DEA with restricted weights and cross-efficiency analysis was applied to determine the FMS with the highest efficiency. In 2002, Karsak (2002) presented a fuzzy MCDM based on closeness of the concepts to the ideal and anti-ideal solutions for the FMS selection from a group of mutually exclusive alternatives. A decision framework, which was constructed based on DEA, was proposed by Sarkis and Talluri (1999) for evaluating alternative flexible manufacturing systems. They implemented the DEA model proposed by Cook et al. (1996). This study incorporated the qualitative and quantitative factors into the consideration without any priori information about factor weights. In addition to studies that are prepared for evaluating and selecting AMTs, Chang and Tsou (1993) presented a chance-constraint linear programming model to evaluate the FMSs according to economic aspects.

14.3.2  AHP in Technology Selection One of widely used MCDM methods applied in technology-related decisions has been the Analytic Hierarchy Process (AHP) model (Tran and Daim, 2008), which was proposed by Saaty (1980). The AHP is accepted as a beneficial approach for technology-related decisions by providing the correct options which can be used in both short and long terms. While the AHP has been a widely used methodology for manufacturing system selection (does not cover the component selection) problems, we reviewed the studies using AHP as the ones used for manufacturing system selection and the ones for other technologies.

14.3.3  Use of AHP for the Selection of Other Technologies Selecting the technologies in Indian telecommunications (Sivarama Prasad and Somasekhara, 1990) has been one of the earliest studies combining the Delphi method and the AHP. 202

MCDM Methods for Technology Selection

Outputs of Delphi were used as inputs to AHP and this provided a rank of technologies alternatives. Sloane et al. (2003) used the AHP to provide evidence for the evolution of the multidisciplinary and interdisciplinary process of selecting neonatal ventilators for a new women’s health hospital. In another study (Lai et al., 2002), the AHP was used for a multi-media authorizing system (MAS) selection problem. A case study was also presented for group decision-making. A decision support model, based on the AHP, for the selection of post-treatment technologies for permeates polishing was presented by Bick and Oron (2005). There is also one study (Tolga et al., 2005) employing fuzzy AHP for operating system selection. For the economic aspect of the decision process, authors employed Fuzzy Replacement Analysis. Another example (García-Cascales and Lamata, 2009) has been the use of AHP for the selection of a “parts cleaning system” for diesel engine maintenance. Tozan (2011) designed a fuzzy/crisp AHP-based decision support system to find the appropriate technology to use in cutting processes of titanium. Due to the complexity of recycling technology selection, Hsu et al. (2010) preferred a multiple-attribute decision process. In the first stage, they have utilized a fuzzy Delphi method to determine the factors regarding selection; subsequently, in the second stage, they applied a Fuzzy Analytic Hierarchy Process to find the importance of factors. Lee and Hwang (2010) also implemented the AHP with the purpose of providing a decision support process for selecting promising nuclear technology for Korea.

14.3.4  Hybrid MCDM in Technology Selection Although there have been numerous studies using AHP for technology selection problems, the AHP does not consider the case of interdependence and feedback as Analytic Network Process (ANP) does; therefore, all of these studies using AHP should have assumed that the criteria and alternatives do not depend on each other. There are also some other studies using ANP as the selection methodology. The main expectation in hybridization of MCDM methods is usually based on strengthening the power of model. The weakness of a model is supported with the influence of other method. There are also studies employing hybrid MCDM models for technology selection purposes. Oztaysi (2014) used the AHP integrated Grey-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of Content Management System (CMS). The main reason of the use of the MCDM was stated as the uncertainty and incomplete information on their hand. Yurdakul (2004) also takes a computer-integrated manufacturing (CIM) investment problem as a multi-attribute problem and implements a combined model of the analytical hierarchy process and goal programming. Kengpol and O’Brien (2001) tested their proposed decision support tool for Time Compression Technologies (TCTs) for rapid product development. The tool was constructed on the combined use of Analytic Hierarchy Process (AHP) cost/benefit and statistical analyses. In their study, Tang et al. (2014) identify key technologies related to silicon solar cells by using a framework facilitating Delphi and AHP. Their framework essentially analyses the potential gap for each technology in the solar cell industry. The Delphi method was used to identify possible technology alternatives. The AHP was used to detect weighted values for each technology by considering responses that were taken over 300 experts by the questionnaires. 203

Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu

Being different than many other approaches, Shen et al. (2010) proposed a technology selection process which integrated fuzzy Delphi method with AHP and patent co-citation approach (PCA). Patent co-citation approach identified the major R&D fields of organic light emitting diode (OLED) technology for Taiwan. Sambasivarao and Deshmukh (1997) utilized multiple methods such as AHP, TOPSIS, risk analysis and linear additive utility model to analyse the automation technology alternatives. Parkan and Wu (1999) handled the robot selection problem using three approaches: OCRA, TOPSIS and utility function model. Subsequently, they ranked the alternatives according to average values of the rankings obtained via those approaches.

14.3.5  Other MCDMs in Technology Selection One of the earliest studies was performed by Huang and Ghandforoush (1984) to evaluate industrial robot vendors by assigning specific weights to both economic and subjective factors. Khouja (1999) also presented an approach for robot selection, which considered technical aspects by real options theory. An AHP model accepts decision factors as in a unidirectional and hierarchical relationship (Meade and Sarkis, 1998). Erdoğmuş et al. (2005) have handled a high technology selection problem by employing analytic network process (ANP). The presented ANP model had a hierarchy, and a network of factors and sub-factors. Benefits (B), Opportunities (O), Costs (C) and Risks (R) were included in the model under the strategic criteria. Then, a combination of alternatives was obtained by using B-O-C-R rates. The same authors also used the ANP (Erdoğmuş et al., 2006) to evaluate the fuel alternatives for residential heating in Turkey. Ayağ and Özdemir (2011) also utilized fuzzy ANP for justifying the decisions about stand-alone machine tool selection among the alternatives. Authors remarkably focus on the fact that the AHP would not handle the several interactions, dependencies and feedback among the sub-criteria. A hybrid model focusing on the economic and industrial aspects for new technology selection was proposed by Shen et al. (2011). Authors have integrated fuzzy Delphi method with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, and the analytic network process (ANP) to be used for technology selection. They have also used the patent co-citation approach (PCA) to detect important technology areas as the alternatives of the selection model. The paper presented by Agrawal et al. (1991) introduced an efficient approach for the problem of an “optimum robot” selection by applying Multiple-Attribute Decision-Making (MADM) approach. As an output of the proposed approach, alternatives were listed by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). As a consequence, they provided an expert system to execute the proposed approach when required. The same authors (Agrawal et al., 1992) also employed their approach for gripper selection. ELECTRE I- and TOPSIS-based decision support model was proposed by Temuçin and Tozan (2016) to rank air conditioning systems alternatives.

14.4  Findings and Critics Technology investments are usually expensive investments which are effective on the survival of the companies. These high amounts of expenditure on technologies involve several serious decisions and require appropriate justification. Researchers have adopted several methods to rationalize these decisions. Selection of a particular technology involves decisions which are serious for the development and cost effectiveness of the company. Strategic and process overview stages can be too late to quit from a technology integration project. 204

MCDM Methods for Technology Selection

Thus, several assessment routes are required for determining a quit time, if the project does not appear as a promising one. However, justification and selection processes for technologies are extensively unstructured problems and must cover economic, analytical and risk factors analysis in this competitive environment. There are different methodologies used in the literature to qualify these selection decisions. This chapter specifically presents a concise review of the methodologies used to select technologies. As a result of concise review on the technology selection decisions, it is apparent that selection approaches are extensively based on three aspects: economic analysis techniques, analytical methods and strategic approaches. MCDM methods have been the most widely used ones for technology selection. However, we are not fully acknowledged which model produces the best result while each paper presents its own case to support its findings. On the other hand, although it has been known fact that AHP models assume a unidirectional, hierarchical relationship among decision levels (Meade and Sarkis, 1998); it has been a favourable approach. Since technology selection problem is known to be a complex problem, omitting the interdependence among the factors (or assuming there is interrelation) can be interpreted as an abstraction of the reality. However, there is no scientific support provided yet to measure the effect of omitting interdependencies. Another issue is about “a group of experts” mentioned in most of the MCDM papers. It is too mysterious sometimes “who these experts are” while the accuracy of the results greatly depends on them. The effect of values of these experts on decision-making is discussed in some studies (Coles and Norman, 2005). Sigmund et al. (2016) ask very important questions about the use of expert knowledge in technology selection. One of these questions is about the correctness of the experiential knowledge employed in the decision. And the second question is on what will happen if the experts in a running project are changed. If experts are accepted as the right people to judge the situation, the consensus (an average of different ideas) is required in MCDMs. As another criticism, some of these studies do not even mention about the consistency of evaluation and readers are not informed if that is a real result provided by “common mind” or not. As a result of straightforward thinking, measuring the expertise level of these experts can be an option. Thereby, their scores can be used during the factor detection or the dependency search for a relationship between them. The situation about the expertise also underlines the importance of the lifetime education in technology and design. Imprecision and vagueness in the existing technology selection problems resulted with the consideration of fuzzy approaches in decision-making. Even though several MCDM methods use a fuzzy number ranking approach to handle the fuzziness, there is not an agreement on the finest approach (Karsak and Ahiska, 2005). Therefore, methodological variances do not make sense mostly. Another issue is about having a real requirement to use fuzzy approaches. In some of the reviewed papers, it is apparent that crisp variables are fuzzifying unnecessarily and artificially. It should be noticed that fuzzy solutions end with the defuzzification process to understand a fuzzy concept. Therefore, fuzzifying something that is apparent may be just loss of time. There are vast numbers of papers attempting to find the best possible alternative indicating a balance in between the price and technical performance by considering the company requirements. This equally means the investigation of the price and the facilities provided by that technology. It is usually assumed that a highly priced technology provides something more. On the other hand, as Karsak (1998) mentions, the performance parameters specified by technology suppliers may not be attainable in practice. In this regard, linking pricing policies with the utilities provided with the technologies can be further investigated by the researchers. 205

Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu

References Agrawal, V.P., Kohli, V. and Gupta, S. (1991), “Computer aided robot selection: The ‘multiple attribute decision making’ approach”, International Journal of Production Research, Vol. 29 No. 8, pp. 1629–1644. Agrawal, V.P., Verma, A. and Agarwal, S. (1992), “Computer-aided evaluation and selection of optimum grippers”, International Journal of Production Research, Vol. 30 No. 11, pp. 2713–2732. Agrell, P.J. (1995), Interactive Multi-Criteria Decision Making in Production Economics, Linkoeping Institute of Technology, Linkoeping, available at: http://pure.iiasa.ac.at/4387/ (accessed 7 March 2017). Ayağ, Z. and Özdemir, R.G. (2011), “An intelligent approach to machine tool selection through fuzzy analytic network process”, Journal of Intelligent Manufacturing, Vol. 22 No. 2, pp. 163–177. Bick, A. and Oron, G. (2005), “Post-treatment design of seawater reverse osmosis plants: Boron removal technology selection for potable water production and environmental control”, Desalination, Vol. 178 No. 1–3, pp. 233–246. Bozdağ, C.E., Kahraman, C. and Ruan, D. (2003), “Fuzzy group decision making for selection among computer integrated manufacturing systems”, Computers in Industry, Vol. 51 No. 1, pp. 13–29. Brown, P.A. and Gibson, D.F. (1972), “A quantified model for facility site selection-application to a multiplant location problem”, A I I E Transactions, Vol. 4 No. 1, pp. 1–10. Chan, C.V. and Kaufman, D.R. (2010), “A technology selection framework for supporting delivery of patient-oriented health interventions in developing countries”, Journal of Biomedical Informatics, Vol. 43 No. 2, pp. 300–306. Chang, D.S. and Tsou, C.S. (1993), “A chance-constraints linear programming model on the economic evaluation of flexible manufacturing systems”, Production Planning & Control, Vol. 4 No. 2, pp. 159–165. Choo, C.W. (1996), “The knowing organization: How organizations use information to construct meaning, create knowledge, and make decisions”, International Journal of Information Management, Vol. 16 No. 5, pp. 329–340. Coles, R. and Norman, E. (2005), “An exploration of the role values plays in design decision-making”, International Journal of Technology and Design Education, Vol. 15 No. 2, pp. 155–171. Cook, W.D., Kress, M. and Seiford, L.M. (1996), “Data envelopment analysis in the presence of both quantitative and qualitative factors”, The Journal of the Operational Research Society, Vol. 47 No. 7, p. 945. Datta, V., Sambasivarao, K.V., Kodali, R. and Deshmukh, S.G. (1992), “Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems”, International Journal of Production Economics, Vol. 28 No. 2, pp. 227–234. Dussauge, P., Hart, S. and Ramanantsoa, B. (1992), Strategic Technology Management, 1 edition., Wiley, Chichester ; New York. Erdoğmuş, Ş., Aras, H. and Koç, E. (2006), “Evaluation of alternative fuels for residential heating in Turkey using analytic network process (ANP) with group decision-making”, Renewable and Sustainable Energy Reviews, Vol. 10 No. 3, pp. 269–279. Erdoğmuş, Ş., Kapanoglu, M. and Koç, E. (2005), “Evaluating high-tech alternatives by using analytic network process with BOCR and multiactors”, Evaluation and Program Planning, Vol. 28 No. 4, pp. 391–399. Farooq, S. and O’Brien, C. (2009), “Risk calculations in the manufacturing technology selection process”, Journal of Manufacturing Technology Management, Vol. 21 No. 1, pp. 28–49. Farzipoor Saen, R. (2006), “A decision model for selecting slightly non-homogeneous technologies”, Applied Mathematics and Computation, Vol. 177 No. 1, pp. 149–158. García-Cascales, M.S. and Lamata, M.T. (2009), “Selection of a cleaning system for engine maintenance based on the analytic hierarchy process”, Computers & Industrial Engineering, Vol. 56 No. 4, pp. 1442–1451. Ghandforoush, P., Huang, P.Y. and Taylor III, B.W.T. (1985), “A multi-criteria decision model for the selection of a computerized manufacturing control system”, International Journal of Production Research, Vol. 23 No. 1, pp. 117–128. Goulart Coelho, L.M., Lange, L.C. and Coelho, H.M. (2017), “Multi-criteria decision making to support waste management: A critical review of current practices and methods”, Waste Management & Research, Vol. 35 No. 1, pp. 3–28. Gregory, M.J. (1995), “Technology management: A process approach”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 209 No. 5, pp. 347–356.

206

MCDM Methods for Technology Selection Haupt, G. (2016), “Hierarchical thinking: A cognitive tool for guiding coherent decision making in design problem solving”, International Journal of Technology and Design Education, available at: https:// doi.org/10.1007/s10798-016-9381-0. Hsu, Y.-L., Lee, C.-H. and Kreng, V.B. (2010), “The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection”, Expert Systems with Applications, Vol. 37 No. 1, pp. 419–425. Huang, P. and Ghandforoush, P. (1984), “Robotics procedures given for evaluating selecting robots”, Industrial Engineering, Vol. 16 No. 4, pp. 44–48. Jaganathan, S., Erinjeri, J.J. and Ker, J. (2007), “Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies”, The International Journal of Advanced Manufacturing Technology, Vol. 32 No. 11–12, pp. 1253–1262. Karsak, E.E. (1998), “A two-phase robot selection procedure”, Production Planning & Control, Vol. 9 No. 7, pp. 675–684. Karsak, E.E. (2002), “Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives”, International Journal of Production Research, Vol. 40 No. 13, pp. 3167–3181. Karsak, E.E. and Ahiska, S.S. (2005), “Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection”, International Journal of Production Research, Vol. 43 No. 8, pp. 1537–1554. Kengpol, A. and O’Brien, C. (2001), “The development of a decision support tool for the selection of advanced technology to achieve rapid product development”, International Journal of Production Economics, Vol. 69 No. 2, pp. 177–191. Khouja, M.J. (1999), “An options view of robot performance parameters in a dynamic environment”, International Journal of Production Research, Vol. 37 No. 6, pp. 1243–1257. Lai, V.S., Wong, B.K. and Cheung, W. (2002), “Group decision making in a multiple criteria environment: A case using the AHP in software selection”, European Journal of Operational Research, Vol. 137 No. 1, pp. 134–144. Lee, D.J. and Hwang, J. (2010), “Decision support for selecting exportable nuclear technology using the analytic hierarchy process: A Korean case”, Energy Policy, Vol. 38 No. 1, pp. 161–167. Linn, R.J., Zhang, W. and Li, Z. (2000), “An intelligent management system for technology management”, Computers & Industrial Engineering, Vol. 38 No. 3, pp. 397–412. Ma, D., Chang, C.-C. and Hung, S.-W. (2013), “The selection of technology for late-starters: A case study of the energy-smart photovoltaic industry”, Economic Modelling, Vol. 35, pp. 10–20. Ma, D. and Hung, S.-W. (2015), “An integrated framework for the selection and acquisition of core technologies: The case of Taiwan’s LED industry”, Long Range Planning, Vol. 48 No. 6, pp. 381–397. Meade, L. and Sarkis, J. (1998), “Strategic analysis of logistics and supply chain management systems using the analytical network process1”, Transportation Research Part E: Logistics and Transportation Review, Vol. 34 No. 3, pp. 201–215. Mohanty, R. p. and Venkataraman, S. (1993), “Use of the analytic hierarchy process for selecting automated manufacturing systems”, International Journal of Operations & Production Management, Vol. 13 No. 8, pp. 45–57. Oztaysi, B. (2014), “A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems”, Knowledge-Based Systems, Vol. 70, pp. 44–54. Parkan, C. and Wu, M.-L. (1999), “Decision-making and performance measurement models with applications to robot selection”, Computers & Industrial Engineering, Vol. 36 No. 3, pp. 503–523. Saaty, T.L. (1980), The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York ; London. Sambasivarao, K.V. and Deshmukh, S.G. (1997), “A decision support system for selection and justification of advanced manufacturing technologies”, Production Planning & Control, Vol. 8 No. 3, pp. 270–284. Sarkis, J. and Talluri, S. (1999), “A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors”, International Journal of Production Research, Vol. 37 No. 13, pp. 2927–2938. Shang, J. and Sueyoshi, T. (1995), “A unified framework for the selection of a flexible manufacturing system”, European Journal of Operational Research, Vol. 85 No. 2, pp. 297–315. Shehabuddeen, N., Probert, D. and Phaal, R. (2006), “From theory to practice: Challenges in operationalising a technology selection framework”, Technovation, Vol. 26 No. 3, pp. 324–335.

207

Zeynep Didem Unutmaz Durmuşoğlu and Alptekin Durmuşoğlu Shen, Y.-C., Chang, S.-H., Lin, G.T.R. and Yu, H.-C. (2010), “A hybrid selection model for emerging technology”, Technological Forecasting and Social Change, Vol. 77 No. 1, pp. 151–166. Shen, Y.-C., Lin, G.T.R. and Tzeng, G.-H. (2011), “Combined DEMATEL techniques with novel MCDM for the organic light emitting diode technology selection”, Expert Systems with Applications, Vol. 38 No. 3, pp. 1468–1481. Sigmund, Z., Radujković, M. and Lazarević, D. (2016), “Decision support model for seismic strengthening technology selection of masonry buildings”, Tehnički Vjesnik, Vol. 23 No. 3, pp. 791, 791– 800, 800. Sivarama Prasad, A.V. and Somasekhara, N. (1990), “The analytic hierarchy process for choice of technologies”, Technological Forecasting and Social Change, Vol. 38 No. 2, pp. 151–158. Sloane, E.B., Liberatore, M.J., Nydick, R.L., Luo, W. and Chung, Q.B. (2003), “Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment”, Computers & Operations Research, Vol. 30 No. 10, pp. 1447–1465. Stam, A. and Kuula, M. (1991), “Selecting a flexible manufacturing system using multiple criteria analysis”, International Journal of Production Research, Vol. 29 No. 4, pp. 803–820. Tang, Y., Sun, H., Yao, Q. and Wang, Y. (2014), “The selection of key technologies by the silicon photovoltaic industry based on the Delphi method and AHP (analytic hierarchy process): Case study of China”, Energy, Vol. 75, pp. 474–482. Temuçin, T. and Tozan, H. (2016), “A fuzzy based decision support system for air conditioner selection and an application to Turkish construction sector”, Technical Gazette, Vol. 23 No. 4, pp. 1117, 1117–1122, 1122. Tolga, E., Demircan, M.L. and Kahraman, C. (2005), “Operating system selection using fuzzy replacement analysis and analytic hierarchy process”, International Journal of Production Economics, Vol. 97 No. 1, pp. 89–117. Torkkeli, M. and Tuominen, M. (2002), “The contribution of technology selection to core competencies”, International Journal of Production Economics, Vol. 77 No. 3, pp. 271–284. Tozan, H. (2011), “Fuzzy AHP based decision support system for technology selection in abrasive water jet cutting processes”, Tehnički Vjesnik, Vol. 18 No. 2, pp. 187, 187–191, 191. Tran, T.A. and Daim, T. (2008), “A taxonomic review of methods and tools applied in technology assessment”, Technological Forecasting and Social Change, Vol. 75 No. 9, pp. 1396–1405. Xia, D., Yu, Q., Gao, Q. and Cheng, G. (2017), “Sustainable technology selection decision-making model for enterprise in supply chain: Based on a modified strategic balanced scorecard”, Journal of Cleaner Production, Vol. 141, pp. 1337–1348. Yu, P. and Lee, J.H. (2013), “A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology”, Expert Systems with Applications, Vol. 40 No. 1, pp. 300–314. Yurdakul, M. (2004), “Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model”, Robotics and Computer-Integrated Manufacturing, Vol. 20 No. 4, pp. 329–340.

208

15 SOFTWARE ASSESSMENT FOR CAPACITY PLANNING AND FEASIBILITY CHECK OF THE MASTER PRODUCTION SCHEDULE Arash Yazdani and Tugrul Daim 15.1 Introduction This section aims to define the scope of the study within the supply chain management and explain the different planning horizons from strategic planning to hourly based schedules. This is accompanied by an explanation of concepts and terms used in this word and a description of the current practices in master scheduling. Probable causes of inaccuracies in the master schedule are discussed and the nature of the problems to be addressed is stated. Finally, the empirical hypotheses are listed which highlight the main aim of this study.

15.1.1 Background It is generally accepted that the competition between the firms is, in fact, within the supply chain network that they are participating in. A functional and effective supply chain is a vital component for the success of every company. The value chain of a typical company includes activities that range from research and development of the products and services through supplier management, production, and logistics and after-sale services to consumption and disposal. The main purpose of these activities is to create wealth for the company by adding value to material and services and selling them as products. Different planning stages within the supply chain determine the actions in different time horizons to satisfy the goals of a company. In the highest level, strategic network planning which dominates the whole supply chain focuses on the main issues of manufacturing, marketing, and the business model. The role of this planning is to establish supply chain policies and objectives (Stevens 1990). The next planning stage which includes tactical planning is concerned with the steps, actions, and means to meet the objectives that were set by the strategic planning. There is tactical planning for each function within the supply chain. In the production function, the sales and operation planning (S&OP) determines the volume of products that need to be produced on the monthly scale and is prepared from three to six months in advance of production. The S&OP planning includes various mid-term planning for procurement of material, production, transport, and sales. DOI: 10.4324/9781003046899-19

209

Arash Yazdani and Tugrul Daim

Master scheduling, in a smaller planning horizon, deals with the mix of products and scheduling activities of the S&OP. This scheduling is performed three months in advance of production (execution). The main focus in this step is the efficiency and optimality of the operation and the mix of means (resources) that need to be employed to execute the strategic plan. Planning horizon narrows further down to daily and hourly basis in detailed scheduling and execution respectively. The detailed scheduling includes the sequence of the activities to be performed and the decisions associated with the selection of resources and jobs when there are plenty of jobs and one resource ( job selection) and when there is one job and multiple resources available (resource selection). Finally, the hourly schedule controls the detailed daily procedures and activities of the production factors (men, machines, and materials) and measures their performance against the plan. In the manufacturing-based companies, the key to save the competitive advantage and answer to the ever-changing needs of customers, it is important to realize the real potentials of the manufacturing systems and plan executable outcomes. Master production schedule (MPS) is often used in a wide range of industries to regulate the activities of production factors and create a balance between demand and supply. MPS acts as a communicator to deliver the processing logic and the relationships between the resources to the factory floor and guide the production processes. This piece of document that is composed of plan and schedule indicates the necessary jobs and their sequences. Nowadays, such documents can be generated with the help of scheduling tools and software that integrate the facilities and resources within the in-bound and out-bound supply chain of the company to keep the plans and schedules within the production constraints. Due to the increasing completion and the large variety of services, the owners of manufacturers and businesses struggle more often with balancing demand and services or products and running a lean production. Therefore, concepts such as demand forecasting, which have always in the center of attention, as well as fast and accurate planning and scheduling are the new challenges to be answered. Besides improving the functionality of the tools and software that there are already being employed, integrated services have become another solution to close the time gap between the functions. By taking advantage of integrated systems, where there is a real flow of information throughout the whole supply chain, from demand forecasting to material procurements and capacity planning and logistics to bring harmony to supply chain and make it as smooth as possible in both tactical and operational levels. The role of integrated services within the supply chain is to increase visibility over the supply chain as well as increase the predictability of the effect of changes in one unit on the other units. There are several management and business systems such as enterprise resource planning (ERP), advanced planning and scheduling (APS) software, and manufacturing execution system (MES). The availability and reliability of the data determine the practicality of this system. These data are gathered and can be analyzed by every one of these management systems, and can be passed to other systems for more informed decision-makings and enabling business intelligence (BI) within an organization. ERP systems, which are often integrated within the BI strategy, can analyze and perform detailed calculations for various key performance indicators (KPIs). The problem that remains as a concern is the manual transformation and reporting of the data. The integration of ERP systems with APS and MES systems has solved this problem to some extent. These systems are needed to be integrated as such to improve the decision-making outcomes and reduce mental and physical efforts associated with it as well as securing and stabilizing production in the supply chain. 210

Capacity Planning and Feasibility Check

The idea of integration of these management systems has been discussed by many authors (Analysis of the planning and scheduling functionality in APS systems 2001, Wiers 2002, McKay and Wiers 2003, Akkermans et al. 2003b, Ważna et al. 2006) and their impacts have been analyzed (Akkermans et al. 2003a). In order to fully exploit the potentials of implemented ERP systems and support the initiated strategy, there should be an integration between that and the systems that include implementation, planning, and execution of the business functions, MES and APS system. Having an integrated system implemented, it is possible to extract the data from manufacturing processes by automatically downloading real data and use them for analyzing and improving the accuracy of estimations, cost analysis, detecting deficiencies, and improving the accuracy and speed of planning. This integrated system may treat the manufacturing processes as a black box, where the production data are only recorded as the production goes on. By considering dynamic variables that exist within the production in real life, it is extensively difficult to come up with value as a capacity that reflects the real-life production. Capacity planning, which helps to balance the demand and production rate, is so far treated in planning and scheduling systems as historical data. The problems associated with historic data include irrelevancy and inaccuracy. Lead time must be a result of planning; however, using averaged production metrics to estimate the lead time and production capacity for planning purposes has been there in practice for an undefined period. This study aims to assess tools and software to come up with a method that replicates the production process as well as the main criteria that define the viability of these tools. The key point of this replication of the processes is to include the variability and reliability of the production factors (men, machines, and materials) into the calculation of capacity planning and improve the planning and scheduling results. This means an effort in closing the gap between “plan” and “execution”.

15.1.2  Problem Definition Whether it is a make-to-stock (MTS), make-to-order (MTO), or engineer-to-order (ETO) type of manufacturing process, the uncertainties and changes in the manufacturing processes can happen at any time during the stages of planning or execution. Some of these random events and risks can be predicted using historical data or preparing a preventive maintenance schedule. However, a variety of random events are far larger than the power of estimation. There are normally two approaches toward preparing a production schedule, forward or backward scheduling. In both approaches, the production lead time of the products is considered and a deterministic correction factor which is used to consider the sum of random events that may occur during production. This correction factor is the average of events that are happened during different times in the past and are recorded as historic data. The problem with using the average of averages in planning is the inaccuracies that are intentionally added to the plan. Considering the engineered production lead times plus some uncertainties to account for random events may always not be accurate and can cause the plan to differ from the execution. Whether the execution underperforms or outperforms the plan, it depends on which end of the capacity range is considered in the estimations. Plans can be optimistically or pessimistically designed but in the nutshell, they can be inaccurate. Plan and execution are two words that are generally used in project management and manufacturing environments to assess the performance of the current activities versus a predetermined estimate. The application of two words implies that there is usually a difference between plan and execution. Production plans, whether optimistic or pessimistic, 211

Arash Yazdani and Tugrul Daim

tend to deviate from the actual production performance. Supply chain planners have always been trying to estimate the production capacities and constraints as accurate as possible and produce executable schedules. However, production schedules can be easily skewed when an event causes the process to delay or deviate from predictions. The emerge of advanced planning and scheduling (APS) software has helped to closely monitor the resources and flow of material beyond the production walls from demand forecast to supplier’s management and generate plans which suite the strategic and financial goals of the companies. Regular APS software can integrate multiple management systems and collaborate across functions. They can reduce time and effort associated with sales and operation (S&OP) planning and allow scenario testing and find the differences that random events can make. However, they are limited to the prediction power of planners and employ deterministic approaches. A large share of random events is dynamic and depends on the current state of the system; these variables are difficult to predict and cannot be precisely measured in order to accurately measure the lead times. Besides, even if the difference caused by these random events is known, their impact on the overall performance of the system is unknown. Some of these random events may include queue times, changeover times, and failure rate productivity of the machines or labors, which are almost impossible to predict. As an example, one of these production variables can be queue times. Queue times are the times that jobs wait for a resource to become available and can be calculated as a measure of time or number of waiting jobs. Queue times exist in many places on the shop floor and almost between every production step. Quantitatively, queue times vary dynamically and sometimes their size affects the performance of machinery or the system. This is the phenomenon that it is referred to as the workload-reliability dependency of the machinery in this project. In reality, queue times are difficult to plan or measure and depend on the current state of the system. The current approach includes calculation of the rough-cut capacity planning (RCCP) in drafting the master production schedule. Master scheduling (Master Planning of Resources 2004) uses various sources as input to generate the master production schedule for production, and some planning activities for distribution, requirements, and logistics. The accuracy of master scheduling has a vital effect on the business and affects many key performance indicators such as utilization, inventory turnover ratio, service level, and tardiness. The accuracy of master scheduling is dependent on the accuracy of the input data. Demand management is one of the key inputs that have been a topic for discussions and improvements to make more accurate estimates of the products’ demand in the future. This is part of the multi-level sequential process to come up with master scheduling. As far as the production has MTO characteristics, demand management plays a more important role. And in many cases, they are faced with seasonality, demand fluctuations, and forecast deviations. The effect of these factors varies between different demand trends and planning horizons; therefore, each trend should be forecasted differently (Chung and Krajewski 1984). Deviations or inaccuracies in demand management may affect the accuracy of the master scheduling and this affects the inventory levels, service level, lost sales, etc. Ultimately, this appears negatively in the financial performance of the firms. The Production Plan, as another input, includes the logical production plan and steps to produce a good or service. There are barely changes within the production plan unless there is an innovation or change of machinery, or know-how. After all, it is relatively easier to predict and implement accurately in master scheduling. There are not many dynamic variables 212

Capacity Planning and Feasibility Check

that influence this step as there are in rough-cut capacity planning. The dynamic variables of the production execution normally appear in capacity planning. The real production capacity in many production processes is so dynamic that it is not possible to transfer an accurate value to master scheduling. Therefore, it is more practical to have a rough-cut estimate of this property, based on averages, to be used for the calculation of the production flow rate on the shop floor. Therefore, there are two important types of data that affect the accuracy and feasibility of the master schedule. These are demand management and capacity planning. This study focuses on capacity planning and tries to explore the dynamic variables within production factors and their interactions. As the name suggests, the rough-cut capacity is an approximate estimate of the production capacity which sets limits and constraints on how much that can be produced and delivered to customers. The problem with this approach is that it is associated with inaccuracies and these inaccuracies can lead to financial losses, ultimately. There are multiple ways that this could lead to financial losses. In one scenario if the production plant cannot be able to execute as planned, it may cause the finished goods inventory level to reduce and potentially miss deadlines and increase backorders. If the production plant can execute more than the planned quantity, in another scenario, an unplanned increase in the inventory level may not be linked to higher sales and can cause holding costs and low inventory turnover ratios. Therefore, inaccuracies cause financial losses in one way or another which may be operational costs or opportunity cost. Three simple questions that can lead to better planning and scheduling, before drafting a master production schedule (MPS) using rough-cut capacity value, are the followings: 1 Is the rough-cut capacity estimation good enough? 2 How can it be improved? 3 Is it worth it to improve? An acceptable master schedule should be feasible and optimized. However, the variations that may enter the master scheduling introduce uncertainties and these cause a deviation between the plan and the execution. With the current practices in planning and scheduling, the feasibility check of the MPS is only possible through comparison of the plan with the historic data and the opinion of the production experts. And there are usually extensive disputes between supply planers, management, and production executers about the feasibility and optimality of the plan. This problem leads to deficiencies between the functions and increased mental and physical effort on tasks which do not add any value. Therefore, this firefighting which is caused by not having a clear view about the production capacities from supply planners adds up with the inaccuracies of the rough-cut capacity planning and creates a great opportunity to improve the accuracy of the plans and schedules and ease the whole process for the functions in the supply chain.

15.1.3  The Goal of Study The goal of this study is to assess different tools and software among advanced planning and scheduling (APS) software and discrete element simulation (DES) software to address the variability of the dynamic production to come up with a more accurate estimate of the capacity. The feasibility of master scheduling lies within the common area of three factors: material, demand, and capacity. 213

Arash Yazdani and Tugrul Daim

In simple words, the focus of this study is to determine the size and the position of the capacity circle with respect to that of demand and material. However, the right tools to estimate this factor are assessed and selected based on a series of predefined criteria in a pairwise comparison using the hierarchical decision model (HDM). This tool is used to simulate the manufacturing processes and implement the dynamic variables to observe the behavior of the system and accurately measure the production capacity. The type of dynamic variables within the production differs between production sectors; however, there are general types of random events that are commonly found in many productions. These events may include: • • • • •

The workload-reliability dependency of machinery Productivity and skills of the workers Changeover and setup times Queue times Incidents and reworks

Implementation of all these random events requires an extensive amount of production data to obtain a clear image of their magnitude for building the model and parameterizing the variables. However, this is a one-time investigation to extract the data required for modeling and simulation. Subsequently, the behavior of the system can be observed by running simulations. In this study, a series of tools and software from different types of functionalities are listed and assessed to see which group and specific tool can be used to capture the changeability and variability of the production factors and obtain a more accurate estimate of the production capacity. The group of systems that the software packages are chosen from are either advanced planning and scheduling (APS) software or discrete element method (DEM) simulation software. The assessment of the tool is followed by its selection and its implication for feasibility and optimization of a master production schedule (MPS). In this work, the implication of the tool to simulate the manufacturing systems and their behavior is called manufacturing systems simulation (MSS). The following empirical hypotheses have been designed to assess the capabilities of manufacturing systems simulation to close the gap between plan and execution. 1 The MSS can be used for capacity planning 2 Feasibility of the MPS can be checked 3 The “current” APS software can be outperformed by a significant improvement in • Production efficiency (setup time, slack time) • Accuracy of planning

15.2  Theoretical Background This chapter looks into industry 4.0 and the advancements that have been drawn from this technology in optimizing the activity of functions in supply chain management. Advancements such as the implementation of various management systems such as enterprise resource planning (ERP), advanced planning and scheduling (APS) tools, and manufacturing execution system (MSS) have altered the way supply chain used to perform. These systems can be integrated and autonomously operate as an enabler for industry 4.0. Also, it has been aimed to find applications for manufacturing systems simulation (MSS) in addressing issues that manufacturers face nowadays due to the increase in product varieties from 214

Capacity Planning and Feasibility Check

competitors and reduced delivery times. Besides defining terms and current challenges in the supply chain, this chapter highlights the possible roles for MSS to implement lean management principles in both make-to-order (MTO) and make-to-stock (MTS) manufacturers.

15.2.1  Industry 4.0 Industry 4.0 is the fourth industrial revolution that envisions the collection of systems and its components such as machinery in a smart factory that is connected globally in a network that autonomously exchanges information and is capable of analyzing and controlling themselves (Tjahjono et al. 2017). Autonomous systems may include, as an example, a series of machines which are mutually connected in a cyber-physical manner and are able to adjust themselves to produce different products based on the product configurations or bill of materials (BOM). The analysis of the effect that industry 4.0 on the supply chain can have is necessary to ensure close collaboration between different functions. Transparency and quick flow and analysis of information are crucial to run an efficient operation between customers, production, and suppliers. This transparency goes beyond production and views all steps from demand management to production planning throughout the whole product lifecycle until recycling and after-sales services. Momentous advancements that are made in information technology (IT) have significantly changed the way that calculations used to be done. The new era of this fast-moving industry is beginning to connect the integrated and automated systems to a wireless network (Kagermann et al. 2013). The rise of IT and industry 4.0 for the companies that are participating in a supply chain network can have opportunities as well as threats. The threats may come from the situations that a company fails to cope with the IT implications effectively and misses opportunities. Therefore, it is essential to recognize the threats and opportunities that IT innovations may offer to supply chain management. Regardless of the notion of these innovative developments, either industry 4.0 in Germany or smart factory, advanced manufacturing or internet of things (IoT) in other European countries, these innovations share some commonalities and have significantly altered the shop floor activities (Tjahjono et al. 2017). The definition of smart factory, as stated by Geissbauer et al. (2016), follows: advanced robotics and artificial intelligence, hi-tech sensors, cloud computing, the Internet of Things, data capture and analytics, digital fabrication (including 3D printing), software-as-a-service and other new marketing models, mobile devices, platforms that use algorithms to direct motor vehicles (including navigation tools, ride-sharing apps, delivery and ride services, and autonomous vehicles), and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries. (Geissbauer et al. 2016) A study that focused on the impact of industry 4.0 on the warehouse, transport logistics, procurement, and fulfillment found that the order fulfillment and transport logistics are expected to be affected mostly by industry 4.0 (Tjahjono et al. 2017). However, this is true while no attention was paid to the production function. The results of 49 studies, as reported by Pfohl et al. (2015), show that industry 4.0 encompasses all innovations implemented in value chain and transforms the trends of digitalization, transparency, modularization, automatization, mobility, collaboration between networks 215

Arash Yazdani and Tugrul Daim

and functions, and socializing of the services and products (Pfohl et al. 2015). The research area of articles and journals which have looked at industry 4.0 from the supply chain point of view can be divided into four categories. The majority of researchers have looked at the impact of industry 4.0 on shop floor activities trying to answer “How” questions, a smaller share have tried to see if such questions can be answered for the management and strategic level too, and some others have aimed to prove the impact of industry 4.0 using quantitative measures such as calculation of opportunity versus threat for the application of industry 4.0 in different functions of supply chain (Tjahjono et al. 2017). On the shop floors, the industry 4.0 implementation has revolutionized the production processes with integrated systems and machinery. Despite the studies in this field such as proof of concept for autonomization in logistics and autonomous decision-makings, planning, and controlling of activities (Cachon 2012), attempts to expand the impact of industry 4.0 toward autonomous production planning and scheduling are still in an infant stage. Therefore, further investigations are required to study the real impact of industry 4.0 and integrated systems on planning and scheduling activities in supply chain management. Generally, the real business impact is about the benefits that can be drawn out of the implementation of industry 4.0. This development would help the supply chain to run with increased flexibility and transparency. It can increase the quality of standards and support decisions in a faster and more effective way. Also, it impacts the life of workers in many functions throughout the supply chain, as it had in the production function.

15.2.2  Discrete Event Simulation Simulations have been used by many companies to perform scenario analysis and support decisions (Oyarbide et al. 2003). Simulations help to move forward with a plan and see the consequences and mark the necessary refinements before actual implementation. For instance, before building a fast speed bottle filling line and installing the machines, simulations can help to find the optimal position of the machinery and the required length of the conveyors or the buffer space behind each machine to enable the design capacity of the filling line. Discrete event simulation (DES) comprises a simulation environment where the states of the system change in discrete steps with respect to time (Andras Varga 2001). The variables and the state of the system take zero time to change between the time steps. The time steps in DES can be referred to as timestamp. This means that the behavior of the model in simulation changes as the states of the system change in discrete steps. DES is solely concerned with the start and end of the timestamps and assumes that nothing interesting happens between the transitions of states (Oyarbide et al. 2003). In general, computer simulations can be recognized as a technique that models a system in an environment and illustrates its behavior and interaction of modeled components. And they belong to the symbolic main class of generic modeling techniques (Oyarbide et  al. 2003). Simulations can be performed to visualize the dynamics and behavior of systems which consist of parts moving along the process and obtain information about the dynamic range of the system. The other advantages may include testing scenarios and optimizing the manufacturing configuration to reduce the final changes in the production assembly and solve complex problems. In principle, three elements are commonly found in many models: “parts or entities” define the tasks or the jobs that need to be processed or treated within the simulated model, “buffers or queues” refer to the waiting time that jobs need to wait for availability of a 216

Capacity Planning and Feasibility Check

resources and finally, “machines or activities” which represent the resources or workstations that “parts or entities” require to be processed. Prior to running simulations, some activities need to be executed to ensure the liability of the simulation. Some of these actions can include dealing with some degree of complexities. However, the aim of the simulation and the question to be answered have to be the guide to tune the level of model complexity. It is essentially important to recognize the objectives of the simulation and realize the dynamics of the system (SD). The complexities and domain of the simulation must be set accordingly to ease the modeling process. This has been proved earlier that the SD, based on the problem identification of causality, can be divided into two phases of qualitative system dynamics and quantitative system dynamics (Wolstenholme 1990). The qualitative system dynamics deals with the cause and effect diagrams and the behavior of the system is determined by its feedback structure while quantitative system dynamics is a more traditional SD phase and deals with the relationship between all variables, calibration of parameters, and derivation of simulation equations. The capability of simulations in capturing SD makes them very suitable for manufacturing processes and analysis of systems behavior (Baines et al. 1998). The simulations can be executed in different levels of details and granularities to address a problem. A simulation with the lowest level of details, a coarse evaluation, can be performed in the first stage of design or decision-making. This type of simulation can guide the discussions and resolve the general problems around an issue. However, the challenges may include verification of the solution and uncertainties about the solution or even the question. The simulation details can be further built up in intermediate and detailed evaluation to solve much-defined problems with specific objectives. To solve detailed problems the models need to be built in detail and with enough accuracy of parameters to reliably calculate the consequence of different alternatives.

15.2.3  Pros and Cons Analysis of Current Practices As it is discussed in Section 1.1, the integration of management systems under the business intelligence (BI) strategy includes the application of advanced planning and scheduling (APS) software. In a competitive supply chain, customer requirements and products need to be manufactured through multiple projects. In such an environment, the due dates become shorter and the products become more customized according to the requirements of customers, which forces lead times to be reduced. Normally, the production or manufacturing of every good consists of a set of jobs that need to be performed consequently (e.g. dispatching, fabrication, configuration, and assembly). These processes occur in the production and every production unit is restricted by the constraints. Besides production constraints, there are constraints concerned with activities beyond production which limit the overall capacity of the supply and define the ground of lead time. Reducing the lead time, timely delivery, availability of stock, and reliability of delivery are important factors for manufacturers to save the competitive edge. For many of these manufacturers meeting deadlines is the only way of survival (Chung et al. 2000). In order to consider these constraints within multiple functions, APS tools can be employed effectively. These tools can be used to plan and schedule the activity of supply chain functions, from order management to delivery and logistics in both unconstrained and constrained scenarios. Therefore, a more visible image can be obtained to assess the supply lead time, available to promise (ATP) and inventory levels. Also, different scenarios can be tested to see the effect of changes in supply and compare the scenarios based on the obtained KPIs. 217

Arash Yazdani and Tugrul Daim Table 15.1  A nalysis of the current practices in planning and scheduling (ASP) Strengths

Weaknesses

Integration with ERP systems Enables lean manufacturing Best production methods using predetermined variables Ease of use and increased visibility Forecasting and scenario analysis

Manufacturing complexities are overseen Dynamic variables not implemented Single-static queue times in rough-cut master plan Lack of flexibility (can be easily skewed) Production planning and detailed scheduling (PP/DS)

Although APS software packages are fairly able to plan and schedule the activity of functions across the whole supply chain, they lack several functionalities which limit the accuracy of these software packages to exactly measure the lead times, risks and probabilities as well as production capacity. Also, production planning and detailed scheduling (PP/DS) is another disability of these tools. A whole summary of the strengths and weaknesses of APS software is presented in Table 15.1. In general, supply chain management has benefited massively from these tools and the functionalities that they have introduced. Some of these functionalities and features were not easily applicable, if not impossible. Problems such as coordinating the raw material capacities with order sizes and fabrication and assembly with different lead times and replenishment times of the warehouse were possible through exhausting mathematical modeling and calculations like mixed-integer programming (LMIP) (Kolisch and Heß 2000). However, this method was still unable to include customers’ due dates for scheduling optimization. One of the main purposes of APS software is their interoperability with the other management systems. This feature of these software packages has enabled them to monitor the movement of goods and observe the inventory levels. Also, forecasting methods can be implemented to forecast the demand and run different scenarios to find the proper ways to respond to the demands as well as forecast the inventory level. The service level, as one of the key KPIs, can be foreseen as a result of different inventory safety levels or production rate and vice versa. These tools have been around for nearly a decade and many companies have implemented them in their supply chain planning. Therefore, it is relatively easy for many employees in these firms to work with such software nowadays and there are many experts around to seek help. This can be recognized as one of their advantages that the industry is moderately familiar with these functionalities and there is a piece of common industrial knowledge. Many of the constraints and capacity values that these software packages need are obtained through external sources. This is currently done either through the integration of APS tools with ERP and MES systems or through a manual interpretation of data. And in many cases, the average of averages has been used to define the production capacity and supply constraints for the APS software due to the lack of more accurate methods. Also, APS software packages are not essentially built and programmed to simulate the activities; therefore, there are some variables and random events which may cause deviations between the plan and the execution. Of course, these random events and variables are present in all parts of the supply chain and affect the overall supply capacity. However, by considering only the production unit as a part of the supply chain, these APS software packages have shown some weaknesses which need to be addressed. Operationally, production or manufacturing is a part of supply chain that conventionally has the longest lead time and can be recognized as bottlenecks. A bottleneck defines the final 218

Capacity Planning and Feasibility Check

throughput of a system and in the supply chain, production capacity defines the throughput. Therefore, it is vital to understand and measure the production constraints and the role they play on the overall performance of the system. APS software packages use the capacity constraints as given or extracted from databases as a fixed value and do not deal with it as they are dynamic. Besides production constraints, APS software cannot calculate the effect of a state of variable on the other variables present in the production. This means that the changeability of manufacturing is overseen by the planning and scheduling tools. Depending on the type of APS system implemented, consideration of the variability of production factors is also far from fulfillment. In drafting production plans and schedules, single queue times are used and are assumed to be unchanged (static) during the whole production for as long as the plan is made for. This, as one of the weaknesses of APS, causes an inaccuracy in the plan. These inaccuracies cause the execution to deviate from the plan and schedule; and once the deviation or time loss occurs, following the schedule is not possible any longer. This conveys that the schedules produced by APS are inflexible and can be skewed easily. Production planning and detailed scheduling are other weak areas of APS software. Since these software packages are not able to calculate the variables and behavior of the production factors, they are not able to perform detailed scheduling and production planning as a result. By APS, production is treated simply as a black box and only the historic averages are used to build this box for planning and scheduling. These are some of the benefits and weaknesses that are associated with the application of APS software for planning and scheduling in supply chain management. These are primarily focused on the representation of the overall capacity of supply and not focused on how the variables and data for constraints and capacity are calculated and treated. These are two different aspects that need to be dealt with separately: a smaller system (capacity planning, constraints management) within a bigger system that is run by APS software. And the information flows from the smaller system as an input for the further calculation of changes in supply performance by the APS system and further planning and scheduling.

15.2.4  Lean Manufacturing Lean manufacturing has been the success key for many manufacturing firms to constantly improve their process and add to their competitive edge. This is more significant for mass car manufacturers and electronic devices supply chain (Melchert et al. 2006). Due to the increase in the number of firms and product varieties in the free market, traditional suppliers of commodities are forced to provide product diversity under high variable demand. Besides, the orders have ever-decreasing due dates and producers need to cope with shorter delivery times and reducing lead time (Suri 1998). There are numerous definitions for lean manufacturing among works of literatures. One of its definitions can be stated as follows: lean manufacturing is a fundamental framework that enhances manufacturing efficiency through the elimination of wastes and is accomplished with minimal costs (Hopp and Spearman 2004). The wastes are the sum of non-value adding activities that may be present on the shop floor such as reworks, repairs, and transportation of material on the production line. Although lean management has proved its capabilities for repetitive processes and medium to large manufacturing sites, it is becoming more difficult to apply the principles of lean management to deal with the variety of products and changes in customers’ tastes. This is particularly of importance when dealing with low-volume and high-mix products, especially for smaller manufacturers. For them, running lean management with the tools and 219

Arash Yazdani and Tugrul Daim

the knowledge that has carried from the second and third industrial revolution is no longer practical and requires new solutions that are adequately fast, accurate, and efficient. In both make-to-order (MTO) and make-to-stock (MTS) productions, the market challenges have twisted the way lean production used to operate. MTO industry that works with the demand-pull mechanism is facing more customized products with shorter due dates. They also face frequent changes in the production schedule and have to effectively handle material management (Henrich et al. 2004). And MTS industry is facing drastic demand variations and an increase in product mixes. The real challenge for the MTO business strategy is to meet the customers’ requirements on time with the right quantity. Therefore, a KPI which is commonly used in supply, on time in full (OTIF), is used to measure the performance of MTO businesses. To achieve a good score in this aspect, companies have tried to continuously improve their manufacturing processes using new lean manufacturing principles. Due to the reductions that can be obtained in lead times, manufacturers have been willing to implement these principles and able to run on MTO basis (Womack et al. 1990). Within the concept of production planning and control (PPC), a company designs its desired PPC system based on the market requirements and the manufacturing processes. And it starts with manufacturing strategy (market characterization and place of manufacturing), followed by PPC and execution on the shop floor (Melchert et al. 2006). The selection of the right PPC is crucial for the success of the company as it determines the way of operation and methods of measurement and control. This selection includes three stages in which manufacturing systems simulation (MSS) may find applications. According to Henrich et al. (2004), the three stages that need to be performed in sequence and are distinguished in the selection and implementation processes are as follows: 1 Preliminary study and evaluation of alternatives 2 Detailed investigation and selection of final design 3 Implementation MSS is capable of helping this decision-making process by simulation and evaluation of the primary models and highlights the opportunities for improvements and testing other scenarios. In the detailed investigation, it is possible to include the details of the model, planning and controlling strategies, and evaluate the final results which lead to the final selection. Achieving a lean and flexible manufacturing line that is able to respond to customers’ needs effectively, as one of the biggest challenges, requires a sound PPC model. The role of MSS becomes more important by realizing that the capabilities of this technique could provide to help the PPC design process and alter the final selection toward a more optimal alternative. Through intensive research and detailed analysis, it has been proved that some of the lean principles such as Kanban and level scheduling are not suitable for low volume and variable demands (Melchert et al. 2006). Perhaps, MSS can be used to test these techniques and find answers for the applicability of lean principles in a faster manner and more specific to the manufacturing settings.

2.4.1  Line Balancing One of the main goals of “smart factory” and industry 4.0 is to enable production lines to produce on time and according to demand within their constraints. Therefore, the manufacturing practice is often based on principles such as lean manufacturing, Kanban, just in 220

Capacity Planning and Feasibility Check

time, or Heijunka (production leveling). As lean management aims to reduce the wastes while manufacturing, excess production can be considered as a type of waste and it is aimed to produce as much as needed by the customers, ideally. An effective technique to reduce this type of waste is the right selection of the manufacturing principle for the pull-driven or push-driven environments. Line balancing was introduced for the first time in 1961 which included the necessities and its early principles (Helgeson and Birnie 1961). Line balancing is the activity of how to dedicate the production lines to tasks and job orders. If (Zupan and Herakovic 2015) this is done effectively, the throughput of lines can be increased while achieving lower costs and minimizing wastes (e.g. overproduction and underutilization of lines) (Zupan and Herakovic 2015). Balancing of production lines has to be done based on the product specifications and the capabilities of the lines as well as the objective functions which try to minimize the final effect of the operation on costs, processing time, or based on priorities. Methods and algorithms exist to perform such calculations and find the best line dedication combinations for manufacturing lines. However, discrete event simulations (DES) have been proved recently to be able to address this problem effectively (Kočiško et al. 2012, Cigolini et al. 2014, Zupan and Herakovic 2015). Zupan and Herakovic (2015) were able to demonstrate that the line balancing could create substantial increment in overall production capacity as much as three times in comparison to the non-balanced model. This was followed by a considerable decrease in waiting time percentage of the jobs (four times). Mathematically, the reduction of waiting time results in a shorter production cycle time and gets closer to the assembly time. This would enhance lean manufacturing as one of the wastes has been minimized. Also, the simulations provided a visualized trace of tasks and resource behavior and this can be used to find the design pitfalls and detect bottlenecks. Generally, line balancing gives better results than non-balanced lines. However, the optimality of the proposed balancing is subjected to questions. Different optimization algorithms and balancing techniques can be used to alter the final results of production performance metrics. Using simulations, different scenarios can be checked and analyzed in a really short time. And scenarios can be proposed by simply changing variables toward a more optimal outcome over the whole range of desired time span.

2.4.2  Detailed Scheduling The importance of detailed scheduling is realized by manufacturers to meet reduced due dates and drafting producible production plans. The detailed scheduling affects the production capacity and, consequently, the promise or delivery dates. This can be done using algorithms and mathematical equations which result in satisfying an objective (Gelders and Kleindorfer 1974). The objective of these functions may include one of the following heuristic techniques or many others as defined by planners: 1 Economic lot scheduling: minimizing setup times or total cost 2 Least slack time: minimizing the tardiness of products on average or per line 3 Weighed flow-time cost: minimizing the products time in the system Detailed scheduling enables manufacturers to operate with the just-in-time production strategy. This strategy reduces the inventory levels significantly and lowers the safety stock level. This is particularly important for manufacturers of high-end products with high production costs or holding costs. In the chemical industry, the inventory levels are a key issue to 221

Arash Yazdani and Tugrul Daim

control due to the difficulties associated with the storage of chemicals such as environmental and health hazards as well as due dates and quality of products. In order to be able to afford to delay in the start of production to keep the inventory level as minimum, accurate planning needs to be carried out to realistically plan the capacities as well as the uncertainties which may occur during production. Detailed scheduling is proved to have an influence on the production rate, tardiness, and inventory level (Bassett et al. 1997). A classic example of detailed scheduling may include the selection of jobs and resources when more than one job or resource is available (Thiesing et al. 2015). This problem is divided into two forms of selection prior to the execution of the tasks: 1 Resource selection 2 Job selection The resource selection decision is necessary when there are multiple resources available and one job to be executed; which resource should be the one to execute the job is the selection that needs to be made at this stage. The other problem is the selection of jobs when there is more than one job waiting for a resource to be available. These problems are addressed using different techniques that aim to optimize the results of these decisions and lead to more efficient schedules. These techniques may vary depending on which of the two problems are being answered, the job selection or resource selection, and can depend on the goal of the schedule. The selection of a resource, if the alternative resources are equally able to execute the jobs, can be affected by the setup times, the load of the resource, the number of the jobs waiting for the resource, reliability and risk dependencies, resource dedication, etc. For the selection of a job out of many, there are other expressions and techniques which may be applied. Some expressions help to maximize/minimize result of objective functions (cost functions, setup time required, least slack time, etc.) and some others are solely depended on the sequence of jobs such as first in first out (FIFO), last in first out (LIFO), and priority of the tasks. In a low-volume high-mix production, where orders are received at different times with different due dates or priorities, updating the detailed scheduling and optimizing the schedule is a tedious task, if not impossible. In this scenario, a re-scheduling is required every time a new order is received. Perhaps, MSS can play a role to solve this problem using simulation of scenarios in further times. MSS, which can be synchronized in parallel to real shop floor activities, could receive order information and run the predetermined objective functions and produce a new optimized detailed schedule for the shop floor based on the new data.

15.2.5  Process Mapping and Simplification Process mapping provides the techniques and methodologies to identify the current state of a process or system, also known as the “as-is” state. The recognition of the “as-is” state is necessary to analyze the system and to design a roadmap for the “to-be” state and the ways to realize it (Biazzo 2002). It can be considered as a management tool that helps to understand the processes, both management and manufacturing processes, and to apply improvements to design or acts like a critical link between design and the bottom-line performance. Process mapping and modeling a manufacturing process can be characterized as challenging and complex. Simplification is a solution to focus on particular questions to be answered about a system and narrow down the focus on those factors that matter the most. This 222

Capacity Planning and Feasibility Check

significantly reduces the study efforts and analysis time. However, simplification is considered as one of the basic activities in building any model (Salt 1993, Shannon 1998) but there are not many frameworks and techniques for general use. Properties of a viable model include its simplicity, flexibility, ease of use, and reliability. Nonetheless, its feasibility with respect to analysis time, resources, and available data determines its applicability (Robinson 2014). By considering these, simplification can help to solve more complex business and manufacturing problems by requiring fewer data in a shorter time. These benefits, which make simulations as feasible decision supports for design and study of processes, can be listed as follows: 1 Faster to develop and easier to communicate 2 Focus on the system elements that matter 3 Reduced cost of data collection and efforts on modeling (coding) Obviously, creating a model for simulation is not possible without simplification to some degree. There is no limit to the extent of activities and efforts in modeling the actual reality for the purpose of simulation. The reality includes the presence and interaction of many variables and choices which are not possible to be comprehensively simulated. However, simplification should be safeguarded by correct rules and assumptions to deliver satisfactory results with a satisfactory level of detail. Simplification is not an easy task and requires experts to come up with the simplest model that could represent the system in question. It requires suitable skills in modeling and simulation as well as relevant knowledge about the manufacturing or business process. These skills include realizing the benefits of simplification and the ability to identify the system components, and the ones which need to be simplified, and also validating the simplifications to ensure the reliability of the simplified model (van der Zee et al. 2018). Although simplification is not a very well-known topic and its techniques are mostly dependent on individual skills and experience and are problem-specific, some general methodologies can be outlined by finding common grounds between the techniques (van der Zee et al. 2018). Principally, these techniques can be divided into two categories as described: 1 Reducing the problem scope 1.1 Model objectives 1.2 Identifying system parts 2 Technical modification to represent parts in a simplified way Either one of the two ways can be chosen for model simplification. The first technique suggests reducing the scope of the problem to that part of the system where it needs to be diagnosed by picking the correct model objective or system parts. For instance, if the model objective is to minimize the waiting time of parts in the manufacturing line or identify the bottleneck there is no necessity for implementing cost functions. The second technique, technical modification to represent parts in a simplified way, suggests treating some system parts as a black box. This would mean to avoid adding complexities to the model by adding details to every part of the system which has a negligible effect on the simulation performance and quality of the final results. It is critically essential to remember that adding details and complexities can have diminishing effects on the model accuracy to some extent. It is possible to build accurate models by 223

Arash Yazdani and Tugrul Daim

adding necessary details at the beginning of the process since the model accuracy outweighs the efforts in collecting data and adding details. However, the slope of model accuracy with respect to the level of complexity declines by adding more details until some point after which it turns negative. It is natural to think that the details and complexities should help the accuracy of the models. But in fact there is a lack of data normally to support high levels of complexities and models may suffer due to lack of data and, sometimes, computation performance or accuracy. A good practice would be setting a desired model accuracy and collecting information to realize that. This means that the model should drive the data requirements and not the other way around. The emphasis should be on the decision or the problem in hand and not the system itself.

15.3 Methodology There are two approaches toward testing the mentioned hypotheses which are performed in sequence. The first approach includes a hierarchical decision-making (HDM) analysis to compare and select a capable platform for manufacturing systems simulation (MSS) purposes. And the next step includes testing the chosen platform by building manufacturing models and performing the simulations to see if the stated problems in this study can be answered through MSS. A simulation logic as a standard reference model for creating simulations is presented and its necessities are discussed. Finally, the case studies are presented and the scenarios are explained together with a description of the manufacturing configuration of the use case and detailed scheduling rules.

15.3.1  Hierarchical Decision Model A hierarchical decision model (HDM) quantitatively assesses the opinion of experts about the suitability of the actions in meeting the goals. This is done through pairwise comparison of components on a single layer and assigning weights according to their importance as a result of mathematical operations series on three matrices (Daim 2016). The number of layers depends on the logic of the problem and the type of criteria and attributes that exist underneath that problem. The HDM method is designed for quantifying the opinion of a single expert as well as many. When there is more than one judgment, different opinions can be analyzed and compared, and a consensus can be achieved by averaging the results. The strength of this method is that different experts are able to include their opinions about the criteria of an HDM layer according to their area of expertise. For example, if we divide the nature of decision-making from mission through objectives and goals to strategies and actions, those experts closer to management proficiencies may judge about the criteria in objective and goals layer and those experts with operational knowledge may judge different strategies and actions to meet the final mission of the decision. This methodology has been proved by various studies in technology standards development for IT and communication industries (Neshati 2014), strategic technology roadmapping (Daim et al. 2018), and policy development for transition frameworks (Abotah and Daim 2017). In order to start with the HDM model, it is necessary to define a final mission for the decision. The mission of this study is to find out the most suitable tool for manufacturing systems simulation. This mission contains perspectives and criteria that need to be assessed and each is associated with different options, as tools, to satisfy the mission. Four perspectives are considered to evaluate all aspects of a software selection for the mentioned mission. These perspectives are listed and described below: 224

Capacity Planning and Feasibility Check

1 2 3 4



Technological Organization Implementation Personal

These aspects are assumed to cover the most important criteria for the selection of software for simulation. These aspects are analyzed from a business perspective to choose software that is valuable and feasible for implementation on a client’s manufacturing and data management platform. Each aspect includes a number of sub-criteria which help to assess the suitability of the software candidates for MSS based on the weighted importance of each sub-criteria as illustrated in Table 15.2. A mutually exclusive and collectively exhaustive Table 15.2  H ierarchical decision model (HDM) architecture Mission

Perspectives

MSS software Technological

Organizational

Implementation

Personal

Sub-criteria

Nodes definition

Capacity planning Complexities

Enabling real capacity realization and planning to meet demand Ability to handle complexities (e.g. changeovers, workers skills, routing options) Risk analysis Perform risk analysis to estimate the probability of meeting production goals Scenario analysis Test scenarios to find the effect of changes on the system Schedule Ability to optimize the schedules for feasibility optimization and utilization purposes Collaboration Ability to optimize the schedules for feasibility and utilization purposes Confidentiality Ability to optimize the schedules for feasibility and utilization purposes Integration with Special authorization to specified users to access Mgmt. Sys. sensitive/protected data Reporting Download and export functionality to present and report plans and schedules Customizability Customizability of the software to meet specific project goals and configurations settings Ease of Ease of implementation at clients’ site for implementation specific projects Market Current market/clients’ knowledge about this knowledge technology The cost/benefit analysis for the software Price implementation performance ratio Scalability The scalability of the software to meet future expansions Skilled people at Availability of knowledgeable personnel to client’s site work with the software at client’s site Visualization Visualization of manufacturing processes and flow of material (2D/3D)

225

Arash Yazdani and Tugrul Daim

(MECE) approach was chosen in drawing up the perspectives and sub-criteria to avoid the conflict of decision elements. The technological aspect of the software is concerned with the capabilities and capacities of the software and built-in algorithms to execute the desired tasks. The organization aspect of the software deals with the possibility of the MSS software to be integrated and synchronized with other management systems such as ERP and MES. The implementation aspect assesses the feasibility and viability of the software to be implemented in an organization with a cost. Finally, the personal aspect considers the personal criteria of using the software and the physical and mental effect that it may have on the daily professional tasks of supply chain planners, managers, and shop floor operators. The assessment of MSS software was based on HDM methodology and with the opinions of supply chain experts in “xxx” company, one of the top firms in supply chain management providing solutions to their clients from Europe and North America. These experts have broad experience in the implementation of new ideas and technologies in clients’ sites and have hands-on experience with some of the candidate software for MSS. A summary of candidate software features and properties attributed to the sub-criteria of HDM is presented in Appendix 1, and the HDM layers are presented in Appendix 2. The candidate software packages are among advanced planning and scheduling (APS) software and discrete element simulation (DES). Although APS software packages, such as Kinaxis RapidResponse (RR-Kinaxis) and AnyLogistix, are not capable of simulating processes in a symbolic visualized manner, they are able to extract information and analyze abstract information to determine the performance of supply chain in tabular forms and with respected designated KPIs. This study focuses on the evaluation of the functionalities that these types of software provide and not only on the brand of the software from the same family. In the following, these candidate software packages are briefly introduced and their main functionalities are highlighted as claimed by their developers.

Kinaxis RapidResponse (RR) Kinaxis RR is a cloud-based supply chain management software package that is suitable for sales and operation planning (S&OP) and combines demand management with supply chain and production planning as well as scheduling for short-term and long-term planning horizons. It allows companies to plan concurrently and collaborate across all supply functions on one platform. RR enables an easier way of managing performance and predicting the turbulences in the supply due to uncertainties in one area of supply and the consecutive chain reactions. It is possible to define standard metrics and design dashboards for more focused management of the situation and the performance. Some of these metrics may be e.g. ending inventory, the estimated value of the inventory at the end of a period, or on-time delivery to request which is the historical record of the percentage of cases where order lines were available on or their due dates. RR can be integrated with other management services for data exchange and updating the planning process as well as allowing all parties in S&OP planning like sales and marketing, supply planners, operation, finance, and executives to take part and collaborate (Kinaxis® 2017).

AnyLogistix AnyLogistix is a special product of Anylogic which is specially designed for supply chain and logistics. The benefit that AnyLogistix offers, for solving supply chain problems, is the virtual representation of the supply chain and modeling different experiments with the proposed 226

Capacity Planning and Feasibility Check

solutions and find out the impact on the business, from financial and performance standpoints. The scope of this tool is an “end-end” supply chain analytics from distribution centers to production and procurement logistics. There are two built-in features within this too, optimization and simulation. The optimization technology of the tool effectively balances the resources and capacities with logistic and supply load using algorithms such as mixed-integer programming, heuristic approaches, and linear programming. The simulation power of this software is able to perform “what-if scenarios” to evaluate different solutions to problems such as supply failure and conserving performance metrics in the presence of risks. The dynamic simulation within AnyLogistix allows observing how the supply chain will perform over time, given uncertainties and real-world variabilities (Wilutis and Magilton 2017).

Simul8 Simul8 is a simulation platform that is used for building models and their simulation as well as sharing them online. It provides a visual and proactive approach to test, verify, and communicate the impact of changes and ideas on the performance of the processes. Improvement decisions need to be verified before implementation; these decisions may vary from, for example, increased throughput of a manufacturing line to solutions to meet increased product demand. Simul8 can be used as an evidence-based decision tool to clearly analyze the weaknesses and benefits of proposals. This can be done collaboratively across functions in real time. This is a strong tool to prove the visualized effect of ideas to stakeholders and other team members for easier understanding and conformity. The flexible simulation software has helped companies across a diverse range of industries, automobiles, aerospace, pharmaceutical, and logistics, to rapidly improve processes, increase efficiency, and reduce costs (SIMUL8 Studio Online Simulation Software 2020).

Simcad Pro Discrete event simulation (DES) tool, Simcad Pro, is a product of CreateASoft, Inc. that combines simulation and planning on one platform. The patented simulation environment of this software allows modeling, and simulates and optimizes operations in visualized forms. It has found applications in various industries such as automation, manufacturing, healthcare, logistics, and warehousing as well as the food and beverage industry. There are different possibilities that can be realized by the application of this tool in these industries. For example, in the healthcare industry, an accurate replica of a hospital or a healthcare system can be made to analyze the waiting time of patients and the traveling distance that doctors and nurses need to walk daily. Subsequently, different solutions for this problem can be collected and tested by using this tool in order to improve these factors and the overall effectiveness of the system. Simcad Pro uses dynamic simulations to test improvement opportunities in an immersive visualized way and improve the processes and schedule activities. The integration of this software with data systems (ERP, SAP, etc.) enables real-time tracking of products, predictive analytics, and prescriptive analytics for improvements and optimization (CreatASoft 2020).

Simio Simio is a DES simulation software package that allows planning and scheduling at the same time. Similar to other simulation tools, Simio simulates processes and systems using intelligent objects, a patented technique. Users can build flexible models and extend the Simio 227

Arash Yazdani and Tugrul Daim

model with custom configurations, data, and algorithms. Features such as “OptQuest” enable users to optimize models and the solutions to a problem using sophisticated built-in metaheuristics and upgrade from conducting “what-if ” scenarios to “what’s-best” scenarios. This tool is capable of capturing risks and reliability of objects and performing a probabilistic analysis to estimate the underlined risks associated with the schedule. Powerful data features allow import, storage, and customization of data across multiple tables for building models and performing simulations. Using the integration function, Simio can exchange data with the ERP system that is used for the model. This software offers a portal that allows users to check and verify the simulation and scheduling results online. There are other methods, as listed below, which have been used as optimization algorithms for a longer period of time. 1 Mixed-integer linear programming (MILP) 2 Constraint-based programing (CBP) Some of these techniques are built in the programming structure of some optimization software. However, these techniques were excluded from this study due to their limitation in detailed scheduling and applicability for large and complex problems in industrial scales (Bassett et al. 1997).

15.3.2  Simulation Logic Prior to running a simulation, a model of the behaving system under the area of scope should be built. This model defines the type, differences, and affinities of the factors (machines, men and material) that are present in the system. Simulation is known as one of the most visualized forms of communication (Mohora et al. 2009); however, it is one the least understood. A standard reference framework would be necessary to design the process of model creation for production and logistic activities in order to create more effective models in a shorter period of time. This framework would also serve as a communication tool between industries to share and discuss the characteristics of the model in an easier way. In designing a simulation model, some conceptual considerations such as model requirements, model development technique, model demonstration, and communication means should be made to help the process (Robinson 2006). Battista et al. (2011) have proposed a modeling structure to facilitate communication between model developers and serve as a common language. This structure is able to represent resources and management policies within the simulation and also help to simplify the implementation of simulation models (Battista et al. 2011). The process starts with defining the “logic layer” on the top of the hierarchy. This layer contains information about manufacturing setup and configurations, the process chart, management and business rules and policies, master production schedule (MPS), and bill of material (BoM) which need to be gathered. This layer is placed in the highest level of planning horizon axis which implies that these activities and information are performed and prepared much ahead of time. The extent of planning horizon highly depends on the type of business and the product life cycles but generally it can be considered from 6 to 12 months. The “communication layer” regulates the activity and interaction of resources on the execution layer. This acts as a communicator which can be in a form of schedule that is created based on the specifications of the logic layer. A business process model and notation (BPMN) can be used to construct this process in a standardized and simplified way (Battista et al. 2011). 228

Capacity Planning and Feasibility Check

In the “execution layer” the actual operation and interaction of production factors occur. It is a repetitive process that is regulated by the logic layer through plans and schedules. The whole layer consists of four elements (workers, machines, buffers, and transporters) that determine the output of the system as they interact. The definition of each one of these elements is crucial in building accurate models; their positions and technical features affect their interaction with each other in the simulation and their effectiveness. In comparison to the logic layer and communication, the execution layer has the highest level details that plan daily and hourly activities of the shop floor. The simulation is used to simulate the activities of the shop floor with respect to the information that is present in all of the layers to build the model. The degree of detail decreases in the higher layers of this hierarchy and the planning horizon expands in dimension. Obviously, the responsiveness to changes decreases by moving down the hierarchy layers. It is relatively easier and less expensive to make changes about the production plan and step in the logic layer with longer planning time spans than on the shop floor where plans are made hourly. Therefore, simulation of this layer should be carried out and analyzed carefully to check the viability and applicability of the ideas in the logic layer.

15.3.3  Case Studies After the assessment and selection of suitable technology for feasibility check of the master production schedule (MPS) and optimization of the production schedule, a case study is performed to simulate a real manufacturing process and discover the shown effectiveness of the tool. In fact, the feasibility check of the MPS gives the same information as the real capacity of the plant. This determines that if the assumed capacity in drafting the MPS was correct or incorrect with what deviations or probabilities. In schedule optimization, detailed scheduling decisions are implemented to test if the selected tool is capable of capturing details and complexities of manufacturing such as due dates, setup times, and other priorities in optimizing the schedule to satisfy the objective functions. In this study, the two distinct forms of manufacturing models, make-to-order (MTO) and make-to-stock (MTS), are differentiated from each other in making the simulation model. The reason is behind the aim of the study which is to analyze the capabilities of MSS in simulating demand-pull and demand-push strategies. Besides these case studies that check the capabilities of the MSS software, it will be tested to see if it is possible to conduct capacity planning and feasibility check of the MPS for the MTO cases with respect to demand variabilities and minimum inventory level. These two production models are unique in how the production plans are produced and how the priorities within the manufacturing processes are assigned to orders or production lines. In the case of MTS, demands are forecasted by sales and marketing teams within an organization and production schedules are generated accordingly to supply the demand. And the role production function is to replenish the inventory levels and keep the level between the safety stock level and the maximum assigned inventory capacity. While in MTO production, the production runs based on the confirmed or forecasted orders and it is assumed, in this study, that there is no safety stock needed due to production and sales of highly customized products. Therefore, the inventory only contains goods that are produced sometime before the delivery due dates. The performance metrics and KPIs differ in these two forms of production as well. For example, in production with push strategy, the manufacturing is concerned with low cost and efficient production system and low inventory levels and in the case of MTO, on time 229

Arash Yazdani and Tugrul Daim Table 15.3  M  TS and MTO characteristics and KPIs

Demand characteristic Challenges Prioritization KPIs

MTS

MTO

• Pull-driven supply chain

• Push-driven supply chain

• • • • • •

Accurate lead time measurements High service level Due dates (least slack time) Order priorities On time in full (OTIF) Customer order cycle time

• • • • • •

Low production costs Demand volatility Service level vs. inventory level Exotic parts vs. high runners Lost sales Inventory turnover ratio (ITR)

in full (OTIF) or delivered in full on time (DIFOT) play a more important role than production costs. These metrics are important to measure the obtained success by using MSS for planning and scheduling. A summary of these metrics and production characteristics that were considered in this study is listed in Table 15.3. The manufacturing setup that was considered to test the applicability of MSS was chosen from a real manufacturer of highly specialized electronic devices that operates on the MTO basis. Due to the intellectual property rights of the client and in order to focus on the feasibility and detailed scheduling capability of the MSS software, the manufacturing part is modeled around the production bottleneck. At some point in the production line where the production rate is limited to the process rate of the bottleneck, the parts pass the “body making” line and go to the “sensor assembly” and then “quality assurance (QA)” and “packaging” before being stored in the “finished goods (FG) inventory”. In this case, the processing time of sensor assembly machines is the longest and requires changeovers; therefore, three machines are considered to execute the task. The changeover logic includes a half an hour setup type for all sensor assembly lines every time the type of product changes. The studied MTO production was modeled to produce parts according to the number of orders received. There are ten different types of products designed to demonstrate the effect of setup time on the capacity and scheduling of the production. The total aggregate demand represents the sum of the demands for all ten products. Actual sales are used to estimate the average number of orders per month and it was plotted with a normal distribution, as shown in Figure 15.1, with a mean of 2,304 (quantity) and a standard deviation of 300. The share of each product in the total aggregate demand is calculated based on the actual order amounts for the top ten products. Due dates are also incorporated in the model to assess the capability of the production activities in meeting the number with respect to not only quantities but also due dates. The due dates are generated based on the conventional promise dates in this industry which is 14 days on average with a standard deviation of 8 days, as represented in Figure 15.2. These due dates are assigned to products as they enter the production and expected to be present in the finished goods inventory before the deadline reaches. In order to avoid having negative due dates due to the nature of normalization with the mentioned mean and standard deviation values, the due dates cannot be less than two days and any value less than two which is sampled from the histogram is considered to be two by the software. The detailed scheduling problem counts for two decisions that may occur as the parts move through processing resources, resource selection, and job selection. The resource selection 230

Capacity Planning and Feasibility Check 0,036 0,033 0,03 0,027

Probability

0,024 0,021 0,018 0,015 0,012 0,009 0,006 0,003 0 1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

3200

3400

Histogram based on 10,000 random samples

Order quantity

Figure 15.1  Total aggregated monthly demand distribution 0,036 0,033 0,03 0,027

Probability

0,024 0,021 0,018 0,015 0,012 0,009 0,006 0,003 0

–15

–10

–5

0

5

10

15

20

25

30

35

40

45

Histogram based on 10,000 random samples

Days

Figure 15.2  Due dates distribution (minimum 2 days, maximum 45 days)

decision is required as the parts leave the body making and need to be attached to a sensor, and the decision to be made here is that to which sensor assembly machine the parts should be sent to. Also, the job selection decision is made by each of the sensor assembly machines. In very often times during production, the sensor assembly machines have jobs waiting in the input buffer and a selection should be made to pick the next job out of the buffer. 231

Arash Yazdani and Tugrul Daim Table 15.4  D etailed scheduling decisions and optimization rules Resource selection

Job selection

Resource load Dedicated resources Optimized setup time

Least setup time Least slack time

In order to compare the optimization power of the software and its result with a predetermined schedule, three scenarios are performed. In one scenario the sensor assembly lines are dedicated to specific products. In the dedication of the sensor assemblies (servers), the share of the products in order quantities is considered to equally balance the load among lines in a discrete manner. Hence, the product orders are distributed across sensor assembly lines in the most equal way without considering the frequency of the changeovers. In the second scenario, the software is programmed to choose the server with the lowest load placed in its input buffer. The difference between this scenario and the first scenario where the servers were dedicated to certain products is that in the second scenario, the load balancing is done according to the current state of the system and not in a predetermined manner and it considers the effect of changeovers on the load as well. A summary of the mentioned scenarios is tabulated in Table 15.4. The third scenario is a fully automated scheduling by the software where the servers are selected in a way that the setup time is minimized. This is enabled by an algorithm that counts the number of product types in the input buffer of the servers and sends the parts to the input buffer where the maximum number of the respective part exists. A visual representation of this algorithm is attached in Appendix 4. By having more parts of the same type waiting for a resource, the total number of changeovers can be reduced. By using this algorithm it is possible to have all parts accumulated in the input buffer of only one resource due to a higher number of the same parts; therefore, a maximum limit for the parts that can enter an input buffer is considered. Through various trials and optimizations, the optimum number for the input buffer was found to be 12. The second detailed scheduling decision is related to the selection of the jobs from the input buffer by the servers. There are various ways that this selection could serve the objective function (Table 15.4). In one way, the total tardiness of the system can be reduced by considering the due dates that the products in work-in-progress (WIP) stage have. This is easily possible by selecting the least slack time as the dispatching rule of the servers. The least slack time is a scheduling rule that reduces the overall time left after a job is processed. The other dispatching rule that was tested in all scenarios is the least setup time. This rule simply reduces the changeover times and increases the processing time ratio of the resource. By selecting this rule, the server tries to dispatch jobs of the same type as processed earlier regardless of the due date or priority of the jobs waiting in the input buffer.

15.4  Results and Discussions The results of the hierarchical decision model (HDM) with the importance of the factors in the selection process as well as the implications of the selected tool for manufacturing systems simulation (MSS) are presented and discussed in this section. Modeling and simulation of a make-to-order (MTO) manufacturing line are performed and the capabilities of the selected tool in scheduling optimization and feasibility check of the production plan are quantitatively assessed. Besides, there are various other applications that can be possibly discovered 232

Capacity Planning and Feasibility Check

by having the right tool for MSS. Some of these applications such as production capacity measurement, a decision support tool for resource management, and enabling re-scheduling are explained in the following.

15.4.1  Hierarchical Decision Model The strengths and weaknesses analysis of the current planning and scheduling practices showed that there is a need to capture complexities of the manufacturing and make flexible schedules. The hierarchical decision model (HDM) was designed to assess the importance of these sub-criteria together with other perspectives that the selection of software would affect. The results of this assessment show that there are differences in the different perspectives of software. These perspectives are considered from the business point of view that aims to test the suitability of this technology to be implemented at clients’ facilities. The average result of the assessments from ten experts in this field shows that technological and implementation perspectives are equally the most important ones. This is followed by organization and person perspectives, later being far less important according to the experts’ judgments. The relative weights of these perspectives are given in Figure 15.3. This can be explained by the huge impact that technological advancements have on organizations. It is a key determinant of the capabilities of software, everything else is secondary. The implementation perspective focuses on the ability of the technology being successfully implemented. A suitable technology is only useful if it is put in the place correctly. Therefore, technology and implementations can be justified to be the most important aspects. The organizational perspective of the software, in this study, is more concerned about the integration ability of the tool with databases, management systems, and collaboration of people from different functions on one platform. The importance of this perspective rises from the synergy that this combination creates. Fast and accurate exchange of data, visibility throughout the whole supply chain, and autonomous planning and scheduling for the supply chain activities are few examples of the potential benefits from system integration. The least important perspective is the personal concerns around a technology selection. This is not as Technological

Organizational

Implementation

Capacity planning

Collaboration

Customizability

Scalability

Complexities

Confidentiality

Ease of implementation

Skilled people

0.38

Market knowledge

Visualization

Reporting & presentation

Price performance

0.29

0.23

0.21

Risk analysis 0.17

Scenario analysis 0.17

0.28

0.29

0.28

0.25

0.16

0.18

Integration

0.19

0.18

Personal 0.15

0.28

0.40

0.32

0.39

Schedule optimization 0.22

Figure 15.3  Hierarchical decision model perspectives and sub-criteria with relative weights

233

Arash Yazdani and Tugrul Daim

highly important as the other perspectives due to the ability of the personnel in learning a technology and flexibilities that a particular technology or software can provide to ease the learning and execution process. This is a state that can be easily tackled; therefore, it should not be a serious disqualifying matter. Within the technology perspective, the sub-criteria capacity planning and schedule optimization were judged to be the most and second most important respectively. Of course, the purpose of the manufacturing systems simulation (MSS) is the optimization of the supply plan and capacity planning to test the feasibility of the master production schedule (MPS). Therefore, these criteria play the most important role. The other objective of MSS is to capture the complexities of manufacturing such as changeovers, buffer sizes, and other dynamic variables. Since the complexities affect the results of planning and scheduling done by the software, it is considered to be the third important factor. The scenario analysis and risk analysis are the additional features that can be developed by software which effectively provide the earlier criteria. Among the four sub-criteria of the organizational perspective, integration with other management software is believed to be the key factor. This is followed by collaboration among functions, reporting and presentation features of the software and confidentiality aspects of the software to hide sensitive information from particular groups or individuals out of the trust circle. It is logical to think about integration with other systems and the collaboration of staff across the supply chain as the essential elements due to the massive opportunities that such a network creates. The assessment and acquisition of technology for a company are always evaluated by the benefits that the technology would offer against the acquisition costs. Therefore, cost and benefit analysis would be the main consideration that is taken by the owners of the businesses. Expectedly, this criterion is ranked to be the most relevant ones from the implementation perspective. Customizability, in the second rank, is defined as the possibility of implementation of software at different manufacturing sites to solve various problems. The market knowledge affects the opinion of the clients about the applicability of technology. If the brand, features and benefits that a viable technology is not known to host company, the chances of implementation of the technology are rather low. The market knowledge is also correlated with the ability of the personnel at the client’s site to work with the software. Therefore, it serves an influential role. The skills of the personnel in the clients’ site are believed to be the most significant criteria from personal perspectives. This affects the ability of the company to execute the functions and the way it can benefit from technology. The people’s resistance against the implementation of new technology can be crucially affected by the level of knowledge they have about the technology and how well they can interact with it. If they are certain about their skills with the technology, there will be less pressure on the personnel and eases the process. The visualization feature of a software package helps to make the communication between the parties and personnel easier. With visualization, it is possible to convey more information in a shorter time. Also, this helps the decision-making process easier by creating evidence-based arguments and convincing the stakeholders. In order to make a general comparison between the sub-criteria from all perspectives, the absolute weights of the items need to be used. This can be calculated by multiplying the relative weight of the sub-criteria with that of their respective perspective. The final values of this calculation, absolute weights, can be used to sort all sub-criteria according to their importance, as shown in Figure 15.4. These can be used later to compare the candidate technologies with respect to their extent in satisfying these criteria. The most prominent criterion is the price performance ratio that the software provides. This criterion estimates the benefits that software brings with regard to the concomitant 234

Capacity Planning and Feasibility Check 0.12

Absolute weigths

0.10 0.08 0.06 0.04 0.02 0.00

Figure 15.4  Absolute weights of the sub-criteria

costs. Also, this is a strong management accounting tool to assess the viability of ideas and technologies. It makes a strong sense to consider this criterion more than others as it summarizes all the benefits and costs into two numbers and makes the comparison easier. In this survey, this criterion is examined by the knowledge of experts about acquisition and implementation costs while having the benefits and suitability of a candidate technology in mind. Integration and collaboration are the next imperative criteria, and this enables system integration for higher visibility throughout the whole supply chain and information update by different functions in the chain. This criterion contributes to a significant synergy that can be realized through the integration of the systems. The advantages of this integration and collaboration are discussed in detail in Chapter 2.1. The sorted list of criteria continues with customizability, capacity planning, and schedule optimization which are expected to address some of the limitations of advanced planning and scheduling (APS) systems and serve the purpose of feasibility check of the production plan. According to the general opinion of experts, the scalability of the software is the least important in assessing a technology. This is a feature that differs among software providers and there are different costing methods depending on the number of licenses or users for an organization. As long as it is possible to ensure an effective collaboration between the functions, it is adequate enough to take benefit from the software regardless of the number of users who can work at the same from one computer per se. Confidentiality of the software is another less important factor. This is probably due to the dependence of this particular software on the whole security of the network. It is up to other technologies and techniques to secure the network against threats outside of the organization. The aim of the MSS technology within the boundaries of the organization is to increase visibility throughout the whole demand management, planning, production, and outbound logistics. Therefore, there is less emphasis in hiding information from some supply chain personnel. Also, the right to use a system or a software package should be granted by the organization to its personnel and this is true for almost all computer software within an organization. 235

Arash Yazdani and Tugrul Daim

In order to select the right technology, all of the five candidate technologies (software) are compared with each other against the mentioned 16 criteria. The results are presented in a multi-dimensional value curve (see Figure 15.5). Clearly, the DES software Simio outperforms the other software in many criteria such as capacity planning, schedule optimization, customizability, and the capability to account for complexities of production processes. There are some criteria in which Kinaxis RapidResponse is found better than Simio, the second most suitable option. These criteria are more related to the organization and the personal perspectives of this assessment. Logically, the importance and absolute weight of all these criteria are not the same as discussed earlier. There are some important criteria which have more influence on the final results based on the mathematical calculations in HDM methodology. Based on the calculated final scores of the candidate software, as presented in Table 15.5, the most suitable software for MSS purposes is Simio with a score of 0.29. Overall, there is a significant difference between the rate capacities of Simio and RapidResponse compared to the other three options. RapidResponse, from the APS family of software, falls after Simio with 0.23 points. The rest of the options are believed to have fewer capabilities in deriving the sub-criteria in this assessment. AnyLogistix is mostly suitable for planning and monitoring of logistics activities beyond the production facilities and other DES simulators such as Simul8 and Simcad Pro are able to simulate processes and improve the quality of decisions about the manufacturing configuration. However, Simul8 lacks the planning and scheduling feature. Therefore, it can be a good candidate if the schedule optimization sub-criteria is an essential requirement, which it is believed to be in this study. Both Simcad Pro and Simio have a scheduling feature; however, Simcad Pro has critical weaknesses in market knowledge, collaboration, and the skill of people at clients’ manufacturing facilities to work with this software (Figure 15.5). It is believed that it is simply less known than Simio and there

Visualization

Capacity planning 0.45

Complexities

0.4 0.35

Skilled people

Risk analysis

0.3 0.25 0.2

Scalability

Scenario analysis

0.15 0.1 0.05

Price performance ratio

Schedule optimization

0

Market knowledge

Collaboration

Ease of implementation

Confidentiality Integration with other Mgmt. Systems Reporting and presentation tools

Customizability

RapidResponse

AnyLogistix

Figure 15.5  Value curve of the candidate software

236

Simul8

Simcad Pro

Simio

Capacity Planning and Feasibility Check Table 15.5  Statistical final scores of the candidate software

Mean Minimum Maximum Std. deviation

RapidResponse

AnyLogistix

Simul8

Simcad Pro

Simio

0.23 0.16 0.40 0.07

0.16 0.12 0.19 0.02

0.15 0.09 0.20 0.04

0.17 0.14 0.20 0.02

0.29 0.09 0.45 0.10

is less learning material about this software. Therefore, Simio possesses a competitive edge against Simcad Pro in these criteria as well as schedule optimization due to its strong capability in the scheduling of the activities and its optimization power. Considering the first five most influential sub-criteria, namely price performance ratio, integration with management systems, customizability, capacity planning, and schedule optimization, Simio is believed to reach the expectations compared to the rest of the options in all these sub-criteria except collaboration. The ability of RapidResponse in providing a platform for collaboration of functions is highly rated. This software has some key advantages in the areas of collaboration, integration, and market knowledge and it is found that there are more skilled people to work this software in the industries. These are the features that can complement Simio, which highly satisfies the technical aspects, in organizational as well as implementation aspects. The value curve reflects that Simio cannot fully substitute the RapidResponse in an implementation process, because there is a match between the weaknesses of RapidResponse and the strengths of Simio and vice versa. Hence, Simio can be used to eliminate the weaknesses and add opportunities to improve the planning and scheduling process whose results will be used in APS software (RapidResponse).

15.4.2  MTO Case Study Results The Simio software was used to simulate the electronics manufacturing lines of a company that operates with the MTO production strategy (Appendix 4). The sales data of the MTO electronic manufacturer shows that there are slightly over 2,000 products delivered to about 2,000 customers in a year. This means that the products are highly customized to the special needs of customers. Therefore, a high number of changeovers are expected which results in a loss in processing time and decreases production efficiency. In order to represent the behavior of the systems in a visualized form, the simulation was carried out to the only model and simulate the manufacturing process around the bottleneck as illustrated in Figure 15.6. The bottleneck exists after the “BodyMaking” server, where the main body is built and around the “SensorAssembly” servers. At this stage, the sensors which are customized to the orders attach to the body and are sent to “QA_Packaging” for quality check and packaging. This model was used to study the feasibility check of the production schedule based on the real demand data and schedule optimization capability of Simio. The detailed scheduling decisions that were considered for the scenarios included a resource selection decision and a job selection decision. The resource selection decision occurs after the “BodyMaking” server before going to one of the three servers and the job selection occurs by all these three servers from their respective input buffer. In a trial experiment, a constant demand of 2,304 products per month was performed to find out if there is an effect in the selection of different dispatching rules. The job selection 237

Arash Yazdani and Tugrul Daim

SensorAssembly_1

ProductionLaunch

SensorAssembly_2

BodyMaking

QA_Packaging

Sink1

SensorAssembly_3

Figure 15.6  Simulated manufacturing processes using Simio

86.8%

Processing

12.9%

93.1%

Processing

6.7% Setup

Setup

Figure 15.7  R  esource status of “SensorAssembly” lines with constant demand (left: least slack time, right: least setup time)

rules were least slack time and least setup time and the resource selection rule was solely based on the equal load in both of the cases. Figure 15.7 shows the average status of the “SensorAssembly” servers. Evidently, the least setup time dispatching rule was able to reduce the setup time by about half (from 12.9% to 6.7%). This implies that the servers spend more time on value-adding activity and reduce the waste times which ultimately increases the productivity of the line at the same time. Therefore, it can be concluded that the software effectively predicts the setup time of the following sequences and picks the next job for processing in a way to minimize the setup time. The least slack time, which prioritizes the jobs based on due dates, is a safe job selection rule to ensure that the parts will be ready by the due dates. Effectively, the jobs will be selected regardless of the number of required changeovers and this increases the setup time and reduces productivity. In the second round of experiments and analysis, the demand (2,304) was varied with a standard deviation of 300 to represent the real-world demand variability and forecasting errors. This time, three different resource selection rules were applied to compare the performance of the manufacturing lines based on the obtained processing time and setup time and check if the variable demand can be answered within the randomized due dates. The scenarios are three forms of resource selection which are equal load, line dedication, and optimized load. And within each scenario, there are other two scenarios for job selection and those are the least setup time and least slack time. It was possible to run these scenarios in a very short time and see the effect of various scheduling modes on the productivity of the model. By looking at the status of the resources in Table 15.6, it can be observed that setup time took between 12.6% and 3.9% of the resources’ total time. The worst productivity with a setup time ratio of 12.6% was achieved by equally distributing the working load on the servers and this would increase the variety of 238

Capacity Planning and Feasibility Check Table15.6  Scenario analysis and Resources status Demand type Variable demand (2,304 ± sd.300)

Resources selection rule

Job selection rule

Processing

Setup

Equal load

Least setup time Least slack time Least setup time Least slack time Least setup time Least slack time

93.9% 87.2%

  5.9% 2,167 12.6% 2,172 Infeasible 11.9% 2,177   3.9% 2,113 12.1% 2,117

Dedicated lines Optimized load

88% 93.2% 85.3%

Production capacity

the products’ type; therefore, more changeovers are needed. Also, choosing the least slack time optimization further lowers productivity. A slightly better result was achieved by dedicating the sensor assembly lines to specific products according to their production quantity and choosing the least slack time. However, with this type of resource selection, if we try to optimize the setup time of the servers, logically, the setup time would be reduced but meeting the deadlines is not possible. The feasibility sign in Table 15.6 indicates that the inventory level for at least one product reached below zero and caused backorders. In fact, this is the feasibility check of the production plan. It is assumed that if the production fails to deliver according to the order quantity and due dates, the designed production plan cannot be feasible under the condition of the model. Therefore, this scenario is not a feasible scenario and is removed from the analysis. The best production performance was achieved by designing an algorithm as described in Chapter 3.3. The purpose of this algorithm is to assign the jobs to servers based on the logic of reducing the setup time. Regardless of the job selection rule, both scenarios with the optimization algorithm could produce the demanded quantities within deadlines. The best scenario with the minimum setup time is achieved by combining the optimization algorithm with the least setup time dispatching rule for the servers. In this scenario, the setup time is reduced to 3.9% and the processing time is 93.2%. However, the produced quantity is not the highest in this case. This does not indicate that the productivity is low, but it only implies that the servers were not occupied for a longer period of time compared to others. In other words, the reduction in setup time was added to processing time and the starving time when the resources were free during the simulation. The effect of lower production in the most efficient scenario can be seen in the lower inventory levels; nevertheless, the demand was met on time.

15.4.3  Applications of MSS in Industries It is essential to realize that the prediction power of the MSS is limited to the production boundaries and it differs from forecasts. Considering production as a part of the supply, it operates as a result of interaction between technologies, material, and labor. The activity level of these factors affects the capacity of the supply chain and, sometimes, it can be the limiting factor. The selected software, Simio, aims to predict and estimate the productivity of this interaction in a more accurate manner by considering their complexities and dynamic factors that exist in real life. The forecasting concept can be used for various aspects such as demand, economic, technological, weather, and demographic and targets the effect of political, environmental, social, technological, legal, and economic (PESTLE) factors on the supply chain. In other words, it aims to reduce the uncertainties about the future outcomes of actions and 239

Arash Yazdani and Tugrul Daim

can include productivity of the manufacturing function. Therefore, Simio is only a tool to help this type of forecast more accurate within a more general concept of forecasting. The long-term objective of MSS being implemented by the manufacturers and process designers is to have a digital twin of the factory. A replicate of the manufacturing processes can be synchronized by various management systems to visualize the real-time status of the jobs and processes. This system can be used by supply chain planners to monitor the daily manufacturing processes and monitor the completion of the jobs or tasks, as well as carry out capacity planning, and plan quickly in the event of incidents. With the help of MSS, it is possible to have a more current and accurate outlook over the production capacity and performance metrics. The APS systems treat the production metrics (e.g. production capacity) by calculating the average of past results. In reality, the production capacity varies over time due to various factors. The variation degree of this measure depends on the type of industry and complexity of the plant; however, there is always a difference between the actual performance and the average performance. Production metrics can be estimated more accurately through the MSS technique by considering the dynamic nature of the production. This would help manufacturers and planners to plan more accurately and promise realistic quantities and deadlines to customers. By realizing the true capacities of the production, the majority of the problems related to delays lost sales and high inventory levels can be resolved. Additionally, MSS can be used as a decision support tool to accurately measure the differences that can be caused by making changes to the manufacturing systems. By assuming that the products lead time is a function of the number of resources, MSS can effectively measure how the lead time can be improved by the addition of resources. The changes in the resources can occur in diverse ways, the resources and machines can be upgraded, workers can be trained, and the type of machinery can transform due to innovations as well as their numbers. All of these factors affect the proactivity and, ultimately, the lead times. Customarily, such decisions are made based on clear evidence about the improvements that can be achieved by making any of the mentioned changes and they are associated with costs, normally. The role of MSS is to measure this improvement and help the cost-benefit analysis by providing a correct measure for the benefits. MSS can be used in different planning zones for various purposes. In the liquid zone of planning, when the plans are made far advanced in time, it is easier to make drastic changes in the plan and alter the processing methods and their capacity. Within this stage, MSS can be used to check the feasibility of the proposed plan with respect to the designed capacity of the production plant and the effect of dynamic variables on the capacity. In the next planning zone which is performed in a shorter time horizon, MSS can be used to optimize the process plans which were proposed in the earlier stage (liquid). Similar techniques, as discussed in this study in Chapter 4.2, can be used for planning and scheduling optimization and guiding the flow of material and activity of the resources in a more efficient way. This optimization may also be applied to meet various objectives of production and KPIs such as production effectiveness, tardiness, inventory turnover, and resource productivity. A great value of MSS lies within the re-scheduling concept of manufacturing plans. As discussed earlier, conventional schedules can be skewed easily in the frozen zone where the schedules are already communicated to the manufacturing and changes in the schedule are not easily possible. Therefore, as soon as deviations occur in manufacturing they cannot be used any longer. The re-scheduling ability of the MSS helps to update the schedules and come up with an optimized schedule as the deviations from the plan occur or random events happen. In these cases, several what-if scenarios can be proposed and tested to see the best way to continue with production by considering the risks and reliability of the proposed scenarios. 240

Capacity Planning and Feasibility Check

15.5 Conclusion Deterministic approaches such as forward scheduling and backward scheduling have been used for planning and scheduling activities of the manufacturing sector for a long time. The problem with this method is the unpredictability of the dynamic variables and random events that occur during manufacturing and cause deviations between plan and execution. Until today, rough-cut capacity planning (RCCP) has been used for master scheduling and balancing the production plan with demand and procurement. The inaccuracies that may exist in the RCCP may cause financial losses that are resulted from missing deadlines, low service levels, lost sales, low inventory turnover, and inefficient production. In this study, manufacturing systems simulation (MSS) has been suggested to tackle the problems associated with the deterministic approaches and provide better visualization of the interaction between the production factors. With the help of MSS, it is aimed to model the manufacturing processes and simulate the performance of the model under the given conditions. This method of planning and optimized scheduling can capture the complexities of manufacturing, simulate the planned processes, and statistically determine the executability of the master production schedule (MPS). The suitability of the candidate software was assessed by the hierarchical decision model (HDM) method through gathering the opinions of supply chain management experts. This assessment included the evaluation of main perspectives and sub-criteria in selecting a software package for the purpose of MSS. The candidate software packages were among popular and powerful technologies from two groups of advanced planning and scheduling (APS) software and discrete element simulation (DES) software in order to compare the functionality of these two groups of software and the best tool for MSS implementation. The results of this survey showed that DES software packages are generally more suitable for MSS and the criteria that were designed to make the assessment, Simio being the best alternative. The Simio software was used to study the implications of its implementation in an MTO manufacturing plant. Based on the results of simulation and performing different scenarios, it can be claimed that Simio could effectively estimate the actual output of the manufacturing lines and optimize the schedules. In this experiment, the feasibility check of the MPS was examined based on the ability of the simulated model in answering the real monthly demand. Changeover times and due dates were the two types of dynamic variables within the simulation to assess the optimization power of the software in reducing the setup time and tardiness of the manufacturing in different scenarios. Out of eight scenarios that were simulated in a short period of time, it was easily possible to discover which scenario results in the best results in terms of the lowest setup time or the highest production capacity. Through possessing a digital replication of the manufacturing processes, different scenarios can be tested by implementing real-life variables and changes in the system and the effect of dynamics on the system’s performance. This methodology provides a better measure to balance the production capacity with load (demand) and check the feasibility of the MPS. Ideally, by having the key process parameters correctly implemented in the model, there is an opportunity to plan executable production schedules and manage resources effectively.

References Abotah, Remal, Daim, Tugrul U. (2017). Towards building a multi perspective policy development framework for transition into renewable energy. Sustainable Energy Technologies and Assessments 21, pp. 67–88. Akkermans, Henk A., Bogerd, Paul, Yücesan, Enver, van Wassenhove, Luk N. (2003a). The impact of ERP on supply chain management: Exploratory findings from a European Delphi study. European Journal of Operational Research 146 (2), pp. 284–301.

241

Arash Yazdani and Tugrul Daim Akkermans, Henk A., Bogerd, Paul, Yücesan, Enver, van Wassenhove, Luk N. (2003b). The impact of ERP on supply chain management: Exploratory findings from a European Delphi study. European Journal of Operational Research 146 (2), pp. 284–301. Analysis of the planning and scheduling functionality in APS systems (2001). Internet-based Enterprise Integration and Management: International Society for Optics and Photonics. Andras, Varga (Ed.) (2001). Discrete event simulation system. Proc. of the European Simulation Multiconference (ESM’2001). Baines, T. S., Harrison, D. K., Kay, John M., Hamblin, D. J. (1998). A consideration of modelling techniques that can be used to evaluate manufacturing strategies. The International Journal of Advanced Manufacturing Technology 14 (5), pp. 369–375. Bassett, Matthew H., Pekny, Joseph F., Reklaitis, Gintaras V. (1997). Using detailed scheduling to obtain realistic operating policies for a batch processing facility. Industrial & Engineering Chemistry Research 36 (5), pp. 1717–1726. Battista, Claudia, Giulia, Dello Stritto, Francesco, Giordano, Raffaele, Iannone, M. M., Schiraldi (Eds.) (2011). Manufacturing systems modelling and simulation software design: A reference model. International DAAAM Symposium. B. Katalinic (1). Biazzo, Stefano (2002). Process mapping techniques and organisational analysis. Business Process Management Journal. Cachon, Gérard P. (2012). What is interesting in operations management? Manufacturing & Service Operations Management 14 (2), pp. 166–169. Chung, Chen-Hua, Krajewski, Lee J. (1984). Planning horizons for master production scheduling. Journal of Operations Management 4 (4), pp. 389–406. Chung, Daeyoung, Lee, Kichang, Shin, Kitae, Park, Jinwoo (Eds.) (2000). An Algorithm for Job Shop Scheduling Problem with Due Date Constraints Considering Operation Subcontract. Cigolini, Roberto, Pero, Margherita, Rossi, Tommaso, Sianesi, Andrea (2014). Linking supply chain configuration to supply chain performance: A discrete event simulation model. Simulation Modelling Practice and Theory 40, pp. 1–11. CreatASoft (2020). Simcad Pro. Simulation Software Applications. Available online at https://www. createasoft.com/, checked on 1/27/2020. Daim, Tugrul U. (2016). Hierarchical decision modeling. Essays in Honor of Dundar F. Kocaoglu. Daim, Tugrul U., Yoon, Byung-Sung, Lindenberg, John, Grizzi, Robert, Estep, Judith, Oliver, Terry (2018). Strategic roadmapping of robotics technologies for the power industry: A multicriteria technology assessment. Technological Forecasting and Social Change 131, pp. 49–66. Geissbauer, R., Vedsø, J., Schrauf, S. (2016). A Strategist’s Guide to Industry 4.0: Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world. Strategy and Business. Gelders, Ludo, Kleindorfer, Paul R. (1974). Coordinating aggregate and detailed scheduling decisions in the one-machine job shop: Part I. Theory. Operations Research 22 (1), pp. 46–60. Helgeson, W. B., Birnie, Dunbar P. (1961). Assembly line balancing using the ranked positional weight technique. Journal of Industrial Engineering 12 (6), pp. 394–398. Henrich, Peter, Land, Martin, Gaalman, Gerard (2004). Exploring applicability of the workload control concept. International Journal of Production Economics 90 (2), pp. 187–198. Hopp, Wallace J., Spearman, Mark L. (2004). To pull or not to pull: What is the question? Manufacturing & Service Operations Management 6 (2), pp. 133–148. Kagermann, Henning, Wahlster, Wolfgang, Helbig, Johannes (2013). Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final Report of the Industrie 4 (0). Kinaxis® (2017). Kinaxis rapid response sales and operations planning. Available online at https:// www.kinaxis.com/sites/default/files/resources/Sales%20and%20Operations%20Planning%20 RapidResponse.pdf, checked on 1/27/2020. Kočiško, Marek, Novák-Marcinčin, Jozef, Baron, Petr, Dobránsky, Jozef (2012). Utilization of progressive simulation software for optimization of production systems in the area of small and medium companies. Tehnički vjesnik 19 (4), pp. 983–986. Kolisch, Rainer, Heß, Karsten (2000). Efficient methods for scheduling make-to-order assemblies under resource, assembly area and part availability constraints. International Journal of Production Research 38 (1), pp. 207–228.

242

Capacity Planning and Feasibility Check Master Planning of Resources. APICS (2004). 3rd ed. 4 volumes. McKay, Kenneth N., Wiers, Vincent C. S. (2003). Integrated decision support for planning, scheduling, and dispatching tasks in a focused factory. Computers in Industry 50 (1), pp. 5–14. Melchert, Eduardo Ruiz, Mesquita, Marco Aurélio de, Francischini, Paulino Graciano (Eds.) (2006). Lean manufacturing on make-to-order suppliers: A case study. 12th International Conference on Industrial Engineering and Operations Management (ICIEOM). Mohora, Cristina, Anania, Dorel, Calin, Oana Andreea (2009). Simulations strategies using Delmia quest. Annals of DAAAM & Proceedings. Neshati, Ramin (2014). Participation in technology standards development: A decision model for the information and communications technology industry. Oyarbide, A., Baines, T. S., Kay, J. M., Ladbrook, J. (2003). Manufacturing systems modelling using system dynamics: Forming a dedicated modelling tool. Journal of Advanced Manufacturing Systems 2 (01), pp. 71–87. Pfohl, Hans-Christian, Yahsi, Burak, Kurnaz, Tamer (2015). The impact of Industry 4.0 on the Supply Chain. Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg International Conference of Logistics (HICL) 20, pp. 31–58. Robinson, S. (2014. September). Simulation: The Practice of Model Development and Use. Macmillan Education UK. Google-Books-ID: Dtn0oAEACAAJ. Robinson, Stewart (Ed.) (2006). Conceptual modeling for simulation: Issues and research requirements. Proceedings of the 2006 Winter Simulation Conference. IEEE. Salt, John D. (Ed.) (1993). Simulation should be easy and fun! Proceedings of the 25th Conference on Winter Simulation. Shannon, Robert E. (Ed.) (1998). Introduction to the art and science of simulation. Winter Simulation Conference. Proceedings (Cat. No. 98CH36274). IEEE. SIMUL8 Studio Online Simulation Software (2020). Available online at https://www.simul8.com/ products/studio/, checked on 1/27/2020. Stevens, Graham C. (1990). Successful supply-chain management. Management Decision 28 (8). Suri, Rajan (1998). Quick Response Manufacturing: A Companywide Approach to Reducing Lead Times. Productivity Press. Thiesing, R. M., Pegden, C. D., Yilmaz, L., Chan, W. K. V., Moon, I., Roeder, T. M. K., Macal, C. (Eds.) (2015). Simio applications in scheduling. Proceedings of the Winter Simulation Conference. Tjahjono, Benny, Esplugues, C., Ares, Enrique, Pelaez, G. (2017). What does industry 4.0 mean to supply chain? Procedia Manufacturing 13, pp. 1175–1182. van der Zee, D.J, Tako, A., Robinson, S., Fishwick, P., Rose, O. (Eds.) (2018). Panel: Education on simulation model simplification–beyond rules of thumb. Winter Simulation Conference (WSC), December. IEEE. Ważna, Lilianna, Bach, Irena, Banaszak, Zbigniew (2006). An effects evaluation of ERP APS system implementation in the uncertain terms with using fuzzy modeling and inference. Applied Computer Science 2 (2), pp. 105–115. Wiers, Vincent C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control 13 (6), pp. 552–560. Wilutis, Mike, Magilton, Derek (2017). anyLogistix Intro Webinar. Available online at https://www. anylogistix.com/resources/videos/anylogistix-webinar-enhance-your-supply-chain-analytics/, checked on 1/27/2020. Wolstenholme, Eric F. (1990). System Enquiry: A System Dynamics Approach. John Wiley & Sons, Inc. Womack, James P., Jones, Daniel T., Roos, Daniel (1990). The Machine that Changed the World. Rawson Associates, New York, 323 p. Zupan, H., Herakovic, N. (2015). Production line balancing with discrete event simulation: A case study. IFAC-PapersOnLine 48 (3), pp. 2305–2311.

243

Presentation tools

Ease of implementation

Visualization Enabling Ind.4.0 Risk analysis Presentation tools Risk analysis

Graphical Modelling

Mgmt. Systems Integration

Monte Carlo Simulation

Presentation Tools

Stochastic Modelling

244

Scenario analysis

Schedule Optimization Enabling Ind.4.0 Capacity planning Planning & scheduling Planning & scheduling Planning & scheduling Capacity planning Planning & scheduling Planning & scheduling Planning & scheduling Planning & scheduling

Scenario Analysis

Schedule Optimization

Capacity Planning

Demand Planning

Inventory Management

Order Fulfilment

Sales & Operations Planning

Shipping Management

Supplier Management

Transportation Management

Warehouse Management

Customizability

Usability

Sources: [1] https://www.capterra.com; [2] https://www.kinaxis.com/en; [3] https://www.anylogistix.com; [4] https://www.simul8.com; [5] https://www.createsoft.com; [6] https://www.simio.com/index.php

Usability

Usability Planning and scheduling Usability

Capacity planning

MRP

Risk analysis

Customizability

Reporting

Usability

Accessibility (availability) Collaboration

Real-time monitoring

Risk analysis

Customizability

Flexibility

Sub-criteria attributes

Complexities

Simio Complexities

Simcad Pro

Dynamic Simulation

Simul8 Complexities

AnyLogistix

Agent-Based Modelling

RR

Direct Manipulation

Features\Technologies

Appendix 1: Software Comparison

Appendices

Arash Yazdani and Tugrul Daim

Capacity Planning and Feasibility Check

Appendix 2: HDM Structure

Manufacturing Systems Simulation (MSS) MSS as a product Perspectives

Organizational

Technological

Implementation

Personal

Sub-criteria

Technologies

RapidResponse

AnyLogistix

Simul8

Figure A15.1  HDM layers and connections

245

Simcad Pro

Simio

246

Set Node

SetNode9

False

Decide

Decide2

False

Decide

Decide2

False

Decide

Decide1

True

True

True

End

SetNode9 Set Node

SetNode9 Set Node

Found

Search

Search1 Original

End

End

Found

Search

Search1 Original Found

Search

Search1

Figure A15.2  Optimization algorithm for the resource selection problem

Begin

Output_ProductionPlan_Entered_2

Original

SetNode9 Set Node

False

Decide

Decide2

False

Decide

Decide3

False

Decide

Decide1

True

True

True

End

SetNode9 Set Node

SetNode9 Set Node

False

Decide

Decide2

SetNode9 Set Node

False

Decide

Decide2

False

Decide

Decide2

Appendix 3:  Detailed Scheduling Algorithm

True

True

True

End

End

SetNode9 Set Node

End

SetNode9 Set Node

Set Node

SetNode9

False

Decide

Decide2 True

End

End

End

SetNode9 Set Node

End

Arash Yazdani and Tugrul Daim

13/02/2020 10/02/2020 07/02/2020 04/02/2020 01/02/2020

Monthly Aggregate Demand

29/01/2020 26/01/2020 23/01/2020 20/01/2020 17/01/2020 11/01/2020 08/01/2020 05/01/2020

Date

14/01/2020

02/01/2020 30/12/2019 27/12/2019 24/12/2019 21/12/2019 15/12/2019 12/12/2019 09/12/2019 06/12/2019 03/12/2019 01/12/2019 28/11/2019 0

1000

2000

3000

25/11/2019

247

Product Mix

18/12/2019

Sensor Assembly 3

Figure A15.3  Simulation of manufacturing processes and states of the system by Simio

16/02/2020

Sensor Assembly 2

0

100

200

19/02/2020

Sensor Assembly 1

Manufacturing Process

FG_Inventory 300

6350

73 Total No. orders

Total Production (Qntt)

22/02/2020

Quantity

Processing Orders

Quantity

Appendix 4:  Simio Simulation Environment

A B C D E F G H I J

Capacity Planning and Feasibility Check

16 TECHNOLOGY ASSESSMENT AND SELECTION Nathasit Gerdsri and Pard Teekasap

16.1 Introduction The development of technological capabilities is essential; therefore, every firm must invest in order to compete. The skills to develop technology and manage technological change are referred to as technological capability. Technological capability is a firm’s ability in utilizing resources to cope with technological and industrial changes in order to create and improve its competitiveness (Bell and Pavitt, 1992; Garcia-Muina and Navas-Lopez, 2007; Jiang, 2001; Panda and Ramanathan, 1996; Sharif, 1995; TDRI, 1989). Even though the development of technological capability is required, an analytical approach to assess the level of technological capabilities and determine a strategic direction to improve the firm’s technological capabilities is limited. Therefore, this study presents the guideline for a firm to assess the current level of technological capabilities, determine the technological capability gaps, and determine the strategic areas needed for the future development. To develop the competitive strategy for improving a firm’s technological capabilities, it requires the clear picture of which strategic position a firm wants to be in the future. However, in many situations, the future picture is unclear because of the dynamics and complexity of the industry and business environment. One method to address uncertain situations and illustrate into a tangible picture is the scenario analysis. Scenario analysis provides the analytical representation of the future possibilities for a firm. From above-mentioned, the key contribution of this chapter is to present the guideline consisting of seven steps starting from determining technological capability dimensions, assessing a current level of technological capability in each dimension, developing strategic scenarios to address the future development, prioritizing the importance of each dimension according to the scenario, setting a desired level of technological capabilities in each scenario, measuring technological capability gaps, and proposing a technology development strategy to fill in the gaps. In this chapter, a case example of how the proposed guideline can be exercised is also demonstrated. The structure of this chapter begins with the literature review on technological capability and how it is important for the development of a firm. The review on assessment and measurement of technological capability is also presented. Later, the guideline with a seven-step 248

DOI: 10.4324/9781003046899-20

Technology Assessment and Selection

process is presented to determine the strategic areas for technological capabilities development. Finally, the case example is analyzed to demonstrate the use of the guideline.

16.2  Literature Review 16.2.1  Technological Capability There are many definitions of the technological capability based on the context and focus of each scholar. However, the key definition of technological capability is a set of abilities that an organization exercises its resources to manage technological and industrial changes in order to create and maintain its competitive advantage (Bell and Pavitt, 1992; Garcia-Muina and Navas-Lopez, 2007; Jiang, 2001; Panda and Ramanathan, 1996; Sharif, 1995; TDRI, 1989). Technological capability is essential because it shows the strength and weakness as well as the ability of a firm to manage itself through technical and technology change (Bell and Pavitt, 1995; Jiang, 2001; Panda and Ramanathan, 1996). With the improvement of technological capability, firms can utilize technology to create competitive advantage and gain benefits from the development of advanced technology such as lowering production cost, improving plant availability, increasing reliability, and creating new opportunity for existing and diversified products and service which result in a better operational and financial performance (Ariffin and Figueiredo, 2004; Bell and Pavitt, 1995; Figueiredo, 2002; Panda and Ramanathan, 1996; Rush et al., 2007). With strong technological capabilities, a firm can develop itself to become a technological self-reliance organization ( Jiang, 2001). Besides that, firms with a strong technological capability can also develop the knowledge network which enhances the technological capability of the industry and becomes a reinforcing force that further improve the technological capability of the firms (Dantas and Bell, 2009, 2011; Giuliani and Bell, 2005).

16.2.2  Technological Capability Assessment 16.2.2.1  Components of Technological Capability Technological capability can be categorized according to the activity of the firms (Lall, 1992). Each scholar groups the activities differently but most of them are similar to each other. For example, Bell and Pavitt (1995) categorized technological capability into facility user’s decision-making and control, project preparation and implementation, process and production organization, product development, linkage development, and capital goods. Facility user’s decision-making and control is the skills needed to search, evaluate, and select the technology or develop new production systems or components. Project preparation and implementation deals with project management, feasibility study, project scheduling, schedule crushing, equipment and plant procurement, and new employees recruitment and training. Process and production organization is the skills to improve plant layout, adjust process, schedule the operation and maintenance plan, and implement organizational changes. Product development is the skills required to adapt products according to market needs, improve product quality, and design new product. Developing linkages is the skills about the collaboration and technology transfer between firms and related industries such as suppliers, customers, and local institutions. Capital goods supply is the skills to improve and create new plant and machinery. 249

Nathasit Gerdsri and Pard Teekasap

Facility user’s decision-making and control can be further elaborated into nine components including change awareness, technology searching, core competency building, technology strategy development, technology assessment and selection, technology acquisition, technology implementation, absorption and operation, technology learning, and external linkage and incentive exploitation (Rush et al., 2007).

16.2.2.2  Measuring the Level of Technological Capabilities The level of technological capability in each dimension can be measured according to what a firm can do or through benchmarking with other local and international firms. The performance-based measurement is more popular. The performance-based measurement method divides the level of technological capability into three levels which are basic or low, intermediate or medium, and advanced or high (Bell and Pavitt, 1995; Lall, 1992; Panda and Ramanathan, 1996). The basic level of production capabilities is a level of capability to which a firm can operate existing production technology but a firm cannot make any change to technology that is used. For intermediate and advanced levels, a firm has capabilities to make contributions to more significant technological changes and create originality. Thailand Development Research Institute (TDRI) (1989) used the scale from 0 to 5. Level 5 means that a firm has technological capabilities comparable to leading international firms. Level 4 means that a firm’s capabilities are comparable to an average of international firms or leading local firms. Level 3 means that a firm’s capabilities are better than an average of local firms. With this level of capabilities, it allows a firm to compete well in domestic market. Level 2 is an average level of capabilities of local firms. Level 1 means that a firm’s capabilities are lower than the local average. Level 0 means that a firm has no capability or does not operate activities in the focused areas. Rush et al. (2007) measured using four scales based on the strength of each capability. The lowest scale is ‘Unaware’, ‘Reactive’, ‘Strategic’, and the highest scale is called ‘Creative’.

16.2.2.3  Determination of Technological Capability Gap To determine the technological capability gap, the current level of technological capabilities needs to be assessed and the desired level of technological capabilities should be targeted. The current level of technological capabilities can be measured by using two main approaches. The first approach is a qualitative method using questionnaire survey and interview which is applied by TDRI (1989). The second approach is a quantitative method using numerical data collection and benchmarking with the target level. This approach was applied by Panda and Ramanathan (1996), Jiang (2001), and Garcia-Muina and Navas-Lopez (2007). For a firm to set the desired level of technological capabilities, it can be benchmarked with the capabilities of advanced firms or the state-of-the-art firms ( Jiang, 2001; Panda and Ramanathan, 1996). However, this approach is not applicable if the measuring firm is already at the advanced level of technological capabilities. Besides, the benchmarking technique cannot be used if the strategic direction of the advanced firms is different from the measuring firms. To overcome these difficulties, the techniques like scenario analysis should be applied so that a firm’s vision can be reflected in the process of setting the target level of technological capabilities (Ringland, 2006). 250

Technology Assessment and Selection

16.2.3  Scenario Analysis Scenario analysis is a tool to illustrate the possible future and push the decision makers to think in advance of what need to be done when the specified scenario happens. Each scenario is the story of how each factor interacts with each other under certain conditions. Scenario analysis is beneficial when the uncertainty and risk are high (Gausemeier et al., 1998; Schoemaker, 1995). However, scenario analysis method has a limitation. Scenario analysis can deal well with predetermined and uncertain conditions, but not for an unknowable condition. A scenario with predetermined conditions is made through the assumption that the outcomes and probabilities are known. A scenario with uncertain conditions is the case that the outcomes are known but the probabilities are not. A scenario with unknowable conditions is the situation that both outcomes and probabilities are not known (Postma and Liebl, 2005). Scenario analysis has been used extensively to study the impact of one factor or factors to the overall situation in various environments. The examples of case studies applying scenario analysis include electricity market (Reneses and Centeno, 2008), agricultural industry (Groenendaal and Zagmutt, 2008), environmental issues (Karjalainen et al., 2003; Matsuoka et al., 1995; Neto et al., 2009), healthcare industry (Satangput et al., 2010; van Genugten et al., 2003), and technology forecasting (Lichtenthaler, 2005; Salo et al., 2003; Yoon and Park, 2005).

16.3  Guideline for Technological Capability Assessment with Scenario Analysis to Determine Technology Development Strategy 16.3.1  Guiding Steps The objective of this study is to introduce a guideline for assessing technological capability of a firm and then determining a firm’s technological development strategy. These steps are derived from an empirical study based on the fieldwork. The guideline as shown in F ­ igure 16.1 consists of seven steps: determining technological capability dimensions, assessing a current level of technological capability in each dimension, developing strategic scenarios to address

Step 1: Determining technological capability dimensions

Step 4: Prioritizing the importance of technological capabilities in each scenario

Step 2: Assessing the current technological capability

Step 5: Setting a desired level of technological capabilities in each scenario

Step 3: Developing strategic scenarios to illustrate the future possibilities

Step 6: Measuring technological capability gaps

Step 7: Proposing a development strategy to bridge the gaps

Figure 16.1  Guideline for strategic determination to fill technological capability gaps

251

Nathasit Gerdsri and Pard Teekasap

the future development, prioritizing the importance of each dimension according to the scenario, setting a desired level of technological capabilities in each scenario, measuring technological capability gaps, and proposing a development strategy to bridge the gaps.

16.3.1.1  Step 1: Determining Technological Capability Dimensions The technological capability dimensions can be grouped according to functional activities. To clearly determine the technological capability gap to develop the strategy, the technological capability dimension should reflect the organization structure and the actual process of the firms. Therefore, we elaborated the technological capability dimension developed by Bell and Pavitt (1995). The comparison of technological capability dimensions proposed by Bell and Pavitt and the one used in this study is shown in Table 16.1. The technological capability dimensions on decision-making, project management, and capital goods between the one proposed by Bell and Pavitt (1995) and used in this study are the same. However, the dimensions on product and process organization, product centered, and developing linkages used in this study are elaborated in more details to match with the characteristics and trends of the industry. For the product and process organization, this study elaborates it into operation and maintenance. The operation dimension refers to an ability to adjust or initiate new production process to maximize outcome. For the maintenance dimension, we measure the ability of firms to maintain and maximize productivity of the systems. The dimension on product centered is categorized into quality and product development. The quality dimension measures the ability of firms to control and improve the quality of product and process. The product development refers to the ability to research and develop new products to serve customers’ need. An expertise in product development can significantly enhance the production skill and the technology development capability (Lakemond et al., 2007). As a result, the product development capability is measured separately from the quality control capability. The dimension on linkage development is elaborated by the type of linking organizations including suppliers, customers, and research and technology organizations (RTOs). The linkage with suppliers and customers refers to the ability of a firm to collaborate with suppliers or customers in developing new products or processes, which can increase the technological competitiveness of the firms through increasing flexibility of the firms and improving Table 16.1  Comparison of technological capability dimension used in Bell and Pavitt’s model and the one used in this study Bell and Pavitt (1995)

This Guideline

Facility user’s decision-making and control Project preparation and implementation Product and process organization

• • • • • • • • • •

Product centered Developing linkages

Capital good supply

252

Investment decision Project management Operation Maintenance Quality Product development Linkage with suppliers Linkage with customers Linkage with RTOs Capital goods

Technology Assessment and Selection

the ability of the firms to handle the industry’s turbulence (Kyrki and Torkkeli, 2009). For the linkage with RTOs, technological capabilities are measured based on the ability of firms to collaborate the research activities for new product or process development.

16.3.1.2  Step 2: Assessing the Current Technological Capability Technological capability is divided into four levels based on Bell and Pavitt’s model. The description of the four levels – routine, basic, intermediate, and advanced – is shown in Table 16.2. The current level of technological capability needs to be assessed from primary data which can be collected from the interview and field visit. The collected data are assessed based on what a firm does instead of what a firm thinks it can do.

16.3.1.3  Step 3: Developing Strategic Scenarios to Illustrate the Future Possibilities Different scenarios are developed to depict the desired target for the future development of technological capabilities in each dimension. The scenarios should reflect the possible situations that a firm can be in the future presenting both optimistic and pessimistic cases.

16.3.1.4  Step 4: Prioritizing the Importance of Technological Capabilities in Each Scenario In each scenario, all technological capability dimensions shown in Table 16.1 are not equally important. Therefore, the relative importance of each dimension should be determined. The method of judgment quantification through pairwise comparison technique can be applied to measure the relative importance of each dimension. The inputs for pairwise comparisons are collected from the management of the firm and the field experts.

16.3.1.5  Step 5: Setting a Desired Level of Technological Capabilities in Each Scenario The desired level of the technological capabilities is the level that a firm should have in order to fulfill the future status as defined in the scenario. The determination of the desired level can be collected through questionnaire or interview with the management of the firm and the experts in the field. Table 16.2  B  road description for each technological capability level Routine Basic Intermediate Advance

The level that a company must have in order to operate the business. Without it, the business would not be in function. The level that a company can apply technologies to operate existing production but a company’s ability in making any change is limited. The level that a company is able to improve their existing technologies and also able to select and implement new technologies. The level that a company can develop new technologies and be able to transform into new product development at the level that leading international companies could in the specified industry.

Note: The numerical indicator is also assigned to each level of technological capability as follows: Routine = 1, Basic = 2, Intermediate = 3, Advance = 4.

253

Nathasit Gerdsri and Pard Teekasap

16.3.1.6  Step 6: Measuring Technological Capability Gaps The size of a technological capability gap in each dimension can be measured from the difference between the target level of technological capability and the current level and also taken the relative importance of each dimension into consideration. The current technological capability level, the relative importance, and target technological capability level of each dimension are obtained from step 2, step 4, and step 5, respectively. This method has been used as a core process to develop the business strategy of the firms (Wheelen and Hunger, 2007). The technological capability gap (TCij) for dimension i and scenario j can be expressed mathematically as follows:

(

)

TC   ij = w ij Xtij − Xc i ; i = 1,… , n,  j = 1,… , m

(1)

where i indicates the technological capability dimension ranging from 1 to n. j represents the scenario ranging from 1 to m. is the weighted technological capability gap between the desired level and the curTCij  rent level of the ith technological capability dimension under the jth scenario. is the relative importance of the ith technological capability dimension under the wij  jth scenario. is the target level of technological capability for the ith technological capability Xtij  dimension under the jth scenario. Xci is the current level of technological capability for the ith dimension.

16.3.1.7  Step 7: Proposing a Development Strategy to Bridge the Gaps The determination of the weighted technological capability gap (TCij) can be used for setting the priority area for strategic development. The wider gap presents that the current level of technological capabilities is far below the target level. Therefore, the technological capability dimensions with a wider gap require high priority for the development support.

16.3.2  Methodology and Data Collection The information used as the inputs for assessing the level of technological capabilities of any firm is collected from three channels: the firm’s factsheet, the structured interviews with the key management staffs, and the shop floor visit. Then, the group of five or six independent technology capability assessors representing industry, government, research institute, and academia work together by using those information to assess the current level of technology capabilities based on the framework and rubric as presented in Appendix A. The interview questions are developed according to the technological capability dimensions as explained in step 1. The interview aims to identify the level of technological capability that the firm can do in each dimension. Usually, three or four key managerial staffs who are responsible for the technological development of a firm were invited for the interview. Their positions range from vice president of business development, R&D director, product development manager, production manager, plant manager, etc. The shop floor visit was conducted after the interview to verify and check for consistency of the information given during the interview sessions. 254

Technology Assessment and Selection

To measure the technology capability gap and determine the strategic areas for future development as addressed in the proposed guideline in Figure 16.1, the information about the target level of technological capability and the relative importance of each technological capability dimension in achieving the desired target are also required. This information is collected based on the analysis and discussion between the group of the key management staffs and industry experts as discussed in steps 4 and 5.

16.4  Case Example: Demonstrating the Use of Proposed Guideline 16.4.1  Case Background The studied firm, which is called Alpha, was the firm producing automotive parts such as suspension and brake. This firm was a joint venture between a Thai firm and a Japanese firm. The key customers of Alpha were the major car assemblers such as Toyota. The key suppliers of Alpha were mainly the small-to-medium local firms. Automotive industry is one of the main industries in Thailand with a significant growth over the past two decades. In 2010, the vehicle production reached the record at 1.56 million units and almost 60% of this amount was exported. According to Thailand Automotive Institute, the production volume is expected to grow to two million units by 2015 which would bring Thailand to be on the top ten list of the largest auto producers in the world. Even though Thailand has an outstanding growth for automotive industry, the rise of automotive industry in China and India has recently become a significant threat. Thailand, China, and India currently pursue similar strategies to leverage its strengths on cost competitiveness, particularly for labor costs, in order to attract foreign investments. To compete, firms in Thai automotive industry must improve itself from being labor-intensive to technology-based manufacturing (Thailand Automotive Institute, 2005). Recently, the new supportive government policies have begun to be developed toward this strategic goal. To determine which technology development path should be followed, a clear technological capability assessment should be systematically conducted. The technological capability gaps between the current level and the desired level should be measured and prioritized.

16.4.2  Technological Capability Gap Analysis 16.4.2.1  Step 1: Determining Technological Capability Dimensions The framework for assessing the level of technological capability is structured according to the technology capability dimensions as shown in Table 16.1 and the classification of technological capability level fitted with the nature of firms in automotive industry is shown in Table 16.2. The detailed framework is shown in Appendix A.

16.4.2.2  Step 2: Assessing the Current Technological Capability The Alpha’s current level of technological capabilities was determined by interviewing with the management of the company for an hour and conducting a factory visit in order to gain the further details and check the accuracy of the interview. The interviewing team consists of the experts in the automotive field and technology management field from government and academic sectors. 255

Nathasit Gerdsri and Pard Teekasap

The results illustrated in Column 3 of Table 16.3 show that Alpha was capable in the areas of project management, operation, and quality as its technological capabilities were at the basic-to-intermediate level. Alpha used the advanced quality control method such as total quality management or just in time method to maintain the effectiveness of its operation. Alpha was also certified for the production quality standard such as ISO/TS 16949. When dealing with projects, Alpha was capable to conduct the feasibility study and prepare the project scheduling. Alpha could also be able to adjust some operating processes to reduce the bottlenecking problem. Alpha had limited capabilities in exploiting the linkage with research and technology organizations (RTOs) as indicated at the routine-basic level. Alpha utilized a linkage with RTOs only to support product testing and personal skill training. The detailed analysis capturing the roles of universities and research institutes in Thailand in supporting the local automotive industry is presented in another study (Intarakumnerd et al., 2012). Also the capabilities for making an investment decision were also limited because Alpha still could not search and evaluate the available technology by itself. Alpha needed the assistance from foreign partners. For the capabilities to deal with customers, Alpha could manage its sale activities but still lacked abilities to gain insight information from the customers. Moreover, Alpha could only make products according to the specifications in a blueprint given by customers. Alpha did not have an ability to give recommendations to customers on how to improve products.

16.4.2.3  Step 3: Developing Strategic Scenarios to Illustrate the Future Possibilities The strategic visions of how Thai automotive firms should develop themselves to continue maintaining their competiveness are referred to the results of the 2010 study on Technology Capability Assessment of Automotive Industry in Thailand. This study was jointly organized between National Science and Technology Development Agency and Thailand Automotive Institute. In their study, a group of experts representing industry, government, and academia who have influence and engagement in the development of automotive industry in Thailand were called in to discuss the possibilities on strategic visions. As per their discussion, two strategic visions were defined. The first scenario was to establish the collaboration with key players in China and India while the second scenario focused on becoming a leading producer for any specific module. The development of industry cluster, particularly for automotive industry, has been pushed forward by industrial policy makers in Thailand. So, the visions as described in the two possible scenarios can also be considered as the strategic targets for the future development of Alpha. Scenario A is a close collaboration with Chinese and Indian manufacturers. In this scenario, a sub-compact car is the term describing a just basic small car with very low price. This type of a car has been the emerging trend in the global automotive industry after Tata Motors announced its Nano car model with the price less than $4,000 in 2008. This car segment is expected to grow and gain around 70% of the total market share in the near future. The key players in this sector are the manufacturers from China and India due to the effectiveness in their labor costs and high domestic demands. For automotive industry in Thailand to respond to this emerging trend, the Thai government can consider to issue the policies supporting Thai automotive part makers to actively collaborate with Chinese and Indian manufacturers in developing and manufacturing this type of small car. Compared to Chinese and Indian manufacturers, Thai manufacturers have advantages in skilled labors 256

Technology Assessment and Selection

and craftsmanship. Therefore, Thai manufacturers can provide quality-sensitive and high value-added automobile parts. Scenario B is a technological leadership development for specific types of module sets. With an intense global competition in automotive industry, many carmakers decided to shift their roles from being automotive part manufacturers and assemblers to focus only being assemblers of automotive part modules provided by their suppliers. According to this new value chain setting, automotive part makers in Thailand can take this opportunity to leverage their current skills and capabilities which currently are accepted in quality by the global standard. To deal with the opportunity, the Thai government can consider issuing the policies to improve technological leadership in product and production quality for specific types of automotive module sets with high-value added such as exterior part module. Through this strategic direction, Thai automotive manufacturers will gain reputation and trust from global customers. Consequentially, they would be invited as a potential strategic partner for major carmakers in the design and supply of those specific automotive part modules.

16.4.2.4  Step 4: Prioritizing the Importance of Technological Capabilities in Each Scenario To determine the relative importance of technological capability dimensions in each scenario, the management of the firm with a group of experts involving automotive industry from industrial, academic, and government sectors were asked to complete the judgmental quantification through pairwise comparison for all dimensions. The results are shown in Figure 16.2. Figure 16.2 shows the comparison of the relative importance of each dimension in each scenario. It indicates that the relative importance of quality control and linkage with customers for Scenario A is higher than that of Scenario B. On the other hand, the investment decision and capital goods are more important for Scenario B than Scenario A.

16.4.2.5  Step 5: Setting a Desired Level of Technological Capabilities in Each Scenario The rating of targeted level of technological capability in all dimensions for each scenario was determined through the focus group discussion of the management of Alpha and the automotive industry experts. The results are shown in Column 4 of Table 16.3 and Table 16.4 for Scenarios A and B, respectively.

16.4.2.6  Step 6: Measuring Technological Capability Gaps The technological capability gap for each technological capability dimension is measured as the difference between the desired level (Column 4) and the current capability level ­(Column 3). Then, the weighted technological capability gap is determined as the product between the relative importance of each technological capability dimension (Column 2) and the gap. The analysis results of Scenarios A and B are shown in Table 16.3 and Table 16.4 consecutively. Figure 16.3 shows the list of technological capability areas targeted for supporting the future development of Alpha according to the requirements in each scenario. The list is prioritized according to the size of weighted technological capability gaps. The larger gap represents the higher priority that dimension would be. 257

Nathasit Gerdsri and Pard Teekasap 100% Capital Goods, 0.06

Capital Goods, 0.08

90%

Linkage with RTO, 0.07

80%

Linkage with Customer, 0.12

Linkage with Customer, 0.09

70%

Linkage with Supplier, 0.09

Linkage with Supplier, 0.1

60%

Product Development, 0.14

Product Development, 0.15

Quality, 0.17

Quality, 0.13

Linkage with RTO, 0.07

50%

40% Maintenance, 0.09 Maintenance, 0.1

30%

Operation, 0.09 Operation, 0.09

20%

Project Management, 0.1

Project Management, 0.08

10%

Investment Decision, 0.09

Investment Decision, 0.11

Collaboration with China and India

Become a module producers

0%

Figure 16.2  Relative importance of each dimension for each scenario Table 16.3  Technological capability index for Scenario A – close collaboration with China and India manufacturers Technological Gap

① Technological capability dimension Investment decision Project management Operation Maintenance Quality Product development Linkage with supplier Linkage with customer Linkage with RTO Capital goods

②

③

④

Gap

Weighted Gap

Weight

Current

Target

④− ③

②x ( ④− ③)

0.09 0.08 0.09 0.10 0.17 0.14 0.09 0.12 0.07 0.06

1.40 2.17 1.97 1.90 2.20 1.47 1.83 1.47 1.23 1.57

3.50 3.00 3.50 3.50 4.00 3.50 3.50 3.50 3.00 3.00

2.10 0.83 1.53 1.60 1.80 2.03 1.67 2.03 1.77 1.43

0.19 0.06 0.13 0.16 0.30 0.28 0.15 0.24 0.13 0.09

258

Technology Assessment and Selection Table 16.4  T  echnological capability index for Scenario B – technological leadership development for specific types of module sets Technological Gap

① Technological capability dimension Investment decision Project management Operation Maintenance Quality Product development Linkage with supplier Linkage with customer Linkage with RTO Capital goods

②

③

④

Gap

Weighted Gap

Weight

Current

Target

④− ③

② × ( ④− ③)

0.11 0.10 0.09 0.09 0.13 0.15 0.10 0.09 0.07 0.08

1.40 2.17 1.97 1.90 2.20 1.47 1.83 1.47 1.23 1.57

3.67 3.00 3.33 3.67 3.67 3.67 3.67 3.67 2.67 3.00

2.27 0.83 1.37 1.77 1.47 2.20 1.83 2.20 1.43 1.43

0.25 0.08 0.13 0.16 0.18 0.33 0.18 0.20 0.09 0.11

16.4.2.7  Step 7: Proposing the Strategy to Bridge the Gaps The technological capability dimensions on which Alpha should focus in order to cooperate with low cost manufacturers in China and India as described in Scenario A were quality and product development, indicated by the largest weighted gaps. With the focus on these two areas, it would allow Alpha to be in a world-leading position and able to set new quality standards. In addition, Alpha would be able to improve product qualifications and perform R&D with customers to develop new products. However, if the Thai automotive industry aimed to be leading producers of any module set as addressed in Scenario B, Alpha needed to focus the improvement on product development and investment decision. With this focus, Alpha would be able to develop new products with customers and develop the R&D strategy to support the firm’s business strategy. After the technological capability gaps were assessed, the management of Alpha who were responsible for the strategic development were gathered to discuss about the development of strategy in order to effectively fill in the gaps. In Scenario A, Alpha should focus its direction on quality-sensitive parts. Alpha should be able to develop products that were reliable with minimal costs. In Scenario B, Alpha needs to be specialized in particular parts. Alpha should be able to co-design the parts with the customers and design the production process as well as the required machine by itself.

16.4.3 Discussion The results from applying the guideline to determine the strategic development for the Thai automotive firm show that Alpha still has limited technological capability. Alpha could be able to meet the quality standard in manufacturing auto parts according to the blueprint or recommendation by carmakers while the company has limited capability to make any modification on part design and development. This finding is in line with the findings in other literatures showing that firms in emerging economies have limited internal technological capability and can only imitate the existing technology (Bell and Figueiredo, 2012; Figueiredo and Brito, 2012). With two scenarios, establishing the close collaboration with China and India manufacturers and becoming the specific module producers, it guides the firm’s top management to 259

Nathasit Gerdsri and Pard Teekasap

Priority Ranking Collaboration with China and India

Become a module producers 10

Proj. Management

9

Capital Goods

Proj. Management Linkage with RTOs

8

Capital Goods

Operation

7

Linkage with RTOs

Operation

6

Linkage with Suppliers

Maintenance

5

Maintenance

4

Investment Decision Linkage with Customers

0.4

Linkage with Suppliers Linkage with Customers

3

Prod. Development

2

Quality

1

0.3

Quality

0.2

0.1

0

Investment Decision Prod. Development

0.1

0.2

0.3

0.4

Figure 16.3  C  omparison of the technological capability areas between two scenarios ranked according to the size of weighted gap in each dimension

decide which strategic direction a firm should pursue and also which areas of technology capability need to be developed in order to achieve the strategic target. If the goal is to collaborate with China and India manufacturers, Alpha should focus on the quality-concerned and high value-added parts which is the advantage that Alpha had over China and India manufacturers. This result is supported by the finding from Lin and Wu (2011) showing that the automotive firms in China still have limited quality management skills compared to its neighbor. If the direction is to be a leading producer for any specific module, Alpha needs to develop its skills in product development with the active investment decision so that Alpha can establish and maintain the strategic partnership with carmakers in order to co-design auto parts with them. Alpha also needs to be proactive in developing processes and machines supporting the development of the future generations of auto parts.

16.5 Conclusions This study introduces a guideline to assess the technological capability gaps and determine a technology development strategy in order to improve the level of technological capability of a firm by using scenario analysis methodology. The guideline consists of seven steps which are determining technological capability dimensions, assessing a current level of technological capability in each dimension, developing strategic scenarios to address the possibilities for the future development, prioritizing the importance of each dimension according to the scenario, setting a desired level of technological capabilities in each scenario, measuring technological capability gaps, and proposing a development direction to bridge the gaps. The guideline was applied to the Thai automotive firm as a case example. By linking the technological capability assessment with the scenario planning, a firm can prioritize the importance of each aspect and develop the detailed technology strategy pinpointing the essential dimensions that need to be developed in order to gain competitive 260

Technology Assessment and Selection

advantage in the future. This advantage of applying scenario analysis to the technological capability assessment can improve the depth of technological capability analysis which is required for detailed technology strategy development.

16.6 Managerial Implication This guideline is useful for the executives of any firm or policy makers who are responsible for the industrial development. For any firm, the management team can use this guideline to assist in the strategic formulation process. This approach can also help in bridging the business strategy with the technology strategy as this approach illustrates what the firms should develop in order to achieve the desired target for the future development. For any government agency, policy makers can also apply this guideline to assess the technological capability at the industry level. The assessment would be based on the collective results of technological capability assessment of widely selected players in that particular industry. With the determination of technological capability gap at the industry level, the government policies or incentives can be issued to assist firms in executing their strategy. For example, if most of firms in the industry need to bridge the technological capability gap in the area of quality enhancement, the government policy should assist firms by providing proper infrastructure and training programs for quality improvement.

Bibliography Ariffin, N. and Figueiredo, P.N. (2004) ‘Internationalization of innovative capabilities: Counter-evidence from the electronics industry in Malaysia and Brazil’, Oxford Development Studies, Vol. 32, No. 4, pp. 559–583. Bell, M. and Figueiredo, P.N. (2012) ‘Building innovative capabilities in latecomer emerging market firms: Some key issues’, in Amann, E. and Cantwell, J. (Eds.), Innovative Firms in Emerging Market Countries, Oxford University Press, Oxford, pp. 24–112. Bell, M. and Pavitt, K. (1992) ‘Accumulating technological capability in developing countries’, The World Bank Research Observer, pp. 257–281. Bell, M. and Pavitt, K. (1995) ‘The development of technological capabilities’, in Haque, I.U. (Ed.), Trade, Technology, and International Competitiveness, The International Bank for Reconstruction and Development/The World Bank, Washington, D.C., pp. 69–101. Dantas, E. and Bell, M. (2009) ‘Latecomer firms and the emergence and development of knowledge networks: The case of Petrobras in Brazil’, Research Policy, Vol. 38, No. 5, pp. 829–844. Dantas, E. and Bell, M. (2011) ‘The co-evolution of firm-centered knowledge networks and capabilities in late industrializing countries: The case of Petrobras in the offshore oil innovation system in Brazil’, World Development, Vol. 39, No. 9, pp. 1570–1591. Figueiredo, P.N. (2002) ‘Does technological learning pay off ? Inter-firm differences in technological capability-accumulation paths and operational performance improvement’, Research Policy, Vol. 31, No. 1, pp. 73–94. Figueiredo, P.N. and Brito, K.N. (2012) ‘MNE subsidiaries’ innovation capability building and learning in emerging economies: Firm level evidence from the ICT industry in Brazil’, International Journal of Innovation and Learning, Vol. 11, No. 1, pp. 12–43. Garcia-Muina, F.E. and Navas-Lopez, J.E. (2007) ‘Explaining and measuring success in new business: The effect of technological capabilities on firm results’, Technovation, Vol. 27, No. 1/2, pp. 30–46. Gausemeier, J., Fink, A. and Schlake, O. (1998) ‘Scenario management: An approach to develop future potentials’, Technological Forecasting and Social Change, Vol. 59, No. 2, pp. 111–130. Giuliani, E. and Bell, M. (2005) ‘The micro-determinants of meso-level learning and innovation: Evidence from a Chilean wine cluster’, Research Policy, Vol. 34, No. 1, pp. 47–68. Groenendaal, H. and Zagmutt, F.J. (2008) ‘Scenario analysis of changes in consumption of dairy products caused by a hypothetical causal link between Mycobacterium avium subspecies paratuberculosis and Crohn’s disease’, Journal of Dairy Science, Vol. 91, No. 8, pp. 3245–3258.

261

Nathasit Gerdsri and Pard Teekasap Intarakumnerd, P., Gerdsri, N. and Teekasap, P. (2012) ‘The roles of external knowledge sources in Thailand’s automotive industry’, Asian Journal of Technology Innovation, Vol. 20, No. S1, pp. 85–97. Jiang, W. (2001) ‘Technological capability assessment as a strategic tool: Cases of Chinese manufacturing firms’, Portland International Conference on Management of Engineering and Technology, PICMET, Portland, Oregon. Karjalainen, T., Pussinen, A., Liski, J., Nabuurs, G.-J., Eggers, T., Lapveteläinen, T. and Kaipainen, T. (2003) ‘Scenario analysis of the impacts of forest management and climate change on the European forest sector carbon budget’, Forest Policy and Economics, Vol. 5, No. 2, pp. 141–155. Kyrki, A. and Torkkeli, M.T. (2009) ‘Subcontracting product development: Creating competitiveness through networking’, International Journal of Technology Intelligence and Planning, Vol. 5, No. 3, pp. 241–257. Lakemond, N., Johansson, G., Magnusson, T. and Safsten, K. (2007) ‘Interfaces between technology development, product development and production: Critical factors and a conceptual model’, International Journal of Technology Intelligence and Planning, Vol. 3, No. 4, pp. 317–330. Lall, S. (1992) ‘Technological capabilities and industrialization’, World Development, Vol. 20, No. 2, pp. 165–186. Lichtenthaler, E. (2005) ‘The choice of technology intelligence methods in multinationals: Towards a contingency approach’, International Journal of Technology Management Vol. 32, No. 3–4, pp. 388–407. Lin, X. and Wu, G. (2011) ‘Technological capability building in the automotive industry: Comparing China with South Korea’, Asia Pacific and Globalization Review, Vol. 1, No. 1, pp. 1–15. Matsuoka, Y., Kainuma, M. and Morita, T. (1995) ‘Scenario analysis of global warming using the Asian Pacific Integrated Model (AIM)’, Energy Policy, Vol. 23, No. 4–5, pp. 357–371. Neto, B., Kroeze, C., Hordijk, L., Costa, C. and Pulles, T. (2009) ‘Strategies to reduce the environmental impact of an aluminium pressure die casting plant: A scenario analysis’, Journal of Environmental Management, Vol. 90, No. 2, pp. 815–830. Panda, H. and Ramanathan, K. (1996) ‘Technological capability assessment of a firm in the electricity sector’, Technovation, Vol. 16, No. 10, pp. 561–588. Postma, T.J.B.M. and Liebl, F. (2005) ‘How to improve scenario analysis as a strategic management tool?’, Technological Forecasting and Social Change, Vol. 72, No. 2, pp. 161–173. Reneses, J. and Centeno, E. (2008) ‘Impact of the Kyoto Protocol on the Iberian electricity market: A scenario analysis’, Energy Policy, Vol. 36, No. 7, pp. 2376–2384. Ringland, G. (2006) Scenario Planning: Managing for the Future. John Wiley & Sons Ltd, West Sussex. Rush, H., Bessant, J. and Hobday, M. (2007) ‘Assessing the technological capabilities of firms: Developing a policy tool’, R&D Management, Vol. 37, No. 3, pp. 221–236. Salo, A., Gustafsson, T. and Ramanathan, R. (2003) ‘Multicritera methods for technology foresight’, Journal of Forecasting, Vol. 22, No. 2/3, pp. 235–255. Satangput, P., Gerdsri, N. and Damrongchai, N. (2010) ‘Scenario analysis for identifying the development areas of future technologies to combat emerging infectious diseases: APEC efforts’, 5th IEEE International Conference on Management of Innovation and Technology, Singapore. Schoemaker, P.J.H. (1995) ‘Scenario planning: A tool for strategic thinking’, Sloan Management Review, Vol. 36, No. 2, pp. 25–40. Sharif, N. (1995) The Evolution of Technology Management Studies: Techno-economics to Techno-metrics. Asian Institute of Technology, Bangkok. TDRI (1989) The development of Thailand’s technological capability in industry, Thailand Development Research Institute (TDRI), Bangkok, Thailand. Thailand Automotive Institute (2005) 2005 Annual Report, Thailand Automotive Institute, Bangkok, Thailand. van Genugten, M.L.L., Heijnen, M.-L.A. and Jager, J.C. (2003) ‘Pandemic influenza and healthcare demand in the Netherlands: Scenario analysis’, Emerging Infectious Diseases, Vol. 9, No. 5, pp. 531–538. Wheelen, T.L. and Hunger, D.L. (2007) Strategic Management and Business Policy, 11th Edition. Prentice Hall, New Jersey. Yoon, B. and Park, Y. (2005) ‘A systematic approach for identifying technology opportunities: Keyword-based morphology analysis’, Technological Forecasting and Social Change, Vol. 72, No. 2, pp. 145–160.

262

Technology Assessment and Selection Appendix A:  Detailed framework of technological capability assessment for automotive industry 1. Routine Investment decision

Abilities to make a decision for technology investment on a turnkey basis and be able to negotiate on price Project Abilities to develop management procurement plan and manage projects based on recommendations from vendors

2. Basic

3. Intermediate

4. Advance

Abilities to search for a new technology and be able to evaluate the potential of that technology

Abilities to align a technology with the corporate vision

Abilities to develop an R&D strategy to support the firm’s strategy

Abilities to conduct a Abilities to feasibility study and prepare project project scheduling staffs through recruiting and training activities. Also, be able to manage project under uncertainties and changing environment Abilities to adjust Abilities to adjust some parts of the the production operation process and process to optimize production schedule the operation efficiency and conduct failure mode and effects analysis

Abilities to arrange project portfolio and manage multiple projects at the same time

Abilities to design a new production process by focusing on lean manufacturing and use computer-aided design tools, e.g. CATIA to assist R&D works Abilities to perform Abilities to Maintenance Abilities to fix non- Abilities to conduct the total productive implement the electronic parts the preventive maintenance through productive maintenance the collaboration maintenance with other departments Abilities to maintain Abilities to Abilities to use the Quality Abilities to a position as the continuously advance quality comply with ISO world leader in improve the standards for quality control methods, e.g. statistical process production quality setting new quality management standard control, TQC/TQM, systems such as zero defect, Kanban/ ISO9000 series JIT, and ERP and be able to comply with the production quality standards such as ISO/TS 16949 Operation

Abilities to operate machines and equipment based on vendors’ recommendations

(Continued)

263

Nathasit Gerdsri and Pard Teekasap 1. Routine Product Abilities to produce development products according to a blueprint and specifications given by customers

2. Basic

3. Intermediate

4. Advance

Abilities to create a product blueprint and specifications according to the customers’ concept and be able to modify products to meet the customers’ needs in each segment

Abilities to perform the basic product design for manufacturing (DFM) certified for the international standards. Be able to improve the product qualifications, e.g. using a lower cost material with the same specification Abilities to transfer technology to suppliers to improve their raw materials, tools, and equipment

Abilities to perform R&D to develop new products with customers and also be able to recommend new products that comply with the nontariff trade barrier Abilities to work with suppliers to develop new raw materials, tools, and equipment

Abilities to search and acquire new information from suppliers to support investment decisions in developing new raw materials, tools. and equipment Abilities to Abilities to deliver products that meet the continuously analyze customers’ requirement the market and customer demands under the price and as well as search delivery schedule and acquire new agreement information from the customers

Linkage Abilities to obtain with supplier raw materials and technologies that meet the requirements in time and within the budget

Linkage with customer

Linkage with RTO

Capital goods

Abilities to transfer technology to customers to improve customers’ product and/or process. Be able to effectively communicate with customers by utilizing electronic data interchange Abilities to work Abilities to work with RTOs to with RTOs to improve a product and redesign a product and production process production process

Abilities to access to RTOs for technical assistance such as training, testing, data mining, and also networking with other companies Abilities to reproduce Abilities to modify some parts of tools and some simple parts of machines that a firm has tools and machines

264

Abilities to work with customers in developing new products

Abilities to work with RTOs in developing a new product and production process Abilities to Abilities to improve accurately the design and design and specification of set the existing tools and machines to optimize specification efficiency. Be able to of new tools develop new tools and and machines. Also be able to machines add automatic features

PART 4

Technology Development and Transfer

17 REGIONAL INNOVATIVE INTENSITY IN AN EMERGING ECONOMY Analyzing China’s Provincial Regions Kenneth G. Huang 17.1 Introduction Innovative capacity refers to the capability of a country or a region, both as a political and economic entity, to produce and commercialize a flow of novel technologies over the long term (Furman et al., 2002). Drawing upon and integrating theoretical perspectives on ideas-driven endogenous growth theory (Romer, 1990), cluster-based theory of industrial competitive advantages (Porter, 1990), and research on national innovation systems (Nelson, 1993), previous studies had developed a national innovative capacity framework that allows the comparison of the production of innovative outputs among countries (Furman et al., 2002; Hu and Mathews, 2008; Lundvall et al., 2002). Usually focusing on developed countries that have more uniform within-country innovation infrastructure, environment, and institutions, this national-level conceptual framework is subject to limitations in examining large, emerging economies that have highly diverse industrial and institutional development across sub-national regions such as states or provinces ( Jia et al., 2019; Li, 2009; Liu and White, 2001). To overcome some of these challenges, a regional innovation system framework has been proposed to explore the underlying drivers of innovation performance and disparity at the regional level (Asheim and Isaksen, 1997; Cooke et al., 1997). However, prior studies had not clearly delineated the rationales of choosing specific conceptual components in the categories of innovation infrastructure, environment, and linkages which contribute to regional innovative capacity building in emerging economies such as China, compared with those in the developed Western economies. Moreover, the innovative capacity framework, which was developed based on mature Western economies, implies that the major components of this framework can apply equally well to account for the variance in innovative capacity across different regions in the same country. However, in transitional economies, a component might play differential roles across regions (or clusters of regions) within the same country. Thus, the component may explain regional innovative capacity to different extents, depending on variations in the concentration of innovative activities or absorptive capacity in a region, and their relationships to the particular component in innovative capacity building.

DOI: 10.4324/9781003046899-22

267

Kenneth G. Huang

To address this gap, we develop a conceptual framework that draws from and builds upon research on regional innovation systems by integrating insights from localized knowledge spillover and concentration of innovative activities, and regional absorptive capacity, which help explain the differential effects of major drivers on the production of innovative output in developed and developing regions. We postulate that more developed regions can enjoy greater localized knowledge spillover due to the concentration of innovative activities and actors, and experience greater regional absorptive capacity due to the prevalence of ­k nowledge-generating and -mediating organizations, association among these organizations, and systemic capabilities in the region. Greater localized knowledge spillover and regional absorptive capacity could accentuate the positive effects of different drivers of regional innovative intensity. We use the world’s largest emerging economy, China, as the empirical context for this study. Due to differences in the levels of developments among provincial-level regions in China, this context provides a suitable opportunity to examine the differential effects of key components on innovative intensity across regions (or clusters of regions). Drawing upon the regional innovation system framework, we provide a systematic, large-scale analysis of the key components and investigate the salience of these components in driving China’s innovation intensity across regions at different levels of developments. This study contributes to the literature on regional innovative capacity framework by integrating theoretical perspectives from localized knowledge spillover and regional absorptive capacity for innovation in an emerging economy. It advances our conceptual understanding of regional innovative capacity building in the context of China, and whether and how certain factors are more salient in developed regions than in developing ones. For policy and decision makers in technology and innovation, this study provides a detailed and timely assessment of the roles played by key components – concentration of scientific and technical talents, technological specialization, R&D spending, government technology and funding support programs, and foreign direct investment (FDI) – in building China’s indigenous innovative capacity in developed and developing provincial regions. An understanding of these roles and associated policies implemented at the provincial level is important as China continues to enhance its technology development and innovative capacity to drive economic growth, move up the value chain, and accelerate the development of the vast Western provinces (State Council of China, 2006).

17.2  Background and Setting: The Development of Modern Technology in China The People’s Republic of China, the world’s largest emerging economy, has made substantial progress in its economy, industrial transformation, and technological development over the past decades. However, socio-economic and technological developments across its provincial-level regions are not homogeneous ( Johnson and Liu, 2011; Sun, 2000). The broader Eastern regions, which consist of Eastern and Central provinces, are among the most developed in China. By contrast, Western provincial regions are largely underdeveloped (Fan et al., 2011). The innovation infrastructure, cluster-specific innovation environment, and institutional linkages are less established in Western provinces. This provides an important and suitable opportunity to investigate the differential roles played by key components in the innovative intensity framework across provincial-level regions. To enable China to “enter the ranks of innovative countries by 2020” and become “a global scientific power by mid-century” (State Council of China, 2006), the State Council 268

Analyzing China’s Provincial Regions

of China formulated “China’s National Medium- to Long-Term Plan for the Development of Science and Technology (2006 to 2020)” in 2006. The national plan emphasizes “zizhu chuangxin” or “indigenous innovation” and technological sovereignty and aims to reduce reliance on foreign technologies. The program also involves a substantial increase in government investments and incentives for R&D and key technology initiatives with commercial applications, including the development of “frontier technologies” such as biotechnology, genetics and life sciences, information and communication technology, advanced materials, structural technology, and nanotechnology. Despite these ambitious targets and the rapid development of technology and innovation in recent years (e.g., for a comprehensive review, see Johnson and Weiss, 2011), China’s innovation system faces several major issues. These issues include the adequate supply of qualified scientists and engineers, the effectiveness of R&D spending, the importance of regional technological specialization, the usefulness of major government technology and funding support programs, and the contribution of FDI to the regional development of innovative capacity. These pertinent issues question the salience of the drivers of China’s regional innovative intensity, especially across regions at different levels of development. Using the regional innovation system framework in the context of China, we develop theory and hypotheses that will predict the differential roles played by key components in shaping innovative intensity in Chinese regions at different stages of development.

17.3  Theory and Hypotheses Development 17.3.1  Localized Knowledge Spillover and Concentration of Innovative Activities and Actors 17.3.1.1  The Concentration of Scientists and Engineers The production of innovation is often localized and region dependent, and thus cannot be well predicted by the more general national innovation system frameworks (Braczyk et al., 1998; Cooke et al., 1997). While knowledge is a non-rivalrous public asset in the production of innovation that can generate positive spillover effects, knowledge spillovers are geographically constrained (Almeida and Kogut, 1999; Jaffe et al., 1993; Rosenkopf and Almeida, 2003; Stuart and Sorenson, 2003). Knowledge flow, exchange, and transfer occur more easily among collocated actors because the efficient transfer of tacit and complex knowledge across organizational boundaries generally requires close, intensive interaction and faceto-face contact that can be facilitated by proximity (Eapen, 2012; Jain and Huang, 2022; Laursen et al., 2012). Such “sticky” knowledge and localized learning in a region underscore the regional innovation system concept. Collaboration and interaction, such as those between industrial enterprises and universities located in close proximity, can facilitate learning (Hicks et al., 1996; Katz, 1994). There are also significant spillovers from university and industrial research and innovations at the regional level such as within the state (ACS et al., 1991; Jaffe, 1989; for a comprehensive review, see Salter and Martin, 2001). As a major component of the innovation infrastructure in the regional innovation system framework, the availability of scientific and technical talents in a region can assert a positive impact on the production of innovations (Furman et al., 2002; Hu and Mathews, 2008). When there is a higher concentration of scientific and technical talents in a particular region, the flow and exchange of ideas through interaction and physical contact tend to increase. 269

Kenneth G. Huang

This facilitates positive knowledge spillovers in the production of innovations by these scientists and engineers. As an example, in the developed coastal provincial region of Guangdong, a well-established and tightly knit community of scientists and engineers from local enterprises, research institutions, and universities facilitates the flow and exchange of ideas and collaborations among these scientific and technical talents, and generate positive spillover to other local communities and organizations. A high concentration of 41 universities is observed in the province, including three leading national universities, namely, South China University of Technology, Sun YatSen University, and Jinan University (Ministry of Education of China, 2013). Guangdong has also placed an emphasis on strategic industrial cluster developments in electronics and information technology, and petrochemical industries. In 2004, 2, 307 enterprises in Guangdong were recognized as new- and high-technology enterprises, while 3,961 enterprises were producing new- and high-technology products in the previously mentioned industries (Greater Pearl River Delta Business Council, 2006). The advanced scientific and technological development and high concentration of industrial clusters (in Shenzhen, Zhuhai, Jiangmen, Foshan, Dongguan, and Guangzhou) further support innovation and facilitate collaboration and knowledge exchange among scientists and engineers from local enterprises and universities. As a result, positive knowledge spillovers increase. Therefore, the concentration of available scientific and technical talents is further enhanced by the tightly integrated and well-developed regional innovation system, which enables and accentuates the positive effect of scientific and technical expertise on the production of innovative outputs. By contrast, in many developing Western provinces in China, such as Ningxia, industrial and technical clusters in the region remain relatively underdeveloped. Furthermore, comprehensive universities and research institutions in the region are few and are located far apart. For example, only six universities are based in Ningxia (Ministry of Education of China, 2013). These conditions impede the flow and exchange of ideas through close interaction and physical contact and restrict local knowledge spillovers. It follows that the positive effects of available scientists and engineers on the intensity of innovation development in these regions become less salient. Therefore, we predict Hypothesis 1. The positive effect of the concentration of available scientists and engineers on the intensity of production of innovative outputs is more salient in developed than in developing regions in China.

17.3.1.2  Specialization in Technology Sectors Further to the concentration of scientists and engineers, the specialization of a region in specific technology sectors can also play an important role for regional innovative capacity development (Archibugi and Pianta, 1992; Bell and Pavitt, 1993). A specialization in particular technology sectors allows regional economies that have access to specific resources, processes, or technical talents in the sectors to extend their advantages and to reduce the costs of production in these sectors. Thus, regional specialization in specific technology sectors enables a region to concentrate its development in those industrial sectors and create the spatial agglomeration of industrial sectors, in which more value-added technological and innovative activities of a sector will be concentrated in a geographic region (Chapman, 2013). Furthermore, a high degree of specialization helps a region attract multinational enterprises (MNEs) with particular technological capabilities (Archibugi and Pianta, 1992), which can reinforce the underlying cluster-specific innovation environment in the region. 270

Analyzing China’s Provincial Regions

Following similar logic, industrial agglomeration through technological specialization produces positive externalities by facilitating the transfer of tacit knowledge and enhancing the benefits derived from knowledge spillovers in a particular region. Furthermore, developed regions with greater competencies in specific industrial sectors can generally derive more benefits from specialization in those sectors through a high concentration of industries and organizations that produce higher quality innovative outputs and generate positive knowledge spillovers (Mahmood and Singh, 2003). In China, such regional specialization is facilitated by and works in tandem with the coordinated policy goals of the provincial and central governments, such as through the establishment of technology clusters or high-technology industry development zones in specific high-technology industries. These technology clusters enhance the industrial capabilities in these sectors, foster economic development, and increase the overall competitiveness of the regions by taking advantage of the well-developed innovation clusters and the positive knowledge spillovers. Compared with the developed regions, many developing Western provinces have less access to sector-specific resources, processes, or technical talents. These regions lack the strong sector-specific technological capabilities, clusters, or complementary assets to take advantage of the knowledge spillover from technological specialization. Until their regional innovation infrastructure and cluster-specific environment become more established, they are less able to maximize the benefits associated with technological specialization. Therefore, we hypothesize Hypothesis 2. The positive effect of increasing technological specialization on the intensity of production of innovative outputs is more salient in developed than in developing regions in China.

17.3.2  Absorptive Capacity of Regions 17.3.2.1  The Role of R&D Spending The capability of a region to produce innovations is also strongly influenced by the region’s absorptive capacity (Mukherji and Silberman, 2013). Absorptive capacity is defined as “the ability to recognize the value of new information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal, 1990, p. 128). The cumulativeness of absorptive capacity suggests that organizations with higher level of absorptive capacity can better identify, acquire, and assimilate external knowledge, thus allowing such organizations to better stimulate and develop innovative outcomes (Zahra and George, 2002). Absorptive capacity has also been theorized and applied at the regional level (Cantwell and Iammarino, 2003; Doloreux and Parto, 2005; Roper and Love, 2006). Regional absorptive capacity can be defined as “the ability of a region to recognize, evaluate, assimilate, and implement new technological knowledge and associated practices commercially” (Dahlman and Nelson, 1995; Mukherji and Silberman, 2013; Roper and Love, 2006). Regional absorptive capacity is influenced by more than the individual absorptive capacity of the organizations in the region. Along with the presence of other knowledge-generating or mediating organizations in the region, the association among these organizational actors and wider systemic capabilities can affect the level of regional absorptive capacity (Cooke and Morgan, 1998). For example, knowledge is absorbed more easily in regions with a larger knowledge stock and a higher level of regional R&D capacity, which could then stimulate and yield more innovative outputs. 271

Kenneth G. Huang

A region’s R&D spending contributes not only to the production of innovations but also to the building of its absorptive capacity (Cohen and Levinthal, 1990; Mukherji and Silberman, 2013). The more a region invests in R&D activities, the more it can fully appreciate, acquire, assimilate, and exploit external knowledge to produce more innovations. Previous studies have established the importance of R&D spending on the production of innovation (Furman and Hayes, 2004; Hu and Mathews, 2005, 2008). These studies of innovative capacity suggest that R&D expenditures have a positive effect on the production of innovative outputs such as patents (Hu and Jefferson, 2009). Following the conceptual logic of regional absorptive capacity, R&D spending is considered more effective in enhancing access to technological frontier and producing further innovations if the region already has a more advanced technological and industrial development, a larger stock of prior technological knowledge, and hence more capacity for R&D and innovation. Therefore, we predict Hypothesis 3. The positive effect of R&D spending on the intensity of production of innovative outputs is more salient in developed than in developing regions in China.

17.3.2.2  Government Technology and Funding Support Programs In addition to R&D spending, which enhances absorptive capacity and innovations, the Chinese government can contribute to the production of innovation and the improvement in regional absorptive capacity through the implementation of major science and technology (S&T) development initiatives and funding support programs in different provincial regions. These important programs (including subsidies), such as the “Torch program”, “Sparkle program”, and “S&T achievements spreading program”, 1 play a key role in connecting and reinforcing the relationships among key components under common innovation infrastructure, such as scientific and technical talents, as well as those under cluster-specific innovation environment, such as specialization in specific technology sectors. The initiatives provide not only the necessary funding but also the expertise and direction by which to translate innovations fostered by the innovation infrastructure into innovative outputs within a regional cluster environment. They also provide the interfaces and linkages for enhancing absorptive capacity across these components in the region. Following the logic of regional absorptive capacity, the aforementioned major technology and funding support programs would have more salient effects on the production of innovative outputs in developed regions with a higher regional absorptive capacity, which are capable of better recognizing, evaluating, assimilating, and exploiting external knowledge, as compared with less developed rural regions of China. Thus, we predict Hypothesis 4. The positive effect of provincial-based technology development and funding programs on the intensity of production of innovative outputs is more salient in developed than in developing regions in China.

17.3.2.3  The Direct and Indirect Roles of FDI Another critical component that can significantly contribute to innovative capacity building in the region is FDI. Due to the large scale and scope of FDI in emerging economies like China, it could directly and indirectly enhance regional innovations in the following ways. First, foreign MNEs and the innovations from their R&D efforts directly and positively 272

Analyzing China’s Provincial Regions

contribute to regional innovative outputs. In the context of China, foreign MNEs and their subsidiaries are gradually moving from low-cost manufacturing focus toward the supply of higher-value technological products. This suggests that their R&D efforts are moving from laboratories that merely support (through product or process adaptation) the effective localization of well-established products to locally integrated laboratories that produce knowledge as well as provide advice and research assistance to other parts of the MNE (Pearce, 1999). This migration of R&D efforts and roles of the MNE laboratories can help reinforce local research and innovative capabilities as well as clusters of technological activities ­(Athreye & Cantwell, 2007). Second, MNEs can also have direct positive impact on regional innovative capacity through advanced practices and innovation management experiences. These practices allow for greater efficiency in innovation through coordinated and high-quality d­ ecision-making, ­ irole, superior planning, R&D management, and better governance structures (Aghion and T 1994; Bessant et al., 1996). MNEs can also transfer effective managerial know-how to local firms and organizations. Third, FDI can indirectly benefit the regional innovative capacity by creating positive spillover effects to the domestic firms and organizations. In the case of China, labor mobility has been historically restricted under the central plan regime due to restrictions on registered permanent residence at the provincial level. Such limitation is reinforced by the sharing of similar convention, culture, and norms by scientific and technical personnel in the same provincial region (Chua et al., 2019). In this case, the local firms stand to gain the most benefits from MNEs in the same region. Moreover, through FDI in the region, these MNEs or foreign-owned entities, located in close proximity with local Chinese firms, can help create and foster co-innovation efforts with the latter, especially in relation to those efforts requiring intensive interaction (Eapen, 2012; Laursen et al., 2012) and in the transfer of tacit and complex knowledge, which is difficult to articulate or express in codified language (Polanyi 1966). Geographic proximity also suggests that local firms and organizations have the opportunity to observe and imitate MNEs in the same region through a demonstration effect (Blomstrom and Kokko, 1998). This is particularly useful given the productivity advantages of MNEs over domestic firms in developing countries (Girma et al., 2001; Li, 2009). More importantly, the direct benefits (i.e., contribution to innovation and transfer of know-how) and indirect benefits (i.e., knowledge spillovers) derived from FDI may be conditional upon and strongly influenced by the overall level of absorptive capacity and complementary assets of a region. In other words, a region with higher absorptive capacity and greater complementary assets can benefit more from the activities of FDI. This suggests that when a region has less established industrial bases and a low level of local innovation infrastructure and clusters environment, it might lack the necessary absorptive capacity and complementary assets to derive full benefits from FDI. Thus, it is important for a region to attain a reasonable level of development in its innovation infrastructure, clusters, and capability to conduct R&D (hence higher regional absorptive capacity) before domestic firms in the region can benefit from FDI-generated externalities. When a region has strong industrial infrastructure, clusters, and R&D capability, which suggest a higher overall level of absorptive capacity and complementary assets, FDI would have a greater positive impact on the production of innovations. Therefore, we arrive at our prediction below: Hypothesis 5. The positive effect of FDI on the intensity of production of innovative outputs is more salient in developed than in developing regions in China. 273

Kenneth G. Huang

17.4  Methodology 17.4.1  Empirical Approach We base our empirical approach on the analytical framework of regional innovation systems to estimate the regional innovative intensity across different provincial-level regions. Whereas prior studies of innovative capacity have focused on the national level (e.g., Furman and Hayes, 2004; Furman et al., 2002; Hu and Mathews, 2008),2 many scholars have suggested that a regional approach in analyzing China’s innovative capacity would be particularly salient and appropriate (Hu and Mathews, 2008, p. 1467). In addition to being the third largest country in the world in terms of land area at approximately 9.6 million square kilometers (United Nations Statistics Division, 2008), each administrative region at the provincial level in transitional China has undergone substantially different developmental stages in terms of economic, institutional, and technological reforms over the past 20 years (Huang, 2010, 2017; Huang et al., 2017). Scholars in Chinese technology and innovation concur that the provincial-level region is China’s basic and constant administrative unit, in which most economic and S&T policies are formulated and where such activities are organized and managed. Thus, considering each province as relatively independent and distinct economic entity in their assessments is analytically more useful and significant (Liu, 2000; Yu, 2010). Although subject to influence by the central government, each province has sufficient autonomy for setting specific governance rules and S&T policies to foster its own regional innovation system and economy. Each Chinese province also imposes some restrictions on mobility owing to restrictions on registered permanent residences at the provincial level (Li, 2009). Furthermore, each province possesses unique customs, language (dialects), norms, and conventions that are locally embedded into its social fabric, shaped by deep cultural and historical roots (Chua et al., 2019).3 So these regions at the provincial level can be perceived as relatively independent innovation systems (Edquist, 2005; Fritsch, 2002) that, together, constitute an important part of the national innovation system in China (Chung, 2002). Therefore, the 31 administrative regions at the provincial level in mainland China are selected as the unit of analysis in this study.4

17.4.2  Data, Variables, and Measures We start by collecting China’s detailed patent data between grant years 1986 and 2008 from the China National Intellectual Property Administration (CNIPA), building upon the data validated in prior research (e.g., Huang, 2010; Huang and Li, 2019; Huang et al., 2021). Other measures of regional innovative capacity are computed and compiled from the China Statistical Yearbook 1986–20105 and the China Statistical Yearbook on Science and Technology 1991–2010,6 which are published respectively by the National Bureau of Statistics of China and the Ministry of Science and Technology of China. To mitigate any data availability and other potential issues related to changes in China’s general economic conditions since 1986, we focus our empirical analyses on the more recent time period in the last ten years, that is, from 1999 to 2008.

17.4.2.1  Dependent Variable For the construction of the dependent variable, the empirical analyses require a consistent region-specific indicator of the level of commercially valuable innovative outputs in a given year. Therefore, we base the operationalization of our dependent variable on the total number of invention and utility model patents (over 865,000) granted by CNIPA to Chinese assignees 274

Analyzing China’s Provincial Regions

(e.g., Chinese enterprises, universities, and research institutions) in each of the 31 administrative regions from 1999 to 2008. As the dependent variable, we use regional innovative intensity (Patents per FTSE), which is a relative and cleaner measure of innovative capacity as it takes into account the available scientific and technical personnel in a region in the production of patent output. It is computed as the number of CNIPA invention and utility model patents awarded divided by the number of full-time scientists and engineers in a given provincial-level region. Furthermore, the use of CNIPA patents helps mitigate potential bias in selection and underrepresentation, which could be a concern in prior studies using patents awarded only by the United States Patent and Trademark Office (USPTO) to foreign and Chinese entities as the primary measure of innovative outputs.7 Despite the benefits of using patents as a measure, the limitations on the use of patents as measures of the introduction of new products, processes, and services have been documented.8

17.4.2.2  Independent and Region-Specific Variables To explain regional innovative intensity (in relative term), we consider in our empirical analyses the following normalized, region-specific key independent variables previously described in the conceptual framework: 1 common innovation infrastructure: number of full-time-equivalent scientists and engineers per capita (FTSE per Capita), accumulated stock of patents per scientist and engineer (Patent Stock Per FTSE), total R&D expenditure per GDP (Total R&D per GDP), and education spending per GDP (EDU Spend per GDP); 2 cluster-specific innovation environment: enterprise R&D expenditure per total R&D expenditure (Enterprise R&D per Total R&D), university R&D expenditure per total R&D expenditure (HL R&D per Total R&D), independent research institution R&D expenditure per total R&D expenditure (IRI R&D per Total R&D), and technological specialization index; 3 quality of linkages: government funding or grant for independent research institutions per GDP (IRI Gov Grant per GDP), government funding or grant for universities per GDP (HL Gov Grant per GDP), provincial-based government technology and funding programs (Program Fund per GDP), and FDI per GDP. We follow previous literature (e.g., Mahmood and Singh, 2003) in using CNIPA patent data to calculate the technological specialization index. It is a χ2 index which measures for each year how specialized (or unevenly) patenting activities of each region are distributed across the major technology sectors where patents are critical, as defined in prior studies (Cohen et al., 2000; Hall and Ziedonis, 2001; Huang, 2010; Levin et al., 1987; Mansfield, 1986).9 It can be calculated using the equation given by

(

χ i 2 = Σ j  pij − pcj 

) / p  2

cj

where j denotes the sector, pcj is the proportion of total China (CNIPA) domestic patents in sector j, and pij is the proportion of patents in sector j held by region i. Here, the more specialized a Chinese region i is in its relative strengths and weaknesses in these sectors, the greater the χ2 value. As the χ2 index calculates the region’s proportionate distribution and not the levels of innovative activities across the technology sectors, this index makes cross-regional comparison in technological specialization especially meaningful. 275

Kenneth G. Huang

Table 16.1 provides the variable definitions and sources, whereas Table 16.2 presents their descriptive statistics. Table 16.3 shows the correlations of these variables.

17.4.3  Model Specification and Estimation We incorporate the innovative capacity model developed in prior studies (Romer, 1990; Furman et al., 2002) into our conceptual framework, in order to estimate our dependent variable, regional innovative intensity. We estimate the relationship between China’s regional innovative intensity and its key drivers by analyzing the following equation: À j ,t = φ Aφj ,t + λ H Aj ,t + δ INF X INF + δ CLUSY jCLUS +δ LINK Z LINK + δ YEARYEARt + ε j ,t j ,t ,t j ,t where, for each region j in year t, Àj, t stands for the intensity of the production of innovations in terms of new patents granted per scientist and engineer in a given provincial-level region; Aϕ denotes the cumulative stock of technological knowledge per scientist and engineer held by a region at a given point in time; and H A refers to the scientific and technical human capital per capita or financial resources per GDP for conducting R&D. Also estimated in relative terms, X INF stands for common innovation infrastructure, YCLUS for cluster-specific innovation environment, and Z LINK for the quality of linkages between innovation infrastructure and environment of cluster-specific innovation. Lastly, YEAR t stands for year-specific technology stock, and ε for the sources of error. Considering an average delay of about two years between all patent applications and grants at the CNIPA, the empirical analyses employ a two-year lag between the indicators of regional innovation system and observed realization of intensity of innovative outputs. This implies that the dependent variable À is set in time t+2.10 To estimate the effects of these key drivers of innovative intensity in developed regions compared with developing regions, we perform separate regression analyses on the sample of developed Eastern provinces and that of developing Western provinces. Then we compare the corresponding coefficients of the same variable across the separate-sample regression models. All the regression models incorporate robust standard errors, clustered on regions (at the provincial level) to account for possible correlations in the errors for patents within each region. Furthermore, in the most stringent models, we include year fixed effects to control for any unobserved heterogeneity in the year when the patents have been granted.

17.5  Results 17.5.1  The Effects of Concentration of Scientists and Engineers and Technological Specialization Drawing upon the conceptual framework on knowledge spillover and geographic concentration of innovative activities, we study the effects of the concentration of scientists and engineers as well as regional technological specialization on the intensity of production of innovative outputs in the developed regions of China compared with the developing regions using split-sample estimations. We first examine the impact of concentration of scientists and engineers in the region. In Table 16.4, Model 1, we find that a 10% increase in the number of scientists and engineers per capita (FTSE per Capita) has increased the number of patents produced per scientist and engineer (Patents per FTSE) significantly by 11.3% (p < 0.05) in the developed Eastern provinces of China. However, Model 2 shows that an increase in the number of scientists 276

Analyzing China’s Provincial Regions Table 17.1  Variable definitions and sources Category Name

Definition

Source(s)

Innovative intensity À

Patents per FTSEj, t+2 Patents granted by CNIPA to an entity or CNIPA and China organization in a Chinese region j in year (t+2), Statistical Yearbook on from 1986 to 2008, per scientist and engineer S&T

Common innovation infrastructure Aϕ

Patent stock per FTSEj, t

HA

FTSE per Capitaj, t

HA

Total R&D per GDPj, t

X INF

EDU spend per GDPj, t

Cumulative total patents from 1986 until (t−1), CNIPA and China per full-time equivalent scientists and engineers Statistical Yearbook on S&T Number of full-time equivalent scientists and China Statistical engineers per capita Yearbook  on S&T and China Statistical Yearbook Total R&D expenditure by enterprise, higher China Statistical Yearbook learning and independent research institutions on S&T and China per GDP Statistical Yearbook Public/government education expenditure per China Statistical Yearbook GDP

Cluster-specific innovation environment YCLUS YCLUS

YCLUS YCLUS

Enterprise R&D per total R&Dj, t Technological specialization Indexj, t

HL R&D per total R&Dj, t IRI R&D per total R&Dj, t

R&D expenditure by large and medium enterprises per total R&D expenditure Technological specialization index measures how specialized (or unevenly) patenting activities of each region are distributed across the 12 major technology sectors. Greater value suggests more specialized (or diverse) regional relative strengths and weaknesses in these sectors R&D expenditure by universities or institutions of higher learning per total R&D expenditure R&D expenditure by independent research institutions per total R&D expenditure

China Statistical Yearbook on S&T Author computation from CNIPA data

China Statistical Yearbook on S&T and China Statistical Yearbook China Statistical Yearbook on S&T and China Statistical Yearbook China Statistical Yearbook on S&T and China Statistical Yearbook

China Statistical Yearbook on S&T China Statistical Yearbook on S&T

Quality of linkages Z LINK

IRI gov grant per GDPj, t

Funds from government grant for research institutions per GDP

Z LINK

HL gov grant per GDPj, t

Funds from government grant for universities or institutions of higher learning per GDP

Z LINK

Program fund per GDPj, t

Z LINK

FDI per GDPj, t

Funds for technology guidance and support programs, or projects of national importance (e.g., the torch program, sparkle program, and achievements spreading program) per GDP Amount of foreign direct investment per GDP

China Statistical Yearbook

Note: All variables are coded based on the 31 provincial-level regions in mainland China.

277

Kenneth G. Huang Table 17.2  Descriptive statistics of variables (years 1999–2008) Category

Variable

Innovative intensity À Patents per FTSE

n

Mean

Std. Dev.

Min

Max

310

0.030

0.016

0.0012

0.098

310 310 310 310

0.27 0.0023 0.0087 0.035

0.14 0.0030 0.0090 0.044

0.022 0.00012 0.00039 0.013

0.95 0.020 0.054 0.69

310 310 310 310

0.60 0.073 0.12 0.28

0.21 0.17 0.073 0.20

0 0.0051 0.0097 0.027

0.93 2.63 0.54 1

310 310 310 310

0.0044 0.0012 0.0049 0.026

0.0079 0.0014 0.0040 0.027

0.00061 0.0000047 0.00030 0

0.052 0.0085 0.029 0.11

Common innovation infrastructure A HA HA X INF

Patent stock per FTSE FTSE per capita Total R&D per GDP EDU spend per GDP

Cluster-specific innovation environment YCLUS YCLUS YCLUS YCLUS

Enterprise R&D per total R&D Technological specialization index HL R&D per total R&D IRI R&D per total R&D

Quality of linkages Z LINK Z LINK Z LINK Z LINK

IRI gov grant per GDP HL gov grant per GDP Program fund per GDP FDI per GDP

and engineers per capita has no significant effect on the number of patents per scientist and engineer in the developing Western provinces. Importantly, a pairwise comparison of the coefficients of these two separate-sample estimations suggests that the coefficients are statistically different (p < 0.05). Models 3 and 4 in Table 16.4 show the results from more stringent fixed effects models by incorporating year fixed effects. Similarly, in Model 3, we find that a 10% increase in the number of scientists and engineers per capita (FTSE per Capita) has significantly increased the number of patents by each scientist and engineer (Patents per FTSE) by 10.4% (p < 0.05) in the developed Eastern provinces of China. Model 4, meanwhile, suggests that the effect of an increase in the number of scientists and engineers per capita on the number of patents per scientist and engineer in the developing Western provinces is not significant. The difference between these two coefficients from the two separate-sample estimations is significant (p