Future-Oriented Technology Assessment E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Comprehensive resource explaining how to evaluate technologies for different purposes in any industry using four different practical approaches
Future-Oriented Technology Assessment offers a comprehensive view of technology assessment structured into three different practical approaches: Technology Evaluation, Technology Roadmapping, and Technology Intelligence.
The first four chapters include studies which utilize technology gap analysis, multiple criteria decision analysis, expert assessment quantification or neural networks to evaluate or forecast technology alternatives. The next five chapters apply bibliometric analysis, patent analysis, and network analysis to identify technology trends and the leaders in the field. The final four chapters use technology roadmapping, which charts a comprehensive plan for implementing technology.
Additional topics covered in Future-Oriented Technology Assessment include:
- Smart grid technology as an alternative to fossil fuel consumption
- Heat pump water heaters that reduce the cost of energy and improve energy efficiency, with particular focus on research from the US and China
- Nanotechnology in construction in Saudi Arabia to improve heat insulation, energy efficiency, and tensile strength in green building designs
With comprehensive, practical insight into evaluating emerging technologies across different industries, Future-Oriented Technology Assessment is an essential read for researchers in technology and professionals in engineering and technology management, along with professionals and graduate students in related disciplines and programs of study.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
A Note from the Series Editor
About the Editors
List of Contributors
Preface
1 Technology Assessment: Smart City Development Initiatives and Issues
1.1 Introduction
1.2 Evolution of the Smart City
1.3 Need for Smart Cities
1.4 Conclusion
1.5 Implication and Future Research
References
2 Technology Assessment: Process Optimization Services in the Cement Industry
2.1 Introduction
2.2 Research Design
2.3 Results of the Survey
2.4 Discussion
2.5 Conclusions
2.A Interview Guide
References
3 Technology Assessment: Energy Storage Technologies
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.4 Model Development
3.5 Results Analysis and Discussion
3.6 Conclusion
Acknowledgments
References
4 Technology Forecasting: A Secure Solar Power Generation Forecasting Framework for Recurrent Neural Networks
4.1 Introduction
4.2 Proposed Secure Solar Power Generation Forecasting Framework
4.3 Deep Learning Techniques
4.4 Adversarial Attack and Mitigation Methods
4.5 Dataset Description, Feature Selection, and Performance Metrics
4.6 Experiments
4.7 Results and Discussion
4.8 Summary
References
5 Technology Intelligence: Transformative Trends and Technological Synergies for the Smart Grid
5.1 Introduction
5.2 Cluster Analysis
5.3 Authors Productivity
5.4 Co-word Analysis
5.5 Discussion
References
6 Technology Intelligence: Cryptocurrencies and Emerging Technologies
6.1 Introduction
6.2 Data and Method
6.3 Discussion
References
7 Technology Intelligence: Geothermal Energy
7.1 Introduction
7.2 Impacts of Geothermal Energy
7.3 Methodology
7.4 Results of Data Analysis
7.5 Discussion
7.6 Conclusions
7.7 Limitations and Future Research
7.A Python Code of Mann-Kendall Test
7.B Python Code of Life Cycle S Curve
References
8 Technology Intelligence: Heat Pump Water Heaters
8.1 Introduction
8.2 Literature Review
8.3 Social Network Analysis
8.4 Methodology
8.5 Heat Pump Water Heaters Bibliometrics Application
8.6 Overall Affiliation Ranking 2010–2021
8.7 Co-word Results
8.8 Interview Results
8.9 Conclusion and Discussion
References
Notes
9 Technology Intelligence: Burst Analysis for RFID in Hospitals
9.1 Introduction
9.2 Methodology
9.3 Data
9.4 Burst Analysis
9.5 Cluster Analysis
9.6 Conclusion
References
10 Technology Roadmapping: Data Science Roadmapping of Networked Organizations’ Strategic Planning for Artificial Intelligence
10.1 Introduction
10.2 Literature Review
10.3 A Case Study of Networked Organizations’ Strategic Planning for AI
10.4 Discussion
10.5 Conclusion
References
11 Technology Roadmapping: Nano Technology in Construction in Saudi Arabia
11.1 Introduction
11.2 Research Methodology
11.3 Background of Nanotechnology
11.4 Nanoarchitecture: Definition and Its Applications
11.5 Building Sector and Sustainable Development Issues
11.6 Using Nanomaterials in the Production of Concrete-based Composites
11.7 Nanoarchitecture Application in Saudi Arabia
11.8 Technology Road Mapping for Green Architecture
11.9 Conclusion and Future Work
References
12 Technology Roadmapping: Standards of Healthcare Data Cybersecurity
12.1 Introduction
12.2 Methodology
12.3 Literature Review
12.4 Results
12.5 Conclusion and Limitations
References
13 Technology Roadmapping: Monitoring the Status of a Technology Roadmap with Data-driven Roadmapping Approach
13.1 Introduction
13.2 Literature Review
13.3 Monitoring the Status of a Roadmap with Data-driven Roadmapping Approach
13.4 The Development of Integrative Restaurant Services and Intelligent Management Published in IEEE Transactions on Engineering Management (Pora et al. 2022)
13.5 Conclusion
References
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Global smart city initiatives and challenges.
Chapter 2
Table 2.1 Scores of the main criteria for global and regional players respon...
Table 2.2 Scores of the main criteria for Brazilian and South African respon...
Table 2.3 Scores of the main criteria for three different levels of seniorit...
Chapter 3
Table 3.1 Sub-criteria of social perspective.
Table 3.2 Sub-criteria of technical perspective.
Table 3.3 Sub-criteria of economic perspective.
Table 3.4 Sub-criteria of environmental perspective.
Table 3.5 Sub-criteria of political perspective.
Table 3.6 Weights of criteria.
Table 3.7 Global weights of criteria and sub-criteria.
Table 3.8 Pairwise comparison of criteria and sub-criteria based on alternat...
Chapter 4
Table 4.1 Feature parameters.
Table 4.2 The best hyperparameters found through a grid search process for t...
Table 4.3 RNN-based models’ performance with RMSE, MSE, and MAE metrics.
Table 4.4 RMSE scores of RNN-based models for a specific
ɛ
value.
Table 4.5 RMSE scores of RNN-based models for a specific
ɛ
value after ...
Chapter 5
Table 5.1 Major clusters.
Table 5.2 Author productivity.
Table 5.3 Social network indicators.
Chapter 7
Table 7.1 Number of patent documents issued by year of publication during 19...
Table 7.2 Number of patent documents issued cumulatively by year of publicat...
Table 7.3 Words without semantic effect in creating topic clusters – emergin...
Table 7.4 Words without semantic effect in creating topic clusters – slow-gr...
Table 7.5 Words without semantic effect in creating topic clusters – rapid-g...
Table 7.6 Results of coherence score of topic clusters in emerging stage.
Table 7.7 Results of coherence score of topic clusters in slow-growth stage....
Table 7.8 Results of coherence score of topic clusters in rapid-growth stage...
Table 7.9 Topic clusters – emerging stage.
Table 7.10 Topic clusters – slow-growth stage.
Table 7.11 Topic clusters – rapid-growth stage.
Table 7.12 Top clusters of emerging stage.
Table 7.13 Top clusters of the slow growth stage.
Table 7.14 Top clusters of the rapid-growth stage.
Table 7.15 Similarities between topic clusters of emerging and slow-growth s...
Table 7.16 Similarities between topic clusters of slow-growth stage and rapi...
Table 7.17 Dominant topics extracted from the Latent Dirichlet Allocation (L...
Table 7.18 Dominant topics extracted from the Latent Dirichlet Allocation (L...
Table 7.19 Dominant topics extracted from the Latent Dirichlet Allocation (L...
Table 7.20 Examples of association rules mining between key terms, support, ...
Table 7.21 Examples of association rules mining between key terms, support, ...
Table 7.22 Examples of association rules mining between key terms, support, ...
Table 7.23 Words extracted from association rules based on the highest degre...
Table 7.24 Words extracted from association rules based on the highest betwe...
Table 7.25 Words extracted from association rules based on the highest close...
Table 7.26 Words extracted from association rules based on the highest Eigen...
Chapter 8
Table 8.1 Heat pump water heater outputs.
Table 8.2 Search queries.
Table 8.3 SNA top scholarly data analysis summary.
Table 8.4 SNA top analysis summary.
Table 8.5 SNA top analysis application.
Table 8.6 Keywords analysis indicated by Pajek software from Scopus Data (SN...
Table 8.7 Overall affiliation ranking 2010–2021.
Table 8.8 List of heat pump water heater.
Chapter 9
Table 9.1 Burst analysis.
Table 9.2 Major clusters on RFID in hospitals Research.
Table 9.3 Institution productivity on RFID in hospital research.
Table 9.4 Country productivity on RFID in hospital research.
Chapter 11
Table 11.1 Technology gaps analysis of the green building product features....
Chapter 12
Table 12.1 Technology roadmap: drivers.
Table 12.2 Technology roadmap: technology.
Table 12.3 Technology roadmap: product features.
Table 12.4 Technology roadmap: resources.
Chapter 13
Table 13.1 Description of a roadmap status and managerial implications.
Table 13.2 Summary of type and source of data.
Table 13.3 Analysis steps for data-driven roadmapping to assess the impact o...
List of Illustrations
Chapter 1
Figure 1.1 Components of smart city.
Figure 1.2 Need and development of smart cities.
Chapter 2
Figure 2.1 The hierarchical model from the client’s point of view.
Figure 2.2 The hierarchical model from the staff’s point of view.
Figure 2.3 Illustration on how the main barrier were compared – each line is...
Figure 2.4 Illustration on how the sub-barrier were compared – each line is ...
Figure 2.5 Example of a pairwise comparison in the online survey.
Figure 2.6 Scores of the main criteria to invest in a process optimization s...
Figure 2.7 Scores of the main criteria and sub-criteria to invest in a proce...
Figure 2.8 Scores of the main barrier and sub-barrier to sell a process opti...
Figure 2.9 Scores of the main barrier and sub-barrier to sell a process opti...
Chapter 3
Figure 3.1 Hierarchical decision model.
Figure 3.2 Criteria/perspectives.
Figure 3.3 Alternative technologies.
Figure 3.4 Weights of criteria.
Figure 3.5 Global weights of subcriteria.
Figure 3.6 TDE diagram.
Chapter 4
Figure 4.1 The proposed secure solar power generation forecasting framework....
Figure 4.2 Recurrent neural networks and Feed-forward neural networks.
Figure 4.3 RNN cell structure.
Figure 4.4 LSTM cell structure.
Figure 4.5 The bidirectional LSTM (BiLSTM) architecture with three consecuti...
Figure 4.6 GRU cell structure.
Figure 4.7 The architecture of the attention block.
Figure 4.8 FGSM attack steps. The input vector
x ∈ ℝ
n
is poisone...
Figure 4.9 Pearson correlation coefficients matrix and the performance metri...
Figure 4.10 RNN-based model forecast results with actual values.
Figure 4.11 Prediction performance changes with various attack power values ...
Figure 4.12 Prediction performance with various attack power values (
ɛ
)...
Chapter 5
Figure 5.1 Major clusters.
Figure 5.2 Keyword map.
Figure 5.3 Country productivity.
Chapter 6
Figure 6.1 Clusters on cryptocurrency research.
Figure 6.2 Timeline analysis cryptocurrency research.
Figure 6.3 All topics.
Figure 6.4 Topic 1.
Figure 6.5 Topic 2.
Figure 6.6 Topic 3.
Figure 6.7 Topic 4.
Figure 6.8 Topic 5.
Chapter 7
Figure 7.1 Impacts of Geothermal Energy.
Figure 7.2 Research steps.
Figure 7.3 Foster S curve model.
Figure 7.4 An example of principal component analysis (PCA) and constructed ...
Figure 7.5 Graph of the number of patent documents issued based on the year ...
Figure 7.6 Graph of the cumulative number of patents issued based on the yea...
Figure 7.7 S-Curve graph of the number of patents issued based on year of pu...
Figure 7.8 S-Curve graph of the number of patents issued based on year of pu...
Figure 7.9 The optimal number of topic clusters in the emerging stage based ...
Figure 7.10 The optimal number of topic clusters in the slow-growth stage ba...
Figure 7.11 The optimal number of topic clusters in the rapid-growth stage b...
Figure 7.12 Word clouds of top clusters at the emerging stage.
Figure 7.13 Word clouds of top clusters at the slow-growth stage.
Figure 7.14 Word clouds of top clusters at the rapid-growth stage.
Figure 7.15 Topic clusters based on principal component analysis (PCA) in Eu...
Figure 7.16 Topic clusters based on principal component analysis (PCA) in Eu...
Figure 7.17 Topic clusters based on principal component analysis (PCA) in Eu...
Figure 7.18 Evolution of topic clusters during emerging, slow-growth, and ra...
Figure 7.19 Co-occurrence network derived from association rules based on th...
Figure 7.20 Co-occurrence network derived from association rules based on th...
Figure 7.21 Co-occurrence network derived from association rules based on th...
Figure 7.22 Co-occurrence network derived from association rules based on th...
Chapter 8
Figure 8.1 HPWH application theory practice data resources.
Figure 8.2 A global co-authoring network for research on heat pump water hea...
Figure 8.3 A global network of co-authors that have researched HPWH from WoS...
Figure 8.4 A global network analysis of co-author conducting patents on heat...
Figure 8.5 Co-occurrence map for author keywords (Jan van Eck and Waltman 20...
Figure 8.6 Co-occurrence map (Jan van Eck and Waltman 2023).
Figure 8.7 Keywords linked to use of HPWH technologies (SNA 2020).
Figure 8.8 Most used heat pump water heater technologies.
Figure 8.9 An overview cluster analysis of the largest 11 co-citation public...
Figure 8.10 Timeline view of cluster analysis of co-cited articles.
Chapter 9
Figure 9.1 Major clusters on RFID in hospitals research.
Figure 9.2 Keyword map.
Figure 9.3 Author collaboration network on RFID in hospitals.
Figure 9.4 Institution collaboration on RFID in hospitals.
Figure 9.5 Country collaboration on RFID in hospitals.
Chapter 10
Figure 10.1 Joint data center architecture.
Figure 10.2 Example roadmap architecture to use beyond the organizational le...
Chapter 11
Figure 11.1 Steps in creating the technology roadmap.
Figure 11.2 Market drivers of green buildings and nanotechnology.
Figure 11.3 QFD market segments and drivers.
Figure 11.4 Product feature mind map of a green building.
Figure 11.5 QFD of market drivers and product features.
Figure 11.6 Green building technology mind map.
Figure 11.7 QFD of green building technologies and product features.
Figure 11.8 Green building technology roadmap.
Chapter 12
Figure 12.1 Proposed methodology.
Figure 12.2 Market drivers mind map.
Figure 12.3 Market segments vs. drivers QFD.
Figure 12.4 Market drivers vs. product features mind map.
Figure 12.5 Market drivers vs. product features QFD.
Figure 12.6 Policy mind map.
Figure 12.7 Technology policy vs. product features QFD.
Figure 12.8 Technology roadmap.
Chapter 13
Figure 13.1 The example structure of a product-technology roadmap (Gerdsri e...
Figure 13.2 The conceptual framework to assess the TRM status signal by cons...
Figure 13.3 Conceptual framework to assess the impacts of changes on the sta...
Figure 13.4 Hierarchical structure of an evaluation model.
Figure 13.5 Generic pattern of a perception curve on the tolerance interval ...
Figure 13.6 Hierarchical structure of an evaluation model for assessing TRM ...
Figure 13.7 Example of TRL calculator.
Figure 13.8 Tolerance intervals indicating how much an organization can tole...
Figure 13.9 Evaluation model to determine the current status of a technology...
Figure 13.10 Managerial procedure for the data-driven roadmapping approach (...
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright
Dedication
A Note from the Series Editor
Series Page
About the Editors
List of Contributors
Preface
Begin Reading
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
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Ekram Hossain
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Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
Future-Oriented Technology Assessment
A Manager’s Guide with Case Applications
Edited by
Haydar Yalçın
Ege University
Turkey
Tugrul U. Daim
Portland State University
United States
IEEE Press Series on Technology Management, Innovation, and Leadership
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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Library of Congress Cataloging-in-Publication Data
Names: Yalçın, Haydar, author. | Daim, Tugrul Unsal, 1967- author.
Title: Future-oriented technology assessment : a manager’s guide with case applications / Haydar Yalçın, Ege University, Turkey, Tugrul U. Daim, Portland State University, US.
Description: Hoboken, New Jersey : Wiley, [2025] | Series: IEEE Press series on technology management, innovation, and leadership | Includes bibliographical references and index.
Identifiers: LCCN 2024011177 (print) | LCCN 2024011178 (ebook) | ISBN 9781119909859 (hardback) | ISBN 9781119909866 (adobe pdf) | ISBN 9781119909873 (epub)
Subjects: LCSH: Technological innovations–Economic aspects. | Business planning–Case studies. | Organizational change–Case studies.
Classification: LCC HC79.T4 Y355 2024 (print) | LCC HC79.T4 (ebook) | DDC 658.5/14–dc23/eng/20240407
LC record available at https://lccn.loc.gov/2024011177
LC ebook record available at https://lccn.loc.gov/2024011178
Cover Design: Wiley
Cover Image: © Weiquan Lin/Getty Images
We dedicate this book to our Dads, who are both in eternity now.
A Note from the Series Editor
IEEE Press is well known for its books on technical and engineering topics. The Technology Management, Innovation, and Leadership series extends the reach of the imprint, from engineering and scientific deep dives to downstream stages of technology value chains and ultimately to societal impact.
The pathway starts with the crucial activities of basic and applied research, but connecting the dots from invention to innovation to the betterment of humanity and our ecosphere has become increasingly critical. Consider some of the other key developments and processes that are essential: new product and service design, system or ecosystem integration, intellectual property protection, manufacturing and supply chain integration, regulatory and compliance and certification, business model design and analysis, cost and price estimation, environmental sustainability assessment, … and much more. The time, effort, and funding required for realizing real-world impact dwarfs what was expended on the research. The skills required for end-to-end success also extend well beyond what is typically covered in STEM disciplines and include effective communication, cross-functional and global collaboration, leadership acumen, and science and technology policy development.
Big-picture insights and perspectives can be offered at an industry-agnostic level, and considerations also vary by industry sector, technology area, geography, and other factors. Accordingly, the series scope encompasses related topics both writ large – e.g. pragmatic assessments of emerging technologies, intrapreneurship and entrepreneurship frameworks, artificial intelligence and digital transformation, cybersecurity and resilience – and in the context of one or more societal application domains such as power and clean energy, logistics and transportation, smart cities and infrastructure, and global healthcare.
I am pleased that, in its first few years, the series has produced several books addressing key topics in its remit. But it’s a vast space to cover and we look forward to publishing more titles that are relevant for researchers, practitioners, policy makers, educators, students, business leaders, and others. For those who are seeking to make a positive difference for themselves, their organizations, and the world, technology management, innovation, and leadership are essential skills to hone.
You can review an up-to-date list of books published and upcoming in the series by navigating to the series page at https://ieee-press.ieee.org.
Tariq Samad
Senior Fellow and Honeywell/W.R. Sweatt Chair in
Technology Management Technological Leadership Institute
University of Minnesota, Minneapolis, Minnesota, USA
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About the Editors
Haydar Yalçın, PhD, is an Associate Professor on Management Information Systems at Ege University in Izmir, Turkey. He is also an affiliated faculty member at Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center, which is a National Academic Center of Excellence for Cybersecurity at Portland State University.
Tugrul U. Daim, PhD, is a Professor of Engineering and Technology Management, Fulbright Scholar, and the Director of Research at Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center, which is a National Academic Center of Excellence for Cybersecurity at Portland State University.
List of Contributors
Ashfaq AlKhalil
Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center
Department of Engineering and Technology Management
Portland State University
Portland, OR 97201, USA
Saeed Alzahrani
King Saud University
Riyadh, 11451 Saudi Arabia
Fayez Alsoubaie
Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center
Department of Engineering and Technology Management
Portland State University
Portland, OR 97201, USA
Serhat Burmaoglu
Izmir Katip Celebi University
Izmir, Turkey
Umit Cali
School of Physics, Engineering and Technology
University of York
Heslington
York, YO10 5DD, UK
Ferhat O. Catak
Electrical Engineering & Computer Science
University of
Stavanger, Rogaland, Norway
Tugrul U. Daim
Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center
Department of Engineering and Technology Management
Portland State University
Portland, OR 97201, USA
Mursel Dogrul
Dational Defense University
Istanbul, Turkey
Ahmet Ergurum
University of Wisconsin-Milwaukee
Milwaukee, WI 53201, USA
Sara Ferdousi
Portland State University
Portland, OR 97201, USA
Tugrul Felipe C. Gelbecke
Hamburg Technical University
Hamburg, Germany
Nathasit Gerdsri
College of Management
Mahidol University
Bangkok, Thailand
Ebru Gökalp
Computer Engineering Department
Hacettepe University
Ankara 06800, Turkey
Mert Onuralp Gökalp
Data Analytics Center
Tüpras, Ankara 06510, Turkey
Kerem Kayabay
HLRS, University of Stuttgart
Stuttgart 70569, Germany
Atilla Kılınç
Energy Institute
Instanbul Technical University
Istanbul 34469, Turkey
Aynur Kirbac
Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center
Department of Engineering and Technology Management
Portland State University
Portland, OR 97201, USA
Vimal Kumar
Department of Information Management
Chaoyang University of Technology
Taichung 41349, Taiwan
Murat Kuzlu
Batten College of Engineering & Technology
Old Dominion University
VA 23507, USA
Kuei-Kuei Lai
Department of Business Administration
Chaoyang University of Technology
Taichung 41349, Taiwan
Hermann Lodding
Hamburg Technical University
Hamburg, Germany
Ali B. Naeini
Iran University of Science and Technology
Tehran, Iran
Alison Nalven
Portland State University
Portland, OR 97201, USA
Ummaraporn Pora
Technopreneurship and Innovation Management Program
Chulalongkorn University
Bangkok, Thailand
Sudatip Puengrusme
College of Management
Mahidol University
Bangkok, Thailand
Salih Sarp
Electrical & Computer Engineering Department
Virginia Commonwealth University
VA 23284, USA
Saumya Saxena
Portland State University
Portland, OR 97201, USA
Nagendra K. Sharma
Department of Management Studies
Graphic Era (Deemed to be University)
Dehradun 248002, India
Sine B. Skaarup
FL Smidth
Copenhagen, Denmark
Nolan Thompson
Portland State University
Portland, OR 97201, USA
Pratima Verma
Department of Strategic Management
Indian Institute of Management Kozhikode
Kozhikode 673570, India
Ronald Vatananan
College of Management
Mahidol University
Bangkok, Thailand
Courtney Wright
Portland State University
Portland, OR 97201, USA
Haydar Yalçın
Department of Business Administration
Ege University
Izmir 35800, Turkey
Aynur Yarga
Izmir Katip Celebi University
Izmir, Turkey
Mehdi Zamani
Kiel University
Kiel, Germany
Jennifer L. Zeitouni
Portland State University
Portland, OR 97201, USA
Hao Zhang
Chengdu Medical College
Chengdu, Sichuan Province, China
Yanxiao Zhao
Electrical & Computer Engineering Department
Virginia Commonwealth University
VA 23284, USA
Preface
This book provides a set of tools for managers tasked with assessing technologies for different purposes.
The first four chapters demonstrate technology assessment and forecasting through case studies including smart cities, solar technology, cement production, and energy storage. Case studies provide insight into how to apply the novel methods used. By providing a wide range of industry applications, the book establishes a set of guidelines for established as well as emerging sectors.
The next five chapters use various technology intelligence approaches to identify technological insight. Through analysis of published papers and patents, we provide intelligence on technologies including crypto, geothermal, smart grid, and heat pump water heaters.
Finally we introduce technology roadmapping, which integrates all the prior methods to provide a path for technology development. Cases provided cover a wide range of sectors, ranging from cybersecurity in health care to nanotechnology in construction.
We hope that this book will enable the readers to manage technology more effectively.
1Technology Assessment: Smart City Development Initiatives and Issues
Nagendra K. Sharma1, Vimal Kumar2, Pratima Verma3, Tugrul U. Daim4, Haydar Yalçın5, and Kuei-Kuei Lai6
1Department of Management Studies, Graphic Era (Deemed to be University), Dehradun 248002, India
2Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
3Department of Strategic Management, Indian Institute of Management Kozhikode, Kozhikode 673570, India
4Mark O. Hatfield Cybersecurity and Cyber Defense Policy Center, Department of Engineering and Technology Management, Portland State University, Portland, OR 97201, USA
5Department of Business Administration, Ege University, Izmir 35800, Turkey
6Department of Business Administration, Chaoyang University of Technology, Taichung 41349, Taiwan
1.1 Introduction
“A smart city is a city well performing built on the ‘smart’ combination of endowments and activities of self-decisive, independent, and aware citizens” (Giffinger et al. 2007). Urban population concentration and other environmental challenges are among the key antecedents to the concept of smart cities around the world. Traditional cities are not adequately structured to provide a good quality of life for people in today’s fast-moving world. The smart cities concept came into the picture with the advent of high-end technologies and corresponding new policies that make it possible to live smartly (Letaifa 2015).
We are facing several kinds of challenges because of population growth in the old cities, which is straining limited, basic resources for their residents. Therefore, a new planning program must be implemented so that cities can be a better place to live. The challenges are compounded because people continue to migrate to urban areas for employment and a better standard of living. Urban jobs and infrastructure attract people looking for a better quality of life. The United Nations (UN) predicts that by 2050 there may be 6.5 billion people living in cities (Streitz 2015).
In some ways, they find this better lifestyle in the cities. However, migration is putting much pressure on the urban established cities and their systems and is not likely to stop in the near future (Okai et al. 2018). The proposal of smart cities is therefore widely accepted as a way to deal with such challenges. Cities play a crucial role in building socioeconomic and ecological counterparts around the world.
Because of the expected and current population growth, cities are suffering from various kinds of challenges as the available resources and the built infrastructure are limited and are under pressure. Thus, there is an urgent need to develop new infrastructure that can have a problem-solving approach to these challenges in the cities. Smart cities offer the hope that everyone can survive in a better way with the use of technology and engineering. For a city to be called a smart city, the smart city (SC) projects require several important criteria to be met: smart mobility, safety and security of the people, smart healthcare system, clean water and air, major dependencies on renewable energy systems, prompt disaster response system, economic development, and higher social and cultural values among people (Kosowatz 2020). Innovative technological solutions will be needed to develop all these factors for smart cities – solutions that can be easily adopted and fixed in the current or a new system.
Figure 1.1 shows several key components of smart cities. Smart cities implement technologies to become smart and it leads to better quality of life, economic development, a traffic management system for a robust supply chain in the city, and an effective health care system in the city with a proper network of emergency vehicles and ambulances. Beyond the listed factors, an effective communication system is necessary for appropriate information travel among the public and authorities (Ismagilova et al. 2019). Understanding the importance of smart cities and their development requires understanding the main priorities identified in the researchers’ smart city concept agendas (Camero and Alba 2019). These researchers are adding knowledge in all the SC areas, such as architecture and planning, civil engineering, information technology, management, policy, and governance.
Figure 1.1 Components of smart city.
Source: Developed by Authors.
In recent studies, it was found that SC development is greatly strengthened with the latest technological developments. The challenges in the cities can be minimized with the appropriate utilization of technologies. Information communication technology (ICT), artificial intelligence (AI), the internet of things (IoT), deep learning, machine learning, neural networking, cognitive computing, and big data analytics are some of the promising technologies used in the development of the smart city projects around the world. The engagement of these technologies is aimed to convert the conventional systems of the city into the autonomous system (Ahad et al. 2020). Inclusion of high-end technologies makes the entire system of the city smart work, such as smart mobility, which connects the vehicles on board to the traffic system and users can easily understand the traffic situation in the real-time. Smart mobility can also reduce fatalities that result when people injured in traffic accidents cannot get immediate treatment. In this case, smart mobility can be helpful for running ambulances or emergency vehicles with the help of an automatic traffics system linked with these vehicles. Real-time monitoring systems and global positioning systems (GPS) showing hospitals and clinics to the users are some of the smart systems that can save lives. These examples confirm how important the smart city project is for every nation in the world and the significance of the innovation agendas of the government, research agencies, and technology partners toward the development and implementation of smart technologies in the cities (Okai et al. 2018). In today’s context, many cities are interested in transforming into smart cities for achieving developmental goals, although the journey is full of challenges and complexities as it includes lots of planning with a public-private partnership (PPP) approach, experts with ICT, government support, and significant funding (Lai et al. 2020; Razmjoo et al. 2021).
This chapter focuses on the technological issues in the development of smart cities around the world and technological engagement in the development process. The major objective is to highlight smart city initiatives adopted by the cities and technology engagement for the same. Another task is to find the issues that are linked to technological aspects. These objectives are important to achieve because it helps in understanding the complexities of adopting the high-end technologies by the countries for smart city development projects. Studies show that most SC projects fail to achieve success because of poor technology adoption or affordability. Hence, this chapter endeavors to highlight these issues with appropriate possible solutions for the successful development of smart cities. This chapter may be helpful for the decision-makers who are engaged with smart city development projects and for researchers who may use this research for exploring future research opportunities.
1.2 Evolution of the Smart City
Urbanization has accelerated the evolution of smart cities. According to De Marco and Mangano (2021), it is expected that by 2050, approximately 66% of the global population will be living in urban areas. The term smart city has gained traction around the world, influencing urban development plans and government policies (Berry 2018). Overpopulation of smart cities also has many space challenges. The challenges are traffic congestion, waste management, pollution, and parking allocation – but opportunities exist to solve these problems. One of the biggest challenges or problems is traditional safety and security infrastructure that arises from rapid urbanization (Isafiade and Bagula 2017).
There is no widely accepted definition of the smart city. It varies according to perspective. The SC concept arose a few years ago as a collection of “ideas on how information and communication technology might improve city functioning” (Camero and Alba 2019). In other words, a smart city is a sustainable city that uses fourth-industrial-revolution technology and stakeholder governance to solve urban challenges and improve inhabitants’ quality of life (Myeong et al. 2022). Smart cities are also known by different names: Digital City (Ishida 2002); Eco-City (Register 1987); Green City (OECD 2013); Intelligent City (Komninos 2006); Knowledge City (Edvinsson 2006); Sustainable City (Haughton and Hunter 1994); and Wired City (Dutton et al. 1987).
1.3 Need for Smart Cities
The increasing burden of population on the planet and certain other types of environmental challenges led by overexploitation of the limited resources made the cities a poor place to live. The basic utilities such as getting safe drinking water are even a challenge for the common public, especially in densely populated places like Mumbai, Manila, Beijing, and Dhaka. The road traffic in these cities is one of the major problems as it wastes precious time and emits more carbon into the air. Traffic congestion kills many people each year who are delayed while seeking emergency medical help. These issues create extensive pressure on the conventional system of older cities as they lack the communication technologies that could strengthen the information system.
On the other hand, there is a lack of access to the latest devices that can help people in living a good and safe life. The increasing population especially in south Asian countries also leads to unemployment and increased crime. To control crime, the police department must have access to devices that help to monitor and track the situation in real-time with wireless technology. Another law enforcement tool is closed-circuit TVs (CCTVs). Police departments could install CCTVs in prominent locations and track the video feed with the help of fixed stations and patrolling cabs. Such tools utilized at a broader scale can be beneficial for the people living in smart cities. The technological potential of SCs has given new wing that can be promising in achieving the target of smart cities.
There are various factors where smart technologies can be implemented to make the city smart, such as safety and security where the police department may be provided with the latest technological tools to track and control crime. Traffic management can be stronger where the congestion can be reduced with automation and machine learning algorithm. The waste needs to be converted into a value with the circular economy concept. Water sewage treatment is necessary so that water can be reused by the public. Pedestrian and cycling paths must be developed so that citizens can ensure good health. The focus should not be only on nonrenewable energy, but there should also good scope of renewable sources of energy such as wind energy and solar energy. Local community parks provide opportunities for exercise and more interaction among citizens for better social development of the public. Although some of these things are present for the holistic development and the establishment of smart cities, all these factors need to be implemented. The need and development of smart cities are shown in Figure 1.2, which illustrates how population and other challenges have provoked greater effort in pairing the concept of smart cities with the development of smart technologies.
Figure 1.2 Need and development of smart cities.
Source: Developed by Authors.
1.3.1 Global Smart Cities Initiatives
Development of smart city projects are taking place across the globe; it is one of the key agendas for almost every nation in the world (Table 1.1). Countries have adopted various kinds of initiatives to make their cities smart. A study conducted by Lai et al. (2020) well highlighted the initiatives of the countries toward SC development. In Africa, the city of Konza has been selected for smart city development. Konza is working with ICT toward the development of a strong network used for efficient management of transportation, utilities, and public safety standards. The initiatives also work toward environmental development and local business development. Other cities in Africa are Slavova and Okwechime that are being included in the smart city project under the African Union’s Agenda 2063. Power generations with renewable resources, rapid adoption of technologies, establishments of smart mobility labs, automatic parking systems, real-time traffic, transport control system, cycle-sharing systems, smart information desks for visitors, and big data projects in smart cities are some of the major initiatives in Africa. In Asian countries, the major SC projects are carried out by China, primarily in the cloud projects, city brain, collection of data from the video at traffic system, and reduction of traffic congestion that made the emergency vehicles 50% faster than before.
Table 1.1 Global smart city initiatives and challenges.
Name of the smart city
Smart city initiatives
Challenges
London
Working well in human capital and international projection. Better urban planning. It is also well known for its smart mobility and transportation for citizens.
Lack of social cohesion and environmental programs.
New York
Better urban planning and smart mobility.
Lack of social cohesion.
Paris
Strong for its international projection, smart mobility, and transportation. Better human capital inclusion.
Lack of social cohesion and environmental programs.
Tokyo
Great initiatives toward environmental and economic development. Better human capital initiatives. Technology-intensive development of the city. Special initiatives for an aging population.
Lack of social cohesion.
Reykjavik
Great initiatives for environmental development. The city is well known for hydroelectric and geothermal energy sources which make the city a world leader in sustainable energy and smart solutions. Better social cohesion.
Lack of urban planning and economy.
Copenhagen
Initiatives for environmental development that gives the city a low level of pollution and contamination. Good governance.
Poor urban planning.
Berlin
Best smart mobility and transportation. Better in human capital and international projection.
Needs improvements in the economy and environmental development.
Amsterdam
Best for international projection; attracts international tourists. Smart mobility and transportation
Lack of social cohesion.
Singapore
The first city to launch driverless taxis and proposed to launch the same kind of buses too. Incorporation of innovation and technology in the city development. Better international projection and environmental development.
Need to work more on mobility and transportation.
Hong Kong
The city is well known for its use of innovative and technology-intensive programs to increase the quality of life and city management through its remarkable initiatives called the Hong Kong Smart City Blueprint project.
Lack of social cohesion.
Source: Adapted from Forbes 2020.
1.3.2 Role of Technology in Smart City Development
Technology plays a significant role in the development of smart cities as it not only makes the city smart but also makes the city utilities convenient for the public. Most smart cities focus on smart mobility that consists of three main elements: ICT, smart cells, and developmental mechanisms. These elements consist of the internet of things (IoT), cloud computing, AI, big data, etc. Smart cells incorporate, for example, smart and automated vehicles and driverless vehicles. In developmental mechanisms, the system is used for smart traffic management and another mechanism for operations (Yan et al. 2020). In cities, increased traffic congestion affects both safety and the environment, as it plays a crucial role in increasing carbon emissions. The technologies in these areas that are broadly used in the development of smart cities are sensors, controller area network (CAN) bus, GPS, light detection and ranging (LiDAR), and others (Xu and Thakur 2021). These technologies have the potential to make the city smart and sustainable in long run. Smart cities around the world also use lithium batteries, which help in running public vehicles like buses and taxis. On the other hand, cities like Singapore are continuously investing in the automation of vehicles and another self-driving effort for the development of the city. Asian Pacific areas, such as China, Taiwan, Malaysia, Korea, Japan, and Hong Kong, use paperless work and much focus on Radio Frequency Identification (RFID) for payments and other related tasks (Asia Pacific Smart Card Association 2002).
Approximately 30% of the people involved in vehicle accidents die because of they reach the hospital too late due to traffic congestion. In the same way, heart attack and stroke patients are not able to reach the hospital in time. To solve such problems and develop a smooth traffic system during an emergency, England has incorporated the use of AI and added emergency crews to the system. A company called Red Ninja in the United Kingdom works well on the project called Life First Emergency Control (LiFE), which is used for managing traffic and taking the emergency patient to the hospital within a few minutes (Ninja 2022). The project uses much of AI and various other algorithms to make this program successful and save precious lives. These are concrete examples of how technology plays a crucial role in smart city development.
1.3.3 Technology Adoption and Development of Smart Cities
A city with strong, forward-looking performance in its governance, mobility, environment, and quality of life, founded on a clever combination of endowments and citizen initiatives from self-aware, independent, and self-determining individuals (Giffinger et al. 2007). The idea of a “smart city” is regarded as a new paradigm for urban development (Bremser et al. 2019). Everyday operations including government, transportation, agriculture, logistics, maintenance, education, and healthcare are all automated in some fashion because of the widespread use of technology, and smart devices can be used to control, monitor, and access these systems remotely (Ahad et al. 2020). This gave rise to the idea of smart cities, in which ICT are combined with a city’s existing traditional infrastructure and then managed and coordinated using digital technology.
A city that inspires its citizens to develop and thrive in their own lives shares culture, information, and life with its citizens (O’Connor and Shaw 2014). The earliest idea was the intelligent city. Top-down strategies are used in the intelligent city, with a concentration on technology (Letaifa 2015). Smart cities are hybrid models that combine centralized city support, coordination, and monitoring with democratized open innovation (Letaifa 2015). Smart people, smart governance, smart mobility, smart environment, and smart lifestyle are the five most frequent markers of the adoption of smart cities (Giffinger et al. 2007).
Planning strategies for smart cities have gathered a lot of traction recently (Kummitha and Crutzen 2017). While supporters of smart cities claim that the adoption of ICTs, improved governance, and human capital among the populace will result in beneficial social transformation, detractors highlight the negative repercussions and flaws in their development and implementation (Kummitha and Crutzen 2017). Transportation, energy, government, and the environment are just a few of the sectors being affected by digital transformation. But it mostly affects people. Consequently, the implementation of the smart city’s various components offers tremendous research possibilities (Manfreda et al. 2021). Bremser et al. (2019) begin with a methodical development of technology and data platforms since they perceive new technology and standardized data interchange as a rare opportunity. Research on innovation uptake serves as the theoretical underpinning (Bremser et al. 2019).
1.3.4 Efforts Toward Overcoming Technological Challenges
SC technologies offer the possibility to manage the repercussions of growing urbanization (Ibrahim et al. 2015). Smart city efforts still struggle to take advantage of technological opportunities (Bremser et al. 2019). Policymakers in metropolitan areas around the world are hurriedly implementing IoT devices, sensors, and other contemporary information and communications technologies to address various governance challenges, boost efficiencies, and empower residents in the race to create smart cities (Mondschein et al. 2021). However, there are several obstacles to the development of smart cities, including laws and regulations, funding, infrastructure, and technological issues that are related to sustainability in terms of the environment, the economy, and society (Ibrahim et al. 2015). Numerous studies have emphasized the significance of sustainability ideas in the growth of smart cities. However, little research has been done on the difficulties in creating sustainable smart cities (Ibrahim et al. 2015).
To give towns a comprehensive knowledge of how SC development should be conducted, overcoming the nature of smart city obstacles is essential. Mora et al. (2019) recommended coordinating efforts when examining the strategic principles that underpin the development of smart cities, reaching an agreement on how to conceptualize, analyze, and standardize such developments, and devising creative monitoring and evaluation systems for such strategies by reflecting on the lessons discovered from existing practices. Rapid urbanization is creating chances for cutting-edge applications of developing technology to identify sectors in the issues of city management (Nyberg 2018).
The creation of sustainable smart cities and the provision of more effective and seamlessly connected services can both be improved by the practitioners by anticipating potential problems (Vu and Hartley 2018). Ignoring the truth about these problems and how to solve them will cause delays and result in the failure to build smart cities. ICT infrastructure support (Hashem et al. 2016), rapid urbanization, and a lack of government wisdom (Nyberg 2018), a lack of coordination and externalities (Warwick 2013), PPPs (European Commission 2013), and implementation issues for sustainable smart cities are some of these challenges (Lytras and Visvizi 2018).
1.3.5 Future of Smart Cities with Technology Adoption
In order to create a blueprint for future smart cities, it is necessary to evaluate the opportunities and difficulties that lie ahead (Murthy Nimmagadda and Harish 2022). Therefore, although being viewed as complex, technological advances are recognized as a unique potential for the city’s future development (Bremser et al. 2019). SC projects have a technological focus, but the current technological environment is thought to be insufficient for future requirements (Bremser et al. 2019). Smart cities use technology that can help inhabitants live better lives by acting as a foundation for future services, such as autonomous mobility, as well as adapt in the future, providing new technology and a supportive environment (Manfreda et al. 2021). A smart city is an entrepreneurial city (Kummitha 2019). Smart cities and entrepreneurship have a reciprocal relationship (Kummitha 2019). First, entrepreneurs launch technological initiatives that aid in the socio-technical transformation of cities into smart cities. Second, as new technologies are embraced in cities, data is produced that aids businesses in looking for new opportunities.
1.4 Conclusion
In this chapter, the focus has been given largely on the technological aspect of smart city projects. The study has well highlighted the significance and need of the smart city for countries that are getting crowded because of the rising level of the urban population. This increase in the population is happening because of the migration of the people from rural areas to urban areas in search of good education and earning a livelihood. This kind of thing is happening in most places around the world. This incident is making cities under pressure as it was not planned for the excessive population that we are noticing today, and therefore the planner, government, and other stakeholders decided to make the city smart so that it can well handle people. As suggested in our very basic example earlier, although traffic management was once handled by a few cops, now it is not possible without the use of sensors and other technological devices to handle the increased traffic. The chapter also talks about the global initiatives toward the development of smart cities. The major objective of the chapter was to highlight the technological role in the development of smart cities. The chapter has also showcased how the implementation of technology is difficult for some countries because of various reasons such as affordability challenges, lack of technical know-how, missing infrastructure support, and other kinds of policy-related challenges. Technology transfer among the countries is also a major challenge.
These challenges hinder the smart city project as already discussed. Few solutions can be implemented toward adopting these technologies such as PPP, public funding support, technology diffusion strategy between the countries, and training for employees working in smart city projects. These methods can help the smart city projects toward adopting new technologies, and it can help in importing technology from other countries. However, with the intervention of international bodies, some countries are ready to help other developing countries with their technologies in the development of the smart city. Many cities have been established as smart cities, and they can serve as a great example or role model in this domain.
1.5 Implication and Future Research
The chapter is about showcasing the significance of the technological aspect in developing smart cities. The knowledge from this book chapter can be used for understanding the basics of smart cities’ concepts and need. Most importantly the technologies that are discussed in the chapter. SC policymakers and experts may get help from this study. The researchers who are working in the field of smart cities may develop some exclusive studies such as how SC technologies can be distributed to developing and underdeveloped countries. The knowledge from the present chapter may be helpful for both researchers and policymakers.
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