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Record Nr. |
UNINA9911019929803321 |
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Autore |
Yalçin Haydar |
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Titolo |
Future-Oriented Technology Assessment : A Manager's Guide with Case Applications |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2024 |
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©2024 |
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ISBN |
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9781119909880 |
1119909880 |
9781119909873 |
1119909872 |
9781119909866 |
1119909864 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (435 pages) |
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Collana |
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IEEE Press Series on Technology Management, Innovation, and Leadership Series |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Technological innovations |
Strategic planning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Title Page -- Copyright -- Contents -- A Note from the Series Editor -- About the Editors -- List of Contributors -- Preface -- Chapter 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 -- 1.3.1 Global Smart Cities Initiatives -- 1.3.2 Role of Technology in Smart City Development -- 1.3.3 Technology Adoption and Development of Smart Cities -- 1.3.4 Efforts Toward Overcoming Technological Challenges -- 1.3.5 Future of Smart Cities with Technology Adoption -- 1.4 Conclusion -- 1.5 Implication and Future Research -- References -- Chapter 2 Technology Assessment: Process Optimization Services in the Cement Industry -- 2.1 Introduction -- 2.2 Research Design -- 2.2.1 Model Development -- 2.2.1.1 Hierarchical Model: Client's Point of View -- 2.2.1.2 Hierarchical Model: Staff's Point of View -- 2.2.1.3 Comparative Judgment of the Variables -- 2.2.2 Supporting Qualitative Data -- 2.2.3 Data Analysis |
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-- 2.3 Results of the Survey -- 2.3.1 Simplified External Survey -- 2.3.2 Complete External Survey -- 2.3.3 Internal Survey -- 2.4 Discussion -- 2.5 Conclusions -- References -- Chapter 3 Technology Assessment: Energy Storage Technologies -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Renewable Energy Sources -- 3.2.2 Assessment of the Energy Storage Technologies -- 3.3 Methodology -- 3.3.1 Hierarchical Decision Model -- 3.3.2 HDM Steps -- 3.3.2.1 Creating Model -- 3.3.2.2 Pairwise Comparison -- 3.3.2.3 Inconsistency and Disagreement -- 3.3.3 Technology Development Envelop (TDE) -- 3.4 Model Development -- 3.4.1 Criteria/Perspectives -- 3.4.1.1 Social Perspective -- 3.4.1.2 Technical Perspective -- 3.4.1.3 Economic Perspective -- 3.4.1.4 Environmental Perspective -- 3.4.1.5 Political Perspective -- 3.4.2 Alternative Technologies. |
3.4.2.1 Flywheels -- 3.4.2.2 Liquid Air Energy Storage Technologies -- 3.4.2.3 Thermoelectric Energy Storage -- 3.4.2.4 Underground Pumped Hydro -- 3.4.2.5 Isothermal Compressed Air Energy Storage (I‐CAES) -- 3.5 Results Analysis and Discussion -- 3.5.1 Model Validation -- 3.5.2 Model Quantification -- 3.5.2.1 Results of the Perspectives and Criteria -- 3.5.3 TDE Diagram -- 3.6 Conclusion -- Acknowledgments -- References -- Chapter 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 -- 4.3.1 Recurrent Neural Networks (RNN) -- 4.3.2 Long Short‐term Memory (LSTM) -- 4.3.3 Bidirectional LSTM -- 4.3.4 Gated Recurrent Units (GRU) -- 4.3.5 LSTM and BiLSTM with Attention -- 4.4 Adversarial Attack and Mitigation Methods -- 4.4.1 Adversarial Attack -- 4.4.2 Adversarial Training -- 4.5 Dataset Description, Feature Selection, and Performance Metrics -- 4.5.1 Dataset Description -- 4.5.2 Feature Selection -- 4.5.3 Performance Metrics -- 4.6 Experiments -- 4.6.1 RNN‐based Solar Power Generation Forecasting -- 4.6.2 Adversarial Machine Learning Attack Against RNN‐based Models -- 4.6.3 RNN‐based Solar Power Generation Forecasting with Adversarial Machine Learning Training -- 4.7 Results and Discussion -- 4.8 Summary -- References -- Chapter 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 -- Chapter 6 Technology Intelligence: Cryptocurrencies and Emerging Technologies -- 6.1 Introduction -- 6.2 Data and Method -- 6.2.1 Cluster Analysis -- 6.2.2 Burst Analysis and Emerging Topics. |
6.2.3 Intertopic Distance Maps and Topic Detection on Cryptocurrency Research Landscape -- 6.2.4 Topic 1: Cryptocurrency Universe and Blockchain Technology -- 6.2.5 Topic 2: Blockchain‐Based Supply Chain Management and Security Optimization -- 6.2.6 Topic 3: Emerging Trends in Internet Science and Security -- 6.2.7 Topic 4: Innovations in Financial Technology and Blockchain Solutions -- 6.2.8 Topic 5: Advanced Systems Analysis and Governance in Current Issues -- 6.3 Discussion -- References -- Chapter 7 Technology Intelligence: Geothermal Energy -- 7.1 Introduction -- 7.2 Impacts of Geothermal Energy -- 7.2.1 Economic Impacts of Geothermal Energy -- 7.2.2 Environmental Impacts of Geothermal Energy -- 7.2.3 Technological Impacts of Geothermal Energy -- 7.2.4 Green Sustainable Science Technology -- 7.2.5 Energy Fuels -- 7.2.6 Thermodynamics -- 7.2.7 Regulation -- 7.2.8 Critical Global Gap of Geothermal Energy -- 7.3 Methodology -- 7.3.1 Data Collection -- 7.3.2 Extraction of Life Cycle of Geothermal Energy Technologies Based on the Number of Cumulative Patents (Foster Curve Model) -- 7.3.3 Mann‐Kendall Test -- |
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7.3.4 Sen's Slope Test -- 7.3.5 Data Preprocessing -- 7.3.6 Latent Dirichlet Allocation (LDA) Topic Modeling -- 7.3.7 Principal Component Analysis (PCA) -- 7.3.8 LDAvis Software Package -- 7.3.9 Text Similarity Calculation -- 7.3.10 Association Rule Mining -- 7.3.11 Social Network Analysis -- 7.4 Results of Data Analysis -- 7.4.1 Data Collection Initial Results -- 7.4.2 Mann‐Kendall Test Result -- 7.4.3 Sen's Slope Test Results -- 7.4.4 Results of Dividing Technology Steps -- 7.4.5 Results of Data Pre‐processing -- 7.4.6 Results of Coherence Score of Topic -- 7.4.7 Latent Dirichlet Allocation (LDA) Topic Modeling with the Number of Optimal Topic Clusters -- 7.4.8 Analysis of Research and Development Status of Geothermal Energy Technologies. |
7.4.9 Breakthrough Inventions and Technologies of Geothermal Energy -- 7.4.9.1 Results of Association Rules Mining -- 7.4.9.2 Results of Analysis for Indicators Related to Social Networks -- 7.5 Discussion -- 7.6 Conclusions -- 7.7 Limitations and Future Research -- References -- Chapter 8 Technology Intelligence: Heat Pump Water Heaters -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 Heat Pump Water Heater Technology -- 8.2.2 Bibliometric Analysis -- 8.3 Social Network Analysis -- 8.4 Methodology -- 8.4.1 Data Collection -- 8.4.2 Findings -- 8.4.3 Co‐authorship Network Analysis: WoS, Scopus, Sumobrain -- 8.4.4 Top Institutions, Countries, Authors, Papers, and Patent -- 8.4.4.1 Scholarly Data -- 8.4.4.2 Patents -- 8.5 Heat Pump Water Heaters Bibliometrics Application -- 8.6 Overall Affiliation Ranking 2010-2021 -- 8.7 Co‐word Results -- 8.7.1 Keywords Related to Use of Heat Pump Water Heater and Measures of Development Degree -- 8.8 Interview Results -- 8.9 Conclusion and Discussion -- References -- Chapter 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 -- Chapter 10 Technology Roadmapping: Data Science Roadmapping of Networked Organizations' Strategic Planning for Artificial Intelligence -- 10.1 Introduction -- 10.2 Literature Review -- 10.2.1 Data Science Roadmapping -- 10.2.1.1 Technology Roadmapping Applications, Methods, and Tools -- 10.2.1.2 Toward Sustainable DSR -- 10.2.2 Strategic Planning for Data‐driven Scenarios -- 10.2.2.1 Big Data, Data Science, and AI -- 10.2.2.2 The Need for Data‐integrated Strategic Planning -- 10.2.2.3 Data Science and AI Challenges at the Organizational Level -- 10.2.2.4 Data Science and AI Challenges Beyond the Organizational Level. |
10.2.2.5 Opportunities for Using DSR -- 10.2.3 Related Studies -- 10.3 A Case Study of Networked Organizations' Strategic Planning for AI -- 10.3.1 Kickoff and Planning -- 10.3.2 Pre‐workshop -- 10.3.3 Workshops -- 10.3.4 Post‐workshop and Final Review -- 10.3.5 Keeping Roadmapping Alive to Implement Data and AI Governance -- 10.4 Discussion -- 10.5 Conclusion -- References -- Chapter 11 Technology Roadmapping: Nano Technology in Construction in Saudi Arabia -- 11.1 Introduction -- 11.2 Research Methodology -- 11.3 Background of Nanotechnology -- 11.3.1 What Is Nano? -- 11.3.2 Nanotechnology and Nanomaterials -- 11.3.3 Role of Nanotechnology -- 11.3.4 Application of Nanotechnology in Sustainable Building Designs -- 11.4 Nanoarchitecture: Definition and Its Applications -- 11.4.1 Definition of Nanoarchitecture -- 11.4.2 Application of Nanoarchitecture -- 11.4.2.1 Nanocement -- 11.4.2.2 Nanotechnology and Steel -- 11.4.2.3 Nano‐glass -- 11.4.2.4 Nanotechnology and Wood -- 11.4.2.5 Nanotechnology and Coating Materials -- 11.5 Building Sector and Sustainable Development Issues -- 11.5.1 Cost‐saving Productive Processes and Environmental Remediation -- 11.5.2 Green Building Concept -- 11.6 Using Nanomaterials in the Production |
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of Concrete‐based Composites -- 11.6.1 Carbon Nanotubes (CNTs) -- 11.6.2 Polymer Cement -- 11.6.3 Advantage of Nanotechnology in the Construction Industry -- 11.6.3.1 Enhanced Thermal Insulation -- 11.6.3.2 Lightweight Materials with Enhanced Strength -- 11.6.3.3 Self‐cleaning and Photocatalytic Properties -- 11.6.3.4 Reduction of Air Pollution -- 11.6.4 Drawbacks of Nanotechnology in the Construction Industry -- 11.7 Nanoarchitecture Application in Saudi Arabia -- 11.8 Technology Road Mapping for Green Architecture -- 11.8.1 Introduction -- 11.8.2 Steps in Technology Roadmapping -- 11.8.3 Market Drivers. |
11.8.3.1 QFD Market Segments and Drivers. |
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Sommario/riassunto |
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This book provides a comprehensive guide to technology assessment and management with a focus on various industry applications. Edited by Haydar Yalçın and Tugrul U. Daim, it includes contributions from experts in fields such as smart city development, process optimization in the cement industry, energy storage technologies, and technology forecasting using neural networks. The book aims to equip managers with the tools needed to assess and plan technology initiatives effectively. Intended for professionals and academics in technology management, it covers methodologies for technology intelligence and roadmapping, offering insights into emerging technologies and strategic planning frameworks. |
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