臺灣博碩士論文加值系統

隨著CMOS半導體的面積縮小達到其物理極限，整個電子產業正面臨著摩爾定律的終結。台積電為目前三家半導體製造商（台積電、英特爾、三星）之中領先挑戰並持續的推動半導體技術發展至物理極限的廠商。

目前，預計台積電將在2027年左右開發出1奈米技術，但在這個時期之後的半導體技術的未來發展，電子產品的開發和與仰賴半導體的新興技術的發展都存在極大的不確定性。其中包含人工智能（AI）、物聯網（IoT）、區塊鏈（包括加密貨幣採礦）和自動駕駛汽車中的汽車電子產品等已被確定為未來十年半導體行業可能的增長動力。

鑑於與摩爾定律的終結相關的技術不確定性以及市場需求的不確定性，加上上述新興技術能否商業化成功尚不明確。爰此，本研究利用情景分析，根據台積電在2030年面臨的主要不確定因素，制定了四種可能的情景及因應策略。不確定軸主要包括：1)半導體技術能否持續突破與否，以及2）半導體市場需求強或弱。由此二根軸，可產生四種情境。藉由台積電內部和外部專家參與，針對每種情景進行了德爾菲調查及技術組合規劃（TPP），依據重要性和風險，評估2030年新興技術的重要性排序。
研究結果共有四個發現。第一個發現：情境一「數位烏托邦」，台積電面臨強大的市場需求以及半導體製造技術的重大突破，可以應用在許許多多新興科技上，而產生重大利潤 ; 此為台積電的最佳情境。相反的，情景四「數位沙漠」，公司缺乏半導體突破，面臨下游市場疲軟，只有少數幾項新興技術可運用，公司勉強達成營運目標 ; 是台積電最糟糕的情況。
第二個發現：Artificial narrow intelligence (ANI), artificial general intelligence (AGI)，協同感知物聯網（IoT）和公共區塊鏈等，皆依賴於技術突破來實現其市場潛力。
Identity-related IoT and Ubiquitous IoT 包括智能城市應用，需靠市場廣泛採用才能成功。Information-aggregation IoT 及和自動駕駛汽車是由技術及市場所驅動，需要技術突破和市場需求強了才能發展成功。
第三個發現：和自動駕駛汽車在四種情境中的平均權重最高，ANI在所有新興技術中，風險為最低。
第四個發現：本硏究在原始TPP模型中，加入了等價線。修改後的TPP模型可以更詳細地描述投資項目，及識別各種新興科技的管理意涵。
最後，根據分析結果，台積電和其他參與者，可更積極地準備應對後摩爾時代的來臨，同時與時俱進，定時修訂本研究結果，適度修訂各情境內容。


關鍵字：後摩爾時代、情景分析、半導體、人工智能、物聯網、區塊鏈、自動駕駛汽車

With the area scaling of CMOS semiconductors reaching its physical limits, the entire electronics industry is facing the inevitable end of Moore’s Law. TSMC is currently one of just three semiconductor manufacturers, along with Intel and Samsung, that continue to push the physical limits at the leading edge of semiconductor technology development. At present, it is expected that TSMC will develop 1 nanometer technology around the year 2027, but beyond this time, there is great uncertainty as to the future development of semiconductor technologies and, in turn, the development of the electronic devices and emerging technologies which rely on semiconductors. Artificial intelligence (AI), the Internet of things (IoT), blockchain including cryptocurrency mining, and automotive electronics in autonomous vehicles have been identified as possible growth drivers for the semiconductor industry over the next decade. Given the technology uncertainty associated with the end of Moore’s Law and uncertainty surrounding market demand, the commercial success of these emerging technologies is uncertain. The current case study utilizes scenario analysis to develop four plausible scenarios based on the key uncertainties facing TSMC in the year 2030. These key uncertainties include; 1) the presence, or lack thereof, of a manufacturing breakthrough in semiconductor manufacturing, and 2) the strength of the market for semiconductor products. Given the resulting four scenarios, a Technology Portfolio Planning (TPP) Delphi survey was administered for each scenario in which internal and external experts to TSMC assessed the importance and risk of the identified emerging technologies in the year 2030. The findings of the study are four-fold. The first finding: Scenario 1 (Digital Utopia) which combines a strong technology market with a technological breakthrough in semiconductor manufacturing has the most Premier Approach technologies and is the best-case scenario for TSMC. On the contrary, scenario 4 (Digital Desert) lacks a semiconductor breakthrough and envisions a weak downstream market and is the worst-case scenario for TSMC. The second finding: Artificial narrow intelligence (ANI), artificial general intelligence (AGI), collaborative-aware Internet of things (IoT), and public blockchain are reliant on a technology breakthrough to achieve their full market potential. Identity-related IoT and Ubiquitous IoT including smart city applications rely on a strong market for widespread adoption. Information-aggregation IoT and automotive electronics are both technology-driven and market-driven and will require a technology breakthrough and a strong market to achieve their greatest potential. The third finding: Automotive electronics have the highest average importance across the four scenarios and ANI provides the lowest level of risk among the emerging technologies analyzed. The fourth finding: A modified TPP model which integrates equilibrium lines in the original TPP model can describe investment group projects in a more detailed manner and is better at identifying managerial implications between various strategic investment opportunities. Finally, based on the results of the analysis, TSMC and other players in the ICT industry can be better prepared to deal with uncertain, but plausible future scenarios in the Post-Moore Era.

Title Page i
Letter of Approval ii
Abstract in Chinese iii
Abstract in English v
Acknowledgements vii
Table of Contents viii
List of Tables x
List of Figures xi
Chapter 1 Introduction 1
1.1 Semiconductor technology development uncertainty 3
1.2 Market demand uncertainty 5
1.3 Emerging technology development uncertainty 6
1.4 The challenges facing TSMC moving toward 2030 8
1.5 Research question and research process 13
Chapter 2 Literature Review 18
2.1 Foresight Studies 18
2.2 Scenario analysis 20
2.2.1 Scenario analysis usage 21
2.2.2 Scenario development 26
2.2.3 Scenario analysis purposes 28
2.2.4 Scenario analysis and the Delphi method 31
2.2.5 Scenario analysis hybrid approach 33
2.3 Technology Portfolio Planning (TPP) model 35
2.5 Emerging technology analysis 36
2.5.1 Artificial Intelligence (AI) 38
2.5.2 Internet of Things (IoT) 49
2.5.3 Public Blockchain 61
2.5.4 Automotive electronics 67
Chapter 3 Methodology 72
3.1 Establishment of a Delphi expert panel 72
3.2 Scenario development 73
3.3 Scenario analysis and TPP model hybrid approach 74
3.4 Identification of key emerging technologies 75
3.5 Assessment of importance and risk factors of emerging technologies 75
3.6 A novel approach to the TPP model with equilibrium lines 77
3.7 Delphi method & consensus measurement 81
Chapter 4 Results: TSMC case study in the year 2030 83
4.1 Scenario analysis orientation 83
4.2 Critical uncertainties facing TSMC in 2030 84
4.2.1 Manufacturing/technological breakthrough uncertainty 84
4.2.2 Semiconductor demand uncertainty 87
4.3 Scenario development 89
4.3.1 Uncertainty Axes 89
4.3.2 Scenario storylines 90
4.4 Emerging technologies in the semiconductor downstream 98
4.5 TPP model results by scenario 100
4.5.1 Digital Utopia modified TPP analysis 100
4.5.2 Digital Myopia TPP analysis 105
4.5.3 Digital Stagnation TPP analysis 110
4.5.4 Digital Desert TPP analysis 113
4.6 TPP results by technology 116
Chapter 5 Conclusion & Suggestions 120
5.1 Research contributions 120
5.2 Research conclusions 120
5.3 Research Limitations 126
5.4 Future research suggestions 128
Appendix A: TPP survey results 129
Appendix B: Delphi panel meeting minutes 130
References: 137

ABBATE, T., CESARONI, F., CINICI, M. C. & VILLARI, M. 2019. Business models for developing smart cities. A fuzzy set qualitative comparative analysis of an IoT platform. Technological Forecasting and Social Change, 142, 183-193.
ABDULJABBAR, R., DIA, H., LIYANAGE, S. & BAGLOEE, S. A. 2019. Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11.
ABRAMS, M., ABRAMS, J., CULLEN, P. & GOLDSTEIN, L. 2019. Artificial Intelligence, Ethics, and Enhanced Data Stewardship. IEEE Security & Privacy, 17, 17-30.
ADAMSON, G. 2018. Achieving trust in artificial general intelligence. 2018 IEEE International Symposium on Technology in Society (ISTAS) Proceedings.
ADNAN, N., MD NORDIN, S., BIN BAHRUDDIN, M. A. & ALI, M. 2018. How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transportation Research Part A: Policy and Practice, 118, 819-836.
AHL, A., YARIME, M., TANAKA, K. & SAGAWA, D. 2019. Review of blockchain-based distributed energy: Implications for institutional development. Renewable and Sustainable Energy Reviews, 107, 200-211.
AI, Y., PENG, M. & ZHANG, K. 2018. Edge computing technologies for Internet of Things: a primer. Digital Communications and Networks, 4, 77-86.
AICARDI, C., FOTHERGILL, B. T., RAINEY, S., STAHL, B. C. & HARRIS, E. 2018. Accompanying technology development in the Human Brain Project: From foresight to ethics management. Futures, 102, 114-124.
AKHTAR, P., KHAN, Z., TARBA, S. & JAYAWICKRAMA, U. 2018. The Internet of Things, dynamic data and information processing capabilities, and operational agility. Technological Forecasting and Social Change, 136, 307-316.
AKINS, R. B., TOLSON, H. & COLE, B. R. 2005. Stability of response characteristics of a Delphi panel: application of bootstrap data expansion. BMC Med Res Methodol, 5, 37.
AL-FUQAHA, A., GUIZANI, M., MOHAMMADI, M., ALEDHARI, M. & AYYASH, M. 2015. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17, 2347-2376.
AL-SALEH, Y. M., VIDICAN, G., NATARAJAN, L. & THEEYATTUPARAMPIL, V. V. 2012. Carbon capture, utilisation and storage scenarios for the Gulf Cooperation Council region: A Delphi-based foresight study. Futures, 44, 105-115.
ALLAM, Z. & DHUNNY, Z. A. 2019. On big data, artificial intelligence and smart cities. Cities, 89, 80-91.
ALTUNTAS, S., DERELI, T. & KUSIAK, A. 2015. Forecasting technology success based on patent data. Technological Forecasting and Social Change, 96, 202-214.
ALVIAL-PALAVICINO, C. & KONRAD, K. 2018. The rise of graphene expectations: Anticipatory practices in emergent nanotechnologies. Futures.
ANDERSEN, M. P., FIERRO, G. & CULLER, D. E. 2017. Enabling synergy in IoT: Platform to service and beyond. Journal of Network and Computer Applications, 81, 96-110.
ANDONI, M., ROBU, V., FLYNN, D., ABRAM, S., GEACH, D., JENKINS, D., MCCALLUM, P. & PEACOCK, A. 2019. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143-174.
ANDREANI, S., KALCHSCHMIDT, M., PINTO, R. & SAYEGH, A. 2019. Reframing technologically enhanced urban scenarios: A design research model towards human centered smart cities. Technological Forecasting and Social Change, 142, 15-25.
ANDREWS-SPEED, P., VAN DER LINDE, C. & KERAMIDAS, K. 2014. Conflict and cooperation over access to energy: Implications for a low-carbon future. Futures, 58, 103-114.
ANGELIS, J. & RIBEIRO DA SILVA, E. 2019. Blockchain adoption: A value driver perspective. Business Horizons, 62, 307-314.
ANTUNES, M., GOMES, D. & AGUIAR, R. L. 2018. Towards IoT data classification through semantic features. Future Generation Computer Systems, 86, 792-798.
APPIO, F. P., LIMA, M. & PAROUTIS, S. 2018. Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change.
ARCOS-NOVILLO, D. A. & GÜEMES-CASTORENA, D. 2017. Development of an additive manufacturing technology scenario for opportunity identification—The case of Mexico. Futures, 90, 1-15.
ARDITO, L., D'ADDA, D. & MESSENI PETRUZZELLI, A. 2018. Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis. Technological Forecasting and Social Change, 136, 317-330.
ARYAL, A., LIAO, Y., NATTUTHURAI, P. & LI, B. 2018. The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management: An International Journal.
ASGHARI, P., RAHMANI, A. M. & JAVADI, H. H. S. 2018. Service composition approaches in IoT: A systematic review. Journal of Network and Computer Applications, 120, 61-77.
ASHIBANI, Y. & MAHMOUD, Q. H. 2017. Cyber physical systems security: Analysis, challenges and solutions. Computers & Security, 68, 81-97.
AUBRY, M. & RENOU-MAISSANT, P. 2014. Semiconductor industry cycles: Explanatory factors and forecasting. Economic Modelling, 39, 221-231.
AVELLA, J. R. 2016. Delphi panels: research design, procedures, advantages, and challenges. International Journal of Doctoral Studies, 11, 305-321.
ÁVILA-ROBINSON, A. & MIYAZAKI, K. 2013. Dynamics of scientific knowledge bases as proxies for discerning technological emergence — The case of MEMS/NEMS technologies. Technological Forecasting and Social Change, 80, 1071-1084.
BANSAL, P. & KOCKELMAN, K. M. 2017. Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95, 49-63.
BANSAL, P., KOCKELMAN, K. M. & SINGH, A. 2016. Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies, 67, 1-14.
BATTISTELLA, C. & DE TONI, A. F. 2011. A methodology of technological foresight: A proposal and field study. Technological Forecasting and Social Change, 78, 1029-1048.
BAUM, S. D. 2016. On the promotion of safe and socially beneficial artificial intelligence. Ai & Society, 32, 543-551.
BAUM, S. D., GOERTZEL, B. & GOERTZEL, T. G. 2011. How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change, 78, 185-195.
BAUR, D. G., HONG, K. & LEE, A. D. 2018. Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, 54, 177-189.
BELLO, O. & ZEADALLY, S. 2019. Toward efficient smartification of the Internet of Things (IoT) services. Future Generation Computer Systems, 92, 663-673.
BIBRI, S. E. 2018. The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230-253.
BIERWISCH, A., KAYSER, V. & SHALA, E. 2015. Emerging technologies in civil security—A scenario-based analysis. Technological Forecasting and Social Change, 101, 226-237.
BILDOSOLA, I., RÍO-BÉLVER, R. M., GARECHANA, G. & CILLERUELO, E. 2017. TeknoRoadmap, an approach for depicting emerging technologies. Technological Forecasting and Social Change, 117, 25-37.
BISWAS, R., JIANG, Z., KECHEZHI, K., KNYSH, S., MANDRÀ, S., O’GORMAN, B., PERDOMO-ORTIZ, A., PETUKHOV, A., REALPE-GÓMEZ, J., RIEFFEL, E., VENTURELLI, D., VASKO, F. & WANG, Z. 2017. A NASA perspective on quantum computing: Opportunities and challenges. Parallel Computing, 64, 81-98.
BOAVENTURA, J. M. G. & FISCHMANN, A. A. 2008. Is your vision consistent? Futures, 40, 597-612.
BÖHME, R., CHRISTIN, N., EDELMAN, B. & MOORE, T. 2015. Bitcoin: Economics, Technology, and Governance. Journal of Economic Perspectives, 29, 213-238.
BONNET, T. W. & OLSON, R. L. 1998. How Scenarios Enrich Public Policy Decisions. In: FAHEY, L. & RANDALL, R. M. (eds.) Learning from the Future: Competitive Foresight Scenarios. New York: John Wiley & Sons
BRADFIELD, R., WRIGHT, G., BURT, G., CAIRNS, G. & VAN DER HEIJDEN, K. 2005. The origins and evolution of scenario techniques in long range business planning. Futures, 37, 795-812.
BRADFIELD, R. M. 2008. Cognitive Barriers in the Scenario Development Process. Advances in Developing Human Resources, 10, 198-215.
BRESCIANI, S., FERRARIS, A. & DEL GIUDICE, M. 2018. The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136, 331-338.
BRILLIANTOVA, V. & THURNER, T. W. 2019. Blockchain and the future of energy. Technology in Society, 57, 38-45.
BUCKLEY, L., KAYE, S.-A. & PRADHAN, A. K. 2018. A qualitative examination of drivers’ responses to partially automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 167-175.
BÜLBÜL, H., ÖMÜRBEK, N., PAKSOY, T. & BEKTAŞ, T. 2013. An empirical investigation of advanced manufacturing technology investment patterns: Evidence from a developing country. Journal of Engineering and Technology Management, 30, 136-156.
CAGNIN, C., HAVAS, A. & SARITAS, O. 2013. Future-oriented technology analysis: Its potential to address disruptive transformations. Technological Forecasting and Social Change, 80, 379-385.
CALLAGHAN, C. W. 2018. Surviving a technological future: Technological proliferation and modes of discovery. Futures, 104, 100-116.
CAPUTO, F., CILLO, V., CANDELO, E. & LIU, Y. 2019. Innovating through digital revolution. Management Decision.
CAPUTO, F., SCUOTTO, V., CARAYANNIS, E. & CILLO, V. 2018. Intertwining the internet of things and consumers' behaviour science: Future promises for businesses. Technological Forecasting and Social Change, 136, 277-284.
CARAYANNIS, E. G., DEL GIUDICE, M. & SOTO-ACOSTA, P. 2018. Disruptive technological change within knowledge-driven economies: The future of the Internet of Things (IoT). Technological Forecasting and Social Change, 136, 265-267.
CASARES, A. P. 2018. The brain of the future and the viability of democratic governance: The role of artificial intelligence, cognitive machines, and viable systems. Futures, 103, 5-16.
CASINO, F., DASAKLIS, T. K. & PATSAKIS, C. 2019. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55-81.
CATH, C., WACHTER, S., MITTELSTADT, B., TADDEO, M. & FLORIDI, L. 2018. Artificial Intelligence and the 'Good Society': the US, EU, and UK approach. Sci Eng Ethics, 24, 505-528.
ČERKA, P., GRIGIENĖ, J. & SIRBIKYTĖ, G. 2015. Liability for damages caused by artificial intelligence. Computer Law & Security Review, 31, 376-389.
CHAN, L. & DAIM, T. 2012. Exploring the impact of technology foresight studies on innovation: Case of BRIC countries. Futures, 44, 618-630.
CHAN, S. W. K. & CHONG, M. W. C. 2017. Sentiment analysis in financial texts. Decision Support Systems, 94, 53-64.
CHEN, H., WAKELAND, W. & YU, J. 2012. A two-stage technology foresight model with system dynamics simulation and its application in the Chinese ICT industry. Technological Forecasting and Social Change, 79, 1254-1267.
CHEN, H., YU, J. & WAKELAND, W. 2016. Generating technology development paths to the desired future through system dynamics modeling and simulation. Futures, 81, 81-97.
CHEN, T.-Y. & HUANG, C.-J. 2019. A Two-Tier Scenario Planning Model of Environmental Sustainability Policy in Taiwan. Sustainability, 11.
CHEN, T.-Y., YU, O. S., HSU, G. J.-Y., HSU, F.-M. & SUNG, W.-N. 2009. Renewable energy technology portfolio planning with scenario analysis: A case study for Taiwan. Energy Policy, 37, 2900-2906.
CHEN, Y. 2018. Blockchain tokens and the potential democratization of entrepreneurship and innovation. Business Horizons, 61, 567-575.
CHERMACK, T. J., LYNHAM, S. A. & RUONA, W. E. A. 2001. A review of scenario planning literature. Futures Research Quarterly.
COULTER, R. & PAN, L. 2018. Intelligent agents defending for an IoT world: A review. Computers & Security, 73, 439-458.
CUI, J., LIEW, L. S., SABALIAUSKAITE, G. & ZHOU, F. 2018. A review on safety failures, security attacks, and available countermeasures for autonomous vehicles. Ad Hoc Networks.
CURRIE, A. & Ó HÉIGEARTAIGH, S. 2018. Working together to face humanity’s greatest threats: Introduction to the Future of Research on Catastrophic and Existential Risk. Futures, 102, 1-5.
DAGHER, G. G., MOHLER, J., MILOJKOVIC, M. & MARELLA, P. B. 2018. Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. Sustainable Cities and Society, 39, 283-297.
DAIM, T. U., RUEDA, G., MARTIN, H. & GERDSRI, P. 2006. Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, 981-1012.
DALKEY, N. 1969. An experimental study of group opinion: The Delphi Method. Futures 1, 408-426.
DAVOUDI, S., WISHARDT, M. & STRANGE, I. 2010. The ageing of Europe: Demographic scenarios of Europe's futures. Futures, 42, 794-803.
DAY, G. S. & SHOEMAKER, P. J. H. 2000. Avoiding the pitfalls of emerging technologies. California Management Review, 42, 8.
DECANIO, S. J. 2016. Robots and humans – complements or substitutes? Journal of Macroeconomics, 49, 280-291.
DECKER, M., FISCHER, M. & OTT, I. 2017. Service Robotics and Human Labor: A first technology assessment of substitution and cooperation. Robotics and Autonomous Systems, 87, 348-354.
DERBYSHIRE, J. & GIOVANNETTI, E. 2017. Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting. Technological Forecasting and Social Change, 125, 334-344.
DHAND, G. & TYAGI, S. S. 2016. Data Aggregation Techniques in WSN:Survey. Procedia Computer Science, 92, 378-384.
DHEBAR, A. 2016. Bringing new high-technology products to market: Six perils awaiting marketers. Business Horizons, 59, 713-722.
DIJKMAN, R. M., SPRENKELS, B., PEETERS, T. & JANSSEN, A. 2015. Business models for the Internet of Things. International Journal of Information Management, 35, 672-678.
DING, I., JR. & CHANG, Y.-J. 2017. HMM with improved feature extraction-based feature parameters for identity recognition of gesture command operators by using a sensed Kinect-data stream. Neurocomputing, 262, 108-119.
DONG, C., SCHOUPS, G. & VAN DE GIESEN, N. 2013. Scenario development for water resource planning and management: A review. Technological Forecasting and Social Change, 80, 749-761.
DUAN, Y., EDWARDS, J. S. & DWIVEDI, Y. K. 2019. Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
DURANCE, P. & GODET, M. 2010. Scenario building: Uses and abuses. Technological Forecasting and Social Change, 77, 1488-1492.
EL-HADDADEH, R., WEERAKKODY, V., OSMANI, M., THAKKER, D. & KAPOOR, K. K. 2019. Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement. Government Information Quarterly, 36, 310-320.
ESSENI, D., BADAMI, O., DRIUSSI, F., LIZZIT, D., PALA, M., PALESTRI, P., ROLLO, T., SELMI, L. & VENICA, S. 2018. New device concepts, transistor architectures and materials for high performance and energy efficient CMOS circuits in the forthcoming era of 3D integrated circuits. 2018 IEEE 2nd Electron Devices Technology and Manufacturing Conference (EDTM), 236-238.
FABER, A., IDENBURG, A. M. & WILTING, H. C. 2007. Exploring techno-economic scenarios in an input–output model. Futures, 39, 16-37.
FAGNANT, D. J. & KOCKELMAN, K. 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.
FAJRIN, N., TAUFIK, I., ISMAIL, N., KAMELIA, L. & RAMDHANI, M. A. 2018. On the Design of Watering and Lighting Control Systems for Chrysanthemum Cultivation in Greenhouse Based on Internet of Things. IOP Conference Series: Materials Science and Engineering, 288.
FARROW, E. 2019. To augment human capacity—Artificial intelligence evolution through causal layered analysis. Futures, 108, 61-71.
FEATHERSTON, C. R. & O'SULLIVAN, E. 2017. Enabling technologies, lifecycle transitions, and industrial systems in technology foresight: Insights from advanced materials FTA. Technological Forecasting and Social Change, 115, 261-277.
FERNANDEZ-CARAMES, T. M. & FRAGA-LAMAS, P. 2018. A Review on the Use of Blockchain for the Internet of Things. IEEE Access, 6, 32979-33001.
FERRÁNDEZ-PASTOR, F.-J., MORA, H., JIMENO-MORENILLA, A. & VOLCKAERT, B. 2018. Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services. Sustainability, 10.
FERRARIS, A., SANTORO, G. & PAPA, A. 2018. The cities of the future: Hybrid alliances for open innovation projects. Futures, 103, 51-60.
FISHER, I. E., GARNSEY, M. R. & HUGHES, M. E. 2016. Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research. Intelligent Systems in Accounting, Finance and Management, 23, 157-214.
FÖRSTER, B. & VON DER GRACHT, H. 2014. Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants. Technological Forecasting and Social Change, 84, 215-229.
FRANCISCO, K. & SWANSON, D. 2018. The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency. Logistics, 2.
FUNK, J. L. 2015. Thinking about the future of technology: Rates of improvement and economic feasibility. Futures, 73, 163-175.
GABRIEL, J. & SCHMELCHER, S. 2018. Three scenarios for EU-China relations 2025. Futures, 97, 26-34.
GALLOWAY, C. & SWIATEK, L. 2018. Public relations and artificial intelligence: It’s not (just) about robots. Public Relations Review, 44, 734-740.
GEORGHIOU, L. 1996. The UK Technology Foresight Programme. Futures, 28, 359-377.
GEORGHIOU, L. & CASSINGENA HARPER, J. 2011. From priority-setting to articulation of demand: Foresight for research and innovation policy and strategy. Futures, 43, 243-251.
GEUM, Y., LEE, S. & PARK, Y. 2014. Combining technology roadmap and system dynamics simulation to support scenario-planning: A case of car-sharing service. Computers & Industrial Engineering, 71, 37-49.
GIANNAROU, L. & ZERVAS, E. 2014. Using Delphi technique to build consensus in practice. International Journal of Business Science and Applied Management, 9.
GIBNEY, E. 2017. D-Wave upgrade: How scientists are using the world’s most controversial quantum computer. Nature, 541, 447-448.
GIGLI, M. & KOO, S. 2011. Internet of Things: Services and applications categorization. Advances in Internet of Things, 27-31.
GOERTZEL, B., GOERTZEL, T. & GOERTZEL, Z. 2017. The global brain and the emerging economy of abundance: Mutualism, open collaboration, exchange networks and the automated commons. Technological Forecasting and Social Change, 114, 65-73.
GONZALEZ-JIMENEZ, H. 2018. Taking the fiction out of science fiction: (Self-aware) robots and what they mean for society, retailers and marketers. Futures, 98, 49-56.
GRAEF, M., SEILER, D. G., DIEBOLD, A. C., MCDONALD, R., CHABLI, A. & SECULA, E. M. 2011. Positioning More Than Moore Characterization Needs and Methods within the 2011 ITRS.
GURKAYNAK, G., YILMAZ, I. & HAKSEVER, G. 2016. Stifling artificial intelligence: Human perils. Computer Law & Security Review, 32, 749-758.
GURSOY, D., CHI, O. H., LU, L. & NUNKOO, R. 2019. Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169.
GYONGYOSI, L. & IMRE, S. 2019. A Survey on quantum computing technology. Computer Science Review, 31, 51-71.
HABIBI, A., SARAFRAZI, A. & IZADYAR, S. 2014. Delphi technique theoretical framework in qualitative research. The International Journal of Engineering and Science, 3, 8-13.
HAMMOUD, M. S. & NASH, D. P. 2014. What corporations do with foresight. European Journal of Futures Research, 2, 42.
HANCOCK, T. & BEZOLD, C. 1994. Possible futures, preferable futures. The Healthcare Forum Journal.
HASHEM, I. A. T., CHANG, V., ANUAR, N. B., ADEWOLE, K., YAQOOB, I., GANI, A., AHMED, E. & CHIROMA, H. 2016. The role of big data in smart city. International Journal of Information Management, 36, 748-758.
HAWLITSCHEK, F., NOTHEISEN, B. & TEUBNER, T. 2018. The limits of trust-free systems: A literature review on blockchain technology and trust in the sharing economy. Electronic Commerce Research and Applications, 29, 50-63.
HEGER, T. & ROHRBECK, R. 2012. Strategic foresight for collaborative exploration of new business fields. Technological Forecasting and Social Change, 79, 819-831.
HEIJDEN, V. D. 1997. Physiotherapy for Patients with Soft Tissue Shoulder Disorders: A Systematic Review of Randomised Clinical Trials. BMJ: British Medical Journal, 315, 25-30.
HENGSTLER, M., ENKEL, E. & DUELLI, S. 2016. Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
HINOJO-LUCENA, F.-J., AZNAR-DÍAZ, I., CÁCERES-RECHE, M.-P. & ROMERO-RODRÍGUEZ, J.-M. 2019. Artificial Intelligence in Higher Education: A Bibliometric Study on its Impact in the Scientific Literature. Education Sciences, 9.
HOFMANN, E. & RÜSCH, M. 2017. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34.
HOJČKOVÁ, K., SANDÉN, B. & AHLBORG, H. 2018. Three electricity futures: Monitoring the emergence of alternative system architectures. Futures, 98, 72-89.
HOLFORD, W. D. 2019. The future of human creative knowledge work within the digital economy. Futures, 105, 143-154.
HOLTEL, S. 2016. Artificial Intelligence Creates a Wicked Problem for the Enterprise. Procedia Computer Science, 99, 171-180.
HORTON, A. 1999. A simple guide to successful foresight. Foresight, 1, 5-9.
HSIEH, C.-H. 2013. Patent value assessment and commercialization strategy. Technological Forecasting and Social Change, 80, 307-319.
HSIEH, Y. H., XUE, F., YANG, T., LIU, H. J., ZHU, Y., CHEN, Y. C., ZHAN, Q., DUAN, C. G., CHEN, L. Q., HE, Q. & CHU, Y. H. 2016. Permanent ferroelectric retention of BiFeO3 mesocrystal. Nat Commun, 7, 13199.
HSU, C.-C. & SANDFORD, B. A. 2007. The Delphi Technique: Making sense Of consensus Practical Assessment, Research & Evaluation, 12.
HSU, C.-L. & LIN, J. C.-C. 2016. An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527.
HUANG, J.-Y. 2016. Patent portfolio analysis of the cloud computing industry. Journal of Engineering and Technology Management, 39, 45-64.
HUANG, M.-H. & RUST, R. T. 2018. Artificial Intelligence in Service. Journal of Service Research, 21, 155-172.
HUGHES, A., PARK, A., KIETZMANN, J. & ARCHER-BROWN, C. 2019a. Beyond Bitcoin: What blockchain and distributed ledger technologies mean for firms. Business Horizons.
HUGHES, L., DWIVEDI, Y. K., MISRA, S. K., RANA, N. P., RAGHAVAN, V. & AKELLA, V. 2019b. Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114-129.
HUSSAIN, M., TAPINOS, E. & KNIGHT, L. 2017. Scenario-driven roadmapping for technology foresight. Technological Forecasting and Social Change, 124, 160-177.
IQBAL, J., KHAN, M., TALHA, M., FARMAN, H., JAN, B., MUHAMMAD, A. & KHATTAK, H. A. 2018. A generic internet of things architecture for controlling electrical energy consumption in smart homes. Sustainable Cities and Society, 43, 443-450.
ISLAM, N., MARINAKIS, Y., MAJADILLAS, M. A., FINK, M. & WALSH, S. T. 2018. Here there be dragons, a pre-roadmap construct for IoT service infrastructure. Technological Forecasting and Social Change.
ISMAGILOVA, E., HUGHES, L., DWIVEDI, Y. K. & RAMAN, K. R. 2019. Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47, 88-100.
JAHANSHAHI, A., KAMALI, M., KHALAJ, M. & KHODAPARAST, Z. 2019. Delphi-based prioritization of economic criteria for development of wave and tidal energy technologies. Energy, 167, 819-827.
JALALI, M. S., KAISER, J. P., SIEGEL, M. & MADNICK, S. 2019. The Internet of Things Promises New Benefits and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products. IEEE Security & Privacy, 17, 39-48.
JAMISON, M. A. & TARIQ, P. 2018. Five things regulators should know about blockchain (and three myths to forget). The Electricity Journal, 31, 20-23.
JAYASHANKAR, P., NILAKANTA, S., JOHNSTON, W. J., GILL, P. & BURRES, R. 2018. IoT adoption in agriculture: the role of trust, perceived value and risk. Journal of Business & Industrial Marketing, 33, 804-821.
JENKINS, A. 2003. Artificial intelligence and the real world. Futures, 35, 779-786.
JHA, S. K., BILALOVIC, J., JHA, A., PATEL, N. & ZHANG, H. 2017. Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-317.
JIANG, R., KLEER, R. & PILLER, F. T. 2017. Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030. Technological Forecasting and Social Change, 117, 84-97.
JØRGENSEN, M. S., JØRGENSEN, U. & CLAUSEN, C. 2009. The social shaping approach to technology foresight. Futures, 41, 80-86.
JORGENSON, D. W. & VU, K. M. 2016. The ICT revolution, world economic growth, and policy issues. Telecommunications Policy, 40, 383-397.
JOUNG, J. & KIM, K. 2017. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technological Forecasting and Social Change, 114, 281-292.
KAGERMANN, H. 2015. Change Through Digitization—Value Creation in the Age of Industry 4.0. Management of Permanent Change.
KAHANE, A. 1999. Changing the winds: Scenarios for people who want to change the world. Whole Earth Review, Spring.
KAHN, H. & WEINER, A. J. 1967. The year 2000: A framework for speculation on the next thirty-three years. , London, MacMillan Publishing Company.
KAMBLE, S. S., GUNASEKARAN, A., PAREKH, H. & JOSHI, S. 2019. Modeling the internet of things adoption barriers in food retail supply chains. Journal of Retailing and Consumer Services, 48, 154-168.
KAMBLE, S. S., GUNASEKARAN, A. & SHARMA, R. 2018. Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107-119.
KANKANHALLI, A., CHARALABIDIS, Y. & MELLOULI, S. 2019. IoT and AI for Smart Government: A Research Agenda. Government Information Quarterly, 36, 304-309.
KAPLAN, A. & HAENLEIN, M. 2019. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62, 15-25.
KAUR, K. & RAMPERSAD, G. 2018. Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48, 87-96.
KELLER, J. & VON DER GRACHT, H. A. 2014. The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey. Technological Forecasting and Social Change, 85, 81-92.
KHADJEH NASSIRTOUSSI, A., AGHABOZORGI, S., YING WAH, T. & NGO, D. C. L. 2014. Text mining for market prediction: A systematic review. Expert Systems with Applications, 41, 7653-7670.
KHAN, H. N., HOUNSHELL, D. A. & FUCHS, E. R. H. 2018. Science and research policy at the end of Moore’s law. Nature Electronics, 1, 14-21.
KHAN, M. A. & SALAH, K. 2018. IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395-411.
KIM, S. & KIM, S. 2016. A multi-criteria approach toward discovering killer IoT application in Korea. Technological Forecasting and Social Change, 102, 143-155.
KING, T. C., AGGARWAL, N., TADDEO, M. & FLORIDI, L. 2019. Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions. Sci Eng Ethics.
KNOBLICH, K., HEAVEY, C. & WILLIAMS, P. 2015. Quantitative analysis of semiconductor supply chain contracts with order flexibility under demand uncertainty: A case study. Computers & Industrial Engineering, 87, 394-406.
KO, T., LEE, J. & RYU, D. 2018. Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10.
KOSE, T. & SAKATA, I. 2018. Identifying technology convergence in the field of robotics research. Technological Forecasting and Social Change.
KROTOV, V. 2017. The Internet of Things and new business opportunities. Business Horizons, 60, 831-841.
KSHETRI, N. 2017. Blockchain's roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 41, 1027-1038.
KUZMINA, K., PRENDEVILLE, S., WALKER, D. & CHARNLEY, F. 2018. Future scenarios for fast-moving consumer goods in a circular economy. Futures.
LANCASTER, A. & KESWANI, M. 2018. Integrated circuit packaging review with an emphasis on 3D packaging. Integration, 60, 204-212.
LEE, I. & LEE, K. 2015. The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58, 431-440.
LEE, S. Y. & LEE, K. 2018. Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154-163.
LEE, Y.-J. & PARK, J.-Y. 2017. Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence. Quality & Quantity, 52, 653-667.
LEHR, T., LORENZ, U., WILLERT, M. & ROHRBECK, R. 2017. Scenario-based strategizing: Advancing the applicability in strategists' teams. Technological Forecasting and Social Change, 124, 214-224.
LEMAIGNAN, S., WARNIER, M., SISBOT, E. A., CLODIC, A. & ALAMI, R. 2017. Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence, 247, 45-69.
LEMINEN, S., RAJAHONKA, M., WESTERLUND, M. & WENDELIN, R. 2018. The future of the Internet of Things: toward heterarchical ecosystems and service business models. Journal of Business & Industrial Marketing, 33, 749-767.
LI, B.-H., HOU, B.-C., YU, W.-T., LU, X.-B. & YANG, C.-W. 2017. Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18, 86-96.
LI, H., HE, H., SHAN, J. & CAI, J. 2019. Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis. Socio-Economic Planning Sciences, 66, 136-148.
LI, M., PORTER, A. L. & SUOMINEN, A. 2018a. Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective. Technological Forecasting and Social Change, 129, 285-296.
LI, S., PAN WANG, P. S. C. P., TRYFONAS, T. & LI, H. 2016. The Internet of Things: a security point of view. Internet Research, 26, 337-359.
LI, Z., WANG, W. M., LIU, G., LIU, L., HE, J. & HUANG, G. Q. 2018b. Toward open manufacturing. Industrial Management & Data Systems, 118, 303-320.
LIN, F., LIN, S.-W. & LU, W.-M. 2018. Sustainability Assessment of Taiwan’s Semiconductor Industry: A New Hybrid Model Using Combined Analytic Hierarchy Process and Two-Stage Additive Network Data Envelopment Analysis. Sustainability, 10.
LINSTONE, H. 1981. The multiple perspective concept: With applications to technology assessment and other decision areas. Technological Forecasting and Social Change, 20, 275-325.
LU, H., LI, Y., CHEN, M., KIM, H. & SERIKAWA, S. 2017. Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23, 368-375.
LU, L., CAI, R. & GURSOY, D. 2019. Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51.
LU, Y. 2017. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10.
LU, Y., PAPAGIANNIDIS, S. & ALAMANOS, E. 2018. Internet of Things: A systematic review of the business literature from the user and organisational perspectives. Technological Forecasting and Social Change, 136, 285-297.
MACKAY, B. & TAMBEAU, P. 2013. A structuration approach to scenario praxis. Technological Forecasting and Social Change, 80, 673-686.
MAINE, E., THOMAS, V. J. & UTTERBACK, J. 2014. Radical innovation from the confluence of technologies: Innovation management strategies for the emerging nanobiotechnology industry. Journal of Engineering and Technology Management, 32, 1-25.
MAJOT, A. & YAMPOLSKIY, R. 2015. Global catastrophic risk and security implications of quantum computers. Futures, 72, 17-26.
MAKHDOOM, I., ABOLHASAN, M., ABBAS, H. & NI, W. 2019. Blockchain's adoption in IoT: The challenges, and a way forward. Journal of Network and Computer Applications, 125, 251-279.
MAKRIDAKIS, S. 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
MANI, Z. & CHOUK, I. 2016. Drivers of consumers’ resistance to smart products. Journal of Marketing Management, 33, 76-97.
MARSAL-LLACUNA, M.-L. 2018. Future living framework: Is blockchain the next enabling network? Technological Forecasting and Social Change, 128, 226-234.
MARTIN, B. R. 1995. Foresight in science and technology. Technology Analysis & Strategic Management, 7, 139-168.
MARTÍNEZ-CARO, E., CEGARRA-NAVARRO, J. G., GARCÍA-PÉREZ, A. & FAIT, M. 2018. Healthcare service evolution towards the Internet of Things: An end-user perspective. Technological Forecasting and Social Change, 136, 268-276.
MCDOWALL, W. 2014. Exploring possible transition pathways for hydrogen energy: A hybrid approach using socio-technical scenarios and energy system modelling. Futures, 63, 1-14.
MCGHIN, T., CHOO, K.-K. R., LIU, C. Z. & HE, D. 2019. Blockchain in healthcare applications: Research challenges and opportunities. Journal of Network and Computer Applications, 135, 62-75.
MEIJERING, J. V., KAMPEN, J. K. & TOBI, H. 2013. Quantifying the development of agreement among experts in Delphi studies. Technological Forecasting and Social Change, 80, 1607-1614.
MEISSNER, P. & WULF, T. 2013. Cognitive benefits of scenario planning: Its impact on biases and decision quality. Technological Forecasting and Social Change, 80, 801-814.
METALLO, C., AGRIFOGLIO, R., SCHIAVONE, F. & MUELLER, J. 2018. Understanding business model in the Internet of Things industry. Technological Forecasting and Social Change, 136, 298-306.
MIETZNER, D. & REGER, G. 2005. Advantages and disadvantages of scenarios. International Journal of Intelligence and Planning, 1.
MILLER, T. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38.
MIORANDI, D., SICARI, S., DE PELLEGRINI, F. & CHLAMTAC, I. 2012. Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10, 1497-1516.
MITROFF, I. & LINSTONE, H. 1996. Unbounded Systems Thinking: The Fifth Way of Knowing. The Unbounded Mind.
MOHAMMED, F. H. & ESMAIL, R. 2015. Survey on IoT services: Classifications and applications. International Journal of Science and Research, 4, 2124-2127.
MONTES, G. A. & GOERTZEL, B. 2018. Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting and Social Change.
MOORE, G. E. Progress in digital integrated electronics. International Electron Devices Meeting IEEE, 1975. Technical Digest.
MORKUNAS, V. J., PASCHEN, J. & BOON, E. 2019. How blockchain technologies impact your business model. Business Horizons.
NAM, T. 2019. Technology usage, expected job sustainability, and perceived job insecurity. Technological Forecasting and Social Change, 138, 155-165.
NANDYALA, C. S. & KIM, H.-K. 2016. Green IoT Agriculture and HealthcareApplication (GAHA). International Journal of Smart Home, 10, 289-300.
NG, I., SCHARF, K., POGREBNA, G. & MAULL, R. 2015. Contextual variety, Internet-of-Things and the choice of tailoring over platform: Mass customisation strategy in supply chain management. International Journal of Production Economics, 159, 76-87.
NGUYEN DANG TUAN, M., NGUYEN THANH, N. & LE TUAN, L. 2019. Applying a mindfulness-based reliability strategy to the Internet of Things in healthcare – A business model in the Vietnamese market. Technological Forecasting and Social Change, 140, 54-68.
NOVOSELOV, K. S., FAL'KO, V. I., COLOMBO, L., GELLERT, P. R., SCHWAB, M. G. & KIM, K. 2012. A roadmap for graphene. Nature, 490, 192-200.
NOWACK, M., ENDRIKAT, J. & GUENTHER, E. 2011. Review of Delphi-based scenario studies: Quality and design considerations. Technological Forecasting and Social Change, 78, 1603-1615.
NYGRÉN, N. A., TAPIO, P. & QI, Y. 2017. Lake management in 2030—Five future images based on an international Delphi study. Futures, 93, 1-13.
OBENG, Y. & SRINIVASAN, P. 2011. Graphene: Is it the future for Semiconductors? An overview of the material, devices, and applications. The Electrochemical Society Interface, 20, 47-52.
OBRECHT, M. & DENAC, M. 2016. Technology forecast of sustainable energy development prospects. Futures, 84, 12-22.
ØLNES, S., UBACHT, J. & JANSSEN, M. 2017. Blockchain in government: Benefits and implications of distributed ledger technology for information sharing. Government Information Quarterly, 34, 355-364.
ÖNKAL, D., SAYIM, K. Z. & GÖNÜL, M. S. 2013. Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80, 772-788.
OUGHTON, E., FRIAS, Z., RUSSELL, T., SICKER, D. & CLEEVELY, D. D. 2018. Towards 5G: Scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure. Technological Forecasting and Social Change, 133, 141-155.
PAN, Y. 2016. Heading toward Artificial Intelligence 2.0. Engineering, 2, 409-413.
PAPA, A., MITAL, M., PISANO, P. & DEL GIUDICE, M. 2018. E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change.
PAPADOULIS, A., QUDDUS, M. & IMPRIALOU, M. 2019. Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis & Prevention, 124, 12-22.
PARREND, P., NAVARRO, J., GUIGOU, F., DERUYVER, A. & COLLET, P. 2018. Foundations and applications of artificial Intelligence for zero-day and multi-step attack detection. EURASIP Journal on Information Security, 2018.
PAZAITIS, A., DE FILIPPI, P. & KOSTAKIS, V. 2017. Blockchain and value systems in the sharing economy: The illustrative case of Backfeed. Technological Forecasting and Social Change, 125, 105-115.
PENG, Z., NG, A. L., KWON, H., WANG, P., CHEN, C. F., LEE, C. S. & WANG, Y. 2017. Graphene as a functional layer for semiconducting carbon nanotube transistor sensors. Carbon N Y, 125, 49-55.
PENMETSA, P., ADANU, E. K., WOOD, D., WANG, T. & JONES, S. L. 2019. Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion. Technological Forecasting and Social Change, 143, 9-13.
PHILIP CHEN, C. L. & ZHANG, C.-Y. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
PIETROBELLI, C. & PUPPATO, F. 2016. Technology foresight and industrial strategy. Technological Forecasting and Social Change, 110, 117-125.
PILLARISETTY, R. 2011. Academic and industry research progress in germanium nanodevices. Nature, 479, 324-328.
PORTER, A. L., ASHTON, W. B., CLAR, G., COATES, J. F., CUHLS, K., CUNNINGHAM, S. W., ADDA, D., DUCATEL, K., VAN DER DUIN, P., GEORGHIOU, L., GORDON, T., LINSTONE, H., MARCHAU, V., MASSARI, G., MILES, I., MOGEE, M., SALO, A., SCAPOLO, F., SMITS, R. & THISSEN, W. 2004. Technology futures analysis: Toward integration of the field and new methods. Technological Forecasting and Social Change, 71, 287-303.
POSTMA, T. J. B. M., BROEKHUIZEN, T. L. J. & VAN DEN BOSCH, F. 2012. The contribution of scenario analysis to the front-end of new product development. Futures, 44, 642-654.
POSTMA, T. J. B. M. & LIEBL, F. 2005. How to improve scenario analysis as a strategic management tool? Technological Forecasting and Social Change, 72, 161-173.
POURGHEBLEH, B. & NAVIMIPOUR, N. J. 2017. Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications, 97, 23-34.
QUEIROZ, M. M. & FOSSO WAMBA, S. 2019. Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82.
RAIKOV, A. 2018. Accelerating technology for self-organising networked democracy. Futures, 103, 17-26.
RAJAN, K. & SAFFIOTTI, A. 2017. Towards a science of integrated AI and Robotics. Artificial Intelligence, 247, 1-9.
RASTOGI, A. P., FOWLER, J. W., MATTHEW CARLYLE, W., ARAZ, O. M., MALTZ, A. & BÜKE, B. 2011. Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations. International Journal of Production Economics, 134, 322-332.
RAYENS, M. K. & HAHN, E. J. 2000. Building consensus using the policy Delphi Method. Policy, Politics, & Nursing Practice, 1, 308-315.
REYNA, A., MARTÍN, C., CHEN, J., SOLER, E. & DÍAZ, M. 2018. On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems, 88, 173-190.
RHISIART, M., MILLER, R. & BROOKS, S. 2015. Learning to use the future: developing foresight capabilities through scenario processes. Technological Forecasting and Social Change, 101, 124-133.
RIBEIRO, B. E. & QUINTANILLA, M. A. 2015. Transitions in biofuel technologies: An appraisal of the social impacts of cellulosic ethanol using the Delphi method. Technological Forecasting and Social Change, 92, 53-68.
RINGLAND, G. 2010. The role of scenarios in strategic foresight. Technological Forecasting and Social Change, 77, 1493-1498.
RISIUS, M. & SPOHRER, K. 2017. A Blockchain Research Framework. Business & Information Systems Engineering, 59, 385-409.
RISTESKA STOJKOSKA, B. L. & TRIVODALIEV, K. V. 2017. A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464.
ROHRBECK, R. 2012. Exploring value creation from corporate-foresight activities. Futures, 44, 440-452.
ROHRBECK, R., BATTISTELLA, C. & HUIZINGH, E. 2015. Corporate foresight: An emerging field with a rich tradition. Technological Forecasting and Social Change, 101, 1-9.
ROHRBECK, R. & GEMÜNDEN, H. G. 2011. Corporate foresight: Its three roles in enhancing the innovation capacity of a firm. Technological Forecasting and Social Change, 78, 231-243.
ROHRBECK, R. & KUM, M. E. 2018. Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and Social Change, 129, 105-116.
ROMANOWSKI, M. & NADOLNY, K. 2018. Technological Foresight – characterisation of research methods used in prospective analysis. Journal of Mechanical and Energy Engineering, 2, 101-108.
RONG, K., HU, G., LIN, Y., SHI, Y. & GUO, L. 2015. Understanding business ecosystem using a 6C framework in Internet-of-Things-based sectors. International Journal of Production Economics, 159, 41-55.
ROTOLO, D., HICKS, D. & MARTIN, B. R. 2015. What is an emerging technology? Research Policy, 44, 1827-1843.
ROWE, G. & WRIGHT, G. 1999. The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15, 353-375.
ROWLAND, N. J. & SPANIOL, M. J. 2017. Social foundation of scenario planning. Technological Forecasting and Social Change, 124, 6-15.
ROZARIO, A. M. & VASARHELYI, M. A. 2018. Auditing with Smart Contracts. The International Journal of Digital Accounting Research, 1-27.
RUFF, F. 2015. The advanced role of corporate foresight in innovation and strategic management — Reflections on practical experiences from the automotive industry. Technological Forecasting and Social Change, 101, 37-48.
RUGGERI, K., KÁCHA, O., MENEZES, I. G., KOS, M., FRANKLIN, M., PARMA, L., LANGDON, P., MATTHEWS, B. & MILES, J. 2018. In with the new? Generational differences shape population technology adoption patterns in the age of self-driving vehicles. Journal of Engineering and Technology Management, 50, 39-44.
SACIO-SZYMAŃSKA, A., MAZURKIEWICZ, A. & POTERALSKA, B. 2015. Corporate foresight at the strategic research institutes. Verslas: Teorija ir Praktika, 16, 316-325.
SANTORO, G., VRONTIS, D., THRASSOU, A. & DEZI, L. 2018. The Internet of Things: Building a knowledge management system for open innovation and knowledge management capacity. Technological Forecasting and Social Change, 136, 347-354.
SARITAS, O. & ONER, M. A. 2004. Systemic analysis of UK foresight results. Technological Forecasting and Social Change, 71, 27-65.
SARITAS, O. & SMITH, J. E. 2011. The Big Picture – trends, drivers, wild cards, discontinuities and weak signals. Futures, 43, 292-312.
SAVELYEV, A. 2018. Copyright in the blockchain era: Promises and challenges. Computer Law & Security Review, 34, 550-561.
SCHIAVONE, F., PAOLONE, F. & MANCINI, D. 2019. Business model innovation for urban smartization. Technological Forecasting and Social Change, 142, 210-219.
SCHOEMAKER, P. J. H. 1991. When and how to use scenario planning: A heuristic approach with illustration. Journal of Forecasting, 10, 549-564.
SCHOEMAKER, P. J. H. 1993. Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14, 193-213.
SCHOEMAKER, P. J. H. 1995. Scenario planning: A tool for strategic thinking. MIT Sloan Management Review, 36, 25-40.
SCHOEMAKER, P. J. H., DAY, G. S. & SNYDER, S. A. 2013. Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change, 80, 815-824.
SCHWARTZ, P. 1991. The art of the long view: planning for the future in an uncertain world, Crown Business.
SCHWARZ, J. O., RAM, C. & ROHRBECK, R. 2019. Combining scenario planning and business wargaming to better anticipate future competitive dynamics. Futures, 105, 133-142.
SEARLE, J. R. 1990. Is the brain a digital computer? Proceedings and Addresses of the American Philosphical Association 64, 21-37.
SESSA, C. & RICCI, A. 2014. The world in 2050 and the New Welfare scenario. Futures, 58, 77-90.
SHEEHAN, B., MURPHY, F., MULLINS, M. & RYAN, C. 2018. Connected and autonomous vehicles: A cyber-risk classification framework. Transportation Research Part A: Policy and Practice.
SHIN, D.-H. 2017. Conceptualizing and measuring quality of experience of the internet of things: Exploring how quality is perceived by users. Information & Management, 54, 998-1011.
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.
SHOEMAKER, E. M. 1995. On the meltwater genesis of drumlins. Boreas, 24, 3-10.
SHROUF, F. & MIRAGLIOTTA, G. 2015. Energy management based on Internet of Things: practices and framework for adoption in production management. Journal of Cleaner Production, 100, 235-246.
SHUHAIBER, A. & MASHAL, I. 2019. Understanding users’ acceptance of smart homes. Technology in Society.
SIEBELINK, R., HALMAN, J. I. M. & HOFMAN, E. 2016. Scenario-Driven Roadmapping to cope with uncertainty: Its application in the construction industry. Technological Forecasting and Social Change, 110, 226-238.
SILVA, B. N., KHAN, M. & HAN, K. 2018. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697-713.
SIMON, J. P. 2019. Artificial intelligence: scope, players, markets and geography. Digital Policy, Regulation and Governance.
SLAUGHTER, R. A. 2018. The IT revolution reassessed part two: Case studies and implications. Futures, 98, 19-31.
SON, H. 2013. Alternative future scenarios for South Korea in 2030. Futures, 52, 27-41.
SPANIOL, M. J. & ROWLAND, N. J. 2018. The scenario planning paradox. Futures, 95, 33-43.
STRANGE, R. & ZUCCHELLA, A. 2017. Industry 4.0, global value chains and international business. Multinational Business Review, 25, 174-184.
SUN, J., YAN, J. & ZHANG, K. Z. K. 2016. Blockchain-based sharing services: What blockchain technology can contribute to smart cities. Financial Innovation, 2.
SUN, T. Q. & MEDAGLIA, R. 2019. Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36, 368-383.
SUN, Y. & GRIMES, S. 2016. China’s increasing participation in ICT’s global value chain: A firm level analysis. Telecommunications Policy, 40, 210-224.
SUNDBO, J. 2016. Food scenarios 2025: Drivers of change between global and regional. Futures, 83, 75-87.
SYDOW, J. & MÜLLER-SEITZ, G. 2018. Open innovation at the interorganizational network level – Stretching practices to face technological discontinuities in the semiconductor industry. Technological Forecasting and Social Change.
TAKANO, Y. & KAJIKAWA, Y. 2019. Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents. Technological Forecasting and Social Change, 138, 45-68.
TALEBIAN, A. & MISHRA, S. 2018. Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations. Transportation Research Part C: Emerging Technologies, 95, 363-380.
TANG, T. & HO, A. T.-K. 2019. A path-dependence perspective on the adoption of Internet of Things: Evidence from early adopters of smart and connected sensors in the United States. Government Information Quarterly, 36, 321-332.
TAVAKKOLI, F., EBRAHIMI, S., WANG, S. & VAFAI, K. 2016. Analysis of critical thermal issues in 3D integrated circuits. International Journal of Heat and Mass Transfer, 97, 337-352.
TAYLOR, P. J., DARGAHI, T., DEHGHANTANHA, A., PARIZI, R. M. & CHOO, K.-K. R. 2019. A systematic literature review of blockchain cyber security. Digital Communications and Networks.
TOPOL, A. W., TULIPE, D. C. L., SHI, L., FRANK, D. J., BERNSTEIN, K., STEEN, S. E., KUMAR, A., SINGCO, G. U., YOUNG, A. M., GUARINI, K. W. & IEONG, M. 2006. Three-dimensional integrated circuits. IBM Journal of Research and Development, 50, 491-506.
TROMMER, J., HEINZIG, A., HEINRICH, A., JORDAN, P., GRUBE, M., SLESAZECK, S., MIKOLAJICK, T. & WEBER, W. M. Material prospects of reconfigurable transistors (RFETs) - From silicon to germanium nanowires. In: PRESS, C. U., ed. MRS Proceedings, 2014. 225-230.
TRUBY, J. 2018. Decarbonizing Bitcoin: Law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies. Energy Research & Social Science, 44, 399-410.
TSENG, F.-M., LIU, Y.-L. & WU, H.-H. 2014. Market penetration among competitive innovation products: The case of the Smartphone Operating System. Journal of Engineering and Technology Management, 32, 40-59.
TU, M., K. LIM, M. & YANG, M.-F. 2018. IoT-based production logistics and supply chain system – Part 2. Industrial Management & Data Systems, 118, 96-125.
TURCHIN, A. 2018. Assessing the future plausibility of catastrophically dangerous AI. Futures.
TURCHIN, A. & DENKENBERGER, D. 2018. Classification of global catastrophic risks connected with artificial intelligence. Ai & Society.
VALTER, P., LINDGREN, P. & PRASAD, R. 2018. Advanced Business Model Innovation Supported by Artificial Intelligence and Deep Learning. Wireless Personal Communications, 100, 97-111.
VAN DEN BUUSE, D. & KOLK, A. 2019. An exploration of smart city approaches by international ICT firms. Technological Forecasting and Social Change, 142, 220-234.
VANDER LAAN, Z. & SADABADI, K. F. 2017. Operational performance of a congested corridor with lanes dedicated to autonomous vehicle traffic. International Journal of Transportation Science and Technology, 6, 42-52.
VANOLO, A. 2016. Is there anybody out there? The place and role of citizens in tomorrow’s smart cities. Futures, 82, 26-36.
VARHO, V. & TAPIO, P. 2013. Combining the qualitative and quantitative with the Q2 scenario technique — The case of transport and climate. Technological Forecasting and Social Change, 80, 611-630.
VECCHIATO, R. 2012. Strategic foresight and environmental uncertainty: a research agenda. Foresight, 14, 387-400.
VECCHIATO, R. 2015. Creating value through foresight: First mover advantages and strategic agility. Technological Forecasting and Social Change, 101, 25-36.
VILLARONGA, E. F., KIESEBERG, P. & LI, T. 2018. Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten. Computer Law & Security Review, 34, 304-313.
VIRIYASITAVAT, W. & HOONSOPON, D. 2019. Blockchain characteristics and consensus in modern business processes. Journal of Industrial Information Integration, 13, 32-39.
VISHNEVSKIY, K., KARASEV, O. & MEISSNER, D. 2015. Integrated roadmaps and corporate foresight as tools of innovation management: The case of Russian companies. Technological Forecasting and Social Change, 90, 433-443.
VON DER GRACHT, H. A. 2012. Consensus measurement in Delphi studies. Technological Forecasting and Social Change, 79, 1525-1536.
VON DER GRACHT, H. A. & STILLINGS, C. 2013. An innovation-focused scenario process — A case from the materials producing industry. Technological Forecasting and Social Change, 80, 599-610.
VON DER GRACHT, H. A., VENNEMANN, C. R. & DARKOW, I.-L. 2010. Corporate foresight and innovation management: A portfolio-approach in evaluating organizational development. Futures, 42, 380-393.
WALDROP, M. M. 2016. More than Moore Nature 530, 144-147.
WANG, C.-T. & CHIU, C.-S. 2014. Competitive strategies for Taiwan's semiconductor industry in a new world economy. Technology in Society, 36, 60-73.
WANG, X., ZHA, X., NI, W., LIU, R. P., GUO, Y. J., NIU, X. & ZHENG, K. 2019a. Survey on blockchain for Internet of Things. Computer Communications, 136, 10-29.
WANG, Y., KUNG, L. & BYRD, T. A. 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.
WANG, Y., SINGGIH, M., WANG, J. & RIT, M. 2019b. Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics, 211, 221-236.
WANG, Y.-H. & HSIEH, C.-C. 2018. Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology. Technological Forecasting and Social Change, 127, 281-290.
WARNKE, P. & SCHIRRMEISTER, E. 2016. Small seeds for grand challenges—Exploring disregarded seeds of change in a foresight process for RTI policy. Futures, 77, 1-10.
WARTH, J., VON DER GRACHT, H. A. & DARKOW, I.-L. 2013. A dissent-based approach for multi-stakeholder scenario development — The future of electric drive vehicles. Technological Forecasting and Social Change, 80, 566-583.
WEBER, K. M., GUDOWSKY, N. & AICHHOLZER, G. 2018. Foresight and technology assessment for the Austrian parliament — Finding new ways of debating the future of industry 4.0. Futures.
WEBER, R. H. 2010. Internet of Things – New security and privacy challenges. Computer Law & Security Review, 26, 23-30.
WEBER, R. H. 2015. Internet of things: Privacy issues revisited. Computer Law & Security Review, 31, 618-627.
WEINBERG, B. D., MILNE, G. R., ANDONOVA, Y. G. & HAJJAT, F. M. 2015. Internet of Things: Convenience vs. privacy and secrecy. Business Horizons, 58, 615-624.
WENG, C.-H., HUANG, T. C.-K. & HAN, R.-P. 2016. Disease prediction with different types of neural network classifiers. Telematics and Informatics, 33, 277-292.
WENG, W.-H. 2015. A Mobile Computing Technology Foresight Study with Scenario Planning Approach. International Journal of Electronic Commerce Studies, 6, 223-232.
WENG, W.-H. & LIN, W.-T. 2013. A scenario analysis of big data technology portfolio planning. International Journal of Engineering Research and Technology (IJERT), 2, 752-762.
WILKINSON, A., KUPERS, R. & MANGALAGIU, D. 2013. How plausibility-based scenario practices are grappling with complexity to appreciate and address 21st century challenges. Technological Forecasting and Social Change, 80, 699-710.
WILSON, C., HARGREAVES, T. & HAUXWELL-BALDWIN, R. 2017. Benefits and risks of smart home technologies. Energy Policy, 103, 72-83.
WOLF, C. & FLOYD, S. W. 2013. Strategic Planning Research: Toward a Theory-Driven Agenda. Journal of Management, 43, 1754-1788.
WRIGHT, D. 2008. Alternative futures: AmI scenarios and Minority Report. Futures, 40, 473-488.
WRIGHT, D. W. M. 2018. Cloning animals for tourism in the year 2070. Futures, 95, 58-75.
WRIGHT, G., BRADFIELD, R. & CAIRNS, G. 2013. Does the intuitive logics method – and its recent enhancements – produce “effective” scenarios? Technological Forecasting and Social Change, 80, 631-642.
WRIGHT, G. & CAIRNS, G. 2011. Scenario Thinking: Practical Approaches to the Future, Basingstoke, UK, Palgrave-Macmillan.
WU, C.-H., DING, C. G., JANE, T.-D., LIN, H.-R. & WU, C.-Y. 2015. Lessons from the global financial crisis for the semiconductor industry. Technological Forecasting and Social Change, 99, 47-53.
WU, H.-Y., CHEN, I. S., CHEN, J.-K. & CHIEN, C.-F. 2019. The R&D efficiency of the Taiwanese semiconductor industry. Measurement, 137, 203-213.
WU, J. & TRAN, N. 2018. Application of Blockchain Technology in Sustainable Energy Systems: An Overview. Sustainability, 10.
XU, X. & FAN, C.-K. 2018. Autonomous vehicles, risk perceptions and insurance demand: An individual survey in China. Transportation Research Part A: Policy and Practice.
YAMPOLSKIY, R. V. 2019. Predicting future AI failures from historic examples. Foresight, 21, 138-152.
YAN, B., JIN, Z., LIU, L. & LIU, S. 2017. Factors influencing the adoption of the internet of things in supply chains. Journal of Evolutionary Economics, 28, 523-545.
YAN, B.-N., LEE, T.-S. & LEE, T.-P. 2015. Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis. Scientometrics, 105, 1285-1300.
YE, L. & YAMAMOTO, T. 2018. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput. Physica A: Statistical Mechanics and its Applications, 512, 588-597.
YOON, J., KIM, Y. J., VONORTAS, N. S. & HAN, S. W. 2017. A moderated mediation model of technology roadmapping and innovation: The roles of corporate foresight and organizational support. Journal of Engineering and Technology Management.
YOUSUF, M. I. 2007. Using experts’ opinions through Delphi technique. Practical Assessment, Research & Evaluation, 12.
ZAWRA, L. M. 2017. Internet of Things (IoT) Realization in the Context of Industry 4.0 (I 4.0). International Robotics & Automation Journal, 3.
ZHANG, P., ZHOU, M. & FORTINO, G. 2018a. Security and trust issues in Fog computing: A survey. Future Generation Computer Systems, 88, 16-27.
ZHANG, T., POTA, H., CHU, C.-C. & GADH, R. 2018b. Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency. Applied Energy, 226, 582-594.
ZHANG, X. & YUE, W. T. 2019. Transformative value of the Internet of Things and pricing decisions. Electronic Commerce Research and Applications, 34.
ZHAO, J., ZHENG, X., DONG, R. & SHAO, G. 2013. The planning, construction, and management toward sustainable cities in China needs the Environmental Internet of Things. International Journal of Sustainable Development & World Ecology, 20, 195-198.