簡易檢索 / 詳目顯示

研究生: 李科逸
Lee, Ko-Yi
論文名稱: 探究國家人工智慧科學、技術與創新之系統性政策工具
Exploring Systemic Policy Instruments for National Artificial Intelligence Based on Science, Technology and Innovation
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 123
中文關鍵詞: 系統性創新科學技術創新政策政策工具人工智慧產業政策台灣
英文關鍵詞: Systemic Innovation, STI Policy, DANP, GRA, Policy Instruments
DOI URL: http://doi.org/10.6345/NTNU202001524
論文種類: 學術論文
相關次數: 點閱:348下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究主要設定聚焦於台灣人工智慧發展做為研究領域,並以「系統性創新」(systemic innovation)理論為基礎,進行相關分析。本研究研析世界人工智慧先進國家(如美國,日本和中國)近年國家政策報告,並歸納各國推動策略及產業面臨困難問題,以系統性創新理論七大面向,綜整出28個系統創新問題。然後,本研究運用DEMATEL based Analytic Network Process(DANP)研究方法,並通過專家問卷,分析探討各系統性創新問題彼此間,所呈現重要影響程度與關連度,並得出影響效益之權重比例,分列優先次序。再者,本研究援引近年來重要國際組織,含聯合國(United Nations, UN)與經濟合作暨發展組織 (Organisation for Economic Co-operation and Development, OECD)所發展,用以評估國家科學、技術、創新政策(Science, Technology and Innovation Policy,STI Policy)之指標,選定十項重要政策工具,對於28個系統性創新問題之解決的重要性,運用灰關聯(Grey Relational Analysis, GRA) 研究方法,探討每個系統性創新問題與每個政策工具之間的關聯和比較,依據所得出重要性排序,提出解決台灣人工智慧發展相關政策工具之優先順序,希望能提供給我國政府推動政策規劃參考。本研究歸納得出研究結論:知識研究和企業創新對台灣人工智慧發展至關重要;政府介入規範並不是台灣現階段產業所需;擴展和市場形成是台灣企業最期盼的政府政策工具及協助措施;經濟性工具和創造市場需求是推動台灣人工智慧發展最重要的政策手段;企業期盼政府能制訂符合AI創新本質及需求的政策工具。最後,本研究期盼所綜整研提的此一整套研究模式,未來能提供我國與其他國家作為相關產業及不同產業之科學、技術、創新政策規劃及政策工具研訂之參考方案。

    This research focused on the field in Taiwanese Artificial Intelligence (AI) development and was based on the theory of systematic innovation for analysis. This research organized the industry problems of U.S., Japan and China and explored 28 systematic innovation problems in 7 dimensions. Then this research used DEMATEL based Analytic Network Process (DANP) method, and analyzed these systemic innovation problems, listed the order of ranking according to the impact weight. Furthermore, this research introduced science, technology and innovation (STI) Policy of United Nations (UN) and Organisation for Economic Co-operation and Development (OECD), and elected 10 policy instruments. And then this research used Grey Relational Analysis (GRA) method, explored the correlations and comparisons between each systemic innovation problem and each policy instrument, and finally propose important policy measures for Taiwanese AI development. The conclusions of this research found the following: knowledge research and enterprise innovation are important to AI development. Government intervention is not the primary need at the current stage. Expansion and market formation are the most anticipated assistance for enterprises. Economic instruments and market demand are the most important policy instruments. And the AI development needs more innovative policy instruments in Taiwan. Finally, this research proposed the whole set of research methods and models; the proposed methodology and models can serve as references for defining future STI policies for other Taiwanese industries or industries belonging to other countries.

    謝誌 i 摘要 ii Abstract iii Table of Contents iv List of Table vi List of Figure vii Chapter 1 Introduction 1 1.1 Research Backgrounds 1 1.2 Research Motivation 4 1.3 Research Purpose 5 1.4 Research Methods and Framework 7 1.5 Research Limitation 9 Chapter 2 Literature Review 11 2.1 Systemic Innovation 11 2.2 Science, Technology and Innovation (STI) Policy 15 2.3 Artificial Intelligence Industry Development Policy 22 Chapter 3 Research Method 29 3.1 DEMATEL based Analytic Network Process (DANP) 29 3.2 Grey Relational Analysis (GRA) 33 Chapter 4 Empirical Study 37 4.1 Artificial Intelligence Industry Policy 37 4.2 Industry Problems and Strategy 47 4.3 Questionnaire Research Framework 56 4.4 DANP Research Results 61 4.5 GRA Research Results 74 Chapter 5 Discussion 77 5.1 Analysis of Research Results 77 5.2 Implications of Research Results 89 Chapter 6 Concluding Remarks 95 References 101 Appendix 109

    Bammer, G. (2008). Integrating policy analysis and complexity: Developing the new specialization of integration and implementation Sciences. In L. F. Dennard, K. A. Richardson, & G. Morçöl (Eds.), Complexity and policy analysis: Tools and concepts for designing robust policies in a complex world. Goodyear, AZ: ISCE Publishing.
    Bergek, A., Hekkert, M., Jacobsson, S., Markard, J., Sandén, B., & Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51-64.
    Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37(3), 407-429.
    Chaminade, C., & Padilla-Pérez, R. (2017). The challenge of alignment and barriers for the design and implementation of science, technology and innovation policies for innovation systems in developing countries. In C. Stefan Kuhlmann & F. Behavioural (Eds.), Research handbook on innovation governance for emerging economies. Cheltenham, England: Edward Elgar Publishing.
    Cheng, C.-H., Liou, J. J., & Chiu, C.-Y. (2017). A consistent fuzzy preference relations based ANP model for R&D project selection. Sustainability, 9(8), 1352.
    Chiu, W.-Y., Tzeng, G.-H., & Li, H.-L. (2010). Improving the e-store business model for satisfying customers’ needs using a hybrid MCDM combined DANP with grey relational model. In Advances in intelligent decision technologies (pp. 113-121). Switzerland: Springer.
    Compston, H. (2009). Policy networks and policy change: Putting policy network theory to the test. London, England: Palgrave Macmillan.
    Deng, J. L. (1982). Control problems of grey systems. Systems and Control Letters, 1(5), 288-294.
    Deng, J. L. (1985). Fundamental methods of grey systems. Wuhan, China: Huazhong University of Science and Technology Press.
    Deng, J. L. (1986). Grey forecasting and decision. Wuhan, China: Huazhong University of Science and Technology Press.
    Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey system, 1(1), 1-24.
    Dong, J., Huo, H., Liu, D., & Li, R. (2017). Evaluating the comprehensive performance of demand response for commercial customers by applying combination weighting techniques and fuzzy VIKOR approach. Sustainability, 9(8), 1332.
    Edquist, C. (1997). Systems of innovation: technologies, institutions, and organizations. East Sussex, England: Psychology Press.
    Executive Yuan. (2017). Digital nation and innovative economic development program. Retrieved from https://english.ey.gov.tw/News3/9E5540D592A5FECD/659df63b-dad4-47e3-80ab-c62cb40a62cd
    Executive Yuan. (2018a). AI small country grand strategy. Retrieved from https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/50a08776-e33a-4be2-a07c-a6e523f5031b
    Executive Yuan. (2018b). Taiwanese AI action plan (2018-2021). Retrieved from https://digi.ey.gov.tw/File/4C622B6A10053DAD
    Geyer, R., & Rihani, S. (2010). Complexity and public policy: A new approach to 21st century politics, policy and society. Abingdon, England: Routledge.
    Huang, C.-Y., Kao, Y.-S., Lu, H.-H., & Wu, M.-J. (2017). Curriculum development for enhancing the imagination in the technology commercialization process. Eurasia Journal of Mathematics, Science and Technology Education, 13(9), 6249-6283.
    Huang, C.-Y., Kao, Y.-S., Wu, M.-J., & Tzeng, G.-H. (2013). Deriving factors influencing the acceptance of pad phones by using the DNP based UTAUT2 framework. Paper presented at the 2013 Proceedings of PICMET'13: Technology Management in the IT-Driven Services (PICMET), San Jose, CA.
    Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.
    Huang, C.-Y., & Ting, Y.-H. (2012). Derivations of factors influencing the word-of-mouth marketing strategies for smart phone applications by using the fuzzy DEMATEL based network process. Paper presented at the 2012 International Conference on Fuzzy Theory and Its Applications (iFUZZY2012), Taichung, Republic of China.
    Integrated Innovation Strategy Promotion Council. (2019). AI strategy 2019-AI for everyone, industry, region and government. Retrieved from https://www.kantei.go.jp/jp/singi/ai_senryaku/pdf/aistratagy2019.pdf
    Itoga, H., Lin, G. T. R., Yang, F. C.-H., & Shyu, J. Z. (2013). Dynamics of industrial cluster scenarios. Review of Research and Social Intervention, 57, 119-132.
    Jacobsson, S., & Johnson, A. (2000). The diffusion of renewable energy technology: an analytical framework and key issues for research. Energy Policy, 28(9), 625-640.
    Jiang, H., Ding, W., Ali, M., & Wu, X. (2012). Advanced research in applied artificial intelligence. Paper presented at the 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China.
    Kao, Y.-S., Nawata, K., & Huang, C.-Y. (2019a). Evaluating the performance of systemic innovation problems of the IoT in manufacturing industries by novel MCDM methods. Sustainability, 11(18), 5-6.
    Kao, Y.-S., Nawata, K., & Huang, C.-Y. (2019b). Systemic functions evaluation based technological innovation system for the sustainability of IoT in the manufacturing industry. Sustainability, 11(8), 2-3.
    Kebede, K. Y., & Mitsufuji, T. (2017). Technological innovation system building for diffusion of renewable energy technology: A case of solar PV systems in Ethiopia. Technological forecasting and social change, 114, 242-253.
    Kieft, A., Harmsen, R., & Hekkert, M. P. (2017). Interactions between systemic problems in innovation systems: The case of energy-efficient houses in the Netherlands. Environmental Innovation and Societal Transitions, 24, 32-44.
    Kuhlmann, S. (2004). Rationales and evolution of public RTD policies in the context of their evaluation. In J. Rojo & W. Polt (Eds.), Handbook on the evaluation of research and technology policy (pp. 70-77). Cheltenham, England: Edward Elgar Publishing.
    Lundvall, B.-Å., Joseph, K., Chaminade, C., & Vang, J. (2009). Handbook of innovation systems and developing countries: building domestic capabilities in a global setting. Cheltenham, England: Edward Elgar Publishing.
    Meissner, D. (2014). Approaches for developing national STI strategies. STI Policy Review, 5(1), 34-56.
    Ministry of Industry and Information Technology. (2017). Three-year action plan to promote the development of the new generation artificial intelligence industry (2018-2020). Retrieved from http://www.miit.gov.cn/n1146295/n1652858/n1652930/n3757016/c5960820/content.html
    National Science and Technology Council. (2019). The national artificial intelligence research and developmen strategic plan: 2019 update. Retrieved from https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf
    Organisation for Economic Co-operation and Development. (2017). Key issues for digital transformation in the G20. Retrieved from https://www.oecd.org/g20/key-issues-for-digital-transformation-in-the-g20.pdf
    Organisation for Economic Co-operation and Development. (2018). OECD science, technology and innovation outlook 2018. Retrieved from https://read.oecd.org/10.1787/sti_in_outlook-2018-en?format=pdf
    Purkus, A., Hagemann, N., Bedtke, N., & Gawel, E. (2018). Towards a sustainable innovation system for the German wood-based bioeconomy: Implications for policy design. Journal of Cleaner Production, 172, 3955-3968.
    Raven, R., Van den Bosch, S., & Weterings, R. (2010). Transitions and strategic niche management: towards a competence kit for practitioners. International Journal of Technology Management, 51(1), 57-74.
    Schmoch, U., Rammer, C., & Legler, H. (2006). National systems of innovation in comparison: Structure and performance indicators for knowledge societies. Dordrecht, Netherlands: Springer Science & Business Media.
    Sixt, G. N., Klerkx, L., & Griffin, T. S. (2018). Transitions in water harvesting practices in Jordan’s rainfed agricultural systems: Systemic problems and blocking mechanisms in an emerging technological innovation system. Environmental Science & Policy, 84, 235-249.
    Smith, K. (2000). Innovation as a systemic phenomenon: rethinking the role of policy. Enterprise and Innovation Management Studies, 1(1), 73-102.
    Smits, R., & Kuhlmann, S. (2004). The rise of systemic instruments in innovation policy. International Journal of Foresight and Innovation Policy, 1(1/2), 4-32.
    Song, W., & Cao, J. (2017). A rough DEMATEL-based approach for evaluating interaction between requirements of product-service system. Computers & Industrial Engineering, 110, 353-363.
    State Council. (2017). Plan for development of the new generation of artificial intelligence. Retrieved from http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm
    Tigabu, A. D., Berkhout, F., & van Beukering, P. (2015). The diffusion of a renewable energy technology and innovation system functioning: Comparing bio-digestion in Kenya and Rwanda. Technological Forecasting and Social Change, 90, 331-345.
    Tzeng, G.-H., & Shen, K.-Y. (2017). New concepts and trends of hybrid multiple criteria decision making. Boca Raton, FL: CRC Press.
    Tzeng, G.-H., & Tasur, S. (1994). The multiple criteria evaluation of grey relation model. The Journal of grey system, 6(2), 87-108.
    United Nations Conference on Trade and Development. (2019). A framework for science, technology and innovation policy reviews. Retrieved from https://unctad.org/en/PublicationsLibrary/dtlstict2019d4_en.pdf
    Van Mierlo, B., Leeuwis, C., Smits, R., & Woolthuis, R. K. (2010). Learning towards system innovation: Evaluating a systemic instrument. Technological forecasting and social change, 77(2), 318-334.
    Von Hippel, E. (2007). The sources of innovation. Wiesbaden, German: Springer.
    Wesche, J. P., Negro, S. O., Dütschke, E., Raven, R., & Hekkert, M. P. (2019). Configurational innovation systems–Explaining the slow German heat transition. Energy Research & Social Science, 52, 99-113.
    White House. (2019). Exec. Order No. 13859: Maintaining American leadership in artificial intelligence. Retrieved from https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/
    Wieczorek, A. J., & Hekkert, M. P. (2012). Systemic instruments for systemic innovation problems: A framework for policy makers and innovation scholars. Science and Public Policy, 39(1), 74-87.
    Woolthuis, R. K., Lankhuizen, M., & Gilsing, V. (2005). A system failure framework for innovation policy design. Technovation, 25(6), 609-619.
    Zhou, F., Wang, X., Lim, M. K., He, Y., & Li, L. (2018). Sustainable recycling partner selection using fuzzy DEMATEL-AEW-FVIKOR: A case study in small-and-medium enterprises (SMEs). Journal of Cleaner Production, 196, 489-504.

    無法下載圖示 本全文未授權公開
    QR CODE