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Industrial Policy for Emerging Technologies: The Case of Narrow AI and the Manufacturing Value Chain as Blueprint for the Industrial Metaverse

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  • Dietlmeier, Simon Frederic

Abstract

In this paper, a qualitative model is inductively developed describing a dynamic “policy mix” -system of innovation enabling and outbalancing dimensions for the deployment of narrow artificial intelligence (AI) in the manufacturing value chain. A literature review first identifies and summarizes general policy recommendations on AI as an emerging technology presented by authors prior to this research. In the empirical part, policy dimensions and suggestions of policy remedies with a focus on the manufacturing value chain were taxonomized based on exploratory interviews with 37 international elite experts on AI across several stakeholder groups. The findings were refined in a survey with participants of the workshop “AI in Manufacturing” organized by the European Commission. The dimensions build the foundation for an industrial policy in the form of a “four-wing industrial policy system model” that can unleash the value of narrow AI in the manufacturing value chain and addresses barriers to scale-up. It represents a qualitative modelling approach and confirms previous views in the literature that innovation policies need to be thought as “policy mix” and systems. A case study of the European Union’s policy mix for AI validates the model empirically based on additional interviews with ten European civil servants.

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  • Dietlmeier, Simon Frederic, 2024. "Industrial Policy for Emerging Technologies: The Case of Narrow AI and the Manufacturing Value Chain as Blueprint for the Industrial Metaverse," MPRA Paper 121183, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:121183
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    1. Padmore, Tim & Schuetze, Hans & Gibson, Hervey, 1998. "Modeling systems of innovation: An enterprise-centered view," Research Policy, Elsevier, vol. 26(6), pages 605-624, February.
    2. Sotarauta, Markku & Srinivas, Smita, 2006. "Co-evolutionary policy processes: Understanding innovative economies and future resilience," MPRA Paper 52689, University Library of Munich, Germany.
    3. Aghion, Philippe & David, Paul A. & Foray, Dominique, 2009. "Science, technology and innovation for economic growth: Linking policy research and practice in 'STIG Systems'," Research Policy, Elsevier, vol. 38(4), pages 681-693, May.
    4. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    5. Heiberger, Richard & Robbins, Naomi, 2014. "Design of Diverging Stacked Bar Charts for Likert Scales and Other Applications," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i05).
    6. Magro, Edurne & Wilson, James R., 2013. "Complex innovation policy systems: Towards an evaluation mix," Research Policy, Elsevier, vol. 42(9), pages 1647-1656.
    7. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    8. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    9. Gawer, Annabelle, 2014. "Bridging differing perspectives on technological platforms: Toward an integrative framework," Research Policy, Elsevier, vol. 43(7), pages 1239-1249.
    10. Ulrich Witt, 2003. "Economic policy making in evolutionary perspective," Journal of Evolutionary Economics, Springer, vol. 13(2), pages 77-94, April.
    11. Robert A. Mundell, 1962. "The Appropriate Use of Monetary and Fiscal Policy for Internal and External Stability," IMF Staff Papers, Palgrave Macmillan, vol. 9(1), pages 70-79, March.
    12. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    13. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    14. Beth‐Anne Schuelke‐Leech & Sara R. Jordan & Betsy Barry, 2019. "Regulating Autonomy: An Assessment of Policy Language for Highly Automated Vehicles," Review of Policy Research, Policy Studies Organization, vol. 36(4), pages 547-579, July.
    15. Padmore, Tim & Gibson, Hervey, 1998. "Modelling systems of innovation: II. A framework for industrial cluster analysis in regions," Research Policy, Elsevier, vol. 26(6), pages 625-641, February.
    16. Giandomenico Majone, 2002. "The Precautionary Principle and its Policy Implications," Journal of Common Market Studies, Wiley Blackwell, vol. 40(1), pages 89-109, March.
    17. van Liebergen, Bart, 2017. "Machine learning: A revolution in risk management and compliance?," Journal of Financial Transformation, Capco Institute, vol. 45, pages 60-67.
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    More about this item

    Keywords

    Artificial Intelligence; Emerging Technologies; Manufacturing ; Value Chain; System; Policy Mix;
    All these keywords.

    JEL classification:

    • A20 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - General
    • B5 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches
    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary; Modern Monetary Theory;
    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • M29 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Other
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Y4 - Miscellaneous Categories - - Dissertations
    • Z1 - Other Special Topics - - Cultural Economics

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