IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/121183.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/121183/1/MPRA_paper_121183.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    6. 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).
    7. Magro, Edurne & Wilson, James R., 2013. "Complex innovation policy systems: Towards an evaluation mix," Research Policy, Elsevier, vol. 42(9), pages 1647-1656.
    8. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    9. 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.
    10. Gawer, Annabelle, 2014. "Bridging differing perspectives on technological platforms: Toward an integrative framework," Research Policy, Elsevier, vol. 43(7), pages 1239-1249.
    11. Ulrich Witt, 2003. "Economic policy making in evolutionary perspective," Journal of Evolutionary Economics, Springer, vol. 13(2), pages 77-94, April.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Giandomenico Majone, 2002. "The Precautionary Principle and its Policy Implications," Journal of Common Market Studies, Wiley Blackwell, vol. 40(1), pages 89-109, March.
    18. van Liebergen, Bart, 2017. "Machine learning: A revolution in risk management and compliance?," Journal of Financial Transformation, Capco Institute, vol. 45, pages 60-67.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fossen, Frank M. & Sorgner, Alina, 2022. "New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    3. Pablo Casas & Concepción Román, 2024. "The impact of artificial intelligence in the early retirement decision," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 583-618, August.
    4. Wang, Huijuan & Ding, Lin & Guan, Rong & Xia, Yan, 2020. "Effects of advancing internet technology on Chinese employment: a spatial study of inter-industry spillovers," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. 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.
    6. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    7. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    8. Matthias Firgo & Peter Mayerhofer & Michael Peneder & Philipp Piribauer & Peter Reschenhofer, 2018. "Beschäftigungseffekte der Digitalisierung in den Bundesländern sowie in Stadt und Land," WIFO Studies, WIFO, number 61633, January.
    9. Flanagan, Kieron & Uyarra, Elvira & Laranja, Manuel, 2010. "The ‘policy mix’ for innovation: rethinking innovation policy in a multi-level, multi-actor context," MPRA Paper 23567, University Library of Munich, Germany.
    10. Ghimire, Ramesh & Skinner, Jim & Carnathan, Mike, 2020. "Who perceived automation as a threat to their jobs in metro Atlanta: Results from the 2019 Metro Atlanta Speaks survey," Technology in Society, Elsevier, vol. 63(C).
    11. Yashiro, Naomitsu & Kyyrä, Tomi & Hwang, Hyunjeong & Tuomala, Juha, 2020. "Technology, Labour Market Institutions and Early Retirement: Evidence from Finland," IZA Discussion Papers 13990, Institute of Labor Economics (IZA).
    12. Wen Zhang & Kee-Hung Lai & Qiguo Gong, 2024. "The future of the labor force: higher cognition and more skills," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    13. Li, Jianqiang & Shan, Yaowen & Tian, Gary & Hao, Xiangchao, 2020. "Labor cost, government intervention, and corporate innovation: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 64(C).
    14. Albrecht, Thorben & Kellermann, Christian, 2020. "Künstliche Intelligenz und die Zukunft der digitalen Arbeitsgesellschaft: Konturen einer ganzheitlichen Technikfolgenabschätzung," Working Paper Forschungsförderung 200, Hans-Böckler-Stiftung, Düsseldorf.
    15. Andreas Irmen, 2021. "Automation, growth, and factor shares in the era of population aging," Journal of Economic Growth, Springer, vol. 26(4), pages 415-453, December.
    16. Huajie Jiang & Qiguo Gong, 2022. "Does Skill Polarization Affect Wage Polarization? U.S. Evidence 2009–2021," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    17. Elizabeth Fisher & Michael A. Flynn & Preethi Pratap & Jay A. Vietas, 2023. "Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review," IJERPH, MDPI, vol. 20(13), pages 1-28, June.
    18. Dario Guarascio & Jelena Reljic & Roman Stollinger, 2023. "Artificial Intelligence and Employment: A Look into the Crystal Ball," LEM Papers Series 2023/34, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    19. Ben Vermeulen & Jan Kesselhut & Andreas Pyka & Pier Paolo Saviotti, 2018. "The Impact of Automation on Employment: Just the Usual Structural Change?," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    20. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: an empirically grounded conceptualization," International Journal of Production Economics, Elsevier, vol. 223(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:121183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.