IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v15y2024i4d10.1007_s13132-023-01707-w.html
   My bibliography  Save this article

Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evidence from the Robot Application

Author

Listed:
  • Shuai Wang

    (Hefei University of Technology)

  • Xin Huang

    (Hefei University of Technology)

  • Mengyue Xia

    (Hefei University of Technology)

  • Xing Shi

    (Hefei University of Technology)

Abstract

While artificial intelligence (AI) is widely acknowledged as a transformative technology with the potential to boost productivity, there is limited understanding of its specific impact on firm innovation efficiency. This study leverages robot data from the International Federation of Robotics (IFR) and detailed data on Chinese manufacturing firms spanning 2015 to 2019. The analysis utilizes the Data Envelopment Analysis (DEA) method to evaluate firms’ innovation efficiency and employs the Tobit model to examine the influence of AI on innovation efficiency. Furthermore, the study delves into the heterogeneity of this impact by considering variations in firm ownership, industries, and regions and explores the mechanisms through which AI affects innovation efficiency. The findings demonstrate that AI application significantly enhances firms’ innovation efficiency, a result that holds robustly even after employing alternative AI proxies and instrumental variable regression. Moreover, the positive effects of AI adoption are primarily observed in state-owned enterprises, traditional manufacturing industries, and developed cities. Further analyses indicate that AI adoption modifies human capital, innovation patterns, and the market environment, thereby influencing innovation efficiency. This research provides valuable insights into unleashing the innovation potential of AI technology.

Suggested Citation

  • Shuai Wang & Xin Huang & Mengyue Xia & Xing Shi, 2024. "Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evidence from the Robot Application," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 16373-16394, December.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:4:d:10.1007_s13132-023-01707-w
    DOI: 10.1007/s13132-023-01707-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-023-01707-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13132-023-01707-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zheng, Zhen & Lin, Yongjia & Yu, Xiaoou & Liu, Xinming, 2021. "Product market competition and the cost of equity capital," Journal of Business Research, Elsevier, vol. 132(C), pages 1-9.
    2. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    3. Shixian Ling & Tianyue Pei & Zhaohui Li & Zhiping Zhang, 2021. "Impact of COVID-19 on Financial Constraints and the Moderating Effect of Financial Technology," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(6), pages 1675-1688, May.
    4. Davies, Benjamin & Gush, Jason & Hendy, Shaun C. & Jaffe, Adam B., 2022. "Research funding and collaboration," Research Policy, Elsevier, vol. 51(2).
    5. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    6. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    7. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    8. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    9. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    10. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    11. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    12. Paul Goldsmith-Pinkham & Isaac Sorkin & Henry Swift, 2020. "Bartik Instruments: What, When, Why, and How," American Economic Review, American Economic Association, vol. 110(8), pages 2586-2624, August.
    13. Jia Li & Zhengying Luo, 2021. "Product market competition and cost stickiness: Evidence from China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(7), pages 1808-1821, October.
    14. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2006. "The Polarization of the U.S. Labor Market," American Economic Review, American Economic Association, vol. 96(2), pages 189-194, May.
    15. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    16. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    17. Dauth, Wolfgang & Findeisen, Sebastian & Südekum, Jens & Wößner, Nicole, 2017. "German robots - the impact of industrial robots on workers," IAB-Discussion Paper 201730, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    18. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    19. Südekum, Jens & Dauth, Wolfgang & Findeisen, Sebastian & Woessner, Nicole, 2017. "German Robots – The Impact of Industrial Robots on Workers," CEPR Discussion Papers 12306, C.E.P.R. Discussion Papers.
    20. Spyros Arvanitis & Euripidis Loukis, 2020. "Reduction of ICT Investment Due to the 2008 Economic Crisis and ICT-Enabled Innovation Performance of Firms," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(1), pages 1-27, March.
    21. Charles J. Hadlock & Joshua R. Pierce, 2010. "New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index," The Review of Financial Studies, Society for Financial Studies, vol. 23(5), pages 1909-1940.
    22. David Bruce Audretsch & Maksim Belitski & Rosa Caiazza, 2021. "Start-ups, Innovation and Knowledge Spillovers," The Journal of Technology Transfer, Springer, vol. 46(6), pages 1995-2016, December.
    23. Furman, Jeffrey L. & Porter, Michael E. & Stern, Scott, 2002. "The determinants of national innovative capacity," Research Policy, Elsevier, vol. 31(6), pages 899-933, August.
    24. Zhen Yu & Zheng Liang & Peiyi Wu, 2021. "How data shape actor relations in artificial intelligence innovation systems: an empirical observation from China [Linking vertically related industries: entry by employee spinouts across industry ," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 251-267.
    25. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    26. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    27. Jiaofeng Pan & Jianxin Guo, 2022. "Innovative Collaboration and Acceleration: an Integrated Framework Based on Knowledge Transfer and Triple Helix," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(4), pages 3223-3247, December.
    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. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    3. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    4. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    5. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    6. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    7. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2021. "Stop worrying and love the robot: An activity-based approach to assess the impact of robotization on employment dynamics," GLO Discussion Paper Series 802, Global Labor Organization (GLO).
    8. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    9. Gallipoli, Giovanni & Makridis, Christos A., 2018. "Structural transformation and the rise of information technology," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 91-110.
    10. Maarek, Paul & Moiteaux, Elliot, 2021. "Polarization, employment and the minimum wage: Evidence from European local labor markets," Labour Economics, Elsevier, vol. 73(C).
    11. Arntz, Melanie & Gregory, Terry & Zierahn-Weilage, Ulrich, 2019. "Digitalization and the Future of Work: Macroeconomic Consequences," IZA Discussion Papers 12428, Institute of Labor Economics (IZA).
    12. Gregory, Terry & Salomons, Anna & Zierahn, Ulrich, 2016. "Racing With or Against the Machine? Evidence from Europe," VfS Annual Conference 2016 (Augsburg): Demographic Change 145843, Verein für Socialpolitik / German Economic Association.
    13. Manav Raj & Robert Seamans, 2019. "Primer on artificial intelligence and robotics," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-14, December.
    14. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    15. Pouliakas, Konstantinos, 2018. "Determinants of Automation Risk in the EU Labour Market: A Skills-Needs Approach," IZA Discussion Papers 11829, Institute of Labor Economics (IZA).
    16. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    17. Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Carlos Molina & William F. Maloney, 2019. "Is Automation Labor-Displacing in the Developing Countries, Too? Robots, Polarization, and Jobs," World Bank Publications - Reports 33301, The World Bank Group.
    19. Goos, Maarten & Rademakers, Emilie & Röttger, Ronja, 2021. "Routine-Biased technical change: Individual-Level evidence from a plant closure," Research Policy, Elsevier, vol. 50(7).
    20. Ana L. ABELIANSKY & Eda ALGUR & David E. BLOOM & Klaus PRETTNER, 2020. "The future of work: Meeting the global challenges of demographic change and automation," International Labour Review, International Labour Organization, vol. 159(3), pages 285-306, September.

    More about this item

    Keywords

    Artificial intelligence; Innovation efficiency; Manufacturing firms; Robot application;
    All these keywords.

    JEL classification:

    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

    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:spr:jknowl:v:15:y:2024:i:4:d:10.1007_s13132-023-01707-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.