IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v81y2023ics0301420723000326.html
   My bibliography  Save this article

Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China's listed companies

Author

Listed:
  • Li, Chengming
  • Xu, Yang
  • Zheng, Hao
  • Wang, Zeyu
  • Han, Haiting
  • Zeng, Liangen

Abstract

Artificial intelligence (AI) offers businesses a way to save expenses and a fundamental shift in innovation tools in the digital era. Whether AI can improve corporate innovation efficiency has been hotly discussed, but there is little empirical evidence. We use text mining to construct a firm-level AI application index innovatively. We investigate how AI affects corporate innovation efficiency using panel data from 3185 listed companies between 2008 and 2020. The results show that AI application significantly improves corporate innovation efficiency. Meanwhile, to avoid endogeneity problems, we use instrumental variables and propensity score matching (PSM) to test and obtain consistent conclusions. Further, we find that intensifying external market competition and flattening internal organizational structure, which are the important economic forms of innovation resource reallocation, play a moderating effect. Furthermore, the impact of AI on corporate innovation efficiency is enormous in companies with a more extensive size and less management power. In addition, the higher the level of AI development in the industry and region where the enterprise is located, the stronger the impact of AI on corporate innovation efficiency. This paper provides micro evidence for the innovation effects of AI.

Suggested Citation

  • Li, Chengming & Xu, Yang & Zheng, Hao & Wang, Zeyu & Han, Haiting & Zeng, Liangen, 2023. "Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China's listed companies," Resources Policy, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000326
    DOI: 10.1016/j.resourpol.2023.103324
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723000326
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103324?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. Nicholas Bloom & Luis Garicano & Raffaella Sadun & John Van Reenen, 2014. "The Distinct Effects of Information Technology and Communication Technology on Firm Organization," Management Science, INFORMS, vol. 60(12), pages 2859-2885, December.
    2. Ying Wang & Jing Zhao, 2015. "Hedge Funds and Corporate Innovation," Financial Management, Financial Management Association International, vol. 44(2), pages 353-385, June.
    3. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    4. Xiaochun Zeng & Suicheng Li & Zahid Yousaf, 2022. "Artificial Intelligence Adoption and Digital Innovation: How Does Digital Resilience Act as a Mediator and Training Protocols as a Moderator?," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    5. Rong, Zhao & Wu, Xiaokai & Boeing, Philipp, 2017. "The effect of institutional ownership on firm innovation: Evidence from Chinese listed firms," Research Policy, Elsevier, vol. 46(9), pages 1533-1551.
    6. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Song, Malin & Ai, Hongshan & Li, Xie, 2015. "Political connections, financing constraints, and the optimization of innovation efficiency among China's private enterprises," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 290-299.
    8. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Working Papers 24301, National Bureau of Economic Research, Inc.
    9. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    10. Sunder, Jayanthi & Sunder, Shyam V. & Zhang, Jingjing, 2017. "Pilot CEOs and corporate innovation," Journal of Financial Economics, Elsevier, vol. 123(1), pages 209-224.
    11. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 61-87, National Bureau of Economic Research, Inc.
    12. Frederick L. Bereskin & Terry L. Campbell II & Po-Hsuan Hsu, 2016. "Corporate Philanthropy, Research Networks, and Collaborative Innovation," Financial Management, Financial Management Association International, vol. 45(1), pages 175-206, March.
    13. Hsu, Po-Hsuan & Tian, Xuan & Xu, Yan, 2014. "Financial development and innovation: Cross-country evidence," Journal of Financial Economics, Elsevier, vol. 112(1), pages 116-135.
    14. 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).
    15. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    16. Azi Ben-Rephael & Zhi Da & Ryan D. Israelsen, 2017. "It Depends on Where You Search: Institutional Investor Attention and Underreaction to News," Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3009-3047.
    17. Walheer, Barnabé & He, Ming, 2020. "Technical efficiency and technology gap of the manufacturing industry in China: Does firm ownership matter?," World Development, Elsevier, vol. 127(C).
    18. 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.
    19. 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.
    20. Yang, Xuehui & Zhang, Huirong & Li, Yan, 2022. "High-speed railway, factor flow and enterprise innovation efficiency: An empirical analysis on micro data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    21. Min, Sujin & Kim, Juseong & Sawng, Yeong-Wha, 2020. "The effect of innovation network size and public R&D investment on regional innovation efficiency," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    22. Komikado, Hiroshi & Morikawa, So & Bhatt, Ayushman & Kato, Hironori, 2021. "High-speed rail, inter-regional accessibility, and regional innovation: Evidence from Japan," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    23. Chen, Xihui Haviour & Tee, Kienpin & Chang, Victor, 2022. "Accelerating Innovation Efficiency through Agile Leadership: The CEO Network Effects in China," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    24. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    25. Xavier Giroud & Holger M. Mueller, 2011. "Corporate Governance, Product Market Competition, and Equity Prices," Journal of Finance, American Finance Association, vol. 66(2), pages 563-600, April.
    26. Haschka, Rouven E. & Herwartz, Helmut, 2020. "Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach," Research Policy, Elsevier, vol. 49(8).
    27. 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.
    28. Henning Kroll & Kou Kou, 2019. "Innovation output and state ownership: empirical evidence from China’s listed firms," Industry and Innovation, Taylor & Francis Journals, vol. 26(2), pages 176-198, February.
    29. Frederik von Briel & Per Davidsson & Jan Recker, 2018. "Digital Technologies as External Enablers of New Venture Creation in the IT Hardware Sector," Entrepreneurship Theory and Practice, , vol. 42(1), pages 47-69, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Chengming & Wang, Yilin & Zhou, Zhihan & Wang, Zeyu & Mardani, Abbas, 2023. "Digital finance and enterprise financing constraints: Structural characteristics and mechanism identification," Journal of Business Research, Elsevier, vol. 165(C).
    2. Gao, Shiya & Guan, Xin & Tang, Run & Zhu, Junfan & Wang, Zeyu & Xu, Wei, 2023. "Resource curse, economic efficiency and green recovery based on three-subject framework," Resources Policy, Elsevier, vol. 85(PB).
    3. Jiang, Zhengyu & Zhang, Xinyi & Zhao, Yingzhi & Li, Chengming & Wang, Zeyu, 2023. "The impact of urban digital transformation on resource sustainability: Evidence from a quasi-natural experiment in China," Resources Policy, Elsevier, vol. 85(PA).
    4. Shuhui Yu & Xin Guan & Junfan Zhu & Zeyu Wang & Youting Jian & Weijia Wang & Ya Yang, 2023. "Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China," Sustainability, MDPI, vol. 15(11), pages 1-14, June.

    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. Nils Grashof & Alexander Kopka, 2023. "Widening or closing the gap? The relationship between artificial intelligence, firm-level productivity and regional clusters," Bremen Papers on Economics & Innovation 2304, University of Bremen, Faculty of Business Studies and Economics.
    2. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    3. 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.
    4. Alexander Kopka & Dirk Fornahl, 2024. "Artificial intelligence and firm growth — catch-up processes of SMEs through integrating AI into their knowledge bases," Small Business Economics, Springer, vol. 62(1), pages 63-85, January.
    5. Kopka, Alexander & Grashof, Nils, 2022. "Artificial intelligence: Catalyst or barrier on the path to sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. 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.
    7. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    9. Joel Klinger & Juan Mateos-Garcia & Konstantinos Stathoulopoulos, 2021. "Deep learning, deep change? Mapping the evolution and geography of a general purpose technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5589-5621, July.
    10. Bernardo S Buarque & Ronald B Davies & Ryan M Hynes & Dieter F Kogler, 2020. "OK Computer: the creation and integration of AI in Europe," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 175-192.
    11. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    12. 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.
    13. J. Klinger & J. Mateos-Garcia & K. Stathoulopoulos, 2018. "Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology," Papers 1808.06355, arXiv.org.
    14. Andrea Borsato & Andre Lorentz, 2022. "Data Production and the coevolving AI trajectories: An attempted evolutionary model," Working Papers of BETA 2022-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    15. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
    16. Todd A. Hall & Sharique Hasan, 2022. "Organizational decision-making and the returns to experimentation," Journal of Organization Design, Springer;Organizational Design Community, vol. 11(4), pages 129-144, December.
    17. Aránzazu Guillán Montero & David Le Blanc, 2019. "Lessons for Today from Past Periods of Rapid Technological Change," Working Papers 158, United Nations, Department of Economics and Social Affairs.
    18. Chen, Zhibin & Xu, Yibin & Tian, Zongtao & Meng, Xu, 2022. "The Impact of Social Insurance Law on Corporate Innovation: Evidence from a quasi-natural experiment," Economic Modelling, Elsevier, vol. 111(C).
    19. Tobias Kollmann & Lucas Kleine-Stegemann & Katharina Cruppe & Christina Then-Bergh, 2022. "Eras of Digital Entrepreneurship," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(1), pages 15-31, February.
    20. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.

    More about this item

    Keywords

    Artificial intelligence; Innovation efficiency; Resource reallocation; Market competition; Organizational structure;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure

    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:eee:jrpoli:v:81:y:2023:i:c:s0301420723000326. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    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.