IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i14p5928-d1433405.html
   My bibliography  Save this article

Artificial Intelligence and Food Processing Firms Productivity: Evidence from China

Author

Listed:
  • Huanan Liu

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

  • Yan Wang

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

  • Zhoufu Yan

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

Abstract

Amidst the tremendous evolution of the digital economy and the expedited establishment of a new development paradigm, the use of artificial intelligence (AI) technologies holds significant importance in achieving superior economic development. While much of the previous research focused on the macroeconomic impact of AI, this study examined how AI technology affects food processing firm performance, productivity, and labor skill structure at the food processing firm level. This study utilized panel data from listed food processing enterprises in Shanghai and Shenzhen spanning from 2010 to 2021, performing textual analysis on the annual reports of listed companies and then creating enterprise-level AI indicators to empirically examine the influence of AI applications on enterprise performance and its underlying mechanisms. The findings indicate a substantial improvement in business performance due to the application of artificial intelligence, which is a conclusion corroborated through a series of stability tests. Exploring channels and mechanisms, the analysis revealed that AI-driven advancements in production technologies stimulated the requirement for highly skilled labor, thereby inducing shifts in the labor force’s structure. Further investigation demonstrated that artificial intelligence contributed to enhancing the total factor productivity, consequently bolstering the overall enterprise performance. A heterogeneity analysis showed that firm-level factors, such as the nature of property rights and factor intensity, had an impact on the influence of AI on firm performance. In addition, the geographic location and time of year of a company also had impacts on the productivity benefits of artificial intelligence. This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency.

Suggested Citation

  • Huanan Liu & Yan Wang & Zhoufu Yan, 2024. "Artificial Intelligence and Food Processing Firms Productivity: Evidence from China," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5928-:d:1433405
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/14/5928/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/14/5928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Daron Acemoglu & Simon Johnson & James A. Robinson, 2001. "The Colonial Origins of Comparative Development: An Empirical Investigation," American Economic Review, American Economic Association, vol. 91(5), pages 1369-1401, December.
    3. Maarek, Paul & Moiteaux, Elliot, 2021. "Polarization, employment and the minimum wage: Evidence from European local labor markets," Labour Economics, Elsevier, vol. 73(C).
    4. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    5. 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.
    6. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    7. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    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. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    2. Lu, Jing & Xiao, Qinglan & Wang, Taoxuan, 2023. "Does the digital economy generate a gender dividend for female employment? Evidence from China," Telecommunications Policy, Elsevier, vol. 47(6).
    3. 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).
    4. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    5. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    6. Du, Junhong & He, Jiajia & Yang, Jing & Chen, Xiaohong, 2024. "How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    7. 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.
    8. Davide Dottori, 2021. "Robots and employment: evidence from Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(2), pages 739-795, July.
    9. Baek, Seungjin & Jeong, Deokjae, 2023. "Factors Influencing Labor Share: Automation, Task Innovation, and Elasticity of Substitution," MPRA Paper 118730, University Library of Munich, Germany.
    10. 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).
    11. Innocenti, Stefania & Golin, Marta, 2022. "Human capital investment and perceived automation risks: Evidence from 16 countries," Journal of Economic Behavior & Organization, Elsevier, vol. 195(C), pages 27-41.
    12. Cao, Yuqiang & Hu, Yong & Liu, Qian & Lu, Meiting & Shan, Yaowen, 2023. "Job creation or disruption? Unraveling the effects of smart city construction on corporate employment in China," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    13. Ni, Bin & Obashi, Ayako, 2021. "Robotics technology and firm-level employment adjustment in Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    14. Stefan Jestl, 2022. "Industrial Robots, and Information and Communication Technology: The Employment Effects in EU Labour Markets," wiiw Working Papers 215, The Vienna Institute for International Economic Studies, wiiw.
    15. Anderton, Robert & Jarvis, Valerie & Labhard, Vincent & Morgan, Julian & Petroulakis, Filippos & Vivian, Lara, 2020. "Virtually everywhere? Digitalisation and the euro area and EU economies," Occasional Paper Series 244, European Central Bank.
    16. Tao Chen & Shuwen Pi & Qing Sophie Wang, 2025. "Artificial Intelligence and Corporate Investment Efficiency: Evidence from Chinese Listed Companies," Working Papers in Economics 25/05, University of Canterbury, Department of Economics and Finance.
    17. Amaresh K Tiwari, 2023. "Automation In An Open, Catching-Up Economy: Aggregate And Microeconometric Evidence," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 144, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    18. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2025. "What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment," Research Policy, Elsevier, vol. 54(1).
    19. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
    20. Lee Ohanian & Musa Orak & Shihan Shen, 2023. "Revisiting Capital-Skill Complementarity, Inequality, and Labor Share," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 479-505, December.

    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:gam:jsusta:v:16:y:2024:i:14:p:5928-:d:1433405. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.