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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
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    References listed on IDEAS

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    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. 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.
    4. Maarek, Paul & Moiteaux, Elliot, 2021. "Polarization, employment and the minimum wage: Evidence from European local labor markets," Labour Economics, Elsevier, vol. 73(C).
    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.
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