A Preliminary Study on Artificial Intelligence and Labour Productivity in China

Authors

  • Lai Zouya School of Distance Education, Universiti Sains Malaysia, 11800 Penang, Malaysia
  • Norhanishah Mohamad Yunus School of Distance Education, Universiti Sains Malaysia, 11800 Penang, Malaysia

DOI:

https://doi.org/10.37134/ibej.Vol17.2.2.2024

Keywords:

Artificial Intelligence, Labour Productivity, Patent Applications, Agriculture Sector

Abstract

Using the total number of patents as a proxy for artificial intelligence (AI), this study adds to the body of knowledge by analysing the relationship between AI applications and labour productivity in China's overall sector and concentrating on China's agriculture sector. Even though this study only employed ordinary least squares (OLS) estimation, the results could still provide a rough idea of the current stage of China’s AI patent applications and their impact on enhancing labour productivity. Our findings demonstrated that the impact of AI patent applications statistically affects the labour productivity of China's overall sector but did not appear to be well supported by our research in the agriculture sector. Our findings suggest that China's agriculture sector has less frequent and lesser experience with patenting to fully exploit innovation activities due to a lack of skilled labour and employee participation in scientific research and innovation activity as a result of the agriculture sector's continued dominance by low-educated labour. To address these challenges, we recommend that the Chinese government continue to invest more in innovation and AI, conduct employee retraining programmes to improve their skills and knowledge, create rules and guidelines to protect the privacy of patents, and promote a climate of openness and accountability when deploying AI in the industry.

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Published

2024-02-22

How to Cite

Zouya, L., & Mohamad Yunus, N. (2024). A Preliminary Study on Artificial Intelligence and Labour Productivity in China. International Business Education Journal, 17(2), 12–25. https://doi.org/10.37134/ibej.Vol17.2.2.2024