The impact of intelligent manufacturing on labor productivity: An empirical analysis of Chinese listed manufacturing companies

Minghao Zhu, Chen Liang, Andy Yeung, Honggeng Zhou

Research output: Contribution to journalArticlepeer-review


Integrating a variety of disruptive information technologies and advanced manufacturing technologies, intelligent manufacturing (IM) has been increasingly adopted by manufacturers around the globe. While previous studies have extensively demonstrated the technological characteristics as well as industrial applications of IM, only a few studies have investigated the likely operational performance effects of IM at the firm-level, presumably due to limited data availability. Accordingly, the motivation of this study is to empirically examine the impact of IM adoption on operational performance in terms of labor productivity, and the conditions under which adopters may reap more benefits from IM. We leverage the resource-based view as the theoretical lens and use the difference-in-differences method to analyze the staggered implementation of IM pilot projects with 16,441 firm-year observations between 2010 and 2020 in China. Our results show that the adoption of IM has positive and significant impacts on Chinese listed manufacturing companies’ labor productivity. In addition, manufacturers with higher employee human capital quality and R&D intensity, as well as operating in more competitive industries will enjoy a more salient IM implementation-labor productivity benefit. Overall, our study contributes to the emerging IM literature by providing empirical evidence of the productivity enhancement effect of IM adoption based on large-scale secondary data, and it also supports the view that successful adoption of innovative technology stems from a proper fit between that technology and diverse internal and external contingencies.
Original languageEnglish
Article number109070
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Production Economics
Early online date17 Oct 2023
Publication statusPublished online - 17 Oct 2023

Bibliographical note

Funding Information:
Andy C. L. Yeung was supported in part by the Research Grants Council of Hong Kong under project No.15507221.

Publisher Copyright:
© 2023 Elsevier B.V.


  • intelligent manufacturing
  • labor productivity
  • quasi-natural experiment
  • resource-based view
  • China
  • Labor productivity
  • Resource-based view
  • Intelligent manufacturing
  • Quasi-natural experiment


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