Data factor marketization, knowledge integration and labor skill upgrading: evidence from a quasi-natural experiment in China

Purpose As data are increasingly incorporated into the system of production factors and become a new engine of economic growth, data factor marketization (DFM) is emerging as a critical force reshaping firms’ knowledge management patterns and labor skill structures. This study aims to examine whether and how DFM promotes labor skill upgrading, with a particular perspective of knowledge integration depth and breadth. Design/methodology/approach This study uses a difference-in-differences model to examine the underlying knowledge integration mechanism linking DFM to labor skill upgrading. Using China’s public data openness policy as a quasi-natural experiment, the authors construct a matched panel data set of 1,054 Chinese A-share listed manufacturing firms across 151 cities over the period 2012–2024. A series of robustness and heterogeneity analyses are conducted to estimate the validity and contextual relevance. Findings The results of this study demonstrate that DFM is positively related to labor skill upgrading. Knowledge integration depth and breadth mediate this relationship. Furthermore, the effects of DFM on both knowledge integration depth and breadth are more pronounced in high technological intensity, competition industries and regional factor endowments. Originality/value This study enriches the literature on data factor by integrating the knowledge-based view. Then, this study deepens understanding of how DFM upgrade labor skill by introducing knowledge integration depth and breadth as a dual-mediating mechanism. Third, this study further advances research by incorporating context variables, highlighting its influence on the effectiveness of knowledge integration depth and breadth. The findings of this study offer both theoretical and empirical contributions to understanding knowledge management in data factor and the digital labor market.