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In the era of the digital economy, data, as a new factor of production, provides critical momentum for driving agricultural modernization and enhancing enterprise efficiency. This study focuses on the data utilization capabilities of agricultural enterprises themselves. Based on data from Chinese A-share listed agricultural companies from 2011 to 2023, it constructs an enterprise-level data element utilization indicator using text analysis. Through the application of a two-way fixed effects model, a mediation effect model, and a threshold regression model, this paper systematically examines the impact, mechanisms, and contextual conditions of data element utilization on the total factor productivity of agricultural enterprises. The findings are as follows: First, the utilization of data elements significantly promotes the improvement of total factor productivity in agricultural enterprises, a conclusion that remains valid after a series of robustness checks and endogeneity treatments. Second, mechanism analysis indicates that alleviating information asymmetry serves as the core transmission pathway through which data elements exert their influence. Third, the empowering effect exhibits significant heterogeneity: its promoting effect strengthens with the deepening of national data strategies and is more pronounced in non-state-owned enterprises, small-scale agricultural enterprise and those located in regions with high levels of talent attraction. Finally, this impact demonstrates a single threshold effect based on the level of regional digital economic development, with its promoting effect showing a non-linear characteristic of increasing marginal returns as the digital ecosystem matures. This research reveals the specific pathways through which data elements drive agricultural transformation from the perspective of micro-enterprise behavior, providing empirical evidence for implementing differentiated and tiered strategies to advance digital empowerment in agriculture.
Xiong et al. (Wed,) studied this question.