Year: 2025
Type: article
Abstract: Introduction Data, as a new production factor, can interact synergistically with traditional factors, such as labour and capital, thus generating amplification, superposition and multiplication effects. China’s vast rural market, rich agricultural data resources and diverse application scenarios offer a robust foundation for the development of data-driven industries. These industries hold promise for advancing the digital intelligence of agricultural production, improving market connectivity and increasing farmers’ income, all of which contribute to the broader process of rural revitalisation. Methods This study constructs a comprehensive panel dataset of 261 Chinese cities from 2014 to 2022. The objective is to empirically analyse the impact and mechanisms through which data-driven industries influence rural revitalisation. A two-way fixed effects model is employed to capture the direct effects, whereas mediation analysis is used to uncover the underlying transmission channels. Results The empirical analysis reveals that data-driven industries have a significant positive impact on rural revitalisation. Further mechanism analysis identifies rural innovation and entrepreneurship as the primary mediating channels through which these industries exert their influence. Additionally, heterogeneity tests indicate that the positive effects are more pronounced in regions with advanced industrial structures and greater fiscal decentralisation. Discussion The findings of this study provide novel empirical insights into the role of data-driven industries in promoting rural revitalisation. Policymakers should focus on enhancing data infrastructure, improving the institutional environment and facilitating the integration of digital technologies into the rural industrial transformation process to maximise the potential of data-driven industries in fostering sustainable rural growth.
Authors: Ping Huang, Yuqing Cui, Xiaohui Chen, Jun Dai, Danni Wang, Qingchen Mei
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,353
Funder: Guizhou University
OpenAlex ID: W4417415047