Driving New Quality Productive Forces through Data Elements
Year: 2026
Type: conference-paper
Abstract: Against the backdrop of global economic restructuring and new technological waves, China is accelerating the cultivation of new quality productive forces as a fresh engine for growth. While data elements, as an emerging production factor, have been recognized for their potential, existing studies remain largely theoretical and lack empirical validation. To address this gap, this study employs provincial panel data from 2012 to 2022 to provide the first systematic empirical assessment of the role of data elements in shaping new quality productive forces, with particular attention to the mediating roles of dynamic capabilities and resource allocation efficiency. Baseline regression results indicate that data elements significantly enhance new quality productive forces, a conclusion robust to stability and endogeneity tests. Mechanism analysis further shows that dynamic capabilities and resource allocation efficiency partially mediate this relationship, while heterogeneity tests reveal stronger effects in eastern provinces, regions with better legal environments, and higher-income areas. This study makes three contributions: it advances theory by identifying the mechanisms through which data elements operate, provides novel empirical evidence bridging conceptual claims and quantitative analysis, and demonstrates the context-dependent nature of data's productivity effects. Beyond China, the findings yield broader implications for other economies by underscoring the importance of data infrastructure, institutional quality, and balanced regional development. More broadly, the study offers timely insights into how data resources, as a foundation of the Fourth Industrial Revolution, can reshape productivity dynamics and foster sustainable economic transformation.
Authors: Cui Fang, Jaehoon Song
Institutions: Taishan University, Yonsei University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204865458