The impact of big data analytics on total factor productivity: the role of corporate equity concentration in Chinese enterprises

This study explores the impact of big data analytics and corporate equity concentration on Total Factor Productivity (TFP) in Chinese enterprises. In today's globalized economy, where efficiency and innovation are crucial for economic growth, understanding the factors that influence TFP is essential. Big data analytics, encompassing models and algorithms, architectures, management, and data security, has become a transformative force in corporate governance and decision-making processes. The integration of big data techniques enables firms to enhance transparency, optimize resource allocation, and improve production efficiency. Concurrently, corporate equity concentration significantly affects decision-making and management oversight within firms. While higher equity concentration can mitigate agency problems and enhance oversight, potentially boosting TFP, it may also lead to issues such as insider control and reduced innovation if excessively high. Using empirical data from A-share listed companies in Shanghai and Shenzhen from 2007 to 2022, this study examines the relationship between equity concentration and TFP, considering the moderating effects of ownership structure, regional financial development, and big data analytics. The findings provide theoretical insights and policy recommendations for optimizing corporate governance and leveraging big data to enhance productivity. This research contributes to the ongoing debate on the role of equity concentration in corporate performance, highlighting its complex influence on TFP and the need for balanced governance structures and advanced data analytics to foster sustainable economic growth.