Using 29,278 firm-year observations of Chinese A-share listed firms from 2012 to 2023, this study examines whether data assetisation improves corporate investment efficiency and whether bank fintech conditions shape this relationship. Data assetisation refers to the process through which firms transform data resources into economically valuable, governable, and deployable assets. We construct a text-based proxy from annual reports using a Word2Vec-expanded lexicon and further distinguish between own-use and transactional data assets. The study finds: (1) Data assetisation significantly enhances corporate investment efficiency, with self-use data assets demonstrating a stronger driving effect. (2) Mechanism analysis reveals that data assetisation alleviates underinvestment by easing financing constraints and leveraging the “talent effect”. Concurrently, it mitigates overinvestment by reducing agency problems and accelerating digital transformation, thereby enhancing investment efficiency. (3) Heterogeneity tests indicate that the positive impact of data assetisation on investment efficiency is more pronounced among growth-stage enterprises, technology-intensive firms, and companies operating in regions with high bank liquidity. (4) Banking fintech positively moderates the enhancement of corporate investment efficiency through data assetisation, with a more pronounced effect on alleviating underinvestment. However, it may also exacerbate overinvestment. This study contributes to sustainable economic development by improving resource allocation efficiency, reducing capital misallocation, and supporting high-quality, low-waste, and sustainable growth of the real economy. Consequently, enterprises should vigorously develop data assetisation, applying different types of data assets to specific use cases to unlock data dividends. This approach supports the scientific development of corporate investment decisions and enhances investment efficiency, laying a micro-level foundation for sustainable socio-economic development.
Luo et al. (Fri,) studied this question.