How digital transformation enhances corporate innovation performance: The mediating roles of big data capabilities and organizational agility.

Digital transformation has emerged as a key driver of high-quality enterprise development and an essential tool in forging an innovation-driven paradigm.Existing studies fail to delve into the specific mechanisms of their impact on firms' innovation performance, and views on their impact are divergent. Some studies suggest that digital transformation can enhance innovation performance, while others point out that it may have negative impacts, and cannot clearly answer how big data capabilities and organisational agility play a role in the digital transformation process. Therefore, based on dynamic capability theory and systems engineering theory, this study adopts the logical framework of "strategy-behaviour-performance" to systematically explore the process of digital transformation that enhances firms' innovation performance through the enhancement of big data capability and organisational agility. By empirically analysing the survey data of 476 manufacturing enterprises in China, the study reveals the chain-mediated effects of big data capability and organisational agility, and confirms the key roles of both in the transformation process. The findings suggest that digital transformation significantly improves firms' innovation performance, and that the dual mediating effects of big data capability and organisational agility are important links in its influencing mechanism. These findings not only provide empirical support for the theoretical model of digital transformation, but also provide practical guidance for enterprises to formulate strategies and optimise resource allocation in the digital era. We suggest that enterprises should strengthen the cultivation of big data capabilities and organisational agility while promoting digital transformation to better adapt to and lead market changes.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2024 The Authors.)