This paper examines how firms’ attention to data assets influences innovation performance through AI investment as a mediating mechanism. To deepen this relationship, it further explores how the AI investment concentration and a firm’s knowledge absorptive capacity moderate this pathway.
The paper is based on a panel dataset of Chinese A-share listed firms from the period of 2010–2023. To analyze the relationships between data asset attention, AI investment and innovation performance, the paper employs fixed-effects regression analysis. Robustness checks are also conducted to ensure the validity of the empirical results.
A greater emphasis on data asset attention significantly promotes a firm’s investment in AI, which in turn enhances innovation performance. The results underscore the pivotal role of AI investment as a catalyst for maximizing the value of data assets. Furthermore, both the concentration of AI investment and a firm’s knowledge absorptive capacity significantly strengthen this positive relationship, showing that a clear strategic focus and strong learning capabilities are crucial.
This paper unpacks the “black box” of how data assets translate into innovation outcomes by identifying AI investment as a critical resource orchestration mechanism. Furthermore, it offers empirical insights into the conditions under which the debated “AI Productivity Paradox” may be resolved, by introducing AI investment concentration and knowledge absorptive capacity as pivotal boundary conditions. Finally, it enriches the digital innovation literature by constructing an “Attention–Behavior–Performance” framework, which yields actionable insights for future research and managerial practice.
