Unlocking the secrets of business intelligence and analytics in driving brand innovation: insights from an empirical study using SEM and fsQCA

While business intelligence and analytics (BI&A) hold significant potential for enterprise innovation, their role in driving brand innovation remains underexplored. This study aims to analyze the underlying mechanism through which antecedent conditions, including BI&A utilization, knowledge sharing, and realized absorptive capacity, influence brand innovation, and which combination of them better leads to higher levels of brand innovation. Drawing on the Process Model for IT business value creation, Dynamic Capabilities View, and Absorptive Capacity Theory, this study develops a research model and employs PLS-SEM and fuzzy-set qualitative comparative analysis (fsQCA) to analyze survey data from 79 employees within China’s traditional gasoline-based automobile industry. SEM results reveal that BI&A utilization enhances knowledge sharing, which in turn strengthens realized absorptive capacity to foster brand innovation, but no direct BI&A-absorptive capacity link is observed. The findings of fsQCA further identify the simultaneous presence of BI&A utilization, knowledge sharing, and realized absorptive capacity constitutes a sufficient condition for generating high brand innovation. These findings advance theoretical integration across information technology, dynamic capabilities, and innovation literature. Practically, they underscore actionable recommendations for traditional automobile enterprises transitioning to data-driven branding strategies, emphasizing cross-departmental knowledge sharing, absorptive capacity cultivation, and BI&A-system alignment to navigate market disruptions.