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Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.
Hao et al. (Fri,) studied this question.