Configurations of Big Data Analytics for Firm Performance: An fsQCA approach
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With big data analytics growing rapidly in importance, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. Early empirical evidence suggests that big data analytics can enhance a firm’s performance; yet, there is a lack of understanding on complementary organizational factors coalesce to drive performance gains, under what conditions they are more appropriate, as well as how they can complement a firm's dynamic capabilities under turbulent and fast-paced market conditions. To address this question, this study builds on the big data analytics capability literature and examines the fit between big data analytics resources and governance practices, dynamic capabilities, and environmental conditions in driving performance gains. Survey data from 175 chief information officers and IT managers working in Greek firms is analyzed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that that different configurations of resources, practices, and external factors coalesce to drive performance gains. We show that there are multiple configurations that can lead in high and low levels of performance.