Big data analytics and firm performance: Findings from a mixed-method approach

TLDR

Big data analytics is viewed as a breakthrough technology, yet firms’ ability to convert it into business value remains poorly understood, with literature suggesting that strong analytics capabilities are essential but often assumes uniform resource importance across contexts. This paper investigates how configurations of resources and contextual factors lead to performance gains from big data analytics investments, drawing on complexity theory. The study employs a mixed‑methods design, surveying 175 Greek CIOs and IT managers and conducting three case studies to examine how contextual factors alter the importance of big data analytics resources for performance. Fuzzy‑set QCA identified four distinct configurations of resources that drive high performance, while the case studies illustrated the inter‑relationships among these elements and the challenges firms face in orchestrating them.

Abstract

Big data analytics has been widely regarded as a breakthrough technological development in academic and business communities. Despite the growing number of firms that are launching big data initiatives, there is still limited understanding on how firms translate the potential of such technologies into business value. The literature argues that to leverage big data analytics and realize performance gains, firms must develop strong big data analytics capabilities. Nevertheless, most studies operate under the assumption that there is limited heterogeneity in the way firms build their big data analytics capabilities and that related resources are of similar importance regardless of context. This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments. Our empirical investigation followed a mixed methods approach using survey data from 175 chief information officers and IT managers working in Greek firms, and three case studies to show that depending on the context, big data analytics resources differ in significance when considering performance gains. Applying a fuzzy-set qualitative comparative analysis (fsQCA) method on the quantitative data, we show that there are four different patterns of elements surrounding big data analytics that lead to high performance. Outcomes of the three case studies highlight the inter-relationships between these elements and outline challenges that organizations face when orchestrating big data analytics resources.