Unveiling data value: A configurational approach to big data and business model design

The rapid growth of big data has attracted academic interest, but extant studies are inadequate to address how to derive data value from the configurations between big data and business model design. To this end, we build a framework that integrates business model design (novelty, efficiency) into big data’s 4Vs (volume, variety, velocity, value) and apply a holistic configurational approach to investigate what combinations among volume, variety, velocity, novelty, and efficiency can affect data value. Based on the sample of 41 consumer internet start-ups in China, we use fuzzy-set qualitative comparative analysis (fsQCA) to reveal the configurational results. The findings suggest that there are four configurations leading to high data value, which are respectively labelled as experience-oriented business models (EBM), operation-enhanced business models (OBM), content-centric business models (CBM), and scale-dominant business models (SBM). Thus, we identify different prototypes of data-driven business models from these configurations, the underlying mechanisms are illustrated by data network effects and representative case practices. By contrast, configurations leading to not-high data value are collectively labelled as business models deficient in data-business synergy. Our study not only advances big data research in management and business and data-driven business model research, but also sheds light on the extension of data network effects’ theoretical boundary and research context.