Big-data business models: A critical literature review and multiperspective research framework

Abstract

The emergence of “big data” offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models. Although big-data business model research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of big-data business models. In particular, our review uses three major criteria (big-data business model types, dimensions, and deployment) to assess the state of the big-data business model literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current big-data business model deployment gap.

Pathways of Data-driven Business Model Design and Realization: A Qualitative Research Study

“…In research, the topic has been investigated under several terms ranging from business intelligence, business analytics, and big data to big data analytics [1]. Scholars have advanced the research on BDA from only the technological perspective, defined by the four characteristics volume, variety, velocity, and veracity as a multisided socio-economic phenomenon [3,6,10].…”

“…Schüritz and Satzger argued that a clear threshold of required data for a DDBM is not defined and that companies shift from a traditional BM to a DDBM, with increased application of the data for the value proposition [14]. However, other scholars such as Wiener et al [3], distinguish between updating the existing BM with data and developing new models. In this study, we clearly distinguish between enhancements of existing BMs and new DDBMs that are centered on data (data as a key resource and/or data processing as a key activity).…”

“…The terms under which data have been investigated have varied in the past decades, ranging from business intelligence, business analytics, and big data to big data analytics [1]. Advancements in information technology, especially in machine learning, big data, cloud, and Internet of Things (IoT) technologies, have further increased the importance of data for business development and innovation [2,3]. Many companies are under pressure or are enhancing their traditional business mode with data or to realize new data-driven business models [3,4].…”

“…Advancements in information technology, especially in machine learning, big data, cloud, and Internet of Things (IoT) technologies, have further increased the importance of data for business development and innovation [2,3]. Many companies are under pressure or are enhancing their traditional business mode with data or to realize new data-driven business models [3,4]. The latter has led to the emergence of a new research field, which investigates data-driven business models (DDBMs).…”

“…Recent literature reviews of DDBMs revealed a considerable number of publications since 2014 in this thriving research field [3,4]. Wiener et al argued that most studies describe the nature of the DDBM phenomenon and emphasize the scarcity of empirical research [3]. In particular, the "dynamic aspects of BDBM deployments (process perspective)" has received very little attention [3:75].…”