Influencing models and determinants in big data analytics research: A bibliometric analysis

Abstract Incorporating big data analytics into a particular context brings various challenges that rest on the model or framework through which individuals or organisations adopt big data to achieve their objectives. Although these models have recently triggered scholars’ attention in various domains, in-depth knowledge of using each of these models in big data research is still blurred. This study enriches our knowledge on emerging models and theories that shape big data analytics adoption (BDAD) research through a bibliometric analysis of 229 studies (143 journal articles and 86 conference papers) published in indexed sources between 2013 and 2019. As a result, twenty models on BDAD have emerged (e.g., “Dynamic Capabilities”, “Resource-Based View”, “Technology Acceptance Model”, “Diffusion of Innovation”, etc.). The analysis reveals that BDAD research to demonstrate attributes suggestive of a topic at an initial stage of development as it is broadly dispersed across different domains employs a wide range of models, some of which overlap. Most of the applied models are generic in nature focusing on variance-based relationships and snapshot prediction with little consensus. There is a conspicuous dearth of process models, firm-level analysis and cultural orientation in contemporary BDAD research. Insights of this bibliometric study could guide rigorous big data research and practice in various contexts. The study concludes with research implications and limitations that offer promising prospects for forthcoming research.