The Perils and Promises of Big Data Research in Information Systems
Grover, Varun, Lindberg, Aron, Benbasat, Izak, Lyytinen, Kalle
Journal of the Association for Information Systems · 2020 · Volume 21 · Issue 2
Abstract
With the proliferation of “big data” and powerful analytical techniques, information systems (IS) researchers are increasingly engaged in what we label as big data research (BDR)—research based on large digital trace datasets and computationally intensive methods. The number of such research papers has been growing rapidly in the top IS journals during the last decade, with roughly 16% of papers in 2018 employing this approach. In this editorial, we propose five conjectures that articulate the potential consequences of increasing BDR prevalence for the IS field’s research goals and outputs. We discuss ways in which IS researchers may be able to better leverage big data and new analysis techniques to conduct more impactful research. Our intent with these conjectures and analyses is to stimulate debate in the IS community. Indeed, we need a productive discussion about how emerging new research methods, digital trace data, and the development of indigenous theory relate to and can support one another.
Keywords
Big Data Research, IS Research, Theory, Empiricism, Digital Traces, IT Artifact
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