The Perils and Promises of Big Data Research in Information Systems

Authors:
  • UAUniversity of Arkansas
    University of Arkansas
  • VGVarun Grover
    Varun Grover
  • ALAron Lindberg
    Aron Lindberg
  • STStevens Institute of Technology
    Stevens Institute of Technology
  • IBIzak Benbasat
    Izak Benbasat
  • UCUniversity of British Columbia
    University of British Columbia
  • KLKalle Lyytinen
    Kalle Lyytinen
  • CUCase Western Reserve University
    Case Western Reserve University
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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.