Paper Info
Title
The Perils and Promises of Big Data Research in Information Systems
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. By analyzing 82 articles (41 BDR articles and a comparison group of 41 non-BDR articles) published in three of the field's top research journals ( MIS Quarterly, Information Systems Research, and Journal of Management Information Systems) during 2016-2018, we show that while the conjectures might appear controversial, they do indeed have merit. Our evidence indicates that compared to non-BDR studies, BDR studies deal less with theory and more with tactical topics. 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.
Year
DOI
Venue
2020
10.17705/1jais.00601
JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS
Keywords
DocType
Volume
Big Data Research,IS Research,Theory,Empiricism,Digital Traces,IT Artifact
Journal
21
Issue
ISSN
Citations 
2
1536-9323
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Varun Grover15604339.27
Aron Lindberg275.20
Izak Benbasat32598174.49
Kalle Lyytinen45078443.19