The Perils and Promises of Big Data Research in Information Systems

Grover, Varun; Lindberg, Aron; Benbasat, Izak; Lyytinen, Kalle

doi:10.17705/1jais.00601

Cited by 38 publications

(42 citation statements)

References 31 publications

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“…As further example, with net-nography (an online research method originating from ethnography employed to study social interaction in digital communication), we observe a phenomenon through the very technologies that we study (thanks to a panellist here). Grover et al (2020) discuss the dramatic shift occurring to big data research (stating 16% of papers in top IS outlets employed this approach in 2018). They offer several conjectures that suggest possible unanticipated consequences of these approaches: the implication being these approaches have not been researched adequately.…”

Section: Information Systems Discipline (Isd) Research Strategymentioning

confidence: 99%

Viewpoint: Information systems research strategy

Gable

2020

The Journal of Strategic Information Systems

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Section: Information Systems Discipline (Isd) Research Strategymentioning

confidence: 99%

Viewpoint: Information systems research strategy

Gable

2020

The Journal of Strategic Information Systems

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“…Because the way datasets are collected and presented is not uniform, combining datasets is difficult [12,16]. With a dataset big enough almost every relationship will become statistically significant causing spurious correlations [7,18,27]. Big data research tends to focus on the "tactical" issues at the expense of "why" settling for correlations [7].…”

Section: Common Challenges With Big Datamentioning

confidence: 99%

“…With a dataset big enough almost every relationship will become statistically significant causing spurious correlations [7,18,27]. Big data research tends to focus on the "tactical" issues at the expense of "why" settling for correlations [7]. In general, big data is better suited to providing the "what", the "where" and the "when" but not the "why" or the "how" [14,18].…”

Section: Common Challenges With Big Datamentioning

confidence: 99%

“…Data is often user-generated rather than explicitly created, collected and stored for research purposes. In 2018, 16% of papers in top IS journals could be classified as big data research [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, big data can uniquely advance the development of theory by revealing anomalies, alternative conceptualizations of constructs and new field experiments. [7]. Interviews and surveys are artificial situations, but discussion forums and other digital sources contain records with candour and the source is also updated and the topics evolve [36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people's behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many "tricks of the trade" but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework.

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