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Utilizing big data analytics for information systems research: challenges, promises and guidelines

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  • Oliver Müller
  • Iris Junglas
  • Jan vom Brocke
  • Stefan Debortoli

Abstract

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user-generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.

Suggested Citation

  • Oliver Müller & Iris Junglas & Jan vom Brocke & Stefan Debortoli, 2016. "Utilizing big data analytics for information systems research: challenges, promises and guidelines," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(4), pages 289-302, July.
    • Handle: RePEc:taf:tjisxx:v:25:y:2016:i:4:p:289-302
    DOI: 10.1057/ejis.2016.2
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    Cited by:

    1. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    2. Ajaya K. Swain & Valeria R. Garza, 2023. "Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution," Information Systems Frontiers, Springer, vol. 25(2), pages 819-834, April.
    3. Torsten Oliver Salge & David Antons & Michael Barrett & Rajiv Kohli & Eivor Oborn & Stavros Polykarpou, 2022. "How IT Investments Help Hospitals Gain and Sustain Reputation in the Media: The Role of Signaling and Framing," Information Systems Research, INFORMS, vol. 33(1), pages 110-130, March.
    4. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    5. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    6. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    7. Godé, Cécile & Brion, Sébastien, 2024. "The affordance-actualization process of predictive analytics: Towards a configurational framework of a predictive policing system," Technological Forecasting and Social Change, Elsevier, vol. 204(C).

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