IDEAS home Printed from https://ideas.repec.org/a/taf/tjisxx/v25y2016i4p289-302.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1057/ejis.2016.2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/ejis.2016.2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tjisxx:v:25:y:2016:i:4:p:289-302. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjis .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.