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Machine learning in information systems - a bibliographic review and open research issues

Author

Listed:
  • Benjamin M. Abdel-Karim

    (Goethe University Frankfurt, Information Systems and Information Management)

  • Nicolas Pfeuffer

    (Goethe University Frankfurt, Information Systems and Information Management)

  • Oliver Hinz

    (Goethe University Frankfurt, Information Systems and Information Management)

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.

Suggested Citation

  • Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:3:d:10.1007_s12525-021-00459-2
    DOI: 10.1007/s12525-021-00459-2
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    References listed on IDEAS

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    Cited by:

    1. Christian Engel & Philipp Ebel & Jan Marco Leimeister, 2022. "Cognitive automation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 339-350, March.
    2. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Rainer Alt, 2021. "Electronic Markets on robotics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 465-471, September.
    4. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    5. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    6. 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.
    7. Jan Zacharias & Moritz Zahn & Johannes Chen & Oliver Hinz, 2022. "Designing a feature selection method based on explainable artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2159-2184, December.

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    More about this item

    Keywords

    Machine learning; Artificial intelligence; Information systems;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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