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

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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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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Mingers, John & Yang, Liying, 2017. "Evaluating journal quality: A review of journal citation indicators and ranking in business and management," European Journal of Operational Research, Elsevier, vol. 257(1), pages 323-337.
    3. Lehmann, Robert & Wohlrabe, Klaus, 2017. "Who is the ‘Journal Grand Master’? A new ranking based on the Elo rating system," Journal of Informetrics, Elsevier, vol. 11(3), pages 800-809.
    4. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    5. Jan Recker, 2013. "Scientific Research in Information Systems," Progress in IS, Springer, edition 127, number 978-3-642-30048-6, June.
    6. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    7. Guest Editors: Hemant Jain & Balaji Padmanabhan & Paul A. Pavlou & Raghu T. Santanam, 2018. "all for Papers—Special Issue of Information Systems Research —Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society," Information Systems Research, INFORMS, vol. 29(1), pages 250-251, March.
    8. William A. Belson, 1959. "Matching and Prediction on the Principle of Biological Classification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 8(2), pages 65-75, June.
    9. van Riessen, B. & Negenborn, R.R. & Dekker, R., 2016. "Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions," Econometric Institute Research Papers EI2016-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Seiler, Christian & Wohlrabe, Klaus, 2014. "How robust are journal rankings based on the impact factor? Evidence from the economic sciences," Journal of Informetrics, Elsevier, vol. 8(4), pages 904-911.
    11. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    12. Jin Li & Kwok Fai Tso & Fangtao Liu, 2017. "Profit earning and monetary loss bidding in online entertainment shopping: the impacts of bidding patterns and characteristics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(1), pages 77-90, February.
    13. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    14. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    15. Mochen Yang & Gediminas Adomavicius & Gordon Burtch & Yuqing Rena, 2018. "Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 29(1), pages 4-24, March.
    16. Michael Scholz & Markus Franz & Oliver Hinz, 2016. "The Ambiguous Identifier Clustering Technique," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 143-156, May.
    17. Ofer Arazy & Johannes Daxenberger & Hila Lifshitz-Assaf & Oded Nov & Iryna Gurevych, 2016. "Turbulent Stability of Emergent Roles: The Dualistic Nature of Self-Organizing Knowledge Coproduction," Information Systems Research, INFORMS, vol. 27(4), pages 792-812, December.
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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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