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

Benjamin M Abdel-Karim,Oliver Hinz,Nicolas Pfeuffer

doi:10.1007/s12525-021-00459-2

Benjamin M Abdel-Karim, Oliver Hinz + Show 1 more

Open Access

https://doi.org/10.1007/s12525-021-00459-2

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Journal: Electronic Markets	Publication Date: Apr 20, 2021
Citations: 18	License type: open-access

Affiliation: Goethe University Frankfurt

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.

Highlights

The constant evolution of Machine Learning (ML) approaches over the past 20 years has led both practice and research to breakthroughs in technological developments in various areas (Jordan and Mitchell 2015, p. 255)
Our bibliographic review and scientometric analysis aim to advance our understanding of the occurrence of research papers concerning ML within the Information Systems (IS) field
This paper aimed to sensitize IS research on applying ML methods based on our bibliographic review in high-level IS journals

Summary

Introduction

The constant evolution of Machine Learning (ML) approaches over the past 20 years has led both practice and research to breakthroughs in technological developments in various areas (Jordan and Mitchell 2015, p. 255). The constant evolution of Machine Learning (ML) approaches over the past 20 years has led both practice and research to breakthroughs in technological developments in various areas Since the generation and mining of data has become more feasible and large amounts of computing power have become considerably more accessible and affordable in the past decade, ML methods, with their ability to automatically solve problems with large sets of parameters, have increasingly been applied in many areas Deep Learning (DL) methods, a subset of ML methods, have gained greater popularity, especially due to the availability of large amounts of complex data To achieve a higher level of innovation and to stay competitive in the market, traditional industrial actors may need to enable their employees to apply more profound analytical methods, especially regarding developments in the fields of Artificial Intelligence (AI) and ML (Aleksander 2017)

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