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A framework for the application of machine learning in IS literature reviews

  • The rapid development and growth of knowledge has resulted in a rich stream of literature on various topics. Information systems (IS) research is becoming increasingly extensive, complex, and heterogeneous. Therefore, a proper understanding and timely analysis of the existing body of knowledge are important to identify emerging topics and research gaps. Despite the advances of information technology in the context of big data, machine learning, and text mining, the implementation of systematic literature reviews (SLRs) is in most cases still a purely manual task. This might lead to serious shortcomings of SLRs in terms of quality and time. The outlined approach in this paper supports the process of SLRs with machine learning techniques. For this purpose, we develop a framework with embedded steps of text mining, cluster analysis, and network analysis to analyze and structure a large amount of research literature. Although the framework is presented using IS research as an example, it is not limited to the IS field but can also be applied to other research areas.

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Author of HS ReutlingenBozkurt, Yusuf; Braun, Reiner; Rossmann, Alexander
Erschienen in:Proceedings of the 15th IADIS International Conference Information Systems, 12-14 March 2022, virtual
Document Type:Conference Proceeding
Year of Publication:2022
Tag:machine learning; research method; systematic literature review; text mining
Page Number:11
First Page:19
Last Page:29
DDC classes:004 Informatik
Open Access?:Nein