Title:	A FRAMEWORK FOR THE APPLICATION OF MACHINE LEARNING IN IS LITERATURE REVIEWS
Author(s):	Yusuf Bozkurt, Reiner Braun and Alexander Rossmann
ISBN:	978-989-8704-37-5
Editors:	Miguel Baptista Nunes, Pedro Isaías and Philip Powell
Year:	2022
Edition:	Single
Keywords:	Systematic Literature Review, Machine Learning, Text Mining, Research Method
First Page:	19
Last Page:	29
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	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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