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Advancing Automated Content Analysis in Knowledge Management Research: The Use of Compound Concepts

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  • Nora Fteimi

    (University of Passau, Passau, Germany)

  • Dirk Basten

    (Department of Information Systems and Systems Development, University of Cologne, Cologne, Germany)

  • Franz Lehner

    (University of Passau, Passau, Germany)

Abstract

This article reports on the development of a knowledge management (KM) dictionary and its application to automated content analysis to investigate topical foci of KM publications and provide an overview of the current research landscape. While automated content analysis gains importance, a problem prevails concerning the use and analysis of compound concepts (e.g., organizational learning). Using a self-developed dictionary of KM-related compound concepts, a sample of 4,290 publications from ten top-ranked KM journals and one KM conference was analyzed using text-mining software. Based on the dictionary approach, this study investigates core research themes of the KM discipline and compares key research interests throughout the IJKM community and those of other outlets. The investigation provides guidance to identify research opportunities in KM and provides useful implications concerning the application of dictionaries. Practitioners might adapt their organizations' approaches to KM accordingly, with regard to prevailing themes and trends in KM research.

Suggested Citation

  • Nora Fteimi & Dirk Basten & Franz Lehner, 2019. "Advancing Automated Content Analysis in Knowledge Management Research: The Use of Compound Concepts," International Journal of Knowledge Management (IJKM), IGI Global, vol. 15(1), pages 53-68, January.
    • Handle: RePEc:igg:jkm000:v:15:y:2019:i:1:p:53-68
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    Cited by:

    1. Nora Nahr & Marikka Heikkilä, 2022. "Uncovering the identity of Electronic Markets research through text mining techniques," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1257-1277, September.

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