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A Research Review and Taxonomy Development for Decision Support and Business Analytics Using Semantic Text Mining

Author

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  • Andrea Ko

    (Department of Information Systems, Corvinus University of Budapest, H-1093 Budapest, Fővám tér 13-15, Budapest, Hungary)

  • Saira Gillani

    (#x2020;Department of Computer Science, Bahria University, Karachi Campus, Karachi, Pakistan)

Abstract

By 2018, business analytics (BA), believed by global CIOs to be of strategic importance, had for years been their top priority. It is also a focus of academic research, as shown by a large number of papers, books, and research reports. On the other hand, the BA domain suffers from several incorrect, imprecise, and incomplete notions. New areas and concepts emerge quickly; making it difficult to ascertain their structure. BA-related taxonomies play a crucial role in analyzing, classifying, and understanding related objects. However, according to the literature on taxonomy development in information systems (IS), in most cases the process is ad hoc. BA taxonomies and frameworks are available in the literature; however, some are excessively general frameworks with a high-level conceptual focus, while others are application or domain-specific. Our paper aims to present a novel semi-automatic method for taxonomy development and maintenance in the field of BA using content analysis and text mining. The contribution of our research is threefold: (1) the taxonomy development method, (2) the draft taxonomy for BA, and (3) identifying the latest research areas and trends in BA.

Suggested Citation

  • Andrea Ko & Saira Gillani, 2020. "A Research Review and Taxonomy Development for Decision Support and Business Analytics Using Semantic Text Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 97-126, January.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019300076
    DOI: 10.1142/S0219622019300076
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    References listed on IDEAS

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