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Profiling internet banking users: A knowledge discovery in data mining process model based approach

Author

Listed:
  • Gunjan Mansingh

    (The University of the West Indies)

  • Lila Rao

    (The University of the West Indies)

  • Kweku-Muata Osei-Bryson

    (Virginia Commonwealth University)

  • Annette Mills

    (University of Canterbury)

Abstract

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

Suggested Citation

  • Gunjan Mansingh & Lila Rao & Kweku-Muata Osei-Bryson & Annette Mills, 2015. "Profiling internet banking users: A knowledge discovery in data mining process model based approach," Information Systems Frontiers, Springer, vol. 17(1), pages 193-215, February.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:1:d:10.1007_s10796-012-9397-2
    DOI: 10.1007/s10796-012-9397-2
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    References listed on IDEAS

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    1. Ritu Agarwal & Jayesh Prasad, 1998. "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, INFORMS, vol. 9(2), pages 204-215, June.
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    3. Karen Furst & William Lang & Daniel Nolle, 2003. "Internet Banking," Journal of Financial Services Research, Springer;Western Finance Association, vol. 23(3), pages 251-251, June.
    4. Kweku-Muata Osei-Bryson & Kendall Giles, 2006. "Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families," Information Systems Frontiers, Springer, vol. 8(3), pages 195-209, July.
    5. Francis Kofi Andoh-Baidoo & Kweku-Muata Osei-Bryson & Kwasi Amoako-Gyampah, 2012. "Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework," Information Systems Frontiers, Springer, vol. 14(2), pages 237-259, April.
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    Cited by:

    1. Ram Ramesh & H. Raghav Rao, 2015. "Foreword: Information Systems Frontiers," Information Systems Frontiers, Springer, vol. 17(1), pages 1-2, February.
    2. Neeru Malhotra & Sunil Sahadev & P. S. H. Leeflang & Keyoor Purani, 2021. "New Insights into e-Loyalty of Internet Banking Users in an Emerging Market Context: A Multilevel Analysis," Information Systems Frontiers, Springer, vol. 23(6), pages 1521-1536, December.

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