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A hybrid decision support framework for generating & selecting causal explanatory regression splines models for information systems research

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  • Kweku-Muata Osei-Bryson

    (Virginia Commonwealth University)

Abstract

Much of Information Systems research is of the behavioral science category that involves the analysis of quantitative data. Typically a confirmatory approach is taken where only unconditional hypotheses are specified based on existing theory and evaluated using techniques such as regression analysis. In this paper we present a knowledge discovery via data mining (KDDM) process model based multi-criteria framework for selecting the most appropriate causal explanatory model based on the researchers subjective preferences including accuracy, simplicity, the relative importance of variables in his/her tentative research model, relative preferences for inclusion of some causal relationships.

Suggested Citation

  • Kweku-Muata Osei-Bryson, 2015. "A hybrid decision support framework for generating & selecting causal explanatory regression splines models for information systems research," Information Systems Frontiers, Springer, vol. 17(4), pages 845-856, August.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:4:d:10.1007_s10796-013-9469-y
    DOI: 10.1007/s10796-013-9469-y
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    References listed on IDEAS

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    1. Aguaron, Juan & Moreno-Jimenez, Jose Maria, 2003. "The geometric consistency index: Approximated thresholds," European Journal of Operational Research, Elsevier, vol. 147(1), pages 137-145, May.
    2. Young Mee Shin & Seung Chang Lee & Bongsik Shin & Ho Geun Lee, 2010. "Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes," Information Systems Frontiers, Springer, vol. 12(5), pages 595-606, November.
    3. Nirup M. Menon & Byungtae Lee & Leslie Eldenburg, 2000. "Productivity of Information Systems in the Healthcare Industry," Information Systems Research, INFORMS, vol. 11(1), pages 83-92, March.
    4. Osei-Bryson, Kweku-Muata, 2006. "An action learning approach for assessing the consistency of pairwise comparison data," European Journal of Operational Research, Elsevier, vol. 174(1), pages 234-244, October.
    5. Myung Ko & Jan G. Clark & Daijin Ko, 2008. "Revisiting the Impact of Information Technology Investments on Productivity: An Empirical Investigation Using Multivariate Adaptive Regression Splines (MARS)," Information Resources Management Journal (IRMJ), IGI Global, vol. 21(3), pages 1-23, July.
    6. MinJae Lee & JinKyu Lee, 2012. "The impact of information security failure on customer behaviors: A study on a large-scale hacking incident on the internet," Information Systems Frontiers, Springer, vol. 14(2), pages 375-393, April.
    7. 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. Babak Daneshvar Rouyendegh & Kazim Topuz & Ali Dag & Asil Oztekin, 2019. "An AHP-IFT Integrated Model for Performance Evaluation of E-Commerce Web Sites," Information Systems Frontiers, Springer, vol. 21(6), pages 1345-1355, December.

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