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

Kweku-Muata Osei-Bryson ()
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Kweku-Muata Osei-Bryson: Virginia Commonwealth University

Information Systems Frontiers, 2015, vol. 17, issue 4, No 10, 845-856

Abstract: 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.

Keywords: Regression splines; Explanatory model; Causal model; KDDM process model; Exploratory data analysis; Multi-criteria decision analysis; MCDM; Decision support system (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10796-013-9469-y

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