Isolating The Key Variables For Regression Models In Enterprise Software Acquisition Decisions: A Blocking Technique

Rolando Pena-Sanchez, Jacques Verville and Christine Bernadas ()
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Rolando Pena-Sanchez: Texas A&M International University [Laredo]
Jacques Verville: Texas A&M International University [Laredo]
Christine Bernadas: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School, Texas A&M International University [Laredo]

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Abstract: Often researchers in the field of information systems face problems related to the variable selection for model building; as well as difficulties associated to their data (small sample and/or non normality). The goal of this article is to present an original statistical blocking technique based on relative variability for screening of variables in multivariate regression models. We applied the blocking-technique and a nonparametric bootstrapping method to the data collected on the USA-South border for a research concerning enterprise software (ES) acquisition contracts. Three mutually exclusive blocks of relative variability for the response variables were formed and their corresponding regression models were built and explained. A conclusion was drawn about the decreasing tendency on the adjusted coefficient of determination (R2adj) magnitudes when the blocks change from low (L) to high (H) condition of relative variability. The obtained models (via stepwise regression) exhibited significant p-values (0.0001).

Date: 2011-02-07
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Published in Journal of Business & Economics Research , 2011, 5 (8), pp.57-68. ⟨10.19030/jber.v5i8.2570⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03218616

DOI: 10.19030/jber.v5i8.2570

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