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The Dangers of Using Intention as a Surrogate for Retention in Brand Positioning Decision Support Systems

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Data Analysis, Machine Learning and Knowledge Discovery

Abstract

The purpose of this paper is to explore the dangers of using intention as a surrogate for retention in a decision support system (DSS) for brand positioning. An empirical study is conducted, using structural equations modeling and both data from the internal transactional database and a survey. The study is aimed at evaluating whether the DSS recommends different product benefits for brand positioning when intention is used as opposed to retention as a criterion variable. The results show that different product benefits are recommended contingent upon the criterion variable (intention vs. retention). The findings also indicate that the strength of the structural relationships is inflated when intention is used. This study is limited in that it investigates only one industry; the newspaper industry. This research provides guidance for brand managers in selecting the most appropriate benefit for brand positioning and advices against the use of intention as opposed to retention in DSSs. To the best of our knowledge this study is the first to challenge and refute the commonly held belief that intention is a valid surrogate for retention in a DSS for brand positioning.

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Authors and Affiliations

  1. Department of Marketing, Ghent University, Ghent, Belgium

    Michel Ballings & Dirk Van den Poel

Authors
  1. Michel Ballings
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  2. Dirk Van den Poel
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Correspondence to Michel Ballings .

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Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Lars Schmidt-Thieme

  3. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Ruth Janning

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Ballings, M., Van den Poel, D. (2014). The Dangers of Using Intention as a Surrogate for Retention in Brand Positioning Decision Support Systems. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_20

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