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Building consumer understanding by utilizing a Bayesian hierarchical structure within the behavioral perspective model

Rogers, Andrew, Foxall, Gordon and Morgan, Peter 2017. Building consumer understanding by utilizing a Bayesian hierarchical structure within the behavioral perspective model. The Behavior Analyst 40 (2) , pp. 419-455. 10.1007/s40614-017-0120-y

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Abstract

This study further develops the theoretical and empirical literature on the Behavioral Perspective Model (BPM) in three ways through an empirical analysis of the Great Britain (GB) biscuit category. First, following a literature review and a category analysis, a more complex model is constructed using the BPM structure and then testing the hypothesis uncovered. Second, the structure of the data theoretically calls for a hierarchical structure of the model, and hence, this is introduced into the BPM framework and is compared to a non-hierarchical structure of the same model. Finally, a discussion is undertaken on the advantages of a Bayesian approach to calculating parameter inference. Two models are built by utilizing vague and informed prior distributions respectively, and the results are compared. This study shows the importance of building appropriate model structures for analysis and demonstrates the advantages and challenges of utilizing a Bayesian approach. It also further demonstrates the BPM’s suitability as a vehicle to better understand consumer behavior

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Business (Including Economics)
Publisher: Springer Verlag
ISSN: 0738-6729
Date of First Compliant Deposit: 27 February 2018
Date of Acceptance: 1 December 2016
Last Modified: 19 Oct 2019 05:18
URI: http://orca.cf.ac.uk/id/eprint/109609

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