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Exploring a Bayesian hierarchical structure within the behavioural perspective model

Rogers, Andrew 2018. Exploring a Bayesian hierarchical structure within the behavioural perspective model. PhD Thesis, Cardiff University.
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Abstract

This thesis focusses on how the behaviour of consumers can be predicted within the Behavioural Perspective Model’s (BPM) theoretical framework. The study focuses on three specific area. First, a complex functional form is created, utilizing the BPM’s Informational and Utilitarian reinforcement in combination with behavioural economic, consumer psychology, marketing and seasonal variables. Second, the text introduces a hierarchical framework to the model. The data are structured as purchases within household and hence the assumption of independence within household purchase is questioned. The hierarchical framework allows the removal of this assumption. Therefore, hierarchical and non-hierarchical models are constructed and compared to investigate this. Third, the text discusses the Bayesian paradigm and the differences this brings to model estimation versus the more traditional frequentist methods of calculation. The debate between the Bayesian and frequentist paradigms has been prevalent within mathematical and statistical literature for some time and this text is not meant to directly contribute to this literature. However, the text does explore the potential advantages to the subject matter through the exploration of a Bayesian framework for model estimation. Hence, model estimation through a Bayesian framework is employed employing both vague and informed prior distribution, with the informed priors calibrated from frequentist estimates.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Business (Including Economics)
Funders: AHRC
Date of First Compliant Deposit: 11 May 2018
Last Modified: 11 May 2018 15:43
URI: http://orca-mwe.cf.ac.uk/id/eprint/111391

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