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Building Consumer Understanding by Utilizing a Bayesian Hierarchical Structure within the Behavioral Perspective

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Summary
This summary is machine-generated.

This study enhances the Behavioral Perspective Model (BPM) by introducing complex and hierarchical structures, and Bayesian analysis for consumer behavior insights in the Great Britain biscuit market.

Keywords:
BayesBehavioral perspective modelConsumer behaviourConsumer psychology

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Area of Science:

  • Consumer Behavior Analysis
  • Marketing Science
  • Econometrics

Background:

  • The Behavioral Perspective Model (BPM) is a key framework for understanding consumer behavior.
  • Existing BPM applications often lack complex structural or advanced inferential approaches.
  • Empirical analysis within specific product categories is needed to refine the BPM.

Purpose of the Study:

  • To extend the theoretical and empirical literature of the Behavioral Perspective Model (BPM).
  • To introduce and test a more complex, hierarchical model structure within the BPM framework.
  • To evaluate the utility of a Bayesian approach for parameter inference in BPM analysis.

Main Methods:

  • Empirical analysis of the Great Britain biscuit category.
  • Construction and hypothesis testing of a complex BPM.
  • Comparison of hierarchical versus non-hierarchical model structures.
  • Application of Bayesian inference with vague and informed priors.

Main Results:

  • A more complex and hierarchical BPM structure provides a better fit for the data.
  • The Bayesian approach offers advantages and presents specific challenges in parameter inference.
  • Model structure and analytical approach significantly impact the understanding of consumer behavior.

Conclusions:

  • Appropriate model structures are crucial for accurate consumer behavior analysis.
  • The Bayesian approach is a valuable, albeit challenging, tool for BPM parameter inference.
  • The Behavioral Perspective Model (BPM) remains a robust framework for understanding consumer behavior.