Rethinking segmentation within the psychological continuum model using Bayesian analysis

Bradley J. Baker, James Du, Mikihiro Sato, Daniel C. Funk

Research output: Contribution to journalArticlepeer-review

Abstract

The Psychological Continuum Model (PCM) represents a theoretical framework in sport management to understand why and how consumer attitudes form and change. Prior researchers developed an algorithmic staging procedure using psychological involvement to operationalize the PCM framework within sport and recreational contexts. Although this staging procedure is pragmatically sound, it rests upon a procedure that, while intuitively sensible, lacks scientific rigor. The current research offers an alternative approach to PCM segmentation using Bayesian Latent Profile Analysis (Bayesian LPA). Comparing three analyses (the conventional PCM segmentation algorithm, K-means clustering, and Bayesian LPA), results demonstrated that Bayesian LPA provides a promising and alternative statistical approach that outperforms the conventional PCM staging algorithm in two ways: (a) it has the ability to classify individuals into the corresponding PCM segments with more distinct boundaries; and (b) it is equipped with stronger statistical power to predict conceptually related distal outcomes with larger effect size.

Original languageEnglish (US)
Pages (from-to)764-775
JournalSport Management Review
Volume23
Issue number4
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Bayesian analysis
  • Psychological Continuum Model (PCM)
  • Psychological involvement
  • Segmentation
  • Staging algorithm

ASJC Scopus subject areas

  • Business and International Management
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management
  • Organizational Behavior and Human Resource Management
  • Management Science and Operations Research
  • Marketing

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