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.

心理连续体模型（PCM）代表了体育管理中的理论框架，旨在了解消费者态度的形成方式和变化方式以及变化的方式。先前的研究人员开发了一种使用心理干预的算法分期程序，以在体育和娱乐环境中实现PCM框架的运作。尽管此分阶段过程在实用上是合理的，但它基于一个虽然直观上明智却缺乏科学严谨性的过程。当前的研究为使用贝叶斯潜在轮廓分析（贝叶斯LPA）的PCM分割提供了另一种方法。比较三种分析（传统的PCM分段算法，K均值聚类和贝叶斯LPA），结果表明，贝叶斯LPA提供了一种有前途的和可替代的统计方法，它在两个方面优于传统的PCM分期算法：（b）它具有更强大的统计能力，可以预测效果较大的概念上相关的远端结局。