Rethinking segmentation within the psychological continuum model using Bayesian analysis Bradley J. Baker a, * , 1 , James Du b, 1 , Mikihiro Sato c , Daniel C. Funk d a University of Massachusetts, Mark H. McCormack Department of Sport Management, Isenberg 255B, 121 Presidents Drive, Amherst, MA, 01003, USA b Florida State University, Department of Sport Management, Tully Gymnasium, 139 Chieftan Way, Tallahassee, FL, 32304, USA c James Madison University, Hart School of Hospitality, Sport and Recreation Management, Godwin Hall MSC 2305, 261 Bluestone Drive, Harrisonburg, VA, 22807, USA d Temple University, School of Sport, Tourism & Hospitality Management, 111 Speakman Hall, 1810 N. 13th St., Philadelphia, PA, 19122, USA A R T I C L E I N F O Article history: Received 9 January 2019 Received in revised form 9 September 2019 Accepted 9 September 2019 Available online xxx Keywords: Bayesian analysis Psychological involvement Segmentation Psychological Continuum Model (PCM) Staging algorithm A B S T R A C T 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. © 2019 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved. 1. Introduction The Psychological Continuum Model (PCM) is a conceptual framework that describes individual and sociological processes that explain the progressive development of a sport consumer’s connection with a sport object (e.g., sport activity, event, team, league, or athlete; Funk & James, 2001,2006). Subsequent empirical work to operationalize the framework (Beaton, Funk, & Alexandris, 2009; Kunkel, Funk, & Hill, 2013) used a staging algorithm with psychological involvement to examine similarities and differences between developmental stages. In this algorithm, three facets of involvement—pleasure, centrality, and sign—are each categorized as low, medium, or high, resulting in 27 profiles. In turn, those profiles are assigned to one of the four developmental stages of the PCM (awareness, attraction, attachment, and allegiance) to categorize participants and spectators into theoretically meaningful clusters within sport and recreational contexts. * Corresponding author. E-mail addresses: bbaker@isenberg.umass.edu (B.J. Baker), jdu3@fsu.edu (J. Du), satomx@jmu.edu (M. Sato), dfunk@temple.edu (D.C. Funk). 1 The first two authors contributed equally to this manuscript. https://doi.org/10.1016/j.smr.2019.09.003 1441-3523/© 2019 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved. Sport Management Review xxx (2019) xxx–xxx G Model SMR 585 No. of Pages 12 Please cite this article in press as: B.J. Baker, et al., Rethinking segmentation within the psychological continuum model using Bayesian analysis, Sport Management Review (2019), https://doi.org/10.1016/j.smr.2019.09.003 Contents lists available at ScienceDirect Sport Management Review journa l homepage : www.e lsevier.com/loca te/smr