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Leisure Sciences
An Interdisciplinary Journal
Volume 26, 2004 - Issue 1
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Original Articles

Exploring Origins of Involvement: Understanding the Relationship Between Consumer Motives and Involvement with Professional Sport Teams

, &
Pages 35-61 | Received 01 Oct 2001, Accepted 01 May 2002, Published online: 12 Aug 2010
 

Abstract

Although the involvement construct has received wide spread theoretical and empirical attention over the past 30 years, its application to a subset of leisure such as spectator sport has gone largely ignored. The present research examines both the multidimensionality of the involvement construct and its origins in sport spectator research. A team sport involvement (TSI) model is introduced that accounts for antecedents of motivation, arousal, and interest related to a professional sport team. A series of focus groups and a pilot test were used to develop and refine the Sport Interest Inventory (SII) that measures 18 distinct antecedents of involvement. The SII was next administered to a random sample of season ticket holders and single game attendees of a sport team (N = 1,600). Confirmatory factor analysis confirmed the psychometric properties of the SII and structural equation modeling analysis supported the TSI model revealing that nine antecedents represented four higher order facets of involvement: Attraction, self-expression, centrality to lifestyle, and risk. Multivariate analysis of variance (MANOVA) followed by post hoc comparisons revealed significant differences in prior attendance behavior could be traced to individual's involvement profile score. The results have important implications for applying the TSI model to enhance managers' understanding of sport consumers. The application of the TSI model and its implications for enhancing managers' understanding of sport consumers are discussed.

Notes

*The 2-index combinational rule presentation strategy using cutoff values for RMSEA of ≤.06 and SMRM of ≤.08 was utilized to limit the number the of Type I and Type II error rates in model specification regardless of sample size CitationHu & Bentler, 1998, 1999).

1F = 4.25, p < .01;

2F = 8.10, p < .01;

3F = 21.74, p < .01;

4F = 19.44, p < .01.

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