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Exploring the Demand Aspects of Sports Consumption and Fan Avidity

Author

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  • Wayne DeSarbo

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Robert Madrigal

    (Lundquist College of Business, University of Oregon, Eugene, Oregon 97403)

Abstract

The sports industry is one of the world's fastest-growing business sectors, and its primary source of revenue is ultimately derived from sports fans. However, little is known about fans' allocations of time, effort, and financial expenditures to the sports they care most about or how they determine their allocations. The objective of our research is to explore the dimensions of sports consumption and fan avidity, and the nature of heterogeneity of such demand aspects vis-à-vis derived market segments. We develop a new constrained latent-structure multidimensional scaling procedure to uncover the underlying dimensions of sports consumption and fan avidity for student college football fans at a large university, and simultaneously derive latent market segments to explore demand heterogeneity. We collected data from a sample of student football fans from a large US public university known for its excellence in college football. We developed 35 expressions of manifestations of fan avidity and investigated how these college fans follow and support their football team. We then extracted four interpretable dimensions and four market segments with the application of this new spatial multidimensional scaling model. This paper discusses the managerial implications of applying this new latent-structure procedure to this college football context.

Suggested Citation

  • Wayne DeSarbo & Robert Madrigal, 2012. "Exploring the Demand Aspects of Sports Consumption and Fan Avidity," Interfaces, INFORMS, vol. 42(2), pages 199-212, April.
    • Handle: RePEc:inm:orinte:v:42:y:2012:i:2:p:199-212
    DOI: 10.1287/inte.1110.0575
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    References listed on IDEAS

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    1. DeSarbo Wayne S., 2010. "A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-24, April.
    2. Brad Hill & B. Christine Green, 2000. "Repeat Attendance as a Function of Involvement, Loyalty, and the Sportscape Across Three Football Contexts," Sport Management Review, Taylor & Francis Journals, vol. 3(2), pages 145-162, July.
    3. Ulf Böckenholt & Ingo Böckenholt, 1991. "Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 699-716, December.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    5. Wedel, Michel & DeSarbo, Wayne S, 1996. "An Exponential-Family Multidimensional Scaling Mixture Methodology," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 447-459, October.
    6. Wayne DeSarbo & Jaewun Cho, 1989. "A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data," Psychometrika, Springer;The Psychometric Society, vol. 54(1), pages 105-129, March.
    7. Wayne S. DeSarbo & J. Douglas Carroll & Donald R. Lehmann & John O'Shaughnessy, 1982. "Three-Way Multivariate Conjoint Analysis," Marketing Science, INFORMS, vol. 1(4), pages 323-350.
    8. Yoshio Takane & Henk Kiers & Jan Leeuw, 1995. "Component analysis with different sets of constraints on different dimensions," Psychometrika, Springer;The Psychometric Society, vol. 60(2), pages 259-280, June.
    9. Hill, Brad & Christine Green, B., 2000. "Repeat Attendance as a Function of Involvement, Loyalty, and the Sportscape Across Three Football Contexts," Sport Management Review, Elsevier, vol. 3(2), pages 145-162, November.
    10. Wayne DeSarbo & Vithala Rao, 1984. "GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 147-186, December.
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    Cited by:

    1. Wayne S. DeSarbo & Qian Chen & Ashley Stadler Blank, 2017. "A Parametric Constrained Segmentation Methodology for Application in Sport Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 4(4), pages 37-55, December.
    2. Baker, Bradley J. & Du, James & Sato, Mikihiro & Funk, Daniel C., 2020. "Rethinking segmentation within the psychological continuum model using Bayesian analysis," Sport Management Review, Elsevier, vol. 23(4), pages 764-775.
    3. Michael J. Fry & Jeffrey W. Ohlmann, 2012. "Introduction to the Special Issue on Analytics in Sports, Part I: General Sports Applications," Interfaces, INFORMS, vol. 42(2), pages 105-108, April.
    4. Ken Sanford & Frank Scott, 2016. "Assessing the Intensity of Sports Rivalries Using Data From Secondary Market Transactions," Journal of Sports Economics, , vol. 17(2), pages 159-174, February.

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