Areas of Expertise

The Center for Sports Business and Research (CSBR) offers collaboration on academic research, practitioner research, and consulting services designed to address industry and sport specific company centered business issues. We tend to focus on the demand side of the various components of the sports industry. We have worked with various sports organizations on issues such as fan avidity, customer satisfaction, market segmentation, consumer perceptions measurement, promotion response, and fan attendance drivers. In a recent project, we examined and identified factors that were driving attendance for Major League Baseball. We also examined the role game day promotions have on fan attendance and subsequent profitability. Through our research, we were able to discover optimal promotion schedules that would maximize expected profitability. This research was featured in an article published in the Sports Business Journal for one particular team. In past projects, we have devised new quantitative methods to perform market segmentation that provides identifiable market segments whose members behave/buy differently within managerial constraints allowing for the determination of ideal segments to target for maximum revenue generation. Our research has also appeared in the top leading academic journals such as Marketing Science, the Journal of Marketing Research, Management Science, Psychometrika, etc. We realize the highly competitive nature of the sports industry and our research allows sport related businesses to maximize the impact of their marketing expenditures. The Center for Sports Business & Research has particular expertise and interest in the following Sports Marketing related application areas of business research including Market Segmentation, Customer Satisfaction/Service Quality Assessment, New Product/Service Design, Analysis of Competition, Positioning, Consumer Choice, Brand Equity, Pricing and Demand Estimation, Promotional Response, Fan Avidity and Preference, Advertising Effectiveness, Consumer Behavior, Optimal Resource Allocation, Demographic Analyses, etc.

A sample list of recent CSBR publications in these areas of business appears below:

    • DeSarbo, W. S., Park, J., and Scott, C., (2008), “A Model-Based Approach for Visualizing the Dimensional Structure of Ordered Successive Categories Preference Data”, Psychometrika, 73(1), 1-20.
    • Park, J., DeSarbo, W. S., and Liechty, J., (2008), “A Hierarchical Bayesian Multidimensional Scaling Methodology for Accommodating Both Structural and Preference Heterogeneity”, Psychometrika, 73(3), 451-472.
    • DeSarbo, W. S., Grewal, R., and Scott, C., (2008), “A Clusterwise Bilinear Multidimensional Scaling Methodology for Simultaneous Segmentation and Positioning”, Journal of Marketing Research, 45(3), 280-292.
    • DeSarbo, W. S., Atalay, S., LeBaron, D., and Blanchard, S., (2008), “Estimating Multiple Consumer Segment Ideal Points from Context-Dependent Survey Data “, Journal of Consumer Research, 35(1), 142-153.
    • Dube, L., Bechara, A., Bockenholt, U., Ansari, A., Dagher, A., DeSarbo, W. S., Hammond, R., Huang, T., Huettel, S., Kooreman, P., and Smidts, A. (2008), “Towards a Brain-to-Society Systems Model of Individual Choice”, Marketing Letters, 19(34), 323-336.
    • Di Benedetto, T., DeSarbo, W. S., and Song, M., (2008),Strategic Capabilities and Radical Innovation: An Empirical Study in Three Countries”, IEEE- Transactions on Engineering Management, 55(3), 420-433.
    • DeSarbo, W. S., Grewal, R., Hwang, H., Wang, Q., (2008), “The Simultaneous Identification of Strategic/Performance Groups and Underlying Dimensions for Assessing an Industry's Competitive Structure”, Journal of Modelling in Management, 3(3), 220-248.
    • DeSarbo, W. S., Blanchard, S., and Atalay, S., (2009), “A Three-Way Clusterwise Multidimensional Unfolding Procedure for the Spatial Representation of Context Dependent Preferences”, Computational Statistics and Data Analysis, 53(8), 3217-3230.
    • DeSarbo, W. S., Grewal, R., and Wang, R., (2009), “Dynamic strategic groups: Deriving spatial evolutionary paths”, Strategic Management Journal, 30(13), 1420-1439.
    • DeSarbo, W. S., (2009), “Measuring fan avidity can help Marketers narrow their focus”, Sports Business Journal, Dec. 21, 13-14.
    • DeSarbo, W. S., Ebbes, P., Fong, D., and Snow, C., (2010), “Revisiting Customer Value Analysis in a Heterogeneous Market”, Journal of Modelling in Management, 5(1), 8-24.
    • Ebbes, P., R. Grewal, W. S. DeSarbo (2010), “Modelling Strategic Group Dynamics: A Hidden Markov Approach”, Quantitative Marketing and Economics, 8(2), 241-274.
    • DeSarbo, W. S., Wang, Q., and Blanchard, S., (2010), “Exploring intra-industry competitive heterogeneity: The identification of latent competitive groups”, Journal of Modelling in Management, 5(2), 94-123.
    • Fong, D., DeSarbo, W. S., Park, J., and Scott, C., (2010), “A Bayesian Vector Multidimensional Scaling Procedure for the Analysis of Ordered Preference Data”, Journal of the American Statistical Society, 105(2), 482-492.
    • DeSarbo, W. S., (2010), “A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity”, Journal of Quantitative Analysis of Sports, 6(2), Art. #3.
    • DeSarbo, W. S., Park, J., and Rao, V. R., (2011), “Deriving Joint Space Positioning Maps from Consumer Preference Ratings”, Marketing Letters, 22(1), 1-14.
    • DeSarbo, W. S. and Madrigal, R. (2011), “Examining the behavioral manifestations of fan avidity in sports marketing”, Journal of Modelling in Management, 6(1), 79-99.
    • Scott, C. J. and DeSarbo, W. S., (2011), “A new constrained stochastic multidimensional scaling vector model: An application to the perceived importance of leadership attributes”, Journal of Modelling in Management, 6(1), 7-32.
    • DeSarbo, W. S. and Madrigal, R., (2012), “Exploring the Demand Aspects of Sports Consumption and Fan Avidity”, Interfaces: Special Issue on Sports Analytics, (March/April) 42, 199-212.
    • Blanchard, S., DeSarbo, W.S., Atalay, A., & Nukhet, H, (2012), “Identifying Consumer Heterogeneity in Unobserved Categories”, Marketing Letters, 23(1), 177-194.
    • Fong, D., Ebbes, P., and DeSarbo, W. (2012), "A Heterogeneous Bayesian Regression Model for Cross Sectional Data Involving a Single Observation per Response Unit", Psychometrika, 77(2), 293-314.
    • Park, J., Rajagopal, P., and DeSarbo, W. S., (2012), “A New Heterogeneous Multidimensional Unfolding Procedure”, Psychometrika, 77(2), 263-287.
    • Kim, S., Fong, D., and DeSarbo, W.S. (2012), “Model Based Segmentation Featuring Simultaneous Segment Level Variable Selection”, Journal of Marketing Research, 49(5), 725-736.
    • Blanchard, S. and DeSarbo, W.S., (2012), "The Heterogeneous P-Median Problem for Categorization Based Clustering", Psychometrika, 77(4), 741-762.
    • DeSarbo, W.S., Blank, A.S., & McKeon, C. (2012), “Proper mix of promotional offerings can produce for teams”, Sports Business Journal, 15(24), p. 18.
    • Blanchard, S., and DeSarbo, W.S., (2013), “A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification”, Psychometrika, 78(2), 320-342.
    • Kim, S., Fong, D., Blanchard, S., & DeSarbo, W.S. (2013), “Implementing Managerial Constraints in Model-Based Segmentation: Extensions of Kim, Fong, and DeSarbo (2012) with an Application to Heterogeneous Perceptions of Service Quality”, Journal of Marketing Research, 50(5), 664-673.
    • Kappe, E., A. Stadler Blank, & W.S. DeSarbo (2014), “A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions”, Management Science, 60(6), 1489-1510.
    • DeSarbo, W.S., H. Hwang, A. Stadler Blank, & E. Kappe (2015), “Constrained Extended Stochastic Constrained Redundancy Analysis”, Psychometrika, 80(2), 516-534.
    • Agarwal, J., DeSarbo, W.S., Malhotra, N., & Rao, V.R. (2015), “An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research”, Consumer Needs and Solutions, 2(1), 19-40.
    • Ross, S. & DeSarbo, W.S. (2015), “A Rapid Reaction to O’Bannon: The Need for Analytics in Applying the Sherman Act to Overly Restrictive Joint Venture Schemes”, Penn State Law Review: Penn Statim, 43.
    • Fong, D., Kim, S., Chen, Z., and DeSarbo, W.S. (2015), “A Bayesian multinomial probit model for the analysis of panel choice data”, Psychometrika, forthcoming.
    • Fong, D., DeSarbo, W.S., Chen, Z., and Xu, Z. (2015), “A Bayesian Vector Multidimensional Scaling Procedure Incorporating Dimension Reparameterization with Variable Selection”, Psychometrika, forthcoming.