Mixed-Method Market Segmentation Typologies in Sports

Introduction and Study Overview

The topic of this study is market segmentation typologies in the sports industry. The purpose of the study was to develop a consumer typology founded on the analysis of consumer participation motivation data by way of a mixed-method approach. The study describes a methodical process for developing a consumer segmentation typology using both demographic variables and self-identified motivations for sport and fitness participation, employing a multivariate statistical method throughout.

Research Questions and Philosophical Underpinnings

The research questions guiding this study were: how can researchers generate a deeper understanding of consumers in the sports industry, and how can that information be effectively analyzed and applied? These questions reflect a pragmatic philosophical orientation, combining qualitative insight with quantitative rigor to produce actionable market intelligence.

Sample and Data Collection Procedure

The mixed-method approach used in this study incorporated qualitative data to help validate subsequent quantitative cluster analysis, and drew upon cluster profiles to establish the structure for market segmentation.

Data for this study were gathered as part of a larger data collection effort. A four-page questionnaire was sent to 2,000 Runner's World subscribers. The questionnaire included a cover letter, a small incentive, and a postage-paid return envelope. A follow-up postcard was mailed approximately two weeks after the initial questionnaire mailing to improve response rates.

Variables and Instrumentation

Variables used in this study included demographic variables and self-identified motivations for sport and fitness participation.

Assessing the reliability of the cluster solutions was accomplished by examining ranges of cluster solutions. A snake-plot of the four-segment solution based on the ten fundamental motivations indicated a rich solution that demonstrated meaningful differences across groups. The external validity of the cluster solution was determined by integrating the closed-form data with cluster solutions founded on qualitative data.

The credibility of the cluster profiles was established through representative quotes drawn from the qualitative data. This approach helped support the labeling process and strengthened the insights provided by the quantitative data. Such triangulation is a hallmark of robust mixed-methods research design.

Data Analysis Methods

The design for this study was founded upon independent coding of open-ended responses using a qualitative analysis software program, followed by principal components analysis and cluster analysis. The first step involved reading the open-ended responses and analyzing their text for particular themes. Two of the authors read all the comments and then jointly agreed on which categories were present in the data.

The next step involved electronically generating coding reports using QSR NVivo, a qualitative analysis software application that combined similar responses across the ten categories identified during the initial coding process. The quantitative phase of the data analysis focused on reducing the dimensionality of these ten categories.

A principal components analysis (PCA) of the ten response categories was then conducted in order to better understand the underlying structure of the data and to create orthogonal linear composites of motivations to serve as metric inputs to the clustering algorithm. This sequential integration of qualitative and quantitative phases is central to the study's mixed-method design.

References

Rohm, A. J., Milne, G. R., & McDonald, M. A. (2006). A mixed-method approach for developing market segmentation typologies in the sports industry. Sport Marketing Quarterly, 15(1), 29–39.