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Technical University of Munich

Using Machine Learning to Understand Performance of Nonprofit Sport Organizations

Lehrstuhl-News |

Previous approaches to model the performance of nonprofit organizations and their determinants largely rely on linearity and monotonicity assumptions. Alumnus Dr. Yanxiang Yang and Prof. Dr. Joerg Koenigstorfer, in collaboration with Prof. Dr. Terri Byers from Canada, jointly use descriptive and predictive models, particularly advanced machine-learning algorithms that allow for the consideration of non-linear and non-monotonic relationships, to understand the relevance of factors associated with the performance of nonprofit sport clubs as well as the nature of relationships. The study appeared in Nonprofit Management & Leadership.
Canadian sport clubs were used as the case

 

Background and Objectives

About 126 representatives of Canadian sport clubs were surveyed and four performance domains were considered: Member relationship, service quality, financial stability, and sporting success. Four machine-learning models (i.e., ridge regression, bagged regression, random forest, and gradient boosting machine) are used. The results reveal that machine-learning models increase the explanatory power compared to linear models. The random forest outperforms the other models in terms of root mean squared error and, partly, mean absolute error, and R square.

 

Findings and Implications

Non-linear relationships are found for several predictors across the four dimensions that were considered, such as the use of outside knowledge, trust, coopetition, age, and tenure of the club representative. We showcase the use of joint computational techniques in not-for-profit research to serve two relevant goals: enhance the explanatory power and maintain the interpretability of predictive models.


About the Study


Contact

Chair of Sport and Health Management

Prof. Dr. Jörg Königstorfer
Uptown München Campus D
Georg-Brauchle-Ring 60/62
80992 Munich

Phone +49.89.289.24559
Fax +49.89.289.24642

info.mgt@mh.tum.de

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