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Success or failure? Predicting the financial performance of United States national non-profit sports organisations using binary logistic regressions.
- Source :
- Managing Sport & Leisure; Nov 2021, Vol. 26 Issue 6, p466-483, 18p, 14 Charts
- Publication Year :
- 2021
-
Abstract
- Rationale/purpose: This study aimed to predict the financial performance of United States national non-profit sports organisations (NNPSOs) using financial effectiveness indicators and financial efficiency ratios – framed under the stakeholder and resource-dependency theories. Design/methodology/approach: Panel data were analysed using binary logistic regression and Kendall Tau correlations. The dependent variable was a financial performance (determined by net income), and the independent variables were financial effectiveness indicators (measured by total assets and total revenues) and financial efficiency ratios (indicated by return on assets and program services ratios). Findings: The overall predictive correctness was 81%. Program services ratio and return on assets were the two best predictors of net income, indicating that efficiency is more superior than effectiveness. Correlations indicated a positive and statistical association between net income and assets, revenues, and return on assets, but not program services ratio. Practical implications: For top management, donors, and policy advocates, the study highlighted the superiority of financial efficiency over financial effectiveness. Research contribution: The study adds to research, theory and practitioners' perspectives by offering a new way of evaluating financial performance with the combination of financial effectiveness and efficiency, but not opinions – a factor uncommon in previous studies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23750472
- Volume :
- 26
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Managing Sport & Leisure
- Publication Type :
- Academic Journal
- Accession number :
- 153048065
- Full Text :
- https://doi.org/10.1080/23750472.2020.1771196