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Using Supervised Learning Algorithms to Predict Discontinued Operations in Nonprofit Organizations

Chengzhang Wu & Richard B. Dull

Journal of Information Systems2025https://doi.org/10.2308/isys-2021-035article
AJG 1ABDC A
Weight
0.37

Abstract

Machine learning has been widely used to predict bankruptcy in for-profit companies. However, prediction of the discontinued operation of nonprofit organizations has rarely been studied. Although discontinued operation of for-profits is primarily due to financial issues, in nonprofits it can sometimes also be attributed to either completion of their mission or a change in needs of those they serve. This study employs machine learning algorithms to predict the discontinued operation of nonprofit organizations by focusing on six factors. Five groups of financial indicators for nonprofits are used to compare prediction performance of Logistic Regression, MultiBoost, Random Forest, Multilayer Perceptron, Support Vector Machine, and Bayesian Network routines. When a combination of all financial indicators is simultaneously used, Random Forest provides the highest accuracy of the procedures examined. Data Availability: Data for this study were obtained from the Internal Revenue Service (IRS) via Amazon Web Services. Recent data are available from IRS website. JEL Classifications: M41; M48; M49.

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https://doi.org/https://doi.org/10.2308/isys-2021-035

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@article{chengzhang2025,
  title        = {{Using Supervised Learning Algorithms to Predict Discontinued Operations in Nonprofit Organizations}},
  author       = {Chengzhang Wu & Richard B. Dull},
  journal      = {Journal of Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/isys-2021-035},
}

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Using Supervised Learning Algorithms to Predict Discontinued Operations in Nonprofit Organizations

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.