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Nonprofit board characteristics and the likelihood of reported programmatic change: evidence from IRS Form 990 and board profile data
Dissertation   Open access

Nonprofit board characteristics and the likelihood of reported programmatic change: evidence from IRS Form 990 and board profile data

Tristan S. Moore
Doctor of Business Administration (D.B.A.), Drexel University
May 2026
DOI:
https://doi.org/10.17918/00011427
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Moore_Tristan_20264.49 MBDownloadView

Abstract

Board characteristics Board governance IRS Form 990 Logistic regression Nonprofit governance Programmatic change
Research examining how nonprofit board characteristics are associated with reported programmatic change remains limited, particularly in studies using IRS Form 990 Part III reporting as an empirical indicator of change. This dissertation examined whether selected nonprofit board characteristics were associated with reported programmatic change among small and medium-sized nonprofits in the San Antonio, Texas, metropolitan area. The analysis used Tax Year 2021 IRS Form 990 data and publicly available LinkedIn board-member information and was guided by resource-based, organizational, and contingency theories. Programmatic change was measured as a binary outcome based on whether an organization reported "Yes" to Part III, Line 2 or Line 3 of Form 990. Binary logistic regression found no statistically significant relationships between the examined board attributes and reported programmatic change. Descriptive results also showed that reported programmatic change was uncommon in Form 990 disclosures, public board-profile visibility was limited, and formal change-management credentials were not identified within the reviewed public board-member profiles. This study contributes to an underexamined area of nonprofit research by documenting how programmatic change appears in Form 990 reporting and by demonstrating a practical, replicable secondary-data approach for future nonprofit research. Future studies can test whether the null findings reflected limited statistical power or whether these board attributes operate differently in larger or more diverse samples.

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