Date of Award

5-2021

Document Type

Dissertation

Degree Name

Doctor of Business Administration (DBA)

Department

School of Business

First Advisor

David Tucker, Ph.D.

Second Advisor

Chengping Zhang, Ph.D.

Third Advisor

Paul Shelton, Ph.D.

Abstract

Charitable organizations are significant contributors to the U.S. economy, and Americans invest billions of dollars into these organizations through their donations. Without these organizations, additional pressure would be placed on governmental agencies to provide certain services or those services would not be provided at all, indicating that these organizations’ long-term survival is necessary. In 1991, Tuckman and Chang published the seminal work on the financial vulnerability of nonprofit organizations and presented a model that describes a financially vulnerable organization. Subsequent studies of this model indicate that the model is predictive; however, those studies did not utilize an actual financial shock. This study tests the predictive ability of the Tuckman-Chang model by applying it to charitable organizations that survived and did not survive the Great Recession, an economic event that negatively affected the charitable sector. Charitable organizations listed in the 2006 IRS Statistics of Income Exempt Organizations Sample File (SOI), hosted by the National Center for Charitable Statistics (NCCS) Data Archive, were compared to those listed in the 2011 IRS SOI File. The organizations listed in both files were considered to have survived the Great Recession and those not listed in the 2011 IRS SOI File were considered to have not survived the Great Recession. The Tuckman-Chang model was applied to all organizations listed in the 2006 SOI file to classify them as financially not-at-risk, at-risk, and severely-at-risk. A second model was developed by adding the debt ratio to the original Tuckman-Chang model. It was applied to the organizations listed in the 2006 SOI file, resulting in a new list of organizations classified as not-at-risk, at-risk, and severely-at-risk. Binary logistic regression was utilized to test the relationship between the classifications of financially at-risk and financially severely-at-risk and organization survival of the Great Recession. Regression results indicate that both models can predict the survival of a charitable organization.

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