Can Signaling Theory Help Agency and Resource Scarcity Theories Explain Franchisee Failure? Predicting SBA-Backed Loan Defaults

This study examines the use of analytic techniques to develop a model that predicts the potential default of Small Business Administration (SBA) backed loans issued to American franchisees. Data collected by World Franchising (WF) in their 2008 survey and by SBA from 2000-2008, covering 271 diverse US franchise chains, on the reported failure rates and charge off percentages of SBA backed loans was used to explore associations between franchisor characteristics and franchisee loan performances. The predictive capability of the derived model was assessed using a data mining technique in which the original data set is split into two different subsets: one for estimation and one for validation. KXEN's (Knowledge Extraction Engines) predictive modeling was used to associate default rates with franchise characteristics. Earnings claims, the requirement for franchisee's industry specific knowledge, total units growth rate, and franchise experience were found as significant predictors linking franchise theories; agency, resource scarcity, and signaling to franchisee failure (default). This study can be used as an important policy tool that will aid franchisors, franchisees, lenders, the SBA, and other stakeholders in making financing decisions