A Business Success versus Failure Prediction Model for Service Industries

ABSTRACT This study developed and tested a success versus failure (S/F) prediction model for the service industries (S/F = f [record keeping and financial control, parents owned a business, management experience, economic timing, staffing, advisors, marketing skill, partners, planning]). Using stepwise discriminant analysis, the service model accurately predicted the surveyed 54 successful and failed matched firms as being successful or failed. Implications for entrepreneurs, investors, lenders, suppliers, educators ,consultants, and public policy makers are discussed. INTRODUCTION Business failure is both frequent and potentially damaging to the efficient operation of a market economy (Storey, Keasey, Watson, & Wynarczyk, 1987). The important role of small business suggests that an understanding of why firms fail or succeed is crucial to the stability and health of the economy (Gaskill, Van Auken, & Manning, 1993). Of major concern to any would-be entrepreneur is the chance of success for the proposed business (Lussier, 1995-96). Success versus failure prediction research benefits entrepreneurs, those who assist, train and advise them, those who provide capital for their ventures, suppliers, and public policy makers (Airman, 1983; D'Aveni, 1989; Hofer & Sandberg, 1987; Ibrahim & Goodwin, 1986; Lussier 1995a; Pech & Alistair, 1993). There are many studies to better understand business success versus failure. However, as Gaskill et al. stated: "mere are many questions still to be resolved and warrant additional exploration-previous studies do not provide a comprehensive or unified explanation for small firm failure. comparisons are needed between successful and failed small business owners" (1993, 28). Cochran (1981) stated that research on business failure for subgroups of the small business sector would prove useful, and that research on business failure for specific industries in regions might be more useful than studies that are national in scope. Robinson and Pearce (1984), Gaskill, Van Auken, and Kim (1994), and Gaskill et al. (1993) have emphasized the importance of focusing research studies on single industries within specific regions of the country. To date, the author has not found any major regional based success versus failure prediction models by industry. This is the first major empirical business success versus failure prediction model applied to service industries by region (New England). Like Lussier (1995a) and Lussier and Corman (1995, 1996) this study also uses the less common matched pairs design to ensure comparisons of successful and failed businesses are not only in the same industry, but are of similar size, age, and location. Purpose The purpose of this research is to develop and test a model that will predict business success versus failure of service firms. DEVELOPMENT OF THE LITERATURE MODEL There are no prior service industries prediction models. Therefore, the model consists of variables identified as generic to business success versus failure. Most studies identify factors contributing to success versus failure without developing predictive models. The major studies that do develop nonfinancial based predictive models include Cooper, Dunkelberg, Woo, and Dennis (1990), Cooper, Gascon, and Woo (1991), Reynolds (1987), Reynolds and Miller (1989). Their studies and those of Ballantine, Cleveland, and Koeller (1992) and Kalleberg and Leicht (1991) use industry as a variable to predict failure, but they do not break their samples into industry subgroupings to compare differences in predictability of success or failure by industry. We need to go beyond current generic prediction models to design a model for service industries (Cochran, 1981; Gaskill, Van Auken, & Kim, 1994; Gaskill, Van Auken, & Manning, 1993; Robinson & Pearce, 1984). Scholars have reported many reasons for business failure. …