A cointegration analysis of the correlates of performance in franchised channels

Not much is known about the primary drivers of performance in franchising systems. With some notable exceptions, much of the franchising literature on performance related issues has focused on either contrasting failure rates of independent small businesses and entrepreneurs with those of franchises and/or system survival issues. The existing literature on franchising performance displays at least three other characteristic patterns. First, most studies have restricted themselves to a single sector, usually, the fast food restaurant industry, since it is often perceived and portrayed as the archetypical franchise sector. Second, existing investigations have tended to focus on a single measure of performance. Finally, with the exception of survival articles, empirical studies have typically confined themselves to cross-sectional examination of the evidence. In other words, we know very little about what fosters long term performance. Our investigation of the correlates of performance, then, contributes to the extant literature in three specific ways. Foremost, we attempt a systematic assessment of the relative effects of a series of firm decision variables on performance. Specifically, we evaluate the impact of four categories of drivers of performance. Besides three covariates, a total of eleven hypotheses focused on drivers of performance are investigated. Second, we utilize three different operationalizations of our dependent variable, performance, in our investigation. Third and finally, we estimate our empirical models using nine years of longitudinal panel data aimed at deciphering the effects associated with our set of predictor variables using cointegration analysis, a relatively new and advanced approach to modeling equilibrium or long term relationships between economic variables in panel data. The results show that seven out of eleven hypotheses were supported by the data using the system size operationalization of performance.