Developing Restaurant Industry Business Cycle Model and Analyzing Industry Turning Point
Choi, Jeong-Gil, Journal of Global Business and Technology
This study developed the restaurant industry business cycle models. The U.S. restaurant industry demonstrated three cycles (peak to peak or trough to trough) for the period of 1970 through 1998. The restaurant industry peaked in 1973, 1979, and 1989. The industry troughed in 1970, 1974, 1980, and 1991. The mean duration of the restaurant industry cycles is 8 years calculated by peak to peak and 6.5 years calculated by trough to trough. Expansion takes an average of 6 years in the restaurant industry but declines sharply after it reaches the peak taking average 1.33 years. The restaurant industry experienced high growth (boom) every five years on average. Restaurant industry growth cycles, then, tend to be relatively symmetrical: since 1970 the average duration was about 2.25 years for both expansion and contraction.
Industries react in different ways to the business cycle fluctuations of the U.S. economy (Berman and Pfleeger, 1997). Some industries are very vulnerable to economic swings, while others are relatively immune to them. For those industries that are characterized as cyclical, the degree and timing of these fluctuations vary widely. The industries that experience only modest gains during expansionary periods may also suffer only mildly during contractions and those that recover fastest from recessions may also feel the impact of a downturn earlier and more strongly than other industries.
One of the most striking aspects of the business cycle is that it is a phenomenon which, sooner or later, is reflected in similar patterns in almost every macro-economic variable, thus illustrating their interdependence (Berk and Bikker, 1995). Such interdependence is not restricted to national macro-economic variables either; it is also an industry phenomenon. It is important to understand the industry business environment if we are to forecast the impact of the cycle on our firm and to fix strategy on the basis of that forecast. In a sense, measuring, monitoring and forecasting business cycles is a relatively new class of methods in investigating the industry's overall phenomena. The systematic analysis of cycles in the restaurant business provides clues to help us forecast future direction and improve our ability to manage. According to previous studies (Choi, 1999, 2003), in the hotel industry, there were many chances to gain competitive advantages over the cycles, but many companies missed the opportunities because there were fears to take business actions at different stages of the industry cycle.
Monitoring and forecasting restaurant industry cycles clearly gives the manager insight into industry turning points. Moreover, a company that quickly recognizes a change in the phase of the industry cycle could use either a recession or a recovery strategy to optimize profit. To take any benefit from this type of analysis, it is necessary to develop the industry cycle models.
In the restaurant industry literature, however, there are no studies using industry cycles. Moreover, the literature on forecasting in the restaurant industry is very limited in terms at least of the number of studies. Some of the studies introduce a menu item forecasting system (Messersmith, Moore, & Hoover, 1978), explain forecasting menu item demand in food service operations (Miller and Shanklin, 1988), forecast restaurant sales (Forst, 1992), introduce general forecasting techniques for restaurant operation (Messersmith and Miller, 1992), and present a case study for demand forecasting (Yavas, 1996). Most of the studies are discussions and thus hard to apply to dynamic and complex economic trends and therefore industry's overall trends. There is no systematic forecasting study for the restaurant industry as a whole and no restaurant industry business cycle study. There is merit to developing a systematic industry cycle model as a forecasting tool and providing a guidepost for the restaurant business managers and investors. …