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Top1. Introduction
The spatial expansion of retail chains is heavily motivated by the desire to increase sales. Given that many of these companies are publicly traded, this motivation is exacerbated by pressures from the stock market. Companies like Walmart have experienced their highest price-to-earnings (P/E) multiples during eras of high new store deployment (Serpkenci & Tigert, 2006). While undoubtedly, individual business practices among retailers lead to variations in the store deployment process, all retailers must consider the locations of their existing stores before deciding upon new store locations. For example, saturating the region where the retailer opened its first store reduces the opportunity for growth in that region and thus, future deployment may have to occur in more distant regions. After expanding to the other regions, the retailer may need to revisit the original saturated region after a period of time to continue growing its store count. On the other hand, if a retailer spread its initial growth over a larger area, then conceivably it could fill in the gaps within the areas where it already has stores.
This article analyzes retail store contagion, or the growth and diffusion of chains over time and space. The objective is to analyze whether the process of new store deployment is the result of a systematic series of stages, and if so, do these stages resemble waves? Cliff & Haggett (2006) examined a similar problem in the epidemiological literature, but this concept has not yet been applied to retail. Other metrics to track the continuity of deployment over space are also presented.
Following the approach of Cliff & Haggett (2006), this article investigates the degree to which waves of store deployment represent a systematic pattern, with periods of swash and backwash. Using the analogy from coastal geomorphology, the initial swash stage involves the introduction of a retailer to a new area through multi-store deployment. Areas closer to the location of the first store of the retailer may be more likely to experience this stage sooner. The retailer shifts to a different area for new store deployment once the closer areas reach saturation. This may be the next closest area, especially for chains that place a high priority on maintaining low distribution costs. The closest area to the first store would experience a backwash stage of fewer store openings than in the previous time period. As the retailer eventually expands to all areas, it becomes incumbent to revisit markets for deployment if it wants to keep growing. Consider that the older stores in the area near the first store of the chain may be some of the most profitable stores because of lower real estate costs as well as stronger brand awareness due to the length of time being deployed in that area. In this case, the retailer accepts the cannibalization of these high performing stores in an effort to secure new sites for store deployment. This represents a re-swash stage for a chain reaching domestic real-estate maturity. This is when new growth often requires significant cannibalization of existing stores (Joseph, 2010).
Our intention is to present this paper primarily as methodological, but it also makes theoretical and empirical contributions. The empirical case study focuses on Target and Walmart. Although similar in many respects, these two chains have been studied for what differentiates them, including their differences in growth patterns, locations, trade areas, customers, and overall value platforms (Graff, 2006; Joseph, 2009; 2010; Shields & Kures, 2007). Our analysis in this paper seeks to identify whether there are systematic processes to their deployment, in the form of swash-backwash waves, with alternating periods of faster and slower growth in an area. Theoretically, this modeling is related to the real-estate maturity of chains, in the sense of saturation of markets. This maturity may affect the locations of new store deployment and could also be linked to alterations in business practices.