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4. Locational strategies of multi-store firms  Most previous discussion on locational decisions has one common feature of making unrealistic and restrictive assumptions and perceives the industry in terms of independent stores.  The subject of location of competing firms with multiple component units 1 seems to have been largely unsung/unheeded in the spatial location literature (Peng and Tabuchi, 2007). This gap is inquisitive/inquiring for the systems which dominate in the market (Karamychev and van Reeven, 2009; Iida and Matsubayashi, 2011 ; Janssen et al., 2005 ; Pal and Sarkar, 2002 ; Peng and Tabu- chi, 2007).  Chu and Lu (1998) note that most previous discussion on locational decisions has one common feature of making unrealistic and restrictive assumptions and perceives the industry in terms of independent stores. The conventional approaches to location selection, i.e., traditional theory and methods, fail (Thill, 1997)  The conventional single-store location theory may not apply to situations wherein individual stores are part of larger organizations under common strategy, intuition, and control, where a centralization is applied to reach global goals and consider the interest of a firm as a whole (Thill, 1997).   Conceptually, a firm selects a distribution of locations instead of choosing a point location (Chu and Lu, 1998).  Major modern international brands sell their goods through chain stores (Takaki and Matsubayashi, 2013 ; Peng and Tabuchi, 2007).  The analysis of multi-store competition has started with the trailbraking work of Teitz (1968) who introduced the idea that a firm can open several facilities in the context of Hotelling’s linear city model and serves the market from multiple locations.   ((The store format and its strategy is supposed to fit well the targeted customer groups identified by their desires, interests, buying requirements, and their expectations (Thill, 1997).))  Our main motivation is to combine these novel solutions into one model and to rectify several mathematical and methodological misconceptions made in numerous existing store-location papers. We incorporate strategic interactions between stores within the same firm and stores that belong to different chains. We consider spatial competition, business stealing and learning effects. We pay a particular attention to correctly capture market segments and to select potential customer groups by observing their characteristics, their mobility patterns, their trip chaining behavior, and activities’ purposes during the day and during the week. A clear distinction between a daytime and nighttime population present in a particular area is needed, and therefore a more appropriate distance measure to a store traveled by a potential customer is carefully proposed and applied in a more realistic manner than it has been done in the existing literature. Further, we consider the markets as being interdependent. A combination of all these elements can create a more authentic/more realistic and original model.  Toivanen and Waterson (2005) emphasize that while traditional industrial organization theories cannot explain the positive effect of rival presence on own entry, learning models can.   Toivanen and Waterson (2005) show that the rival presence increases the probability of entry due to the firm learning, yet, profits are decreasing in the number of rival stores and are increasing in the number of own outlets. The Toivanen and Waterson’ (2005) results further suggest that learning effects are strong enough to dominate any negative effects that competition between firms may have on entry decisions.  Nwogugu (2006) finds the issue with distance measure particularly disturbing. The author claims that existing store-location models do not incorporate the distance element appropriately, erroneously assuming that all residents of each community travel the full length of the distance between their community and the store location each time they visit a store. However, in reality most potential buyers do not work in their immediate communities and tend to stop over a store on their way to other destinations and so will prefer locations that are on the route to destinations that they regularly visit. Most people drop in on the store before going to work, during lunch, after work, or on weekends.   Fransen et al.’ (2015) method adds a behavioral realism to the existing metrics that are typically based on static, nighttime representations of population.  Nishida (2015) and Toivanen and Waterson (2005) incorporate some form of spatial competition, showing that a store’s revenue is influenced not only by other stores in the same location but also by those in adjacent locations.   Degree of local competition is keen between neighboring firms, but weak between remote ones (Peng and Tabuchi, 2007).  Schiraldi et al. (2013) allow business stealing and cannibalization effects to operate not only within the boundary of a particular location, but also across locations which may be the source of the spatial competition. Igami and Yang (2015) notice the future need to include the shops’ distances within geographical market stating that closer shops seem to compete more fiercely.   Firms are expected to carefully choose locations to ease the access to the highest number of spatially dispersed potential customers.  Igami and Yang (2015) observe that fast food stores seem to compete within relatively small markets concentrating their efforts on a micro- level location. Their results indicate a highly localized nature of competition among fast food stores. The authors state that a distance criterion equal or greater than a mile (1,61 kilometer) would appear to be useless for an empiri- cal analysis of competition among fast food stores. According to calculations of Thomadsen (2005) for the fast food market (McDonald’s and Burger King) in Santa Clara County, only outlets within 0.5 mile (800 meters) will compete as close substitutes, even in car-obsessed California.   Fransen et al. (2015) suggest that not the entire working population should be treated as potential customers for a simple reason, that not every person commuting to work make use of a particular facility (in our application, service or store). This can lead to an overestimation of the facility’s (service’s or store’s) demand.  one of the most common mistakes is targeting people instead of targeting money. A market segment may represent a large percentage of population, but a small part of the market. There is always a need to look at the money potential of market segments, not just the number of people in the segments”. One of the results of Chu and Lu (1998) seems not to tell the full story when indicating that more populated areas will encourage the player to establish more stores.  In addition, Gira Conseil evaluates that a potential customer is willing to walk 10-12 minutes or drive a car for 5-7 minutes to reach a particular restaurant.  Igami and Yang (2015) show that typically used population and income might not adequately capture the demand. Population statistics usually reflect the number of residents but not necessarily the real daytime population, which might be much more critical in the fast food industry.   Thomadsen (2007) shows that firms will choose to locate near large sources of demand.   Following the remark of Nishida (2015), a clear distinction between a daytime versus nighttime population present in a particular area is to be made.  As a consequence, a more appropriate distance between a potential customer and a store definition will be carefully proposed and applied in a more realistic manner than in most of the existing papers (see the arguments of Nwogugu, 2006).