deletions | additions       diff --git a/If_you_wait_to_do__.tex b/If_you_wait_to_do__.tex  index 75128f5..0e8f279 100644  --- a/If_you_wait_to_do__.tex  +++ b/If_you_wait_to_do__.tex 
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4) The motivation of the last chapter comes from the fact 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 analysis of multi-store competition has started already 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. Yet, the subject of location of competing firms with multiple component units 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).   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).  Our main motivation for this chapter is to combine a number of 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.  The subject of location of competing firms with multiple component units 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).   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).  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.   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.