پویایی انتخاب فروشگاه مصرف کننده: تجزیه و تحلیل ساختار بازار رقابتی برای فروشگاه های مواد غذایی

This study aims at formulating and testing a model of store choice dynamics to measure the effects of consumer characteristics on consumer grocery store choice and switching behavior. A dynamic hazard model is estimated to obtain an understanding of the components influencing consumer purchase timing, store choice, and the competitive dynamics of retail competition. The hazard model is combined with an internal market structure analysis using a generalized factor analytic structure. We estimate a latent structure that is both store and store chain specific. This allows us to study store competition at the store chain level such as competition based on price such as EDLP versus a Hi-Lo pricing strategy and competition specific to a store due to differences in location. Competition in the retailing industry has reached dramatic dimensions. New retailing formats appear in the market increasingly more rapidly. A focus on a particular aspect of the retail mix (e.g., service or price) means that retailers can compete on highly diverse dimensions. Scrambled merchandising and similar developments have implied that particular retailers are now competing against retailers they did not compete with in the past. These trends can be observed in all segments of the retailing industry including the grocery industry, albeit perhaps in different form and intensity. As a result of these developments, consumers face a retail environment in constant flux. They continuously must decide to stay loyal, try out new formats, or use the complete system to obtain benefit from discounts on specific days or for specific items. Previous research has reported low store loyalty and significant store switching for grocery store purchases Kau and Ehrenberg 1984, ____and Kathy A Hammond 1995 and ____and Harry J P Timmermans 1997. Given these findings, it is important to incorporate the store switching behavior in the study of consumer store choice. Furthermore, consumer reactions to a rapidly changing retail environment will additionally depend upon idiosyncratic preferences and socio-economic characteristics that either allow or restrain them from pursuing some of the options. For example, active search requires a substantial amount of time that households working long hours may not have. For the retailer, the problem is how to cope with the increased competition in light of the dynamics of consumer shopping behavior. Should retailers invest in loyal consumers and not worry too much about the customer who is cherry-picking the market? Or, should one try to aggressively attract new customers? Or perhaps should they try to capture a substantial share of the switching population of shoppers? To make better informed decisions on this issue, retailers need to know more about the timing of shopping trips, store choice, and switching behavior of consumers, together with those factors that influence this relationship, to develop appropriate strategies. Hence, according to this framework, it is pertinent to know the magnitude of store loyal/store switching behavior, the nature of the competitive structure in their market and how it is changing, and to be aware of any differences in these regards between consumer segments. The dynamic store choice decision can be conceptualised as a problem of deciding where and when to shop. The first decision is the traditional store location choice problem. The second is the shopping trip incidence problem relating to the timing of shopping trips and implies information about intershopping trip times. Information on a sequence of shopping trip events yields information about the number or percentage of consumers choosing the same store on subsequent shopping trips (repeat shopping or store loyalty). Transitions between stores on successive shopping trips provide measures of store-switching behavior. These two choice processes are, of course, interrelated. Store choice is dependent on the timing of shopping trips, as consumers may go to a smaller local store for short ‘fill-in’ trips and go to a larger store for regular shopping trips (Kahn and Schmittlein, 1989). Also, store choice and shopping trip timing decisions tend to differ for individuals and households as a result of personal differences, household composition, and activity patterns ____and Harry J P Timmermans 1997 and Kim and Park 1997. Most previous research has focused only on the timing or the store choice decision. Furthermore, the majority of research studying store choice behavior has applied cross-sectional data. To the extent that prior research has considered the dynamics of store choice, it has been limited by the assumptions made. For example, the dynamic Markov model (Burnett, 1973) is based on the assumptions that the average number of shopping trips is the same in each successive, equal-length time period, and that the transition matrix is time-invariant. Hence, store choice probabilities are constant over time. The NBD and Dirichlet models, which have been applied to store choice (see, e.g., Kau and Ehrenberg 1984, Wrigley and Dunn 1984 and ___and___ 1985) combine purchase timing and store choice. However, they employ the assumption that shopping trips are made in equal time periods, and that the number of purchases at a particular store by a single consumer or household in successive equal time periods is independent. This research further limited consideration of shopping trips to separate product classes. Shopping trips for all other purchases are not included in the analysis. To overcome the shortcomings of previous research, we propose a dynamic model of store choice, a hazard model, where store choice is dependent on the timing of shopping trips. The hazard model is a Semi-Markov or Markov Renewal process (Howard, 1964). Store choice is modeled by a discrete-state space consisting of the choice set of stores, and intershopping time is incorporated by allowing the time between shopping trips (the transition rates) to be a random variable after some distribution function. Hazard or transition rates are estimated for transitions between all stores, including repeat shopping trips and switching between different stores (e.g., Kalbfleisch and Prentice, 1980). In terms of modeling consumer store choice dynamics, the use of hazard functions is appropriate in the sense that the hazard rate, defined in this context as the rate at which a new shopping trip is made at some time t, given that the consumer has not shopped until time t, can be linked to covariates that indicate how and to what degree these influence consumer store choice dynamics and the competitive structure of the retail market. In particular, hazard models allow one to derive the effects of explanatory variables on intershopping timing and store choice from scanner panel data. This model specification has been used to study brand switching behavior ( Vilcassim and Jain 1991, Gonul and Srinivasan 1993 and and Frank M 1998; and Chintagunta, 1998), but applications to store choice are lacking. We employ this hazard model to estimate competition both at the store and store chain level by combining it with an internal market structure analysis. We estimate a generalized factor analytic structure model (Sinha, 2000). This has a latent structure with an idiosyncratic component that is store chain specific and a common factor that is both store and store-chain specific. This structure allows us to study store competition at the store-chain level (e.g., competition based on price, such as EDLP vs. a Hi-Lo pricing strategy) and competition specific to a store (e.g., due to differences in location). Furthermore, the idiosyncratic component estimates the unique unobserved components of store chains. This is an extension of the model by Chintagunta (1998). Different from Chintagunta (1998), we specify a factor analytic structure on the self-transitions and the shape parameters of the hazard model. This provides a more general and parsimonious model. We begin by discussing the relevant literature on store choice dynamics. Next, we discuss the methodology and our model’s properties. Then, the data are briefly discussed, followed by a general summary of estimation results. Finally, we provide conclusions and areas of future research.

This research aims to obtain a better understanding of consumer shopping behavior by deriving a model of consumer store choice dynamics. Store choice dynamics has received only limited attention in the marketing literature. Furthermore, the models used rely on limiting and rigid assumptions. The Dirichlet model assumes independence of intershopping timing and store choice. Results indicate duration dependence in the intershopping time distribution, which is in disagreement with the assumptions of the Dirichlet model. The logit model only considers store choice, and empirical evidence shows that shopping trip timing and store choice behavior are dependent processes, and that omitting the timing of shopping trips may lead to biased results and different managerial implications. Implications from the market structure maps also differ substantially between the logit and hazard model. The logit model provides a more stationary representation based on market shares, whereas the hazard model provides a dynamic representation derived from the timing and switching between stores. Therefore, the logit model may be more appropriate for the modeling of brand choice data, and the hazard model for store choice data as manifested by the market structure maps. Due to switching between regular and fill-in trips, the hazard model provides a better spatial representation. Hence, the hazard model is a natural candidate to study store choice dynamics. We proposed a novel hazard model that has several unique components. The model allows for the dependence of store choice and the timing of shopping trips, duration dependence, and state dependence. Our model includes a market structure analysis showing the competitive retail structure. The market structure analysis utilizes latent attributes that are both specific to stores and store chains. Furthermore, we incorporate heterogeneity using a latent class model, identifying different segments of shoppers. In addition, our model provides important insights into consumer shopping behavior not obtained from other models. Analysis identified two different segments, which differ in their shopping behavior. Although both segments revealed significant switching between stores and chains, the nature of switching varied. For segment one, we observed state dependence in store switching, whereas for segment two, switching was zero-order. The internal market structure analysis revealed a competitive market structure based on pricing strategies and spatial competition between stores. In general, managers can use these results to determine the positioning strategy of their major competitors or for altering positioning strategies to increase the competitiveness of the store or chain. Finally, the purchase timing distribution offered information about the timing of shopping trips. We observed significant differences between the intershopping times for different stores and for switchers versus repeat shoppers. The different segments of shoppers also differed in the timing of shopping trips, and we observed asymmetry in the timing for switching trips. These results provide important information to retail management who focus on increasing store traffic. This is especially important for shopping behavior as consumers make multiple purchases during a trip.