Asymmetric competition in retail store formats: Evaluating inter- and intra-format spatial effects

Journal of Retailing 81 (1, 2005) 59–73

Asymmetric competition in retail store formats: Evaluating inter- and intra-format spatial effects ´ Oscar Gonz´alez-Benito a,∗ , Pablo A. Mu˜noz-Gallego a,1 , Praveen K. Kopalle b,2 a

Dpto. Administraci´on y Econom´ıa de la Empresa, Universidad de Salamanca, Campus Miguel de Unamuno, 37007 Salamanca, Spain b Tuck School of Business at Dartmouth, 100 Tuck Hall, Hanover, NH 03755, USA

Abstract Our study aims to analyze the role of store format in retail competitive interactions, specifically, the relationship between growth, location strategy, and market response. To assess this relationship, we propose an extension of the classic models of spatial interaction, which incorporate the asymmetric competitive effects linked to the concept of store format. An empirical application allows us to confirm greater spatial rivalry within store formats (intra-format) than between store formats (inter-format). This implies a certain hierarchical organization when consumers select a retail store, first choosing the type of store at which they will shop and later a particular store within this format. The results are important for retailers who want to configure an optimal network of store locations as well as public administrators who must regulate commercial activity. © 2005 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Retail store format; Spatial analysis of demand; Inter-format competition; Intra-format competition

Introduction One of the consequences of the intense transformation experienced by the retail sector in recent decades has been the diversification of store formats (Kumar 1997; Morganosky 1997). For example, in the context of purchasing groceries, the introduction of the supermarket as a generic self-service format was followed by the hypermarket as a larger version of supermarket and the development of the discount store as a low-price-oriented supermarket. Although definitions of these store formats, or types, are often inexact and sometimes confusing (Dawson 2000), they reveal the increasing variety of store models in the ever more heterogeneous retail market. From the perspective of demand, store formats might be defined as broad, competing categories that provide benefits to match the needs of different types of consumers and/or ∗

Corresponding author. Tel.: +34 923 294400x3508. ´ Gonz´alez-Benito), E-mail addresses: [email protected] (O. [email protected] (P.A. Mu˜noz-Gallego), [email protected] (P.K. Kopalle). 1 Tel.: +34 923 294400x3127. 2 Tel.: +1 603 646 3612.

different shopping situations. Many research studies have attempted to identify the benefits sought by, as well as the motivations and sociodemographic characteristics of, customers of different store formats (e.g., Bloch, Ridgway, & Dawson 1994; Hern´andez, Munuera, & Ru´ız 1995; LaBay & Comm 1991; Morganosky & Cude 2000; Redondo 1999; Reynolds, Ganesh, & Luckett 2002; Roy 1994). While these studies stress the link between store format and customer response (Bhatnagar & Ratchford 2004), they do not explore competition across different retail store formats (Gonz´alez-Benito 2005). Store formats determine a retail competitive structure. They define the pattern by which stores may be grouped based on the degree of overlap among their target segments (i.e., basis of the intensity of the competitive interaction). This structuring entails distinguishing between an interformat competitive level that relates to the rivalry between store formats, and an intra-format competitive level that relates to the rivalry among stores of the same format (Dunne & Lusch 1999; Ghosh 1994). The role of store format in the retail competitive structure, as well as in market response, does influence the growth and location strategy decisions of retail operators (Goldman

0022-4359/$ – see front matter © 2005 New York University. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jretai.2005.01.004

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2001)—that is, whether to expand through new stores and diverse store formats. Many studies have tried to analyze the structural, economic, and social consequences of the development of new store formats (Arnold, Handelmann, & Tigert 1998; Arnold & Luthra 2000; Brennan & Lundsten 2000; Farhangmehr, Marques, & Silva 2001; Hallsworth & Worthington 2000; Jones & Douce 2000; Miller, Reardon, & McCorkle 1999; Ozment & Martin 1990; Seiders, Simonides, & Tigert 2000). These studies focus on the effects of new store formats on consumer shopping habits and the size and distribution of demand. Understanding how location, closing, restructuring, or acquisition of a new store affects demand is fundamental for retail firms that must configure their store networks and public administrators who regulate commercial activity. By explicitly identifying the competitive effects of store formats, firms and administrators may gain important benefits. First, when retail firms develop growth and market coverage strategy, they must consider store format, regardless of how ambiguous the definition of various formats may be. Because many retailers diversify product offers through different store formats and commercial names (Bell, Davies, & Howard 1997; Dawson & Burt 1998; Pellegrini 1994), they also must take into account the effects these varied store formats have on sales. From an analytical perspective, determining the optimal configuration of store locations therefore requires retailers to consider both spatial competition (i.e., the location of each store) and the competition derived from distinct positioning in the retail setting. If the store formats are explicitly differentiated, retailers do not need to focus as intently on the role of each differentiating attribute. In short, if retailers evaluate spatial competition without distinguishing inter- and intra-format competitive interactions, the interformat rivalry might be inflated at the expense of intra-format competition. Second, store formats are an obvious classification criterion for public administrators when they evaluate retail infrastructure and competitive equilibrium. Some legislation even focuses on competitive effects among formats and defines the requirements for a license to open a store according to its retail format. For example, Spanish Law 7/1996 for the Regulation of Retail Commerce requires a retailer to obtain a specific license before it opens a large store. Some regional Spanish legislation even has attempted to regulate the establishment of discount stores. For example, Law 16/1999 for Domestic Trade in the Community of Madrid makes the establishment, enlargement, or modification of a “hard discount” store subject to authorization from the Regional Ministry of Economy and Employment. Therefore, our goal in this paper is to analyze the role of retail store format in spatial competitive interaction. Presumably, the resulting competitive effects will be consistent with the definition of store format, in that the impact of a store’s spatial accessibility (i.e., location) on competitors will be more severe within each store format than among different store formats. In this study, we incorporate the possible

asymmetric effects of different store formats. In this respect, we extend traditional models of spatial analysis and capture the spatial competitive interactions of store formats. We use research from three areas: (1) market response models and asymmetric competition, (2) spatial analysis and spatial interaction models, and (3) inter-format competition. Our study also provides some theoretical insights by calibrating the proposed models to a real context and empirically evaluating the spatial competitive effects caused by different store formats. Prior studies have examined the role of store format in the choice of a store or retail chain. For example, Solgaard and Hansen (2003) analyze competition among conventional supermarkets, hypermarkets, and discounters. Bell, Ho, and Tang (1998), Bell and Lattin (1998), and Galata, Bucklin, and Hanssens (1999) examine competition among price-based supermarket formats, specifically, everyday low price and promotional pricing stores. In addition, Fox, Montgomery, and Lodish (2002) consider competition among grocery stores, mass merchandisers, and drugstores. These studies acknowledge that the store format concept captures stores’ generic positioning and, consequently, partly determines their attraction, competitive structure, and market response. These studies also capture market heterogeneity based on geodemographic, socioeconomic, and behavioral explanatory variables (Fox et al. 2002; Galata et al. 1999), a priori segmentation (Bell & Lattin 1998), and/or unobserved heterogeneity as detected by the random-effects approach (Solgaard & Hansen 2003) or, particularly, the latent segmentation approach (Bell et al. 1998; Galata et al. 1999). However, extant research has not considered how store format affects the relationship between retail marketing variables and market response. For example, explanatory variables that stem from the store format, such as the distance between the stores within the format and consumer residence, should be included in any examination of market response. We address this issue in this study. Our study also complements some recent research in the field of spatial analysis. Bronnenberg and Mahajan (2001) developed a model in which the effect of marketing variables, such as price and promotion, depends on the spatial distribution of retailers. The underlying argument is that retailers’ unobserved actions cause a measurable spatial dependence among the marketing variables. In this study, we examine the effect of location depending on the store format, that is, considering the competition within and across store formats.

A model of store format and spatial competition Our model is similar to logit-type competitive interaction models used in both aggregate analyses of market share (Cooper 1993; Cooper & Nakanishi 1988) and individual analyses of discrete consumer choice (Ben-Akiva & Lerman 1985; Lilien, Kotler, & Moorthy 1992). These models form a common methodological framework for analyzing competitive market structures and asymmetric competition (e.g.,

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Carpenter & Lehmann 1985; Carpenter et al., 1988; Cooper & Inoue 1996; Kannan & Wright 1991; Midgley, Marks, & Cooper 1997), though their emphasis usually has been on competition between products. In this paper, we consider spatial competitive effects at both the store-format level and the store level. The application of these tools to the spatial analysis of demand mirrors research in the field of spatial interaction theory and gravitational models (Fotheringham & O’Kelly 1989; Haynes & Fotheringham 1984). We first provide the traditional approach in spatial competitive interaction. We then analyze the role of store format by adapting the model to the inter-format level of spatial competition. Finally, we assess the implications of the inter-format level of competition for competitive structure. Traditional spatial modeling As a starting point, we assume the market share πi (j) of a retail store j in a residential area i is directly proportional to the attraction Aij generated as a result of marketing effort. Specifically (where J is set of all stores), πi (j) =


Aij

j ∈ J

Aij

(1)

In pioneering applications of this approach, mainly inspired by Huff (1962), two determining dimensions of the attraction exerted by a store have been contemplated: (1) the spatial accessibility Dij of the store, generally measured as the distance of store j from residential area i, and (2) the variety of products and services Sj offered at the store, generally measured by the store’s size. Bucklin (1967) points out the limitations of the above approach and the need to consider another dimension of attraction—image. Note that image may be viewed as the convergence of other attributes, such as price, service, ambience, brand equity, and other strategies and tactics, that determine the way consumers perceive the store. This image can be assumed to be constant and broken down into two components: a first component, γ f , that refers to the store format f, and a second component, αr , that refers to the store chain r. Following a multiplicative competitive interaction model, the retail attraction exerted by a store or shopping center can be expressed as Aij = exp(γf + αr ) × Sj βS × Dij βD ,

(2)

where βS and βD denote parameters that represent the impact of the size of the offer and accessibility, respectively. The above formulation assumes the following: 1 The market is homogeneous—i.e., sensitivity to image or spatial coverage variables is independent of the residential area. Possible extensions to incorporate the heterogeneity of consumers include, for example, a priori differentiation of market segments (e.g., Gensch 1985; Hortman, Allaway, Mason, & Rasp 1990), incorporation of consumer characteristics as determinants of attraction (e.g., Fotheringham & Trew 1993; Guadagni & Little 1983), or interpretation

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of the parameters as random coefficients distributed over the population (e.g., Chintagunta, Jain, & Vilcassim 1991; Kamakura & Russell 1989). 2 The model is a static representation of competitive interaction; it avoids the time dimension, especially with regard to interpreting the attraction that derives from the image as a constant magnitude. Thus, the model is only suitable for a cross-sectional analysis of intra-urban markets. 3 The store image is represented by the intrinsic attractiveness of the format in which it operates and the marketing strategy of the chain to which it belongs. In this respect, the application of micro-marketing principles to the retail context favors a decentralization of market actions to the store level (Hoch, Kim, Montgomery, & Rossi 1995; Montgomery 1997), which may imply a certain divergence among stores regarding the common image of the chain. Furthermore, the model assumes a symmetric competitive structure, similar to Cooper and Nakanishi’s (1988) conception. That is, the store’s marketing actions have homogeneous effects on competing stores. With regard to market coverage strategy, an increase in market share deriving from an improvement in the store’s spatial accessibility affects competitors equally. This is a well-known independence of irrelevant alternatives (IIA) property. The premise that underlies the objective of this study is that the IIA property cannot be assumed in a heterogeneous retail situation. Instead, the diversity of stores, as evident in the explicit definition of wide-ranging store formats, makes a complex competitive structure likely (Popkowski Leszczyc, Sinha, & Timmermans 2000). In particular, changes in the spatial accessibility of one store could possibly have differential effects on each competitor. Because a greater similarity in the benefits supplied by particular stores implies greater substitutability, specific store formats may delimit groups of stores for which competitive rivalry is most intense. Accordingly, there will be less rivalry across store formats, which may even reach situations of complementarity (Miller et al. 1999). In other words, the patterns of spatial competitive interaction show asymmetries linked to the concept of store format, and these asymmetries reflect higher rivalry within a store format relative to, say, a store in format A versus another store in format B. This is because a store format reflects a specific type of retail product in the consumer’s choice of shopping destination. Accordingly, we propose the following hypotheses and build a more comprehensive model to test them: H1. There are significant asymmetric competitive effects due to the spatial configuration of store formats; i.e., the rivalry within store formats is different from rivalry across store formats. H2. There is more rivalry within store formats relative to the rivalry across store formats.

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Inter-format competitive effects in the traditional model

retail attraction also depends on the inter-format competitive context, such that

In order to test the above hypotheses, i.e., before assessing the role of store format in spatial competitive interaction, we conduct an intermediate step where we consider market response separately on the inter-format level of spatial competition. The traditional model assumes market share πi (f) of store format f in area i can be understood as determined by the attraction Aif of this format relative to all formats. That is (where F is set of all store formats), πi (f ) =


Aif f ∈F

Aif 

(3)

Analogously, the attraction of a store format can be understood as fragmented into two components: (i) the intrinsic attraction γ f of the generic positioning profile that the format represents, and (ii) the attraction that derives from the degree of spatial coverage Cif reached by store format f in residential area i, measured as the distance of the closest store in format f from residential area i. The resulting model can be expressed as Aif = exp(γf ) × Cif δ ,

(4)

where the parameter δ represents the impact of spatial coverage. Again, the competitive structure implicit in the model is symmetric, whereas actual experience suggests the existence of asymmetric competitive effects. The development of new store formats does not affect mature formats equally; instead, it is especially harmful to those that offer consumers similar benefits. One way to overcome this limitation is to assume the retail attraction of a store format depends not solely on its characteristics, but also on the specific context in which it operates (Meyer & Johnson 1995). In this respect, Cooper and Nakanishi (1988) suggest a fully extended model capable of differentiating competitive cross-effects. The model assumes that attraction depends on the characteristics and marketing efforts of all competitors. Adapted to the problem posed herein, the model can be adjusted as follows:  δff  Aif = exp(γf ) × Cif (5)  , f ∈F f  = f

where the parameter δff denotes the cross-effects of the spatial coverage of all store formats competing in the market—i.e., the effect of store format f on f. The above formulation is similar to that used by Gonz´alez-Benito (2005). Positive and differential cross-effects (δff ) in Eq. (5) suggest asymmetric inter-format competition; i.e., as the distance of the closest store in the competing format decreases, the attraction of other formats decrease. Eq. (5) assumes asymmetric competitive effects linked to inter-format competition, and we integrate this model with the traditional approach, as shown in Eq. (2). Therefore, a store’s

Aij = exp(γf + αr ) × Sj βS × Dij βD ×

 f ∈F

δff  Cif 

(6)

f  = f

This extended model accounts for competitive asymmetries linked to store format (H1)—i.e., differential crosseffects (δff ). The location of a store affects its competitors differently because it changes the spatial coverage of the format in which it operates. Moreover, consistent with the link between store format and generic store positioning, these asymmetries should lessen the competitive interaction between stores of different formats (H2). Note that in Eq. (6), intra- and inter-format competition should be evaluated in relative terms. For example, a negative cross-effect implies that if a store moves closer to a residential area, that store takes away more share from other stores of the same format than from stores of other formats; i.e., intra-format competition is higher than inter-format competition. Similarly, a positive cross-effect implies that if a store moves closer to a residential area, that store will take away more share from stores of other formats; i.e., inter-format competition is higher than intra-format competition. We elaborate more on this aspect in the results section. From the disaggregated perspective of consumer behavior, the hypotheses posed can be justified by assuming a certain degree of hierarchy in shopping decisions in a nested setting. As depicted in Fig. 1, store choice seems to be broken down into two decision levels: one related to the format of the store at which the consumer wishes to shop, and one related to the specific store chosen from the selected store format. A reason for this argument is that consumers simplify their choices in a growing heterogeneity of retailers by means of a previous choice of store format. The choice of store format allows consumers to reduce the consideration set according to the benefits sought and the type of buying situation. In short, this perspective implies a nested structure in the set of choices considered by the consumer. For this reason, we extend our modeling framework by adopting the methodological approach of nested-choice models (Ben-Akiva & Lerman 1985; McFadden, 1980), which have proven useful for modeling and testing in the contexts of competitive market structures (Kannan & Wright 1991) and retail competition (e.g., Ahn & Ghosh 1989; Sinha 2000; Suarez, Rodr´ıguez, & Trespalacios 2000). According to the nested approach, the market share πi (j) of store j is determined by the market share πi (f) of store format f in which it operates and its market share πi (j|f) within this format. Thus (where J is set of all stores within a format f, and F is the set of all formats), πi (j) = πi (f )×πi (j|f ) =


Aif

f ∈F

Aif 

×


Aij j ∈ J

Aij

, (7)

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63

Fig. 1. Nested choice structure.

for which the explanatory configuration is as follows: Aij = exp(αr ) × Sj βS × Dij βD ,  δff  (1−θ) Aif = exp(γf ) × Cif ,  × Iif Iif =



f  ∈ F,f  =f

Aij ,

j ∈ J

The parameter, θ, relates to the extent of hierarchy in the competitive structure. When its value is 0, the model reduces to Eq. (6). When its value is 1, the model implies the independence of the inter- and intra-format competitive models. In the latter case, spatial competitive interaction is explained separately by Eqs. (2) and (5), though Eq. (2) must be understood as a representation of the competitive structure within each store format; i.e., the choice of format is independent of the choice of store within each format. Thus, the nested model is an extension of the integrated model suggested in Eq. (6). Note the proposed nested model differs from a typical nested logit approach (e.g., Kannan & Wright 1991) in the way that explanatory variables are included in the model. The typical nested logit approach is based on the multinomial logit specification; that is, the attraction is a linear parameterized function of the explanatory variables transformed by the exponential function. By contrast, our model is based on the multiplicative competitive interaction approach (Cooper & Nakanishi 1988); that is, the attraction is a multiplicative parameterized function of the explanatory variables. Note that θ captures the effect of the inclusive value at the store format level (McFadden 1980). Also, the significance of θ captures the appropriateness of the nested approach and provides the contribution of the combined attractiveness of all the stores within a format to the attraction of that format.

Empirical analysis To test our hypotheses, we empirically estimate the proposed model in a real intra-urban scenario. The applica-

tion was carried out in the context of food shopping by analyzing the structural implications of three nonspecialized store formats that usually are differentiated in the literature (Burt & Sparks 1995; Tordjman 1994): (1) the supermarket as a generic self-service form, (2) the hypermarket as a large supermarket with a greater assortment of products, and (3) the discount store as a supermarket oriented toward an aggressive price policy to the detriment of its other retail services. Research setting We selected the city of Salamanca, Spain, for our analysis. Salamanca provides a census population of 162,370 inhabitants and 61,669 households,3 though it has a considerable floating population (mainly students). At the time we collected our information (April, 2002), the city’s retail infrastructure consisted of 63 self-service food stores belonging to 16 different retail chains. Of these stores, 43 were supermarkets, two were hypermarkets, and 18 were discount stores (see Table 1). Although none of the chains operated in more than one of the formats considered, some did belong to the same retail firm. We collected information about shopping behavior by surveying households in April 2002. The questionnaire was administered by personal interview and addressed to the household member responsible for food shopping. We asked respondents for a list of stores at which he or she usually shopped and a subjective estimation of the allocation of expenditure to each store during the previous month. Thus, the percentage of purchases made for each of the 63 stores was collected from each household. The maximum number of different stores patronized by a household was seven. Our survey aimed to obtain a generic expenditure pattern, independent of the effects of specific marketing actions on the part of the stores. We carried out a random sampling, stratified by neighborhoods with proportionate allocation, and based the selection of the households to interview 3 This information was provided by the Office of Statistics of the City Council of Salamanca and corresponds to June 15, 2000.

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64 Table 1 Retail infrastructure of the scenario studied Format

Chain

Number of stores

Mean size (m2 )

Hypermarket

Carrefour Leclerc El Arbol Consum Caprabo Supersol Champion

1 1 18 9 1 2 2

8129 7200 732 657 1500 1125 1788

Supermarket

Superchamber´ı Gadis Gama Herbu’s Maxcoop Hnos. cosme Dia

1 2 2 3 2 1 16

160 790 240 461 617 360 242

Discount

Lidl Plus superdescuento

1 1

750 750

Source: Alimarket Supermarket Census.

in each neighborhood on a random route procedure. We received a total of 584 valid questionnaires. For the explanatory variables considered in our model, we obtained measurements at the store level from the Alimarket Supermarket Census and the MOSAIC Iberia geographic information system. The former provided the location and size of each store, as well as a classification into supermarkets, hypermarkets, and discount stores.4 The latter provided the means to obtain a spatial interpretation of the study scenario by assigning geographic coordinates to consumers and stores. Consistent with the aggregate focus of this study, the location of consumers and stores can be distinguished at the census-track level.5 Specifically, we use the following measurements: 1. To approximate the range of the store’s offer Sj , we used the selling space, measured in hundreds of square meters. 2. To approximate the store’s accessibility Dij , we used the Euclidean distance, measured in kilometers, between the store and the consumer’s residential area. Note that while this measure may have some limitations—for example, it does not consider multipurpose and multi-destination

shopping trips (Dellaert, Arentse, Bierlaire, Borgers, &Timmermans 1998; Ingene & Ghosh 1990)—future research could refine this measure. 3. To approximate the spatial coverage Cif achieved by a store format in the consumer’s residential area, we used Euclidean distance, measured in kilometers, to the closest store of this format. Each variable was divided by its geometric mean to establish a correspondence between each unit value and the average distance in the specific intraurban area. We acknowledge that this measure captures only one of the many aspects that reflect the accessibility of a store format and ignores, for example, the intensity of coverage to which the consumer is exposed (Recker & Kostyniuk 1978). Presumably, when the homogeneity of the stores in the format is greater, the variable becomes more appropriate because the nearest store assumption grows more conceivable. Although the model is proposed from an aggregate perspective at the residential-area level, the estimation uses individual information about the food budget distribution of residents in the areas, not aggregate information about market shares. Interpreting budget allocation quotas as shopping probabilities makes it possible to calibrate the model using the maximum likelihood technique. However, this technique usually requires correspondence between the observations and stores (i.e., a qualitative dependent variable). We do so by linking consumers and stores, matching each consumer with the store in which he or she makes the greatest expenditure. Consistent with other research in this area (Popkowski Leszczyc, & Timmermans 1997; Uncles & Hammond 1995), we found that to a great extent, store formats share their customers; in other words, consumers are not loyal to the stores. We therefore modified our estimation by incorporating budget allocation quotas (Gonz´alez-Benito and Santos-Requejo 2002), which altered the likelihood functions as follows:  L= πi (j)gij i

for the models entered in Eqs. (2), (6) and (7)  πi (f )gif L= i

4

The definition of hypermarket is based on size and applies specifically to those supermarkets larger than 2500 m2 of selling space. It is the classification criterion used by Spanish legislation and most of the trade information sources. The definition of discount stores is based on a more detailed analysis of assortment, price, service, etc. Dia, Plus Superdescuento, and Lidl are widely known chains in Spain and are usually referred to as examples of the discount store format, characterized by their emphasis on low prices. 5 Census tracks considered by the Spanish administration are areas with a maximum of 2500 inhabitants. In contrast, MOSAIC Iberia has developed a more precise partition of urban geography. Each official census track is broken into approximately fifteen of MOSAIC’s census tracks. Bearing in mind that Spanish urban geography is characterized by a high population density, the resulting spatial units are acceptably small. Spatial coordinates are assigned in consideration of the central point of the census track in which the store is located or the consumer resides.

j

f

for the model entered in Eq. (5),where gij and gif denote the expenditure quotas of consumer i in store j or format f, respectively. The dependent variables thus focus on market share in terms of expenditure rather than preference, primary shopping, or any other criterion. The estimations were programmed directly using GAUSS statistical software and the OPTMUN optimization library. Comparisons of the extended models with their restricted versions were based on two common indicators linked to maximum likelihood estimation procedures (Ben-Akiva & Lerman 1985). First, the likelihood ratio index (also named ρ2 or pseudo R), a measure of the goodness of fit, is defined

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as follows:

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sponse, Table 2 provides the results of the estimation of the model specified in Eq. (2). The explanatory power of the model is high compared with a basic specification of the model in which only the attraction constants of the chain and the format are included. The associated likelihood ratio test is highly significant, and the coefficient ρ2 reflects a reduction in variance of nearly 30%, which is acceptable in this type of estimation technique. The signs of the parameters associated with the explanatory variables reflect a significant relationship between the market share in a specific residential area and both store size and store proximity to the residential area. Note that lower values of distance indicate closer store proximity and, therefore, higher market share; this reflects the relevance of store location for consumers. The attraction constants for chains and formats reflect their intrinsic store attraction, relative to the base-case store format and chain, for which the values are zero after accounting for the effects of size and distance. The sum of the constants in each category enables us to evaluate the comparative, nonspatial store attraction of each retail chain. To understand the spatial competitive effects linked to store format, the traditional spatial competitive interaction model must be interpreted at the inter-format competitive level. Table 3 provides the estimation results of the model

ln(likelihood of the extended model) 1− ln(likelihood of the restricted model) Although it is interpreted in a fashion similar to R2 in regression analysis, a lower value is required for a better fit (Guadagni & Little 1983). Second, the likelihood ratio test assesses whether the improvement in the goodness of fit is significant. According to the null hypothesis that model extension is redundant, the statistic, −2(ln(likelihood of the restricted model) − ln(likelihood of the extended model)), is χ2 distributed with as many degrees of freedom as there are additional parameters. Analysis and results To establish a comparative reference by which to evaluate the relevance of asymmetric competitive effects that are linked to the supermarket, hypermarket, and discount store classification of retail stores, it is advisable to start from a symmetric interpretation of the competitive scenario. In reTable 2 Classic model of spatial competitive interaction (Eq. (2)) Parameter

Standard error

t-Statistic

Significance

0.5169 −0.2039 0

0.5011 0.2256 –

1.0315 −0.9039 –

0.3027 0.3663 –

Supermarket Caprabo Supersol Champion Superchamber´ı El Arbol Consum Gadis Gama Herbu’s Maxcoop Hnos. cosmeb

−4.1719 2.5713 0.7062 2.8415 1.6109 2.1225 1.6730 1.6865 1.2136 1.1019 1.3935 0

0.9726 1.0233 1.0864 0.9678 1.2318 0.9354 0.9442 0.9600 1.0791 1.0103 1.0117 –

−4.2891 2.5126 0.6501 2.9360 1.3077 2.2690 1.7718 1.7567 1.1246 1.0907 1.3773 –

0.0000 0.0122 0.5158 0.0034 0.1914 0.0236 0.0769 0.0794 0.2612 0.2758 0.1689 –

Discounta Dia Lidl Plus superdescuentob

0 −1.3094 −1.2140 0

– 0.3722 0.4581 –

2.9360 −3.5176 −2.6499 –

– 0.0004 0.0082 –

0.5518 −1.8167

0.1517 0.0513

3.6366 −35.3987

0.0003 0.0000

Attraction constants (γ, α) Hypermarket Carrefour Leclercb

Spatial coverage (β) Size Distance

Goodness-of-fit statistics Log-maximum likelihood = −1542.9226 Likelihood ratio test (on trivial) = 1253.222 (Significance χ22 = 0.0000) Likelihood ratio coefficient (on trivial) = ρ2 = 0.2888 a b

Format constant taken as a reference with null value. Chain constant taken as a reference with null value.

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Table 3 Inter-format spatial competition interaction model (Eq. (5)) Parameter

Standard error

t-Statistic

Significance

−0.3680 0.4207 0

0.1136 0.0895 –

−3.2392 4.6994 –

0.0012 0.0000 –

Inter-format coverage (δ) Hypermarket coverage; effect on Hypermarketb Supermarket Discount

0 0.4027 0.3244

– 0.2331 0.2473

– 1.7274 1.3118

– 0.0846 0.1900

Supermarket coverage; effect on Hypermarket Supermarketb Discount

0.5162 0 0.3964

0.1621 – 0.1380

3.1846 – 2.8720

0.0015 – 0.0042

Discount coverage; effect on Hypermarket Supermarket Discountb

0.6929 0.4052 0

0.1647 0.1234 –

4.2071 3.2828 –

0.0000 0.0010 –

Attraction constants (γ) Hypermarket Supermarket Discounta

Goodness-of-Fit Statistics Log-maximum likelihood = −591.5209 Likelihood ratio test (on trivial) = 45.0176 (Significance χ62 = 0.0000) Likelihood ratio coefficient (on trivial) = ρ2 = 0.0366 a b

Format constant taken as a reference with null value. Inter-format asymmetric effect taken as a reference with null value.

specified in Eq. (5). One of the effects linked to each spatial coverage variable in the model has been set to zero. As a criterion, the format pertaining to the coverage variable in question has been taken as reference so that the parameters quantify the cross-effects for competitors. The overall fit of the model is still highly significant, despite the difficulty quantifying a concept as broad as the coverage achieved by a store format or, from the consumer’s perspective, its accessibility. The positive sign of the parameters is consistent with the competitive interaction pattern at the store-format level; the greater the distance to a specific store format, the greater is the market share for the competing formats. With regard to the intrinsic attraction constants linked to store formats, the supermarket appears to be the most attractive format, whereas the hypermarket seems to be the least attractive. While the above intermediate results reflect coherence between competitive interaction patterns at the store-format and the store levels, our main interest centers on the integration of the two models. Table 4 provides the estimation results of the integrated model, as specified in Eq. (6). The argument implicit in this integration is that the retail attraction exerted by a store depends not only on its location, but also on the spatial coverage achieved by competing formats. The fit of the integrated model is a significant improvement over the classic interaction model, as reflected by the associated likelihood ratio test. It corresponds to a slight increase in the coefficient ρ2 . This result confirms H1—that is, the addition of asymmetric competitive effects improves the model fit significantly.

The parameters for size and distance show a pattern similar to that in Table 2 and indicate the role of the spatial coverage variables on market share. The sign change in the parameters for the competitive effects, compared with those obtained for the inter-format model (Table 3), may seem to indicate that increased distance to the closest store within a format diminishes the attraction of other store formats. However, the signs confirm H2: there is greater rivalry between stores within each format. Because the location variables at the store level (Dij ) and the spatial coverage variables at the format level (Cif ) are based on distance from the consumer, the parameters associated with the format-level spatial coverage must be interpreted in relation to the parameters associated with the store-level distance variables. The parameters that represent asymmetric effects moderate the consequences of the distance variable on competing store formats. For example, the higher the distance to hypermarket A, the lower the hypermarket attraction (−2.0604); however, a higher distance to the closest hypermarket will decrease the attraction of supermarket and discount formats (−1.2150 and −1.3954), although less severe than the impact on attraction to hypermarket A. Therefore, although the consequences of higher distance to hypermarket A on competitors are always beneficial, they are more beneficial for other hypermarkets than for supermarkets and discounters. To facilitate interpretation of the resulting asymmetric competitive effects, a simplified sample retail context is simulated in the Appendix A. At least two conclusions can be drawn based on the analysis in the Appendix A. First, Eq. (6) captures competitive asymmetries linked to the store for-

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Table 4 Integrated model of spatial competitive interaction (Eq. (6)) Parameter

Standard error

t-Statistic

Significance

0.5364 −0.0142 0

0.4680 0.2370 –

1.1461 −0.0601 –

0.2522 0.9520 –

Supermarket Caprabo Supersol Champion Superchamber´ı El Arbol Consum Gadis Gama Herbu’s Maxcoop Hnos. cosmeb

−4.4831 2.7449 0.7803 2.9945 2.0508 2.1807 1.7397 1.6060 1.1970 1.2072 1.5146 0

1.0222 1.0269 1.1040 0.9893 1.0536 0.9608 0.9661 0.9754 1.0935 1.0318 0.9924 –

−4.3855 2.6729 0.7068 3.0268 1.9463 2.2697 1.8006 1.6464 1.0945 1.1699 1.5261 –

0.0000 0.0077 0.4799 0.0025 0.0520 0.0235 0.0722 0.1002 0.2741 0.2425 0.1275 –

Discounta Dia Lidl Plus superdescuentob

0 −1.5576 −1.3299 0

– 0.3967 0.4644 –

– −3.9261 −2.8637 –

– 0.0000 0.0043 –

Spatial coverage (β) Size Distance

0.5388 −2.0640

0.1384 0.0624

3.8919 −33.0419

0.0001 0.0000

Inter-format coverage (δ) Hypermarket coverage; effect on Hypermarketc Supermarket Discount

0 −1.2150 −1.3954

– 0.2553 0.2677

– −4.7590 −5.2117

– 0.0000 0.0000

Supermarket coverage; effect on Hypermarket Supermarketc Discount

−0.7513 0 −0.8485

0.1810 – 0.1647

−4.1492 – −5.1509

0.0000 – 0.0000

Discount coverage; effect on Hypermarket Supermarket Discountc

−0.8259 −0.9442 0

0.1817 0.1484 –

−4.5433 −6.3626 –

0.0000 0.0000 –

Attraction constants (γ, α) Hypermarket Carrefour Leclercb

Goodness-of-fit statistics Log-maximum likelihood = −1513.8645 Likelihood ratio test (extension) = 58.1162 (Significance χ62 = 0.0000) Likelihood ratio test (on trivial) = 1311.3382 (Significance χ82 = 0.0000) Likelihood ratio coefficient (on trivial) = ρ2 = 0.3022 a b c

Format constant taken as a reference with null value. Chain constant taken as a reference with null value. Inter-format asymmetric effect constant taken as a reference with null value.

mat. When the distance to discount store B decreases, the consequences for competing stores differ according to their format. Discount store A is the most severely affected because the cross-effects are negative. For the cross-effects to be positive, the increased share of discount store B would have to be from stores of competing formats. Thus, we believe that intra-format competition is more severe than interformat competition. In addition, the consequences on the supermarket are less severe than those on the hypermarket; this should be expected because the moderating effect of discount store coverage on the supermarket is higher than on the hy-

permarket. Second, the asymmetric effects disappear when the distance to discount store B increases because the spatial coverage reached by the discount store format is not affected. The distance to the nearest discount store remains the same. This limitation in the spatial coverage variable provides additional support for the nested model extension in Eq. (7), where we consider the overall attractiveness of all the stores within each format. While the above analysis provides evidence in support of the proposed hypotheses, it would be interesting to explore a nested extension of the model of market response, especially

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Table 5 Nested integrated model of spatial competitive interaction (Eq. (7)) Parameter

Standard error

t-Statistic

Significance

−0.0301 −0.2447 0

0.2336 0.2484 –

−0.1287 −0.9851 –

0.8975 0.3249 –

Supermarket Caprabo Supersol Champion Superchamber´ı El Arbol Consum Gadis Gama Herbu’s Maxcoop Hnos. cosmeb

−1.2668 2.9896 0.8884 3.1664 1.8513 2.3227 1.8567 1.7323 1.1423 1.4603 1.7279 0

0.6468 1.0750 1.1792 1.0204 1.2585 0.9876 0.9978 1.0353 1.1426 1.0651 1.0557 –

−1.9584 2.7810 0.7533 3.1031 1.4710 2.3517 1.8607 1.6731 0.9997 1.3709 1.6366 –

0.0506 0.0055 0.4515 0.0020 0.1418 0.0190 0.0633 0.0948 0.3178 0.1709 0.1022 –

Discounta Dia Lidl Plus superdescuentob

0 −1.5035 −1.4450 0

– 0.3907 0.4877 –

– −3.8478 −2.9628 –

– 0.0001 0.0031 –

Spatial Coverage (β) Size Distance

0.6038 −2.1268

0.1564 0.0681

3.8595 −31.1869

0.0001 0.0000

Inter-format coverage (δ) Hypermarket coverage; effect on Hypermarketc Supermarket Discount

0 −0.1325 −0.2514

– 0.2645 0.2792

– −0.5009 −0.9003

– 0.6165 0.3683

Supermarket coverage; effect on Hypermarket Supermarketc Discount

0.0685 0 –0.0472

0.2019 – 0.1857

0.3394 – –0.2539

0.7343 – 0.7996

Discount coverage; effect on Hypermarket Supermarket Discountc

0.1569 −0.0708 0

0.2109 0.1770 –

0.7436 −0.4001 –

0.4573 0.6892 –

0.6678

0.0986

6.7683

0.0000

Attraction constants (γ, α) Hypermarket Carrefour Leclercb

Nesting Inclusive value (θ)

Goodness-of-fit statistics Log-maximum likelihood = −1506.8530 Likelihood ratio test (extension) = 14.023 (Significance χ12 = 0.0002)(Sign. ␹2 1 = 0.0002) Likelihood ratio test (on trivial) = 1325.36 (Significance χ92 = 0.0000) Likelihood ratio coefficient (on trivial) = ρ2 = 0.3054 a b c

Format constant taken as a reference with null value. Chain constant taken as a reference with null value. Inter-format asymmetric effect constant taken as a reference with null value.

if some of the limitations added in the empirical configuration of the model were taken into account. Contrary to expectations, the asymmetrical competitive effects appear only when the location decisions imply a change in the spatial coverage variable at the format level (i.e., distance to the closest store within the same format changes). Table 5 summarizes the estimation results of the nested model specified in Eq. (7).

The extension detailed herein constitutes a significant contribution to the integrated model. The significance of the added parameter proves a certain degree of hierarchical organization in the competitive structure and makes it possible to assume two decision levels for store choice: a first-level choice of store format that reflects the consumer’s needs or the specific circumstances of a shopping occasion, and a second-

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level choice of a store within that format. The loss of significance of the inter-format asymmetric effects reflects that the nested approach captures most of the asymmetric competitive effects linked to store format. In other words, after accounting for the intrinsic attractiveness of each of the store formats and the overall attractiveness of all the stores within each store format, captured via the nested approach, we find no residual significant inter-format competitive effects.

Conclusions Store formats form a classification criterion for retail stores that is linked to the degree the target segments overlap and, consequently, to the degree of rivalry in capturing demand. Based on this premise, the role of store format in the relationship between store location and market response has been approached through a model of spatial interaction, where we jointly consider the attraction of stores within a format as well as the attraction of the store format itself. The model has been empirically estimated in the context of food shopping. We also examine the results of a nested specification of the above model. The results from the joint model confirm that spatial competitive interaction is asymmetric and affected by the attraction dimensions that characterize competing retail stores. In addition, these results demonstrate that the concept of store format plays a prominent role in the relationship between market share and spatial competition. Specifically, the spatial accessibility of a store differently affects the demand for its competitors according to the store format in which it and its competitors operate. Competitive intensity seems to be more severe at the intra-format than at the inter-format level. This implies a two-step hierarchy in the process of retail store choice in which the consumer chooses first the type of store in which to shop and second, the specific store within that format. The spatial dimension is significant in the second level of decision making. From a methodological perspective, the results reveal that given the heterogeneous retail stores, models of market share based on the symmetry of spatial competitive interaction are inadequate. By considering the combined effects of location at both the store and format levels, this paper provides a modeling approach that also accounts for asymmetric competitive interaction. In contrast to prior research in this field, the explanatory configuration of our model depends directly on the classification of competing stores into specific, distinct formats. Therefore, the relationship between spatial configuration and market response now can be quantified at both the store and format levels of competition. Our results are relevant to retailers who want to create a portfolio of store formats and a network of stores for which market coverage is optimized and cannibalization effects are minimized. To obtain this optimization, those retailers must anticipate the consequences of new locations, closures, mergers, or competitors’ diversification strategies.

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In this regard, this study demonstrates that spatial competitive analysis should not be isolated from the effect of other attraction dimensions because the effect of location depends on the retail positioning of competitors, specifically, on the degree the target segments overlap. However, an explicit consideration of store formats reduces the need to consider other image dimensions in the understanding of retail spatial competition. In addition, this study shows that spatial analysis should not focus exclusively on a specific store format. Although intra-format competitive interaction is more intense, the consequences of inter-format rivalry should not be dismissed. Rather, the current competitive retail context reflects a greater prominence of spatial competition between formats where there is an emphasis on occupying new markets—at the expense of overlapping areas of influence—by retailers engaged in spreading new store formats (Valentin 1991). The model also provides retailers with an analytical instrument that outperforms traditional models that are based on a symmetric conception of competition. As shown in the Appendix A, demand in a residential zone, as derived from changes in the location of formats and stores, can be quantified by means of the model. Moreover, global demand can be forecasted through aggregation across the residential zones that constitute the market in question. Besides, the model preserves two benefits critical to retailers searching for effective location models (Simkin 1996). First, it is based on a standard logit-type attraction model. Second, it can be replicated in many urban settings using commercially available secondary data. Instead of subjective survey data, retailers can substitute objective data from consumer panels, which are available in most western countries as are geographic information systems and store census information. The results are relevant not only to retailers, but also to public administrators who must develop retail planning policies and regulatory laws. Although many such decisions are based on aggregate coverage ratios or similar basic analytic methods, an exhaustive assessment of the advisability of new retail spaces or possible mergers between retailers requires an understanding of how these changes affect demand. By understanding market responses to the spatial configuration of retail stores, chains, and formats, public policymakers can better evaluate the scope of the existing retail infrastructure, identify deficiencies in retail services coverage, detect monopolistic situations, and anticipate future consequences. Therefore, just as retailers can use the proposed model to optimize their own performance, public policymakers might employ it to guarantee competitive equilibrium, optimize consumer welfare, and ensure economic and social sustainability. As a direction of future research, the proposed model can be extended to a decision model. For example, it could be used to determine the demand allocation in a location–allocation approach (Ghosh & McLafferty 1987; Ghosh & Rushton 1987). Optimal locations thus emerge from the optimization of the objective functions or from the equilibrium that game theory might suggest, according to competitors’ reactions.

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Note that this study is only an initial approach to the study of the role store format plays in spatial competitive interaction. It has several limitations, one of which is the definition of store formats. Although the notion is intuitive, employing it presents at least two difficulties. First, the delimiting of store formats is not always obvious because it implies divisions in the continuum of competitive positioning. Second, greater precision in the differentiation of store formats implies there may be correspondence within each chain or even each store. The analysis addresses only one of the many possible ways to incorporate the heterogeneity of retailers in spatial analyses of demand. Another limitation is the precision of the variables used in the empirical specification of the model. Spatial accessibility and spatial coverage are complex multidimensional concepts that are difficult to apply in practice. More precise and complete data might improve the explanatory possibilities substantially. In this line, it is important to point out that the study focused exclusively on the spatial dimension. Its role within the rest of the retail marketing mix variables has only been addressed insofar as they relate to the store format. The variance of other retail marketing mix variables within store formats and retailers should be considered in future research. Finally, it is important to call attention to the static nature of the model. By considering dynamic effects, it may be possible to combine the impact of changes in the spatial configuration of the retail stores with the evolution of store formats, thereby anticipating the repercussions of changes in demand on the retail structure.

and Culture of Castile and Leon, Spain (Research Project SA04/00F).

Appendix A A simulation of the competitive effects of location decisions in a simplified retail setting Assume that a residential zone i is surrounded by three stores: - a hypermarket with 8000 m2 , 1.5 km away, - a supermarket with 600 m2 , 0.5 km away, - a discount store (A) with 300 m2 , 0.5 km away. Assume also that another discount store, B, has the same conditions as discount store A; namely, it is 300 m2 and 0.5 km away. If the intrinsic attraction of all stores is assumed to be equal (i.e., the attraction constants α and γ equal 0) and the coverage parameters β and δ are assumed to be those obtained in Table 4, the resulting estimated market shares at i, according to Eq. (6), are given by the central column of Table A.1. They have been calculated as follows: δf,hypermarket Aij = exp(γf + αr ) × SjβS × DijβD × Ci,hypermarket δf,supermarket δf,discount × Ci,supermarket × Ci,discount

Ai,hypermarket = exp(0 + 0) × 800.5388 × 1.5−2.0640 × 1.50.0000 × 0.5−0.7513 × 0.5−0.8259 = 13.700

Acknowledgments

Ai,supermarket = exp(0 + 0) × 60.5388 × 0.5−2.0640

The authors are grateful for the collaboration of MOSAIC Iberia S.A., Publicaciones ALIMARKET, and the Office of Statistics of the City Council of Salamanca, Spain, on the empirical part of this study. The authors also appreciate the useful commentaries and suggestions of JR editors and three anonymous reviewers on previous versions of this paper. This research was financed by the Regional Ministry of Education

× 1.5−1.2150 × 0.50.0000 × 0.5−0.9442 = 12.908 Ai,discount A = exp(0 + 0) × 30.5388 × 0.5−2.0640 × 1.5−1.3954 × 0.5−0.8485 ×0.50.0000 = 7.729

Table A.1 Market response on alternative locations

Attraction Hypermarket Supermarket Discount store A Discount store B

Discount store B 200 m closer

Discount store B 100 m closer

Initial decision

Discount store B 100 m farther

Discount store B 200 m farther

20.890 20.910 7.729 22.182

16.472 15.936 7.729 12.250

13.700 12.908 7.729 7.729

13.700 12.908 7.729 5.305

13.700 12.908 7.729 3.859

Market share (variation with respect to initial decision) Hypermarket 0.291 (−10.6%) Supermarket 0.292 (−5.0%) Discount store A 0.108 (−41.3%) Discount store B 0.309 (+68.4%)

0.314 (−3.5%) 0.304 (−0.9%) 0.148 (−19.7%) 0.234 (+27.3%)

0.326 0.307 0.184 0.184

0.346 (+6.1%) 0.326 (+6.1%) 0.195 (+6.1%) 0.134 (−27.2%)

0.359 (+12.9%) 0.338 (+12.9%) 0.202 (+12.9%) 0.101 (−45.0%)

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Ai,discount B = exp(0 + 0) × 30.5388 × 0.5−2.0640 × 1.5−1.3954 × 0.5−0.8485 ×0.50.0000 = 7.729

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Ai,discount B = exp(0 + 0) × 30.5388 × 0.5−2.0640 ×1.5−1.3954 × 0.5−0.8485 × 0.50.0000 = 12.250 Aij πi (j) =
j  Aij 

Aij πi (j) =
j  Aij  πi (hypermarket) =

Ai,hypermarket + Ai,supermarket Ai,hypermarket

πi (hypermarket) =

+ Ai,discount A + Ai,discount B = 0.314

+ Ai,discount A + Ai,discount B = 0.326 πi (supermarket) = πi (supermarket) =

Ai,supermarket + Ai,supermarket Ai,hypermarket + Ai,discount A + Ai,discount B = 0.307

πi (discount A) =

πi (discount B) =

Ai,discount B + Ai,supermarket Ai,hypermarket

Ai,supermarket + Ai,supermarket Ai,hypermarket + Ai,discount A + Ai,discount B = 0.304

πi (discount A) =

Ai,discount A + Ai,supermarket Ai,hypermarket + Ai,discount A + Ai,discount B = 0.814

Ai,hypermarket + Ai,supermarket Ai,hypermarket

Ai,discount A + Ai,supermarket Ai,hypermarket + Ai,discount A + Ai,discount B = 0.148

πi (discount B) =

Ai,discount B + Ai,supermarket Ai,hypermarket + Ai,discount A + Ai,discount B = 0.234

+ Ai,discount A + Ai,discount B = 0.814 References Now assume that alternative locations are found for discount store B, so that the distance can be reduced 100 and 200 m or increased 100 and 200 m. Table A.1 contains the resulting estimated market shares and comparisons with the original location decision, according to Eq. (6). For example, when distance is reduced 100 m, market shares are calculated as follows: β

β

δ

f,hypermarket Aij = exp(γf + αr ) × Sj S × DijD × Ci,hypermarket

δ

δ

f,supermarket f,discount × Ci,discount × Ci,supermarket

Ai,hypermarket = exp(0 + 0) × 800.5388 × 1.5−2.0640 ×1.50.0000 × 0.5−0.7513 × 0.5−0.8259 = 16.472 Ai,supermarket = exp(0 + 0) × 60.5388 × 0.5−2.0640 ×1.5−1.2150 × 0.50.0000 × 0.5−0.9442 = 15.936 Ai,discount A = exp(0 + 0) × 30.5388 × 0.5−2.0640 ×1.5−1.3954 × 0.5−0.8485 × 0.50.0000 = 7.729

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