A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet?

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A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet? Kristina Melis a,∗ , Katia Campo b , Lien Lamey a , Els Breugelmans b a

b

KU Leuven, Faculty of Economics and Business, Naamsestraat 69, 3000 Leuven, Belgium KU Leuven, Campus Carolus Antwerp, Faculty of Economics and Business, Korte Nieuwstraat 33, 2000 Antwerp, Belgium

Abstract A switch from single- to multichannel grocery shopping can affect consumers’ purchase allocations. This study investigates whether and to what extent a consumer’s decision to start buying groceries online at a particular chain can lead to expansion of the share in grocery spending allocated to that chain. To obtain a better insight into the underlying mechanisms and to provide more accurate and refined managerial guidelines, we further investigate the moderating effects of a comprehensive set of factors that may stimulate or withhold these expansion effects. To analyze these effects, we use a large and representative U.K. household panel data set, composed of multichannel grocery shoppers and a matched group of single-channel shoppers, for a 2.5-year period across all multichannel grocery chains. The empirical analysis confirms that multichannel grocery shoppers expand the share of wallet allocated to the online-visited chain, and that the extent of this expansion depends on chain as well as consumer characteristics. Expansion effects appear to be more profound for chains that adopt an integrated multichannel strategy for product prices and category assortments. In addition, customers who face more stringent time constraints, live farther away from the chain’s offline store, and/or purchase fewer competitive private-label or hard discounter products, are more likely to increase their share of grocery spending at the online-visited chain. © 2016 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Multichannel retailing; Grocery shopping; Expansion

Introduction Despite some initial implementation delays, online grocery is now a booming business: nine percent of U.S. consumers and six percent of European customers are already frequently using the online channel to buy groceries, and a recent Nielsen survey revealed that 51 percent (U.S.) and 46 percent (Europe) is willing to do so in the near future (Nielsen 2016). These figures and expected growth rates are confirmed by several other studies (e.g. IGD 2015; Steiman 2014). By promoting their online channels as an alternative shopping venue for consumers, grocery retailers strive to increase customer satisfaction and loyalty, retain and increase spending by existing customers, and improve the chain’s overall performance and competitive position (IGD 2012). Changes in the grocery

∗

Corresponding author. Tel.: +32 016 379 043. E-mail addresses: [email protected] (K. Melis), [email protected] (K. Campo), [email protected] (L. Lamey), [email protected] (E. Breugelmans).

retail market – such as increased globalization, the success of discounter formats and the increasing tendency of grocery shoppers to visit multiple chains – has intensified the already harsh competition in this sector (Gijsbrechts, Campo, and Nisol 2008; McKinsey & Company 2014). The importance of defending and possibly improving the market position therefore becomes pertinent for all grocery chains. While adding an online store to the chain’s distribution system can provide interesting opportunities in this respect, selling products through the offline and online channel often also leads to substantially higher operational costs (e.g. information and communication technology, labor, and delivery costs; McKinsey & Company 2013). It further adds to the complexity of the grocery retailers’ multiproduct marketing mix strategy, given that marketing mix instruments like price (Wolk and Ebling 2010), assortment (Melis et al. 2015) and promotion (Breugelmans and Campo 2016) not only exert effects on competing grocery chains (across-chain effects) but also influence purchase behavior in the other channel of the same chain (within-chain effects). This is especially pertinent for grocery shopping, because most online shoppers keep visiting the chain’s offline store to combine

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convenience advantages of online shopping with self-service advantages of offline stores (Alba et al. 1997; Chu et al. 2010; Chu, Chintagunta, and Cebollada 2008; Konus¸, Verhoef, and Neslin 2008; Venkatesan, Kumar, and Ravishanker 2007). To exploit the opportunities and challenges provided by multichannel retailing, grocery retailers therefore need better insights into consumers’ multichannel shopping behavior. The questions of whether and to what extent shoppers adjust their allocation of grocery expenditures when starting to shop online are especially relevant in a grocery shopping context, where consumers do not only switch to multichannel shopping – rather than fully migrating to the online channel – but often also regularly visit multiple chains. Previous research has indicated that more than 60 percent of the grocery shoppers frequently visits more than one grocery chain, to take maximum advantage of (category-specific) price/assortment differences and/or to minimize shopping costs by only visiting more distant stores for major shopping trips and nearby stores for minor trips (Fox, Montgomery, and Lodish 2004; Gijsbrechts, Campo, and Nisol 2008; Vroegrijk, Gijsbrechts, and Campo 2013). Consumers who engage in these more complex shopping patterns – by interchangeably buying online and offline, and visiting multiple chains – may try to take maximum advantage of the provided opportunities to increase their shopping value, and hence may adjust their purchase allocation patterns when switching from single-channel (offline) to multichannel grocery shopping. Multichannel shoppers can for instance not only reallocate category purchases across chains but also across channels within the chain; and the specific nature of the multichannel, multiple-chain context can have a substantially different effect on how factors that were previously investigated in a(n) (offline and/or online) shopping context influence purchase allocation decisions (e.g. distance in the offline context vs. convenience advantages in the online context). Given that a consumer’s decision to start shopping online creates opportunities for expansion, but also entails risks for within-chain cannibalization, it is crucial to gain a better insight in how this decision affects purchase allocation patterns. The research objective in this study is therefore twofold: to investigate whether and to what extent a consumer’s decision to start shopping in the online channel expands her or his share of spending within that chain, and to explore which factors stimulate or hinder expansion. Table 1 provides an overview of related papers on multichannel retailing and shopping. We contribute to this literature stream in multiple ways. As is clear from Table 1, most prior research (i) concentrates on non-grocery contexts, (ii) uses outcome variables that measure the impact of multichannel retailing on absolute chain performance (like sales and stock market returns), and (iii) investigates no or a very small set of moderators. Apart from Pozzi (2013), most prior multichannel studies investigate non-grocery shopping sectors, such as apparel (Venkatesan, Kumar, and Ravishanker 2007) or computer hardware/software (Kumar and Venkatesan 2005). Purchase behavior in the grocery sector differs substantially from these purchase contexts, because of the low level of involvement, the

multiproduct and habitual purchases (requiring, for instance, less pre-purchase information search), and the competitive setting with a high frequency of multiple-store shopping. As a result, most of the previous findings obtained in other shopping contexts cannot be simply generalized to the grocery sector, where channels are used interchangeably rather than sequentially (search online, purchase offline) or in competition (buying online or offline) as is more likely for other, durable, sectors. Additional research is needed, taking the specifics of the grocery sector into account, to provide a better insight into what drives allocation decisions in the multichannel, multiple-chain grocery shopping context. Second, most prior studies on multichannel retail performance use aggregate and absolute measures, such as sales and stock market returns (Bilgicer et al. 2015; Kushwaha and Shankar 2013; Pozzi 2013). We aim to obtain a better insight into shopping behavior effects – and thereby into the chain’s competitive position – by performing an individual-level analysis of shifts in grocery purchase allocations following a consumer’s decision to use, rather than the retailer’s decision to introduce, the online channel. As a result, we can detail the spending allocation shifts that come from competing retailers and accrue to the rapidly increasing segment of customers who are starting to use the online grocery channel (Planet Retail 2014). The question of whether, to what extent, and in which circumstances, a consumer’s (individual) decision to shop online will expand their share of spending at the multichannel chain will therefore remain pertinent, long after the retailer’s decision to open up an online store. What is more, by using (an individual-level) share-of-wallet (SoW) measure as the dependent variable, we explicitly account for consumer spending at competing retailers. Aggregate-level analyses, that focus for instance on overall changes in sales following a retailer’s decision to open an online store, only disclose “net” effects of a multichannel chain in which multiple consumers start shopping online. Third, we identify and investigate a comprehensive set of factors that can drive SoW expansion, by integrating insights from the multiple-store (offline) shopping and online shopping literature. Online shopping brings along costs and benefits that are on the one hand similar in nature as offline shopping costs (fixed vs. variable, and acquisition vs. transaction benefits/costs of shopping), but on the other hand are determined by different store characteristics (e.g. convenience advantages of pick-up and home delivery service, interactive and attribute-specific search tools; Chintagunta, Chu, and Cebollada 2012). The online shopping environment can for this reason also cause a change in the otherwise habitual purchase pattern (Pozzi 2013). The set of factors we use are derived from those two underlying mechanisms. Previous research takes a fragmented view, considering either no (Ansari, Mela, and Neslin 2008; Bilgicer et al. 2015) or a very small set of (e.g. Kushwaha and Shankar 2013; Pozzi 2013) moderating variables. In addition, with an individual-level analysis across multiple multichannel grocery chains, we can determine the moderating impact of consumer characteristics that we operationalize as relative measures (i.e. shares at competitors), in addition to those of overall (across all chains) and chain-specific consumer characteristics. Including the effect of

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Table 1 Literature review.

Author(s)

Grocery Yes No

Dependent variable

Moderators Level

Factor

Chain

Product overlap

Firm

Channel power

Introduction strategy Marketplace

Order of entry

Retailer decision Deleersnyder et al. 2002 Geyskens, Gielens, and Dekimpe 2002

Pozzi 2013

x x

x

Sales and spending

Ansari, Mela, and Neslin 2008

x

Bilgicer et al. 2015 Kumar and Venkatesan 2005 Kushwaha and Shankar 2013 van Baal and Dach 2005 Venkatesan et al. 2007 This study

x x x x x x

Volume sales and advertising income Stock market return

Customer (at chain)

Consumer decision Channel selection, – purchase incidence and order size Spending – Purchase behavior and – channel adoption Spending Product category Retailer switching Product category Customer profitability – Share of wallet

Customer (overall) Customer (at competitor) Customer (at chain)

individual-level moderating factors contributes to a better understanding of potential expansion effects and can provide better managerial guidelines to stimulate these effects. Hence, previous studies vary across industries, moderating factors investigated, and dependent variables considered. We extend and refine prior research, by focusing on the unique but important grocery sector and by providing a comprehensive framework and empirical analysis that maps differences across categories, consumers and chains. Gaining a better understanding of whether and why grocery shoppers’ purchase allocations are updated and, even more interestingly, which drivers influence their purchase allocation pattern following the decision to start shopping online at a particular chain therefore constitutes an important academic contribution. Our research also has important implications for multichannel retail management, and can provide valuable guidelines on how to exploit multichannel opportunities to improve the retail chain’s performance and competitive position. For example, by revealing the critical factors that drive or inhibit expansion, we provide multichannel retailers with new insights into which consumers to target, how to approach them, and how to stimulate them to shift a larger part of their grocery purchases to the multichannel chain to take maximum advantage of online shopping convenience. Our findings may thus help multichannel retailers with optimizing their communication and marketing mix

Product demand growth Distance to chain

Hedonic vs. utilitarian Dominance of search characteristics

Time constraint Multiple-store shopper Heavy/bulky share Sensory share Private-label share Distance to store Price integration Assortment integration

strategy when promoting and improving their online shopping platform. Conceptual Framework Consumers who start to buy groceries online can shift (part of) their purchases from the offline to the online store belonging to the same chain (shift in purchase allocation within the chain, but not across chains). In that case, the online-visited chain retains essentially the same share of the consumer’s grocery purchases, and the switch toward multichannel shopping has no expansion effect for the chain, but only leads to cannibalization of offline purchases. An alternative scenario is that multichannel shoppers shift (part of) their purchases from competitive offline visited chains to the online store’s chain. In this case, they reevaluate their allocations across chains and increase the (online plus offline) share of wallet (SoW) they assign to the chain that hosts the online channel, which signals expansion. However, expansion and cannibalization are not orthogonal events: Consumers can increase the SoW assigned to an online-visited chain by shifting part of their expenditures from competitive chains to the focal chain, but also cannibalize some of the chain’s offline sales. We consider the net results of these between- and withinchain SoW shifts, with positive effects being indicative of SoW expansion.

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Fig. 1. Conceptual framework.

By using SoW as our outcome of interest, we focus on mere allocation (competitive) effects, without the need to incorporate other consumer-level or environmental effects that only or predominantly affect overall spending levels (Cooil et al. 2007; Leenheer et al. 2007; Mägi 2003). Our goal is not to explain whether and why a consumer decides to use the online channel of a particular chain to buy groceries, but rather to define the implications of this decision. In our conceptual framework, we therefore focus on expansion effects triggered by the decision to use the online channel, rather than other factors that could influence spending allocations across chains (e.g. changes to the offline marketing mix). Instead, we control for these effects in our model (see the Methodology section). To identify drivers and inhibitors of the potential expansion effects, we focus on the factors that may stimulate grocery shoppers to reallocate their purchases from competitive chains to the chain that hosts an online channel they have started to use. Previous studies of habitual purchase behavior cite extrinsic events (e.g. stockout; Campo, Gijsbrechts, and Nisol 2000; Corstjens and Corstjens 1995) or changes in the shopping environment (e.g. new store introduction; Nagengast et al. 2014) as reasons for altered behavior. Accordingly, we predict that a substantial change to a shopping pattern, such as the decision to start using the online channel of a chain, can trigger a reallocation of grocery spending across chains.1 Building on multiple-store (offline) shopping and online shopping literature, we further postulate that this reallocation of spending, and thus the potential for expansion, depends on two mechanisms: (1) the costs and benefits triggered by the online environment and (2) consumers’ willingness to change the habitual purchase patterns they formed before going online. To the

1

In this research, we focus on the reevaluation of the spending allocations toward chains, that is, a reevaluation of where to buy products and brands; not a reevaluation of which products or brands to buy.

extent that the benefits and costs triggered by the online channel environment also affect the relative position of a chain, they may stimulate a shift in purchases across chains. Moreover, purchase behavior – especially for repeated and low involvement purchases such as groceries – tends to be characterized by inertia and a reluctance (or lack of motivation) to deviate from habitual purchase patterns. As we indicate in Fig. 1, we focus on the specific costs and benefits of online shopping that can change the value balance between chains, and the major factors that can reduce (or reinforce) the resistance to change purchase allocation patterns across chains. These factors constitute important drivers or inhibitors of SoW expansion effects triggered by the decision to shop online. Costs and Benefits of Online Shopping The online channel differs from traditional, offline grocery stores in terms of convenience (e.g. 24 h ordering capabilities), access (no transportation, home delivery) and transaction (ease or risk of acquiring specific products online) benefits and costs (Chintagunta, Chu, and Cebollada 2012; Jiang, Yang, and Jun 2013). The magnitude and importance of these benefits and costs depends on several general and chain-specific consumer characteristics. First, convenience advantages tend to be especially appreciated by time-constrained, goal-oriented consumers. We account for this consumer trait by including the frequency of shopping trips in the analysis as an indicator (Degeratu, Rangaswamy, and Wu 2000; Morganosky and Cude 2000).2 Previous research has 2 In our data, we only have information about the frequency (number) of shopping trips and the amount spent per shopping trip, but we unfortunately do not have information about the duration. While there is a negative correlation between the frequency and amount (suggesting that respondents that engage in less shopping trips tend to engage in major trips), we cannot rule out that the number of shopping trips is negatively correlated with the duration of the

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indicated that time-constrained, goal-oriented consumers with a utilitarian shopping attitude wish to spend as less time on shopping activities as possible (Babin, Darden, and Griffin 1994; Baltas, Argouslidis, and Skarmeas 2010; Dellaert et al. 1998; Umesh, Pettit, and Bozman 1989). They have a preference to consolidate grocery shopping efforts and for that reason may try to economize on (grocery) shopping time by engaging in less frequent (major) shopping trips. Driven by a similar motivation, these shoppers can also be more inclined to shift a larger share of the grocery purchases from a competitive offline patronized chain to the online-visited chain. We therefore expect that consumers with high time constraints are more likely to expand their SoW at the chain where they shop online. Second, if consumers use home delivery services, they no longer bear the physical costs of a trip to the store (Chintagunta, Chu, and Cebollada 2012). If they live farther from an offline store, consumers experience relatively higher transportation costs, so they may be more inclined to shift more of their SoW to the online store, for which no distance disadvantage remains (see also Pozzi 2013). The likelihood of SoW expansion therefore should be higher for shoppers who live farther from the offline store, because they benefit more from the access advantages of the online channel. Third, online shopping convenience advantages (lower transaction costs, as a result of the picking and home delivery service) are especially valuable for heavy and bulky products (Campo and Breugelmans 2015; Chintagunta, Chu, and Cebollada 2012). The expected benefits of a switch to the online channel of the focal chain depend on the extent to which consumers purchase in these categories from competitive chains. Consumers who buy more heavy or bulky categories may have more to gain (lower transaction costs) by shifting part of these product purchases from (offline patronized) competitive chains to the online-visited chain. We expect contrasting effects for sensory product categories, because the online channel imposes a disadvantage in this case. When customers cannot see or touch such products, their evaluation process is more difficult and uncertain, increasing the perceived risk of online purchasing (Campo and Breugelmans 2015; Laroche et al. 2005; Pauwels et al. 2011; Weathers, Sharma, and Wood 2007). That is, higher spending levels for heavy/bulky (sensory) product categories at competitive chains should have a positive (negative) effect on the degree of SoW expansion. Habitual Purchase Patterns Grocery shopping usually is characterized by habitual, routine decision-making processes (Hoyer 1984; Hoyer and Brown 1990; Hoyer, MacInnis, and Pieters 2013; Rhee and Bell 2002). The degree of expansion following the decision to go online therefore depends on the consumers’ willingness to change

trips (fewer shopping trips might be longer in duration). Nevertheless, taking into account traveling and transportation costs, we believe that low shopping frequency (with large shopping baskets) is a proxy for time-constrained, goaloriented consumers.

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purchasing habits and practices developed prior to going online. Consumers who have a strong preference for a chain and/or some of its ‘unique’ products may be less willing to shift purchases from this chain to the online-visited store. Resistance to change likely is stronger when shifting purchases would require different product choices, such as when consumers frequently buy private-label products or patronize hard discounters. Resistance to change also depends on consumers’ familiarity with the online-visited chain, which is captured by the level of integration of marketing mix across the chain’s online and offline channel. Consumers who prefer the private-label products offered by competitive chains experience higher switching costs if they must substitute these products by less preferred alternatives (Ailawadi, Pauwels, and Steenkamp 2008; Corstjens and Lal 2000). However, some recent studies indicate that private-label – based store loyalty might only hold at intermediate levels of private-label purchases. Heavy buyers instead tend to be less loyal to a specific store and prefer private-label products in general, irrespective of the chain that sells them (Ailawadi, Pauwels, and Steenkamp 2008; Koschate-Fischer, Cramer, and Hoyer 2014). Therefore, we predict a U-shaped relationship, such that private-label buyers are less inclined to reallocate purchases from competitive chains when they exhibit intermediate private-label purchase shares with competitors but more willing to reallocate purchases to the chain whose online channel they use when they purchase either very low or very high shares of private-label products from competitors. Consumers who buy a substantial share of their groceries at hard discounters also may be reluctant to shift purchases to the chain where they have started to shop online. Vroegrijk, Gijsbrechts, and Campo (2013) find that consumers include hard discounters in their shopping process to take advantage of category value complementarities, by buying some categories (e.g. quality-sensitive ones) in traditional supermarkets, and other categories (e.g. price-sensitive ones) at the hard discounter. Shifting away from a hard discounter to higher-priced substitutes available through the focal chain thus may entail significant switching costs, so consumers who shop at hard discounters may be less inclined to expand their SoW at the online-visited chain.3 Multiple-store shoppers that regularly visit different chains may have different motives to do so (Gijsbrechts, Campo, and Nisol 2008). On the one hand, they may visit multiple chains, because they want to take advantage of chain differences to match their category-specific preferences (Gijsbrechts, Campo, and Nisol 2008). In line with the reasoning for hard discounter shoppers, this increases switching costs, leading to the expectation that multiple-store shoppers are more reluctant to change their allocation patterns and experience expansion when starting to shop online. On the other hand, there are also reasons to expect lower switching costs for multiple-store shoppers, when they visit multiple stores because of fixed cost complementarity (e.g. driven by distance). In this case, these shoppers

3

Because of their low-cost, back-to-basics strategy, the major European hard discounter chains (Aldi and Lidl) do not offer online stores and restrict their operations to traditional offline stores.

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are familiar with different chains, are more used to compare these chains’ offer, and can be expected to be less changeaverse (Baltas, Argouslidis, and Skarmeas 2010; Gijsbrechts, Campo, and Nisol 2008), making them more willing to alter their allocation patterns. Hence, depending on the motivation for visiting different stores, we posit that the degree of multiplestore shopping negatively or positively influences the tendency to expand SoW. Finally, previous research and actual practice suggest that the online assortment and price level do often not match offline counterparts exactly; most multichannel grocery chains charge higher prices and carry smaller assortments online (Cheng 2010; Grewal et al. 2010). A stronger degree of assortment and price integration, which reflects the similarity of the offline and online marketing mix (price and assortment) for a particular chain, facilitates consumers to rely on their previous (offline) assortment and price knowledge. This simplifies the decision process and may have a positive effect on the attitude toward and preference for the chain (from offline to online and vice versa). The brand extension literature has demonstrated that a positive attitude toward a well-known original brand can be transferred to a new product introduced under the same brand name, especially when there is a perceived fit and experience advantage between the existing and newly introduced product (Bottomley and Holden 2001). For similar reasons, the likelihood for expansion when using a new, unfamiliar online channel may depend on the familiarity with offline stores of the same chain, and the similarity or ‘fit’ between a multichannel retailer’s online and offline stores. As a result, we expect that a stronger integration in marketing mix may reduce the consumers’ resistance to change their purchase allocation patterns across chains after starting to buy online, and therefore facilitate SoW expansion effects. Methodology Our dependent variable is a household h’s share of wallet for chain c in period t (SoWhct ) (Mägi 2003). For stability, we use a four-month (16-week) period. We use a logit transformation to restrict the SoW variable to the 0–1 range (see Cleeren, van Heerde, and Dekimpe 2013; Lesaffre, Rizopoulos, and Tsonaka 2007), which results in the following model specification4 :   SoWhct = αc1 + αc2 ∗ Trendt ln 1 − SoWhct + γhct ∗ Online channel usehct + βc ∗ Online channel use comphkt  + βj ∗ Xj,hct + εhct (1)

where α1c captures intrinsic preference for chain c, and α2c indicates chain-specific changes over time (Trendt ). Our focal variable, online channel use (Online channel usehct ), indicates whether the household is an online channel shopper of chain c in period t.5 This step dummy variable equals 1 if the household started to use the online channel of chain c in period t or before (several alternative, more complex, operationalizations of online channel use were tested but failed to improve model fit; see Robustness checks section). The online channel use at competitors (Online channel use comphkt ) is a similar step dummy, equal to 1 if household h started visiting the online channel of a competitive chain (k = / c) in period t or before. With Xj,hct , we account for marketing mix variables, such as the chain’s price, assortment, and (log-transformed) distance,6 relative to competitors. Using γ hct , we capture household-specific expansion effects of online channel use at chain c in period t, to examine if expansion occurs. If γ hct is significant and positive, online channel use leads to an expansion of SoW for chain c (which does not rule out cannibalization). Finally, to investigate how the moderating factors influence SoW expansion, we allow the parameter γ hct to vary with the costs and benefits of online shopping (online) and habitual purchase patterns (habit): γhct = γc0 + Onlinehct + Habithc ,

(2)

where Onlinehct = γ11 ∗ Time constrainth + γ12 ∗ ln(RelDistancehct + 1) + γ13 ∗ Heavy/Bulky sharehc + γ14 ∗ Sensory sharehc ,

(2a)

Habithc = γ21 ∗ PL sharehc + γ22 ∗ (PL sharehc )2 + γ23 ∗ HardDiscount shareh + γ24 ∗ Multiple store shopperh + γ25 ∗ Price integrationhc + γ26 ∗ Assortment integrationhc

(2b)

The factors that influence the costs and benefits of online shopping include the consumers’ time constraints (approximated by the number of shopping trips consumers make before they start shopping online; Time constrainth ), the distance to the nearest offline outlet of chain c relative to the competition (RelDistancehct ),7 and the share of heavy/bulky and sensory categories allocated to competitive chains (Heavy/Bulky sharehc

j

4 We correct for the occurrence of zero SoW observations with a Heckman (1979) selection model (see Appendix). To avoid a specification of the dependent variable in Eq. (1) that leads to division by 0 (i.e. if SoW equals 1), we subtracted a small value (Bass et al. 2007; Cleeren, van Heerde, and Dekimpe 2013), so the dependent variable of Eq. (1) equals ln((SoWhct − 0.00001)/(1 − (SoWhct − 0.00001))).

5 The period in which a household starts to shop online in a chain differs across households. For straightforward notation, we do not use the subscripts h and c for the period indication. 6 In line with Briesch, Chintagunta, and Fox (2009) and Vroegrijk, Gijsbrechts, and Campo (2013), we log-transform distance, to account for marginal decreasing effects. 7 For a few households, distance varies over time due to changes in the household’s residence (e.g. a move).

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and Sensory sharehc ). The factors related to the reluctance to change habitual purchase patterns include how much of a household’s budget is allocated to competing chains’ private-label products (PL sharehc and PL sharehc 2 ) and to hard discounters (HardDiscount shareh ). We also include the household’s multiple-store shopping tendency (Multiple store shopperh ) and the level of integration in the marketing mix instruments (Price integrationhc and Assortment integrationhc ). We meancentered all these factors in the model estimations, so we can interpret online channel use at their means. In estimating the model in Eqs. (1) and (2), we acknowledge two issues. First, a household’s decision to shop online with a particular chain is not exogenous; households tend to choose the online store hosted by the chain that they prefer offline (Melis et al. 2015). A potential self-selection thus arises, because the dependent variable influences this explanatory variable, which could lead to biased estimates. In line with Degeratu, Rangaswamy, and Wu (2000), we match a sample of multichannel households with a group of single-channel, offline households, using variables that significantly influence the decision to shop online with a specific chain (e.g. Chintagunta, Chu, and Cebollada 2012; Degeratu, Rangaswamy, and Wu 2000). In addition, we must address the occurrence of null SoW observations, caused by a decision not to visit the chain. Excluding these observations would lead to biased online channel use estimates (Hamilton and Nickerson 2003; Leenheer et al. 2007). Instead, we simultaneously model the household’s decision to shop at chain c in period t; conditional on this choice, we model household h’s SoW at chain c, using a Heckman (1979) selection model. We detail both model estimation correction procedures in Appendix. Finally, both SoW and chain choice are determined for each household across each multichannel chain. To control for potential within-household correlations, we employed cluster-robust standard errors, in which the error terms are allowed to correlate within households (see ter Braak, Dekimpe, and Geyskens 2013).

Data Description We use data from a representative U.K. household panel, run by Kantar Worldpanel and obtained from AiMark for July 2006–December 2008 (with July–December 2006 as initialization period, and January 2007–December 2008 as estimation period). The U.K. online grocery market is the most developed among all major European online grocery markets (Centre for Retail Research 2014) and can be considered as the first mover. A study of Kantar Worldpanel (2015) points to a major growth potential for online grocery shopping for countries with similar characteristics as the U.K. What is more, our focus is on the consumer’s decision to start shopping online rather than the chain’s decision to open up an online channel. While most chains nowadays have an online store, the percentage of online grocery shoppers is still very small in many countries, but is expected to rise substantially in the near future (e.g. IGD 2015; Steiman 2014). In this light, the results of this paper can show the path

7

for grocery chains in markets where online grocery retailing is still in its infancy but growing in importance. During our observation period, four chains offered an online channel along with their brick-and-mortar stores: Tesco, Asda, Sainsbury’s, and Waitrose. Each of these multichannel chains provides home delivery service (pick-up services were not yet available), introduced to the market long before the start of our observation period. Therefore, we did not need to control for the impact of the retailer’s decision to open an online channel and can focus instead on the impact of a household’s decision to shop online. Across the multichannel chains, we selected a balanced sample of (i) multichannel shoppers, defined as households that started to shop online in 2007–2008 and visited at least two chains after this decision (a necessary but not sufficient condition for expansion), and (ii) single-channel (offline) multiple-store shoppers that are comparable to the multichannel multiple-store shoppers, using the matching procedure described in Appendix. The sample includes 7907 households, split between 4069 multichannel and 3838 single-channel entries.8 To operationalize SoW and the explanatory variables that capture purchase behavior at competing chains (heavy/bulky, sensory, private-label and hard discounter share), we used data from the top ten retail chains, including those that did not offer an online channel during the observation period (e.g. Aldi, Morrisons, Spar). Table 2 contains the descriptive statistics related to households’ SoW (i.e. allocation of spending across all grocery chains) for each multichannel chain and also distinguishes between single- and multichannel shoppers. The SoW of the multichannel shoppers before they began shopping online (i.e. only offline purchases) and that of single-channel shoppers is notably similar. This also applies to other variables like purchase frequency, number of household members and social class (not in table), suggesting identical profiles for multichannel and single-channel shoppers, as was intended with the matching procedure. For multichannel shoppers, the change in SoW due to the initiation of online shopping is positive on average for each multichannel chain (i.e. expansion in general), but we also note substantial variation in the SoW ranges across consumers, as illustrated in Table 2.9 Because SoW is a composite measure – combining both shopping trip frequency and shopping trip spending – we separately report changes in spending and in shopping frequency in Table 2. Both measures increase after the initiation of online shopping, across all chains. Again variation exists across chains.

8 To rule out any left-censoring bias in the analyses, we received data from 2004 onwards (2.5 years before the start of our initialization period). We only retained multichannel households in our model estimation that did not make any online shopping trip in the period before our estimation period (i.e. January 2007–December 2008). The percent of multichannel shoppers in our sample, that is, those that start to use the online grocery channel for the first time (10.84 percent; 4069 of 37,535 in total) is in the ballpark of figures that are reported on the online grocery shopping growth increases in that period (Nielsen 2009). 9 T-test results show that in the 16-week period before starting to shop online, there is no difference in SoW in the first month and last month that a household shops in the period. In other words, there is no (increasing) trend in SoW before shopping online.

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Table 2 Household descriptive statistics. Asda Mean

Sainsbury’s SD

Mean

Multichannel households (N = 4069) SoW before onlinea .21 .29 SoWb,c .04 .21 [−.68; .94] Range b,c 2.85 9.41 Shopping tripsb,c 113.52 241.37 Spendingb,c Single-channel (offline) households (N = 3838) .23 SoWa a b c

Tesco SD

Mean

.17 .27 .02 .20 [−.76; 1.00] 2.98 8.41 129.11 284.53

.32

.16

.27

Waitrose SD

.37 .04

Mean

SD

.35 .21

.02 .03

2.98 122.95

8.70 255.55

1.90 41.99

5.12 135.30

.38

.36

.02

.09

[−.85; .97]

.09 .10 [−.52; .65]

Variation is a result of differences over households and periods. Variation is a result of differences over households. Online + offline purchases.

As we show in Table 3, in line with Leenheer et al. (2007) and Mägi (2003), SoW is measured as a household’s proportion of overall grocery expenditures at chain c relative to the total expenditures over all chains in that period.10 As we noted previously, the key explanatory variable, household h’s online channel use of chain c, is measured as a step dummy that equals 1 as soon as household h starts to shop online with chain c. The moderating variables (time constraint, heavy/bulky share, sensory share, multiple-store shopper, private-label share, and hard discounter share) are operationalized using the period before the household starts to shop online.11 We also operationalize these variables (except for time constraint and multiple-store shopper) with the shares defined at competitive chains (i.e. all top ten chains except the focal chain). The multiple-store shopper variable reflects a relative entropy degree, measured as the spread in spending across all chains, before the household starts to shop online (Bustos-Reyes and González-Benito 2008). The classification of heavy/bulky (e.g. mineral water, toilet tissue) and sensory (e.g. fruit, vegetables) products relied on a review by a panel of four experts. We also checked this classification for compatibility with previous research (e.g. Chintagunta, Chu, and Cebollada 2012; Degeratu, Rangaswamy, and Wu 2000). To define the price and assortment integration variables and the chain-level price and assortment variables, we selected the top 67 categories, covering two-thirds of all purchases in the U.K. grocery retail market. In line with Briesch, Chintagunta, and Fox (2009), we made the integration and marketing mix variables household-specific, by using a weighted sum of the category-level variables over the selected categories, using longterm household category purchase shares as weights (Vroegrijk, Gijsbrechts, and Campo 2013; Zhang and Breugelmans 2012). The price and assortment integration measures are weighted averages over the entire observation period for a household

10 Online prices tend to be higher than offline prices (Wolk and Ebling 2010), so we use offline prices to compute online sales. The expansion effect thus is driven purely by changes in purchase behavior, rather than different price settings across the online and offline channel. 11 Every multichannel household has a different starting point for its online shopping, so this period differs across households, but includes a minimum of one four-month period (the initialization).

(maximum of eight four-month periods). For both integration measures, we use a ratio to compare the online and offline stores of the same chain (Sullivan 1998). The chain-specific, timevarying price and assortment variables are relative measures, capturing the chain’s store attribute level, compared against all top ten retail chains (Fox, Montgomery, and Lodish 2004). We use a similar relative measure for the distance variable in Eq. (1), operationalized as the Euclidean distance to the closest store outlet (Vroegrijk, Gijsbrechts, and Campo 2013). The descriptive statistics in Table 4 refer to the marketing mix variables and moderators. Here, we report the price, assortment, and distance values before making them relative to other chains and before making them household-specific. Tesco, the market leader (both offline and online) and known as the industry changer, carries the largest average offline assortment (42.4 subbrands per category), charges an average offline category price (10 pounds per equivalent unit), and is the store closest to most households (3.29 km). Waitrose, the smallest supermarket chain we focus on, offers the smallest offline assortment (20.96 subbrands per category), the highest offline price (13 pounds per equivalent unit), and the greatest distance from consumers on average (19.22 km). The other two, Asda and Sainsbury’s, hold an intermediate position in terms of assortment and distance, with Asda as EDLP chain charging the lowest offline price (9 pounds per equivalent unit). We further observe in Table 4 that on average, all the chains charge higher prices online than offline (ratio > 1) and maintain a smaller online than offline assortment (ratio < 1), in line with prior literature (e.g. Cheng 2010; Wolk and Ebling 2010). We thus expect negative and positive effects of the price and assortment integration variables respectively. The amount of the grocery budget allocated to a chain’s competitors before shopping online includes, on average, four percent for heavy and bulky products, 29 percent for sensory products, four percent to hard discount chains, and approximately 55 percent to private-label items.12 An average multichannel, multiple-store

12

In line with our findings, in 2014, total private-label market share for fast moving consumer goods in the U.K. was 51.5 percent, and unit private-label market share was 59.2 percent (IRI 2014).

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Table 3 Variable operationalizations. Variable

Description

SoWhct

Sum of all grocery purchases for household h per period t (four months) and multichannel chain c, divided by the sum of all grocery purchases for household h per period t across all multichannel and single-channel top ten chains (online prices replaced with offline prices).

Purchase frequencyh

wh,cat

Price integrationhc

Assortment integrationhc

Heavy/Bulky sharehc

Sensory sharehc

Multiple store shopperh

Average four-month (offline) shopping trip frequency for household h across all multichannel and single-channel chains in the period before household h starts to shop online for the first time (th ), which includes at minimum the initialization period (July 2006–December 2006) plus any period in the estimation period where the household did not yet buy online. Category weights of household h for each of the 67 categories (cat), defined as the sum of category purchases (offline and online) for household h in the estimation period, divided by the sum of all grocery purchases (offline and online) for household h in the estimation period, which includes eight four-month periods.

Formula hct Sales 10 c=1

1 th −1

t=1

Sum over 67 categories of the ratio of the number of online brands (national brands + private labels) over the number of offline brands (national brands + private labels) for each category (cat) of the multichannel chain c, weighted with household category shares (wh,cat ) and averaged over the eight estimation periods.

1 8

The degree of dispersion in offline grocery purchases of household h across all multichannel and single-channel retailers in the period before household h starts to shop online.

number shopping tripsht

8 Salesht,cat t=18

1 8

Sum of offline purchases for household h on sensory categories at the competing multichannel and single-channel chains (i.e. all top ten chains except c), divided by the sum of all offline grocery purchases at the competing multichannel and single-channel chains in the period before household h starts to shop online for the first time.

th −1  t=1

Sum over 67 categories of the ratio of the average online unit price over the average offline unit price for a set of products (available in both channels) for each category (cat) of the multichannel chain c, weighted with household category shares (wh,cat ) and averaged over eight estimation periods.

Sum of offline purchases for household h in heavy/bulky categories at competing multichannel and single-channel chains (all top ten chains except c), divided by the sum of all offline grocery purchases at competing multichannel and single-channel chains in the period before household h starts to shop online for the first time.

Saleshct

Salesht

67 8  

wh,cat ∗

Online pricect,cat Offline pricect,cat

t=1 cat=1

8 67  

wh,cat ∗

Online assortmentct,cat Offline assortmentct,cat

t=1 cat=1

th−1 9 Heavy/Bulky saleshtk t=1 k=1 k= / c th−1 9 t=1

k=1

Saleshtk

th−1 9 Sensory saleshtk t=1 k= / c th−1 k=1 9 t=1

k=1

th−1 c=10 t=1

c=1

Saleshtk

SoWhc ∗ln(SoWhc )

ln(10)

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Table 3 (Continued) Variable

Description

Distancehct

Euclidian distance between household h’s residence and the nearest store outlet of chain c in kilometers in period t. Latitude (Lat) and Longitude (Long) refer to the geographic coordinates of household h’s residence (Lat1 and Long1) and the nearest store outlet of a chain c (Lat2 and Long2). Radius Earth is a unit of distance expressed in kilometers (∼6378).

PL sharehc

HardDiscount shareh

RelPricehct

RelAssortmenthct

RelDistancehct

Sum of offline purchases by household h of private-label brands at competing multichannel and single-channel chains (all top ten chains except c), divided by the sum of all offline grocery purchases at the competing multichannel and single-channel chains in the period before household h starts to shop online. Sum of offline purchases by household h at hard discounters (Aldi and Lidl), divided by sum of all offline grocery purchases in the period before household h starts to shop online. Offline unit price summed over 67 categories, across all units in the category (cat) for each period t and each multichannel chain c, weighted with household category shares (wh,cat ) and relative to the average of all multichannel and single-channel chains. Sum over 67 categories of the number of offline brands (national brands + private labels) in each category (cat) for each period t and for each multichannel chain c, weighted with household shares (wh,cat ) and relative to the average of all the multichannel and single-channel chains. The distance from household h’s residence to the nearest store of chain c for each period t (because some households move during the study period), relative to the average of all multichannel and single-channel chains.

household shops approximately 26 times in a four-month period, or once or twice per week (M = 26.18, SD = 19.09). Finally, the average multiple-store shopper value (.32) is closer to 0 than 1, where 0 indicates total loyalty to a single chain. Estimation Results The estimation results in Table 5 include insignificant chain intercepts that show, after controlling for the explanatory variables, no difference in intrinsic attractiveness across chains. The trend variable indicates that two out of four multichannel chains lose SoW over time, with the loss being most profound for the largest chain Tesco (αTESCO2 =− .06, p < .01), followed by the smallest chain Waitrose (αWAIT2 =− .04, p < .05) and insignificant for Asda and Sainsbury’s (αASDA2 =− .01, p > .01; αSAINS2 = .004, p > .01). The relative price charged by a chain has no significant effect, whereas the chain’s assortment, relative to

Formula



[(−Lat1ht ) − (−Lat2hct )]2 + [(Long1ht − Long2hct )∗ SIN(.5 ∗ ((−Lat1ht ) + (−Lat2hct )))]2 ∗



π 180



∗ Radius Earth

th−1 9 PL saleshtk k= / c t=1th−1 k=19 t=1

k=1

Saleshtk

th−1 9 Hard t=1 tk=1 9 h−1 t=1

67 

k=1

Saleshtk



k= / c

 10Pricecat,ct

wh,cat ∗

c=1

cat=1

67 

Discount saleshtk

Pricecat,ct /10

 wh,cat ∗

 10Assortcat,ct c=1

cat=1



Assortcat,ct /10

 10Distancect c=1

Distancect /10

its competitors, has a positive, significant effect (βPRICE =−1.19 p > .10; βASSORT = 1.84, p < .01). The farther away a household lives from the chain’s nearest offline store, relative to the other chains, the lower its SoW for that chain (βDIST =−1.58, p < .01). For online channel use at competitors, we find support for our expectation that it lowers SoW at the focal chain (βASDA =−1.17, p < .01; βSAINS =− .91, p < .01; βTESCO =−1.61, p < .01; βWAIT =− .54, p < .01). Turning to our key variable of interest, we find a significant, positive effect of online channel use on a household’s SoW for all multichannel chains. Because our explanatory variables are mean-centered, this finding implies that, on average, consumers expand their SoW at the chain where they start to shop online. While the differences are small, Sainsbury’s and Asda, two chains in an ‘intermediate’ position, gain the most from consumers who start shopping for groceries in their online stores (γ SAINS0 = 2.05, p < .01; γ ASDA0 = 1.76, p < .01), whereas

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Table 4 Variable descriptive statistics. Asda

Pricea,c Assortmenta,c Distanceb Price integrationa,c,d Assortment integrationa,c,d Heavy/Bulky shareb,d Sensory shareb,d Private-label shareb,d

Sainsbury’s

Tesco

Waitrose

Mean

SD

Mean

SD

Mean

SD

Mean

SD

.09 31.12 7.67 1.01 .61 .04 .29 .54

.27 24.76 10.21 .16 .14 .03 .11 .16

.10 31.83 6.04 1.03 .63 .04 .28 .55

.30 24.81 11.85 .11 .13 .03 .11 .15

.10 42.40 3.29 1.01 .69 .04 .29 .54

.28 32.75 5.26 .08 .11 .04 .13 .18

.13 20.96 19.22 1.04 .66 .04 .29 .56

.36 17.47 27.85 .20 .19 .03 .09 .13

Time constraintb,d Hard discount shareb,d Multiple-store shopperb,d

Mean 26.18 .04 .32

SD 19.09 .11 .18

a

Variation is a result of differences over categories and periods. Variation is a result of differences over households. c For variables for which we used household-specific weights to correct for differences in category importance between households, we report the original (non-weighted) values for each multichannel retailer. d These variables serve as moderators to explain SoW expansion, so these descriptive statistics are calculated for multichannel shoppers only. b

Table 5 SoW regression model. Intercept and control variables Intercept Asda (αASDA1 ) Sainsbury’s (αSAINS1 ) Tesco (αTESCO1 ) Waitrose (αWAIT1 ) Trend Asda (αASDA2 ) Sainsbury’s (αSAINS2 ) Tesco (αTESCO2 ) Waitrose (αWAIT2 ) Online channel use competitors Asda (βASDA ) Sainsbury’s (βSAINS ) Tesco (βTESCO ) Waitrose (βWAIT )

Online channel use −1.02 (1.08) −1.46 (1.26) −.96 (1.22) −.80 (1.26) −.01 (.01) .005 (.01) −.06** (.02) −.04* (.02) −1.17** (.06) −.91** (.06) −1.61** (.07) −.54** (.10)

Marketing mix RelPrice (βPRICE ) RelAssortment (βASSORT ) RelDistance (βDIST )

Online channel use Asda (γ ASDA0 ) Sainsbury’s (γ SAINS0 ) Tesco (γ TESCO0 ) Waitrose (γ WAIT0 ) Online shopping environment Time constraint (γ 11 ) Distance (γ 12 ) Heavy/Bulky share (γ 13 ) Sensory share (γ 14 ) Habitual purchase patterns PL share (γ 21 ) PL share2 (γ 22 ) HardDiscount share (γ 23 ) Multiple store shopper (γ 24 ) Price integration (γ 25 )

−1.19 (.77) 1.84** (.46) −1.58** (.08)

Assortment integration (γ 26 ) Inverse Mills Ratio Adjusted R2

1.76** (.14) 2.05** (.13) 1.17** (.12) 1.31** (.17) −.01** (.00) .51** (.16) −1.28 (.76) .49 (.36) −3.55** (.38) 7.34** (.78) −1.88** (.28) −5.32** (.25) −7.49* (3.57) 4.47** (1.91) 1.12** (.01) 18.78%

Note: Standard errors are in parentheses. * p < .05„ all two-sided. ** p < .01, all two-sided.

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Waitrose and Tesco (resp. the smallest and largest chain) gain the least (γ WAIT0 = 1.31, p < .01; γ TESCO0 = 1.17, p < .01) (all parameters significantly differ at p < .05). But what factors drive these expansion effects? In line with our conceptual framework, we discuss the potential drivers and inhibitors using the costs and benefits triggered by the online environment, and the consumers’ willingness/reluctance to change habitual purchase patterns as underlying mechanisms. Regarding the costs and benefits of the online shopping environment, the findings provide support for the assumption that time-constrained consumers (lower purchase frequency as proxy variable) benefit more from allocating a higher SoW to the chain in which they shop online (γ 11 =− .01, p < .01), enabling them to economize on grocery shopping time. In line with expectations, distance also has a positive and significant moderating effect, and consumers living farther away from the offline store are more inclined to reap the accessibility advantages of the online channel by shopping in chains whose physical stores are more difficult to reach (γ 12 = .51, p < .01). In contrast, expectations on the effect of transaction (dis)advantages linked to online shopping, are not supported by the empirical results. The share of heavy/bulky or sensory categories purchased at competitive chains has no significant impact on SoW expansion of the online-visited chain (γ 13 =−1.28, p > .05; γ 14 = .49, p > .05). In terms of habitual purchase patterns, we find that privatelabel share exhibits the predicted U-shaped relationship with SoW expansion. Consumers who previously allocated an intermediate share of their budget to private-label products of competitive chains, are less inclined to expand their SoW at the (other) online-visited chain (γ 21 =−3.55, p < .01). Private-label

prone consumers (with a high share of private-label purchases), however, more easily shift part of their purchases to the online chain (which is revealed by the positive quadratic term; γ 22 = 7.34, p < .01). Hard discounter proneness also hinders the expansion of SoW (γ 23 =−1.88, p < .01), in line with our predictions. Consumers expand their SoW less when they used to allocate their grocery budget over multiple grocery chains in their habitual (pre-online) purchase pattern (γ 24 =−5.32, p < .01). Consumers who visit multiple offline stores thus appear to take advantage of chain differences to fulfill their categoryspecific store preferences and are therefore hindered to expand SoW. This suggests that in our research, consumers may have been more motivated by category preference complementarity rather than fixed cost complementarity, when visiting multiple stores (Gijsbrechts, Campo, and Nisol 2008). Finally, a stronger integration in terms of both price and assortment leads to more expansion of SoW at the focal chain (γ 25 =−7.49, p < .05; γ 26 = 4.47, p < .05). Robustness Checks Recent research in the consumer durables industries shows that multichannel consumers eventually revert to their pre-online shopping spending levels with a chain (Bilgicer et al. 2015). Therefore, we tested alternative operationalizations of the online channel use variable to capture these dynamic effects, including an online experience variable based on recency and frequency; an experience variable based on recency, frequency, and monetary value; a declining experience variable; a pulse dummy; and a count dummy (see Table 6 for the definition of these variables

Table 6 Alternative operationalizations of online channel usage.a Variables

Operationalizations

Bayesian information criterion

Step dummy

A step dummy for each multichannel chain c: equal to 1 during and following the period t in which household h starts to shop online at that chain c, 0 otherwise (‘current’ operationalization; results as reported in Table 5). A dummy for each multichannel chain c that equals 1 during period t each time a household h shops online at that chain, 0 otherwise. The cumulative number of times household h shopped online at multichannel chain c within each four-month period. Weighted sum of previous online shopping trips in the estimation period across a four-month period t (16 weekly periods) for each multichannel chain c, with a decay parameter δ to capture fading effects. Starting value equals number of online in first online shopping  shopping period thc of household h at that chain c. q=t trips δt−q ∗ #Tripshct q=thc Weighted sum of previous total online spending (monetary value) in the estimation period across a four-month period t (16 weekly periods) for each multichannel chain c, with a decay parameter δ to capture fading effects. Starting value equals expenditure infirst online shopping period thc at chain c for household h. the online q=t t−q ∗ #Exp δ hct q=thc Four-monthly (16 weekly periods) updates of experience, starting with the first online purchase at the store (first online shopping period = thc ), which can only decline thereafter with a decay parameter δt−thc * step dummyhct Step dummy + dummy for each multichannel chain that equals 1 during the period t in which household h shops online for the first time at that chain, 0 otherwise.

678,556.30

Pulse dummy Count variable Recency and frequencya

Recency, frequency and monetarya

Declining OCUa

Step dummy + first online purchase dummy

683,268.70 685,029.40 684,760.30

685,069.40

680,786.30

678,592.40

a These operationalizations involve a decay parameter. We used a decay parameter with value .7. None of the ‘intermediate’ decay measures resulted in a fit improvement.

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and an overview of resulting Bayesian information criteria [BIC] measures). The alternative models all provided worse fit than our proposed model, so a step dummy representing online channel use reflected the data best. These robustness checks also provided evidence that, once consumers start to shop online, they expand SoW for that chain, and this effect persists in the medium term in our data set. To disentangle the temporary, immediate effect of the first purchase from the permanent, enduring effects of a consumer’s decision to shop online at a specific chain, we also extended Eq. (1) with a first online purchase dummy which equals one only when the household makes its first online purchase in a specific chain in period t, 0 otherwise. The online channel use effect was not temporary in this analysis but instead remained in subsequent periods, after the first online purchase. However, the model fit decreased (see last row, Table 6). Because we obtained the same substantive insights, we reported our results without the first online purchase dummy. To test for state dependence and carryover effects, we included a lagged SoW and lagged online channel use variable in the model. The model with lagged online channel use resulted in worse fit. The model with the lagged SoW resulted in a better fit, but it masked some of the results. Including a lagged version of the dependent variable biases marketing mix variable parameters, due to correlation issues (Bell and Lattin 1998; Fox, Montgomery, and Lodish 2004; Vroegrijk, Gijsbrechts, and Campo 2013). Our goal is to explain the drivers and inhibitors of SoW expansion, so we chose not to include the lagged SoW. Conclusion In this research, we focus on changes in purchase allocation patterns across channels and chains following the decision to shop online in the unique but important grocery sector. It is clear that findings from other sectors cannot be transferred to the FMCG sector, where channels are used interchangeably rather than sequentially (search online, purchase offline) or in competition (buying online or offline) as is more likely for other, durable, sectors. In view of the rapid increase in multichannel grocery retailing and the lack of insights into multichannel shopping behavior that can support strategic decisions in this domain, we have examined two important research questions. First, can a consumer’s decision to start buying groceries online at a particular chain increase the SoW this consumer allocates to the chain (expansion effect)? Second, what factors drive or inhibit this expansion effect? Investigating whether, to what extent, and in which circumstances, a consumer’s decision to shop online will expand its share of spending at the multichannel grocery chain is an important academic contribution. To answer these questions, we examined a representative U.K. household panel data set of a large, balanced sample of multi- and single-channel grocery shoppers. We estimated a Heckman selection model with SoW as the main dependent variable and obtained several interesting findings. From these results, we also derived managerial implications and some directions for further research.

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Does Expansion Occur? Many grocery shoppers develop habitual purchase behaviors and use choice heuristics or decision cues to simplify their decision processes (Hoyer 1984; Hoyer and Brown 1990; Hoyer, MacInnis, and Pieters 2013). Substantial changes in the shopping environment (such as a new store introduction) can cause a break point in this routine shopping behavior (Rhee and Bell 2002). This is confirmed by our results, indicating that the decision to start shopping online can prompt a substantive reevaluation of grocery purchase allocations across chains. Prior literature has primarily focused on the non-grocery retailing context, mainly used aggregate outcome measures, and has reported mixed results of online channel introductions (retailer perspective) or online channel use (consumer perspective) (positive effects: Bilgicer et al. 2015; Deleersnyder et al. 2002; Geyskens, Gielens, and Dekimpe 2002; Kumar and Venkatesan 2005; Kushwaha and Shankar 2013; Venkatesan, Kumar, and Ravishanker 2007 and negative effects: Ansari, Mela, and Neslin 2008; van Baal and Dach 2005; Wallace, Giese, and Johnson 2004). As grocery shopping differs substantially from other purchase contexts (Hoyer, MacInnis, and Pieters 2013), with most consumers using multiple channels interchangeably (visiting both the online and offline store to buy groceries; Melis et al. 2015) and visiting multiple chains (Gijsbrechts, Campo, and Nisol 2008), findings of previous studies are not directly transferrable to our multichannel multiple chain grocery shopping context. This is why our research substantially adds to existing research. Our results affirm that for each of the grocery chains we examine, multichannel grocery shopping expands consumers’ SoW at the chain where they start buying online. Consumers tend to select the online store maintained by the chain that they prefer to visit offline (Melis et al. 2015) and stay with the same chain online (i.e. positive and significant loyalty effect in the choice part of the model; see Appendix, Table A2), so this finding suggests some interesting opportunities for grocery retailers to improve their market position. The robustness checks illustrate that this SoW expansion effect does not immediately fade but rather persists for the medium term. This finding contradicts (non-grocery) studies that indicate a negative or fading effect of online channel introductions and usage on sales or loyalty for consumer durables retailers (Ansari, Mela, and Neslin 2008; Bilgicer et al. 2015). Instead, it conforms with the habitual nature of grocery shopping. The decision to start shopping online indicates a reevaluation (‘shake up’) of the purchase allocation patterns, to the advantage of the online-visited chain and at the cost of purchases normally made at competitive chains. Instead of reverting to pre-online shopping patterns, purchase behavior quickly becomes habitual and resistant to change again, after the first few purchases. This finding is in line with research of Shi and Zhang (2014), who show that consumers over time gravitate to online habitual decision behavior, for instance spurred by personalized shopping lists, or previous order lists made available in the online shopping environment.

Please cite this article in press as: Melis, Kristina, et al, A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet? Journal of Retailing (xxx, 2016), http://dx.doi.org/10.1016/j.jretai.2016.05.001

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Drivers and Inhibitors of Expansion The extent of expansion varies across consumers and chains; we explain this observation with a comprehensive analysis of the potential drivers and inhibitors of expansion effects, including overall and chain-specific consumer characteristics, as well as consumer characteristics that explicitly take the competitive setting (multiple grocery chains) into account. In this respect, we are the first to focus on relative measures, including relative explanatory variables next to a relative dependent variable (SoW) that captures shifts in sales and competitive effects that are canceled out when other, more aggregated outcome measures are used. While prior multiple-store (offline) and online shopping literature was helpful to identify relevant factors affecting multichannel purchase behavior, more research was needed to obtain a better insight into how these factors play a moderating role on changes in purchase allocation decisions following the decision to start buying online. First, regarding the costs and benefits that follow the decision to use an online channel, we find that consumers take advantage of online shopping convenience and access benefits, and for that reason shift more of their grocery purchases to the chain where they start buying online. More specifically, expansion effects appear to be stronger for consumers who face stronger time constraints (reflected by a lower shopping frequency) and who live farther away from the offline store. However, consumers seem to be less sensitive to transaction (dis)advantages. We did not find any effects of the shares of heavy/bulky and sensory product purchases from competitive stores on SoW expansion at the online-visited chain. Second, expansion effects are strongly affected by consumers’ willingness or reluctance to change their habitual purchase patterns formed before they went online. We find strong support for our prediction that required changes to product choices, as emerge when favored products are not available in the online store, can keep consumers from shifting more of their purchases to the online-visited chain. This finding holds especially among consumers who buy more private-label products at competitive chains or buy a larger share of products from hard discounters. In line with previous private-label research, we find that the relationship between competitive private-label share and the magnitude of the expansion effect is U-shaped (Ailawadi, Pauwels, and Steenkamp 2008). Up to a certain point, privatelabel share reflects chain loyalty (resulting in a reluctance to shift a larger share to the online store’s chain), but beyond this point, it suggests private-label proneness, or loyalty to private labels in general rather than the specific private label of a retail chain. We also find a negative impact of multiple-store shopping on SoW expansion. Instead of finding that consumers are more willing to change when they regularly visit and become familiar with various chains (which would suggest that they patronage multiple chains because of fixed cost complementarity), we uncovered category preference complementarity in the decision to visit different chains. That is, consumers who regularly visit multiple chains offline are less inclined to shift some of their purchases from competitive chains to the online store’s chain

(Gijsbrechts, Campo, and Nisol 2008). When consumers adopt a multiple-store shopping strategy before starting to shop online, they seek to maximize their overall shopping utility by buying each grocery category from the chain that provides the best value (benefit/cost ratio). Any change in this allocation pattern might be associated with increased shopping costs (i.e. less optimal value distribution). Finally, a stronger integration of price and assortment across multiple channels appears to help consumers in allocating more of their grocery purchases to the online-visited chain. That is, when consumers can rely on their offline assortment and price knowledge developed prior to going online, they are less reluctant to change existing purchasing practices, which helps SoW expansion. Managerial Implications Our research provides valuable insights into the possibility, nature, and source of expanded consumer spending with a chain. Many companies already offer an online channel and contemplate various ways to improve their overall competitive market position. Our study shows that consumers’ decision to start using an online channel creates interesting opportunities for SoW expansion; this effect persists over the medium term. Therefore, it is critical for multichannel retailers to attract consumers to their online channel and stimulate them to shift some of the purchases they currently make from competitors to the chain immediately, because grocery purchase behavior quickly becomes habitual and resistant to change again, after just a few purchases. Online grocery retailers should offer features such as saved personalized online shopping lists or previous order lists to nurture such (online) habitual purchase behavior. To gain more purchase share from competitors, our results also suggest some effective marketing mix and communication strategies for retail chains, as well as targeting strategies that they might use to attract a specific group of online channel users. Multichannel retailers have the ability to determine the marketing mix integration across channels. As consumers can much easier adapt ‘old’ purchasing habits when they can rely on previous marketing mix knowledge, grocery retailers can benefit from a more integrated online–offline price and assortment strategy. Managers should weigh the costs of extra integration against the benefits of extra revenues due to SoW expansion. Retailers also can monitor their customers’ shopping basket composition in their first few online purchases. Using data mining, they can assess which product categories or types the household is not buying. The online environment is an ideal place to customize promotions and communication, to draw consumers’ attention to particular brands (e.g. private labels) or categories that they currently do not buy at their chain. Obviously, the use of (sensitive) purchase behavior data in data mining applications should be in line with privacy regulations. Different sources, however, state that consumers are willing to trade privacy for personalization, with for example, 61 percent of surveyed consumers in the U.S. and U.K. willing to share data with companies, to receive personalized offers (promotions, coupons, product suggestions) (mashable.com 2012).

Please cite this article in press as: Melis, Kristina, et al, A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet? Journal of Retailing (xxx, 2016), http://dx.doi.org/10.1016/j.jretai.2016.05.001

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Finally, with regard to targeting strategies, retailers should emphasize the benefits of online shopping, such as its convenience and lack of distance restrictions. In so doing, they bring the “store” closer to the consumer, without having to open new outlets. Consumers that make few shopping trips and that are therefore likely to be time constrained, goal oriented, and that have limited access to the chain’s offline stores experience the largest SoW expansion, so retailers should review their market environments to identify these consumers, as well as ways to reach them. For example, they could target business centers located at some distance or remote neighborhoods with timeconstrained residents to attract them to their online channel. Limitations and Research Directions This study provides interesting new insights into if and how a consumer’s decision to start shopping online with a particular chain affects the SoW allocated to that chain. But it also has some limitations that point to areas for additional research. First, we analyzed overall SoW, though Campo and Breugelmans (2015) and Chintagunta, Chu, and Cebollada (2012) argue that online channel (dis)advantages influence which categories people tend to buy online. We did not find any significant effect of the share of heavy/bulky or sensory product categories, but a more refined category-level analysis, such as one that uses category-specific shares as dependent variables, could provide more insight into these effects. It also would be interesting to extend the range of category characteristics (e.g. storability, impulse purchase) to determine whether they can induce shifts in purchase allocation patterns, driven by the decision to start buying online. Second, we had no information about the promotions run by the retailers in their online or offline channel or about other external events (e.g. service failures, new website designs, and changes to the service delivery policy) that could influence allocations of spending across chains. Further research should incorporate these aspects in the models. Third, offering an online channel comes with extra costs, and retailers aim to cover these costs by gaining additional revenue from existing or new clients. When all retail chains offer online channels, competitive pressure might require each retailer to offer its own online channel to retain existing customers. However, we lack any specific cost information, so investigating the precise margins and profit levels would be an interesting next step. Fourth, we focus on net changes in total SoW for the primary chain in the short till medium term. By using a single SoW model, we ensure only one dependent variable, which takes values between 0 and 1. However, further research should attempt to disentangle the SoW expansion effects in more detail. Do our results reflect more online shopping at the focal retailer or more shopping in both channels? Do consumers increase their spending or shopping frequency with a particular retailer? To what extent does expansion go hand-in-hand with cannibalization? What happens when consumers engage in online chain switching? And, do results remain stable (SoW expansion), even in the long run (>2 years)? We further made an important assumption in our analyses, namely, that expansion results mainly from

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consumers shifting (part of) their expenditures to the chain where they have started to shop online, at the cost of purchases they would normally make with competitive chains. Accordingly, we assumed that consumers would not change their total grocery spending as a result of their decision to go online. Explicitly controlling for this effect in model estimations would offer a worthwhile extension of our research. Acknowledgments The authors thank AiMark for data access and greatly appreciate the valuable suggestions by the editor, the anonymous JR reviewers and the participants at the 2014 Emac Conference in Valencia. Special thanks go to Prof. Dr. Marnik G. Dekimpe and Prof. Dr. Michaela Draganska for pointing to relevant issues leading to improvements in this article. The authors also acknowledge financial support from the National Bank of Belgium under grant number NBB/14/004. Appendix. Model Estimation Correction In estimating our model, we account for two additional issues: (1) the endogenous nature of a household’s decision to shop online and (2) the occurrence of zeros in SoW, because households might decide not to visit chain c in a certain period t. Matching Procedure To correct for the endogeneity of online shopping decisions, we match multichannel households with single-channel households, according to their household characteristics (e.g. Degeratu, Rangaswamy, and Wu 2000), then estimate our model on both groups of households. That is, we include a sample of households with a similar profile and probability to start shopping online at a particular chain but that has not (yet) started to shop online or made the shift to multichannel shopping. To define the household characteristics that influence a household’s h decision to shop online at a specific chain c, we perform the following logistic regression for each multichannel chain separately: Online shoppingh = π1 + π2 ∗ Time constraint h + π3 ∗ Time contraint 2h + π4 ∗ Overall Heavy/bulky shareh + π5 ∗ Overall Sensory shareh + π6 ∗ HH membersh + π7 ∗ Social classh + π8 ∗ SoW Asdah + π8 ∗ SoW Asdah + π9 ∗ SoW Sainsh + π10 ∗ SoW Tescoh + π11 ∗ SoW Waith + εh

(A.1)

We relied on prior online shopping literature and data availability criteria to select the influence variables. Following Degeratu, Rangaswamy, and Wu (2000) and Morganosky and Cude (2000), we predict that the decision to engage in online

Please cite this article in press as: Melis, Kristina, et al, A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet? Journal of Retailing (xxx, 2016), http://dx.doi.org/10.1016/j.jretai.2016.05.001

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Table A1 Logistic regressions. Variables

Asda

Sainsbury’s

Tesco

Waitrose

Intercept (π1 )

−4.97**

−4.93**

−3.86**

(.17) .05** (.004) −.0004** (.0001) .27 (.49) .12 (.21) .16** (.02) .15 (.33) .23** (.09) .20** (.08) .04 (.10) −.09 (.11) 1.84** (.09) .38** (.15) .08 (.12) −1.28* (.65)

(.21) .04** (.005) −.0003** (.0001) −1.10 (.71) −.05 (.26) .06* (.03) .13 (.33) .48** (.11) .19 (.10) .28* (.13) −.24 (.15) −.16 (.17) 2.65** (.12) .09 (.14) .70 (.39)

(.12) .04** (.003) −.0003** (.00004) −.28 (.38) −.07 (.15) .14** (.01) .36 (.19) .39** (.06) .19** (.06) −.09 (.07) −.25** (.08) .03 (.09) .48** (.10) 1.55** (.07) .05 (.29)

−5.03** (.22) .04** (.005) −.0003** (.0001) −1.80* (.80) .22 (.28) .05 (.03) .34 (.33) .54** (.12) .34** (.11) .22 (.14) .04 (.15) −.42* (.18) 1.48** (.14) .34* (.14) 3.50** (.19)

Time constraint (π2 ) Time constraint2 (π3 ) Overall heavy bulky share (π4 ) Sensory share (π5 ) Household members (π6 ) Social class1 (π71 ) Social class2 (π72 ) Social class3 (π73 ) Social class5 (π75 ) Social class6 (π76 ) SoW Asda (π8 ) SoW Sains (π9 ) SoW Tesco (π10 ) SoW Wait (π11 ) Note: Standard errors are in parentheses. * p < .05, all two-sided. ** p < .01, all two-sided.

grocery shopping depends primarily on time constraints and the need for convenience. We include time constraint (measured by purchase frequency) (Table 3) and its squared term.13 To approximate the need for convenient shopping, we use the overall share of heavy and bulky items that a household purchases, among all its expenditures (Overal Heavy/bulky shareh ), Buying these items online can provide a valuable convenience advantage, because they can be picked by online grocery staff and delivered directly to the household (Chintagunta, Chu, and Cebollada 2012).14 We include a sensory product share variable (Overall Sensory shareh ), which should act as barrier; the process of buying sensory products online is more difficult, because

13 We expect an inverted U-shaped relationship between time constraint and the decision to shop online, such that at some point (i.e. extremely high purchase frequency), the likelihood of being a multichannel shopper at a chain decreases. Households with very high purchase frequencies probably have substantial shopping time available and thus are less likely to start shopping online at the chain. 14 In contrast with the moderating variables in Eq. (2), this heavy/bulky share variable refers to the overall share of buying bulky and heavy categories (sum of offline expenditures on bulky and heavy categories in all chains/sum of offline expenditures on top 67 categories in all chains in the initialization period). The same distinction applies to the sensory variable, which in Eq. (A1) is also measured as the overall share.

consumers cannot physically inspect the products (Chintagunta, Chu, and Cebollada 2012; Degeratu, Rangaswamy, and Wu 2000). To account for consumers’ preference for a particular chain when deciding to shop online, we included the share of wallet for each multichannel chain in the models (SoW Asdah ; SoW Sainsh ; SoW Tescoh ; SoW Waith ) (Melis et al. 2015), which we operationalized using the period before the household started shopping online. Finally, we control for the number of members in a household (HH membersh ) and add indicators of social status (Social classh ), formed by the levels of education and income. Multichannel households tend to be bigger in size and have higher income and education levels than singlechannel, offline households (Degeratu, Rangaswamy, and Wu 2000; Morganosky and Cude 2001; Raijas and Tuunainen 2001; Rohm and Swaminathan 2004). Table A1 contains the results of the logistic regression models for each chain. Time constraint has a consistently significant, inverted U-shaped effect on the decision to shop online at each of the chains; consumers who have a moderate time constraint are more likely to adopt the online channel, but when they have an extremely low time constraint, they are less likely to adopt the online channel. However, the overall shares of heavy/bulky and sensory items were not significant in most models. As expected, households with more members are more likely to

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adopt the online channel offered by each of the multichannel chains. Compared with a reference (Social class 4), consumers in the social classes ranked second and third in the hierarchy (Social class2 and Social class3) have a higher likelihood of being multichannel shoppers, and this effect is consistent for all four chains. Next, we selected single-channel households with similar values on the significant determinants of being an online shopper with a specific chain. Time constraint and a chain’s SoW are not categorical, so we first determine a manageable number of categories, according to the values of these variables in each quartile (using the first period in the panel for single-channel, offline households to operationalize the variables). Then we match, for all significant determinants, the frequencies and values exhibited by the single-channel households, to ensure they align with those in the multichannel group. To obtain two groups that are comparable in size, we randomly selected single-channel, offline shoppers with each chain that approximately matched the size of the respective multichannel group. Because we considered four chains, we repeated this procedure four times. Finally, we deleted any duplicate households resulting from the repeated matching procedure.

Table A2 Selection equation. Intercept Asda (τ ASDA ) Sainsbury’s (τ SAINS ) Tesco (τ TESCO ) Waitrose (τ WAIT ) Trend Asda (δASDA1 ) Sainsbury’s (δSAINS1 ) Tesco (δTESCO1 ) Waitrose (δWAIT1 ) Lagged choice Asda (δASDA2 ) Sainsbury’s (δSAINS2 ) Tesco (δTESCO2 )

Selection Model

Waitrose (δWAIT2 )

To control for observations in which SoW equaled 0, we used a Heckman selection model (Hamilton and Nickerson 2003; Leenheer et al. 2007), in which we simultaneously model the decision of household h to choose chain c in period t (Chain choicehct ) and, conditional on household h choosing chain c in period t (Chain choicehct = 1), its share of wallet for chain c in period t (SoWhct ). The outcome (SoW) equation is described in the text (Eqs. (1) and (2)); the selection equation related to chain choice appears in the following probit model:

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Marketing mix RelPrice (δPRICE ) Relassortment (δASSORT ) Reldistance (δDIST ) Rho

−1.59** (.28) −2.05** (.32) −1.62** (.31) −1.86** (.31) −.10** (.003) −.07** (.002) −.14** (.004) −.04** (.004) 1.45** (.02) 1.52** (.02) 1.40** (.02) 1.69** (.03) −.07 (.18) 1.54** (.13) −1.09** (.02) −.33** (.006)

Note: Standard errors are in parentheses. ** p < .01, all two-sided.

Chain choicehct = τc + δ1c ∗ Trendt + δ2c ∗ Chain choicehc,t−1 + δ3 ∗ RelPricehct + δ4 ∗ RelAssortmenthct + δ5 ∗ RelDistancehc + εhct

(A.2)

To capture intrinsic utility, we included chain-specific intercepts (τ c ) and used a trend variable to reflect chain-specific time-dependent changes in intrinsic attractiveness (Trendt ). These variables capture the effect of characteristics that differ over chains and possibly over time (e.g. promotions, website design, distribution of offline stores, costs of delivery) but for which we have no information. The lagged choice variable (Chain choicehc,t−1 ) captures chain loyalty effects (i.e. behavioral loyalty), which we expect to be highly significant and positive. The marketing mix variables we include are the relative offline price level (RelPricehct ) for chain c in period t, the relative size of the offline assortment (RelAssortmenthct ), and the relative distance between a household’s residence and the nearest store of each chain c (RelDistancehc ) (identical to the operationalizations in the main model; see Table 3).

Table A2 provides an overview of the selection Eq. (A.2). The error correlation (rho) between the selection model and the regression (SoW) model is significant (χ2 (1) = 3111.80, p < .01), so the selection equation should precede the SoW equation. All multichannel chains have negative intrinsic attractiveness (τ ASDA =−1.59, p < .01; τ SAINS =−2.05, p < .01; τ TESCO =−1.62, p < .01; τ WAIT =−1.86, p < .01) and become less attractive over time (δASDA1 =− .10, p < .01; δSAINS1 =− .07, p < .01; δTESCO1 =− .14, p < .01; δWAIT1 =−0.4, p < .01). Yet the previous choice predicts the chain choice quite well (δASDA2 = 1.45, p < .01; δSAINS2 = 1.52, p < .01; δTESCO2 = 1.40, p < .01; δWAIT2 = 1.69, p < .01). In accordance with prior store choice literature (e.g. Briesch, Chintagunta, and Fox 2009; Fox, Montgomery, and Lodish 2004), we also find that households choose chains with more assortment attractiveness (δ4 = 1.54, p < .01) and that are closer to their home (δ5 =−1.09, p < .01). Price level had no significant influence in this selection model (δ3 =− .07, p > .10).

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Please cite this article in press as: Melis, Kristina, et al, A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet? Journal of Retailing (xxx, 2016), http://dx.doi.org/10.1016/j.jretai.2016.05.001