A bigger slice of the multichannel grocery pie: When does consumers’
online channel use expand retailers’ share of wallet?
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 singlechannel 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.
Keywords: multichannel retailing, grocery shopping, expansion
1
Introduction
Despite some initial implementation delays, online grocery is now a booming business:
9% of U.S. consumers and 6% of European customers are already frequently using the online
channel to buy groceries, and a recent Nielsen survey revealed that 51% (U.S.) and 46%
(Europe) is willing to do so in the near future (Nielsen 2015). 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 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 et al.
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 ànd 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 convenience
advantages of online shopping with self-service advantages of offline stores (Alba et al. 1997;
2
Chu et al. 2010; Chu, Chintagunta, and Cebollada 2008; Konuş, 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% 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 ánd 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
3
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.
[Insert Table 1 about here]
Apart from Pozzi (2013), most prior multichannel studies investigate non-grocery
shopping sectors, such as apparel (Venkatesan, Kumar, and Ravishankar 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 –
4
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 et al. 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
5
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 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 strategy when promoting and improving
their online shopping platform.
6
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 towards 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 within-chain SoW shifts, with positive effects being
indicative of SoW expansion.
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 pre-dominantly 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).
7
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 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 Figure 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.
[Insert Figure 1 about here]
1
In this research, we focus on the reevaluation of the spending allocations towards chains, i.e., a reevaluation of
where to buy products and brands; not a reevaluation of which products or brands to buy.
8
Costs and Benefits of Online Shopping
The online channel differs from traditional, offline grocery stores in terms of
convenience (e.g., 24 hour ordering capabilities), access (no transportation, home delivery)
and transaction (ease or risk of acquiring specific products online) benefits and costs
(Chintagunta et al. 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 indicated that time-constrained, goaloriented consumers with a utilitarian shopping attitude wish to spend as less time on shopping
activities as possible (Babin, Darden, and Griffin 1994; Baltas et al. 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
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 trips (fewer shopping trips might be longer in duration). Nevertheless, taking into account
travelling and transportation costs, we believe that low shopping frequency (with large shopping baskets) is a
proxy for time-constrained, goal-oriented consumers.
9
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 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
10
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-Fisher, 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 privatelabel 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., pricesensitive 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
11
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 categoryspecific preferences (Gijsbrechts, Campo, and Nisol 2008). In line with the reasoning for hard
discounter shoppers, this increases switching costs, leading to the expectation that multiplestore 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 are familiar with
different chains, are more used to compare these chains’ offer, and can be expected to be less
change-averse (Baltas, Argouslidis & Skarmeas 2010; Gijsbrechts et al. 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 multiple-store 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 towards and preference for
the chain (from offline to online and vice versa). The brand extension literature has
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.
12
demonstrated that a positive attitude towards 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 specification:4
(1)
𝑆𝑜𝑊ℎ𝑐𝑡
𝑙𝑛 (
) = 𝛼𝑐1 + 𝛼𝑐2 ∗ 𝑇𝑟𝑒𝑛𝑑𝑡 + 𝛾ℎ𝑐𝑡 ∗ 𝑂𝑛𝑙𝑖𝑛𝑒_𝑐ℎ𝑎𝑛𝑛𝑒𝑙_𝑢𝑠𝑒ℎ𝑐𝑡 + 𝛽𝑐 ∗ 𝑂𝑛𝑙𝑖𝑛𝑒_𝑐ℎ𝑎𝑛𝑛𝑒𝑙_𝑢𝑠𝑒_𝑐𝑜𝑚𝑝ℎ𝑘𝑡
1 − 𝑆𝑜𝑊ℎ𝑐𝑡
+ ∑ 𝛽𝑗 ∗ 𝑋𝑗,ℎ𝑐𝑡 + 𝜀ℎ𝑐𝑡
𝑗
,
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
4
We correct for the occurrence of zero SoW observations with a Heckman (1979) selection model (see the
Appendix). To avoid a specification of the dependent variable in Equation 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
𝑆𝑜𝑊ℎ𝑐𝑡 −0.00001
).
1−(𝑆𝑜𝑊ℎ𝑐𝑡 −0.00001
dependent variable of Equation 1 equals 𝑙𝑛 (
13
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 γhct, we capture
household-specific expansion effects of online channel use at chain c in period t, to examine if
expansion occurs. If γhct 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 γhct to vary with the
costs and benefits of online shopping (online) and habitual purchase patterns (habit):
(2) 𝛾ℎ𝑐𝑡 = 𝛾𝑐0 + 𝑂𝑛𝑙𝑖𝑛𝑒ℎ𝑐𝑡 + 𝐻𝑎𝑏𝑖𝑡ℎ𝑐
,
where,
(2a)
𝑂𝑛𝑙𝑖𝑛𝑒ℎ𝑐𝑡 = 𝛾11 ∗ 𝑇𝑖𝑚𝑒_𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡ℎ + 𝛾12 ∗ ln(𝑅𝑒𝑙𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒ℎ𝑐𝑡 + 1) + 𝛾13 ∗ 𝐻𝑒𝑎𝑣𝑦/𝐵𝑢𝑙𝑘𝑦_𝑠ℎ𝑎𝑟𝑒ℎ𝑐 + 𝛾14
∗ 𝑆𝑒𝑛𝑠𝑜𝑟𝑦_𝑠ℎ𝑎𝑟𝑒ℎ𝑐
,
(2b)
𝐻𝑎𝑏𝑖𝑡ℎ𝑐 = 𝛾21 ∗ 𝑃𝐿_𝑠ℎ𝑎𝑟𝑒ℎ𝑐 + 𝛾22 ∗ (𝑃𝐿_𝑠ℎ𝑎𝑟𝑒ℎ𝑐 )2 + 𝛾23 ∗ 𝐻𝑎𝑟𝑑𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡_𝑠ℎ𝑎𝑟𝑒ℎ + 𝛾24
∗ 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑒_𝑠𝑡𝑜𝑟𝑒_𝑠ℎ𝑜𝑝𝑝𝑒𝑟ℎ + 𝛾25 ∗ 𝑃𝑟𝑖𝑐𝑒_𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛ℎ𝑐 + 𝛾26
∗ 𝐴𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡_𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛ℎ𝑐
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 logtransform distance, to account for marginal decreasing effects.
14
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 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 mean-centered all these factors in the model estimations, so we
can interpret online channel use at their means.
In estimating the model in Equations 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
7
For a few households, distance varies over time due to changes in the household’s residence (e.g., a move).
15
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 the 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 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
16
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 the Appendix. The sample includes 7,907
households, split between 4,069 multichannel and 3,838 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
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 % of multichannel shoppers in our sample, i.e., those that start to use the online grocery channel for the first
time (10.84%; 4,069 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.
17
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.
[Insert Table 2 about here]
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).
[Insert Table 3 about here]
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).
18
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 long-term 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 (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, time-varying 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 Equation 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 kilometers).
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 kilometers). 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).
19
[Insert Table 4 about here]
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, 4% for heavy and bulky products, 29% for sensory products, 4% to hard discount
chains, and approximately 55% to private-label items.12 An average multichannel, multiplestore 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 (𝛼 𝑇𝐸𝑆𝐶𝑂2 =-.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 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
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%, and unit private-label market share was 59.2% (IRI 2014).
20
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).
[Insert Table 5 about here]
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 (𝛾𝑆𝐴𝐼𝑁𝑆0=2.05, p < .01; 𝛾𝐴𝑆𝐷𝐴0=1.76, p < .01),
whereas Waitrose and Tesco (resp. the smallest and largest chain) gain the least
(𝛾𝑊𝐴𝐼𝑇0=1.31, p < .01; 𝛾𝑇𝐸𝑆𝐶𝑂0=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 (𝛾11=-.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 (𝛾12=.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
21
share of heavy/bulky or sensory categories purchased at competitive chains has no significant
impact on SoW expansion of the online-visited chain (𝛾13=-1.28, p > .05; 𝛾14 =.49, p > .05).
In terms of habitual purchase patterns, we find that private-label 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 (𝛾21=-3.55, p < .01). Privatelabel 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; 𝛾22=7.34, p < .01). Hard discounter proneness also hinders the expansion of SoW
(𝛾23=-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 (preonline) purchase pattern (𝛾24=-5.32, p < .01). Consumers who visit multiple offline stores
thus appear to take advantage of chain differences to fulfill their category-specific 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 (𝛾25=-7.49, p<.05; 𝛾26=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
22
definition of these variables 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.
[Insert Table 6 about here]
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 Equation 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
23
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.
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
24
Venkatesan; Kushwaha and Shankar 2013; Venkatesan, Kumar, and Ravishankar 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 et al. 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 the Appendix, Table A.2), 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
25
gravitate to online habitual decision behavior, for instance spurred by personalized shopping
lists, or previous order lists made available in the online shopping environment.
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
26
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 Ushaped (Ailawadi, Pauwels, and Steenkamp 2008). Up to a certain point, private-label 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.
27
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
28
(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% 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).
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 time-constrained 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)
29
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
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.
30
Acknowledgments
The authors thank AiMark for providing the data and the National Bank of Belgium for
financial support (NBB/14/004).
31
References
Ailawadi, Kusum L., Koen Pauwels, & Jan-Benedict E.M. Steenkamp (2008), “Private-Label Use and
Store Loyalty,” Journal of Marketing, 72(6), 19-30.
Alba, Joseph, Lynch, John., Barton, Weitz, Janiszewski, Chris, Lutz, Richard, Sawyer, Alan, & Wood,
Stacy (1997). Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives
to Participate in Electronic Marketplaces. Journal of Marketing, 61(3), 38-53.
Ansari, Asim, Mela, Carl F., & Neslin, Scott A. (2008). Customer Channel Migration. Journal of
Marketing Research, 45(1), 60-76.
Babin, B.J., Darden, W.R., & Griffin, M. (1994). Work and/or Fun: Measuring Hedonic and
Utilitarian Shopping Value. Journal of Consumer Research, 20(4), 644-656.
Baltas, G., Argouslidis, P. C., & Skarmeas, D. (2010). The Role of Customer Factors in Multiple Store
Patronage: A Cost–Benefit Approach. Journal of Retailing, 86(1), 37-50.
Bass, Frank M., Bruce, Norris, Majumdar, Sumit, & Murthi, B.P.S. (2007). Wearout Effects of
Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales
Relationship. Marketing science, 26(2), 179-195.
Bell, David R., & Lattin, James M. (1998). Shopping Behavior and Consumer Preference for Store
Price Format: Why “Large Basket” Shoppers Prefer EDLP. Marketing Science, 17(1), 66-88.
Bilgicer, Tolga, Jedidi, Kamel, Lehmann, Donald R., & Neslin, Scott A. (2015). The Long-Term
Effect of Multichannel Usage on Sales. Customer Needs and Solutions, 2(1), 1-16.
Briesch, Richard A., Chintagunta, Pradeep, & Fox, Edward J. (2009). How Does Assortment Affect
Grocery Store Choice? Journal of Marketing Research, 46(2), 176-189.
Bustos-Reyes, César Augusto, & González-Benito, Óscar (2008). Store and Store Format Loyalty
Measures Based on Budget Allocation. Journal of Business Research, 61(9), 1015-1025.
Butler, Sarah (Januari 6 2014). Grocers Rush to Open 'Dark Stores' as Online Food Shopping
Expands, The Guardian. Retrieved May 21 2015 from The Guardian
http://www.theguardian.com/business/2014/jan/06/supermarkets-open-dark-stores-onlinefood-shopping-expands.
Campo, Katia, & Breugelmans, Els (2015). Buying Groceries in Brick and Click Stores: Category
Allocation Decisions and the Moderating Effect of Online Buying Experience. Journal of
Interactive Marketing, 31(3), 63-78.
Campo, K., Gijsbrechts, E., & Nisol, P. (2000). Towards Understanding Consumer Response to Stockouts. Journal of Retailing, 76(2), 219-242.
Centre for Retail Research (2014). Online Retailing: Britain, Europe and the US 2014, Retrieved May
22 2015 from http://www.retailresearch.org/onlineretailing.php.
Cheng, Jacqui (2010). The New Age of Online Grocery Shopping, Retrieved May 22 2015 from
http://arstechnica.com/business/2010/03/the-new-age-of-online-grocery-shopping/.
Chintagunta, Pradeep, Chu, Junhong, & Cebollada, Javier (2012). Quantifying Transaction Costs in
Online/Off-line Grocery Channel Choice. Marketing Science, 31(1), 96-114.
Chu, Junhong, Arce-Urriza, Marta, Cebollada, Javier, & Chintagunta, Pradeep (2010). An Empirical
Analysis of Shopping Behavior Across Online and Offline Channels for Grocery Products:
The Moderating Effects of Household and Product Characteristics. Journal of Interactive
Marketing, 24(4), 251-268.
Chu, Junhong, Chintagunta, Pradeep, & Cebollada, Javier (2008). Research Note—A Comparison of
Within-Household Price Sensitivity Across Online and Offline Channels. Marketing Science,
27(2), 283-299.
Cleeren, Kathleen, van Heerde, Harald J., & Dekimpe, Marnik G. (2013). Rising from the Ashes: How
Brands and Categories can Overcome Product-Harm Crises. Journal of Marketing, 77(2), 5877.
Cooil, Bruce, Keiningham, Timothy L., Aksoy, Lerzan, & Hsu, Michael (2007). A Longitudinal
Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of
Customer Characteristics. Journal of Marketing, 71(1), 67-83.
Corstjens, Judy, & Corstjens, Marcel (1995). Store Wars: The Battle for Mindspace and Shelfspace
(Vol. 5): Wiley Chichester.
32
Corstjens, Marcel, & Lal, Rajiv (2000). Building Store Loyalty through Store Brands. Journal of
Marketing Research, 37(3), 281-291.
Degeratu, Alexandru M., Rangaswamy, Arvind, & Wu, Jianan (2000). Consumer Choice Behavior in
Online and Traditional Supermarkets: The Effects of Brand Name, Price, and Other Search
Attributes. International Journal of Research in Marketing, 17(1), 55-78.
Dellaert, B.G.C., Arentze, T.A., Bierlaire, M., Borgers, A.W.J., & Timmermans, H.J.P. (1998).
Investigating Consumers’ tendency to Combine Multiple Shopping Purposes and Destinations.
Journal of Marketing Research, 25(2), 177-188.
Deleersnyder, Barbara, Geyskens, Inge, Gielens, Katrijn, & Dekimpe, Marnik G. (2002). How
Cannibalistic is the Internet Channel? A Study of the Newspaper Industry in the United
Kingdom and the Netherlands. International Journal of Research in Marketing, 19(4), 337348.
Fox, Edward J., Montgomery, Alan L., & Lodish, Leonard M. (2004). Consumer Shopping and
Spending across Retail Formats. Journal of Business, 77(2), 25-60.
Geyskens, Inge, Gielens, Katrijn, & Dekimpe, Marnik G. (2002). The Market Valuation of Internet
Channel Additions. Journal of Marketing, 66(2), 102-119.
Gijsbrechts, Els, Campo, Katia, & Nisol, Patricia (2008). Beyond Promotion-Based Store Switching:
Antecedents and Patterns of Systematic Multiple-Store Shopping. International Journal of
Research in Marketing, 25(1), 5-21.
Grewal, Dhruv, Janakiraman, Ramkumar, Kalyanam, Kirthi, Kannan, P. K., Ratchford, Brian, Song,
Reo, & Tolerico, Stephen (2010). Strategic Online and Offline Retail Pricing: A Review and
Research Agenda. Journal of Interactive Marketing, 24(2), 138-154.
Gupta, Sumeet & Kim Hee-Woong (2010), Value-driven Internet shopping: The Mental Accounting
Theory Perspective. Psychology & Marketing, 27(1), 13-35.
Hamilton, Barton H., & Nickerson, Jackson A. (2003). Correcting for Endogeneity in Strategic
Management Research. Strategic organization, 1(1), 51-78.
Heckman, James J. (1979). Sample Selection Bias as a Specification Error. Econometrica: Journal of
the Econometric Society, 47(1), 153-161.
Hoyer, Wayne D. (1984). An Examination of Consumer Decision Making for a Common Repeat
Purchase Product. Journal of Consumer Research, 11(3), 822-829.
Hoyer, Wayne D., & Brown, Steven P. (1990). Effects of Brand Awareness on Choice for a Common,
Repeat-purchase Product. Journal of Consumer Research, 17(2), 141-148.
Hoyer, Wayne D., MacInnis, Deborah J., & Pieters, Rik (2013). Consumer Behavior (6th ed.). South
Western: Cengage Learning.
IGD (2012). The Connected Shopper. Retrieved May 21 2015 from IGD http://www.igd.com/ourexpertise/Shopper-Insight/shopper-outlook/4136/The-Connected-Shopper/.
IGD (2015). The Growth Potential of Online Grocery. Retrieved February 1 2016 from IGD
http://www.igd.com/Research/Shopper-Insight/The-growth-potential-of-online-grocery/.
IRI (2014). Private Label in Western Economies: Closing the price gap, losing share. Retrieved May
22 from http://www.iriworldwide.eu/Portals/0/articlepdfs/PrivateLabel2014/PrivateLabel_report_Dec2014_nolinks.pdf.
Jiang, Ling A., Yang, Zhilin and Jun, Minjoon (2013). Measuring Consumer Perceptions of Online
Shopping Convenience. Journal of Service Management, 24(2), 191-214.
Kantar Worldpanel (2015). Accelerating the Growth of E-commerce in FMCG. Retrieved from
http://us.kantar.com/media/1026452/kantar_worldpanel_-_accelerating_the_growth_of_ecommerce_in_fmcg_2015_final_report.pdf.
Konuş, Umut, Verhoef, Peter C., & Neslin, Scott A. (2008). Multichannel Shopper Segments and their
Covariates. Journal of Retailing, 84(4), 398-413.
Koschate-Fischer, Nicole, Cramer, Johannes, & Hoyer, Wayne D. (2014). Moderating Effects of the
Relationship Between Private Label Share and Store Loyalty. Journal of Marketing, 78(2), 6982.
Kumar, Vipin, & Venkatesan, Rajkumar (2005). Who Are the Multichannel Shoppers and How Do
They Perform?: Correlates of Multichannel Shopping Behavior. Journal of Interactive
Marketing, 19(2), 44-62.
33
Kushwaha, Tarun, & Shankar, Venkatesh (2013). Are multichannel customers really more valuable?
The moderating role of product category characteristics. Journal of Marketing, 77(4), 67-85.
Laroche, Michel, Yang, Zhiyong, McDougall, Gordon H. G., & Bergeron, Jasmin (2005). Internet
versus Bricks-and-Mortar Retailers: An Investigation into Intangibility and its Consequences.
Journal of Retailing, 81(4), 251-267.
Leenheer, Jorna, van Heerde, Harald J., Bijmolt, Tammo H., & Smidts, Ale (2007). Do Loyalty
Programs Really Enhance Behavioral Loyalty? An Empirical Analysis Accounting for SelfSelecting Members. International Journal of Research in Marketing, 24(1), 31-47.
Lesaffre, Emanuel, Rizopoulos, Dimitris, & Tsonaka, Roula (2007). The Logistic Transform for
Bounded Outcome Scores. Biostatistics, 8(1), 72-85.
Mägi, Anne W. (2003). Share of Wallet in Retailing: the Effects of Customer Satisfaction, Loyalty
Cards and Shopper Characteristics. Journal of Retailing, 79(2), 97-106.
Mashable. (2012). 61% of Online Shoppers Would Trade Privacy for Personalization. Retrieved
February 23 2016 from http://mashable.com/2012/11/20/online-shopping-privacystudy//#KbO.02DqZ8qq.
McKinsey & Company. (2014). Retail 4.0: The Future of Retail Grocery in a Digital World. Retrieved
from
http://www.mckinseyonmarketingandsales.com/sites/default/files/pdf/Future_of_grocery_in_d
igital_world.pdf.
McKinsey & Company. (2013). The Future of Online Grocery in Europe. Retrieved May 22 2015
from http://www.mckinsey.com/client_service/retail/latest_thinking.
Melis, Kristina, Campo, Katia, Breugelmans, Els, & Lamey, Lien (2015). The Impact of the
Multichannel Retail Mix on Online Store Choice: Does online experience matter? Journal of
Retailing, 91(2), 272 288.
Morganosky, Michelle A., & Cude, Brenda J. (2000). Consumer Response to Online Grocery
Shopping. International Journal of Retail & Distribution Management, 28(1), 17-26.
Nagengast, Liane, Evanschitzky, Heiner, Blut, Markus, & Rudolph, Thomas (2014). New Insights in
the Moderating Effect of Switching Costs on the Satisfaction–Repurchase Behavior Link.
Journal of Retailing, 90(3), 408-427.
Pauwels, Koen, Leeflang, Peter S. H., Teerling, Marije L., & Huizingh, K. R. Eelko (2011). Does
Online Information Drive Offline Revenues?: Only for Specific Products and Consumer
Segments! Journal of Retailing, 87(1), 1-17.
Planet Retail. (2014). European Grocery Retailing: Change is the Only Constant. Retrieved May 22
2015 from http://www.planetretail.net/presentations/ApexBrasilPresentation.pdf.
Pozzi, Andrea (2013). The Effect of Internet Distribution on Brick-and-mortar Sales. The RAND
Journal of Economics, 44(3), 569-583.
Raijas, Anu, & Tuunainen, Virpi Kristiina (2001). Critical Factors in Electronic Grocery Shopping.
The International Review of Retail, Distribution and Consumer Research, 11(3), 255-265.
Rhee, Hongjai, & Bell, David R. (2002). The Inter-Store Mobility of Supermarket Shoppers. Journal
of Retailing, 78(4), 225-237.
Rohm, Andrew J., & Swaminathan, Vanitha (2004). A Typology of Online Shoppers Based on
Shopping Motivations. Journal of Business Research, 57(7), 748-757.
Shi, Savannah W., & Zhang, Jie (2014). Usage Experience with Decision Aids and Evolution of
Online Purchase Behavior. Marketing Science, 33(6), 871-882.
Steiman, Hannah Clark (2014). Why 2014 Will Finally Be the Year of the Online Grocer. Retrieved
May 22 2015, from Bloomberg Businessweek http://www.businessweek.com/articles/201401-16/why-2014-will-finally-be-the-year-of-the-online-grocer.
Sullivan, Mary W. (1998). How Brand Names Affect the Demand for Twin Automobiles. Journal of
Marketing Research, 35(2), 154-165.
ter Braak, Anne, Dekimpe, Marnik G., & Geyskens, Inge (2013). Retailer Private-Label Margins: The
Role of Supplier and Quality-Tier Differentiation. Journal of Marketing, 77(4), 86-103.
The Nielsen Company (2009). Opportunities Abound for Online Grocers. Retrieved February 24 from
http://www.nielsen.com/us/en/insights/news/2009/opportunities-abound-for-onlinegrocers.html.
34
Umesh, U.N., Pettit, K., & Bozman, C.S. (1989). Shopping Model of the Time-Sensitive Consumer.
Decision Sciences, 20(4), 715-729.
van Baal, Sebastian, & Dach, Christian (2005). Free Riding and Customer Retention across Retailers'
Cchannels. Journal of Interactive Marketing, 19(2), 75-85
Venkatesan, Rajkumar, Kumar, Vipin, & Ravishanker, Nalini (2007). Multichannel Shopping: Causes
and Consequences. Journal of Marketing, 71(2), 114-132.
Vroegrijk, Mark, Gijsbrechts, Els, & Campo, Katia (2013). Close Encounter with the Hard Discounter
Entry: A Multiple-Store Shopping Perspective on the Impact of Local Hard-Discounter Entry.
Journal of Marketing Research, 50(5), 606-626.
Wallace, David W., Giese, Joan L., & Johnson, Jena L. (2004). Customer Retailer Loyalty in the
Context of Multiple Channel Strategies. Journal of Retailing, 80(4), 249-263.
Weathers, Danny, Sharma, Subhash, & Wood, Stacy L. (2007). Effects of Online Communication
Practices on Consumer Perceptions of Performance Uncertainty for Search and Experience
Goods. Journal of Retailing, 83(4), 393-401.
Wolk, Agnieszka, & Ebling, Christine (2010). Multichannel Price Differentiation: An Empirical
Investigation of Existence and Causes. International Journal of Research in Marketing, 27(2),
142-150.
Zhang, Jie, & Breugelmans, Els (2012). The Impact of an Item-Based loyalty Program on Consumer
Purchase Behavior. Journal of Marketing Research, 49(1), 50-65.
35
Figure 1: Conceptual Framework
36
Table 1. Literature Review
Author(s)
Grocery
Yes No
Dependent variable
Moderators
Level
Factor
Retailer Decision
Deleersnyder et al.
2002
x
Volume sales and
advertising income
Chain
Product overlap
Geyskens, Gielens,
and Dekimpe 2002
x
Stock market return
Firm
Channel power
Introduction
strategy
Marketplace
Order of entry
Pozzi 2013
x
Sales and spending
Ansari, Mela, and
Neslin 2008
x
Bilgicer et al. 2015
x
Kumar and
Venkatesan 2005
Kushwaha and
Shankar 2013
x
van Baal and Dach
2005
Venkatesan et al. 2007
This study
x
Consumer Decision
Channel selection,
purchase incidence and
order size
Spending
Customer (at
chain)
Product demand
growth
Distance to chain
/
/
x
Purchase behavior and
channel adoption
Spending
Product category
Hedonic vs. utilitarian
x
Retailer switching
Product category
Dominance of search
characteristics
x
Customer profitability
Share of wallet
/
/
Customer
(overall)
Customer (at
competitor)
Customer (at
chain)
Time constraint
Multiple-store
shopper
Heavy/bulky share
Sensory share
Private-label share
Distance to store
Price integration
Assortment
integration
37
Table 2. Household Descriptive Statistics
Multichannel households (N = 4,069)
Asda
Mean
SD
SoW before
onlinea
Δ SoWbc
Range Δbc
Δ Shopping
tripsbc
Δ Spendingbc
a
SoW
.21
.29
.04
.21
[-.68; .94]
2.85
9.41
113.52 241.37
.23
Sainsbury's
Mean
SD
.17
.27
.02
.20
[-.76; 1.00]
2.98
129.11
8.41
284.53
Tesco
Mean
SD
.37
.35
.04
.21
[-.85; .97]
2.98
122.95
8.70
255.55
Waitrose
Mean
SD
.02
.09
.03
.10
[-.52; .65]
1.90
41.99
Single-channel (offline) households (N = 3,838)
.32
.16
.27
.38
.36
.02
5.12
135.30
.09
a
Variation is a result of differences over households and periods.
b
Variation is a result of differences over households.
c
Online + offline purchases.
38
Table 3. Variable Operationalizations
Variable
𝑆𝑜𝑊ℎ𝑐𝑡
𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒_𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦ℎ
𝑤ℎ,𝑐𝑎𝑡
𝑃𝑟𝑖𝑐𝑒_𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛ℎ𝑐
𝐴𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡_𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛ℎ𝑐
𝐻𝑒𝑎𝑣𝑦/𝐵𝑢𝑙𝑘𝑦_𝑠ℎ𝑎𝑟𝑒ℎ𝑐
𝑆𝑒𝑛𝑠𝑜𝑟𝑦_𝑠ℎ𝑎𝑟𝑒ℎ𝑐
𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑒_𝑠𝑡𝑜𝑟𝑒_𝑠ℎ𝑜𝑝𝑝𝑒𝑟ℎ
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒ℎ𝑐𝑡
Description
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).
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 (t h), 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.
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 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.
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.
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.
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.
Euclidian distance between household h’s residence and the nearest store
outlet of chain c in kilometers in period t. Latitude (Lat) and Longitude
Formula
𝑆𝑎𝑙𝑒𝑠ℎ𝑐𝑡
10
∑𝑐=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑐𝑡
𝑡ℎ −1
1
∑ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑠ℎ𝑜𝑝𝑝𝑖𝑛𝑔 𝑡𝑟𝑖𝑝𝑠ℎ𝑡
𝑡ℎ − 1
𝑡=1
∑8𝑡=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡,𝑐𝑎𝑡
∑8𝑡=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡
8
67
1
𝑂𝑛𝑙𝑖𝑛𝑒 𝑝𝑟𝑖𝑐𝑒𝑐𝑡,𝑐𝑎𝑡
∑ ∑ 𝑤ℎ,𝑐𝑎𝑡 ∗
8
𝑂𝑓𝑓𝑙𝑖𝑛𝑒 𝑝𝑟𝑖𝑐𝑒𝑐𝑡,𝑐𝑎𝑡
𝑡=1 𝑐𝑎𝑡=1
8
67
1
𝑂𝑛𝑙𝑖𝑛𝑒 𝑎𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡𝑐𝑡,𝑐𝑎𝑡
∑ ∑ 𝑤ℎ,𝑐𝑎𝑡 ∗
8
𝑂𝑓𝑓𝑙𝑖𝑛𝑒 𝑎𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡𝑐𝑡,𝑐𝑎𝑡
𝑡=1 𝑐𝑎𝑡=1
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝐻𝑒𝑎𝑣𝑦/𝐵𝑢𝑙𝑘𝑦_𝑠𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑘≠𝑐
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑆𝑒𝑛𝑠𝑜𝑟𝑦_𝑠𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑘≠𝑐
𝑡
ℎ−1
∑𝑡=1
∑𝑐=10
𝑐=1 𝑆𝑜𝑊ℎ𝑐 ∗ ln(𝑆𝑜𝑊ℎ𝑐 )
ln(10)
39
𝑃𝐿_𝑠ℎ𝑎𝑟𝑒ℎ𝑐
𝐻𝑎𝑟𝑑𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡_𝑠ℎ𝑎𝑟𝑒ℎ
𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒ℎ𝑐𝑡
𝑅𝑒𝑙𝐴𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡ℎ𝑐𝑡
𝑅𝑒𝑙𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒ℎ𝑐𝑡
(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).
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.
√[(−𝐿𝑎𝑡1ℎ𝑡 ) − (−𝐿𝑎𝑡2ℎ𝑐𝑡 )]2 + [(𝐿𝑜𝑛𝑔1ℎ𝑡 − 𝐿𝑜𝑛𝑔2ℎ𝑐𝑡 ) ∗ 𝑆𝐼𝑁 (. 5 ∗ ((−𝐿𝑎𝑡1ℎ𝑡 ) + (−𝐿𝑎𝑡2ℎ𝑐𝑡 )))]
*(
𝜋
2
) ∗ 𝑅𝑎𝑑𝑖𝑢𝑠_𝐸𝑎𝑟𝑡ℎ
180
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑃𝐿_𝑠𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑘≠𝑐
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝐻𝑎𝑟𝑑 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡_𝑠𝑎𝑙𝑒𝑠ℎ𝑡𝑘
𝑡
ℎ−1 ∑9
∑𝑡=1
𝑘=1 𝑆𝑎𝑙𝑒𝑠ℎ𝑡𝑘
67
∑ 𝑤ℎ,𝑐𝑎𝑡 ∗ (
𝑐𝑎𝑡=1
𝑘≠𝑐
𝑃𝑟𝑖𝑐𝑒𝑐𝑎𝑡,𝑐𝑡
)
10
∑𝑐=1 𝑃𝑟𝑖𝑐𝑒𝑐𝑎𝑡,𝑐𝑡
10
67
∑ 𝑤ℎ,𝑐𝑎𝑡 ∗ (
𝑐𝑎𝑡=1
(
𝐴𝑠𝑠𝑜𝑟𝑡𝑐𝑎𝑡,𝑐𝑡
)
∑10
𝑐=1 𝐴𝑠𝑠𝑜𝑟𝑡𝑐𝑎𝑡,𝑐𝑡
10
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑐𝑡
)
10
∑𝑐=1 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑐𝑡
10
40
Table 4. Variable Descriptive Statistics
Priceac
Assortmentac
Distanceb
Price integrationacd
Assortment integrationacd
Heavy/Bulky sharebd
Sensory sharebd
Private-label sharebd
Time constraintbd
Hard discount sharebd
Multiple-store shopperbd
Asda
Sainsbury's
Mean
SD
Mean
SD
.09
.27
.10
.30
31.12
24.76
31.83
24.81
7.67
10.21
6.04
11.85
1.01
.16
1.03
.11
.61
.14
.63
.13
.04
.03
.04
.03
.29
.11
.28
.11
.54
.16
.55
.15
Mean
26.18
.04
.32
Tesco
Waitrose
Mean
SD
Mean
SD
.10
.28
.13
.36
42.40
32.75 20.96
17.47
3.29
5.26 19.22
27.85
1.01
.08
1.04
.20
.69
.11
.66
.19
.04
.04
.04
.03
.29
.13
.29
.09
.54
.18
.56
.13
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
41
Table 5. SoW Regression Model
Intercept and Control Variables
Intercept
Online Channel Use
Online channel use
Asda (𝛼𝐴𝑆𝐷𝐴1 )
-1.02
(1.08)
Asda (𝛾𝐴𝑆𝐷𝐴0 )
1.76**
(.14)
Sainsbury's (αSAINS1 )
-1.46
(1.26)
Sainsbury's (γSAINS0 )
2.05**
(.13)
Tesco (𝛼 𝑇𝐸𝑆𝐶𝑂1 )
-.96
(1.22)
Tesco (γTESCO0 )
1.17**
(.12)
Waitrose (αWAIT1 )
-.80
(1.26)
Waitrose (γWAIT0 )
1.31**
(.17)
Trend
Online shopping environment
Asda (𝛼𝐴𝑆𝐷𝐴2 )
-.01
(.01)
Time_constraint (γ11 )
-.01**
(.00)
Sainsbury's (𝛼𝑆𝐴𝐼𝑁𝑆2 )
.005
(.01)
Distance (γ12 )
.51**
(.16)
Tesco (𝛼 𝑇𝐸𝑆𝐶𝑂2 )
-.06**
(.02)
Heavy/Bulky_share (γ13 )
Waitrose (𝛼𝑊𝐴𝐼𝑇2 )
-.04*
(.02)
Sensory_share (γ14 )
Online channel use competitors
Asda (𝛽𝐴𝑆𝐷𝐴 )
Sainsbury's (βSAINs )
Tesco (βTESCO )
Waitrose (βWAIT )
RelAssortment (βASSORT )
RelDistance (βDIST )
.49
(.36)
Habitual purchase patterns
-1.17**
(.06)
PL _share (γ21 )
-3.55**
(.38)
-.91**
(.06)
PL_share² (γ22 )
7.34**
(.78)
-1.61**
(.07)
-.54**
(.10)
Marketing mix
RelPrice (βPRICE )
-1.28
(.76)
-1.19
(.77)
1.84**
(.46)
-1.58**
(.08)
HardDiscount_share (γ23 )
-1.88**
(.28)
Multiple_store_shopper (γ24 )
-5.32**
(.25)
Price_integration (γ25 )
-7.49*
(3.57)
Assortment_integration (γ26 )
4.47**
(1.91)
Inverse Mills Ratio
Adjusted R²
1.12**
(.01)
18.78%
Notes: Standard errors are in parentheses.
*p < .05, **p < .01, all two-sided.
42
Table 6. Alternative Operationalizations of Online Channel Usagea
Variables
Operationalizations
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).
Step dummy
Bayesian
Information
Criterion
678556.30
Pulse dummy
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.
683268.70
Count variable
The cumulative number of times household h
shopped online at multichannel chain c within each
four-month period.
685029.40
Recency and frequencya
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 shopping
trips in first online shopping period thc of household
h at that chain c.
𝑞=𝑡
(∑
𝑞=𝑡ℎ𝑐
Recency, frequency and
monetarya
𝑞=𝑡
𝑞=𝑡ℎ𝑐
Declining OCU
a
𝛿 𝑡−𝑞 ∗ #𝑇𝑟𝑖𝑝𝑠ℎ𝑐𝑡 )
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 the
online expenditure in first online shopping period
thc at chain c for household h.
(∑
684760.30
685069.40
𝛿 𝑡−𝑞 ∗ #𝐸𝑥𝑝ℎ𝑐𝑡 )
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
𝛿 𝑡−𝑡ℎ𝑐 ∗ 𝑠𝑡𝑒𝑝_𝑑𝑢𝑚𝑚𝑦ℎ𝑐𝑡
680786.30
Step dummy + dummy for each multichannel chain
678592.40
that equals 1 during the period t in which household
h shops online for the first time at that chain, 0
otherwise.
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.
Step dummy + first online
purchase dummy
43
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:
(A.1)
𝑂𝑛𝑙𝑖𝑛𝑒_𝑠ℎ𝑜𝑝𝑝𝑖𝑛𝑔ℎ
= 𝜋1 + 𝜋2 ∗ 𝑇𝑖𝑚𝑒𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡 ℎ + 𝜋3 ∗ 𝑇𝑖𝑚𝑒𝑐𝑜𝑛𝑡𝑟𝑎𝑖𝑛𝑡 2ℎ + 𝜋4 ∗ 𝑂𝑣𝑒𝑟𝑎𝑙_𝐻𝑒𝑎𝑣𝑦/𝑏𝑢𝑙𝑘𝑦_𝑠ℎ𝑎𝑟𝑒ℎ
+ 𝜋5 ∗ 𝑂𝑣𝑒𝑟𝑎𝑙𝑙_𝑆𝑒𝑛𝑠𝑜𝑟𝑦_𝑠ℎ𝑎𝑟𝑒ℎ + 𝜋6 ∗ 𝐻𝐻_𝑚𝑒𝑚𝑏𝑒𝑟𝑠ℎ + 𝜋7 ∗ 𝑆𝑜𝑐𝑖𝑎𝑙_𝑐𝑙𝑎𝑠𝑠ℎ + 𝜋8
∗ 𝑆𝑜𝑊_𝐴𝑠𝑑𝑎ℎ + 𝜋9 ∗ 𝑆𝑜𝑊_𝑆𝑎𝑖𝑛𝑠ℎ + 𝜋10 ∗ 𝑆𝑜𝑊_𝑇𝑒𝑠𝑐𝑜ℎ + 𝜋11 ∗ 𝑆𝑜𝑊_𝑊𝑎𝑖𝑡ℎ + 𝜀ℎ
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 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
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.
44
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 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 single-channel,
offline households (Degeratu, Rangaswamy, and Wu 2000; Morganosky and Cude 2001;
Raijas and Tuunainen 2001; Rohm and Swaminathan 2004).
Table A.1 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 adopt the online channel offered by each of the multichannel
chains. Compared with a reference (Social_class 4), consumers in the social classes ranked
14
In contrast with the moderating variables in Equation 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 Equation A.1 is also measured as the overall share.
45
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.
[Insert Table A.1 about here]
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 singlechannel, 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.
Selection Model
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
(Equations 1 and 2); the selection equation related to chain choice appears in the following
probit model:
(A.2)
𝐶ℎ𝑎𝑖𝑛_𝑐ℎ𝑜𝑖𝑐𝑒ℎ𝑐𝑡
= 𝜏𝑐 + 𝛿1𝑐 ∗ 𝑇𝑟𝑒𝑛𝑑𝑡 + 𝛿2𝑐 ∗ 𝐶ℎ𝑎𝑖𝑛_𝑐ℎ𝑜𝑖𝑐𝑒ℎ𝑐,𝑡−1 + 𝛿3 ∗ 𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒ℎ𝑐𝑡 + 𝛿4
∗ 𝑅𝑒𝑙𝐴𝑠𝑠𝑜𝑟𝑡𝑚𝑒𝑛𝑡ℎ𝑐𝑡 + 𝛿5 ∗ 𝑅𝑒𝑙𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒ℎ𝑐 + 𝜖ℎ𝑐𝑡
46
To capture intrinsic utility, we included chain-specific intercepts (𝜏𝑐 ) 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 A.2 provides an overview of the selection Equation A.2. The error correlation
(rho) between the selection model and the regression (SoW) model is significant (²(1) =
3111.80, p < .01), so the selection equation should precede the SoW equation.
[Insert Table A.2 about here]
All multichannel chains have negative intrinsic attractiveness (𝜏𝐴𝑆𝐷𝐴 =-1.59,
p<.01; 𝜏𝑆𝐴𝐼𝑁𝑆 =-2.05, p<.01; 𝜏 𝑇𝐸𝑆𝐶𝑂 =-1.62, p<.01; 𝜏𝑊𝐴𝐼𝑇 =-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<0.1; 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).
47
Table A.1 Logistic Regressions
Variables
Intercept (π1 )
Asda
-4.97**
(.17)
Time_constraint (π2 )
.05**
(.004)
Time_constraint² (π3 )
-.0004**
(.0001)
Overall heavy_bulky share (π4 )
.27
(.49)
Sensory_share (π5 )
.12
(.21)
Household_members (π6 )
.16**
(.02)
Social_class1 (π71 )
.15
(.33)
Social_class2 (π72 )
.23**
(.09)
Social_class3 (π73 )
.20**
(.08)
Social_class5 (𝜋75 )
.04
(.10)
Social_class6 (π76 )
-.09
(.11)
SoW_Asda (π8 )
1.84**
(.09)
SoW_Sains (π9 )
.38**
(.15)
SoW_Tesco (π10 )
.08
(.12)
SoW_Wait (π11 )
-1.28*
(.65)
Notes: Standard errors are in parentheses.
*p < .05, **p < .01, all two-sided.
Sainsbury’s
-4.93**
(.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)
Tesco
-3.86**
(.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)
Waitrose
-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)
48
Table A.2 Selection Equation
Intercept
Asda (τASDA )
Sainsbury's (τSAINS )
Tesco (τTESCO )
Waitrose (τWAIT )
-1.59**
(.28)
-2.05**
(.32)
-1.62**
(.31)
-1.86**
(.31)
Trend
Asda (δASDA1 )
Sainsbury's (δSAINS1 )
Tesco (δTESCO1 )
Waitrose (δWAIT1 )
-.10**
(.003)
-.07**
(.002)
-.14**
(.004)
-.04**
(.004)
Lagged choice
Asda (δASDA2 )
Sainsbury's (δSAINS2 )
Tesco (δTESCO2 )
Waitrose (δWAIT2 )
Marketing mix
RelPrice (δPRICE )
Relassortment (δASSORT )
Reldistance (δDIST )
Rho
1.45**
(.02)
1.52**
(.02)
1.40**
(.02)
1.69**
(.03)
-.07
(.18)
1.54**
(.13)
-1.09**
(.02)
-.33**
(.006)
Notes: Standard errors are in parentheses.
*p < .05, **p < .01, all two-sided.
49