A model of retail format competition for non-durable goods

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Intern. J. of Research in Marketing 21 (2004) 39–59

A model of retail format competition for non-durable goods

Amit Bhatnagara,*, Brian T. Ratchfordb

aDepartment of Marketing, University of Wisconsin-Milwaukee, Milwaukee, WI 53209, USAbRobert H. Smith School of Business, University of Maryland, College Park, MD 20742-1815, USA

Received 8 January 2001; accepted 19 May 2003

Abstract

In the academic literature pertaining to store choice, studies have traditionally limited the choice to stores within a certain

format. The role played by different retail formats has not been studied extensively. This paper, therefore, develops a general

model of retail format choice for non-durable goods. Using one common model, we are able to isolate the states under which

patronizing supermarkets, convenience stores, and food warehouses would be optimal. The optimality of the different formats is

shown to depend on membership fees, travel costs, consumption rates, perishability of products, inventory holding costs of

consumers, and cost structures of retailers. We develop several hypotheses regarding format choice by consumers. We test the

hypotheses on self-reports of shopping behavior in hypothetical situations.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Format choice; Self-selection; Supermarkets; Convenience stores; Food warehouses

1. Introduction actual practice, if we consider say grocery products,

Retail patronage issues have engaged academic

minds ever since the dawn of marketing as a scientific

discipline. In most of these studies, consumers eval-

uate a group of stores on a set of attributes and then,

depending upon their individual preferences, patron-

ize the best store. There has been an implicit assump-

tion in these studies that all the stores in the choice set

would be within the same format. The driving logic

has been that since different formats retail different

products, the type of the product to be purchased

would automatically determine the retail format. In

0167-8116/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.ijresmar.2003.05.002

* Corresponding author. Tel.: +1-414-229-2520; fax: +1-414-

229-6957.

E-mail address: [email protected] (A. Bhatnagar).

we will find several products that are retailed by more

than one format, e.g., milk, eggs, bread, and sodas are

sold by convenience stores, supermarkets, food ware-

houses, etc. This would indicate that there are other

factors in addition to the absence or presence of

product categories that determine whether a store

should be included in the choice set or not. In this

paper, we try to identify these determinant factors by

building a theoretical model of format choice.

In this study, we focus on convenience stores,

supermarkets, and food warehouses, as they are the

most popular retailing formats in the grocery sector.

Among the three, convenience stores have the lowest

breadth of assortment, but the highest price. In turn,

supermarkets have higher breadth as compared to

convenience stores but lower prices. A food ware-

house has lower prices as compared to a competitive

Exhibit 2

Percentage of Nielsen’s household panel buying at each retail

format (includes multiple responses)

Product Convenience

store (%)

Supermarket

(%)

Food

warehouse

(%)

Analgesics 1 48 8

Apple juice 1 42 4

Beer 5 26 3

Cat food 1 28 3

Cat litter – 22 3

Catsup 1 68 7

Cereal RTE 2 92 12

Cheddar cheese 1 47 5

Cheese, processed – 18 –

Cigarettes 17 28 5

Coffee, ground 1 59 10

Cough remedies – 15 1

Cups, disposable – 13 1

Dog food 1 27 4

Eggs 3 89 9

Frozen poultry – 23 9

Gelatin – 40 –

Greeting cards 2 55 1

Gum 3 26 3

Hair spray – 21 1

Hosiery – 19 –

Ice cream 5 78 2

Jelly – 35 2

Light bulbs – 45 4

Lunch meat – 43 2

Mayonnaise 1 60 4

Milk 24 94 12

Motor oil 1 8 2

Orange juice 2 57 3

Paper towels 1 78 7

Pens and pencils – 15 3

Pizza, frozen 1 60 4

Popcorn, unpopped 1 54 8

Potato chips 12 84 9

Razor blades – 14 2

Rice – 39 3

Salad and cooking oil 1 69 6

Shampoo – 46 6

Soap, bar 1 64 11

A. Bhatnagar, B.T. Ratchford / Intern. J. of Research in Marketing 21 (2004) 39–5940

supermarket, but similar breadth of assortment. The

breadth of assortment at a food warehouse is higher

than that of a convenience store but the prices are

much lower. The food warehouse is distinguished

from the other formats by the fact that it charges a

membership fee. This is an example of two-part

pricing, which has been studied in the economics

literature (Goldman, Leland, & Sibley, 1984; Oi,

1971; Willig, 1978). We also assume that, in general,

it takes more time to travel to and shop at a warehouse

as compared to a supermarket and at a supermarket as

compared to a convenience store, because of lower

levels of services. For instance, convenience stores

have more retail outlets as compared to supermarkets

leading to lower travel time costs for their patrons.

Similarly, supermarkets have a greater number of

outlets as compared to warehouses leading to lower

travel time costs. There are a number of other services

provided by retail stores that influence consumers

shopping time. We describe these services in detail

in subsequent sections. Please see Exhibit 1 for a

definition of the different formats.

An in-depth analysis of format choice issue in the

grocery sector should enable us to explain many in-

teresting observations of consumer behavior recorded

in the popular literature. ACNielsenmaintains a house-

hold panel of 40,000 consumers across the US and

Exhibit 2 would show that even though groceries are

retailed by all the three formats, some sell more of one

kind than the other. Why? In a universe abounding

with stock keeping units (SKUs), can we offer some

guidelines to the retailers about what categories and

sizes they should carry and what not? Again, it has

been empirically observed in a large number of studies

that the interpurchase time at different formats is

different. Why? Why should food warehouses charge

membership fees? Do the different retail formats target

different segments of buyers and if they are under the

Exhibit 1

Store attributes at different retail formats

Store attribute Convenience

store

Supermarket Food

warehouse

Prices High Low Lowest

Breadth of assortment Small Large Large

Consumer shopping

time costs

Low Moderate High

Membership fee No No Yes

Soft drinks 23 91 11

Soup, canned 2 77 4

Toilet bowl cleaners – 43 2

Toilet tissue 3 85 9

VCR tapes – 16 12

Yogurt, refrigerated 1 60 1

Supermarket Business: April 1996, p. 37.

same management can different formats attract their

targeted segment while blocking out non-targeted seg-

ments. In the backdrop of different supermarkets

A. Bhatnagar, B.T. Ratchford / Intern. J. of Research in Marketing 21 (2004) 39–59 41

operating under nearly perfect competitive conditions

and offering the lowest price, how can food ware-

houses offer even lower prices (McLaughlin, Hawkes,

& Perosio, 1992)? Do household refrigeration capacity

and house size play any role in format choice? It is

questions like this that provide a rationale for this

study.

In order to analyze the above issues, we put

forward a general model of store choice, where stores

in the choice set have different formats. The model is

grounded in economic theory of consumer behavior

and firm’s profit maximization. The aim here is to

unify the several disparate strands of knowledge about

retail formats in one parsimonious model. The model

is used to identify consumer states under which

patronizing supermarkets, convenience stores, and

food warehouses would be optimal. The key insight

is that the variation in retail formats can be explained

by the interplay between consumer travel costs, mem-

bership fees, consumer inventory capacity constraints,

and retail cost structures. The model is used to

generate several hypotheses about consumers’ format

choice. An interesting issue, not studied in detail in

previous marketing literature, which we highlight is

that format choice would be influenced by household

refrigeration capacity and house size. We test the

hypotheses on self-reports of shopping behavior in

hypothetical situations.

The rest of the paper is arranged as follows. In the

next section, we discuss the existing literature on

format choice. In Section 3, we set up the basic utility

structure of the consumer and profit equation charac-

teristic of the retailer. We put forward a general model

of format choice. In Section 4, we study the compe-

tition between supermarkets and convenience stores,

and in Section 5, between supermarkets and food

warehouses. Empirical tests of hypotheses developed

from our analysis are presented in Section 6, followed

by the conclusion part in Section 7.

2. Literature review

Store choice literature has a rich tradition and

some of the notable works to date are Arnold, Ma,

and Tigert (1978), Arnold, Oum, and Tigert (1983),

Arnold, Handelman, and Tigert (1996), Burke, Bari,

Harlam, Kahn, and Lodish (1992), Darden (1979),

Dawson, Bloch, and Ridgway (1990), Eagle (1984),

Keng and Ehrenberg (1984), Louviere and Gaeth

(1987), Mason, Durand, and Taylor (1983), Monroe

and Guiltinan (1975), and Spiggle and Sewall

(1987). These studies have variously tried to ratio-

nalize store choice in terms of store attributes,

importance weights, store attitudes, general shopping

patterns, household demographics, and situational

factors. For instance, a recent study (Kenhove, Wulf,

& Waterschoot, 1999) examined the impact of task

definition on store attribute salience and store choice.

They examined five different tasks, and for each

task, they found different attributes to be salient.

One of the tasks that they study is when consumers

have to buy in large quantities. For this task, con-

sumers retrieve a store that has large enough stock,

low prices, and a store that sells these products. Both

supermarkets and food warehouses would satisfy this

requirement. We study the competition between

supermarkets and food warehouses. We also provide

explanations for why different stores carry different

products. In addition, we study the role of member-

ship fees, product perishability and household capac-

ity constraints.

Most of the store choice studies have been restrict-

ed to stores within the same format, i.e., supermar-

kets, discount stores, department stores, etc. In a path

breaking paper written by Bucklin (1963), all stores

were classified as convenience, shopping or specialty

stores, depending upon whether they retailed conve-

nience, shopping, or specialty goods. At the other

extreme are studies by Krider and Weinberg (1998),

Lal and Rao (1997), etc., that assume a complete

overlap of assortments between formats. However,

the reality lies somewhere in between. Alderson and

Shapiro (1964) studied grocery retailers in Philadel-

phia and empirically observed that there is consider-

able overlap in the assortment carried by different

formats. This is all the more true for grocery prod-

ucts. If some goods are available at more than one

retail format (e.g., milk, bread, eggs are carried by

convenience stores, supermarkets, and food ware-

houses), it raises the interesting question of why for

a certain product, some consumers patronize one

format and others another. This question has been

left unanswered by most of the store patronage

studies as they limit choice to stores within the same

format.


Some recent studies have examined the role of

retail pricing formats on consumer shopping behavior.

For instance, Bell, Ho, and Tang (1998) and Ho, Tang,

and Bell (1998) have shown that consumers would

visit HiLo stores more than Every Day Low Pricing

(EDLP) stores and would also buy in smaller quanti-

ties there. Bell and Lattin (1998) have demonstrated

that consumers would buy larger baskets at EDLP

stores and smaller baskets at HiLo stores. Here,

differences in consumer behavior arise due to the

variability in pricing at different formats, and uncer-

tainty about prices at different stores. We identify

some additional sources of differences in patronage

behavior, such as travel costs, membership fees,

perishability of products, capacity constraints. Bell

et al. and Bell and Lattin assume that one format

has higher prices than the other. By modeling the

supply side, we are able to offer insights into why

different formats have different levels of prices for the

same product. In our model, formats also differ across

the size of assortment and we explain why different

formats carry different assortments.

Our modeling approach is similar to Bell et al.

(1998), which shows that there are fixed and variable

costs to shopping, and a consumer prefers a shop

where the total costs are minimized. In their model,

the fixed costs are the travel costs, which are mod-

ified by store loyalty. In our model, in addition to

travel costs, we also include fixed costs due to

membership fees. This issue becomes important when

we model the competition between supermarket and

Fig. 1. Market share of supermarkets, conv

food warehouse. Additionally, we model the problem

from the supply side to show how different formats

can have different price levels under perfect compe-

tition. We also examine the reasons why interpurch-

ase time at different formats is different and what

impact it has on the assortments offered by different

formats.

Messinger and Narasimhan (1997) argue that large

assortments become more important as time costs

increase, which would lead one to predict that super-

markets, which offer large assortments, would become

more popular over time relative to convenience stores.

However, Fig. 1 shows that over the last 10 years, the

market share of supermarkets and convenience stores

has remained constant. This would suggest that there

are market segments that prefer smaller assortments

and the reasons for that need to be investigated.

Additionally, Messinger and Narasimhan show that

supermarkets owe their success to one-stop shopping.

We show that supermarkets can be attractive under

two additional states. One is lower prices: when a

consumer perceives a large enough price difference

between a supermarket and a convenience store to

offset the added travel and time costs of shopping

there. A less obvious insight is that the supermarket

becomes attractive even when the consumer is inter-

ested in just one product category, if he intends to buy

in large quantities. This demand for quantity per trip

increases as the rate of consumption increases and as

inventory holding cost decreases. Messinger and Nar-

asimhan, in their conclusion section, provided the

enience stores, and food warehouses.


blueprint for future research in this field and therefore

needs to be quoted in full,

. . .we think it would be desirable to develop

extensions that incorporate such critical issues as

store location, purchase frequency and consumer

holding of inventory, imperfect information and

product heterogeneity. . .we especially look for-

ward to examination, along the lines of analysis

used herein, of other growing retail formats.

Varying forms of consumer economies are provid-

ed by such emerging store formats as wholesale

clubs, convenience stores. . .cost shifting, which

goes beyond the shopping tradeoffs studied in this

paper, would seem to explain much about store

formats.

Taking our cue from Messinger and Narasimhan

(1997), we study some of the more relevant formats in

the grocery sector. We also incorporate variables like

location, purchase frequency, consumer holding of

inventory, and perishability. We study wholesale clubs

(food warehouses) and convenience stores and study

the different ways that cost-shifting takes place be-

tween the retailer and the consumer. The building

blocks of our analysis are a model of a utility max-

imizing consumer planning shopping trips in the face

of travel and inventory holding costs, and a model of

profit-maximizing retail firms in a competitive setting

with free entry.

3. Analytic scenario

The economic agents in this scenario are the

consumers and the retail firms. We first discuss

consumers’ decision-making model. We start with a

specification of a consumer’s basic utility structure,

and lay out a stylized model of shopping that we think

captures the essential aspects of choice between the

formats we have chosen to study. A model of any

individual consumer’s demand for a format as a

function of relative prices and travel costs (or serv-

ices) is formulated. This model holds for any given set

of prices and services, and determines the shape of

any consumer’s demand function. The prices and

services that are actually offered to consumers depend

on the decisions of retailers, which are considered in

the second section. This general model is used to

identify states under which consumers would patron-

ize convenience stores, supermarkets, and food ware-

houses. Finally, we present a model for the supply

side, in particular, we analyze the profit function of a

retailer. Because we wish to focus on the role of

shopping costs in determining retail formats, we make

a number of simplifying assumptions. These are as

follows:

(a) There are no temporal price fluctuations at a

store. That is, the prices at all stores remain

constant over the purchase cycle.

(b) All consumers have the same utility function,

which is stable over time.

(c) As implied by assumptions (a) and (b),

household i’s consumption rate cji, of product

category j, is stable over time. To avoid stock-

outs, consumers will either increase their number

of trips for the entire market basket or do fill-

in shopping. Their choice depends on tradeoffs

between prices and travel costs.

(d) The following household factors are also treated

as exogenous—household income, space con-

straint, and shopping cost at different retail

formats.

(e) Consumers do not indulge in impulse shopping.

3.1. Consumer’s utility structure

Consumers need to acquire goods to produce

household commodities like food, shelter, clothing,

etc., and they are willing to invest time and money

into acquiring these goods. A household’s preference

is represented by an ordinal, increasing, strictly quasi-

concave, twice differential utility function. Utility at

any time interval is derived from the flow of con-

sumption at that instant of time of the items in the

market basket. Because this flow is stable through

time by assumption (c) above, we can write the utility

of any household i over a time horizon of V periods

as:

Ui ¼ VUðci1; ci2; . . . ; cikÞ; ð3:1:1Þ

where we assume that discounting can be ignored.

Assume that the consumer wishes to maximize Eq.

(3.1.1) over the horizon V. We need to define a

1 Since S must be in integer units in practice, it is possible that

the consumer will satisfy this integer constraint by taking a mixture

of major trips and smaller trips, that interpurchase times will not be

the same for all trips, and that the capacity constraint will not be

binding for all trips. We address some of these issues later. If the

capacity constraint is not binding for T, then the required number of

trips is one.


relation between the flow of consumption, and quan-

tity of items purchased; without loss of generality we

can define one unit quantity as the amount needed to

obtain one unit of consumption in a time interval t.

Thus, assuming that the consumer does not maintain a

beginning or ending inventory, quantity purchased of j

over the horizon V must be qji(V) =Vcj

i.

Using this utility concept, we develop a model of

format choice based on the assumption that a con-

sumer maximizes the utility over the V period hori-

zon subject to a budget constraint, shopping costs

and a capacity constraint. Let pj be the price of good

j, Ii the income allocated to these purchases, and Ki

the total capacity of consumer i to stock grocery

items. A consumer has to incur certain time costs in

shopping at any store. These costs can arise due to

travel costs. The underlying theme of all the retail

gravity attraction models (Huff, 1964) is that travel

distance provides disutility to the consumer and all

else being equal a consumer would patronize the

nearest store. Additional sources of cost could be

time costs involved with finding a suitable parking

spot, assembling the market basket, time costs at the

check out counter, etc. These costs, which are sum-

marized by Gi, are proportional to the number of

shopping trips S that the consumer takes over the

relevant horizon: Gi = giSi. These costs reduce the

discretionary income available to a consumer. Con-

sumer i therefore maximizes his utility subject to the

following two constraints:

Xj

pjqijðV Þ ¼ I i � Gi ¼ I i � giSi ð3:1:2Þ

Xj

qijðMiÞVKi; ð3:1:3Þ

where qji(Mi) is the amount of j bought on any

shopping trip. Here, Mi is the interpurchase shopping

duration for household i. The first constraint (Eq.

(3.1.2)) ensures that a household does not spend

more than the total discretionary income available

to it and the second constraint (Eq. (3.1.3)) ensures

that the total quantity of the basket purchased on any

trip is less than or equal to the household’s capacity

to stock.

Assume initially that the goods purchased for the

market basket do not perish over the purchase cycle so

that perishability is not an issue. Then the capacity

constraint determines interpurchase times and indi-

rectly determines shopping costs by determining the

number of trips that are necessary. Since the cost per

shopping trip gi is fixed, the consumer will wish to

minimize the number of shopping trips. The imped-

iment to this is the capacity constraint. Since qji(Mi)=

Micji, the constraint (Eq. (3.1.3)) implies that the

interpurchase time is Mi ¼ Ki=ðP

j cijÞ , if the con-

straint is binding. The fraction of the time horizon

between trips, and the fraction of the consumer’s

requirements bought on any trip becomes ðMi=V Þ ¼Ki=ð

Pj q

ijðV ÞÞ, and the number of trips over the time

horizon is Si =V/Mi.1 Obviously, the quantity pur-

chased on any trip increases with capacity, and

interpurchase times increase with capacity, and

decreases with increases in the rate of consumption.

Since shopping costs Gi are indirectly determined by

the capacity constraint in Eq. (3.1.2), consumer i’s

unique optimal demands at price vector p̄ can be also

represented by the indirect utility function vi, and the

indirect utility function of consumer i can be written

as, vi(p̄,I i�Gi).

If the consumer does not have to worry about items

perishing, the consumer will adjust all purchase quan-

tities to be just sufficient to cover consumption over

the interval Mi. If some items perish before this time,

the consumer has three choices: do without, reduce

the time between shopping trips for the entire market

basket, do smaller fill-in trips for the perishable items.

As discussed later in the paper, the option of fill-in

trips can be attractive if the cost per trip needed to

acquire perishable items is less than the cost of

acquiring the entire market basket. Perishability could

be modeled formally by adding constraints to those

expressed above in Eqs. (3.1.2) and (3.1.3), and

format choice can be modeled by extending the model

outlined above to allow explicitly for different price

levels and travel costs for different formats. The


remainder of the paper is devoted to examining the

issue of format choice.

3.2. Model of format choice

In this section, we develop a general model of

consumers store-format choice for a consumer having

the utility structure as defined in Section 3.1. Given

our assumptions framing the problem in terms of the

entire time horizon V, or the time horizon covered by

the next purchase, Mi, gives equivalent results. We

choose to work with the latter, studying the choice of

the next shopping trip. Suppose a consumer is plan-

ning a shopping trip and has to decide between stores

having different formats. One criterion in format

choice would be the general prices at the different

formats. Let the price vector at a store of format A be p̄

and at a store of format B be ðp� xÞ, where x is some

positive number. For simplicity, we assume that the

price difference between corresponding items at the

two formats remains the same. All consumers do not

automatically prefer the cheaper store, because they

incur higher time costs at the cheaper store due to

several factors. One factor could be that the cheaper

store offers lower level of services, such as fewer

outlets leading to higher travel costs, poor presenta-

tion, poor atmospherics, etc. This would increase the

time costs at the cheaper store. Another factor could be

lower depth of assortment. All consumers have an

ideal brand-size that they would like to buy in each

product category. If the consumers cannot find their

ideal brand-size at a store, then from the available

brand-sizes they buy the brand-size that is nearest to

their ideal. The cost of buying less than ideal can be

represented by D, where D is increasing in the distance

between the consumer’s ideal brand-size and the

nearest available brand-size at a store. This cost, i.e.,

depth cost, would be more in stores with lower depth

of assortment, i.e., convenience stores, as opposed to

supermarkets. An additional factor could be the pay-

ment of membership fee F in order to shop at that

store. We will discuss these factors in greater detail in

Section 5. The extra time cost reduces the discretionary

income of the consumer. Now, the format where the

consumer incurs higher time cost will have to offer

some incentive to the consumer. And the incentive that

they offer is lower prices. The consumer choice at any

given trip can be shown to be (here, we should note

that consumer choice of format would differ from trip

to trip based on shopping objective):

Format A: p̄; I i � TiA � DA

Format B: ðp� xÞ; I i � TiB � F � DB

ð3:2:1Þ

Here, TAi , TB

i are the shopping costs, DA, DB the depth

costs at formats A and B, and F the membership fee at

format B. A consumer would self-select format B, if

the following two conditions are satisfied:

Participation constraint :

viððp� xÞ; I i � TiB � F � DBÞ > 0

Incentive constraint :

viððp� xÞ; I i � TiB � F �DBÞ> viðp̄; I i �Ti

A�DAÞ:

The participation constraint simply ensures that a

consumer’s discretionary income is sufficient to cover

the prices of the products and the accompanying cost

of shopping. A consumer would not go to a store

where he has to exert too much effort or if the product

is priced out of his budget (e.g., caviar for a graduate

student). The incentive constraint ensures that a con-

sumer derives greater utility at format B as compared

to format A. So if by lowering the prices, a higher cost

format can make itself more attractive than a lower

cost format, a consumer would prefer it. For further

mathematical analysis, we rewrite (TBi� TA

i) as c1i x , F as

as c2x, and (DB�DA) as c3x. Here, c1i , c2, and c3 are

constants, and x is the price difference between any

two products at formats A and B. We should note that

TBi and TA

i are individual specific and, therefore, c1i is

individual specific. On the other hand, membership fee

is common to all and therefore c2 has no i index.

Similarly, depth of assortment is store specific and

common for all consumers and, therefore, it has no i

index. Therefore, the total extra costs incurred by

consumer i at the format with lower prices would be

(c1i + c2 + c3)x. For small x, we can also write the

incentive constraint as:

dvi

dx

��x!0

> 0

dvi

dx

��x!0

¼ �Xj

Bvi

Bpj� Bvi

BI iðci1 þ c2 þ c3Þ

ð3:2:2Þ


Since Roy’s identity implies that Bvi/Bpj=� qji(0)(Bvi/

BI i), we can write Eq. (3.2.2) as:

¼ Bvi

BI i

Xj

qijðMiÞ � ðci1 þ c2 þ c3Þ !

> 0:

Since, Bvi/BI i>0, consumer i would prefer format B

over format A only if the following condition is

satisfied:

Xj

qijðMiÞ > ðci1 þ c2 þ c3Þ: ð3:2:3Þ

Since we must haveP

j qijðMiÞVKi , then, if the

capacity constraint is binding, format B would be

preferred over format A, if the following condition is

satisfied:2

Ki > ðci1 þ c2 þ c3Þ: ð3:2:4Þ

In other words, format B is preferred if total quantity

purchased across all the j categories exceeds the extra

cost of shopping at lower price store (for example,

added travel cost) per dollar of the price differential.3

Small capacity favors the high priced store because

the consumer saves less per shopping trip from the

lower priced store, making it harder to overcome the

higher shopping costs. From the preceding section, we

know that qji(Mi) =Micj

i. While Eqs. (3.2.3) and (3.2.4)

are general conditions.

3.3. Profit function of the retail firm

The other economic agents in our model are the

retail firms. We next analyze the profit function of

retail firms to show that stores with lower prices

would have lower marginal costs of operation. A

2 For the entire time horizon, the equivalent result would

involve multiplying both sides by Si=V/Mi.3 A second-order effect that we do not consider is that the cost

differential between stores could trigger some change in the size of

the market basket, with lower costs per item leading to more

expenditures on the focal items. Unless cost differentials are very

large these effects are likely to be relatively small for the non-

durable items that we study.

store with format B charges a price vector p� x,

while constrained by competition to operate on the

iso-profit line of 0 profit. Let NB be the number of

customers patronizing a store within format B. The

profit pB earned by a retail store can be expressed

as the difference between total revenue and total

cost:

pB ¼ NB

Xj

ðpj � xÞqjðpj � xÞ

� C aB;NB

Xj

qjðpj � xÞ !

ð3:3:1Þ

where qj( pj� x) is the quantity of product category

j purchased by a prototypical customer at store B

at price ( pj� x). The total quantity, sold by the

store, multiplied by price gives the total revenue.

The cost structure of the retail firm is expressed

by CðaB;NB

Pj qjðpj � xÞÞ, which includes both the

wholesale prices and the cost of retailing. The total

cost is strictly increasing and twice differentiable in its

arguments. The argument aB in the cost structure

indexes the production technology of the retail firm.

As aB increases, a retail firm faces higher costs due to

greater provision of retail services, which are bundled

with the goods being sold (see Betancourt & Gautschi,

1988 for an in-depth explanation of these retail serv-

ices). We assume that a retail firm can lower its cost by

transferring the services it traditionally performs to the

consumer, in return for lower prices. As the total

turnover increases, the total costs also increase but at

a decreasing rate due to increasing returns to scale.

Therefore,

BC aB;NB

Xj

ðpj � xÞ !

B NB

Xj

qjðpj � xÞ ! > 0;

B2C aB;NB

Xj

qjðpj � xÞ !

B NB

Xj

qjðpj � xÞ !2

< 0: ð3:3:2Þ


If the store maximizes profit, we can write the

familiar marginal revenue-marginal cost condition for

maximum profit:

dpB

dx¼ �NB

Xj

qjðpj � xÞ þXj

ðpj � x� CVÞ

� ððdNB=dxÞqjðpj� xÞþNBdqjðpj�xÞ=dxÞ¼ 0

ð3:3:3ÞHere, CV is the marginal cost of retailing. The term

inside parentheses on the far right of Eq. (3.3.3)

represents the volume increase due to the price cut,

which is due to attracting new customers, and to

greater volume purchases from existing customers.

This term must be positive. Obviously, the markup on

marginal cost, ( pj� x�CV), must be positive if Eq.

(3.3.3) is to hold, i.e., retail price should be higher

than the marginal cost. Different formats follow

different strategies to keep this markup positive and

the details are in the following sections.

4. Supermarket–convenience store competition

On average, consumers have to travel a much larger

distance to reach supermarkets than convenience

stores, making the time costs for supermarkets much

higher than that for convenience stores. The difference

in shopping time costs arise essentially due to travel

costs, as all other factors, like atmospherics, presenta-

tion, etc., would be the same at the two formats. So, the

more distant supermarket has to provide some incen-

tive to a consumer to convince him to self-select the

supermarket. The supermarket can do so by lowering

the price of its assortment (how it can do so is some-

thing we will leave for the second half of this section).

Eq. (3.2.3) indicates that a consumer would prefer the

supermarket if the following condition is met:

xXj

qijðMiÞ > ðTiB � Ti

AÞ þ ðDB � DAÞ

ifXj

qijðMiÞ < Ki; ð4:1Þ

xKi > ðTiB � Ti

AÞ þ ðDB � DAÞ; otherwise:

Here, TBi stands for the time cost and DB for the

depth cost at the supermarket, and TAi for the time cost

and DA for the depth cost at the convenience store.

Since, neither supermarket nor convenience store

charge membership fee, c2x would be 0. Since, the

depth of assortment at the convenience store is smaller

than that at the supermarket, DA>DB. It suggests five

conditions that affect the optimality of going to the

supermarket. While some of these conditions are

cross-sectional in nature, i.e., different consumer seg-

ments would patronize different retail formats, some

others are inter-temporal in nature, i.e., at different

points of time, the same consumer may rationally

choose to select a format that is different from the one

chosen previously. The first condition is inter-tempo-

ral in nature, whereas the next four are cross-sectional.

4.1. Market basket

A consumer would patronize a supermarket, if he

has to buy from a large number of categories, i.e., as j

increases. There, thus, exists a market basket size

threshold barrier beyond which consumers self-select

supermarkets. This is the result arrived at byMessinger

and Narasimhan (1997). Therefore, at those shopping

incidences, where a consumer has to buy from a large

number of categories, he would prefer supermarkets.

Hypothesis 1a: The likelihood of patronizing super-

markets is increasing in the number of product

categories purchased by the consumer.

4.2. Price difference

When the price difference, i.e., x, between the

supermarket and the convenience store is large

enough. Therefore, those consumer segments that

perceive the price difference between the supermarket

and the convenience store to be large would prefer

supermarkets.

Hypothesis 1b: The likelihood of patronizing super-

markets is increasing in the perceived price saving per

item offered by the supermarket.

4.3. Depth of assortment

Consumers are likely to value deeper assortments

more as they have a higher probability of finding a

brand-size close to their ideal.


Hypothesis 1c: The likelihood of patronizing super-

markets is increasing in the perceived depth of

assortment at the supermarket.

4.4. Time costs

When the difference in travel costs to the super-

market and the convenience store, TBi� TA

i , is very

small. The travel cost to a store is determined by the

(i) physical distance between the consumer’s house

and the store and (ii) the availability of automobiles.

Therefore, if a particular consumer lives very close to

a supermarket, the difference in travel costs for her

would be very small, and her probability of shopping

at the supermarket would increase. Similarly, if two

consumers live at the same distance from a store, but

if one owns a car and the other one does not; the one

with the car would experience lower travel cost.

These considerations suggest the following two

hypotheses regarding cross-sectional differences in

supermarket patronage.

Hypothesis 1d: The likelihood of patronizing super-

markets is decreasing in the consumer’s physical

distance from the supermarket.

Hypothesis 1e: The likelihood of patronizing super-

markets is increasing in the consumer’s ownership of

cars.

Now, it needs to be seen whether it makes eco-

nomic sense for the supermarkets to lower the prices.

A supermarket can increase its customer base by

lowering its prices and it should lower it to a level

that cannot be matched by the competing convenience

store. At the same time, it should make at least as

much profit as the convenience store. It needs to be

checked whether it is economically possible to do so,

if the requirement in Eq. (3.3.3) is met, i.e., if

( pj� x�CV) is positive. It would be so due to

increasing efficiencies in operation brought about by

the economies of scale, due to which marginal costs of

operation decline at higher levels of turnover. Since

the turnover at the supermarket is much higher than

that at the convenience store, the marginal costs at the

supermarket are comparatively much lower and thus

the supermarket is able to compete successfully with

the convenience store. Another factor leading to the

lower costs of operation would be that supermarkets

have fewer retail outlets as compared to convenience

stores. Therefore, supermarkets have better distribu-

tion efficiency.

One reason for patronizing convenience stores can

be time constraint. If a consumer needs to buy

something in a hurry, i.e., an inexpensive emergency

good, he cannot wait till the end of week to buy it with

the rest of grocery basket and he would rush to the

nearest convenience store. Additionally, it needs to be

studied whether there are any non-emergency situa-

tions where the consumer, in spite of all his knowl-

edge of higher prices at the convenience store is

compelled to go to a convenience store.

From the discussion in Section 4.3, we see that a

consumer would prefer the convenience store if Eq.

(4.1) is not satisfied, or whenP

j qijðMiÞ is very small.

There are two conditions under whichP

j qijðMiÞ

would be small. First, if the number of product

categories is kept constant, then as qij (M

i) becomes

very small, the left-hand side in Eq. (4.1) becomes

small and the consumer self-selects convenience

stores. Thus, even if the consumer is buying his

market basket, if he purchases in small quantities, he

would prefer the convenience store. Therefore, there

exists a quantity size barrier. That helps to explain

why convenience stores retail only small packet sizes

as compared to supermarkets. Second, a consumer

would prefer convenience store, if he is planning to

purchase from a few product categories, or if j is very

small. Next, we identify conditions under which these

two conditions are met. The first condition explains

inter-temporal variations in format patronage, whereas

the remaining conditions explain cross-sectional var-

iations in format patronage.

4.5. Perishability

As stated above, if the life of some item is less

than Mi, the consumer must either do without or

increase the frequency of shopping trips. Assume that

the latter yields more utility, and that the consumer is

faced with the choice of increasing the frequency of

major trips, or of engaging in fill-in shopping at a

convenience store. To fix ideas, suppose that some

items have a life of Mpi <Mi, where Mi is the inter-

purchase time determined by the consumer’s capacity

constraint. For the time horizon V, the consumer has

A. Bhatnagar, B.T. Ratchford / Intern. J. of R

the option of making V/Mpimajor trips at a cost of gi per

trip, or to continue with V/Mi major trips interspersed

with one or more fill-in trips in between. The cost of

the major trips continues to be gi, while the costs of the

fill-in trips are ci per cycle. These costs include the

costs of any increased prices the consumer may have to

pay at a convenience store. The fill-in shopping

alternative will be preferred if ( gi + ci)(V/Mi) < gi(V/

Mpi). This can be expressed as ci < gi((Mi/Mp

i)� 1). Fill-

in shopping at the convenience store becomes more

attractive as its cost per shopping trip is lower relative

to the cost of the main trip to the supermarket, and as

the life of perishable items decreases, making Mi/Mpi

larger. The latter leads to the following hypothesis:

Hypothesis 2a: The likelihood of patronizing conve-

nience store as compared to the supermarket is

increasing in the perishability of the goods in the

shopping basket.

4.6. Capacity constraints

A consumer i would prefer the convenience store if

Ki is very small. Therefore, those consumer segments

that face a capacity constraint, i.e., they have a small

house or small refrigerator, will shop in small quan-

tities. This may explain why the supermarkets are

more popular in the suburbs as compared to the

downtown area. In urban areas, citizens occupy small

apartments, with limited storage space and so they are

compelled to buy the goods used in household pro-

duction in small quantities. That is perhaps the reason

that the assortment that downtown convenience stores

offer is much broader than the assortment at a subur-

ban convenience store.

Hypothesis 2b: The likelihood of patronizing conve-

nience store as compared to supermarket is increasing

in the capacity constraint faced by the consumer.

Krider and Weinberg (1998) were perhaps the

4 A neighborhood variety store is a retail store that sells a wide

assortment of non-grocery products and complements a neighbor-

hood grocery store in many parts of the world.

first to study the role of perishability in the context

of multi-store shopping and obtained somewhat

similar results. As the perceived perishability de-

creases, consumers are more likely to shop at the

low priced discount store (analogue of supermarket

in our case). We model the problem from the supply

side to show how the supermarket is able to offer lower

prices.

4.7. Household income

From constraint (3.1.2), we can deduce that con-

sumer segments with low income would buy in small

quantities, i.e., qji would be small.

Hypothesis 2c: The likelihood of patronizing conve-

nience store as compared to the Supermarket is

decreasing in the household income.

4.8. Household size

It seems intuitive that consumer segments charac-

terized by small households sizes would consume

smaller quantities as compared to large household

sizes, i.e., qji would be small.

Hypothesis 2d: The likelihood of patronizing conve-

nience store as compared to the supermarket is

decreasing in the household size.

We can use the insights gained above to explain

Exhibit 2. Eggs, ice cream, and milk are normally

stored in the refrigerator and consumed in large quan-

tities. Therefore, households face a space constraint for

these goods. These items are also perishable. Beer,

potato chips, and soft drinks are consumed in large

quantities and are needed quickly if a household runs

out of stock. Most of the convenience store soft drink

sales are in single containers to teenagers and work-

men, who consume these on the premises or on the job.

On these occasions, soft drink sales cannot be com-

bined with the market basket. Gum is an impulse item

and therefore its purchase is not planned and part of the

market basket. Wertenbroch (1998) offers an interest-

ing insight into why convenience stores are more

heavily patronized by smokers. According to this

study, consumers ration their purchase quantity to

solve their self-control problem by limiting their stock

and hence consumption opportunities. Since smokers

ration their purchase quantities, they would have to

make several fill-in trips to the convenience store.

The above analysis may also provide an explana-

tion for the disappearance of the neighborhood variety

store.4 The two—convenience store and the variety

store, till recently used to be regular features in the

esearch in Marketing 21 (2004) 39–59 49


neighborhoods in US and between them covered most

of society’s needs, but, whereas the convenience store

is still flourishing, the variety store has disappeared.

This is because any of the above reasons, which

ensure the success of convenience stores, do not apply

in the case of variety stores. Non-grocery items are

neither perishable nor are they bought in bulk. While

variety stores did sell emergency goods such as

electric fuses, the market for such products is highly

limited and has been taken over by the convenience

store. For example, convenience stores sell electric

fuses and similar items.

5. Food warehouse–supermarket competition

The most interesting and promising development

in grocery retailing in the US in the last decade has

been the emergence of food warehouses. The food

warehouses are now a standard fixture on the retail-

scape of most major American cities and command a

loyal electorate. Food warehouses charge a member-

ship fee F and limit their access to their members only.

The price at the food warehouse is lower than that at

the supermarket. Membership fee F reduces a con-

sumer’s income by an amount c2x. In general, food

warehouses are located farther away than supermar-

kets and consumers have to incur not only extra

membership fee but also extra travel cost. Addition-

ally, food warehouses offer lower level of services,

and depth of assortment as compared to supermarket,

which increases consumers shopping times and depth

costs. In case of food warehouses and supermarkets,

DA <DB. Assuming the extreme example of time and

depth costs being same for both formats, Eq. (3.2.3)

suggests that a consumer would self-select food ware-

house over supermarkets, if the following condition

is met: xP

j qijðMiÞ > F. If time and costs were not

the same, then the right-hand side would be F+

(TBi� TA

i )+(DB�DA), where (TBi� TA

i ) is the differ-

ence in shopping time cost and DB�DA is the differ-

ence in depth costs between the food warehouse and

the supermarket. In reality, the number of product

categories bought at the food warehouse would be less

than that at the supermarket. However, we consider the

extreme case where the number of product categories

bought at the two formats is the same. All the follow-

ing results would hold, even when the number of

product categories bought at the food warehouse is

less than that at the supermarket. There are two ways

the inequality condition can be met and both the

conditions are cross-sectional in nature.

5.1. Household size

If a household buys goods in quantity sizes beyond a

threshold barrier, i.e., qji is very large. Therefore, heavy

users would prefer the food warehouse. In general, a

bigger household would consume in large quantities.

Since the heavy users prefer the food warehouse, all

the packaged goods there are sold in large sizes.

Hypothesis 3a: The likelihood of shopping at the

food warehouse is increasing in the household’s size.

5.2. Price difference

If the price difference between the supermarket and

the food warehouse is large enough.

Hypothesis 3b: The likelihood of shopping at the

food warehouse is increasing in the price saving per

item offered by the food warehouse.

Nowadays, a large number of supermarkets are

setting up food warehouses in their own patronage

areas. The food warehouses do not cannibalize the

sales of existing supermarkets because they segment

the market on the basis of usage level. The consumers

with high consumption amount are more price sensi-

tive due to the higher outlay involved. Therefore, their

indifference curve will be less steep than that of

households with low consumption rates (Fig. 2).

Therefore, as we can see in Fig. 2, when price sen-

sitivity arises due to higher usage rates, the more price

sensitive consumers will patronize food warehouse.

The different prices at the food warehouse and the

supermarket are therefore an example of Pigou’s third

degree price discrimination as it exploits heterogene-

ity across consumers. The two central requirements of

Pigou’s model are ‘direct accessibility’ and ‘consumer

isolation’. ‘Direct accessibility’ means enforcing a

mechanism that allows separating the two segments

and is also technically and legally enforceable. This is

what the membership fee ensures. Since it is offered to

all customers, it is legally valid and it allows one to

separate the heavy and light users. The idea of

Fig. 2. Effective prices at supermarket and food warehouse.


‘consumer isolation’ deals with preventing trade

among customers, which because of the high transac-

tion costs is quite limited among consumers. This

explains why a large number of supermarkets are suc-

cessfully setting up food warehouses without canni-

balizing their existing sales.

From the discussion in Section 3.1, we can show

that, for a given rate of consumption, as the purchased

quantity qij increases, the interpurchase time Mi also

increases. Therefore, we hypothesize:

Hypothesis 3c: The interpurchase time is higher for

food warehouses as compared to supermarkets.

As the interpurchase time increases, the proportion

of a consumer’s inventory that is held in perishable

items must decrease, all else equal. From Exhibit 2,

we see that the main items that consumers buy at the

food warehouse are analgesics, catsup, cereal RTE,

ground coffee, eggs, frozen poultry, paper towels,

potato chips, cooking oil, shampoo, bar soap, soft

drinks, toilet tissue, VCR tapes. With the possible

exception of eggs, all are non-perishables.

In addition, the prices at warehouses, in general,

are the lowest among all the other formats. The food

warehouses would be able to charge the lowest price,

if the marginal cost is sufficiently lower than the price,

i.e.,

ðpj � x� CVðaB;NBqjÞÞ > 0:

The price at food warehouses is lower than that at

supermarkets, which itself due to competitive pressure

is barely above the cost of retailing. In a survey of

consumers and grocery stores in northeast US,

McLaughlin et al. (1992) found that, on the average,

consumers’ monthly expenditure at food warehouses

was 17% of that at supermarkets, whereas the prices at

food warehouses were only 26% lower than that at

supermarkets. So, obviously, consumers buy from

more categories at supermarkets. The question is

how can the food warehouses, with even lower prices

and lower volumes, lower their marginal costs. They

can do so by employing a different production tech-

nology as compared to the supermarkets. Some of the

mechanisms that food warehouses employ to reduce

the marginal costs of retailing are the following

(McLaughlin et al., 1992),

� They typically carry 5000 SKUs as compared to

20,000 SKUs for typical supermarkets.� The SKUs themselves are larger sizes and multi-

pack sizes, which have higher value per SKU than

typical supermarket items.� The receiving and merchandising ends are de-

signed to utilize full pallets to minimize handling

of individual packages.� They rely on shelf price cards rather than individ-

ually pricing each package. These SKU efficiencies

account for 5–6% of the operating difference

between warehouse stores and supermarkets.� They do not offer shopping bags or bagging leading

to lower staff and management requirements.� They have shorter operating hours than

supermarkets.


� The stock is drop-shipped leading to lower

warehouse and transportation costs.� They employ minimal advertising and promotion.

This is due to their following everyday low pricing

policy and membership requirements.� They are normally not located in high visibility or

high traffic locations that normally push the rent

upwards. Food warehouses enjoy this advantage

because most of them enjoy monopoly positions in

their trade area. This is unlike supermarkets, which

have to compete with each other, forcing them to

choose high visibility and high traffic locations.� They require less administrative and general

expense due to the simplicity of their operations,

fewer levels of management, less staff training

needed and less maintenance and repair.� They offer fewer in-store services and lower

atmospherics.� They stock only the fast moving items leading

sometimes to negative net inventory. This means

that they get paid by the consumers before they pay

the manufacturers. This allows them to operate

with less working capital. So, why cannot the

supermarkets follow the food warehouse approach,

keep only the fast moving items and reduce their

working capital requirement. We have shown in

Section 4 that supermarkets are self-selected by

consumers when they have to buy their market

basket. Due to heterogeneity in consumer needs,

this basket varies from consumer to consumer. This

forces the supermarket to offer very large assort-

ment sizes to satisfy all the consumers.

We do not model the competition between the food

warehouse and the convenience store because it would

be similar to the competition between the supermarket

and the convenience store. It is easy to show that

consumers who are members of food warehouses

would prefer convenience store for their fill-in trips,

if they face capacity constraints. They are also more

likely to buy perishables at the convenience stores.

6. Marketplace data

The hypotheses developed earlier in this paper

were tested by carrying out a survey of consumers’

format choices in Buffalo and Amherst, USA. It was

thought prudent to choose a city like Buffalo, along

with a suburban town like Amherst, because in the

suburbs, almost everybody has a car and goes to the

supermarket. One can afford to stay in the suburbs,

only if one has a car and therefore the sample may get

biased. On the other hand, in Buffalo, we chose a

residential area that is inhabited by lower to middle

income class consumers. By choosing these two

residential areas, we could get a good income spread

in our sample. Since Amherst is a suburb, the houses

are bigger than in Buffalo and space constraint is not

that important. Also, in a city, one sees more demo-

graphic variability as compared to the suburbs. A total

of 1988 surveys were mailed out and 526 completed

surveys were received back. The response rate was

augmented by incorporating four lotteries of a total

value of US$350 and sending a reminder postcard

after two weeks. The Select Deluxe software was used

to pull out names and addresses of individuals and

1988 households were selected at random.

In the survey, local examples of different retail

formats were given. Then the respondents were asked

to state a store within each format that they frequent

most often. (After the surveys were received, the

responses to these questions were checked to make

sure that the consumers understood the definitions of

different format and the wrong ones were removed.)

Then the respondents were asked to rate each one of

the different stores on the following set of attributes—

time taken for traveling to the store, prices, number of

brands in any one product category (proxy for depth),

number of product categories, such as milk, eggs

(proxy for breadth). The respondents rated each one

of the stores on each one of the above attributes on a

seven-point Likert scale, anchored by Poor and Ex-

cellent. Each consumer therefore rated one conve-

nience store, one supermarket, and one food ware-

house on the set of attributes. For each store, the

respondents also had to state how many trips they

make to the store in a month and how much they

spend on average on each trip. The mean values of the

store characteristics for the different formats are

presented in Table 1. We also did a one-way ANOVA

to see if the attribute perceptions differ across formats.

According to the F statistics, the attribute differences

between the different formats do differ statistically.

The average perception of the stores of different

formats on different attributes is as per expectations.

Table 1

Average attribute ratings of different formats

Convenience

store

Supermarket Food

warehouse

ANOVA

F statistics

P value

Time taken for shopping, parking, traveling to the store, etc. 6.148 (1.10) 5.616 (1.20) 4.825 (1.26) 87.89 0.000

Prices 3.848 (1.31) 5.158 (1.01) 5.289 (1.06) 185.86 0.000

Depth (no. of brands in any one product category) 3.441 (1.21) 5.316 (1.17) 4.570 (1.25) 279.63 0.000

Breadth (no. of product categories, e.g., milk, eggs) 3.768 (1.29) 5.624 (1.20) 4.945 (1.37) 237.43 0.000

Number of trips per month 4.982 (4.93) 6.295 (4.52) 0.765 (0.85) 125.09 0.000

Expenditure per trip 8.686 (12.77) 65.616 (64.70) 79.170 (61.83) 228.82 0.000

The above formats were rated on a seven-point Likert scale, with 1 =Poor and 7 =Excellent.


After this, the consumers were presented with a

purchase problem and asked to choose a format.

In the last section, the respondents had to answer

some demographic questions. The demographic char-

acteristics of the sample are discussed in Table 2.

There were approximately equal number of consum-

ers from the age group 35–44 years, 45–54 years,

and 65 years or older. There were comparatively fewer

younger respondents. Most of the respondents had

some college education, with nearly half the sample

having some college degree. Most of the respond-

ing households had an annual income between

US$20,000 and US$99,999, with roughly one-fourth

of the sample earning between US$60,000 and

US$99,999. An overwhelming majority of the sample

was married. Females outnumbered males by a ratio

of 2:1.

The correlation matrix among the different

household characteristics is presented in Table 3.

Most of the household characteristics are very

heavily correlated.

6.1. Test for Hypothesis 1a

The respondents were presented with two different

situations with the difference that under one scenario

they had to buy only one item and in the second they

had to buy a large number of items. The first question

was, ‘Suppose you are feeling thirsty. You are out of

soda. Where will you go shopping?’ The second

question was ‘If you are not only short of soda, but

also vegetables, frozen dinners, ice creams, snacks,

bread and butter, where will you go shopping?’ A

preliminary survey was carried out of the trade area

where the surveys were sent to ensure that all the

items were those that were available at all the super-

markets and the convenience stores. In each scenario,

they had to choose between supermarket and conve-

nience store. While in the first scenario, 47.1% of the

consumers preferred convenience store, in the second

scenario, only 0.7% preferred convenience store. This

is despite the fact that all these items are available at

the local convenience store. The v2 statistics was

307.74 with one degree of freedom.

6.2. Test for Hypotheses 1b and 1d

The respondents were asked to state how much

they spend on each trip to the supermarket and the

convenience store as well as the number of trips they

make to each format in any given month. This

information was used to determine the household

average monthly expenditure at the two formats.

The log of the ratio of the household monthly expen-

diture at supermarket and convenience store was

regressed on the store attributes. The results are

presented in Table 4. The breadth of assortment was

measured by asking the respondents to rate the dif-

ferent stores on the number of product categories

available. The respondents were asked to give the

dimensions of the refrigerators in their house. This

was used to calculate the cubic volume of refrigera-

tion capacity available in every house. The positive

value of the intercept indicates the consumer’s inher-

ent preference for the supermarket. As the prices at the

supermarket increase as opposed to the prices at the

convenience store, probability of shopping at the

supermarket decreases, as measured by the decrease

in the household expenditure at the supermarket.

Similarly, as time taken to travel to the supermarket

increases, the threshold barriers increase and the

household expenditure at the supermarket decreases.

Table 4

Test for Hypotheses 1b, 1d, and 2a

Dependent variable: log(monthly household expenditure at the

supermarket/monthly household expenditure at the convenience

store)

Independent variable* Parameter

estimate

Standard

error

P value

Intercept 0.9384 0.3480 0.0074

Time(supermarket)�Time(convenience store)

0.0993 0.0479 0.0393

Price(supermarket)�Price(convenience store)

0.1834 0.0459 0.0001

Breadth(supermarket)�Breadth(convenience store)

0.0880 0.0453 0.0532

Household refrigeration capacity 0.0024 0.0024 0.3200

Number of rooms 0.1597 0.0455 0.0005

R2 = 0.1458; adjusted R2 = 0.1322; F value = 0.0001; number of

observations = 319.

*All the variables were rated on a seven-point Likert scale, with

1 =Poor and 7 =Excellent.

Table 3

Correlation matrix of household characteristics

A1 A2 A3 A4 A5

A1 Household income 1 0.23 0.32 0.37 0.21

A2 Number of members

in the household

1 0.28 0.58 0.14

A3 Number of rooms 1 0.45 0.04

A4 Number of cars 1 0.20

A5 Household refrigeration

capacity

1

Table 2

Descriptive statistics of the sample

Variables Percentage of respondents

in this category

Age

Under 25 0.4

25–34 years 9.1

35–44 years 19.2

45–54 years 26.0

55–64 years 16.3

65 years or older 29.0

Education

Less than high school 1.1

Completed high school 21.1

Some college education 32.6

Graduate degree 35.0

Postgraduate degree 10.1

Household annual income

Less than US$20,000 8.7

US$20,000–39,999 26.4

US$40,000–59,999 25.8

US$60,000–79,999 16.0

US$80,000–99,999 10.8

US$100,000–119,999 6.4

US$120,000–139,999 1.9

US$140,000–159,999 0.8

US$160,000–179,999 0.8

Greater than US$180,000 2.3

Marital

Single 17.8

Married 68.3

Other 13.9

Gender

Male 32.6

Female 67.4

Number of members in household

1 20.0

2 37.1

3 17.1

4 16.3

5 6.8

6 2.1

7 0.2

8 0.4


These two results support Hypotheses 1b and 1d. We

also see that as the breadth of assortment at the

supermarket increases, the household expenditure

increases, because the chances of the consumer find-

ing his market basket there increases. The variables on

household refrigeration capacity and number of rooms

were incorporated to test Hypotheses 2a and 2b.

6.3. Test for Hypothesis 1c

The correlation between the differences in the

breadth of assortment, and depth of assortment at

the convenience store and the supermarket was very

high (0.56). Therefore, we tested Hypothesis 1c by

repeating the analysis for Hypothesis 1b, with the

difference that instead of breadth of assortment we

had depth of assortment. We obtained the same

results as Table 4 and a positive significant value

for the coefficient of depth difference. This indi-

cates that as differences in depth of assortment

increases, consumers start patronizing supermarket

more.

Table 5

Test for Hypothesis 1e



store)

Independent variable Parameter

estimate

Standard

error

P value

Intercept 1.6317 0.1629 0.0001


0.1010 0.0453 0.0265


0.1471 0.0445 0.0011


0.1354 0.0436 0.0021

Number of cars in the household 0.2540 0.0714 0.0004


observations = 319.

Table 6

Test for Hypotheses 2a and 2b

Dependent variable: probability of shopping at the convenience

store for different items


estimate

Standard

error

P value

Intercept � 0.5663 0.1992 0.0045


� 0.0563 0.0284 0.0480


� 0.0686 0.0284 0.0157


� 0.0777 0.0282 0.0059

Household refrigeration capacity 0.0020 0.0014 0.1626

Number of rooms � 0.0609 0.0263 0.0205

Perishability 0.5320 0.0820 0.0001

Log-likelihood =� 771.3420; number of observations = 2085.


6.4. Test for Hypothesis 1e

We ran the above regression again, but with

Number of Cars in the Household as one of the

independent variables. The dependent variable and

store attributes were the same as in Table 5. We had to

run separate regressions, i.e., we did not combine

Tables 4 and 5, because number of rooms in the

house and number of cars in the household were

heavily correlated. From Table 5, we observe that as

the number of cars in the household increases, the

probability of shopping at the supermarket as com-

pared to the convenience store also increases.

6.5. Test for Hypothesis 2a and Hypothesis 2b

The respondents were presented with a list of items

i.e., Milk, Beer, Fruit, Frozen Food, Bread, Vegetable

Oil. Before mailing the surveys, we surveyed all the

convenience stores in the neighborhood, where sur-

veys were sent, to ensure that these categories were

indeed being retailed there. Two new variables Per-

ishability and Capacity Constraint were created. The

respondents were asked to state the number of trips

they make to the supermarket in a month. Respon-

dent’s average interpurchase time at the supermarket

was determined by dividing 30 by the number of trips.

The respondents were asked to state for how many

days after purchase each one of the items in the list

retains its freshness. If this time period was less than

the interpurchase period of a consumer, the variable

Perishability was assigned the value 1 for that con-

sumer, otherwise 0. The respondents were asked if

they store each one of the items in the refrigerator and

if they did the variable Household Refrigeration Ca-

pacity was assigned the value 1, otherwise 0. Finally,

for each one of the items, the respondents were asked if

they buy it regularly at the convenience store and their

response became the dependent variable in a probit

model with store attributes, Perishability, Capacity

Constraint, and Number of Rooms in the House

(another proxy for Capacity Constraint) as the inde-

pendent variables. The result of this analysis is pre-

sented in Table 6. The probability of shopping at con-

venience store as opposed to supermarket decreases as

prices and time taken at convenience store increase.

On the other hand, the probability increases in depth of

assortment (defined as number of brands in any

product category), perishability of products and the

consumer capacity constraints. If an item is stored in a

refrigerator the household is likely to face capacity

constraint and again is more likely to shop at conve-

nience stores for that particular item. However, this

parameter is not statistically significant. Again, from

Table 4, we see as the number of rooms in the house

increases, the average household monthly expenditure

at the supermarket increases.

6.6. Test for Hypotheses 2c and 2d

To test these hypotheses, we did a regression

analysis similar to the regression analysis for Hypoth-

Table 7

Test for Hypotheses 2c and 2d



store)


estimate

Standard

error

P value

Intercept 1.5581 0.17445 0.0001


0.1050 0.04749 0.0277


0.1534 0.04690 0.0012


0.1220 0.04557 0.0078

Number of members

in the household

0.0802 0.04528 0.0772

Household income 0.1099 0.03470 0.0017


observations = 319.

Table 8A

Test for Hypothesis 3a

Dependent variable: probability of shopping at the food warehouse


estimate

Standard

error

P value

Intercept � 0.6595 0.1266 0.0001

Number of members

in the household

0.1491 0.0428 0.0005

Log-likelihood =� 343.4918; number of observations = 521.


esis 1d. The dependent variable and the store attrib-

utes are the same as in Table 5, with the difference that

instead of number of cars in the household, we include

the number of members in the household and house-

hold income as the explanatory variables. We did not

combine this analysis with that of Table 5, by treating

all three, i.e., number of cars in the household, number

of members, and household income, as explanatory

variables, because the number of members in the

household is heavily correlated with the number of

cars in the household. From Table 7, we can see that

as the household income increases, proportion of food

expenditure at the convenience store decreases. This

supports Hypothesis 2c. Similarly, we can see that as

the number of members in the household increases,

the probability of shopping at the convenience store

decreases, which supports Hypothesis 2d.

6.7. Test for Hypotheses 3a and 3b

The respondents were asked if they shop at food

warehouses and if they do, then to state how much

they spend on each trip. As per hypothesis, the

greater the household size, the greater the probability

of shopping at the food warehouse. We see that as

the number of members in the household increases,

the probability of shopping at the food warehouse

increases (Table 8A). The household expenditure at

the food warehouse should increase, as the house-

holds perceive lower prices or greater price savings.

We have expenditure data only if the respondents

shop at the food warehouse and the expenditure data

for all those who do not shop is censored. Let the

expenditure at the store be measured by yi and the

store attributes by the vector wi. Then the household

expenditure would be given by:

yi ¼ wVwi þ ei if yi > 0

yi ¼ 0; otherwise: ð6:7:1Þ

Here, w is the vector of importance weights which

has to be estimated and q are residuals that are

independently and normally distributed with mean 0

and a common variance, r2. However, since some of

the observations are censored, the mean value of the

residuals is not 0 and this can be corrected by

incorporating the Mills ratio into Eq. (6.7.1), so that

the equation estimated by OLS becomes,

yi ¼ wVwi þ r/i

1� Ui

þ ei; ð6:7:2Þ

where /i and Ui are the density function and the

distribution function of the residual of the probit

choice model estimated in the first stage. Eq. (6.7.2)

is then estimated in the second stage. In Table 8B, we

see that the expenditure decreases as the perceived

prices at the food warehouse increase. The two

significant attributes at the food warehouse are there-

fore prices and family size (which effects through the

Mill’s ratio). As family size increases, the probability

of shopping increases and the consumers are willing

to accept a lower price cut. Or, in order to attract a

small family, a food warehouse will have to give very

deep price cuts to reduce the quantity size threshold

barrier.

Table 8B

Test for Hypothesis 3b

Dependent variable: annual household expenditure at the food

warehouse


estimate

Standard

error

P value

Intercept 67.9877 473.3098 0.8860

Time(food warehouse)�Time(supermarket)

142.3636 85.8732 0.0992

Price(food warehouse)�Price(supermarket)

145.8399 62.3534 0.0205

Depth(food warehouse)�Depth(supermarket)

12.8246 73.0826 0.8609

Breadth(food warehouse)�Breadth(supermarket)

� 30.3328 74.1858 0.6831

Household income 25.2633 47.5988 0.5963

Mill’s ratio 1131.7644 677.8171 0.0968

R2 = 0.0747; adjusted R2 = 0.0424; F value = 2.314 ( P value =


6.8. Test for Hypothesis 3c

The respondents were asked how many trips they

make to the supermarket in a month. We obtained the

interpurchase time at the supermarket by dividing 30

by this number. Similarly, respondents were asked to

state the number of trips they make on average to a

food warehouse and this information was used to

determine the interpurchase time at the food ware-

house. We subjected the interpurchase times at the

supermarket and food warehouse to a t test. The mean

difference between the times was 1.7468 and the

standard error was 0.6053. The t value was 2.89,

and it was statistically significant (P value being

0.0043). This indicates that the interpurchase time at

the food warehouse is significantly higher than that at

the supermarket.

0.0357); number of observations = 166.

7. Conclusion

We provided a theoretical model of format choice

to complement the standard models of store choice.

The model is based on the assumption that a consumer

will choose the format that provides the most attrac-

tive combination of price, inventory cost and travel

cost. In most cases, this will be the format that

minimizes the sum of these costs, which may be

termed the full price. On the supply side, formats will

emerge if they can profitably provide a way for the

consumer to minimize these costs. The price mecha-

nism provides a way for consumers to pay for a

service that a particular format provides, or to decide

to perform certain services themselves. The conve-

nience store requires high prices because of its small

scale of operation, but can be attractive in certain

situations because it minimizes travel and inventory

costs. Conversely, the food warehouse is attractive to

consumers who have low inventory costs because they

can perform the inventory service efficiently them-

selves, but benefit from the low prices charged by the

warehouse.

We showed the flexibility of our model by ap-

plying it to three different retail formats. We showed

that supermarkets would be preferred by consumers

when they have to buy more than a threshold

number of categories and therefore the supermarkets

should carry extremely broad assortments. Conve-

nience stores are expected to carry perishables,

goods that are stored in the refrigerator, and emer-

gency goods. Food warehouses are expected to be

preferred by the heavy users, i.e., large families. That

explains why food warehouses carry only large

packet sizes. These hypotheses were supported by

our empirical analysis.

An implication of our model is that managers

interested in exploring possible opportunities to

create new retail formats should pay special attention

to changes in technologies available to consumers to

acquire and store goods, or to changes in the costs

of using existing technologies for doing these things.

Larger and better refrigerators, larger houses, and

availability of automobiles to transport items have

all lowered inventory holding costs, making pur-

chases in large quantities feasible. Conversely,

higher time costs have made shopping more expen-

sive, leading to demand for services that minimize

shopping time.

In general, any format that can either lower travel

costs or inventory holding costs is something that a

segment of consumers should be willing to pay for.

In this study, we focused on only the traditional

formats. However, there are a number of new for-

mats that retailers are currently experimenting with

and which can be the focus of any future research

endeavor. Two of the most significant new formats

are the mass merchandisers and the online grocery

stores. During the last five years, the mass merchan-


disers Wal-Mart has become one of the largest re-

tailer of groceries in the US. Many strip malls

traditionally featured both mass merchandisers and

supermarkets side by side as they complemented each

other. It would be interesting to study the competition

between these two formats, now that their assortment

overlaps. Online grocery stores offer another level of

competition to the traditional grocery stores. It would

be interesting to analyze whether Internet would draw

its customers equally from all the traditional formats,

or would it compete more intensely with one of

them.

A consumer’s choice of a retail format to pa-

tronize on any given shopping trip is the output of a

complex dynamic problem, where the consumer has

to decide upon (1) how much to consume from each

category, (2) how to organize the purchases into

shopping trips over time, and (3) how to choose

different store formats. In doing so, the consumer

has to take into account the fact that these decisions

are linked both over time and across categories, as a

result of shared shopping costs, income, and space

constraints. To simplify the issues, and to focus on

the role of shopping costs and space constraints, in

this study, we have assumed constant prices and con-

sumption rates. In reality, the markets in question

are characterized by heavy dealing, and consump-

tion rates are likely to exhibit seasonal patterns.

Future researchers can extend this study by withdraw-

ing our assumptions about prices and consumption

rates.

Acknowledgements

This paper is based in part on a doctoral disserta-

tion submitted to the State University of New York

at Buffalo. We wish to thank Govind Hariharan,

Minakshi Trivedi, Denis Gensch, and Peter Morgan

for their insightful comments on earlier drafts of this

paper. We also acknowledge the helpful comments

of the editor and reviewers.

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