This paper analyzes consumer preferences toward grocery store choices given a set of attributes of stores. This information will then be used to make inferences on how the opening of a Wal-Mart supercenter would affect the other grocery stores in a small city.Consumer/Household Economics, Institutional and Behavioral Economics,

Emerson, Robert D.

House, Lisa

Palma, Marco A.

English

Research Papers in Economics

 
 
Evaluating Factors Influencing Grocery Store Choice 
by 
Marco Palma, Robert Emerson, and Lisa House
 1 
July, 2003 
 
 
Paper prepared for presentation at the American Agricultural Economics Association Annual 
Meeting, Montreal, Canada, July 27-30, 2003 
                                                 
1 Authors are research assistant, professor, and associate professor, respectively, Department of Food and Resource 
Economics, University of Florida. Copyright 2003, Palma, Emerson, and House. All rights reserved.  Readers may 
make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright 
notice appears on all such copies.    
 
Introduction 
The food sector, in particular at the retail level in the food distribution chain, continues to 
have structural changes, with some of the changes being reflected by consumer choices regarding 
when and where to make food purchases. Supermarkets have grown substantially and become 
more concentrated, while there has been entry of new grocery store forms such as super-centers 
and large food distribution warehouses. These new options provide customers with alternatives 
that may increase competition (Medina and Ward, 1999). 
In recent years, Wal-Mart has made significant impacts on the retailing business by 
combining general merchandise stores with full size supermarkets to form what is known as 
Wal-Mart Supercenters. The first Wal-Mart Supercenter opened its doors in Washington, 
Missouri in 1988. Eleven years after the opening of the first supercenter, the Wal-Mart Company 
was the fourth largest food retailer in the nation and expanded to international markets (Huang et 
al, 2002).   
This paper analyzes consumer preferences toward grocery store choices given a set of 
attributes of stores. This information will then be used to make inferences on how the opening of 
a Wal-Mart supercenter would affect the other grocery stores in a small city. 
 
Background 
  The number of non-traditional grocery outlets has increased substantially in the past few 
years.  Nontraditional outlets target specific high volume categories of dry grocery products, 
paper products, frozen foods, limited perishable produce and meat products, health and personal 
care products, and general merchandise. Low operating margins provide attractive low priced    
products to consumers while ensuring high volume shipments by suppliers (Capps and Griffin, 
1998). 
  In this study, residents of a small city (population approximately 22,000 residents), were 
surveyed about their choice of grocery stores.  At the time of the survey, three grocery stores 
existed in the city, with residents aware a Wal-Mart Superstore was opening within two months.  
The three existing grocery stores represented three different types of grocery stores: Store A 
(generally regarded as higher quality, higher price); Store B (generally regarded as medium 
quality, medium price); and Store C (generally regarded as low-quality, low price).  All three 
grocery stores and the planned Wal-Mart Supercenter were located within six miles on the same 
main thoroughfare.  
 
Data and Methods 
  The data for this study was obtained through a mail survey. Four hundred surveys were 
mailed to randomly selected households (fitting the criteria of having children in the house and 
incomes above $25,000).  An effective response rate of 18.75% (75 surveys) was achieved.  The 
survey included questions ranking the three grocery stores currently in the market on quality 
attributes and price; which store the respondent shopped at for major and minor purchases; which 
store they were located closest to; and what the top three reasons were for choosing a grocery 
store.  Demographics were also collected. The demographic questions included number of people 
in the household, age, gender, level of education, income, etc.   
  A discrete choice framework is used to analyze respondent preferences for a grocery 
store.  Although the questionnaire asked for preferences among three different stores, the third 
store had an inadequate number of complete observations (5) for analysis. The dependent    
variable representing the preferred grocery store is therefore a binary variable.  Among available 
explanatory variables are demographic variables, respondent rankings of the most important 
qualities in a grocery store,  and likert scale variables representing the respondents’ evaluations 
of each of the existing grocery stores. 
  The basic starting point for unordered choice models is typically random utility theory. 
Given two alternatives, the utility for the choice 1 may be represented for the i
th consumer as: 
      1 1 1 ' i i i z U ε β + =      ( 1 )  
If the consumer chooses alternative 1, then it is assumed that the  1 i U  is the larger of the 2 
utilities. Then the statistical model is driven by the probability that choice 1 is made, which is 
(Greene, 2000): 
     ) . Pr( 2 1 i i U U >       (2) 
  The available data permit the estimation of alternative forms of the discrete choice model.  
The basic logit model explains the consumer’s choice on the basis of characteristics of the 
individual decision-maker.  The existing data set includes various demographic variables for the 
individual as well as the individual’s top three reasons for their choice of store.  In this case, the 
logit estimation is based on the following specification of the probabilities: 
   


















= =
∑∑
∑
==
=
2
11
1
1
exp
exp
) | 1 Pr(
j
K
k
k jk
K
k
k k
x
x
x y
α
α
     ( 3 )  
The explanatory variables  k x  in equation (3) include the individual demographic characteristics 
and their top three reasons for choice of store .  Although equation 3 indicates two different sets 
of α parameters to be estimated, only one set can be estimated since they must always be 
normalized relative to a base set; in a model with m choices, only m-1 independent sets of    
parameters can be estimated.  The questions that can be answered with this type of specification 
are what types of individuals are most likely to select one store over another based on their 
characteristics.  For example, does a particular store appeal to low-income or high-income 
consumers?  An important aspect of the approach is that can only answer questions pertaining to 
the stores under consideration in the sample. 
  An alternative approach is one that is based on McFadden’s conditional logit model.  The 
conditional logit model utilizes the individual’s evaluation of specific attributes of each of the 
alternatives under consideration in explaining the discrete choice decision. In this case, the 
probability model is specified as: 
    
∑
=
= =
2
1
1
) ' exp(
) ' exp(
) | 1 Pr(
j
ij
i
i
z
z
z y
β
β
           (4) 
There are K zij variables, each representing the evaluation of the k
th attribute of the j
th choice by 
the i
th individual.  Note that there is only one set of β parameter estimates for this specification; 
they are not specific to the choices considered.  Among the attributes available in the data set for 
inclusion in this specification are each respondent’s evaluation via a likert scale of a set of 
grocery store attributes for each store.  An indication of which store is closest for them is also 
available and suitable in this specification.  An important characteristic of this specification is the 
ability to make predictions about store choice for a store not in the sample.  Given an evaluation 
of attributes of a new store not in the sample, a prediction may be made about the probability of 
selection of the new store.  This is not possible with the basic logit model specified earlier. 
  A third form of logit model combines the above two models, including both individual 
characteristics and evaluations of attributes of the choices.  This form of the model permits both 
types of predictive statements:  which type of consumer is most likely to select a particular store 
Comment: Probably needs to be taken 
out    
among those in the sample, but in addition, permits an evaluation of the probability of selection 
of a new store not in the sample based on an evaluation of the attributes of the new store by an 
individual.  The specification of the probabilities is: 
   
∑
=
+
+
= =
2
1
1 1
) ' ' exp(
) ' ' exp(
) , | 1 Pr(
m
i m im
i i
i
x z
x z
z x y
α β
α β
     (5) 
The different forms of the logit model are estimated using standard maximum likelihood 
techniques.  
Results And Discussion 
  Estimates of the standard logit model are reported in Table 1.  Among the respondents, 
42 selected the Store A store and 28 selected Store B as their preferred store. The average 
number of people in the household was 3.67. About half of the respondents preferred the grocery 
store closest to them. The average age of the respondents was 45 years.  
  The log likelihood function for the estimated model had a value of –29.30504; the 
restricted log likelihood function (no explanatory variables included) was –47.11082. The 
resulting estimated model is highly significant with a likelihood ratio test statistic value of 
35.61156.  With 19 degrees of freedom, the null hypothesis of zero values for the parameters is 
rejected at better than the 5% level.  
These results from the binomial logit model reveal that three characteristics are 
statistically significant in determining store choice: price, variety and closest store. The results 
for the demographic characteristics were disappointingly statistically insignificant. The general 
manager of the Store A store identified one of the same characteristics that is indicated as 
discriminating between the two stores: variety of product.     
Among the 70 complete observations used in the model, 42 reported Store A as the 
preferred grocery store and 28 reported Store B as the preferred grocery store.  Table 2 presents 
the frequencies of actual and predicted outcomes. Despite the limited statistical significance of 
individual parameters, the model predicts 53 of 70, or 75.71 percent, of the observations 
correctly. This percentage was obtained by adding up the diagonal elements (34+19) and 
dividing by the number of observations (70) in table 2. A naïve model which always predicts y=0 
because P<0.5, predicts 42 of 70, or 60 percent of the observations correctly.  
The conditional logit estimates are displayed in table 3.  Responses were available for the 
following 12 attributes of the stores as evaluated by the respondents: service, quality, price, 
variety, convenience, friendliness, knowledge of staff, location, cleanliness, lighting, layout, and 
return policy.  Each was rated by the respondents on a scale of one to five where one represents 
“needs improvement,” three is “satisfactory,” and five is “excellent.”  Although a number of the 
questions appear the same as the questions used in the standard logit, there is a significant 
difference: the questions for the standard logit were a 1-3 ranking of the selected store; the 
responses used for the conditional logit are a rating of each attribute for each store by each 
person. 
  Since a number of the attributes appear fairly similar, a principle components analysis of 
the attributes was done to determine the extent to which they are measuring the same 
characteristics.  Since the first five principle components represented over 85 percent of the 
variation, five of the attribute variables were selected to avoid a lack of identification of any of 
the attributes.  The ones selected were on the basis of those most likely to have consistent 
perceived benefits to the consumer:  price, variety, quality, cleanliness, and service.  In addition,    
whether or not the store was closest for the consumer was a choice based attribute as well.  With 
this set of variables, there are 61 complete observations. 
  The conditional logit estimates in table 3 suggest that the closeness of the store is one of 
the stronger variables.  The partial effect (evaluated at the means) suggests that the probability of 
shopping at the closer store is 0.13 larger than at the more distant store.  In addition, price and 
service are at the margin of standard significance levels.  Better price performance and better 
service as evaluated by the consumer also increase the probability of shopping at the store 
performing better on those attributes. 
  The mixed logit estimates are also displayed in table 3.  While including the same 
attribute variables as the conditional logit, the specification also includes the individual 
characteristics of the respondents.  The comparison of the two specifications yields a likelihood 
ratio statistic of 23.13.  With nine degrees of freedom, the individual characteristics are jointly 
significant at better than the one percent level.  Among the attributes, closeness of the store 
remains strongly significant.  While price performance is only marginally significant, quality 
perception is strongly significant.  All three of the significant attribute variables have a positive 
effect on store selection.  Cleanliness and variety do not appear to be determining factors in store 
choice, at least between the two stores under consideration. 
  Among the individual characteristics, household size is statistically significant as is being 
in the $60,000 - $99,999 income group.  Given the normalization, the positive parameter for 
household size implies that the larger the household size, the less likely is the individual to shop 
at Store A, or conversely, the more likely they are to shop at Store B.  Since the parameter on the 
significant income group is negative, the upper middle-income group has a higher probability of    
choosing Store A for grocery shopping.  Education, gender, and age appear to have little 
systematic effect on store choice. 
  In contrasting the estimates for the three specifications, the mixed logit model appears to 
have some advantages in interpreting the data.  It is able to identify the effects of some of the 
individual characteristics which the standard logit did not.  In addition, it is no weaker in 
identifying the significant attributes for consumers (recall that the measurement of the attributes 
differs in the two cases).   The straight conditional logit handles the attributes well, but the mixed 
logit specification appears to represent a modest improvement. 
 
Concluding Remarks 
Traditional grocery stores face stronger competition from mass merchants, such as Wal-
Mart. In order to maintain market share traditional grocery stores must identify a way to 
compete. Information on what factors influence a customer to choose a grocery store can aid in 
understanding customer reaction to new competitors, including new competitors such as Wal-
Mart.  According to the conditional and mixed logit models, the characteristics that have a 
statistically significant influence on grocery store choice are price and location of the grocery 
store (distance).  In the mixed model, quality was significant, and in the conditional model, 
service marginally significant.  The standard logit model isolated price, variety and location.  
The two variables consistently significant across the different specifications are price 
performance and location.   
Since one of Wal-Mart’s prime characteristics is perceived price performance, the 
estimation results suggest that consumers will respond and existing stores are likely to face a loss 
of customers.  Additionally, Store A can expect a bigger impact in the study as it was located    
near the Wal-Mart.  In other words, respondents who were located closest to Store B would 
remain located closest to Store B, but respondents who were closest to Store A now could be 
closest to Wal-Mart. 
Some of the questions incorporated within the market survey included if customers had 
shopped at a Wal-Mart super-center before, if they plan to shop at the new Wal-Mart super-
center. Most (91 percent) of the respondents have shopped at a Wal-Mart super-center before, 
and 100 percent plan to shop at the Wal-Mart super-center. These responses in conjunction with 
the results from the model presented in this paper suggest that Wal-Mart will be a strong 
competitor for the existing stores. Finally, the recommendation to the grocery stores analyzed in 
this paper (i.e. Store A and Store B) is to emphasize price performance.   
 
References 
Capps, O. and J. M. Griffin.  “Effects of a Mass Merchandiser on Traditional Food Retailers”.  
Journal of Food Distribution Research 29(1):1-7. 
Greene, W.H.  Econometric Analysis, Fourth Edition.  Prentice Hall, Upper Saddle River, New 
Jersey, 2000. 
Greene, W.H.  Limdep Version 7.0 User’s Manual Revised Edition. Econometric Software, 
Plainview, New York, 1998. 
Huang, L.C., J.E. Epperson, B.J. Cude, and B. Woo.  “Wal-Mart Supercenter The New Low 
Price Food Retailer in Town.”  Choices Summer 2002:6-9. 
McFadden, D.  Conditional Logit Analysis of Qualitative Choice Behavior, in P. Zarembka, ed., 
Frontiers in Econometrics.  New York: Academic Press, 1973.    
Medina, S. and R.W. Ward. “A Model of Retail Outlet Selection for Beef.”  International Food 
and Agribusiness Management Review, 2(2)1999: 195-219.     
Table 1.  Standard logit model estimates of store choice (Pr[yi=Store B]) 
Variable Coefficient  Estimated Standard Error 
Constant -4.2287  2.9634 
Service
a  -0.2642 0.4676 
Price
a  0.7002 0.4020 
Variety
a  0.9463 0.4632 
Convenience
a  -0.2101 0.4064 
Location
a  0.2553 0.3995 
Atmosphere
a  0.1813 0.3692 
High quality produce
a  0.3929 0.5129 
High quality meat
a  0.0994 0.4645 
Overall quality
a  -0.2045 0.3490 
Knowledgeable staff
a  -0.8527 0.7925 
Closest store  2.9560  1.0749 
Household size  -0.7012  3.9393 
Age 0.3791  0.4535 
Gender -0.9326  0.9411 
College education  1.5600  1.0886 
Some Graduate School  -0.4242  1.09612 
Income: $30,000 - 59,999  0.3527  0.9610 
Income: $60,000 – 99,999  -1.019  1.2109 
Income: >$99,999  -1.5258  1.4915 
Log Likelihood:  -29.3050   
Observations:  70   
aVariable was given a rank of 1, 2 or 3 by the respondent as most important reason for selecting 
the store. 
    
Table 2. Frequencies of Actual and Predicted Outcomes 
  Predicted 
Actual   0  1  Total 
0   34  8 42 
1   9 19  28 
Total    43 27 70 
                                                                                                                                                                                    Table 3.  Conditional logit and mixed logit estimates 
  Variable Model 
   Conditional Logit  Mixed Logit 
   Coefficient Std.  Error  ∂Pr(A)/∂x Coefficient Std.  Error ∂Pr(A)/∂x 
Closest Store  1.1469  0.4582  0.1347  4.8839  2.1402  0.2709 
Price 0.8155  0.4374  0.0958  1.3032  0.8254  0.0723 
Variety 0.4364  0.5753  0.0513  -1.8651  1.2963  -0.1034 
Quality 0.7106  0.6525  0.0835  4.2483  2.0541  0.2356 
Cleanliness 0.0953  0.5478  0.0112  -0.8880  0.9861  -0.0492 
Store 
Attributes 
Service 0.6277  0.3913  0.0737  1.1681  0.8479  0.0648 
Constant      -8.7006  4.9397  0.4825 
Household size        1.8527  0.8623  -0.1027 
Age       0.1111  0.0760  -0.0062 
Gender       0.0486  1.4347  -0.0027 
College Edcn        -1.1800  2.0857  0.1043 
Graduate School        -4.6468  3.0813  0.2577 
Income: 
$30,000-59,999 
     4.9487  3.4510  -0.2744 
Income: 
$60,000-99,999 
     -5.7739  2.5830  0.3202 
Individual 
Characteristics 
Income: 
>$99,999 
     -3.0425  3.0961  0.1687 
 Log  Likelihood  -22.6007  -11.0356 
 Observations  61  61 
 Choices  2  2 
 

EVALUATING FACTORS INFLUENCING GROCERY STORE CHOICE

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