Food Quality and Preference 46 (2015) 113–118Contents lists .docx

Food Quality and Preference 46 (2015) 113–118
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Food Quality and Preference
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c
a t e / f o o d q u a l
Modeling store brand choice: Minimal effects of households’
demographic features
http://dx.doi.org/10.1016/j.foodqual.2015.07.011
0950-3293/� 2015 Elsevier Ltd. All rights reserved.
⇑ Corresponding author.
E-mail addresses: [email protected] (A. Cotes-Torres),
[email protected] (P.A.
Muñoz-Gallego), [email protected] (Ó. González-Benito).
Alejandro Cotes-Torres a,⇑ , Pablo A. Muñoz-Gallego b, Óscar
González-Benito b
a Universidad Nacional de Colombia, Sede Bogotá, Ciudad
Universitaria, Edificio 561, Bogotá D.C., Colombia
b Universidad de Salamanca, Campus Miguel de Unamuno,
Edificio FES, 37007 Salamanca, Spain
a r t i c l e i n f o a b s t r a c t
Article history:
Received 11 November 2012
Received in revised form 8 June 2015
Accepted 18 July 2015
Available online 18 July 2015
Keywords:

Agribusiness marketing
Agri-food market
Consumer behavior
National brands
Private labels
Retailer strategy
Demographic characteristics are factors that both managers and
business consultants use to explain con-
sumer behavior. However, their usefulness has been questioned
by some researchers; this study consid-
ers their effect on store brand choice. The authors analyze
purchases in 13 food categories by 2011
households over the course of two years using a binomial logit
mixed model. The results reveal that
the household’s social class, household size, and the head of
household’s age affect this choice. A weak
relationship emerges between the demographic variables and
store brand choice though, indicating that
for many years, retailers and business managers have been
allocating vast financial resources to obtain
data about demographic variables that barely affect real
consumer behavior. The results also confirm
prior research that indicates retailers should not segment by
households’ demographic features but
rather should offer store brands and target all buyers with them
similarly.
� 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Retail managers almost invariably wind up conducting some
market research to support their strategic decisions, such as col-
lecting data about demographic variables that describe shoppers
or their households. But do classical demographic variables
really
affect consumer behavior, and if so, which of them has the

greatest
influence on consumers’ intentions to purchase? In the agri-
food
industry, Piccolo and D’Elia (2008) argue that demographic
charac-
teristics are relevant for explaining consumer behavior, such
that
they offer insights for improving the production process as well
as developing new food products. Yet perhaps the most
important
recent phenomenon in the agri-food market has been the
consider-
able increase of store brands (Akbay & Jones, 2005; Cotes,
2010;
Olsen, Menichelli, Meyer, & Næs, 2011; Soler, 2005). Store
brands
have come a long way, especially in Europe, shifting from a
busi-
ness strategy that distributors initially introduced to increase
cus-
tomers’ loyalty toward their establishments. As a result, store
brands historically have been dismissed as cheap brands, with
poorer quality than offered by national brands (Boyle &
Lathrop,
2013; Nenycz-Thiel & Romaniuk, 2009). Despite some recent
shifts
that have reduced the differentials between the two types of
brands, some researchers assert that the traditional distinction
remains in evidence (Nenycz-Thiel & Romaniuk, 2011).
Accordingly, marketers need to determine which demographic
variables push consumers to buy store-branded foodstuffs. Hoch
(1996) finds that in locations where shoppers are older and have
low incomes, larger households, and more education, the likeli-
hood of store brand purchases is greater. Mittal (1994), Urbany,
Dickson, and Kalapurakal (1996) also suggest that demographic

variables are better predictors of store brand choice than are
psy-
chographic variables; Ailawadi and Harlam (2004) propose
match-
ing demographic and psychographic consumer characteristics to
quantify the effects on store brand choice. Other researchers
(Bonnet & Simioni, 2001; Kim, Srinivasan, & Wilcox, 1999;
Resano, Sanjuán, & Albisu, 2012) confirm that consumer demo-
graphic characteristics and buying habits directly affect
people’s
price sensibility. Despite these contributions though, research
offers no consensus about the true relevance of demographic
vari-
ables for consumer behavior models; several researchers also
sug-
gest very weak or no effects (Akbay & Jones, 2005; Baltas,
2003;
Blattberg, Buesing, Peacock, & Sen, 1978; Bucklin & Gupta,
1992;
Burt, 2000; Grunert et al., 2012; Gupta & Chintagunta, 1994;
Hansen, Singh, & Chintagunta, 2006; Narasimhan, 1984; Rossi
&
Allenby, 1993; Uncles, Kennedy, Nenycz-Thiel, Singh, &
Kwok,
2012).
Such contrasting findings imply the need to investigate vari-
ables that previously may have been overlooked (Brown &
Dant,
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114 A. Cotes-Torres et al. / Food Quality and Preference 46
(2015) 113–118
2008b) to uncover new insights and reconcile the results (Brown
&
Dant, 2008a). In particular, to determine if retailers should con-
sider other types of demographic variables, we propose a
theoret-
ical framework of store brand-oriented customer behavior that
includes time trends. Our binomial logit model with mixed
effects
enables us to estimate the main demographic factors that might
affect store brand choice.
2. Conceptual framework
Some studies (Ailawadi, Neslin, & Gedenk, 2001; Guerrero,
Colomer, Guàrdia, Xicola, & Clotet, 2000) consider the
influence
of a household’s primary shopper’s gender on store brand
choice
but not the effect of the gender of the head of the household.
Because of changing family structures, modern women
frequently
provide the only financial support for their families, and two
ele-
ments might converge to create a predisposition among them to
buy store brands. First, single-parent households tend to have
less
discretionary income to spend. Second, these households have
less
time available for shopping, so they carefully evaluate the attri-
butes of a product in relation to its price. A store brand can be

both
a time- and a money-saving offering for these consumers,
because
these brands ensure a low-price purchase with acceptable
quality.
Alternatively, if the head of household is a man, current global
trends suggest higher household income, because their female
spouses contribute financially to the upkeep of the home (de
Boer, McCarthy, Cowan, & Ryan, 2004). These families, with
their
higher monetary income, might be more willing to pay for high
quality foodstuffs (Cotes & Muñoz, 2009) or buy the best
brands,
such that they lean more toward national brands. As de Boer
et al. (2004) state, the increasing participation of women in the
labor market has increased the income of households (and coun-
tries) but also has formed a society of ‘‘cash-rich, time-poor
con-
sumers.’’ Accordingly, we propose:
H1: If the head of household is a man, the chances of that
household buying store brands decreases.
When it comes to age, Baltas (2003) and Richardson, Jain, and
Dick (1996) argue that this feature has no effect on store brand
choice; Enneking, Neumann, and Henneberg (2007) instead
assert
that age affects store brand choice. These results may reflect the
reality of the agri-food market in particular, in that the
purchasing
decision process tends to be affected by concerns about the
preva-
lence of age-related disease and predispositions to eat healthy,
high-quality foods, regardless of the price of the product.
Various

authors (e.g., Dean et al., 2007; Rozin, 1999) demonstrate that
not only are older people more aware of their health, but they
also
tend to be more interested in consuming healthy food products
(Roininen, Tuorila, Zandstra, de Graaf, & Vehkalahti, 2001) and
high
quality food (Ngapo & Dransfleld, 2006; Quagrainie,
Unterschultz,
& Veeman, 1998; Sánchez & Barrena, 2006; Sánchez, Beriain,
&
Carr, 2012). Thus, we propose:
H2: The older the head of the household, the less likely the
household is to buy store brands.
A more complex structural demographic characteristic similarly
might influence store brand choice, namely, household size.
Regardless of the level of income or economic capacity, a
house-
hold with more people may develop a specific brand choice pro-
cess. Some researchers (Baltas, 2003; Enneking et al., 2007)
argue
that it does not have any impact on willingness to purchase
store
brands, yet in the agri-food market specifically, it appears that
the number of members of a family affects the household’s
valua-
tion of foodstuff quality (e.g., Quagrainie et al., 1998; Sánchez
et al.,
2012). For example, Frank and Boyd (1965) cite a positive
relation-
ship between household size and propensity to buy store brands,
as confirmed by Richardson et al. (1996), though they also warn
that its impact is relatively less than that of psychographic con-
sumer characteristics. Accordingly, we posit that household size
may impose a budgetary constraint on the possible expenses that

households can meet each month; a large family would have a
lower income per each member, and we predict:
H3: A larger household is more likely to purchase store brands.
Although the relationship between family income and store
brand choice might seem obvious, Akbay and Jones (2005)
stress
that this relationship might not be straightforward in the
agri-food industry; depending on the type of food, consumers
might have specific preferences for a specific product attribute
and thus assign less importance to the type of brand. Coe (1971)
finds that consumers with medium incomes prefer store brands
more than those with low incomes, whereas Murphy (1978)
indi-
cates that high-income consumers tend to buy more store brands
than medium income shoppers. Frank and Boyd (1965) report
sim-
ilar results. Other research (Richardson et al., 1996) indicates
less
willingness to buy store brands when households increase their
income, though this influence appears weak.
In contrast, some researchers (e.g., Quagrainie et al., 1998;
Sepulveda, Maza, & Mantecón, 2008) argue for a positive
relation-
ship between consumer income and the acquisition of high
quality
foods; Sánchez and Barrena (2006) call income level more
impor-
tant than demographic characteristics at the moment the shopper
must pay for a high-quality food. Thus, low-income consumers
may decrease their spending on food baskets by buying more
store
brands (Binkley & Connor, 1996; Kaufman, MacDonald, Lutz,
&

Smallwood, 1997). Alternatively, Akbay and Jones (2005) find
that
higher income consumers prefer to buy national brands. Many
studies refer to social classes or income to designate households
according to their purchasing capacity. Thus, we propose:
H4: The higher a household’s social class, the less likely it is to
buy store brands.
Occupancy levels and employment situations are less often
studied demographic characteristics in relation to store
brand-oriented consumer behavior. Baltas (2003) finds no
relation-
ship between the propensity toward store brands and buyer
occu-
pancy level; in the agri-food market, several researchers (e.g.,
Grunert et al., 2012; Piccolo & D’Elia, 2008) highlight it as
funda-
mental to the food purchasing decision process, though these
authors focus mainly on the intrinsic characteristics of the
product,
not the choice of any brand in particular. Therefore, we propose
analyzing this variable by accounting for time-versus-money
trade-offs. A consumer with more time available might assess
the price–quality relationship offered by a brand more
positively
and thus lean more toward store brands. Consider, for example,
an unemployed person compared with a gainfully employed, and
thus less time-rich, consumer. Unemployment could be a proxy
for consumers with more available time but poor economic
resources. We propose:
H5: If the (a) head of the household or (b) homemaker works for
income, the household is less likely to buy store brands.
In food markets, Sánchez et al. (2012) find that consumers with

more education seek higher quality products. Therefore,
education
and store brand choice might have a negative relationship.
Another
argument in support of this relationship holds that education
could
proxy for consumer income, such that people with more
purchas-
ing power can select a greater variety of products and brands
and
thus might prefer national brands. However, Richardson et al.
(1996) find no relationship between individual education and
store
brand choice; the relationship instead pertains to household
income. Thus, we investigate consumers’ schooling level; a per-
son’s good education is reflected in the number of academic
degrees achieved, which generally indicates his or her maximum
schooling level. Specifically, we assert that a consumer with
more
academic degrees prefers to eat the best foodstuffs, whose
safety
A. Cotes-Torres et al. / Food Quality and Preference 46 (2015)
113–118 115
and quality generally tends to be insured by national brands that
invest more in quality seals than store brands. We propose:
H6a: The higher the head of household’s schooling level, the
less likely the household is to choose store brands.
Notwithstanding this argument, Richardson et al. (1996) sug-
gest that educated people can better assess the different
intrinsic
attributes of a product and balance them against its price, which

eventually leads them to accept store brands. In the case of the
food industry, Grunert et al. (2012) show that consumers with
the highest academic degrees have a greater capacity to analyze
the key information presented about foodstuffs. Yet compared
with national brands, store brands are relatively new, and con-
sumers’ reactive attitudes toward new products might influence
their decisions. According to Barrena and Sánchez (2012), food
neophobia may be transient, due to the consumers’ negative
reac-
tion to a specific situation or food, or it may be a structural
feature
of a person’s personality. In that regard, Flight, Leppard, and
Cox
(2003) find that more educated consumers accept novelty in the
food industry more easily, which may explain why various
authors
(e.g., Burger & Schott, 1972; Cunningham, Hardy, & Imperia,
1982;
Frank & Boyd, 1965) find positive relationships between the
pre-
disposition to buy store brands and education. The direct
evalua-
tion of the purchaser at the point of sale finally determines the
convenience of carrying a certain brand, so we propose the
follow-
ing hypothesis:
H6b: The higher the primary shopper’s schooling level, the
more likely the household is to acquire a store brand.
3. Materials and methods
Using a data panel provided by the market research firm Kantar
Worldpanel Spain, we analyzed 13 product categories: canned
tuna, coffee, fruit preserves, cured ham, cooked ham, processed
vegetables, olive oil, pasta, cheese, sausage, yogurt, juice, and

ketchup (Table 1).
We gathered data from all households that had bought in at
least 10 of the 13 categories, as well as those that offered
complete
information about at least three purchases per semester in each
of
the categories over two continuous years (June 2006–June
2008).
In total, we analyzed 591,319 individual shopping occasions by
2011 households distributed across Spain. We thus assured a
rep-
resentative sample of consumers and their behavior. We summa-
rize the empirical model in Fig. 1.
Store brand choice in Fig. 1 refers to any store brand bought by
an individual on each purchase occasion; thus we used a
binomial
logit model, where pijk was the store brand choice likelihood
for
category i in year j for household k, or:
pijk ¼
euijk
1 þ euijk
Table 1
Relative share per foodstuff category.
Category Frequency Percentage
Canned tuna 36,288 6.14
Cheese 159,711 27.01
Coffee 33,604 5.68
Cooked ham 17,432 2.95
Cured ham 11,709 1.98

Fruit preserved 8131 1.38
Juice 48,790 8.25
Ketchup 4259 0.72
Olive oil 20,942 3.54
Pasta 52,733 8.92
Processed vegetables 13,620 2.30
Sausage 23,591 3.99
Yogurt 160,509 27.14
To estimate uijk, we used the following linear mixed model:
uijk ¼ l0 þ hj þ
X2
d¼1
bd xijkd þ
X6
s¼1
ms þ si þ nij þ uk þ kijk:
We define uijk as the store brand choice in category i during
year
j of household k with a logit transformation; l0 was the
intercept.
In addition, hj is the fixed effect of the year j = {1, 2}.
Furthermore,
we define bd as the fixed effect of the household level of
several
quantitative demographic variables d = {1, 2}, including
household
size and the head of household’s age. With xijkd, we observe
the
demographic variable d of category i in year j for the household
k = {1, . . ., hjk}. We also include ms as the fixed effect at the
house-

hold level for the qualitative demographic variables s = {1, . . .,
6}:
head of household’s gender, household’s economic status, head
of
household’s employment (i.e., worker with income, housework
and retired, or other), homemaker’s employment (same three
employment levels), head of household’s education, and
primary
shopper’s education. Finally, we consider four random effects,
where si is the random effect of category i = {1, . . ., 13}; nij is
the
random effect of category i in year j; /k is the random effect of
household k; and kijk is the residual random effect of category i
in
year j for household k.
To estimate the household’s social class, we used the four-level
classification from Kantar Worldpanel Spain. To assign the
sample
to these classes, we used detailed data about the households’
prop-
erties, equipment, and habits, though we did not have access to
disaggregated data in this regard. Another alternative to
determine
the effect of families’ economic constraints and the influence on
their decisions to buy a store brand would use household income
level. However, we did not have access to that specific
information,
which is among the most difficult data to obtain in marketing
stud-
ies, because most consumers protect this information carefully,
such that many of them refuse to answer such questions or offer
inaccurate responses. Therefore, we considered the social class
determined by Kantar Worldpanel Spain a preferable option for
reflecting the economic reality of the households in our sample.

4. Results and discussion
We applied classic multicollinearity tests (Hair, Black, Babin,
&
Anderson, 2009) but found no evidence of collinear
relationships
among the studied demographic variables. Some researchers
have
suggested a possible quadratic relationship between the head of
household’s age and other demographic variables; therefore, we
tested for this link but did not find collinear relations of the
demo-
graphic variables with the square of the head of household’s
age.
Thus, we obtained the multivariate model in Table 2.
We found a significant year effect on store brand choice
(p = 0.0002), such that consumers were 9.1% more predisposed
to
buy store brands between the third trimester of 2007 and second
trimester of 2008 (year 2 of this study), compared with the four
previous trimesters (year 1). This outcome might have been a
con-
sequence of two factors related to the macroeconomic environ-
ment. First, expanded store brand markets, especially in Europe,
have given consumers more options for choosing them instead
of
national brands. Second, a global economic crisis started, and
its
effects began to be evident in Spain during the second half of
the
2007. Thus the year effect might suggest that the economic
crisis
affected store brand choice, in line with Quelch and Harding
(1996) argument that the economic contraction of 1981–1982
increased U.S. store brand market share from 14% to 17%. Hoch

and Banerji (1993) also claim that between 1971 and 1993, as a
consequence of the different economic cycles in the United
States, decreasing available revenues led to larger store brand
mar-
kets. In another cultural setting, Ang, Leong, and Kotler (2000)
Hypothesized negative factors Hypothesized positive factors
H1: Head of household’s gender (man) r
H2: Head of household’s age c
H4: Household’s social class p
H5a: Head of household’s employment
(worker with income) r
H5b: Homemaker’s employment (worker
with income) r
H6a: Head of household’s schooling level r
H3: Household size c
H6b: Primary shopper’s schooling level r
c Hypothesis confirmed.
p Hypothesis partially confirmed.
r Hypothesis rejected.
Fig. 1. Factors hypothesized to affect store brand choice.
Table 2
General model of the effect of households’ demographic

variables on store brand choice.
Effect Estimator Pr > |t| Odds ratio 95% confidence limits
Intercept 0.2297 0.3564 . . .
Year 1 �0.0949 0.0002* 0.909 0.875 0.945
2 0 .
Head of household’s gender Woman �0.0157 0.7898 0.984
0.877 1.105
Man 0 . . . .
Head of household’s age �0.0048 0.0312* 0.995 0.991 1.000
Household size 0.0620 0.0003* 1.064 1.028 1.101
Household’s social class High and medium-high �0.1258
0.0336* 0.882 0.785 0.990
Medium �0.0508 0.3088 0.950 0.862 1.048
Medium-low �0.0408 0.3631 0.960 0.879 1.048
Low 0 . . . .
Head of household’s employment Worker with income 0.0290
0.6553 1.029 0.906 1.169
Housework and retired �0.0060 0.9358 0.994 0.860 1.149
Others 0 . . . .
Homemaker’s employment Worker with income �0.0184 0.7018
0.982 0.894 1.079
Housework and retired 0.0023 0.9651 1.002 0.903 1.113
Others 0 . . . .
Head of household’s schooling level Superior university degree
�0.2162 0.1703 0.806 0.591 1.097
Half university degree �0.2243 0.1436 0.799 0.592 1.079

Superior high schoola �0.2162 0.1261 0.806 0.611 1.063
Elemental high schoolb �0.1543 0.2673 0.857 0.652 1.126
Primary studies �0.0401 0.7673 0.961 0.737 1.253
Incomplete studiesc 0 . . . .
Primary shopper’s schooling level Superior university degree
�0.0004 0.9978 1.000 0.731 1.366
Half university degree 0.1397 0.3615 1.150 0.852 1.553
Superior high school 0.0865 0.5455 1.090 0.824 1.443
Elemental high school 0.0672 0.6291 1.070 0.814 1.405
Primary studies 0.0678 0.6203 1.070 0.818 1.400
Incomplete studies 0 . . . .
a This category included BUP-COU-FP2 Spanish education
degrees.
b This category included EGB-FP1-ESO Spanish education
degrees.
c This category included people without any schooling or
incomplete studies.
* Significant effect.
(2015) 113–118
assert that the Asian financial crisis heightened store brand
buying
trends.
Generally speaking, in recessionary economic periods, con-
sumers tend to postpone some purchases until the situation
improves or else reduce the amount of products they buy
(Katona, 1975). However, for nondurable products (e.g., food,
per-
sonal care), the only viable option is to save on the price of the
pro-
duct (Shama, 1981). During economically challenging times,
consumers attend more to price information (Estelami,

Lehmann,
& Holden, 2001; Guerrero et al., 2000; Wakefield & Inman,
1993),
and many consider store brands their best option, because these
brands offer average prices that are 25–30% lower than those of
national brands (Kumar & Steenkamp, 2007).
Regarding the gender effect for the head of the household, we
noted no statistically significant differences between genders
(p = 0.7898) and thus reject H1. Whether the head of the
household
was a man or a woman did not really affect store brand choice.
However, we found a negative relationship of age with store
brand
choice (p = 0.0312), in support of H2. That is, the likelihood of
buy-
ing a store brand decreased by 0.5% for each year that the head
of
the household was older than 53 years of age.
Household size also emerged as an important determinant of
store brand-oriented consumer behavior (p = 0.0003).
Specifically,
we found a 6.4% of increase in the probability that a household
would buy store brands with each additional member of the
household beyond 3.55 persons. Thus, in big households,
Table 3
Variances of the general model of the effect of households’
demographic variables on
store brand choice.
Variance Estimator Standard error Z-value Pr Z
si (category effect) 0.1970 0.0780 2.53 0.0057

nij (category � year effect) 0.0006 0.0006 1.04 0.1492
uk (household effect) 0.6650 0.0239 27.80 <0.0001
kijk (residual effect) 4.8900 0.0414 1180.30 <0.0001
A. Cotes-Torres et al. / Food Quality and Preference 46 (2015)
113–118 117
household size strongly determines store brand choice. This
posi-
tive relationship confirms H3.
The social class of the household influenced store brand choice
too, maybe due to purchasing power differences, but also likely
as
a result of customs or the particular cultural values of wealthier
consumers. The households in high or medium-high social
classes
exhibited a 11.8% lower likelihood (p = 0.0336) of buying store
brands compared with other social classes. Thus, we found
partial
support for H4.
The head of household’s employment did not affect store brand
choice; this interesting finding contrasted with our expectations,
so we reject H5a. Instead, we determined that households did
not tend to buy store brands when the head of household had
less
time and more income, that is, when he or she was an employee
who received remuneration for work. Nor did we find a positive
relationship between the homemaker’s employment and store
brand choice, so we also reject H5b.
For education, we did not find any statistically significant evi-
dence to lead us to conclude that the head of household’s or the
primary shopper’s schooling level influenced willingness to buy
a
store brand. Therefore, we reject both H6a and H6b. As a side

note,
we highlight that the primary shopper’s schooling level is a
classi-
cal variable, used widely in both surveys and consumer panels
for
marketing research.
Finally, in Table 3 we estimate the variation in each food cate-
gory and its effect on store brand-oriented consumer behavior
(si), variation in each category per year (nij), and variation for
each
household (uk).
The effect achieved by each demographic variable in combina-
tion thus effectively explains the reality of the agri-food market
(in Spain). In other words, the conclusions we achieved appear
valid for the entire Spanish food industry.
5. Conclusions
According to their effect on store brand choice, we can divide
the demographic variables we studied into two major groups.
The first contains those variables that had an effect on
consumers’
decisions, namely, the household’s social class (i.e., high and
medium-high classes were less prone to buy store brands), as
well
as household size and the head of the household’s age, both of
which depend on the magnitude of the change, with greater or
smaller effects on store brand choice. The effects of these latter
two variables can be considerable when the magnitude of the
change is substantial. Using 3.5 members per family as a
reference
(i.e., the mean in our database), a large family of 5 individuals
should exhibit a 9.3% greater likelihood of buying store brands.
Similarly, using a baseline age of 53 years for the head of the

household (the mean in our database), an age decrement to
25 years would increase the likelihood of buying store brands
by
13.44%, whereas an increasing age, to 70 years, would decrease
this
likelihood by 8.16%.
The second major group contained the factors with no effect on
store brand choice. The head of the household’s gender,
employ-
ment, and education level were three such variables; others
were
the homemaker’s employment and primary shopper’s schooling
level. Yet these latter two demographic variables are the ones
most
frequently used in both academic and professional market
research. Our findings confirm the challenges issued by several
authors (e.g., Burt, 2000; Frank & Boyd, 1965; Uncles et al.,
2012)
and suggest that retailers should not segment their markets by
households’ demographic features. Instead, they should
introduce
their store brands and target all buyers similarly. Our research
thus
confirms a weak relationship between demographic variables
and
store brand choice. For many years, retailers and business man-
agers have been allocating huge financial resources to obtain
data
about demographic variables, but those factors either barely or
do
not affect real consumer behavior at all.
We have focused on store brand choice among Spanish con-
sumers; to enhance the validity of our findings, it would be use-
ful to examine our framework in other countries. Furthermore,

we studied only the direct demographic effects that we antici-
pated would influence brand decisions; other researchers have
focused on indirect effects (e.g., Ailawadi et al., 2001),
excluding
possible time effects. Ongoing research therefore should
develop
more complex, combined models that include both direct and
indirect effects in a linear mixed models paradigm (e.g.,
Chabanet & Pineau, 2006; Piccolo & D’Elia, 2008), as well as
pos-
sible intermediation relationships or interaction effects
(Enneking
et al., 2007). Although it would be difficult to derive, such a
com-
plex model could provide a more holistic, detailed view of store
brand choices.
Another interesting path for further research would be to vali-
date our model in product categories outside the food sector, to
discern similarities and differences across economic sectors.
With
such information, it would be possible to propose unique
manage-
rial strategies, appropriate for each sector, or perhaps develop
gen-
eral consumer behavior models if the optimal strategies appear
similar.
The effects of the economic crisis might have been one of the
factors underlying the year effect; however, with our data panel,
we could not test this claim. It seems clear that periods of eco-
nomic contraction increase store brand choice, but what happens
when the economic crisis ends? Do national brand consumers
return to their original preferences in all product categories?
Or, more likely, do product categories vary in the rate at which
they return to their previous equilibrium, such that some never

achieve it again? Evidence to answer these questions has
remained contradictory: some authors claim consumers who
choose store brands during a recession revert to national brands
when the crisis ends (e.g., Ward, Shimshack, Perloff, & Harris,
2002). Others argue that store brands earn their best revenues
during economic contractions and then that a portion of the
mar-
ket continues to choose store brands, even after the crisis ends
(Lamey, Deleersnyder, Dekimpe, & Steenkamp, 2007). It would
be interesting to pursue this issue in the aftermath of the recent
economic crisis.
We did not have enough information to classify the different
types of store brands that have been developed in last years.
However, Nenycz-Thiel and Romaniuk (2012) warn about
potential
differences in consumer behavior toward traditional store
brands,
which are usually associated with low prices, and premium store
brands, which seek to differentiate themselves by their quality
level rather than their low price.
Finally, the study variables associated with each household’s
social class might reflect the matched effects of efforts to
manage
household resources and social product value. Additional
research
might try to separate these effects, because social product value
could help explain the low store brands shares in some regions
(e.g., Latin America), where households confront strict
economic
restrictions but have not embraced store brands as viable
purchase
options.

(2015) 113–118
Acknowledgments
This research was supported by the Programme Alban, the
European Union Programme of High Level Scholarships for
Latin
America, scholarship N�. E06D100306CO. We thank the editor
and reviewers for their insightful comments and suggestions on
previous versions of this article. We also thank Kantar
Worldpanel Spain for providing the study data. This research
also
was supported by Ministerio de Economía y Competitividad,
Grant ECO2014-53060-R (Spain).
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http://refhub.elsevier.com/S0950-3293(15)00174-3/h0005

http://refhub.elsevier.com/S0950-3293(15)00174-
3/h0315Modeling store brand choice: Minimal effects of
households’ demographic features1 Introduction2 Conceptual
framework3 Materials and methods4 Results and discussion5
ConclusionsAcknowledgmentsReferences
1
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