Population Demographics

Food Quality and Preference 46 (2015) 113–118

Contents lists available at ScienceDirect

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,

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

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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.

116 A. Cotes-Torres et al. / Food Quality and Preference 46 (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.

118 A. Cotes-Torres et al. / Food Quality and Preference 46 (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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