Abstract
Purpose
This paper aims to explain variations in store brand penetration using trust. It aims to help both retailers and manufacturers predict store brand purchases through an improved understanding of the impact of trust in store brands across 10 different store brand product categories and among nine different grocery retailers.
Design/methodology/approach
Data were collected through a telephone survey of 904 participants responsible for the household grocery shopping with a quota of 100 respondents from each of the nine leading grocery retailers in Greece.
Findings
The findings provide empirical support that store brand purchases are positively influenced by the consumers’ perceived level of trust toward the retailer’s store brands. Results also confirmed variations in store brand penetration across the ten product categories that were tested, variations among the retailers and variations in the level of trust.
Originality/value
This paper is adding to the store brand literature from a quantitative perspective and is contributing to the theory, as there is no clear theoretical view on the effect of trust on store brand purchases.
Propósito
El objetivo de este artículo es explicar las variaciones que se producen en la adopción y penetración de la marca de distribución a partir de la confianza que los consumidores depositan en esta. Con ello se pretende ayudar tanto a los distribuidores como a los fabricantes en la predicción de las compras de marcas de distribución a través del impacto de la confianza en diez categorías de producto distintas y diferentes cadenas de distribución de alimentación.
Diseño/metodología/enfoque
Se recogen datos a través de encuestas telefónicas a una muestra de 904 individuos responsables de la compra de productos de alimentación en el hogar distribuidos equitativamente entre las nueve marcas líderes de distribución alimentaria en Grecia.
Resultados
Los resultados empíricos obtenidos apoyan el planteamiento del trabajo de que las compras de marcas de distribución están influidas positivamente por el nivel de confianza que los consumidores manifiestan hacia las mismas. Los resultados también confirman que la variación en el grado de penetración de las marcas de distribución en las distintas categorías de productos y cadenas de supermercados analizadas viene explicada por las variaciones en los niveles de confianza manifestados.
Originalidad/valor
Este trabajo contribuye a la literatura de las marcas de distribución no sólo en aspectos teóricos sino también empíricos al no existir hasta la fecha un posicionamiento teórico claro sobre el efecto de la confianza en la adquisición de las marcas de distribución.
Keywords
Citation
Sarantidou, P.P. (2018), "Store brand adoption and penetration explained by trust", Spanish Journal of Marketing - ESIC, Vol. 22 No. 3, pp. 359-376. https://doi.org/10.1108/SJME-04-2018-0025
Publisher
:Emerald Publishing Limited
Copyright © 2018, Paraskevi P. Sarantidou.
License
Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
One of the most significant changes in the grocery industry is the success and growth of store brands. Store brands have been growing in sales in both the US and European markets (Sethuraman and Gielens, 2014; Hökelekli et al., 2017). They have experienced a fast growth in their market share and have become key players in many markets around the world with a global value share of 16.7 per cent during 2016 (Nielsen, 2018). Recent researchers suggest that their perceived value for money positioning is a key driver to their wide acceptance (Beneke and Carter, 2015; Beneke et al., 2015; Rubio et al., 2015; Konuk, 2018). Because of their success, they are present in almost every product category, especially in Europe, store brands are present in more than 90 per cent of the product categories in consumer-packaged goods (Cuneo et al., 2015). Furthermore, European grocery retailers such as Tesco, Sainsbury and Asda have developed, for their store brands, similar brand management practices with those of the national brands. They are also trying to respond to environmental changes and to the changes in the brand management practices (Veloutsou and Guzmán, 2017). Overall, European grocery retailers are increasingly adopting a multi-tier strategy by offering in addition to their standard store brands a line of economy as well as a line of premium quality store brands (Hökelekli et al., 2017; Konuk, 2018). However, there are variations in their level of acceptance among regions, countries as well as product categories. In the US market, store brands have reached an 18.4 per cent dollar share in supermarkets during 2016 whereas in Europe their dollar share is at 31.4 per cent during the same period and within Europe their share ranges from a maximum of 42 per cent in Spain to 19 per cent in Italy (PLMA, 2017; Nielsen, 2018).
Store brands are being used by retailers as a tool to increase profit margin, increase their bargaining power with the national manufacturers, gain a competitive advantage over other retailers, help them to improve store image and increase store loyalty (Ailawadi et al., 2008; Coelho do Vale et al., 2016; Seenivasan et al., 2016). Because of their importance, to both retailers and manufacturers of national brands, there is a plethora of studies that have focused on identifying the driving forces behind store brand growth (Muruganantham and Priyadharshini, 2017). Some of the factors that have been identified by previous researchers are: market factors such as economic (Cotterill et al., 2000), retail concentration and geographic expansion (Myers and Alexander, 2007; Cuneo et al., 2015), consumer characteristics (Baltas and Argouslidis, 2007; Glynn and Chen, 2009; Muruganantham and Priyadharshini, 2017), store image (Tsung-Chi and Chung-Yu, 2008; Beneke et al., 2015; Ruiz-Real et al., 2017), perceived value and perceived quality of store brands (Beneke et al., 2013; Beneke and Carter, 2015; Beneke et al., 2015).
Despite all previous research on store brands, the impact of trust on store brand adoption and penetration has not been fully explored. A thorough search of the relevant literature yielded five related articles. Out of these, four study the influence of trust to the intention to buy store brands rather than store brand purchases (Chaniotakis et al., 2009 and 2010; Lymperopoulos et al., 2010; Konuk, 2018) whereas the other article studies the influence on both purchase intention and loyalty to store brands (Calvo Porral and Levy-Mangin, 2016). Among these five studies, there is no agreement on whether trust in store brands has a direct or an indirect impact to either the intention to buy or to loyalty toward the store brands. Furthermore, out of these five studies, three were limited to one single product category (Chaniotakis et al., 2009 and 2010; Lymperopoulos et al., 2010); one study was focusing on the general category of food products (Calvo Porral and Levy-Mangin, 2016) while the other was focusing on organic store brand food products (Konuk, 2018). Thus, none of these studies have tested the impact of trust on both food and non-food store brand purchases as well as among different retailers. This is an important gap because previous research has shown that different store brand product categories exhibit different levels of success and that store brand proneness is more category specific than consumer-specific (Muruganantham and Priyadharshini, 2017). In addition, previous research indicates that there are variations in consumers’ perceptions and preference across different retailers (Semeijn et al., 2004; Mieres et al., 2006; Baltas and Argouslidis, 2007; Muruganantham and Priyadharshini, 2017). So, in this context, the major contribution of this study is to examine whether consumers of nine different grocery retailers exhibit similarities in their store brand purchase behavior based on their level of trust on 10 different store brand product categories (seven food and three non-food categories).
Specifically, this research attempts to investigate and provide useful insights into:
the relationship between trust in store brands and store brand purchases;
the variations in the level of trust among several store brand product categories;
the variations in the behavioral attitude toward store brands across different retailers; and
the variations in the level of trust in store brands across different retailers.
To meet the objectives of this study, the next section of the paper presents the construct of trust as well as the theoretical links under investigation. It then explains the methods used for the data collection and analysis; it presents the results and discusses them in terms of their practical contribution and their limitations. Finally, it provides recommendations for further research.
2. Theoretical background and development of HH2.1 Trust
Trust is widely recognized as an important variable that affects human relationships at all levels and has received a great deal of attention in social sciences literature and particularly in the marketing literature (Delgado-Ballester and Munuera-Aleman, 2005; Veloutsou, 2015). In marketing, we have witnessed a shift from the traditional activities of “attracting customers” toward “building relationships” with customers (Veloutsou and Guzmán, 2017). Therefore, trust is considered as a key element of the relationship marketing approach: a prerequisite for building long-term relationships between the company and its customers, intermediaries and suppliers as well as all other members in its micro-environment (Delgado-Ballester and Munuera-Aleman, 2005; Veloutsou et al., 2013; Veloutsou, 2015).
Trust is recognized as being broad and diverse in nature (Li et al., 2015; Hegner and Jevons, 2016). Trust is subjective and exists when there is confidence that the other party is reliable (Morgan and Hunt, 1994; Yannopoulou et al., 2011), credible (Doney and Cannon, 1997) and has the ability (Mayer and Davis, 1995). Delgado-Ballester and Munuera-Aleman (2001, p. 1242) defined brand trust as “[…] a feeling of security held by the consumer that the brand will meet hihe/sher consumption expectations […] brand reliability and brand intentions towards the individual”. This definition incorporates the dimension of reliability and the dimension of intentionality (or benevolence). Reliability is based on the belief that the brand will fulfill its promises and the intentionality on the belief that the brand will not take advantage of the consumer’s vulnerability and uncertainty. From a theoretical point of view, it is apparent that both dimensions are necessary for trust to exist (Li et al., 2015; Hegner and Jevons, 2016). Chaudhuri and Holbrook (2001) have added a behavioral dimension and conceptualized trust as “[…] the willingness of the average consumer to rely on the ability of the brand to perform its stated function”.
Overall, a review of the literature on trust reveals the diversity of definitions and approaches across disciplines. Lewicki et al. (2006) conducted a detailed review of the literature on trust and identified four theoretical approaches in conceptualizing trust: the behavioral approach, and the three psychological approaches (unidimensional, two-dimensional and transformational). In this study, the unidimensional approach was taken, and trust is considered as bipolar opposites (trust and distrust) of a single dimension. Thus, trust is conceptualized as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another” (Rousseau et al., 1998, p. 395). Absent from our definition of trust is the behavioral intention of “willingness” (Chaudhuri and Holbrook, 2001). We argue that this behavioral intention, as well as the reliability and intentionality, are implicit in the conceptualization of trust; once there is a feeling that the brand is “trusted”, willingness, reliability and intentionality are outcomes of trust (Morgan and Hunt, 1994). Thus, trust in store brands is conceptualized as an aggregate evaluation of how a brand will fulfill expectations.
2.1 The role of trust in store Brand purchases
When we are referring to trust toward a brand then the construct of trust becomes part of the brand-consumer relationship and therefore part of the brand equity (Delgado-Ballester and Munuera-Aleman, 2005; Veloutsou et al., 2013). There are several studies that emphasize the existence of different levels of consumer commitment or loyalty with the brand (Delgado-Ballester and Munuera-Aleman, 2001; Ball et al., 2004). Brand trust is one of the strongest commitments, as it creates a highly valued brand-consumer relationship. Furthermore, the brand trust will influence the intention to continue purchasing the brand and gain higher market share. Thus, brand trust is expected to influence attitudinal and behavioral loyalty as well as the brand’s financial performance (Chaudhuri and Holbrook, 2001; Veloutsou, 2015).
Relative to the store brands, Chaniotakis et al. (2009) in their study found that consumers’ intention of purchasing store brands is directly affected by their attitudes and that consumers’ attitudes are directly affected by the perceived benefits and indirectly by the consumer trust toward the store brands. This indirect positive effect on the consumers’ intention to buy store brands was confirmed by Lymperopoulos et al. (2010). Specifically, they found that trust in store brands has a direct positive effect on the perceived benefits which affect attitudes and then attitudes influence consumers’ intention to buy store brands. Another study contacted in the food category by Calvo Porral and Levy-Mangin (2016) in Spain, a market with high store brand high market share, found that consumer trust in store brands exhibits a moderating influence on store brand loyalty, that is, depending on the level of trust there are variations in the loyalty. Overall, consumers develop trust toward a brand based on positive beliefs and expectations and this is expected to influence their evaluation as well as their purchase intention (Calvo Porral and Levy-Mangin, 2016; Konuk, 2018). Therefore, it is hypothesized that:
The level of trust in Store Brands affects Store Brand purchases.
Furthermore, consumer perceptions for different product categories are different, as they are affected from the different characteristics of each category, which has a significant effect on their buying behavior (Veloutsou et al., 2004). Consumers’ purchase intentions are greatly influenced by the perceived value and the perceived risk associated with product purchases (Beneke et al., 2015; Rubio et al., 2015; Muruganantham and Priyadharshini, 2017; Konuk, 2018) and consumers exhibit higher perceived risk with store brands compared to national brands (Beneke and Carter, 2015). Moreover, there are variations in the perceived quality of different store brand categories and different product categories influence brand trust differently because of their different characteristics (Chaudhuri and Holbrook,2001; Konuk, 2018). Therefore, it is hypothesized that:
The level of trust in Store Brands varies among product categories.
2.2 The role of the retailer in store Brand purchases
Traditionally, the retailer as a brand and the store brands are treated independently. Many researchers have explored the influence of store brands on variables that are related to retailer brand equity such as store loyalty and store satisfaction (Martenson, 2007; Ailawadi et al., 2008; Seenivasan et al., 2016). However, to achieve synergies, we expect to see a consistency between the overall retail brand strategies and the strategies for their store brands. For example, the low-price strategy of a discounter, which is based on offering low-priced products, must be accompanied by a low store brand price strategy.
Previous studies have found that there are variations in consumers’ perceptions and preferences for store brands across different retailers (Mieres et al., 2006; Baltas and Argouslidis, 2007). Consumers perceive different levels of risk when buying from different retailers (Sheinin and Wagner, 2003). Retailers, through their overall marketing strategies, could influence store brand sales and penetration to a large extent; they try to influence consumer evaluation toward their store brands, create confidence and reduce the perceived risk (Bao et al., 2011; İpek et al., 2016). In addition, the retailer’s image has a positive impact on both the perceived quality, purchase intention and trust in store brands (Beneke et al., 2015; Konuk, 2018; Ruiz-Real et al., 2017). However, different retailers have a different image. Therefore, it is hypothesized that:
Store Brand penetration varies across different retailers.
The level of trust in Store Brands will vary by retailer.
3. Methodology
3.1 Empirical context
Grocery retailing in Greece was selected as the empirical context of the study for several reasons. First, the nature of grocery retailing is characterized as task-oriented with a short sales cycle. Second, the specific setting is very competitive with low loyalty levels with consumers patronizing multiple chains. Also, because of the economic crisis consumers have become more price-sensitive (IRI Topline Report, 2017). Third, the study is supported by a large grocery retailer that wants to investigate the level of trust in its store brands as well as on other retailers and the effect to store brand purchases.
3.2 Sampling and description of data
Data were collected through a telephone survey using the CATI process. The target population was those responsible for the household grocery shopping, who shop at the nine leading grocery retailers that sell store brands in Greece and live in the two largest cities in Greece (Athens and Thessaloniki). Respondents were asked to respond to the questions for the grocery retailer that they make most of their purchases. Because the study is focusing on those retailers that offer store brands, a non-proportional quota sample was selected to guarantee enough responses per retailer and thus to ensure the validity of our analysis at the level of the selected retailer. A total of 904 respondents were interviewed based upon a quota of 100 respondents for each of the nine retailers. Women represent 77.4 per cent of the whole sample whereas most of the respondents are in the age group of 25-64 (91 per cent). Comparing the demographic characteristics of the sample with the data from the National Statistics, we note that women and the age group of 25-64 are over-represented in the sample. This is to be expected, as the target population was those responsible for the household grocery shopping. The sampling frame was the electronic telephone directories for the two cities selected and CATI randomly selected and dialed the numbers.
3.3 Method of analysis
Partial least squares structural equation modeling (PLS-SEM) was selected for testing H1 (The level of trust in Store Brands affects Store Brand purchases). PLS was selected as an appropriate method of analysis because the objective, in this case, is the prediction (Hair et al., 2011). Also, it was selected because we use formative indicators (the observed variables) as measures that form or cause the creation or change in the latent variable, and PLS can model formative indicators. In this study, SB purchases represent the dependent variable that is to be predicted by the level of trust in SBs. For the execution of the analyses, the SmartPLS 3.0 software was used (Ringle et al., 2015). In addition, the Statistical Package for the Social Science (SPSS) version 17.0 was chosen as the computer program for the univariate and bivariate data analysis.
Friedman’s Test was selected for testing H2 (The level of trust in store brands varies amongst product categories). It was selected over the Repeated Measures ANOVA, as a nonparametric test is more robust, that is, more linear in conditions. Also, it is appropriate for testing differences between more than two conditions when the same participants have been used in all conditions. The Friedman’s Test ranks the data for each respondent, adds up the ranks for each condition and then calculates the test statistics Fr (Diamantopoulos and Schlegelmilch, 1997; Field, 2009).
The one-way analysis of variance (ANOVA) was selected to test H3 (Store brand penetration varies across different retailers) and H4 (The level of trust in store brands will vary by retailer). In H3, there are nine groups that are being compared (the nine grocery chains selected each one with a sample of 100 respondents) for variations in their store brand penetration. In H4, we also have the nine grocery retailers that are being compared for variations in the level of trust with their store brands. In both cases, the level of measurement is an interval. Therefore, the one-way ANOVA is appropriate (Diamantopoulos and Schlegelmilch, 1997). The one-way ANOVA was selected to examine whether the mean values of the constructs among the nine groups are different (so the null hypothesis is that the group means are the same). To test the null hypotheses (H3 and H4), the amount of systematic variance in the data to the amount of unsystematic variance was calculated, that is, the F-ratio and to indicate the level of significance the effect size ω was added (Field, 2009).
3.4 Measures and instrument design
In line with our unidimensional definition of trust, we measured trust in store brands with a single-item scale. Thus, we measured expectations that were expressed in the form of beliefs about the trustworthiness of the retailer’s store brands (Selnes, 1998; Lewicki et al., 2006). A qualitative study was used for the selection of different store brand product categories for measuring the construct of trust in store brands. Ten product categories were selected to capture the scope of the construct based on five focus groups with consumers and three interviews with retail marketing managers. Specifically, seven store brand product categories were selected from the food category (Luncheon meat and cheese, soft drinks, dairy, juices, wine, beer, other food) and three from the non-food category (detergents, Shampoo, paper products). Therefore, we conceptualized the trust in food and the trust in non-food store brands as two interrelated constructs that can provide us with the overall level of trust in store brands. Respondents were asked to evaluate on a four-point Likert scale to what degree they trust the specific SB product category (from “trust a lot” = 4 to “do not trust at all” = 1). Thus, the level of trust was measured as the average level of trust (minimum of “1” to a maximum of “4”) for the 10 product categories. A four-point scale was selected because it is quick to administer and can be easily grasped by respondents over the phone. Also, there is no midpoint, so the respondents had to make a choice (O’Loughlin and Coenders, 2004).
Store brand purchases in the store were directly measured. Respondents were classified into two categories, purchasers and non-purchasers of store brands. Purchasers were all those that indicated purchase from any one of the 10 product categories. To measure the level of store brand penetration, we created a new variable, called “Store Brand Variety” that measures the level of store brand purchases. Depending on the number of product categories that respondents have purchased “Store Brand Variety” can range from a minimum value of “0” to a maximum of “10”. Respondents who do not buy store brand are represented with the minimum value and respondents who buy store brands are represented based on the number of product categories that they buy (e.g. “1” for those that buy one product category, “2” for two categories, etc.). Thus, we have an interval scale because we establish an ordered relationship between respondents with regard to the number of store brand categories that they buy (Diamantopoulos and Schlegelmilch, 1997; Hair et al., 2010).
4. Data analysis and results
4.1 Descriptive statistics
The study was conducted in Greece, which is an undeveloped market for store brands comparing with the other European countries. This is reflected in the findings, as only 60 per cent of the respondents indicated that they had purchased a store brand from any of the 10 product categories, and out of them approximately 30 per cent had purchased only from a single product category, whereas 35 per cent had bought store brands in more than four product categories. Furthermore, our results confirmed previous research on the variations in store brand penetration among grocery retailers (Mieres et al., 2006; Baltas and Argouslidis, 2007). As it was expected, the discount grocery retailer (Lidl) had the highest level of penetration (82 per cent of its customers indicated that had purchased at least one store brand).
The findings also confirmed significant differences in store brand penetration among the 10 product categories. Figure 1 illustrates that paper products and other food exhibited the highest level of acceptance (43 per cent and 38 per cent respectively of the respondents had purchased from these two categories). In contrast, store brand purchases in the wine and beer category showed the lowest level of acceptance (10 per cent and 11 per cent, respectively). In addition, Figure 1 illustrates that the product categories with the highest purchase rate also exhibit the highest level of trust. The category with the highest level of trust is paper products, which is explained by the high penetration of this product category.
4.2 The level of trust in store brands affects store Brand purchases
The formative measurement model (outer) and the structural model (inner) were assessed following the suggested procedure by Hair et al. (2017). Overall, the construct measures of both the measurement model and the structural model proved to be valid and reliable.
In formative measurement models, it is required to assess collinearity before analyzing outer weights for their significance and relevance (Cenfetelli and Bassellier, 2009). To assess collinearity, the variance inflation factor (VIF) was computed. Initially, when assessing the collinearity between the indicators, the wine and beer were identified with a VIF value of 5.8 and 6.7 respectively, that is, above the threshold value of five. Thus, it was decided to remove the beer indicator and retest for collinearity (Cenfetelli and Bassellier, 2009). After removing this indicator all other indicators have a VIF below the threshold value of five; thus, collinearity is not an issue (Table I). Furthermore, to assess the contribution of the indicators to forming the construct, the bootstrapping procedure (1,000 samples) was followed. To deal with the low weight issue, because of the large number of indicators, trust in store brands was treated as a second-order construct (Cenfetelli and Bassellier, 2009). All the weights and t-values were found significant at a 5 per cent level except for two indicators in the food category (dairy and wine) indicating their low importance in forming the construct. Even though the weights for these two indicators were not significant, we retained the indicators to better capture the trust in food store brands construct and to possible managerial input.
The structural model provides us with measures of the relationships between the constructs; Table II presents the structural model results aggregated over all grocery retailers. The results indicate that the construct of trust in store brands (R2 = 0.233) explains 23 per cent of store brand purchases (Hair et al., 2017). The findings indicate that trust toward store bands is a moderate predictor of store brand sales and as suggested by Chin (1998) a moderate R2 value can be accepted when a few exogenous latent variables are used to explain an endogenous latent variable. Furthermore, our results indicate that for the formation of the overall level of trust in store brands (second-order construct), the level of trust in nonfood has a higher impact (β = 0.687, t = 6.373) than the level of trust in food store brands (β = 0.370, t = 2.773). Thus, in relation to the impact of the level of trust in the store brand purchases, the level of trust in non-food has a higher impact than the trust in food store brands. Overall, the results indicate a positive impact of the level of trust in store brands on store brand purchases (β = 0.457, t = 14.850). Therefore, H1 can be supported.
Moreover, Table II presents the structural model results for four selected retailers (AV, Carrefour, Sklavenitis and Lidl). AV and Carrefour were selected as the two multinational retailers, Sklavenitis as a local strong player, and Lidl as a discounter. It is important to note that because store brands in Greece have a much lower market share than in other European countries (e.g. Germany, Spain, U.K), Lidl has adopted its strategy by displaying more products from manufacturer brands in Greece. The findings indicate that trust toward store brands is an important predictor of their store brand sales. Even though there are variations in the predictive power of the model across different retailers, the general direction and strength of the model are similar. Looking at the selected retailers, the predictive power of the model ranges from 39 per cent in the case of AV, to 22 per cent in the case of Sklavenitis.
4.3 The level of trust in store brands varies among product categories
In our hypothesis, we test differences in the level of trust between the 10 store brand product categories. Therefore, the sample of respondents (a single group) provides 10 different measurements, which are then contrasted. The null hypothesis is that there are no differences in the level of trust among the product categories. The level of trust can range from a minimum of one (“do not trust at all”) to a maximum of four (“trust a lot”). Therefore, we have 10 ordinal level measures, one per product category that needs to be compared to one another. The responses for each product category are ranked and the chi-square distribution with nine degrees of freedom (number of variables – 1) as well as the significance was calculated. Table IV shows the mean ranks in each condition and demonstrates that there are variations in the ranks across the conditions. Overall, the results indicate that there is a significant difference between the median levels of trust among the ten product categories, Χ2(9) = 581.203, p < 0.001. Therefore, H2 can be supported (Table III).
4.4 Store Brand penetration varies across different retailers
The results of our analysis are presented in Table IV. The table provides some descriptive statistics such as the group means, and standard deviation for each of the nine retailers. In addition, 95 per cent confidence intervals for each group means are indicated. For example, there is a 95 per cent confidence that the true value of the “Store Brand variety” mean for Carrefour is likely to be between 1.44 and 2.42. Regarding the variation of store brand penetration across different retailers, the total amount of variation is captured by the total sum of squares, that is, SST = 6048,680 with df = 899. To identify how much of this variation can be explained by the different groups, the F-ratio needs to be calculated (Diamantopoulos and Schlegelmilch, 1997). Which is the variability between groups SSM = 477.861, df = 8 over the variability within groups SSR = 5570.819, df = 891. If the null hypothesis is accepted the F-ratio should be close to 1. The results indicate that the there is a medium variation on the “SB variety” across the nine retailers F (8, 891) = 9.597, p < 0.001, ω = 0.27. Therefore, H3 can be supported.
4.5 The level of trust in store brands will vary by retailer
The results of our analysis are presented in Table V. Regarding the variation in the level of trust in SBs across different retailers, the total amount of variation is SST = 461.608 with df = 627. The variability between groups is SSM = 22.813, df = 8 while the variability within groups is SSR = 438.795 df = 619; F (8, 619) = 4.023, p < 0.001, ω = 0.19. Thus, our results indicate that there is a medium variation in level or trust in SBs across the nine retailers. Therefore, H4 can also be supported.
5. Discussion
This paper investigates the impact of trust as a driver of store brand purchases, and through that explains the reasons for the observed differences in the level of store brand penetration among different retailers and product categories. Previous studies led us to expect a cause and effect relationship between the level of trust in store brands and store brand purchases. However, in comparison to previous studies (Chaniotakis et al., 2009 and 2010; Lymperopoulos et al., 2010; Calvo Porral and Levy-Mangin, 2016; Konuk, 2018), the contribution of this study is that it examines the impact of trust in multiple product categories (three in non-food and 7 in food) and in nine leading grocery retailers.
The findings of this study add to the literature by emphasizing the impact of trust in store brand purchases across all product categories as well as across all nine retailers. Thus, heightened levels of trust are deemed to influence consumers’ evaluation and have a positive effect on their store brand purchases. This finding is consistent with the work of previous researchers which suggest that trust is a central variable to purchase intention and to the development of brand loyalty and that trust is a process that evolves from past experiences and interaction with the brand (Chaniotakis et al., 2010; Lymperopoulos et al., 2010; Veloutsou, 2015). While the variable of trust toward the store brands by no means can be considered as the only one that can accurately predict store brand penetration, it can provide with a good yardstick of how customers will react to their decision-making based upon their attitude towards the store brands.
The study also shows that there are significant differences in the level of trust among the product categories as well as substantial differences in the behavioral attitude toward the 10 product categories. Categories characterized by the lowest levels of functional and social or psychological risk exhibited the highest level of purchase. In contrast, categories that are mostly consumed within a social context and therefore exhibit a higher level of psychological risk (Semeijn et al., 2004) showed the lowest level of acceptance. The findings also indicate that there is a balance between the behavioral and the cognitive component of the attitude toward store brands. The product categories with the highest purchase rate also exhibit the highest level of trust. These differences in the level of trust and penetration among the different store brand product categories can be explained by the differences in the perceived risk. Previous researchers have indicated that if the perceived risk in a specific product category is low, consumers display greater price sensitivity and are more motivated to seek lower prices, that is, they might be more likely to purchase the lower-priced store brands in that product category (Sethuraman and Gielens, 2014).
Finally, another important finding of this study is that there are variations in the level of store brand penetration and in the level of trust among the nine different retailers. These variations among the different retailers can be explained with their different strategies (Calvo Porral and Lang, 2015; Schnittka et al., 2015; İpek et al., 2016; Hökelekli et al., 2017). Despite these variations, it was observed that there is a balance between the behavioral and the cognitive component of the attitude toward the retailers’ store brands. Thus, the results of the current research support the notion that the variations in the level of trust among the different retailers can explain the variations in the level of store brand penetration.
6. Managerial implications
In terms of the implications to retailers, the findings suggest that trust-building strategies is an appropriate way of influencing the behavioral attitude toward store brands. However, the challenge for retailers is to overcome consumers’ perceptions toward store brands as being homogeneous across retailers (Ailawadi et al., 2008). Retailers through their strategies should try to break this perception, differentiate their store brands and increase the level of trust for their own store brands rather than the overall category. They should try to create positive attitudes and perceptions toward their store brands so that customers feel secure that the brand will meet their expectations. They should promote their ability to offer quality store brands so purchase decisions will not be based on price alone. Each one of the nine retailers included in the study sells many different manufacturer brands and a wide assortment of store brands; each one has developed a different image in the consumer’s mind and a different store brand strategy. For instance, there are variations in how they have decided to brand their store brands. For example, AV and Carrefour show their retail brand name on their store brand packaging and follow a family brand policy for their store brands (e.g. AV uses the brand name “AV” in several product categories). On the other hand, Sklavenitis and Lidl use several different “phantom” brand names for their store brands and their retail brand name is revealed only in the legal declarations on the back of the pack. Recent research indicates that store brand perceptions and attitude are more favorable when the retailer is using the chain-label and this effect is stronger for the standard rather than the economy store brands (Schnittka et al., 2015). Hence, consumers’ perceptions toward the retailer’s brand name can be transferred to the perceptions of the store brands that the retailer carries.
Furthermore, variations in the rate of adoption by product category and by a retailer, as well as variations in the level of trust in different store brand categories suggest that the development and implementation of store brand strategies should be retailer- and market-specific. Retailers must understand that there are different segments of shoppers (buyers vs non-buyers, light/medium/heavy store brand buyers) with different shopper behavior, different level of involvement with the purchase and different criteria for evaluating products. Thus, they should try to target them by implementing differentiated strategic approaches.
From the other side, manufacturers of national brands, in their effort to protect their market share, should try to maintain or increase the consumers’ level of trust towards their brand or to decrease the level of trust in store brands. Using communication effectively they should try to enhance the perceived value of their brands so that consumers will be willing to pay higher prices. According to Batra and Sinha (2000), the experience characteristics of a category positively affect sales of manufacturers’ brands rather than the search characteristics. Therefore, they should try to increase trust toward their brands by motivating consumers to form their own perceptions through trial rather than encouraging them to read the information provided on the packaging. To decrease the level of trust in store brands they should try to increase the uncertainty toward them and thus make it more difficult for consumers to purchase the lower-priced store brands. Moreover, because there are variations in the level of trust and penetration of the store brands among the different retailers, they need to assess the environment of each of their key retail customers and develop their strategies on a per customer basis.
7. Limitations and further research
The present study exhibits several limitations that might provide direction for future research. Methodologically, a choice was made to focus on the grocery store industry and the data were collected from the Greek market. Thus, the findings might lack generalizibility to different national contexts or to different retail sectors. Also, in relation to the methodological choices, respondents were asked to indicate whether they buy store brands or not and if their response was positive, then they were asked to declare which store brand categories they buy (from the list of the 10 categories). This is a limitation because we are not measuring actual purchases, so future research might use actual data from supermarket scanners.
Another limitation is related to the way that trust was measured. For example, trust in store brands was measured by using a single item. A sensitivity analysis was conducted to see whether there were differences in the structural relationships of the model at different levels of trust and no difference was indicated. Even though the use of single-item measures is widely used in the marketing literature (Drolet and Morrison, 2001; Varki and Colgate, 2001), it is recognized that using a single item for measuring a complex construct such as trust probably constitutes a limitation. A single item measure has some important practical advantages (parsimony and easier administration) but it ignores the multidimensional nature of the construct and considers only the overall trust, that is, it ignores trust in specific aspects of the brand (Lewicki et al., 2006; Diamantopoulos et al., 2012; Li et al., 2015). Therefore, future research might measure trust using other established scales (Sirdeshmukh et al., 2002; Guenzi, et al., 2009; Li et al., 2015).
Furthermore, although a score for the “overall level” of trust was obtained from the respondents, there was no indication as to the reason behind their response. Respondents were asked to indicate their overall trust perceptions, for the 10 product categories, without using any attribute specification. Therefore, future research might add more constructs in the model to see the effect they have on store brand trust and to explain how much trust can be explained from these constructs (e.g. store brand perceived quality and perceived value, the overall perceptions toward the specific retailer, the level of trust on the specific retailer). Another limitation is associated with the Likert scale used and to how the distances between the scale points are interpreted by the respondents (Lewicki et al., 2006).
Finally, another problem occurs because of the differences in how retailers are branding their store brands. A common strategy among discounters or economy store brands at service-oriented retailers is that their store brands are not chain labeled. In this case, respondents might not be able to recognize a store brand as belonging to the specific retailer (Schnittka et al., 2015).
Figures
Figure 1.
Penetration of store brand product categories and level of trust
Assessing the measurement model: collinearity, outer weights and significance
| Indicator food SBs |
VIF | Path weights | t-values* | Indicator non-food SBs |
VIF | Path weights | t-values* |
|---|---|---|---|---|---|---|---|
| Dairy | 3.381 | −0.047 | 1.347 | Detergents | 3.026 | 0.125 | 2.930 |
| Juices | 3.257 | 0.268 | 6.939 | Paper Products | 2.252 | 0.402 | 4.917 |
| Luncheon meat/cheese | 2.669 | 0.063 | 1.689 | Shampoo and BF | 2.961 | 0.249 | 7.097 |
| Other food | 2.782 | 0.362 | 8.423 | ||||
| Soft drinks | 4.171 | 0.147 | 3.670 | ||||
| Wine | 3.427 | 0.022 | 0.740 |
*PLS t-values are based on bootstrapping with 1,000 samples
Structural model results
| Total (All 9 RT) | AV | Carrefour | Sklavenitis | Lidl | AV | Carrefour | ||
|---|---|---|---|---|---|---|---|---|
| R2 | 0.233 | 0.388 | 0.228 | 0.218 | 0.29 | 0.388 | 0.228 | |
| Path coefficients | t-value* | Path coefficients | t-value | Path coefficients | t-value | Path coefficients | t-value | |
| Trust in SBs >> SB | 0.457 | 14.859 | 0.587 | 4.416 | 0.451 | 5.238 | 0.391 | 1.731 |
| Trust in Food SBs >> Trust in SBs** | 0.370 | 2.773 | 0.539 | 2.834 | 0.384 | 1.084 | 0.973 | 3.914 |
| Trust in non-food SBs >> Trust in SBs** | 0.687 | 6.373 | 0.577 | 3.092 | 0.701 | 2.181 | 0.049 | 0.204 |
PLS t-values are based on bootstrapping with 1,000 samples;
Significance of first-order constructs to second-order
Friedman’s test results for similarities in the level of trust among product categories
| Descriptive statistics | ||||
|---|---|---|---|---|
| n | M* | SD | Mean Rank | |
| Meat and cheese | 622 | 1.80 | 0.99 | 5.03 |
| Soft drinks | 622 | 1.88 | 1.02 | 5.29 |
| Detergents | 622 | 2.13 | 1.07 | 6.03 |
| Dairy | 622 | 1.78 | 0.99 | 4.99 |
| Wine | 622 | 1.82 | 1.02 | 5.11 |
| Beer | 622 | 1.85 | 1.01 | 5.23 |
| Shampoo/BF | 622 | 1.87 | 1.02 | 5.28 |
| Other food | 622 | 2.08 | 1.03 | 5.85 |
| Paper | 622 | 2.43 | 1.08 | 6.87 |
| Juices | 622 | 1.90 | 1.04 | 5.33 |
| Friedman test | ||||
| N | 622 | |||
| Chi-square | 581.203 | |||
| df | 9 | |||
| Asymp. Sig. | 0.000 | |||
*Average level of trust (1 = do not trust at all, 2 = trust a little, 3 = trust somehow, 4 = trust a lot)
ANOVA results for similarities in store brand penetration across different retailers
| Descriptive statistics | |||||
|---|---|---|---|---|---|
| n | M* | SD | 95% confidence interval for mean | ||
| Lower bound | Upper bound | ||||
| AV | 100 | 1.65 | 2.15 | 1.22 | 2.08 |
| Atlantik | 100 | 1.36 | 2.32 | 0.90 | 1.82 |
| Veropoulos | 101 | 2.12 | 2.67 | 1.59 | 2.65 |
| Carrefour | 100 | 1.93 | 2.49 | 1.44 | 2.42 |
| Lidl | 100 | 3.91 | 3.29 | 3.26 | 4.56 |
| Champion | 102 | 2.28 | 2.48 | 1.80 | 2.77 |
| Masoutis | 101 | 1.90 | 2.25 | 1.46 | 2.34 |
| My Market | 100 | 1.50 | 2.56 | 0.99 | 2.01 |
| Sklavenitis | 100 | 1.51 | 2.04 | 1.11 | 1.91 |
| Total | 904 | 2.02 | 2.59 | 1.85 | 2.19 |
| Analysis of variance | |||||
| Source | Sum of squares | df | Mean square | F-ratio | p |
| Between groups | 477.861 | 8 | 59.733 | 9.597 | 0.000 |
| Within groups | 5570.819 | 895 | 6.224 | ||
| Total | 6048.680 | 903 | |||
*Average number of store brands purchased by retailer (minimum value of 0 to a maximum of 10)
ANOVA results for similarities in the level of trust among retailers
| Descriptive statistics | |||||
|---|---|---|---|---|---|
| n** | M* | SD | 95% confidence interval for mean | ||
| Lower bound | Upper bound | ||||
| AV | 76 | 1.84 | 0.81 | 1.66 | 2.03 |
| Atlantik | 72 | 1.77 | 0.85 | 1.57 | 1.97 |
| Veropoulos | 65 | 2.06 | 0.87 | 1.84 | 2.27 |
| Carrefour | 66 | 1.92 | 0.93 | 1.69 | 2.14 |
| Lidl | 71 | 2.47 | 0.82 | 2.27 | 2.66 |
| Champion | 68 | 1.95 | 0.78 | 1.76 | 2.14 |
| Masoutis | 77 | 1.91 | 0.83 | 1.72 | 2.10 |
| My Market | 69 | 1.95 | 0.85 | 1.75 | 2.15 |
| Sklavenitis | 64 | 1.91 | 0.84 | 1.70 | 2.12 |
| Total | 628 | 1.97 | 0.86 | 1.91 | 2.04 |
| Analysis of variance | |||||
| Source | Sum of squares | df | Mean square | F-ratio | p |
| Between groups | 22.813 | 8 | 2.852 | 4.023 | 0.000 |
| Within groups | 438.795 | 619 | 0.709 | ||
| Total | 461.608 | 627 | |||
*average level of trust (1 = do not trust at all, 2 = trust a little, 3 = trust somehow, 4 = trust a lot);
missing values
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Further reading
Erdem, T., Zhao, Y. and Valenzuela, A. (2004), “Performance of store brands: a Cross-Country analysis of consumer Store-Brand preferences, perceptions, and risk”, Journal of Marketing Research, Vol. 41 No. 1, pp. 86-100.
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