Accepted Manuscript

Brand awareness – brand quality inference and consumer´s risk perception instore brands of food products

Natalia Rubio, Javier Oubiña, Nieves Villaseñor

PII: S0950-3293(13)00152-3DOI: http://dx.doi.org/10.1016/j.foodqual.2013.09.006Reference: FQAP 2686

To appear in: Food Quality and Preference

Received Date: 29 May 2013Revised Date: 11 September 2013Accepted Date: 12 September 2013

Please cite this article as: Rubio, N., Oubiña, J., Villaseñor, N., Brand awareness – brand quality inference andconsumer´s risk perception in store brands of food products, Food Quality and Preference (2013), doi: http://dx.doi.org/10.1016/j.foodqual.2013.09.006

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BRAND AWARENESS – BRAND QUALITY INFERENCE AND CONSUMER´S RISK

PERCEPTION IN STORE BRANDS OF FOOD PRODUCTS

Natalia Rubio1

Javier Oubiña

Nieves Villaseñor

Autónoma University of Madrid

1 Natalia Rubio (Ph.D., Autónoma University of Madrid -SPAIN-), Associate Professor of Marketing, Department of Finance

and Marketing Research, Business Studies Faculty, natalia.rubio@uam.es.

Javier Oubiña (Ph.D., Autónoma University of Madrid -SPAIN-), Associate Professor of Marketing, Department of Finance

and Marketing Research, Business Studies Faculty, javier.oubinna@uam.es.

Nieves Villaseñor (Ph.D., Autónoma University of Madrid -SPAIN-), Professor of Marketing, Department of Finance and

Marketing Research, Business Studies Faculty, nieves.villasenor@uam.es.

Contact Address: Natalia Rubio Benito, Departamento de Financiación e Investigación Comercial. Facultad de CC.

Económicas y Empresariales, Universidad Autónoma de Madrid, c/ Francisco Tomás y Valiente, 5.

Campus de Cantoblanco, 28049 Madrid, España. Tel.: +34914973567; Fax.: +34914978725.

Acknowledgements: The authors wish to acknowledge the financial support of the Fundación Ramón Areces (research

project: “El capital cliente en mercados minoristas de gran consumo”), Ministry of Economy and

Competitiveness (research project ref.: ECO2012-31517) and UAM+CSIC (research project ref.:

CEMU-2012-34).

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BRAND AWARENESS – BRAND QUALITY INFERENCE AND CONSUMER´S RISK

PERCEPTION IN STORE BRANDS OF FOOD PRODUCTS

Abstract:

Store brands (SBs) have currently become consolidated in the food market, have achieved an objective

quality similar to that of manufacturer brands and a competitive price. However, food retailers have

invested little in communication about these brands, considering it enough to use proximity to the

consumer and economies of scope derived from the presence of their own brands throughout the

establishment. This paper explores the consequences of this communication strategy about SBs on the

functional risk perceived by consumers for these brands and the consumer’s identification with them.

We propose a theoretical model, contrast it empirically for food products, and perform a multigroup

analysis of quality conscious and non-quality conscious consumers. The results obtained reveal a

negative effect of the inference brand awareness – brand quality on the consumer’s identification with

the SB as a result of the greater functional risk perceived for these brands. This effect is substantially

greater in quality conscious consumers, a key segment for retailers since it constitutes the target of

their premium SBs. The results show retailers that investment in communication of SBs is absolutely

necessary to dismiss SB functional risk and expand customer base by appealing to quality conscious

consumers. The investigation has significant implications for the retailer’s strategy for marketing SBs

in food products.

Keywords:

Consumers, store brands, brand awareness – brand quality inference, functional risk, risk-reduction

strategies, brand identification, and quality consciousness.

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1. Introduction

Brand identification is a fundamental antecedent of brand loyalty and thus plays a crucial role in the

consumer’s brand choice and buying behavior (Ahearne, Bhattacharya and Gruen, 2005). The study of

brand identification has focused predominantly on manufacturer brands (Marin, Ruiz and Rubio,

2009), and we find no research that focuses on the consumer’s identification with store brands (SBs).

Although SBs were initially introduced in food markets as low-quality, low-price brands,

distributors are attempting to improve the quality of SBs and position them as the best choice on the

shelves in terms of price/quality ratio. Currently, consumers perceive SBs as value brands, and

manufacturers and distributors consider them as real brands that are beginning to have their own

identity. In fact, the academic literature has produced some recent studies praising their brand equity

(Cuneo, López and Yagüe, 2012 a, b).

SBs possess a set of characteristics that differentiate them from manufacturer brands and make it

necessary to rethink study of the consumer’s identification with the brand in the specific context of

SBs. Perceived value and low investment in advertising communication are common features

distinctive to SBs. Taking advantage of their closer position to the consumer as members of the

channel, food retailers have communicated their brands fundamentally through the establishment.

Retailers have also benefitted from economies of scope in communication deriving from

commercialization of their brand in a large number of product categories.

Different studies stress the importance of advertising in brand awareness (Aaker, 1996; Buil, De

Chernatony and Martínez, 2013; Kirmani and Wright, 1989), as well as the importance of brand

awareness in the perception of brand quality (Aaker, 1996; Buil et al., 2013; Keller and Lehman,

2003). While consumers draw conclusions about the quality of a brand based on their evaluation of its

intrinsic attributes (Shaharudin, Mansor and Elias, 2011; Sule, Paquin and Levy, 2002), its packaging

(Lavenka, 1991; Rigaux-Bricmont, 1982) or its price (Cronley, Posavac, Meyer, Kardes and Kellaris,

2005; Kardes, Cronley, Kellaris and Posavac, 2004; Lim and Olshavsky, 1988), one of the elements

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that most strongly conditions perception of a product’s quality is the brand name (Dawar and Parker,

1994). We will call the causal association that many consumers make between recognized brand and

high quality the brand awareness - quality inference. This paper analyzes the importance of this

inference in the consumer’s identification with the SB in food markets, and proposes its negative

indirect effect on identification through the functional risk the consumer perceives for these brands.

Although the perceived quality of SBs has improved due to favorable evolution of their intrinsic

and extrinsic attributes, there is still a significant difference between the perceived risk of

manufacturer and SBs to the disadvantage of SBs. The risk entailed in buying a brand conditions

significantly the entire process of the consumer’s evaluation and comparison of manufacturer and SBs

(Erdem, Zhao and Valenzuela, 2004; Glynn and Chen, 2009; Richardson, Jain and Dick, 1996). This

risk affects the perceived value and the consumer’s satisfaction with these brands and is thus also very

likely to affect the consumer’s identification with the SB.

On the other hand, consumers differ considerably in their quality consciousness (Sproles and

Kendall, 1986), and analysis of the effect of brand awareness - quality on the consumer’s

identification with the SB should control for this issue. In this study, we therefore perform a multi-

group analysis based on the consumer’s quality consciousness and compare the results obtained. We

find that quality consciousness moderates the effect of brand awareness - quality on perceived

functional risk for SBs. Quality conscious consumers are more brand conscious and place more trust in

the performance of recognized and advertised brands. They perceive more functional risk for SBs and

thus tend to dismiss their satisfaction and identification with these brands. The study results have

interesting implications for retail management of these brands.

2. Literature review

SBs have greater penetration in the European market than in the U.S. One of the reasons the academic

literature gives for this difference is the greater perceived value of SBs in Europe than in the U.S.

(Erdem et al., 2004). The perceived value of a brand involves considering brand quality not in absolute

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terms but relative to its price (Richardson et al., 1996). Perceived value forms as a result of the

consumer’s experiences purchasing and consuming a brand (Cronin, Brady and Hult, 2000).

Sethuraman (1992) finds that, although most consumers choose SBs primarily for their advantage in

price, quality plays a more significant role than price in the success of SBs, especially if we

understand this success from the long-term strategic perspective. Since the perceived quality of SBs

affects their perceived risk, their perceived value, consumer satisfaction, and consumer brand

identification, it is important to understand the aspects of the brand that determine perceived quality

and how perceived quality affects and is related to these key elements in the success of any brand.

2.1. Perceived functional risk of SBs

One of the issues most strongly influencing the perceived quality of a brand is its brand awareness

(Aaker, 1991, 1996; Buil et al., 2013; Dawar and Parker, 1994; Keller and Lehman, 2003). Consumers

assign high quality to prestigious brands. Such brands therefore enjoy greater credibility for the

consumer and ultimately greater value (Erdem and Swait, 1998; Erdem, Swait and Louviere, 2002).

Awareness encourages the perceived quality of the brand and thus also its credibility due to lower

perceived functional risk for the brand.

This investigation defines perceived risk as the expectation of certain results or events that may

occur and that are negative or suspect. The consumer faces four main kinds of risk in the process of

deciding to buy a product: functional, financial, psychological, and social (Dowling and Staelin, 1994;

Kaplan, Szybillo and Jacoby, 1974). Functional risk indicates the performance or utility conceived for

the product, financial risk the potential loss of money that can occur in any transaction, psychological

risk the possible consequences of mental uneasiness connected with a transaction, and social risk the

bad image that consuming a product may give an individual in the eyes of others. This study analyzes

the effect of the brand awarenesss - quality inference on perception of the functional risk of SBs.

Functional risk is directly linked to perceived quality, an issue that constitutes the main point of

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resistance to acquiring SBs in products of mass consumption (DelVecchio, 2001; Liljander, Polsa and

Van Riel, 2009; Méndez, Oubiña and Rubio, 2011; Richardson et al., 1996).

In choosing a brand, the consumer faces uncertainties that make it difficult to evaluate the

functional risks involved in the purchase. To reduce these uncertainties the consumer uses risk-

reduction strategies such as (1) information gathering from personal (friends, family) and commercial

(packaging, salespersons) sources, (2) well-known brands, (3) reassurance (e.g., through private

testing, free samples), (4) brand loyalty, (5) price, and (6) store image (Schiffman and Kanuk, 2006;

Mitchell and McGoldrick, 1996). Knowing the main risk reduction strategies that the consumer uses in

choosing a manufacturer brand vs. an SB is vital to retailers in managing their service and in providing

a risk-reduction marketing mix for consumers (Johnson and Bruwer, 2004; Lacey, Bruwer and Li,

2009).

Based on a review of over 100 articles, Mitchell and McGoldrick (1996) highlight the search for

information and well-known brands as the main risk-reduction strategies used by consumers.

However, the use and hierarchy of risk-reduction strategies vary by product, individual profile, and

type of purchasing establishment (among other issues), making it advisable to limit study of these

strategies to specific contexts. Bruwer, Fong and Saliba (2013) find that the main risk reduction

strategy for purchasing wine in specialty wine stores is information gathering, for which they obtain

significant differences between low and high perceived risk individuals, followed by seeking

reassurance through tasting and price. For wine acquired in restaurants, however, Lacey et al. (2009)

identify tangible product attributes as the most significant risk-reduction strategy, followed by advice

from staff and the restaurant’s reputation.

To evaluate store and manufacturer brands comparatively for products of mass consumption,

consumers seek and use information from the extrinsic attributes (e.g., brand name, price) and the

intrinsic attributes of the product (e.g., ingredients, texture). These attributes help the consumer to

make his or her own evaluation of quality (Wernerfelt, 1988; Zeithaml, 1988). Extrinsic attributes

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have been traditionally valued more highly in manufacturer brands than in SBs. It is thus reasonable to

think that consumers who are guided more strongly by extrinsic attributes as indicators of perceived

quality perceive lower functional risks in manufacturer brands than in SBs. Erdem et al. (2004) find

that the buyer’s initial uncertainty is usually greater for SBs than for manufacturer brands; and Erdem

and Swait (1998), Erdem et al. (2002), and Schiffman and Kanuk (2006) argue that consumers use a

risk-reduction strategy when they buy prestigious manufacturer brands, to which they assign high

perceived quality.

Consumer reliance on extrinsic attributes of a product is likely to be the main explanation for the

lower perceived quality of SBs. In fact, González, Díaz and Trespalacios (2006) find that consumer

reliance on extrinsic attributes of the product is negatively related to the perceived quality in SBs vs.

manufacturer brands and positively related to the difference in risk between SBs and manufacturer

brands.

Based on the aforementioned, we propose the following hypothesis:

H1: The greater the inference brand awareness – brand quality, the greater the functional risk

perceived in the SB.

2.2. Perceived value of SBs

Richardson et al. (1996) suggest that retailers put more emphasis on quality than on low prices to

improve consumers’ perception and increase sales of their own brands. These authors recommend that

retailers use different methods to increase perception of SB quality. To achieve this goal, retailers can

invest in advertising, encourage consumer product experience through sampling (Sprott and Shimp,

2004; Goldsmith, Flynn, Goldsmith and Stacey, 2010), invest in store image, or choose to develop SBs

in food products that have low quality variation across brands and are strongly associated with quality

in consumers’ mind (i.e. the signature product) (Bao, Bao and Sheng, 2011). This strategic orientation

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would help retailers to expand their customer base by appealing to quality conscious consumers (Bao

et al., 2011; Sprott and Shimp, 2004).

Consumers perceive manufacturer brands as safe brands, brands with less variation in the quality of

their products than SBs (Montgomery and Wernerfelt, 1992). The credibility and trust achieved by

manufacturer brands over time have contributed considerably to their perceived value (De Wulf,

Odekerken-Schröder, Goedertier and Van Ossel, 2005). In contrast, the higher perceived risk of SBs

has acted to the detriment of their perceived value (Richardson et al., 1996). Therefore, both

manufacturer brands and SBs give the consumer value, but manufacturer brands have consolidated

their value through quality, whereas SBs have consolidated theirs through price. Although it is true

that the quality of SBs has improved substantially over time, recent studies find that, although their

objective quality is similar to that of manufacturer brands, their perceived quality continues to be

significantly lower (Méndez, Oubiña and Rubio, 2008; 2011).

Based on the foregoing, we establish the following study hypotheses:

H2. Quality consciousness positively moderates the effect of the inference brand awareness – brand

quality on the functional risk perceived in the SB, making this effect stronger for consumers with

greater quality consciousness.

H3: The greater the functional risk perceived for the SB, the lower the perceived value of these brands.

2.3. Consumer’s satisfaction and identification with SBs

Perceived risk is related to the losses and negative consequences anticipated if one makes the wrong

choice. It is thus reasonable to expect that, when the perceived risk of a service or a brand increases,

the satisfaction with that service or brand decreases (Cronin et al., 2000; Dowling and Staelin, 1994;

Gocek and Beceren, 2012; Hallowell, 1996; Hu, Kandampully and Juwaheer, 2009; Johnson,

Garbarino and Sivadas, 2006 and Johnson, Sivadas and Garbarino, 2008, among others). Further,

Gocek and Beceren (2012) indicate that perceived risk is one of the main factors influencing customer

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satisfaction. Nador (2012) warns that perceived risk affects the success of marketing strategy for the

brand or firm and underscores the importance of managing it effectively. Along these lines, the study

by Johnson et al. (2006 and 2008) proposes that the company reduce the risk that the consumer

perceives for its brand by providing purchase guarantees that increase the brand’s perceived value and

thus satisfaction with it as a possible management strategy.

Based on the foregoing, we propose the following hypotheses concerning SBs:

H4: The greater the functional risk perceived for the SB, the lower the consumer’s level of satisfaction

with it.

H5: The greater the perceived value of the SB, the greater the consumer’s satisfaction with it.

Satisfying the consumer involves achieving the consumer’s pleasure in consuming the brand and

desire to repeat this experience. This is one of the fundamental strategic concepts in marketing, since it

generates an explicit relationship between the processes of purchase and consumption and the post-

purchase phenomenon (Hunt, 1983).

Brand satisfaction has a positive influence on the relationship between consumer and brand (Gocek

and Beceren, 2012), more specifically, on one of the most important aspects of this relationship, the

brand identification. According to the theory of social categorization and identification, an individual

identifies with a social category when the social category (and the social identity associated with it)

improves his or her self-esteem. An identified individual will show positive pro-category behavior to

preserve the attractiveness of this social identity (and thus his/her self-esteem) (Bhattacharya and Sen,

2003; Tajfel and Turner, 1985).

Brands can be social categories with which consumers identify. The academic literature defines the

consumer’s identification with the brand as self-connection with a brand (Fournier 1998), congruence

of self-image between consumers and brands (Kressmann, Sirgy, Herrmann, Huber, Huber and Lee,

2006), or the consumer’s involvement with a brand (Pritchard, Havitz and Howard, 1999). Ahearne et

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al. (2005) define it as “an active, selective, deliberate act motivated by the satisfaction of one or more

needs for self-definition (for example: “Who am I?”).”

Empirical studies (Ahearne et al., 2005; Marin et al., 2009) find that consumers with stronger brand

identification are more likely to “get hooked” on pro-brand activities in favor of the brand, such as

word of mouth and brand loyalty. When articulating their self-perception, individuals go beyond their

personal identity and develop a social identity. Brand identification shows the degree to which the

brand expresses and improves individual identity (Kim, Han and Park, 2001). Individuals who identify

with a particular brand experience a positive psychological result in the form of improved self-esteem

and are more likely to act favorably toward the brand (Donavan, Janda and Suh, 2006).

SBs can express and improve the identity of individuals satisfied with the purchase experience at

the retailer (Bitner, 1992; Verhoef, 2009) and their experience consuming these brands (Brakus et al.,

2009). They can also express and improve the identity of individuals who perceive a value for these

brands that reinforces their self-image as intelligent customers. The value-conscious consumer and

smart shopper are concepts associated with the attitude and purchase of SBs in various academic

studies (Burton, Lichtenstein, Netemeyer and Garretson, 1998; Dabholkar et al., 1996; Garretson,

Fisher and Burton, 2002; Jin and Suh, 2005).

Based on the foregoing, we formulate the last two hypotheses:

H6: The greater the consumer’s satisfaction with the SB, the greater the identification with the SB.

H7: The greater the perceived value of the SB, the greater the degree of identification with the SB.

Figure 1 presents the theoretical research model.

Figure 1 here.

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3. Methodology

To contrast the hypotheses proposed, we performed an empirical study of people responsible for

shopping in their households who stated that they purchase SBs in food products. In the first phase of

the research, we performed twenty in-depth interviews of individuals from the study group. From

these, we obtained exploratory starting information that enabled us to determine the relationship

between consumers and SBs and thus subsequently to design a better questionnaire. Specifically, the

questions posed to the consumers in the in-depth interviews enabled us to determine in detail the

perceptions and attitudes of the individuals responsible for buying these brands. We could then

determine possible issues that influenced their purchase, explore consumers’ image of the

manufacturers, retailers, and consumers of these SBs, and analyze how these consumers explain and

interpret the lower price of SBs, among other issues.

The in-depth interviews were performed with Spanish residents chosen through non-random

sampling with quotas for gender, age, occupation, and number of members in the household.

Participants were mainly recruited by the snowball sampling method (the authors’ friends and

colleagues were asked to refer a friend with whom the authors were not acquainted). One of the main

advantages of this sampling method is the ease and speed in obtaining the quotas desired, while one of

its main disadvantages is that respondents engaged in similar social circles may share attitudes and

beliefs (May, 2001). The results of the in-depth interviews allowed us to design the questionnaire, use

language adapted to the interviewee, and adapt the items used in past research to the specific context.

The second phase of the research was quantitative. Data were collected through self-administered

questionnaires from Spanish residents in Spain. The information came from a personal survey at the

exit of 54 purchase establishments belonging to seven hypermarkets and supermarkets from five

significant commercial groups in Spain (Mercadona, Eroski, Carrefour, Día, and Auchan). These five

groups have a very strong presence in Spain, in terms of both commercial surface (around 52% of the

commercial surface in m2 in 2010) and sales volume in food products (around 64% of the sector’s

income in 2010) (The Brattle Group, 2012). The number of establishments per group was determined

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according to the commercial surface share in m2 of each group in 2010

2 (14 establishments for

Mercadona, 13 for the Eroski group, 12 for the Carrefour group, 10 for Día, 5 for Auchan). The

establishments were chosen according to different urban areas based on income, retail competition,

and means of transportation.

At the exit of the commercial establishments, students in the final year of graduate courses in

marketing who possessed general training in market research and specific training in SBs recruited

shoppers who agreed to complete a self-administered questionnaire approximately 10 minutes in

length. We obtained a total of 877 questionnaires (20 per establishment); 804 were considered valid.3

The items that we used to measure the concepts proposed come from the adaptation of scales used

previously in the academic literature. Specifically, to measure the brand awareness – quality inference,

we adapt the items employed by Richardson et al. (1996), Dick, Jain and Richardson (1997), and

Sethuraman and Cole (1997), as well as those from DelVecchio (2001) in extrinsic cue reliance. To

measure perceived value, we use the items that refer to value from the scale for SB attitude developed

by Burton et al. (1998). We adapt the measure for brand satisfaction from the research of Oliver

(1980), and we construct the scale for functional risk following the operationalizations of Stone and

Gronhaugh (1993) and Dholakia (1997). For consumer identification with the SB, we adapt the well-

established scale proposed by Mael and Asforth (1992). Finally, we measured the consumer’s quality

consciousness with three statements from the scale developed by Sproles and Kendall (1986): “Getting

very good quality is very important to me,” “In general, I usually try to buy the best overall quality,”

and “I make a special effort to choose the very best quality products.” We obtained an Alpha

Cronbach of 0.73 for this scale, which guaranteed its reliability.

Not all consumers are equally sensitive to quality, nor do they show the same degree of

involvement in it (Sproles and Kendall, 1986). To determine the moderating effect that quality

2 Mercadona (13.2%), Eroski (12.2%), Carrefour (12%), Día (9.3%), and Auchan (5.2%). We use share of

commercial surface instead of share of sales to avoid possible bias in the results due to Mercadona’s outstanding

leadership in sales and in its own brands in Spain. 3 Mercadona (201), Eroski (191), Carrefour (185), Día (149), Auchan (78).

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consciousness has on the causal effect of the brand awareness – quality inference on SBs’ perceived

functional risk (H2), we classify consumers into two segments, those with a high degree of

involvement in quality and for whom quality is the main factor determining their purchase decisions

(designated as “quality conscious consumers (QC consumers)”), and those for whom the attribute of

quality does not have as much relevance (designated as “non-quality conscious consumers (NQC

consumers).” We identify these two segments by applying a TwoStep cluster analysis to the survey

respondents’ replies on the three items used to measure quality consciousness. The results obtained

with TwoStep cluster analysis were validated with the results obtained using Ward’s method. Both

procedures provide nearly identical results.

Table 1 shows the differences between the two segments for the variables in the model. The

variance analysis performed shows statistically significant differences in brand awareness – quality

inference and functional risk. QC consumers use brand as an indicator of quality more often

(MeanQC=3.37; MeanNQC=3.08) and perceive greater functional risk in purchasing SBs (MeanQC=3.55;

MeanNQC=3.27).

Table 1 here.

We used multi-item 7-point Likert scales from 1 (disagree completely) to 7 (agree completely).

Tables 2 and 3 describe the scales used with their corresponding items. We estimated the empirical

model using structural models of covariance with the statistical package AMOS 19.

4. Results

4.1. Measurement model

For each of the samples, we confirmed the quality of the measurement scales, as recommended by

Byrne (2001). We performed a confirmatory factor analysis with the AMOS 19.0 program. The results

showed very satisfactory fits in both samples for modeling the five factors proposed. In the sample of

QC consumers, the relationship X2/df was 1.994, close to the maximum threshold of 2 recommended

by Bentler (1989). The values of the CFI and AGFI were 0.979 and 0.924, respectively, higher than

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the minimum value of 0.9 recommended by Bentler (1989) and Bentler and Bonnet (1980). The value

of the RMSEA was 0.048, lower than the maximum value of 0.06 suggested by Hu and Bentler

(1999). In the sample of NQC consumers, the values obtained also respect the limits suggested in the

academic literature (X2/df=1.673, CFI=0.976, AGFI=0.924 and RMSEA=0.043).

Tables 2 and 3 show the results for reliability and validity for both samples (QC consumers and

NQC consumers). In all cases, the statistics used for reliability – the Alpha Cronbach and composite

reliability – are higher than the minimum value of 0.70 (Hair, Anderson, Tatham and Black, 1998).

The variance extracted is in all cases lower than or equal to a variance of 0.5, and all items have

sufficient convergent validity, since all of the parameters are statistically significant.

Table 2 here.

Table 3 here.

We also confirmed the discriminant validity in both samples. In Table 4, we see that the percentage

of variance extracted for each construct is in all cases higher than the square of the correlation between

each pair of concepts.

Table 4 here.

Finally, we examine the measurement invariance between the two groups as follows. First, we

perform a multigroup confirmatory analysis and find that the results show satisfactory fit

(X2=381.382; d.f=208; X2/df=1.834; CFI=0.978; NFI=0.953; IFI=0.978; GFI=0.948; AGFI=0.924;

RMSEA=0.032). Second, we impose the restriction of equality of parameters for the two samples and

compare the results for goodness of fit for the restricted model with the results for the goodness of fit

for the unrestricted model (∆X2=12.58; ∆df=12; p=0.4>0.01). Since we do not see significant

worsening in the model’s fit, this result guarantees that measurement invariance is fulfilled. Thus, the

differences that we observe between the causal relationship models will be due to the causal

relationships themselves and not to the measurement of the constructs.

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4.2. Causal relationship model

We estimate the model in Figure 1 using structural equations modeling, without including the

moderating effect (H2). The fit obtained is satisfactory (X2=238.946; d.f=108; X2/df=2.212;

CFI=0.983; NFI=0.970; IFI=0.983; GFI=0.967; AGFI=0.953; RMSEA=0.039), and all of the

proposed hypotheses are confirmed. The path coefficients for the overall model appear in Table 5.

Table 5 here.

We now consider the moderating effect and perform a multi-group structural analysis for the

segments of QC consumers and NQC consumers. We compare the results of the two models—the

first, unrestricted model, and a second model, on which we impose the restriction of equality for the

structural parameters in the two segments (restricted model). The results for goodness of fit show a

significant worsening in the model when we impose the restrictions of equality on the structural

relationships. This suggests that some restrictions cannot be sustained. See Table 6.

Table 6 here.

Table 7 shows the non-standardized structural parameters for each of the segments considered and

the critical ratios obtained for the differences. In comparing the models, we must remember that we

use non-standardized parameters due to the possible presence of differences in the standard deviation

of each construct between the samples (Iglesias and Vázquez, 2001). We use a t-test based on the

expression t = (βi-βj)/square root (Si2+Sj

2), in which βi and βj represent the coefficients to be contrasted

and Si and Sj their respective standard errors (Hair et al., 1998), to calculate statistical significance of

the differences between parameters.

Table 7 here.

Quality consciousness moderates the effect of the brand awareness – brand quality inference on

perceived functional risk in SBs (H2); that is, the intensity of the relationship proposed in H1 differs

16

statistically for QC and NQC consumers. The rest of the hypotheses proposed in this study are

contrasted in both samples and yield similar intensity for both groups (see Table 7).

In the sample of QC consumers, the negative relationship for the brand awareness – brand quality

inference and perceived functional risk of SBs is significantly more intense (0.88 in QC vs. 0.65 in

NQC). The consumers more interested in quality, those for whom quality is a determining factor in

their purchase choices, use brand as an indicator of quality and perceive greater risk in the

performance of SBs.

The total effect of perceived functional risk for SBs on satisfaction with these brands is similar in

both samples, specifically -0.31 [(-0.36*0.57) -0.10] in QC consumers and -0.33 [(-0.25*0.46) -0.21]

in NQC consumers. However, in the segment of QC consumers, the indirect effect through value (-

0.21) is considerably more intense than the direct effect (-0.10). The opposite occurs in the segment of

NQC consumers, where the direct effect (-0.21) is considerably more intense than the indirect effect

through value (0.12).

It is also interesting to note that the total effect of the brand awareness – brand quality inference in

each of the constructs is higher (in absolute value) for the segment of QC consumers than for the

segment of NQC consumers. Thus, the total effect on perceived functional risk of SBs is 0.88 in QC

consumers and 0.65 in NQC consumers. The effect on perceived value of SBs is -0.31 in QC

consumers and -0.16 in NQC consumers, the effect on satisfaction with SBs -0.27 in QC consumers

and -0.21 in NQC consumers, and the effect on identification with SBs -0.25 in QC consumers and -

0.17 in NQC consumers.

Finally, we would point out the high proportion of variance explained by the different constructs in

both samples, although the explanatory values achieved in the segment of QC consumers are higher

than those obtained in the segment of NQC consumers. Specifically, the variance explained for

identification with the SB is R2=0.73 in QC consumers and R

2=0.66 in NQC consumers; the variance

explained for the perceived functional risk of SBs is R2=0.52 in QC consumers and R

2=0.33 in NQC

17

consumers; and the variance explained for satisfaction with SBs is R2=0.52 in QC consumers and

R2=0.37 in NQC consumers.

5. Conclusions

The results obtained in this research enable us to contrast our hypotheses and thus confirm the correct

functioning of the proposed model to explain the influence of the brand awareness – brand quality

inference on the consumer’s degree of identification with the SB.

Brand communication contributes to brand awareness, and consumers use brand awareness on a

great number of occasions as both a guarantee of quality and a risk-reduction strategy. The more

aware consumers are of a brand and the more this awareness influences the attribution of quality to the

brand, the greater the perceived risk in the proper functioning of SBs as compared with manufacturer

brands. Perceived functional risk is a decisive factor in the configuration of SBs’ value and in

consumers’ satisfaction with these brands. Specifically, the effect of perceived functional risk on

satisfaction is not only direct but also indirect through perceived value. Both the perceived value of

SBs and satisfaction with them affect the consumer’s identification with these brands positively and

significantly.

The results obtained from our modeling have significant strategic implications for food retailers,

since they show the importance of brand awareness as a strategy for reducing risk for buyers of SBs in

general, and especially for quality-conscious buyers.

Decreasing the perceived risk in the functioning of a product should constitute one of the main

challenges retailers undertake in managing their own brands, especially in the case of consumers for

whom quality is the fundamental purchase attribute. Retailers should engage in sufficient

communication on the SB to increase its recognition and prestige and thus to encourage its connection

with quality.

18

In designating their brands, distributors have used primarily their own name on the label (label

brand) or umbrella names for sets of product categories. These brand strategies have permitted the

retailer to use its establishments as showcases for communication and to take advantage of economies

of scope derived from the repetition of the name/names of their brand throughout the establishment.

In this way, food retailers have achieved significant successes with their own brands, among these,

gaining high market participation in European countries and consolidating SBs’ positioning in value.

However, academic research on this positioning shows consensus on the evaluation of price, perceived

as competitive relative to manufacturer brands, but no consensus on the evaluation of quality.

Numerous studies find that the objective quality of SBs and manufacturer brands is similar, but

consumers continue to perceive manufacturer brands as having higher quality as compared to SBs

when the brands are known.

The results obtained in this study warn of that minimal retail investment in communication about

these brands can have a negative effect on their perceived performance. Retailers’ communication on

their own brands throughout the establishment has been insufficient to achieve perceptions of quality

adequate to these brands’ levels of objective quality. Further, if the results obtained show that the retail

investment in communication is important in food products, it will surely be crucial in markets for

non-perishable goods, where the consumer is less familiar with these brands and uses extrinsic

attributes to a greater extent in his/her purchase choice. Insufficient communication on SBs in these

markets is surely the reason that they have failed to take off.

The results of this study also show that communication about SBs is crucial in segments of quality

conscious consumers. The greater acceptance of SBs in food products has encouraged many retailers

to adopt a segmented brand strategy for portfolios of their own brands. Currently, in a large number of

establishments, distributors commercialize not only their traditional SBs, which are positioned in

value, but also their premium SBs, with a higher quality that sometimes exceeds the quality of the

19

leading manufacturer brands. The target of these premium SBs is consumers who are concerned with

quality and do not mind paying a higher price for the better product.

Retail investment in brand awareness is extremely important in improving QC consumers’

perception of SB value. The negative effect of the inference brand awareness – brand quality on

perceived value of the SB is significantly greater in QC consumers than in NQC consumers. In QC

consumers, the inference brand awareness – brand quality has a strong negative effect on perceived

functional risk of SBs as compared to manufacturer brands, and this perceived functional risk

decreases SBs’ perceived value. In this segment of QC consumers, manufacturer brands continue to

hold the leading position. Retailers have not yet invested sufficiently in awareness of their own brands

to be able to consolidate them in this segment.

Traditionally, SBs have targeted NQC consumers. These consumers are more sensitive to price and

less sensitive to quality. Here, the effect of the brand awareness – brand quality inference on the

perceived value of the SB is lower than in the segment of QC consumers, but the sign obtained is also

negative and significant. This result warns us that any strategy to consolidate and grow SBs in this

segment must include the importance of attempting to create greater brand consciousness through

appropriate investment in communication. Lack of investment increases the perceived functional risk

of SBs as compared to manufacturer brands and decreases SBs’ potential for success.

It is thus important for retail establishments to contribute to decreasing the perceived functional

risk of SBs by improving their image and how they are evaluated. Companies should do this not only

through the presence of SBs throughout the establishment but also by advertising investment and

informal control of the purchase experience (Aurier and Lanauze, 2011). It is true that such a strategy

requires a certain level of investment and a sacrifice for the retail firm. But future gains in respect for

the chain, trust, and value of the brand, satisfaction and identification are likely to reward that

sacrifice. SBs should seek to achieve high levels of identification if they wish to compete successfully

with manufacturer brands. Under no circumstances should retailers abandon the effort to achieve high

20

identification with their brands, since brand identification is crucial to true loyalty to the brand. Insofar

as consumers perceive value in SBs, they will identify with SBs, that is, will perceive what these

brands represent to them on the levels of both self-image and social image. The main route available to

retailers to increase the perceived value of these brands is reducing their perceived functional risk

through greater investment in awareness. To the extent which retailers achieve this, they will also get

greater satisfaction and identification of the consumer with the SB.

This study is not exempt from limitations, which could be taken into account in future research

First, our study analyzes data from the Spanish food market and for SBs in general. It is advisable to

consider other countries and product categories in which SBs are less accepted (e.g., categories of non-

perishable goods) and to differentiate between value and premium SBs and among the SBs of different

commercial labels. As to the origin of the model, it would be interesting for future research to

investigate in greater depth the factors determining consumers’ associations of brand awareness and

perceived quality, as well as to identify other possible issues that condition the perceived functional

risk of SBs. Finally, it would be useful to perform other investigations that incorporate other

components of the brand’s perceived risk into this model, such as financial, psychological, and social

factors.

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Figures and Tables

Figure 1: Theoretical model

Brand awareness-

quality inference

SB

satisfaction

SB

identification

SB perceived

value

SB functional

risk

H3

H4

H7

H5

H1

H6H2

Quality

consciousness

Source: Developed by the authors

Table 1. Differences between QC consumers and NQC consumers

Variable/Construct QC n=439

NQC n=365

Total F-Snedecor

Brand awareness-

quality inference

3.37

(1.30)

3.08

(1.11)

3.23

(1.27)

10.605***

SB functional risk 3.55

(1.60)

3.27

(1.29)

3.42

(1.47)

6.991***

SB perceived value

4.09

(1.48)

4.21

(1.17)

4.15

(1.35)

1.840

SB satisfaction 4.44

(1.34)

4.55

(1.09)

4.49

(1.23)

0.594

SB identification 4.16 (1.51)

4.20 (1.24)

4.17 (1.39)

0.128

Standard deviation in parentheses.

***: p<0.01.

31

Table 2. Analysis of reliability and validity of measurement scales for the sample of QC consumers

Reliability Validity

Variables

Li Ei Alpha

Cronbach

Composite

Reliability

(CR)

Average

Variance

Extracted (AVE)

Convergent

Validity

Awareness - quality inference

v1: When the product is not from a recognized brand, it is lower quality.

v2: The “more famous” the brand name, the better the

quality of a product that carries that name. v3: Brand name is a determining issue in purchasing a

product.

v4: The more a product is advertised, the better its

quality.

v5: I feel secure purchasing a brand whose advertising I

have seen in some communications medium.

0.77

0.87

0.70

0.73

0.61

0.41

0.25

0.51

0.47

0.63

0.86

0.86

0.55

t =11.44 ***

t=12.69*** t =11.80***

t=15.14***

---

SB functional risk

v6: Purchasing a well-known manufacturer brand (MB)

is safer than purchasing a well-known SB.

v7: SBs have worse performance than MBs.

v8: A product with a “famous” MB will perform better

than an SB, even if the SB is from a recognized

establishment.

0.81 0.86

0.79

0.34 0.26

0.37

0.86

0.86

0.68

t=17.84*** t=18.85***

t = ---

SB perceived value

v9: For many products, the best purchase (for

price/quality ratio) is generally the SB.

v10: Considering value for money, I prefer SBs to MBs.

v11: When I buy an SB, I always feel that I am getting a

good deal.

0.82 0.88

0.83

0.33 0.23

0.32

0.88 0.88 0.71

t= 19.65*** t=21.52***

t= ---

SB satisfaction

v12: With SBs, I always get what I am looking for.

v13: SBs always meet my expectations as a consumer.

v14: My choice to buy SBs is a wise one.

0.84

0.84

0.92

0.30

0.30

0.16

0.88 0.90 0.75

t =18.17 ***

t= ---

t =20.56***

SB identification

v15: I feel identified with SBs. v16: When someone praises SBs, I feel exactly the same

way.

v17: I feel proud of SBs.

0.75 0.90

0.91

0.44 0.19

0.18

0.88 0.89 0.73

--- t= 19.41***

t= 19.51***

Significance level: *** p< 0.001.

Li: Standardized loading; Ei = (1- R2): error variance;

32

Table 3. Analysis of reliability and validity of measurement scales for NQC consumers

Reliability Validity

Variables

Li Ei Alpha

Cronbach

Composite

Reliability

(CR)

Average

Variance

Extracted

(AVE)

Convergent

Validity

Awareness - quality inference

v1: When the product is not from a recognized brand, it

is lower quality.

v2: The “more famous” the brand name, the better the

quality of a product that carries that name.

v3: Brand name is a determining issue in purchasing a product.

v4: The more a product is advertised, the better its

quality. v5: I feel secure purchasing a brand whose advertising I

have seen in some communication medium.

0.75

0.78

0.62 0.78

0.56

0.45

0.40

0.62 0.40

0.69

0.82

0.83

0.49

t =8.37***

t=8.88***

t =8.68*** t=10.69***

---

SB functional risk v6: Purchasing a well-known manufacturer brand (MB)

is safer than purchasing a well-known SB.

v7: SBs have worse performance than MBs. v8: A product with a “famous” MB will perform better

than an SB, even if the SB is from a recognized

establishment.

0.75

0.82 0.83

0.43

0.32 0.32

0.84

0.84

0.64

t=14.80***

t=16.00***

t = ---

SB perceived value

v9: For many products, the best purchase (for

price/quality ratio) is generally the SB. v10: Considering value for money, I prefer SBs to MBs.

v11: When I buy an SB, I always feel that I am getting a

good deal.

0.77

0.79 0.81

0.38

0.41 0.35

0.83 0.83 0.62

t= 14.62***

t=14.97*** t= ---

SB satisfaction

v12: With SBs, I always get what I am looking for. v13: SBs always meet my expectations as a consumer.

v14: My choice to buy SBs is a wise one.

0.82 0.80

0.84

0.34 0.37

0.30

0.83 0.86 0.67

t =13.30 ***

t= --- t =13.68***

SB identification v15: I feel identified with SBs.

v16: When someone praises SBs, I feel exactly the same

way. v17: I feel proud of SBs.

0.79

0.80

0.82

0.37

0.36

0.33

0.85 0.85 0.65

---

t= 15.74***

t= 16.08***

Significance level: *** p< 0.001.

Li: Standardized loading; Ei = (1- R2): error variance;

33

Table 4. Analysis of discriminant validity for both pairs of samples (QC consumers and NQC consumers) according to the method of Average Variance Extracted

Brand

awareness-

quality

inference

SB

functional

risk

SB

perceived

value

SB

satisfaction

SB

identification

QC consumers 0.74 0.72 -0.26 -0.22 -0.21 Brand awareness - quality inference NQC consumers 0.70 0.58 -0.15 -0.18 0.09

QC consumers 0.82 -0.37 -0.37 -0.35 SB functional risk

NQC consumers 0.80 -0.28 -0.39 -0.23

QC consumers 0.84 0.71 0.82 SB perceived value

NQC consumers 0.79 0.56 0.75

QC consumers 0.86 0.76 SB satisfaction

NQC consumers 0.82 0.68

QC consumers 0.85 SB identification

NQC consumers 0.81

The data in the table that appear in bold on the diagonal are the square root of the AVE of each construct The data above the diagonal correspond to the correlations between pairs of constructs

Table 5. Estimation of the relationship model

Model relationships Standardized

coefficient

t-value

H1: Inference brand awareness –

brand quality �Perceived SB

functional risk

0.67 15.21***

H3: Perceived SB functional risk

� Perceived SB value for

money

-0.34 -8.36***

H4: Perceived SB functional risk

� SB satisfaction

-0.16 -4.79***

H5: Perceived SB value for

money � SB satisfaction

0.61 15.53***

H6: SB satisfaction � SB identification

0.36 8.62***

H7: Perceived SB value for

money �SB identification

0.56 12.59***

***p<0.01

Table 6. Comparison of nested models in the multigroup analysis

Fit statistics X2

(d.f.)

CMIN/DF ∆X2 (d.f.) p CFI GFI AGFI RMSEA

Structural model without

restrictions

388.204

(216)

1.797 0.978 0.947 0.925 0.032

Structural model with restricted

parameters

415.847

(234)

1.777 27.643

(18)

0.068 0.977 0.943 0.926 0.031

34

Table 7. Results of the multigroup analysis

Model relationships QC consumers

(non-

standardized

coeffs.)

NQC

consumers

(standardized

coeffs.)

CR

H1: Inference brand awareness –

brand quality �Perceived SB

functional risk

0.88*** 0.65*** 2.18**

H3: Perceived SB functional risk

� Perceived SB value for money

-0.36*** -0.25*** -1.47

H4: Perceived SB functional risk

� SB satisfaction

-0.10*** -0.21*** 1.78

H5: Perceived SB value for

money � SB satisfaction

0.57*** 0.46*** 1.58

H6: SB satisfaction � SB

identification

0.36*** 0.41*** -0.61

H7: Perceived SB value for

money �SB identification

0.49*** 0.55*** -0.68

CQ: Quality conscious NCQ: Non-quality conscious

CR: Critical Ratio for differences between parameters

t=1.65 for p<0.1; t=1.96 for p<0.05, and t=2.58 for p<0.01

35

BRAND AWARENESS – BRAND QUALITY INFERENCE AND CONSUMER´S RISK

PERCEPTION IN STORE BRANDS OF FOOD PRODUCTS

Highlights

• Brand awareness-brand quality inference affects negatively the consumer´s identification

with store brands.

• The more aware consumers are of a brand and the more this awareness influences the

attribution of quality to the brand, the greater the perceived risk in the functioning of SBs

as compared with manufacturer brands.

• Perceived risk is a decisive factor in the configuration of the SB’s value, and SB´s value

affects the consumer’s identification with these brands positively and significantly.

• Differences in the modelling proposed are shown for quality conscious consumers and

non quality conscious consumers of SBs.

• Investment in brand awareness is absolutely necessary to expand customer base of

store brands by appealing to quality conscious consumers, a key segment for retailers

since it constitutes the target of their premium store brands.