Beyond Brand Attitude: Individual Drivers of Purchase for Symbolic Cobranded

Products

Marc Mazodiera,b* and Dwight Merunkac,d

aUniversity of Nottingham Ningbo, 199 Taikang East Road, 315100 Ningbo, China

bISG Business School, 147 Avenue Victor Hugo, 75116 Paris, France

cAix-Marseille University, IAE Aix-Marseille, Cergam, Rue de la Quille, Puyricard 13540,

France

d KEDGE Business School, Domaine de Luminy, BP 921, Marseille Cedex9 13288, France

Submission: June 2013

Revision: August 2013

Acceptance: November 2013

*Corresponding author at: University of Nottingham Ningbo, 315100 Ningbo, China, Tel:

+86 574 8818 3042; e-mail: marc.mazodier@nottingham.edu.cn

Dwight Merunka, Tel : +33 442 280 829; e-mail: dwight.merunka@iae-aix.com

____________________________________________

The authors thank the guest editors, Luk Warlop and L.J. Shrum, as well as two anonymous

reviewers, for their constructive comments and suggestions.

Abstract

Cobranding, or the use of two brand names on a single product, generally coincides

with higher purchase intentions. Prior research focuses on ingredient co-branding and

suggests that attitude towards both the primary and the secondary brands and congruence

between the brands are important drivers of cobranded products’ success. This research shows

that self-congruity with the secondary brand and need for uniqueness have significant positive

impacts on symbolic co-branding purchase, in addition to perceived congruence and attitude

toward the primary brand. In contrast, attitude toward the secondary brand does not relate to

purchase of symbolic cobranded products. Therefore, managers should rely on self-congruity,

instead of attitude toward the secondary brand, when choosing a partner for symbolic

cobranded products. Moreover, product category involvement enhances the impact of self-

congruity on purchase intent.

Keywords: cobranding, self-congruity, brand attitude, need for uniqueness

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1. Introduction

An increasing number of companies use co-branding, which pairs two brands to form a

separate, unique product, to generate sales. The literature distinguishes two types of co-

branding: symbolic co-branding and ingredient co-branding (Cegarra and Michel, 2001;

Kotler and Pfoertsch, 2010). This research focuses on symbolic co-branding which ”consists

in associating to the host brand a second brand that will give it symbolic additional attributes”

(Kotler and Pfoertsch, 2010, p. 23). For a cobranded mobile phone that features both Samsung

and Hugo Boss for example, Samsung represents the host or primary brand (PB), and Hugo

Boss is the secondary brand (SB). This symbolic cobranding aims to enhance both symbolic

and self-expressive associations for the cobranded product, through associations linked to the

Hugo Boss brand. Some of the symbolic associations are well known (such as the Lagerfeld -

Coca-Cola light), while others go unnoticed (like the Aston Martin – Nokia mobile phone).

Symbolic co-branding, has become increasingly prevalent for several reasons. Symbolic

cobranded products appeal to new target segments by featuring a secondary partner’s brand

equity (Park, Jun and Shocker,1996), and through their association, partner brands can borrow

the equity of other entities in the form of transfers of attitudes, thoughts, images, feelings,

awareness and experience (Keller, 2003). A signaling perspective also suggests that the

combination of two brands provides greater assurance about product quality than can a single-

branded product (Rao and Ruekert, 1999). Furthermore, symbolic cobranded products may

increase usage among consumers, by providing new products that appeal to consumers’ need

for uniqueness (Bloch, 1995).

To ensure the effectiveness of their cobranding strategies, marketers need to know what

drives cobranded product purchase. Surprisingly, prior research studying the conditions in

which cobranding is effective focuses on ingredient branding, which incorporates functional

attributes of one brand into an existing, dominant brand as an ingredient (Helmig, Huber and

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Leeflang, 2008). For example, Simonin and Ruth (1998) study a partnership between a car

brand and an ingredient microprocessor brand, and Helmig, Huber and Leeflang (2007) use a

hypothetical fruity quark/pudding. These studies show that prior attitudes toward both partner

brands and congruence between the brands are critical drivers of ingredient co-branding

effectiveness (Simonin and Ruth, 1998; Helmig et al., 2007). Specific research focusing on

symbolic cobranding is necessary, because symbolic attributes and functional attributes do not

influence consumer behavior in the same way (Gill, 2008; Luo, Kannan and Ratchford, 2008).

Symbolic features fulfill consumers’ social identity-related motivations, whereas functional

features relate more to utilitarian motivations (Maehle and Supphellen, 2011). Moreover,

drivers of symbolic consumption are ambiguous and need clarification (Aaker, 1999; Keller

and Lehmann, 2006; Park, MacInnis and Priester, 2009; Stern, 2006). This research stresses

the role of self-congruity in symbolic consumption. Self-congruity theory advocates that

consumers behave according to the match between brands’ symbolic attributes and

consumers’ own self-concept (Sirgy 1985). Prior research suggests that self-congruity is a

critical factor in symbolic consumption because symbolic products, such as luxury products,

facilitate the expression of the consumer’s internal self (Mason, 1981; Tsai, 2005). Moreover,

symbolic co-branding is usually associated with SB that few consumers have ever used or

experienced. Prior research suggests that brand attitude is a poor predictor of consumer

behavior when consumers have never used the brand (Bird, Channon and Erhenberg, 1970),

whereas self-congruity with a brand influences consumer behavior even if consumers have

never used it (Park and John 2010). Therefore, this research posits that self-congruity is a key

predictor of symbolic co-branding purchase intent. In addition to self-congruity, the current

study combines constructs from different streams of literature into an integrative model to

improve understanding of symbolic cobranding consumption and to answer the managerial

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questions: How to select symbolic co-branding partners? And who to target with symbolic co-

branded products? The findings make several contributions to research and practice.

First, this investigation extends the generalizability of Helmig et al.’s (2007) model by

validating it for symbolic cobranding. It confirms the importance of perceived fit between the

two brands (PB and SB). More importantly, the findings demonstrate that self-congruity with

the secondary brand is a better driver of purchase intentions toward symbolic co-branded

products than is attitude. Second, this study identifies need for uniqueness as another original

explanatory variable of symbolic cobranded product purchase intentions in the case of

symbolic consumption, indicating that cobranded products are a means for consumers to

differentiate themselves from others. Third, results indicate that product category involvement

not only affects product purchase intentions directly but also strengthens the role of self-

congruity in symbolic cobranding consumption. Therefore, marketers should select partners

for symbolic co-branding strategies according to the level of self-congruity of their target with

the potential secondary brands, and the level of perceived fit between the two brands.

Moreover, managers should use symbolic co-branding strategies to target first and foremost

highly-involved consumers in the product category with positive attitudes toward branding,

and who need to show their uniqueness.

2. Conceptual background and hypotheses

Extensive research examines the potential positive and negative effects of ingredient

cobranding on attitudes or behaviors towards cobranded products (Norris, 1992; Simonin and

Ruth, 1998; for an extensive review, see Helmig et al., 2008). The focus here is on ingredient

branding, which incorporates functional attributes of the secondary brand into the primary

one. Cobranding may also be designed to transfer symbolic attributes from the secondary

brand into the cobranded product in order to enhance its symbolic consumption experience

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(Keller, 2003). Because symbolic consumption relies on different underlying motives than

functional consumption (Keller and Lehmann, 2006; Park et al., 2009), the purpose of this

research is to develop and validate a comprehensive model based on both established

relationships in ingredient branding literature and novel relationships relevant to symbolic

cobranding. In particular, this research introduces two concepts linked to symbolic

consumption behavior: self-congruity with the secondary brand and need for uniqueness.

Mason (1981) suggests that self-concept is a central driver of symbolic consumption,

because consumers use value-expressive symbolic products to create and maintain their sense

of self. Consumers often purchase goods to express their identity and evaluate brands

according to the match between their symbolic attributes and the person’s self-concept

(Aaker, 1999). This matching process is referred to as self-congruity, and it plays an important

role in both pre- and post-purchase behaviors (Johar and Sirgy, 1991; Sirgy, 1985). According

to this process, a match between the brand and the consumer’s self-image arises when a

value-expressive brand triggers the consumer’s self-schema, which contain self-knowledge

related to the product’s perceived image (Sirgy, 1985). Self-congruity reflects the consumer’s

perceived fit of the brand with both the consumer’s actual and ideal selves (Aaker, 1999). In

the case of symbolic cobranded products, secondary brands are often luxury or status brands

(for instance the mobile phones: LG-Prada, Sharp-McLaren) or famous events (for examples

the mobile phones: Sagem-Roland Garros, Samsung-Olympic Games) and primary brands

often belong to product categories associated with strong social meanings (like cars and

mobile phones). The purpose of the SB is to enhance the symbolic aspects of the product.

Therefore symbolic co-branding represents an opportunity to study the role of self-congruity

in symbolic consumption. H1: Self-congruity with the secondary brand relates positively to

purchase intent of symbolic cobranded products.

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Ingredient branding research indicates that attitude toward the SB also drives behavioral

intentions toward cobranded products (Helmig et al., 2007; Simonin and Ruth, 1998).

However, because symbolic SBs are usually brands that few consumers likely have

experienced directly (i.e., prestigious brands or events), attitude toward the SB, even if

positive, might be weak. Whether brand experience is direct (purchase or possession) or

indirect (exposure to communications or information) strongly influences the link between

attitude and behavior (Fazio and Zanna, 1978). A direct experience reinforces the link

between attitude and behavior; indirect experience weakens it. Direct experience also implies

more confidence in the judgment, higher attitude accessibility and more available information

(Fazio and Zanna, 1978; Fazio, Zanna and Cooper, 1978). Thus, direct experience positively

influences attitude strength, which then positively moderates the link between attitude and

behavior (Chaiken and Baldwin, 1981; Krosnick, Boninger, Chuang, Berent and Carnot,

1993). When indirect experience is the only type available, consumers should have a weak

attitude toward the SB. In symbolic cobranding, SBs may be prestigious brands (e.g., Prada

associated with LG) or famous events (e.g., the Roland Garros Grand Slam tennis tournament

associated with Sagem, or the Olympic Games associated with Samsung). Few consumers

actually attend these events or buy the luxury brands directly, so the strength of attitude

toward the SB likely is weak and may have a limited impact on behavior.

In contrast, brands with appealing personalities attract consumers with no prior brand

usage, because consumers may enhance their sense of self (Swaminathan, Stilley and

Ahluwalia, 2009). Park and John (2010) show that consumers may perceive themselves as

more good-looking and glamorous after using a Victoria’s Secret shopping bag, and more

intelligent and leader-like after using an MIT pen. In line with Park and John (2010), self-

congruity with the SB may be a better predictor of consumer choice than brand attitude

toward the SB, especially if consumers have never or rarely used the brand or have no

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intention of purchasing the SB itself. H2: Self-congruity with the secondary brand has a

stronger effect on purchase intent than attitude toward the secondary brand.

Self-congruity theory also proposes that consumers behave according to the match

between a brand’s symbolic attributes and their self-concepts (Aaker, 1999; Sirgy, 1985). If

people attempt to construct, express and enhance their self-image through consumption, the

impact of self-congruity on consumer behavior likely is most prominent in value-expressive

consumption activities, which are driven by symbolic or self-expressive product features

(Mason, 1981). According to Bloch (1982, p. 416), “being highly involved with a product that

carries an appropriate symbolic meaning provides a way to project and enhance part of one’s

self-image”. Information regarding the self probably is not activated when consumers

encounter less involving products (Barone, Shimp and Sprott, 1999), such that they may be

influenced less by self-congruity than by functional congruity (Johar and Sirgy, 1991). In

contrast, high product category involvement should augment the likelihood of self-congruity

effects by increasing the activation of self-schemas (Barone et al., 1999). Because highly

involving categories provide better vehicles for self-expression (Bloch, 1982), self-congruity

with the SB should relate more to purchase intentions for the symbolic cobranded product

when consumers are strongly involved in the product category. H3: Product category

involvement moderates the relationship between self-congruity and purchase intent for

symbolic cobranded products, such that the relationship is stronger among consumers who are

highly involved in the product category.

Finally, cobranded products differ from the standard products offered by the PB because

they are modified by the SB. Cobranded products often are offered in limited quantities or for

a limited period of time, which increases perceptions of rarity and enables consumers to

express their difference. Tian, Bearden, and Hunter (2001, p. 50) acknowledge that

“Consumers acquire and display material possessions for the purpose of feeling differentiated

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from other people and, thus, are targeted with a variety of marketing stimuli that attempt to

enhance self-perceptions of uniqueness”. Possessing a cobranded product enables consumers

to distinguish themselves from others, which leads to introduce the concept of need for

uniqueness. Need for uniqueness is an individual trait that might favor the purchase of

cobranded products, given their relative scarcity. Higher need for uniqueness drives people to

possess products that differentiate them from others (Simonson and Nowlis, 2000). For

example, these consumers likely prefer distinct product designs (Bloch, 1995) with attributes

that define them as different from members of their reference groups (Snyder and Fromkin,

1980).

Thus, because symbolic cobranded products are usually limited series that help

consumers differentiate themselves from others. H4: Need for uniqueness relates positively to

purchase probability for symbolic cobranded products.

Helmig et al. (2007) further suggest that cobranding efficiency reflects four factors:

attitude toward the PB, perceived fit between the PB and the SB, variety seeking and brand

consciousness. The integrative model depicted in Figure 1 includes these four factors.

Figure 1 here.

3. Method

To study the proposed independent variables explaining symbolic cobranded product

purchase intent, this research uses an experiment with real brands. Following Simonin and

Ruth (1998), this study uses mobile phone brands, which allows high variance in the

explanatory factors. Cobranded mobile phones are common (e.g., Motorola–Dolce & Gabana,

Sagem–Agnès B), and Samsung is selected as the PB, because the brand already produced

many cobranded products in limited series (e.g., Samsung–Cacharel, Samsung–Olympics,

Samsung–Adidas). A pretest with 36 students to measure category involvement and attitude

8

toward Samsung indicates substantial variance. In addition, this research investigates two

types of secondary brands: a luxury brand and a notorious prestigious event, both of which

have been cobranded with mobile phone brands (e.g., Vertu–Ferrari, Sagem–Porsche, Sagem–

Roland Garros, Huaqi–Manchester United). To select the SBs, 24 students read the definition

of symbolic cobranded products and estimated which luxury car brands and sport events they

would expect to see associated with a Samsung mobile phone; they listed 11 car brands and 9

sport events. Next, visual depictions of the 20 possible cobranded products were created based

on pictures of real Sagem–Roland Garros and Vertu–Ferrari mobile phones. Forty students

(each exposed to 10 cobranded products) then indicated their self-congruity with, attitude

toward and perception of the congruence of the SBs with Samsung. Co-branded products

Samsung–Jaguar and Samsung–Wimbledon were finally retained for being the car- and event-

based cobranded products with the greatest variance across the three concepts.

A sample of mobile phone users evaluated these two cobranded products. A Web panel

enabled recruiting 979 representative participants according to quota sampling, in terms of

age, gender and occupation as prior research confirms the reliable response quality of online

surveys (Deutskens, de Jong, de Ruyter and Wetzels, 2006). A filter question excluded 54

participants who had never purchased a mobile phone for themselves, leaving a final sample

size of 925. Participants were randomly assigned to either product: 468 to the Samsung–

Jaguar and 457 to the Samsung–Wimbledon mobile phone and the entire sample was used to

test the model and hypotheses. The sample is composed of 52% women; 25% of the sample is

between the ages of 18 and 26 years, 25% between 26 and 38 years and 25% are older than 51

years; 42.6% are white-collar workers, 14% are retired and 11% are students. All the

independent variables were measured before exposing the participants to the picture of the

cobranded products. To create a sufficient interval between cobranding exposure and the

purchase intentions measure, a diversion task exposed participants to 12 pictures of other

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mobile phones (8 mono-branded and 4 cobranded). Finally, purchase intentions for the

experimental cobranded products, as well as for the other mobile phones were collected.

Previously validated scales were used to measure all constructs (self-congruity, brand

attitude, purchase probability, need for uniqueness, product category involvement, perceived

fit between brands, variety seeking and brand consciousness). Given the superiority of

purchase probability measures over purchase intention scales (Wright and MacRae, 2007),

this research used purchase probability as a measure of purchase intention with Juster’s

(1966) 11-point scale. All scale items appear in Table 1.

Table 1 here.

4. Results

The psychometric properties of the scales and the distinctiveness of the variables were

examined using confirmatory analysis (AMOS 19.0) with maximum likelihood estimation.

The eight-factor model yields a good fit to the data: χ²(249) = 1016.65 (p = .000), confirmatory

fit index (CFI) = .963, normed fit index (NFI) = .952 and root mean square error of

approximation (RMSEA) = .058. For all scales, the factor loadings are significant (p < .01) in

support of convergent validity. The Cronbach’s alphas are at least .84, which demonstrates

good reliability. The results also confirm discriminant validity, because the average variance

extracted exceeds the square of the correlations between constructs (Table 2). It is noteworthy

that these results confirm that variety seeking and need for uniqueness are two distinct

constructs (Maimaran and Wheeler, 2008).

Table 2 here.

4.1. Common method variance

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Self-reports may introduce a common method variance (CMV) bias. To limit this bias, this

research followed Podsakoff, MacKenzie, Lee and Podsakoff (2003) recommendations. First,

the predictors and criterion variables were separated in the questionnaire, response

confidentiality was ensured and participants were explicitly informed that there is no right or

wrong answers. Second, a Harman’s single-factor test revealed a poor fit to the data (χ²(275) =

18433.36, p = .00, CFI = .136, NFI = .134, RMSEA = .267). Third, the baseline measurement

model with an additional latent common method factor on which every item in the model was

allowed to load (in addition to loading on their original constructs) was tested. This model

offers a better fit (χ²(248) = 966.45, p = .000), but the indicator loadings on the theoretical

factors all remain significant. The common factor accounts for only 16.8% of total variance,

which is less than the median method variance (25%), according to studies of self-reported

perceptions (Williams, Cote and Buckley,1989). Finally, the correlations among substantive

latent factors are virtually the same whether the common method factor is included or not.

These results indicate an absence of common method bias (Podsakoff et al., 2003).

4.2. Hypotheses testing

To estimate the relationships among the concepts, this research uses structural equation

modeling with AMOS 19.0 and maximum likelihood estimation. The moderating effect is

modeled with a latent interaction term (self-congruity ´ product category involvement) to

indicate the latent interaction (Cortina, Chen and Dunlap, 2001; Ping, 1995). The loading and

error of every unique indicator was fixed, following Ping’s (1995) procedure, and to reduce

multicollinearity, the predictors were standardized prior to forming the interaction term

(Cohen, Cohen, West and Aiken, 2003). The main effect of product category involvement is

also introduced in order to identify the type of moderation (Sharma, Durand and Gur-Arie,

1981).

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The proposed structural model and the hypotheses were first tested for each cobranded

product separately. For both cobranded mobile phone versions (Samsung–Wimbledon and

Samsung–Jaguar), the overall fit statistics demonstrate acceptable model fit (χ²(289) = 833.305,

p = .000, CFI = .951, NFI = .928 RMSEA = .064; and χ²(289) = 673.40, p = .000, CFI = .965,

NFI = .940, RMSEA = .054, respectively). All the hypothesized paths are consistent between

brands (i.e., same direction and similar statistical significance). Therefore, data were pooled,

and the model was estimated on the whole sample including the two cobranded products.

The hypothesized model with interactive effects indicates a strong fit of the data to the

model: χ²(289) = 1151.14 (p = .000), CFI = .961, NFI = .948 and RMSEA= .057. Table 3

contains the standardized path coefficients.

Table 3 here.

Self-congruity relates significantly to purchase probability for the cobranded product (β =

.19, p < .001), in support of H1. In contrast, attitude toward the SB is not significantly linked

to purchase probability (β = .02, ns). Therefore, self-congruity with the SB is a better

predictor of cobranded product purchase probability than brand attitude toward the SB, in

support of H2. For comparison purposes, we test the model without self-congruity and find a

positive, statically significant effect of attitude toward the SB on purchase probability (β =

.09, p < .01). However, attitude toward the SB becomes non-significant when self-congruity is

introduced into the model. The results obtained might be sensitive to the level of self-

congruity of consumers with the SB. It might be that consumers with a lower level of self-

congruity rely more on attitude toward the SB. Since self-congruity is not manipulated,

gender is used to test this boundary condition given that men find Jaguar (M=9.4, SD=4.8)

and Wimbledon (M=9.5, SD=4.8) more self-congruent than women (M=8.5, SD=4.6; M=8.3,

SD=4.3 respectively).

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A multiple analysis of variance (MANOVA) examines the relationships between

gender and self-congruity and attitude toward the SB. Gender influences significantly self-

congruity with the SB (F=4.010; p<.01) and attitude toward the SB (F=4.830; p<.01). A

multi-group analysis shows that the path loading from self-congruity to purchase intent is .12

(p<.01) for males and .26 (p<.001) for females and the magnitude difference is statically

significant (χ²=4.18, df=1, p<.05). However, groups are not different at the attitude level.

Attitude toward the SB does not influence significantly purchase intent for both men and

women (χ²=.983, df=1, p>.05). These results confirm that self-congruity is a better predictor

of symbolic co-branded product purchase intent than attitude for consumers either with high

levels or with low levels of self-congruity and attitude. The latter analysis enables to test the

boundary conditions and to strengthen the validity of the results.

The interaction of self-congruity and product category involvement also relates positively

and significantly to purchase probability, in support of H3 (β = .25, p < .01). Because product

category involvement has a direct positive impact on cobranded purchase probability (β = .14,

p < .01), product category involvement offers a quasi-moderating effect between self-

congruity and cobranded product purchase probability (Sharma et al., 1981). The direct effect

of product category involvement confirms Helmig et al.’s (2007) findings in an ingredient

branding context. To clarify this interaction, the regression lines of self-congruity on purchase

probability, showing ±1 SD from the mean level of product category involvement are shown

in Figure 2 (Aiken and West, 1991). When product category involvement is high, self-

congruity with SB exerts a stronger impact on cobranded product purchase probability. The

interaction of attitude toward the SB and product category involvement is not significant.

Attitude toward the SB has no impact on purchase probability for either highly or less

involved consumers. Overall, these results demonstrate that self-congruity is a better predictor

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of cobranded product purchase probability than attitude, especially for consumers with high

levels of involvement in the category.

Figure 2 here.

The structural equation model parameters also indicate that need for uniqueness has a

positive impact on symbolic cobranded product purchase probability (β = .16, p < .001), in

support of H4. Therefore, cobranded products are material possessions that contribute to

satisfy consumers’ need for uniqueness. Finally, the control variables, perceived fit, brand

consciousness and prior attitude toward the primary brand positively influence symbolic

cobranded product purchase probability (β = .29, p < .001;β = .11, p < .001; β = .15, p < .001,

respectively). Variety seeking has a positive yet weaker effect on symbolic cobranding

acceptance (β = .06, p = .052).

5. Discussion and conclusion

In contrast with previous research (Helmig et al., 2007; Simonin and Ruth, 1998), this

study suggests that attitude toward the SB does not influence substantially symbolic

cobranded product purchase, at least among consumers who have never or rarely used the

secondary brand. This finding is in line with the theory that attitude strength is lower for

brands that consumers do not experience directly (Fazio et al., 1978; Krosnick and

Schumanm, 1988). Yet self-congruity with the SB represents a significant driver of purchase

behavior, especially when consumers are strongly involved in the product category. These

findings re-affirm that consumers choose brands with appealing personalities to enhance their

sense of self (even brands not previously used) (Swaminathan et al., 2009), and demonstrates

the substantial role of self-congruity in symbolic consumption.

Further research may be helpful to learn whether or not self-congruity is always a

better predictor of consumer behavior than attitude or whether this is limited to symbolic co-

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branding. These results might hold for other forms of alliances, such as sponsorships or

celebrity endorsements. Similar to cobranding, these communication strategies aim to develop

brand equity through associations with a prestigious event or celebrity (Keller, 2003). Self-

congruity might predict the effects of these communication strategies better than attitude.

Further research should also confirm that self-congruity influences less functional cobranding

than symbolic co-branding.

This study also demonstrates that need for uniqueness is important for understanding

symbolic cobranding efficiency, probably because symbolic cobranded products are relatively

rare, produced in limited quantities and sold for short periods of time. In line with previous

research on need for uniqueness, the desire to possess unique products is an individual-level

trait that drives many consumption decisions (Tian et al., 2001). This influence is

demonstrated in the case of symbolic cobranded products. This finding is managerially

relevant, because it implies that manufacturing short series of cobranded products may satisfy

the need for uniqueness of consumers who might not have bought the widely available

primary brand on its own. Therefore, a symbolic cobranding strategy likely leads to higher

volume sales and possibly higher margins, if the price of the cobranded product reflects the

value that high need for uniqueness consumers attribute to it. These results should be

confirmed in other product categories and in ingredient co-branding strategies. Need for

uniqueness should impact less substantially the purchase of ingredient co-branding product

when the products have a long life cycle, such as the Intel – IBM or Haagen Dazs – Baileys

products. However, need for uniqueness may influence as well ingredient co-branding in

limited series.

Furthermore, this research confirms that the perceived fit between two brands, variety

seeking, brand consciousness, attitude toward the primary brand and product category

involvement all are substantial predictors of symbolic cobranded purchase (Helmig et al.,

15

2007). The representativeness of our sample, the controls for potential common method

variance bias and the investigation of symbolic cobranded products increase the reliability and

the generalizability of these relationships. In addition, the findings show that product category

involvement enhances the role of self-congruity in symbolic consumption. Overall, results

indicate that managers should consider self-congruity rather than attitude to choose partners

for symbolic cobranded products, especially in high involvement product categories.

This study is not without limitations. The cross-sectional nature of the data limits the

ability to draw strong inferences about the sequence of effects and the short time gap between

exposure to the cobranded products and the measure of purchase intentions is a

methodological weakness of the experiment. Some testing effects might have introduced

some bias if respondents adjusted their answers to meet what they perceived to be the purpose

of the study. However, the diversion task we used after the exposure to the experimental

cobranded product and the measures of the respondents’ impressions of other brands should

have minimized these effects. Moreover, the results of the tests for common method biases

show that proximity between independent and dependent variables measures does not produce

artifactual covariance independent of the content of the constructs themselves.

Whereas this paper studies two SBs that few consumers had directly purchased or

experienced (i.e., Jaguar and Wimbledon), additional research should test whether brand

experience moderates the relationships between self-congruity with the SB and symbolic

cobranded product purchase intentions or between attitude toward the SB and purchase

intentions. On the one hand, Barone et al. (1999) show that product ownership increases the

effects of self-congruity on product evaluation. On the other hand, direct brand experience

implies higher attitude strength, which might increase the role of attitude toward the SB.

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Another avenue for further research is the role of self-congruity with the PB. Self-

congruity with the PB should have a direct effect on symbolic co-branded product purchase

intent. Strong self-congruity with the PB may influence negatively symbolic co-branding

acceptance if consumers feeling a close self-connection to the brand perceive the association

with the SB as a threat, which could damage the fit between their self-concept and their image

of the PB products. Similar to the role of self-congruity with the SB, self-congruity with the

PB should influence more substantially the purchase of symbolic rather than functional

cobranded products. Moreover, self-congruity with the PB may also interact with self-

congruity with the SB. One objective of symbolic co-branding is to transfer positive equity

from the SB to the co-branded product. The role of self-congruity with the SB may be

especially substantial when the level of self-congruity with the PB is low or moderate. When

the PB does not trigger the consumer’s self-schema, a value-expressive SB that fits

consumers’ self-concept may improve more co-branded product purchase intent than when

the PB already fulfills consumer’s need to enhance their sense of self.

Finally, the results of this study also need to be replicated in less value-expressive product

categories, because consumers may not value as much the fit between the secondary brand

image and their self-concepts for these products.

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Table 1 Measurement scales Constructs (Sources): Items

Type Item Loadings

Self-congruity (adapted from Sirgy et al., 1997, 2008) I feel like I am part of the [SB]* family. People who use/attend [SB] are very different from me. The image of the user/spectator of [SB] is highly consistent with how I see myself.

Likert (1 to 7)

.96 .81 .98

Attitude toward constituent brands (adapted from Batra & Stayman, 1990) Negative/positive Unfavorable/favorable Bad/good

SD (7 points)

.94

.96

.96 Need for uniqueness (adapted from Franke & Schreier, 2008; Tian et al., 2001) I am generally more likely to buy a product if it is rare. In general, I enjoy having things that others do not. I actively seek to develop my personal uniqueness by buying special products or brands.

Likert (1 to 7)

.93

.94

.85

Category product involvement (adapted from Helmig et al., 2007) I’m very interested in mobile phones. Mobile phones are very important to me. I’m very enthusiastic about mobile phones.

Likert (1 to 7)

.92

.95

.89 Fit (adapted from Speed & Thompson, 2000) The image of Samsung and [SB] are similar. It makes sense to me that Samsung associates itself with [SB]. Samsung and [SB] fit together well.

Likert (1 to 7)

.85 .68 .86

Variety seeking (adapted from Helmig et al., 2007) I generally like to try out something different. I switch brands very often to try something different. Always buying the same brand is boring.

Likert (1 to 7)

.92 .78 .66

Brand consciousness (adapted from Helmig et al., 2007) I generally buy branded products. Famous brands are best for me. Brands that are more expensive are much more attractive for me.

Likert (1 to 7)

.80 .91 .95

Purchase probability (Juster, 1966)

11-point probability

scale

Notes: [SB]* = either Jaguar or Wimbledon; SD = semantic differential.

23

Table 2 Psychometric properties of the scales

Notes: M = mean, SD = standard deviation, α = Cronbach’s alpha, AVE = average variance extracted, S-C = self-congruity.

M SD α AVE 1 2 3 4 5 6 7 8 1. Secondary brand affect 11.1 4.71 .97 .92 1.0

2. Primary brand affect 14.5 4.37 .98 .92 .14 1.0

3. Perceived fit 11.5 3.76 .84 .64 .14 .26 1.0

4. S-C with the SB 8.9 4.68 .94 .84 .42 .20 .24 1.0

5. Need for uniqueness 10.0 5.05 .94 .83 .15 .30 .21 .35 1.0

6. Category involvement 12.4 4.99 .94 .85 .02 .43 .18 .21 .45 1.0

7. Variety seeking 13.1 4.16 .84 .63 .13 .23 .14 .14 .33 .22 1.0

8. Brand consciousness 12.8 5.2 .92 .79 .18 .22 .08 .12 .16 .16 .20 1.0

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Table 3 Standardized linear and moderated effects Predictor Purchase Probability Perceived fit between PB and SB .29** Attitude toward the PB .15** Variety seeking .06 Brand consciousness .11** Attitude toward the SB .02 Self-congruity .19** Need for uniqueness .16** Product category involvement .14** Self-congruity ´ Product category involvement .25*

R² of hypothesized model

.30 Notes: N = 909. Structural path coefficients are standardized. ** p < .001 * p < .01

25

Fig. 1. Conceptual and structural model

26

Fig. 2. Interaction between self-congruity and product category involvement for predicting purchase intentions

30.5

31

31.5

32

32.5

33

33.5

34

34.5

35

1 2 3 4 5 6 7

Purc

hase

Inte

nt

Self-congruity

High Involvement Low Involvement