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CUSTOMER-BRAND IDENTIFICATION AS A SUSTAINABLE COMPETITIVE ADVANTAGE: A MULTINATIONAL AND LONGITUDINAL EXAMINATION
Son K. Lam
Doctoral StudentUniversity of Houston
Dissertation Proposal
Chair:
Dr. Michael Ahearne
Committee:
Dr. Ed Blair
Dr. Ye Hu
Dr. C. B. Bhattacharya
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CUSTOMER-BRAND IDENTIFICATION AS A SUSTAINABLE COMPETITIVE ADVANTAGE: A MULTINATIONAL AND LONGITUDINAL EXAMINATION
Dissertation ProposalSon K. Lam
Abstract
Previous marketing research has been struggling to find a deeply-rooted cognitive variable that might be more predictive of customer loyalty than customer satisfaction both in the short run and in the long run. Drawing from the customer-company identification and brand health literatures, this dissertation proposes that customer-brand identification (CBI), defined as the extent to which customers define themselves in terms of psychological oneness with a brand, should be highly predictive of important customer behavior, both in-role (e.g., loyalty) and extra-role (e.g., social promotion). The fusion of the brand and the self makes CBI a “sticky prior” that is more enduring than either perceived value or switching costs, creating a sustainable competitive advantage due to its value, rareness, inimitability, and non-substitutability. Two empirical essays in this dissertation explore this research proposition and its boundary conditions in cross-cultural and longitudinal contexts.
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INTRODUCTION
Building a strong and healthy relationship with customers, the sine qua non in the era of
hypercompetition, tops the Marketing Science Institute’s 2006-2008 research priorities. In the
relationship marketing literature, the inter-relationships among perceived value, satisfaction,
loyalty, and ultimately market share figure predominantly. However, it does not take a
microscope to identify two major concerns. First, while there is consensus that satisfaction is
positively related to customer loyalty, marketing researchers concur that “satisfaction is not
enough” (Oliver 1999; Jones and Sasser 1995; Reichheld 1996). In this vein, researchers suggest
that perceived value might represent a construct at a higher level of abstraction with broader
implications for predicting customer loyalty than customer satisfaction (Sheth et al. 1991; Bolton
and Drew 1991; Rust and Oliver 1994). In its most general definition, perceived value represents
customers’ perception of what is received and what is given (Zeithaml 1988). However,
perceived value is like a ghost that is hard to chase because it varies across situations, time,
experience, types of offering, and competitive landscape (for a review, see Whittaker et al. 2007;
Huber et al. 2007). Meager empirical research on perceived value has produced mixed results
(Huber et al. 2007, p. 555). Second, the focus on current market share in cross-sectional research
on loyalty is valid but near-sighted (Bhattacharya and Lodish 2000). Is there a deeply-rooted
cognitive variable that might be more predictive of customer loyalty both in the short run and in
the long run?
A review of brand health, customer loyalty, and customer-company identification
literatures suggests that this variable does exist. Drawing from the epidemiological literature,
Bhattacharya and Lodish (2000) propose that the health of a brand has two related yet distinct
components: current well-being and resistance. Brand current well-being is generally reflected in
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the current market share, baseline sales (i.e., sales when there is no promotion), and customer-
based brand equity (Keller 1993) under normal conditions. Brand resistance refers to the focal
brand’s vulnerability to abnormal fluctuations in the market, such as competitors’ promotion or
changes in regulations. This vulnerability manifests itself primarily in the form of switching
behavior (c.f., Bhattacharya and Lodish 2000, p. 8-10), bringing to light the segment of “spurious
loyalty” (Day 1969; Jacoby and Chesnut 1978). It remains unclear, however, as to what variables
can serve as valid antecedents to brand health.
Research on loyalty suggests that “authentic” brand loyalty exists only when there is “a
deeply held commitment to rebuy or repatronize a preferred product/service consistently in the
future, thereby causing repetitive same-brand or same-brand-set purchasing, despite situational
influences and marketing efforts having the potential to cause switching behavior” (Oliver 1999,
p. 34). It follows immediately that this deeply-rooted cognitive variable can serve as an
antecedent to brand health. Drawing from the customer-company identification literature
(Bhattacharya and Sen 2003; Ahearne, Bhattacharya, and Gruen 2005), we propose that
customer-brand identification (CBI), defined as the extent to which customers define themselves
in terms of psychological oneness with the brand, is the missing link in predicting brand health
even when perceived value and switching costs are controlled for.
The brand management literature has postulated several brand concepts such as brand
knowledge, brand loyalty, and brand awareness (Keller 1993). New constructs reflecting
customer relationship with brands have recently been introduced (e.g., brand love, Carroll and
Ahuvia 2006; self-brand connection, Escalas and Bettman 2005; brand commitment, Hess and
Story 2005). However, CBI is distinct from its predecessor conceptualizations in that CBI
reflects and captures the psychological oneness (Ashforth and Mael 1989) while the plethora of
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these constructs does not. Hogg, Terry, and White (1995) contend that the notion of identity is
descriptive (e.g., using the brand to describe the self), prescriptive (e.g., behaving in a way
consistent with the brand image), and evaluative (e.g., treating the identified brand more
favorably than other brands, resisting negative information about the identified brand). The
fusion of the brand, the self, and self-schemata makes CBI a “sticky prior” (Bolton and Reed
2004) that is more enduring than either ephemeral satisfaction or manipulable switching costs.
Consequently, CBI should be highly predictive across contexts and social settings of several
important customer behaviors: in role and extra role behavior, current behavior and future
intentions, support for the identified brand and resistance to competitive attractions. In other
words, CBI might constitute a sustainable competitive advantage due to its value, rareness,
inimitability, and non-substitutability (Barney 1991; Porter 1985; Reed and DeFillipi 1990).
Previous research that is based on the conceptual framework of customer-company
identification (Bhattacharya and Sen 2003) has received preliminary empirical support that
customer-company identification results in higher product utilization and customer extra role
behavior such as positive word of mouth (Ahearne, Bhattacharya, and Gruen 2005; Bagozzi and
Dholakia 2006; Donavan, Janda, and Suh 2006). However, there has been little empirical
research examining the phenomenon of customer-company identification longitudinally or
outside of the U.S. More specifically, it remains unclear as to (1) How important it is in the long
run, (2) How important it is compared with perceived value and switching costs, (3) How stable
it is in the long run, (4) How it behaves in a competitive environment, and (5) Whether its
importance is universal and generalizeable to countries outside of the U.S.
This dissertation adopts a strategic application of social identity theory (Ashforth and
Mael 1996; Elsbach 1999; Fiol 1991) and builds upon the conceptual framework by
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Bhattacharya and colleagues (Bhattacharya and Sen 2003; Ahearne, Bhattacharya, and Gruen
2005) to achieve a deeper understanding of CBI and its correlates in four important areas. First,
we compare the validity of CBI with that of perceived value and switching costs in predicting
customer behavioral loyalty. Second, we explore the moderating role of cultures of the
relationship between CBI and its consequences by adapting Hofstede’s (1980, 2001) cultural
dimensions to the consumer behavior context. Third, we examine a broader array of CBI
consequences. Some of these consequences are emergent phenomena in the booming internet era
and might not be explainable by perceived value and switching costs. Fourth, we examine the
longitudinal impact of CBI on behavioral loyalty in a competitive context. More specifically, we
study how enduring the effect of CBI on customer loyalty is over time in markets where a new
entrant tries to uproot customer’s identification with incumbent brands.
We believe this dissertation will make at least four contributions to the existing
marketing literature. First, we build on and extend the emerging literature on customer-company
identification (Bhattacharya and Sen 2003; Ahearne, Bhattacharya and Gruen 2005; Donavan,
Janda, and Suh 2006) by zeroing in on a closer level of analysis, which is the customer-brand
level. This close-up will enable an in-depth juxtaposition of the antecedents to true, attitudinal
loyalty versus marketing-induced, behavioral loyalty (MSI Research Priorities 2006-2008).
Second, the longitudinal examination of CBI over time and its consequences will extend the
current limited understanding of the loyalty processes. Third, the explicit incorporation of
competition into the customer-company identification literature by applying rigorous analytical
method provides a fresh approach to research in this area. Finally, this research will be the first
to examine cultures as the boundary conditions on the relationship between CBI and customer
behavioral intentions. The insights gained from the studies will be of instant interests to both
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academics and practitioners in terms of branding strategy and building global brand
communities.
In the next section, we review the relevant literatures that provide the theoretical
foundation for two empirical essays. These streams of research include social identity theory,
identity theory, and customer-company identification.
LITERATURE REVIEW
Social Identity Theory, Identity Theory and Their Marketing Applications
Social identity theory (Tajfel and Turner 1985) posits that individuals define their self-
concepts by their connections with social groups or organizations. For example, individuals
might identify with social entities such as well-known organizations to bask in their reflected
glory (Cialdini et al. 1976). In so doing, individuals engage in a self-categorization process
through which an ingroup to which one belongs is clearly demarcated against an outgroup
(Turner et al. 1987). Based on social identity theory, organizational behavior researchers develop
the concept of organizational identification (Albert and Whetten 1985; Ashforth and Mael 1989;
Dutton, Dukerich, and Harquail 1994), defined as the extent to which organizational members
define themselves in terms of oneness with the organization. Individuals who identify strongly
with organizations are more likely to engage in identity-affirming and identity-enhancing
behaviors such as higher performance, embracing organizational values, and extra-role behavior
(Riketta 2005). Furthermore, they also interact with other individuals who are attracted by the
same social identity. As an example, brand communities reflect a strong ingroup/outgroup
juxtaposition, with favorable attitude toward the ingroup members. Terms such as Mac-users and
Red Sox fans abound in daily life. Members of these brand communities - sometimes even brand
cults - engage in rituals to celebrate their memberships and to extol their beloved brands
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(Bagozzi and Dholakia 2006; Muniz and O’Guinn 2001; McAlexander, Schouten and Koenig
2002).
At a more micro level, identity theory (Stryker and Serpe 1982) focuses on the social
roles individuals play in various social settings. Each role constitutes an identity; identities are
organized hierarchically. Marketing research based on identity theory tends to focus on how
individual customers behave in agreement with the most salient (i.e., most internalized, high in
the hierarchy) identity because it provides the most meaning for the self (Arnett et al. 2003). This
stream of research also frames customer-product relationship in light of what is “me” and what is
“not me” (Kleine et al. 1995; Reed 2002). Although social identity theory and identity theory
evolve in two different streams of research (social psychology and sociology, respectively), these
interrelated theories share several similar concepts that have been introduced into the marketing
literature such as identity salience, identity-congruent behavior, and multiple levels and layers of
identification. Most relevant to this dissertation are customer-company identification and its
consequences, identity-congruent behavior.
Customer-Brand Identification
Under the overarching theme of relationship marketing (Sheth and Parvatiyar 1995;
Berry 1995), previous research on customer-company relationship develops along two streams.
The first stream of research focuses on almost exclusively interpretive consumers’ account about
their relationship with brands (Fournier 1998; McAlexander, Schouten, and Koenig 2002). One
of the tenets of this stream of research is that possessions can be viewed as the extended self
(Belk 1988), or the self (Kleine et al. 1995). This way, consumer-product relationships resulting
from frequent interactions are anthropomorphized. In other words, this research stream treats
brands as relationship partners and view consumer-brand relationships as affect laden (Thomson,
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MacInnis, and Park 2005; Park and MacInnis 2006).
Taking a cognition-based approach, the second stream of research proposes that
consumers identify with companies to satisfy one or more self-definitional needs (Bhattacharya
and Sen 2003; Ahearne, Bhattacharya, and Gruen 2005; Einwiller et al. 2006). Most importantly,
this identification is not contingent upon interaction with specific organizational members
(Turner 1982), or direct experience with the object of identification (Reed 2000). At the brand
level, individuals might identify with a brand that fits with their personalities without actually
being able to afford it. As individuals age, identification changes from an unconscious process of
merely mimicking role models to a more conscious and sophisticated one (Chaplin and John
2005; Reed 2000).
This dissertation builds primarily upon this second stream to examine customer-brand
relationship. Customer-brand identification (CBI) is defined here as the extent to which
customers define themselves in terms of psychological oneness with a brand. Consistent with the
widely accepted perspective in organizational identification research ((Bergami and Bagozzi
2000; Dutton et al. 1994), social categorization theory (Turner et al. 1987), and research on close
relationships (Aaron et al. 1991), CBI is treated here as a cognitive construct that reflects the
extent to which the brand has been assimilated into the self of customers. Previous research on
brand loyalty has tried to distinguish “action loyalty” from lower levels of loyalty such as
conative loyalty, affective commitment, and cognitive loyalty (Oliver 1999; Day 1969). In this
vein, CBI might be considered the most powerful antecedent to the highest form of loyalty
because CBI reflects an on-going fusion of the brand and the self into one entity.
Table 1 details the conceptual distinction between CBI and existing brand-related
constructs. To establish empirical evidence of its discriminant validity, we will include some of
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these measures in the data collection phase.
TABLE 1Customer-Brand Identification and Similar Brand-Related Concepts
Concept Definition and Distinction
Customer-Brand Identification The degree to which a customer defines himself in terms of psychological oneness with a brand.
Self-brand connection (Escalas and Bettman 2005)
Self-brand connection is used to describe situations when brand associations are used to construct the self or to communicate the self-concept to others. CBI reflects this notion from a social identity theory (Tajfel and Turner 1985) perspective.
Brand affect (Chaudhuri and Holbrook 2001)
A brand’s potential to elicit a positive emotional response in the average consumer as a result of its use. CBI is not contingent on previous use.
Brand love (Carroll and Ahuvia 2006)
The degree of passionate emotional attachment a satisfied consumer has for a particular trade name. Brand love is affect-based, whereas CBI is a cognitive construct.
Brand trust (Chaudhuri and Holbrook 2001)
The willingness of the average consumer to rely on the ability of the brand to perform its stated function. This might be one of the antecedents of CBI. Consumers might trust a number of brands, but not identify with all of them.
Brand loyalty
(Oliver 1999)
A deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same-brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior. The new construct CBI is an antecedent to brand loyalty.
Antecedents to CBI. It is important to note that customer-brand emotional attachment has
recently received some academic attention (Thomson, MacInnis, and Park 2005). Instead of
viewing the cognition- and emotion-based streams of research as separate, we believe the
interplay of cognition and affect in forming CBI is inherent in the conceptual frameworks of
organizational identification in the forms of “perceived attractiveness of the organization”
(Bhattacharya and Sen 2003) and “emotional significance attached to that membership” (Tajfel
1981, p. 255). It should be emphasized, however, that the antecedents to CBI are heavily leaned
toward cognitive processes for at least two reasons: (1) Identification involves an effortful
comparison to detect the fit between the self and the brand along dimensions such as personality
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and values (c.f., Sirgy 1982; Aaker 1997), and (2) Identification requires perceived
distinctiveness of the brand in the consideration set. There exists mounting empirical evidence
that cognitive variables such as construed external image of the organization, perceived
organizational prestige, and organizational stereotypes serve as strong predictors of members’
organizational identification (e.g., Ahearne, Bhattacharya, and Gruen 2005; Bhattacharya, Rao,
and Glynn 1995; Kreiner and Ashforth 2004; Dukerich, Golden, and Shortell 2002; Bergami and
Bagozzi 2000).
Consequences of CBI. Previous research on organizational identification suggests that
organizational identification has important implications for organizations. Organizational
identification has been found to be predictive of organizational members’ in-role behavior such
as performance as well as extra-role behavior like organizational citizenship behavior (Riketta
2005). Recent marketing research on customer-company identification also supports this claim
(Ahearne, Bhattacharya and Gruen 2005; Donavan, Janda, and Suh 2006). In terms of affective
consequences, organizational identification researchers suggests that organizational identification
can result in “hot affects” such as passion, strong bonding, captivation, or even addiction, a
viewpoint that is consistent with the emotion-based research stream reviewed above. In the
terminology of switching costs, organizational identification can lead to high emotional
switching costs (Burnham, Frels, and Mahajan 2003).
In the next section, we present two essays. The organization of each essay is as follows.
We first identify important gaps in the existing literature on customer-company identification
and related streams of research. Then, we propose our research questions along with the
conceptual framework and hypotheses. This is followed by research methodology and sampling
plans. The proposal ends with a detailed work plan.
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ESSAY 1CONSEQUENCES OF CUSTOMER-BRAND IDENTIFICATION:
A CROSS-NATIONAL EXAMINATION
There has been a rekindled interest in studying the link between perceived value and
customer loyalty (Johnson, Herrmann, and Huber 2006). Researchers seem to concur that
perceived value might be more predictive of customer future intentions than satisfaction (Cronin
et al. 1997; Whittaker et al. 2007). Research on perceived value, however, pays little attention to
customer intentions other than repurchase (Johnson, Herrmann, and Huber 2006). Meanwhile,
research on customer-company identification suggests that customer-company identification can
result in not only customer in-role but also extra-role behavior such as positive word of mouth
(Ahearne, Bhattacharya, and Gruen 2005), collecting company-related collectibles (Bagozzi et
al. 2008), and symbol passing (Donavan et al. 2006). Furthermore, less attention has been paid to
identity-preserving behavior such as customer forgiveness (Chung and Beverland 2006),
resistance to negative information (Ahluwalia et al. 2000), brand defense, or stronger claims
(Bhattacharya and Sen 2003). These behaviors might be uniquely related to CBI and not
customers’ perceived value. Another important gap in the literature is that the boundary
conditions of the relationship between customer-company identification and its consequences
have not been empirically examined.
In this essay, we seek the answers to three important questions: (1) What are the
important in-role and extra-role consequences of CBI? (2) How strong is the predictive validity
of CBI relative to that of perceived value in explaining customer behavior? and (3) What is the
nature of the moderating effects of cultural orientations on the relationships between CBI and
perceived value and these customer behavioral intentions?
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We focus on cultural dimensions as moderators for a number of reasons. First, cultural
values program personalities which in turn might influence relational behavior, such as
developing and maintaining relationship with brands. Second, previous marketing research has
also found these dimensions to be important moderators of phenomena such as consumer
information processing, expectations, relationship proneness, attitude toward advertising, and
brand trust (for a complete review, see Soares et al. 2007; Arnold and Bianchi 2001; Mooij
2005). Third, this emphasis is helpful in understanding cross-cultural consumer psychology in
the era of globalization.
CONCEPTUAL FRAMEWORK AND HYPOTHESES
Figure 1.1 depicts the conceptual framework of the first essay. We first focus on the
baseline model of the main effects of CBI and perceived value on customer behavioral
intentions. We then lay out the rationale for the moderating effects of cultural orientations on
these simple effects.
----------------------------------------------Insert Figure 1.1 about here
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The Baseline Model: Consequences of CBI and Customer Perceived Value
CBI can induce two groups of identity-congruent behaviors: in-role behavior to maintain
the identity (repurchase intention, willingness to pay more), and extra-role behavior to promote
the identity (e.g., positive word of mouth, social promotion of brands). Extra-role behavior can
be targeted at the focal brand (e.g., recommending friends to buy the focal brand, resilience to
negative publicity, higher tolerance for product/service failure, defending the brand in web blogs
and social groups) as well as competitors’ brands (e.g., badmouthing competitors’ brands,
looking for negative information about competitors’ brands). Consistent with previous research,
we posit that there will be a positive relationship between CBI and these consequences. These
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behaviors can be organized into a continuum, with stronger claims being the most intense extra-
role behavior.
We pay particular attention to the higher level of behavioral intentions as these might be
unique consequences of CBI. Customers who strongly identify with brands develop a deeply-
rooted preference for those brands. Therefore, when faced with preference-inconsistent
information such as negative publicity, they are more likely to process the information
systematically and tend to refute or counterargue such information to maintain cognitive
consistency (Heider 1946; Johnson and Eagly 1990; Kruglanski 1990; Jain and Maheswaran
2000). Stronger forms of customer extra-role behaviors such as customer forgiveness and brand
defense require a high level of internalization of the brand into the self such that customers make
generous attributions when transgressions occur, and take attacks on the brand personally. This
phenomenon has also been documented in the literature on interpersonal close relationship
(Fournier 1998; Thomson et al. 2005). Furthermore, customers are more likely to express
stronger claims to the brand only when they think they deserve to receive preferential treatment
by the brand as a relationship partner, which is the case for customers who identify strongly with
brands (Bhattacharya and Sen 2003; Mitchell et al. 1997). Hence:
H1: There is a positive relationship between CBI and (a) repurchase intention, (b) willingness to pay more, (c) social promotion, (d) consumer forgiveness, (e) brand defense, and (f) stronger claims.
Perceived value provides customers with a rational reason to continue their relationships
with the brands. Furthermore, perceived value elevates customer satisfaction (Patterson and
Spreng 1997), which in turn results in higher intention to repurchase and disseminate positive
word of mouth, and higher willingness to pay more (Oliver 1980; Bolton and Lemon 1999;
Zeithaml et al. 1996). By definition, perceived value forms the foundation of relationships
characterized by reciprocity and calculation. Inasmuch as higher-level customer behaviors such
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as forgiveness and brand defense are costly to the customers and perceived value does not
necessarily lead to higher levels of brand internalization to ignite stronger claims, we do not
expect perceived value to be related to these behaviors. This suggests,
H2: There is a positive relationship between perceived value and (a) repurchase intention, (b) willingness to pay more, and (c) social promotion.
H3: Perceived value is not related to (a) customer forgiveness, (b) brand defense, and (c) stronger claims.
Cultural Dimensions
Among various conceptualizations of cultural orientations (e.g., Schwartz 1992; Rokeach
1973; Peabody 1985; Hofstede 1980, 2001), Hofstede’s five cultural dimensions remain the most
widely-accepted perspective (Steenkamp et al. 1999). These dimensions include individualism
/collectivism, uncertainty avoidance, power distance, masculinity/femininity, and long-term
orientation. While Hofstede analyzes these dimensions at the national level, the fact that cultures
exert an impact on how individuals embedded in those cultures behave makes it possible to
extend these national dimensions to individual cultural orientations (Triandis and Suh 2002).
Previous marketing research has found mixed support for the role of masculinity/femininity and
power distance dimensions (c.f., Erdem, Swait, and Valenzuela 2006; Roth 1995; Blodgett et al.
2001) and largely ignored the long-term orientation dimension possibly due to limited
availability of national scores for this dimension in the Hofstede’s reports. Therefore, we focus
on three cultural dimensions: individualism /collectivism, uncertainty avoidance, and long-term
orientation.
Cultural Dimensions as Moderators of the Relationship between CBI, Perceived Value and
Customer Behavioral Intentions
Cultural dimensions can moderate the relationships between CBI, perceived value and
their consequences because certain cultural orientations drive individuals to develop different
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propensities in maintaining and nurturing existing relationships and to assign perceived value
different weights in forming their behavioral intentions. In the next section, we first define each
cultural dimension, then present the rationale for the hypotheses.
Individualism/Collectivism. This cultural orientation is defined as the degree to which
individuals are supposed to look after themselves or remain integrated into groups (Hofstede
2001). This dimension has received the most attention in marketing research. While researchers
disagree as to whether they are two ends of a continuum or are independent, most research treats
these two as opposites (Van den Bulte and Stremersch 2004). Related constructs include
independence/interdependence self-construal (Markus and Kitayama 1991) and idiocentrism-
allocentrism (Triandis 1989). Collectivistic individuals believe that group decisions are better.
They place much emphasis on belonging (vs. variety seeking), survival (vs. hedonism), and rely
more on social networks to acquire information (vs. media). Because collectivistic individuals
value consensus rather than dispute, conformity to group norms rather than uniqueness, and
relationship rather than novelty (Roth 1995), they should be less likely to switch to other brands
from the brands they identify with. They should also have a higher willingness to pay more to
maintain the relationship with the brands as well as to receive the acceptance of the social groups
to which they belong. Moreover, since collectivistic customers enjoy interacting with other
customers, they will be more likely to pass along word of mouth about brands they identify with
or they perceive as having high value.
Perceived value, a computation of cost/benefits, is of the highest importance for
customers with individualistic orientation (Triandis et al. 1993). They only maintain
relationships that give something valuable in return. Paying more will tip the balance of equity
that is high on the mind of these individualistic customers. Collectivistic customers, on the
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contrary, are more concerned about being harmonious with social groups, and might trade off
personal benefits such as value for money for brands that readily receive social acceptance.
Collectivistic customers are also more likely to attribute failure of products to external forces
such as fate and luck rather than holding the company responsible (Schutte and Ciarlante 1998),
and are therefore more forgiving. These customers also view equal treatment as more important
than equity (McFarlin and Sweeney 2001), and as a result do not put forth stronger claims to be
treated differently. Therefore,
H4: The relationship between CBI and (a) repurchase intention, (b) willingness to pay more, and (c) brand social promotion will be stronger (weaker) when the customer is collectivistic (individualistic).
H5: The relationship between perceived value and (a) repurchase intention will be stronger (weaker) when the customer is individualistic (collectivistic). However, the relationship between perceived value and (b) willingness to pay more, and (c) brand social promotion will be weaker (stronger) when the customer is individualistic ( collectivistic).
H6: The relationship between CBI and (a) forgiveness, and (b) brand defense will be stronger (weaker) when customers have collectivistic (individualistic) orientation. However, the relationship between CBI and (c) stronger claims will be weaker (stronger) for collectivistic (individualistic) customers.
Uncertainty avoidance. Uncertainty avoidance refers to the extent to which a culture
programs its members to feel either uncomfortable or comfortable in unstructured situations
(Hofstede 2001). High uncertainty-avoidance individuals prefer stability, loyalty, simplicity in
consumption. They possess strong resistance to changes and a high need for clarity and structure
(“what is different is dangerous”). Hence, they will be less likely to go through the hassle of
brand experimentation (Broderick 2007). Because giving out word of mouth might reduce the
uncertainty that goes along with consumption and reduce post-purchase cognitive dissonance
(Liu et al. 2001; Festinger 1957), customers who are high in uncertainty avoidance will also be
more likely to engage in this extra-role behavior.
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It is risk and structure that customers with high uncertainty avoidance are after, not
perceived value. We therefore do not expect to see any difference among customers with
different levels of uncertainty avoidance when it comes to evaluating the importance of
perceived value. On the contrary, customers who are high in uncertainty avoidance should value
the existing relationships they have built with old brands. The cumulative trust resulting from
identification process gives them more commitment to engaging in the behaviors just described.
As non risk-takers, customers with high uncertainty avoidance orientation tend to
overemphasize the negative aspects of information, and are less likely to forgive brands during
transgressions or when negative information exists. With the propensity to be risk-averse and
preference for a stable status quo, they should also be less likely to engage in behavior to defend
the brands or asking the companies which own those brands to do extra things for them even
when they identify with those brands. Hence,
H7: The relationship between CBI and (a) repurchase intention, (b) willingness to pay more, and (c) brand social promotion will be stronger (weaker) when the customer is high (low) in uncertainty avoidance.
H8: The relationship between perceived value and (a) repurchase intention, (b) willingness to pay more, and (c) brand social promotion is not moderated by the customer’s uncertainty avoidance orientation.
H9: The relationship between CBI and (a) forgiveness, (e) brand defense, and (f) stronger claims will be weaker (stronger) when customers have high (low) uncertainty avoidance orientation.
Long-term orientation. This cultural dimension refers to the extent to which a culture
programs its members to accept delayed gratification of their material, social and emotional
needs (Hofstede 2001). Individuals with long-term orientation possess persistence, thrift,
synthetic thinking (vs. analytic), and structured problem solving. They also tend to build long-
term relationship. Long-term oriented customers should assign particular importance to
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perceived value as this value will be instrumental in relationship maintenance in the long-run.
Consequently, the relationship between perceived value and its consequences will be elevated
among these customers.
With this long-term orientation in mind, these customers are also more likely to request
brands to do more to enrich the existing relationship. This should be particularly true for brands
that they have had a relationship with, or identify with, as they want to invest (and expect the
same from the brand) in existing relationships rather than building too many loosely-knit brand
relationships. This suggests the following:
H10: The relationship between CBI and (a) repurchase intention, (b) willingness to pay more, and (c) brand social promotion will be stronger (weaker) when the customer is high (low) in long-term orientation.
H11: The relationship between perceived value and (a) repurchase intention, (b) willingness to pay more, and (c) brand social promotion will be stronger (weaker) when the customer is high (low) in long-term orientation.
H12: The relationship between CBI and (a) forgiveness, (e) brand defense, and (f) stronger claims will be stronger (weaker) when customers have high (low) long-term orientation.
Cultural Dimensions as Antecedents of CBI
The above discussion on the characteristics of cultural orientations also suggests that
cultural dimensions might motivate individuals to build relationships beside maintaining and
fostering existing ones. By definition, CBI is a form of close relationship between the brand and
the self. Thus, the three cultural orientations can also function as antecedents to CBI. We do not
expect these cultural dimensions to impact perceived value. Therefore,
H13: There is a positive relationship between collectivism and CBI.
H14: There is a positive relationship between uncertainty avoidance and CBI.
H15: There is a positive relationship between long-term orientation and CBI.
19
METHODOLOGY
Sample
To pretest the baseline model, data were collected from high-prescribing physicians in a
B2B setting. To test the full conceptual framework, data will be collected from approximately
3000 actual consumers in 12 countries in Europe and Asia. These consumers will be asked about
their relationships with brands in highly hedonic or symbolic categories (see Roth 1995; Park,
Jaworski, and MacInnis 1986).
We choose products that are at least in the growing phase in their product life cycle. This
is to ensure that consumers have had enough time to develop their preference and identification
with a number of brands. To ensure that we have the same brands across a number of countries
and to control for category effect, we focus on corporate brands only (i.e., the name of the
company is also the brand) and reserve specific product-brands for future research.
Key Measures
CBI will be measured using the 2-item scale (Bergami and Bagozzi 2000), of which one
item is a Venn diagram, and the other in words. Cultural orientation scales will be adapted from
marketing research in a multinational setting (Erdem, Swait, and Valenzuela 2006; Roth 1995)
and social psychological research on self construal (Markus and Kitayama 1991; Singelis 1994).
A pretest in a B2C context was conducted in July 2007. Other scales include perceived value
(Dodds et al. 1991; Sheth et al. 1991), and behavioral intentions (Zeithaml et al. 1996). New
scales measuring intense extra-role behavior are adapted from Bhattacharya and Sen (2003),
Bagozzi et al. (2008). We will also include socio-demographic, consumer reciprocity as control
variables.
Level of Analysis
20
It should be noted that although the formal hypotheses are stated at the individual
consumer level, the conceptual framework depicted in Figure 2 can be tested at two levels of
analysis: national and individual. The individual level of analysis is attractive and appropriate
because (1) There might exist within-culture variations; This is particularly important for
countries with high mobility as in the European Union, (2) It allows for testing interactions with
statistical power. However, it is also possible to test the model using national scores and
applying Hierarchical Linear Modeling (HLM, Raudenbush and Bryk 2002). In this regard, there
are two possibilities: (1) Using Hofstede’s raw score, and (2) Using the average scores reported
by subjects within each country. The former approach has been adopted in previous research.
This approach has the disadvantage that Hofstede’s raw scores were published a long time ago,
and was measured using IBM employees almost exclusively. These scores might not be
representative of the general consumers. The latter approach runs the risk of ignoring high
within-country variations. When HLM is used, the Level-1 variables consist of CBI and its
consequences. Level-2 variables are the national cultural orientations. In other words, the
moderating effects will be modeled as cross-level interactions. The following equations express
this idea.
DVij = β0j + β1j(CBI) + β2j(VALUE) + rij . (1.1)
β0j = γ00 + γ01(COL) + γ02(UAI)+ γ03(LTO)+ u0j. (1.2)
β1j = γ10 + γ11(COL) + γ12(UAI)+ γ13(LTO)+ u1j. (1.3)
β2j = γ20 + γ21(COL) + γ22(UAI)+ γ23(LTO)+ u2j. (1.4)
where DV = dependent variables, CBI = Customer-Brand Identification, VALUE = Perceived
value, COL = Collectivism/Individualism, UAI = Uncerntainty avoidance, LTO = Long-term
orientation, rij ~ N(0,σ2).
21
Analytical approach
This large-scale study needs to address important methodological challenges. First, the
study has multiple dependent variables. With several interaction terms, multiple regression
analysis appears to be the most viable solution. Second, it is generally believed that subjects in
varied cultures might respond differently to a battery of questions. For example, extreme
response sets are popular (Hui and Triandis 1989; De Jong, Steenkamp, Fox and Baumgartner
2008). To address this issue, cross-cultural researchers have proposed two possible solutions.
First, tests of measurement invariance should be conducted prior to testing the structural model
(Steenkamp and Baumgartner 1998). This is to ensure that the scales behave somewhat
uniformly across countries. The advantage of this approach is that standard SEM softwares have
incorporated multiple-group analysis and mean-variance modules. However, when there are
many countries, as in the case of this study, pair-wise comparisons across some 12 countries will
be impractical (12 x 11/2 = 66 possible comparisons). Second, item scores can be standardized
twice (within-subject, then between-subject) before formal analysis (Leung and Bond 1989).
22
FIGURE 1.1Essay 1 - Conceptual Framework
Consumer-Brand Identification
Control Socio-demographic variables Reciprocity Switching costs (if applicable)
Cultural OrientationsIndividualism/Collectivism Uncertainty avoidance Long-term orientation
Perceived Value
High-level Behavior
Stronger claims
Brand defense
Consumer forgiveness
Low- to Medium-level Behavior
Brand social promotion
Willingness to pay more
Repurchase intention
Intensity of
Behaviora
l Intentions
Behavioral Intentions
23
ESSAY 2CUSTOMER-BRAND IDENTIFICATION AS AN ANTECEDENT TO BRAND HEALTH
Previous marketing research has struggled with finding a strong predictor of customers’
long-term behavioral loyalty in the presence of competitive moves. This second essay
complements the first essay by investigating why it is important to build CBI in a competitive
setting using a longitudinal study design. We propose and test a conceptual framework in which
customer-brand identification serves as a predictor of switching behavior, an important indicator
of customer behavioral loyalty that underlies both the current well-being of a brand and all
measures of brand resistance proposed in the brand health literature (Bhattacharya and Lodish
2000).
Our research departs from previous research in several ways. First, previous research on
customer-company identification has been cross-sectional. Hence, the dynamic nature of CBI in
competitive markets is largely ignored. This is surprising because research in the non-profit
marketing literature suggests that participation in activities other than those organized by the
focal organization impairs the identification with the focal organization (Bhattacharya, Rao and
Glynn 1995). There has also been a call for the incorporation of competition into customer
relationship models (Rust, Lemon, and Zeithaml 2004). Furthermore, it should also be noted that
previous research has not examined the dynamic of switching costs and how they will influence
customer behavior. We believe a longitudinal examination of CBI, its consequences, and other
loyalty predictors is critical, especially in highly competitive markets. Second, previous research
on loyalty relies heavily on satisfaction and disconfirmation/confirmation of expectations. While
these are important, marketing researchers concur that “satisfaction is not enough” (Oliver 1999;
Jones and Sasser 1995; Reichheld 1996) and speculate that a deeply-rooted cognitive variable
might be more predictive of customers’ action loyalty. Surprisingly, empirical marketing
24
research has not thoroughly investigated this research proposition. Recent research that does
compare the predictive validity of satisfaction and commitment either tests it cross-sectionally
(Garbarino and Johnson 1999), uses lagged-variable analysis (Johnson, Herrmann, and Huber
2006; Mittal, Kumar, and Tsiros 1999), or largely ignores competition. There has also been little
research on customer’s perceived value of brands, much less on its longitudinal effects on brand
loyalty. More importantly, previous research on perceived value has shown mixed findings,
ranging from very strong effects (Sweeney et al. 1999) to marginal ones (Sirohi et al. 1998).
These equivocal results might be an artifact of important missing predictors such as CBI or
switching costs. By simultaneously considering CBI, customer’s perceived value, switching
costs, and competition, we are able to put the above critical research propositions to the most
stringent test.
More specifically, we study the dynamic of CBI when there is a new entrant and its
impact on behavioral loyalty at the individual level by seeking the answers to two research
questions: (1) How predictive is CBI with a focal brand compared with its perceived value and
customers’ switching costs in predicting switching behavior from the focal brand to the new
entrant?, and (2) How does this pattern change over time?
CONCEPTUAL FRAMEWORK AND HYPOTHESES
As defined above, CBI represents the extent to which customers perceive themselves and
the brand as sharing self-definitional attributes. While cognitive in nature, CBI has important
affective and conative consequences. We propose that three groups of variables will predict
customer loyalty behavior. These include customer dispositions and characteristics and customer
perceptions of both the focal, incumbent brands and the new competitor. In this essay, we focus
our attention on the latter two groups while controlling for the first. Furthermore, to keep the
25
scope of the study manageable while attending to the longitudinal effects of the focal constructs,
we confine ourselves to investigating only switching behavior from the incumbent brands to the
new brand. The conceptual framework is depicted in Figure 2.1.
----------------------------------------------Insert Figure 2.1 about here
----------------------------------------------
At the aggregate market level, the product diffusion literature focuses on word of mouth
and consumer innovativeness as two important drivers of new product diffusion, largely ignoring
psychological variables (e.g., Bass 1969; Krishnan, Bass, and Kumar 2000). Shifting the focus to
a subgroup of the market, we extend this diffusion literature by incorporating CBI and other
psychological variables into the model while simultaneously controlling for perceived value and
switching costs.
The relationship between perceived value, defined as the difference between benefits and
costs, and repurchase intention has been found to be partially mediated by customer satisfaction
(Patterson and Spreng 1997; Whittaker et al. 2007). Satisfaction is the response to pleasurable
fulfillment of needs, desires, and expectations (Oliver 1993). Most researchers treat satisfaction
as transaction-specific (e.g., Oliver 1980), while others consider satisfaction customers’ overall
evaluation of the total purchase and consumption experience with a good or service over time
(Anderson, Fornell, and Lehmann 1994; Bolton and Lemon 1999). In either conceptualization,
satisfaction appears to be an affective outcome of a highly rational evaluation of the discrepancy
between expectations and performance, as posited in the disconfirmation paradigm (Oliver 1980;
Oliver, Rust, and Varki 1997) or perceived value in the value research stream (Sheth et al. 1991;
Zeithaml 1988). Furthermore, both the accumulation of satisfaction and perceived value are not
enough to reach “ultimate loyalty” (Oliver 1999, p. 34) because they are subject to deterioration
and competitive promotions. It should also be noted that transaction-specific satisfaction is
26
highly correlated with positive affect (Oliver, Rust, and Varki 1997) but not enduring “hot
affects.” In the aggregate, the direct and indirect effects of perceived value on customer
behavioral loyalty, while cognitive and affective in nature, do not reflect a high level of
internalization of brand values into the self.
In addition to customer perceived value, switching costs might be another reason why
customers keep buying a brand. We consider two types of non-relational switching costs:
procedural costs and financial costs. Procedural switching costs have been defined as consisting
of economic risk, evaluation, learning, and setup costs. Financial switching costs refer to benefits
loss and financial-loss costs (Burnham, Frels, and Mahajan 2003). We do not consider relational
switching costs because it is embedded as a consequence of CBI. This distinction of relational
vs. non-relational switching costs is consistent with recent theorization by Bendapudi and Berry
(1997) that customers may be motivated to maintain relationships with brands because of either
constraints such as time and money or dedication such as expected emotional distress if they
switch to another brand. Customers with high non-relational switching costs feel trapped as
hostage in the relationship with brands (Jones and Sasser 1995). With such a calculative
commitment in mind, these customers are likely to defect when competitors offer incentives to
unlock them from their marriage with the incumbent brands, or when they themselves feel they
are able to afford termination of the relationship (Jones et al. 2007).
Customers with high CBI consider the brand to be part of their “self.” One of the
consequences of this cognitive assimilation is customers’ deep emotional attachment to the brand
such that parting with the brand amounts to great “psychological distress” (Ashforth and Johnson
2001). However, this emotional switching cost is not the only route through which CBI exerts its
impact on loyalty behavior. Customers with high CBI enter the phase of “determined self-
27
isolation” wherein they have “generated the focused desire to rebuy the brand and only that
brand” and “acquired the skills necessary to overcome threats and obstacles to this quest” (Oliver
1999, p.37-38). Social identity theory and identity theory refer to these behaviors as identity-
congruent behavior (Tajfel and Turner 1985; Stryker and Serpe 1982). In its ultimate form,
customers entered the phase of “immersed self-identity” when they participate in brand
communities. In other words, CBI carries with it the notion of personal determination and social
support that are not reflected in either switching costs or satisfaction (Oliver 1999, p. 42).
While the impact of perceived value, switching costs, and CBI are all subject to
deterioration due to competitive attractions, the first two are likely to be eroded faster as they are
not linked closely to the self. On the contrary, the impact of CBI on loyalty is more likely to be
enduring over time as changing self-related schema does not easily take place, and if it does, it
takes time (Bolton and Reed 2004). Consequently, we predict that:
H1: At the time of the introduction of the new brand, (a) the higher the CBI with the focal brand, (b) the higher the customer’s perceived value of the focal brand, and (c) the higher the customer’s non-relational switching costs, the lower the probability that the customer will switch to the new brand.
H2: At the time of the introduction of the new brand, the effect of (a) the customer’s
perceived value of the focal brand, and (b) the customer’s non-relational switching costs on the probability that the customer will switch to the new brand is weaker than that of the customer’s CBI with the focal brand.
H3: Over time, the effect of (a) the customer’s perceived value of the focal brand, and (b) the customer’s non-relational switching costs on the probability that the customer will switch to the new brand is less enduring (i.e., decays faster) than that of the customer’s CBI with the focal brand.
METHODOLOGY
Sample
The conceptual framework will be tested using longitudinal survey(s). To capture the
change, we choose research settings such that the imminent entrance of a new, innovative
28
competitor can trigger observable fluctuations in the market. At this point, we are confident that
the introduction of a high-tech product in a number of countries in Europe will take place in
2008. The advantage of this research sample is that it is possible to measure all of the
independent variables over 4-6 periods. Furthermore, while the switching behavior is self-
reported, it is fairly objective.
Measures
Depending on the expected launch of the brand, we will conduct a series of surveys and
ask subjects to record customer diaries. CBI will be measured using the 2-item scale (Bergami
and Bagozzi 2000), of which one item is a Venn diagram, and the other in words. Other
measures are adapted from existing scales.
Control Variables
Because this study will be tested in the context of a new brand introduction into existing
markets, we believe it is necessary to control for customer characteristics. Consistent with the
product diffusion literature (Bass 1969) and customer innovativeness (Lynn and Gelb 1996;
Steenkamp et al. 1999), we predict that customer innate innovativeness and susceptibility to
social influence (Bearden, Netemeyer, and Teel 1989) will influence their probability of adopting
new brands that are innovative, and/or symbolic and/or publicly consumed.
In a competitive market, consumers might develop multiple identifications with brands.
Furthermore, the brand extension literature suggests that positively evaluated symbolic
associations between a company’s existing brands and its new brand enhance the extendibility of
a corporate brand (Park, Milberg, and Lawson 1991). Therefore, we control for the CBI with the
company that owns the new brand, and the customer’s perceived brand concept consistency of
the new brand. Finally, we also control for socio-demographic variables.
29
Analytical approach
For this study, since the dependent variable is an event (switch/not switch), we adopt
survival analysis as the analytical methodology. Because the underlying metric for time in this
particular study is truly continuous but we only measure discretized values, we specify the clog-
log link (Allison 1995; Hosmer and Lemeshow 2000; Singer and Willett 2003). It should also be
noted that when the probability of the event is low, the clog-log link produces results that are
similar to that by the logit link (Singer and Willett 2003).
Modeling Switching Behavior
Let T be a non-negative continuous random variable with probability density function
f(t) and cumulative distribution function F(T) = Pr {T ≤ t}, giving the probability that the event
has occurred by duration t. In this study, the event is “switching to the new brand”. The survival
function S(t) reflects the probability of not experiencing the event or “surviving” through time
period j.
. (2.1)
The hazard function represents the instantaneous rate of occurrence of the event and is
defined as
. (2.2)
It can easily be shown that
. (2.3)
Discrete-time hazards assume an underlying continuous time model, but with survival
times grouped into intervals. Specifically, the durations or failure times between aj-1 and aj are
30
recorded as a single value. Assume that the underlying continuous time model is of proportional
hazard form:
, (2.4)
where z represents a vector of covariates.
Rewrite the hazard specification as
. (2.5)
We have
. (2.6)
which is equivalent to
. (2.7)
By definition, . Integrating both sides of Equation 2.7 over time (0, t) leads to
(2.8)
Let
. (2.9)
We then can calculate the conditional probability of surviving through the jth interval given that
a subject has survived the previous j-1 intervals as
. (2.10)
The corresponding hazard rate in the jth interval [aj-1, aj) is
31
. (2.11)
The total survivor function until the start of the jth interval is expressed as
. (2.12)
Define
, (2.13)
the likelihood of an event in interval [aj-1, aj) given survival until then can be written as
. (2.14)
Let wj = 1 for an event in the jth interval and wj = 0 otherwise. Via a complementary log-log
(clog-log) link, the likelihood for an individual i observed for ri intervals until either an event or
censoring is:
wij ~ Bernoulli( ) i = 1,…, n, j = 1,…, ri
. (2.15)
where βj denotes a regression effect that is fixed within intervals but may vary between intervals,
and z[aj] represents potentially time-varying predictors. Further detailed discussion of discrete-
time survival analysis is available in Singer and Willett (2003), Muthén and Masyn (2005), and
Congdon (2007).
For this study, time-invariant variables will include customer characteristics, namely
innate innovativeness (INNOV), socio-demographic variables such as gender (GEN), age (AGE),
income (INC), and susceptibility to social influence (SUS). We also control for the brand concept
consistency of the new brand (NB_CON). Time-varying variables include two groups. These
variables are summarized in Table 2.1.
32
(1) For the incumbent focal brands: customer identification with the brand (IB_CBI),
perceived value of using the focal brand (IB_VAL), switching costs (IB_SC), overall
satisfaction (IB_SAT), and peer brand use (PEER), and
(2) For the new entrant: customer identification with the new brand (NB_CBI), perceived
value of the new brand (NB_VAL), interbrand similarity (SIM), promotion (if any)
during the period observed (PRO), and word of mouth about the new brand
(NB_WOM).
TABLE 2.1Summary of Constructs
Measures
Endogenous variableSWITCH What brand of (product type) do you currently use?
Predictor variablesCustomer characteristics
INNOV Consumer innate innovativeness (Gielens and Steenkamp 2007).SUS Consumer susceptibility to social influence (Bearden et al. 1989).GENDER Self-reported.AGE Self-reported.INCOME Self-reported.
Focal brand predictorsIB_CBI Consumer-Brand Identification with the focal incumbent brand
(Bergami and Bagozzi 2000).IB_VAL Perceived value of the focal brand (Dodds et al. 1991; Netemeyer et al. 2004).IB_SC Switching costs (Jones et al. 2007; Burnham et al. 2003).IB_SAT Overall satisfaction.IB_QUA Perceived product quality of the focal brandIB_SER Perceived services of the cell phone carrier of the focal brandPEER Peer brand use.IB_PRO Promotion during the time interval IB_LEN Length of previous use
New brand predictorsNB_CBI Consumer-Brand Identification with the company that owns the new brand
(Bergami and Bagozzi 2000).NB_VAL Perceived value of the new brand (Dodds et al. 1991; Netemeyer et al. 2004).SIM Interbrand similarity, using product attribute-level comparisons.NB_PRO Promotion during the time interval (yes/no).NB_CON Brand concept consistency with the other brands under the same umbrella brand.NB_QUA Perceived product quality of the new brandNB_SER Perceived services of the cell phone carrier of the new brandNB_WOM Word of mouth within the social group about the new brand.
33
Analytical Strategy
To test hypotheses 1 and 2, there are two options. The first is to run a logistic regression:
SWITCH = f (control variables, IB_CBI, IB_VAL, IB_SC) (2.16)
H1 is supported if all of the slopes of the three focal independent variables (CBI with the
focal brand, perceived value of the focal brand, switching costs from the focal brand to the new
brand) are negative. To test H2, we compare the slopes of these three focal independent variables.
Alternatively, these hypotheses can be tested directly by looking at the coefficients at time t1,
using the clog-log link described next.
To test H3, estimation will be conducted on the clog-log transformation of the hazard
function. More specifically, dummies will be used to denote each time period (D1, D2, ..,DJ). The
hazard function, with clog-log transformation, is specified as
, (2.17)
where
(1- ) is the probability of surviving (not switch) a given interval conditioned on survival of previous intervals.
Dj are time-period dummies, j = {0,1,2,3,4} if data were collected over 5 time periods.Vector X consists of time-invariant predictors.Vector Z consists of time-varying predictors. We only focus on IB_CBI, IB_VAL, and
IB_SC. The other time-varying variables are assumed to have stable effects on the hazard rate.
We specify an AR-1 process for each of the coefficients of these three time-varying
predictors as follows
. (2.18). (2.19)
. (2.20)
where ~ iid N(0,1). We predict that and .
34
Alternatively, the non-proportional model (i.e., with time-varying effects) can be
specified by creating interaction terms between the time-invariant (vector X) and time-varying
(vector Z) predictors and the time-period dummies (Dj). The model can also be specified as a
split hazard model (Hess and Mayhew 1997).
35
FIGURE 2.1Essay 2 - Conceptual Framework
SW (t0)
Time-invariant antecedents
New entrant CBI with the new brand and the brand owner (+) Perceived value (+) Control variables: Interbrand similarity, Brand concept consistency (+), Word of mouth (+), Promotion (+), Product quality (-), Service quality of the carrier (-).
SW (t1) SW (…) SW (tj)
Switching to the new brand (SW)
Control variablesConsumer characteristics
Innate innovativeness (+) Susceptibility to social influence (+/-) Socio-demographic variables
Focal brand(s) CBI with the focal brand (-) Perceived value (-) Switching costs to the new brand (procedural, financial costs) (-) Control variables: Overall satisfaction (-), Peer brand use, Promotion (-), Length of use (-), Product quality (-), Service quality of the carrier (-)
Time-varying antecedents (t0-tj)
36
Work Plan
Major steps in the development of the dissertation Target date
Finalize sources of data February 2008Refine study measures January 2008-March 2008Pretest January 2008-March 2008Collect data April 2008 – December 2008Analyze data August 2008 – January 2008Write up analysis results and prepare for defense February 2009Defend dissertation May 2009
37
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