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They’re Just Not That Into You: How to Leverage Existing Consumer–Brand
Relationships through Social Psychological Distance
Abstract
While prevailing marketing practice is to encourage ever stronger relationships between
consumers and brands, such relationships are rare and many consumers are relationship-averse or
content with the status quo. The authors examine how marketers can more effectively manage
existing brand relationships by focusing on the psychological distance between consumers and
brands in order to match close (distant) brands with concrete (abstract) language in marketing
communications. Through such matching, marketers can create a beneficial mindset-congruency
effect leading to more favorable evaluations and behavior, even for brands that are relatively
distant to consumers. Study 1 demonstrates the basic mindset-congruency effect and Study 2
shows it is capable of affecting donation behaviors. Study 3 documents two brand-level factors
(search versus experience goods, brand stereotypes) that moderate this effect in managerially
relevant ways. Study 4 shows that activation of the mindset-congruency effect influences
consumers to spend more, and that these behaviors are moderated by consumer category
involvement. The authors conclude with marketing and theoretical implications.
Keywords: consumer–brand relationships, psychological distance, construal level, brand-
relationship type, brand relationship management, mindset congruency
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“Just as parents find it difficult to be objective about their children, so it is with
marketing managers and their brands. It is hard to see the brand from the
consumer’s perspective. It is difficult to appreciate the minor role the brand plays
in the life of the consumer… No one else loves your brand as much as you do.”
(Heckler and Till 2009, pp. 6–7)
Marketing managers want consumers to form strong connections with their brands. Building
on Fournier’s (1998) consumer–brand relationship (CBR) framework, research has mapped more
than 50 relationship types (Wittenbraker, Zeitoun, and Fournier 2015), many of them meaningful
and closely tied to the consumer’s sense of self. In the academic literature there has been a
notable propensity to focus on these kinds of relationships, such as committed partnerships and
best friendships (Fournier and Alvarez 2013). This focus is also reflected in prevailing brand-
management approaches that seek to move consumers from “weak or indifferent” relationships
(Fournier and Alvarez 2013, p. 253) to stronger ones where the consumer is more attached,
connected to, or in love with a brand (Malar et al. 2011). After all, if stronger brand relationships
are commercial assets that “offer the greatest economic profit potential” (Park, MacInnis, and
Priester 2009, p. 379), then pursuing stronger relationships seems to be a sound strategy.
However, as indicated by the sheer volume and variation of CBR types, consumers often do
not experience or seek strong brand relationships. Strong relationships are, in fact, “rare in a
brand context” (Thomson, MacInnis, and Park 2005, p. 89), and as many as 77% of consumers
report that they do not forge strong relationships with brands (Freeman, Spenner, and Bird 2012).
Similarly, large-scale practitioner research by Havas (2020) suggests that a majority of brand
content is not meaningful, and consumers would not care if most brands “disappeared”
(meaningful-brands.com). To underscore this point, we conducted a simple test: we recruited 323
consumers from a private research panel (Mage = 37; 47% male) and asked them to list all the
brands that were important to them. On average, respondents listed only 2.15 brands (SD = 1.50),
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and fewer than 1% of respondents listed 10 or more brands. Thus, while most consumers have at
least one brand that they feel strongly about, there appears to be a low ceiling to this
phenomenon. All of this speaks to a situation lamented decades ago that persists today (Fournier,
Dobscha, and Mick 1998, p. 44):
Every company wants the rewards of long term, committed partnerships. But
people maintain literally hundreds of one-on-one relationships in their personal
lives… and clearly, only a handful of them are of a close and committed nature.
How can we expect people to do any more in their lives as consumers?
The implication is that marketers are fixated on building the types of relationships that
countless consumers simply may not want, in essence choosing a potentially wasteful
relationship-upgrading strategy as a result of disregarding consumer preferences. In response, we
highlight the value of marketers’ embracing the relationship status quo and argue in favor of a
simplified strategy based upon a phenomena-to-construct (MacInnis et al. 2020) assessment of
CBRs. We leverage the fact that all the major CBR constructs (e.g., love, attachment,
identification) implicitly or explicitly reflect the idea of self–brand distance, defined as the
psychological proximity between a brand and the consumer’s self-concept. We show that
different types of brand relationships are associated with varying levels of psychological distance
(Trope and Liberman 2010) and expand upon this theoretical mapping to demonstrate how to
better leverage existing CBRs. Drawing on Construal Level Theory (Trope and Liberman 2010),
we establish a congruency effect, showing that matching the psychological distance associated
with a CBR to an appropriate level of construal or message concreteness improves brand
evaluations and spending for both close and distant consumers. In five studies, we offer the first
empirical demonstration that social psychological distance is common to many major CBR
constructs, that matching distance and construal level in marketing communications results in
superior consumer evaluations and behaviors, and that these effects are moderated by variables
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with strong implications for how marketers can respond more effectively to consumers’ existing
brand relationships.
Theoretical Framework
Consumers engage in many types of brand relationships, and most implicate their self-
concept. This basic idea is captured with different terms such as self–brand connection (Escalas
2004), self–brand overlap (Aron, Aron, and Smollan 1992), self–brand distance (Park,
Eisingerich, and Park 2013), self-connection (Fournier 1998), and self-concept connection (e.g.,
Swaminathan, Page, and Gürhan-Canli 2007). While each possesses its own nuance, they
converge on reflecting “the extent to which the brand overlaps with or is included in the self; that
is, the extent to which the brand is me and I am the brand” (MacInnis and Folkes 2017, p. 364).
Extensive evidence of this self–brand distance is embedded in core CBR concepts such as brand
identification, commitment, attachment, and love. For example, earlier work proposed that self-
connection—the extent to which a brand reflects and expresses important aspects of the self—is
a vital component of how brands can become meaningful relationship partners (Fournier 1998).
Other work draws from self-expansion theory (Reimann and Aron 2009) to posit that brand
relationships are formed as part of an unconscious motivation to expand the self and include
close others in the self-concept. What these differing accounts make clear is that many brand
relationships largely implicate closeness of the brand to the self-concept (see Table 1).
Consumer–Brand Relationships and Psychological Distance
We employ psychological distance as a useful complement to the CBR literature as a
means of conceptualizing self–brand distance. Psychological distance refers to the “subjective
experience that something is close or far away from the self, here, and now” (Trope and
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Liberman 2010, p. 440). At its core, psychological distance reflects the subjective feeling of how
far, in abstract psychological space, a target (e.g., object or event) is perceived to be from the self
(Alter and Oppenheimer 2008). We suggest that psychological distance can be construed as the
foundation underlying the numerous conceptualizations of self–brand distance in the CBR
literature. Supporting this contention, we report empirical evidence from a large-scale pilot study
implying that the common self–brand constructs in the marketing literature (e.g. Self-brand
Connection [Escalas and Bettman 2003] or Self-Connection [Aaker, Fournier, and Brasel 2004])
load on a single factor interpretable as psychological distance (see Web Appendix W1).
While psychological distance can vary based upon geographic, temporal, or probabilistic
proximity (e.g., Murdock and Rajagopal 2017), numerous aspects of social cognition have also
been shown to alter perceptions of psychological distance. For example, the psychological
distance of a target is smaller for an ingroup member (psychologically close; e.g., sister) and
larger for an outgroup member (psychologically distant; e.g., waiter) (Linville, Fischer, and
Yoon 1996). Further, similar others are perceived to be more psychologically close than
dissimilar others (Liviatan, Trope, and Liberman 2008), and objects are perceived to be
psychologically closer when imagined from the first-person versus third-person perspective
(Pronin and Ross 2006).
Findings in the literature are consistent with the idea that the social component of
psychological distance may explain how consumers interact with brands. For example,
examining moral identity in the context of outgroup brands, Choi and Winterich (2013) do not
explicitly address the social dimension of psychological distance but suggest that “although
psychological distance tends to be examined as the distance between two people rather than
between a consumer and a brand, it is possible that the perception of distance from others applies
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to brands given the relationships and group associations with brands” (p. 100). Even so, there is
nearly no explicit consideration of the social component of psychological distance in the
branding literature.
Since CBRs represent socially-construed dyads that are in many ways akin to an
interpersonal relationship (Fournier 1998), the array of consumer–brand relationships identified
in past research should vary predictably along the social dimension of psychological distance,
based upon the relational norms and behaviors that constitute each relationship. Consider two
examples: With committed brand relationships, consumers are faithful to the brand in some
lasting way and think about these brands relatively similarly to their more intimate interpersonal
connections (Miller, Fournier, and Allen 2012). In this case, much like personal relationships
(e.g., Linville, Fischer, and Yoon 1996), it is clear that the brand will be perceived as
psychologically close and incorporated into the self-concept (Fournier 1998). Conversely, secret
affair brand relationships, also characterized by high levels of affect, imply that brands are kept
hidden in order to avoid a public association. Indeed, their nearest relational neighbor is the
“complete stranger” type (Zayer and Neier 2011), underlining that secret affair brands lie more
in the domain of “not me.” In this case, despite positive feelings towards the brand, secret affair
brands will be perceived as more psychologically distant, since the brand is incorporated into the
self-concept to a lesser extent and the consumer actively seeks distance from it (Arsel and
Stewart 2015). More generally, we use close brand-relationship types to refer to those associated
with a low level of perceived psychological distance between the self and the brand, and distant
brand-relationship types to refer to those associated with a high level of perceived psychological
distance between the self and the brand. Pilot Study B provides empirical support for the level of
psychological distance as a common foundation to a number of CBR types (Web Appendix W2).
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Construal Level Theory
If brand relationships indeed involve consumers’ perceptions of psychological distance, it
should benefit the brand to align this social distance with the construal level of brand information
offered by marketers. Construal Level Theory (Trope and Liberman 2010) suggests that the
greater an object’s psychological distance from a person, the greater the likelihood that it will be
conceptualized at a higher level of abstraction. On the other hand, objects that are
psychologically close are represented by more concrete, low-level construals. Abstract, high-
level construals are “schematic, decontextualized representations that extract the gist from the
available information” whereas concrete, low-level construals are “relatively unstructured,
contextualized representations that include subordinate and incidental features of events” (Trope,
Liberman, and Wakslak 2007, p. 83).
Matching the psychological distance of an object with an appropriate level of construal or
concreteness of brand information results in mindset-congruency effects that have been shown to
lead to information being perceived as more persuasive (e.g., Lee and Aaker 2004; Trope,
Liberman, Wakslak 2007) and more likely to be accurately stored and retained in memory
(Kisielius and Sternthal 1986). Such mindset-congruency effects have been observed in
marketing. For example, research on message framing and construal suggests that high-level,
abstract versus low-level, concrete language improves conservation behavior (White,
MacDonnell, and Dahl 2011), increases effectiveness of charitable appeals and health messaging
(Han, Duhachek, and Agrawal 2016; MacDonnell and White 2015), and explains consumer
evaluations of brand extensions (Meyvis, Goldsmith, and Dhar 2012).
We harness the concept of psychological distance that underlies the various brand-
relationship types to establish actionable strategies focused on leveraging the relationships that
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form organically between consumers and brands, rather than attempting to lead often-unwilling
consumers into stronger relationships. Specifically, we expect that evaluations and behavior
directed towards psychologically close brand relationships will be more favorable when
consumers are presented with low-level, concrete brand information, whereas evaluations and
behavior directed towards psychologically distant brand relationships will be more favorable
when consumers are presented with high-level, abstract brand information.
The Role of Processing Fluency
The concept of processing fluency has been defined in a number of ways, generally referring
to the ease with which a person is able to process information and assess meaning (Alter and
Oppenheimer 2008; Lee and Labroo 2004). Fluency has been shown to increase as a result of
construal-based mindset congruencies (e.g., matching loss- [gain]-framed messages with
concrete [abstract] mindsets), leading to more favorable behaviors (White, MacDonnell, Dahl
2011; White, Habib, and Hardisty 2019). Research has further shown that heightened processing
fluency resulting from a fit-based mindset congruency can lead to greater message persuasion
(Lee and Aaker 2004) and positively influences a variety of judgments such as liking (Allard and
Griffin 2017). Thus, further corroborating our psychological distance-based account, we expect
perceptions of fluency will increase when the degree of psychological distance implied by a
particular brand relationship is matched with an appropriate construal level or concreteness of
brand information.
We report four studies. Study 1 demonstrates our mindset-congruency effect by embedding
a construal manipulation within brand information and documents processing fluency as a
mediator. Study 2 embeds a manipulation of construal level in an advertisement and shows that
the mindset-congruency effect can increase donations to a brand-supported cause. Study 3 elicits
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psychologically close and distant brands and examines both brand stereotypes (i.e., warmth and
competence) and the search versus experience nature of the brand as moderators to shed light on
how to more effectively manage existing CBRs. Finally, Study 4 employs a concreteness
manipulation in a field study to demonstrate that the mindset-congruency effect is sufficiently
strong to influence consumer spending and establishes an actionable segmentation moderator:
category usage rate.
Study 1
Study 1 examines the brand-relationship mindset-congruency effect using a construal
manipulation embedded within a brand communication. If different brand-relationship types are
associated with varying levels of closeness to the self, this should result in a mindset-congruency
effect when processing that brand information at an appropriate construal level. To achieve this,
we elicit two types of brand relationships—“committed” and “secret affair”—based upon the
results of Pilot Study B (see Web Appendix W2) that examined twelve brand relationships along
the psychological-distance dimension in addition to other dimensions currently used to
conceptualize CBRs. As committed relationships are closer to the self (Fournier 1998; Miller,
Fournier, and Allen 2012), we expect to see improved brand evaluations following low-construal
brand information processing. Conversely, since “secret affairs” are more distant from the self
(Fournier 1998; Miller, Fournier, and Allen 2012), brand evaluations should be more positive
following exposure to high-construal brand information. Furthermore, by showing mediation by
processing fluency, we provide further evidence to support our claim that we are documenting a
construal–mindset congruency effect driven by psychological distance.
Method and Procedure
We recruited 266 undergraduates (33% female, Mage = 18.2 years) from a large public
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university in exchange for partial course credit. Twelve were removed for failing an attention
check or for not following instructions, and five were removed due to incomplete responses (N =
249). Participants were randomly assigned to conditions in a 2 (brand-relationship type:
committed, secret affair) × 2 (construal level: low, high) between-subjects design.
Participants were informed that they would be evaluating the quality of various brands using
an online system, International Standard, which (ostensibly) compiles information from a
variety of online sources (e.g., consumer reports, online product reviews) and produces brand-
quality scores (see Web Appendix W3). After learning about the system, participants completed
an established brand-relationship elicitation task in which they were asked to nominate a brand
that fit the given brand-relationship type: committed or secret affair (Miller, Fournier, and Allen
2012; see Web Appendix W1). In all conditions, participants were asked to reflect upon brands
that they use regularly in their daily life, so the only difference between the conditions was the
nature of the brand relationship elicited. On the following screen, participants were asked to wait
ten seconds while the system calculated the International Standard quality score for their
nominated brand. The system then informed all participants that their brand had scored 9.2 (out
of 10). To manipulate construal level of the brand information, the next screen provided a list of
the “top five factors that contributed to the International Standard score that the brand received,”
which varied by condition based upon a “how versus why” manipulation of construal level.
Repeatedly focusing on how something is done elicits a low-level, concrete mindset, whereas
focusing on why it is done elicits a high-level, abstract mindset (Freitas, Gollwitzer, and Trope
2004). In the low-level condition, participants were shown five claims about the brand pertaining
to how (concrete) the brand earned the score that it did (e.g., by creating products that
continually meet or exceed the expectations of its customers—according to consumer reports). In
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the high-level condition, participants were shown five claims about the brand pertaining to why
(abstract) it received the score that it did (e.g., because it creates products that continually meet
or exceed the expectations of its customers—according to consumer reports). The content of the
five claims did not differ between the two conditions—the only difference was the manipulation
of construal level. We pretested the manipulation to assess the construal level of the brand
information and attitudes towards the information (for pretest results for construal manipulations
used in all studies see Web Appendices W4 and W5).
Next, participants were asked to complete a three-item measure of processing fluency (α =
.91; Lee and Aaker 2004; e.g., “How easy was this information to comprehend?” “How easy was
this information to process?”) on a 1-7 scale (very difficult – very easy). This was followed by
evaluations of the brand using indices of attitudes (α = .95; nine items from Batra and Stayman
1990; e.g., unfavorable – favorable, dislike – like), trust (α = .81; three items from Chaudhuri
and Holbrook 2001; e.g., “I trust this brand”, “I rely on this brand”), and satisfaction (α = .96;
three items of Fletcher, Simpson, and Thomas 2000; e.g., “How satisfied are you with this
brand?”, “How content are you with this brand?”), all measured on 1-7 scales. Finally,
participants completed basic demographics and manipulation checks for psychological distance
using the IOS zipper (Choi and Winterich 2013; i.e., “Please indicate which case best describes
the level of overlap between your own self-definition or identity and this brand”) and self–brand
connection (α = .92; seven items from Escalas 2004; e.g., “This brand reflects who I am”, “I can
identify with this brand”) scales. The latter measure was captured on a 1-7 scale (not at all –
extremely well; see Web Appendix W6 for measures used in all studies).
Results
Manipulation check. Manipulation checks revealed that the brand closeness manipulation
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was successful. A brand relationship type × construal level condition ANOVA on psychological
distance revealed a significant main effect of brand relationship type F(1, 243) = 16.73, p <
.001). Participants in the committed brand relationship condition perceived the brand to be closer
to the self (M = 4.80) than those in the secret affair condition (M = 3.96). As an additional check
of psychological distance, we examined self-brand connection: as expected, committed brand
relationships were perceived to be closer (M = 4.27) than secret affair brand relationships (M =
2.97), F(1, 245) = 55.86, p < .001.
Brand evaluations. A principal components analysis indicated that all three dependent
variables (attitudes, trust, satisfaction) were unidimensional (all loadings > .89) so we indexed
them to form a brand evaluations composite (α = .88). We use a composite in all remaining
analyses in order to economize reporting, though the choice is bolstered by research showing
strong correlations and theoretical links across these variables (e.g., Garbarino and Johnson
1999). A 2 (brand-relationship type: committed, secret affair) × 2 (construal level: high, low)
ANOVA yielded a significant main effect of brand relationship on brand evaluations (F(1, 245)
= 48.79, p < .001; η2 = .17). In general, brands were evaluated more positively in the committed
condition than in the secret affair condition. Importantly, the results show a significant
interaction (F(1, 245) = 11.81, p < .01; η2 = .05) that remained significant after controlling for
both age and gender (F(1, 243) = 11.73, p < .01; η2 = .05), which we include as covariates in all
further studies.
Follow-up simple effects revealed that for committed relationships, brand evaluations were
significantly more favorable in the low-level condition (M = 6.08) than the high-level condition
(M = 5.67; F(1, 243) = 5.08, p < .05; η2 = .03). On the other hand, for secret-affair relationships,
brand evaluations were significantly more favorable in the high-level condition (M = 5.20) than
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in the low-level condition (M = 4.71; F(1, 243) = 6.74, p < .05; η2 = .02).
Processing fluency. We conducted a conditional process analysis with the PROCESS macro
(Model 8; Hayes 2018) using a bootstrap procedure (5,000 draws) to construct bias-corrected
confidence intervals. Results suggest that processing fluency mediates the focal relationship
since the indirect effect of the highest-order interaction (brand-relationship type × construal
level) through fluency was significant (B = .25, SE = .10, 95% CI = .09, .48). That is, the effect
of construal level on brand evaluations through processing fluency is conditional upon
relationship type. For committed relationships, results show a significant indirect effect (B = .09,
SE = .06, 95% CI = .0004, .2224) while for secret-affair relationships, the direction of the
indirect effect reverses (B = -.16, SE = .07, 95% CI = -.31, -.05).
Discussion
Study 1 provides evidence that consumers’ relationships with brands can be effectively
managed by attending to the associated degree of psychological distance. For consumers who are
in psychologically close brand relationships (e.g., committed), claims made using low-level,
concrete language result in increased processing fluency, leading to the brand being perceived
more favorably than when high-level abstract claims are made. Conversely, for consumers who
are in a more distant relationship with a brand (e.g., secret affair), high-level abstract claims
result in increased processing fluency, leading to the brand being perceived more favorably than
when low-level concrete claims are made. However, it should be noted that past research
associating construal level with positive affect (e.g. Labroo and Patrick 2009) suggests a
potential alternative explanation for our congruency effect. To address this, we conducted a
replication of our congruency effect to experimentally and statistically rule out the role of affect
and to enhance robustness by using a stronger, direct manipulation of construal level (see
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Replication Study in Web Appendix W7). Finally, although the evidence of mediation by
processing fluency supports our contention that our results are attributable to a construal–mindset
congruency effect (Allard and Griffin 2017; Lee and Aaker 2004; White, MacDonnell, and Dahl
2011; 2019), in Study 2 we aim to provide further evidence that this congruency effect is driven
by the brand’s psychological distance and address selection concerns.
Study 2
Study 1 examined the impact of the mindset-congruency effect on brand evaluations. It
remains to be seen whether the effect is strong enough to affect consumer spending. To this end,
Study 2 employs a more realistic application of construal level by embedding a construal-based
manipulation in an advertisement for a charitable cause. We examine differences in consumer
donations as a joint function of the construal level of brand communications and the
psychological distance of the target brand. Finally, Study 2 uses a single target brand assigned to
all respondents, an approach that avoids selection effects and more closely represents the type of
decisions typically made by a marketer managing a single brand.
Method and Procedure
We recruited 156 student and non-student community volunteers (75% female, Mage = 22.2
years) through a large public university in exchange for $8.00. Participants were informed that
they would be taking part in a study to assess their thoughts, feelings, and attitudes towards a
brand. Following general demographic questions, participants rated their brand closeness using
two measures (Choi and Winterich 2013; Escalas 2004; α = .94). We chose the brand Molson
Canadian because a pretest (N = 48) suggested it elicited considerable variance in self–brand
distance in the study population. Next, participants viewed a fundraising advertisement entitled
“Lend a Hand to Man’s Best Friend” (Web Appendix W8) that was co-branded by Molson
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Canadian and the local Society for the Prevention of Cruelty to Animals (SPCA). We created a
between-subjects construal level manipulation by embedding “how” versus “why” language
similar to that used in Study 1. In the low-level-construal condition, the advertisement featured
the question “How does your donation make a difference?” along with four statements
answering the question (e.g., “by providing medical care”). In the high-level construal condition,
the advertisement featured the question “Why is your donation important?” along with four
statements (e.g., “because it ensures healthy animals”). Finally, after viewing the advertisement,
participants were told that Molson Canadian was raising money for the SPCA and asked how
much of their $8.00 payment they would like to donate to the cause, with their donation to be
deducted from their payment at the end of the study (donation amount). Though all participants
actually received payment of the $8.00 at the end of the study, they did not know this at the time
they were asked to make a donation.
Results
We regressed donation amount on construal level, brand closeness, the two-way interaction
term, and the age and gender covariates. The expected construal level × brand closeness
interaction was significant (B = .70, t(150) = 2.85, p < .01; f 2 = .05). A floodlight analysis
revealed that the effect of construal level was significant and negative for brand closeness scores
below 1.77 (B = -1.00, t(150) = -1.98, p = .05) and significant and positive for brand closeness
scores above 5.12 (B = 1.35, t(150) = 1.98, p = .05). That is, for more distant brands, donations
were at least 67% higher when the advertisement featured high-level (versus low-level)
language, whereas for closer brands, donations were at least 88% higher when the ad featured
low-level (versus high-level) language.
Discussion
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Study 2 measured consumers’ perceived closeness to a brand in order to demonstrate that
the construal–mindset congruency effect can impact the amount of money donated to a charity
affiliated with a target brand. These findings reveal that the mindset-congruency effect is strong
enough to shift consumers’ brand-endorsed donation behavior, providing behavioral support for
our primary finding. Furthermore, it shows that embedding a construal-level manipulation in an
advertisement is a practical, effective means of establishing a construal–mindset congruency
effect. Importantly, the effect is replicated using a different brand-selection procedure
(experimenter-provided versus self-selected brand) that guards against idiosyncratic brand
effects.
Study 3
Study 3 was designed to address key brand-level moderators to provide greater insight into
the practical application of our mindset congruency effect. Since our congruency effect is
predicated on the construal level of brand information, we focus on two brand-level moderators
that pose specific implications for how consumers process this brand information. First, we
examine search versus experience brands to examine how our congruency effect is impacted by
differences in the availability and diagnosticity of brand information. Second, we examine how
strongly held brand stereotypes (e.g. warmth, competence) can inhibit the processing of new
brand information.
Prediction
Search versus experience brands. Search attributes (Nelson 1970) are those “qualities of a
brand that the consumer can determine by inspection prior to purchase” (Ford, Smith, and Swasy
1990 p. 434) and can be effectively discovered without the consumer interacting with the brand
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or product (Huang, Lurie, and Mitra 2009). In contrast, experience attributes refer to those
product attributes that cannot be determined prior to inspection as they typically require purchase
to understand (Alba et al. 1997). Adapting these definitions to the brand level, we define search
(experience) brands as those for which the brand attributes most important to consumers can be
effectively evaluated using the information available before (only after) purchase – that is, the
brand is primarily characterized by search (experience) attributes.
Recall that our theorizing suggests that consumers will prefer low-level concrete information
for close brands and high-level abstract information for distant brands. However, in the context
of search brands, we expect this pattern will be reversed. For search brands, the information
typically sought by consumers is readily available prior to purchase (Nelson 1970) as consumers
will typically have extensive knowledge of (Mitra, Reiss, and Capella 1999) and be less skeptical
of claims made by such brands (Ford, Smith, and Swasy 1990). Therefore, search brands are
likely to be characterized by information saturation: close (distant) search brands provide all
concrete (abstract) information consumers need in advance. Since most CBRs are based on
consumers’ regularly interacting with brands, additional construal-congruent information
becomes highly redundant and is unlikely to gain attention or be processed extensively. Rather,
with search brands, we expect that the novelty of being exposed to information that is
incongruent with the associated construal mindset—that is, abstract information for close brands
and concrete information for distant brands—will better capture consumer attention and
influence their subsequent evaluations. This view is supported by research suggesting that
information is novel when it breaks from pre-existing schemas and can lead to heightened
attention, arousal, and more favorable responses (Ang, Lee, and Leong 2007).
On the other hand, since the qualities of experience brands are difficult to evaluate in
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advance of purchase, consumers tend to expend more effort gathering information about them
(Mitra, Reiss, and Capela 1999) and undertake more processing in relation to them (Huang,
Lurie, and Mitra 2009), yet they still often end up in a state of greater ambiguity and uncertainty
compared to search brands (Hoch and Deighton 1989). Thus, as a result of this subjectivity, it is
less likely that consumers will reach a point of information saturation when considering
experience brands, meaning that information that is congruent with their construal-mindset will
continue to be evaluated more favorably. Therefore, we expect to obtain our construal–mindset
congruency effect for such brands.
Brand stereotypes. Building on the Stereotype Content Model of interpersonal interaction
(Fiske et al. 2002), extant literature suggests that consumers typically maintain two fundamental
perceptions or beliefs about brands (Kervyn, Fiske, and Malone 2012). The first is brand warmth,
which captures the extent to which a brand is perceived as having positive intentions. The second
is brand competence, indicating whether the brand is perceived to have the ability to carry out
these intentions. Both warmth and competence share characteristics with other constructs such as
a brand’s personality (i.e., sincerity and competence; MacInnis and Folkes 2017) and power (i.e.,
communion and agency; Yang and Aggarwal 2019). Warmth and competence are pivotal in the
management of product, service, human, and destination brands (Anholt Ipsos Nation and City
Brands Indices 2020; Bennett et al. 2019; Malone and Fiske 2013; Packard, Moore, and
McFerran 2020) due to their importance in shaping consumer evaluations and behaviors (e.g.,
Aaker, Garbinsky, and Vohs 2012; Aaker, Vohs, and Mogilner 2010).
Brands that are consistently positioned over time (e.g., Sam Adams) may become
stereotyped by virtue of being perceived as being very warm and/or competent (Freling and
Forbes 2005; Kervyn, Fiske, and Malone 2012), a situation that we expect will present a
19
boundary condition for our mindset-congruency effect. Specifically, we anticipate that highly
stereotyped brands will lead consumers not to attend to information provided to them in
marketing communications but to rely on their existing brand beliefs. These strongly stereotyped
beliefs are highly accessible, stable, enduring (Aaker, Garbinsky, and Vohs 2012; Freling and
Forbes 2005; Puzakova, Kwak, and Taylor 2013) and “resistant to change, regardless of the
nature of the new information” (Johar, Sengupta, and Aaker 2005, p. 468). As a result, people
with very strongly held stereotypes tend to expend less cognitive effort on stereotype-consistent
information (Sherman et al. 2005). In the current context, any mindset-congruency effect that
results from a matching of self–brand distance with the concreteness of brand communications
would be eliminated for those with strong beliefs about the warmth or competence of a brand,
since this new information would not change existing stereotyped beliefs about the brand.
Method and Procedure
Two hundred and one participants (59% female, Mage = 38.0 years) were recruited from
Amazon’s Mechanical Turk in exchange for a nominal fee. Of those participants, seven were
removed for failing an attention check (N = 194). Participants were randomly assigned to
conditions in a 2 (brand closeness: close, distant) × 2 (construal level: low, high) between-
subjects design. We used the same International Standard approach as Study 1 with three key
differences. First, rather than using specific brand-relationship types (i.e., committed versus
secret affair) to manipulate psychological distance, we took a more direct approach by eliciting
psychologically close versus distant brands. Pilot Study B and extant research (e.g., Fournier
1998) show that brand relationships vary on dimensions other than psychological distance, such
as their valence, hierarchy, and whether the products or services tend to be publicly consumed.
Because some of these dimensions are uncorrelated with psychological distance (e.g., hierarchy,
20
public/private—see Web Appendix W2), they are unlikely to confound our results. Still, for the
remaining studies, we thought it prudent to take different approaches, which is why going
forward we either explicitly manipulate or measure psychological distance. Participants were
shown an image of Aron, Aron, and Smollan’s (1992) IOS scale with large overlap (close) or
separate (distant) pairing circled (see Web Appendix W9) and asked to think of a brand they use
in their daily lives that they felt best characterized this high level (low level) of self–brand
overlap.
Second, we strengthened our “how versus why” construal level manipulation by altering the
concreteness of the information returned by the International Standard procedure (see Web
Appendix W10). Prior research shows that construal level can be manipulated by varying the
level of concreteness or abstraction of written language (e.g., Trope and Liberman 2010; White,
MacDonnell, and Dahl 2011) such that concrete language engages low-level construals and
abstract language engages high-level construals. Third, we measured brand stereotypes of
warmth (α = .95; e.g., “warm”, “friendly”) and competence (α = .93; e.g., “competent”,
“effective”; both 4-item scales from Aaker, Vohs, and Mogilner 2010) on 1-7 scales (not at all –
very much). We further captured the extent to which the brand is primarily a search versus
experience good (α = .85; lower scores = experience good; higher scores = search good; Sharma,
Sivakumaran, and Marshall 2014) and single-item measures
1
of public/private,
symbolic/utilitarian, and political orientation (1 very liberal – 7 very conservative). Two sample
1
Symbolic/functional (p = .40) and public/private (p = .12) were included on an exploratory basis and were found to
not moderate the construal level brand closeness interaction. We also explored whether Political Orientation (Jost
2003) moderated our mindset-congruency effect. The results show a significant three-way interaction with the
congruency interaction significant for more liberal respondents and not significant for more conservative
respondents, but the key spotlight analyses were non-significant (for liberal participants [-1 SD], the effect of
concreteness was negative for distant brands (p > .10) and positive for close brands (p > .13)). We further
investigated this effect using a separate replication of Study 3 (see Web Appendix W11) where all the spotlight
analyses are significant (p < .05).
21
items for search versus experience are: “I can get all the information about this brand before
buying it” and “I can evaluate the quality of this brand before buying it”. In all other respects,
this study mirrored Study 1.
Results
First, a manipulation check revealed that the brand closeness manipulation was
successful. A brand closeness × construal level condition ANOVA on self-brand connection
revealed only a significant main effect of brand closeness F(1, 190) = 134.05, p < .001.
Participants in the close condition perceived the brand to be closer to the self (M = 5.87) than
those in the distant condition (M = 3.30). Next, we created a brand evaluations composite (α =
.98) based on brand attitude (α = .97), trust (α = .89), and satisfaction (α = .97). Second, results
from detailed analyses indicate that the focal brand closeness, search versus experience, and
brand stereotypes variables represent independent constructs (see Web Appendix W12). Finally,
each of the three moderators were examined in separate regression analyses in which we used
PROCESS Model 3 (Hayes 2018) to regress brand evaluations on construal level (dummy
coded), brand closeness (dummy coded), the continuous moderator, all two-way interactions, the
three-way interaction term, and age and gender covariates.
Search versus Experience brands. Results indicated a significant three-way interaction (B =
-1.41, t(184) = -5.71, p < .001; f 2 = .11) (see Figure 1). Floodlight analysis indicated two
significant Johnson-Neyman inflection points. Specifically, the simple interaction effect of
construal level and brand closeness was significant and positive for any search score below 5.31
(B = .64, t(184) = 1.97, p = .05) and significant and negative for any search score above 6.34 (B
= -.83, t(184) = -1.97, p = .05). That is, while the congruency effect holds for brands containing
some level of experience attributes, the effect reverses for brands characterized predominantly by
22
search attributes. Next, we used spotlight analyses to probe the simple interaction of construal
level brand closeness at two levels: experience (-1 SD) and search (+1 SD). Supporting our
mindset-congruency hypothesis, for experience brands the effect of concreteness was significant
and positive for close brands (B = 1.41, t(184) = 4.58, p < .001) and significant and negative for
distant brands (B = -1.02, t(184) = -3.20, p < .01). Thus, for experience brands, evaluations of
close (distant) brands were higher when concrete (abstract) information was used. In contrast,
for search brands the effect of concreteness was significant and negative for close brands (B = -
.69, t(184) = -2.02, p < .05) and marginally significant and positive for distant brands (B = .61,
t(184) = 1.85, p < .07). Thus, for search brands, evaluations of close (distant) brands were higher
when abstract (concrete) information was used.
--Insert Figure 1 HERE--
Brand stereotypes. For competence, there was a significant three-way interaction (B = -.69,
t(184) = -2.25, p < .05; f 2 = .01). Floodlight analysis indicated that the simple interaction effect
of construal level and brand closeness was significant and positive for any competence score
below 6.22 (B = .58, t(184) = 1.97, p = .05). That is, the mindset-congruency effect was
attenuated at high levels of competence. Next, a spotlight analysis showed that for low-
competence brands (-1 SD) the effect of concreteness was significant and negative for distant
brands (B = -.57, t(184) = -2.31, p < .05) and significant and positive for close brands (B = .82,
t(184) = 2.16, p < .05). Thus, evaluations of close (distant) brands were higher when concrete
(abstract) information was used. In contrast, no significant effects were found for high-
competence brands (+1 SD).
23
For warmth, the results yielded a similar three-way interaction (B = -.73, t(183) = - 4.00, p <
.001; f 2 = .04). Floodlight analysis indicated that the simple interaction effect of construal level
and brand closeness was significant and positive for any warmth score below 5.23 (B = .59,
t(183) = 1.97, p = .05). A spotlight analysis showed that for low-warmth brands (-1 SD), the
effect of concreteness was significant and negative for distant brands (B = -.45, t(183) = -1.94, p
= .05) and significant and positive for close brands (B = 1.60, t(183) = 4.70, p < .001). In
contrast, no significant effects were found for high-warmth brands (+1 SD). Thus, the results
indicate that our mindset-congruency effect is effectively attenuated at high levels of either the
warmth or competent brand stereotype (see Web Appendix W13 for figures).
Discussion
Study 3 provides further replication of our mindset-congruency effect, identifies boundary
conditions to inform its practical application, and highlights the unique implications of search
brands, for which our effect is reversed. Specifically, our findings suggest that marketers can
enhance the success of strategies that match brand closeness with construal level by focusing on
brands without strongly developed brand stereotypes. Furthermore, we argue that managing
brands predominantly characterized by search attributes necessitates that marketers follow a
reversed strategy by matching close (distant) search brands with abstract (concrete) language.
Study 4
Study 2 demonstrated that the impact of the mindset-congruency effect was sufficient to
positively influence consumer spending. However, this effect was observed indirectly in the
context of a charity co-brand, using dollars donated to the charity partner as the dependent
variable. In contrast, Study 4 was conducted to increase the robustness and ecological validity of
our findings through a field study examining direct purchase behavior. Furthermore, while our
24
previous studies primarily employed more subtle “how versus why” construal-based
manipulations, Study 4 adopts a purely concreteness-based manipulation of construal level in
order to improve the practical application of our effect. Finally, Study 3 examined how brand-
based differences in the availability of brand information (i.e. search versus experience brands)
and the strength with which this brand knowledge is held in memory (i.e. brand stereotypes)
moderate our mindset congruency effect. In Study 4, we build on our exploration of boundary
conditions by examining a brand-level moderator, category usage rate, that affects consumers’
motivation to process brand information.
Prediction.
Category Usage Rate. We use category usage rate, capturing the amount of a product
consumed by an individual in an average week, to tap the notion of category involvement and to
reflect a useful segmentation variable (Dillon and Gupta 1996). Extant research has found that
low involvement levels are associated with a lack of active information seeking, little motivation
to compare across product attributes, and limited personal relevance of what the product has to
offer (Zaichkowsky 1985). We suggest that consumers who are less involved (i.e., low-volume,
light users) are unmotivated and unlikely to be sufficiently attentive to the content of a brand
communication effort for the mindset-congruency effect to emerge. On the other hand, more
regular and involved users should be more able and willing to attend to (and be impacted by)
subtle differences in how brand information is presented. Thus, we expect that since light users
are comparably less involved in the category, they will be less likely to attend to and process
brand communications, mitigating any potential effect that the concreteness or abstractness of
this message may have.
25
It may be useful to compare this prediction to Study 3, where we suggested that search
brands are characterized by information saturation, meaning consumers should pay attention to
and process construal-incongruent (versus congruent) information. Here, we propose that when
consumers are uninvolved with a product category, they will be less motivated to process any
brand information. Rather than flipping the mindset-congruency effect as search brands did in
Study 3, we anticipate the lack of attention associated with low involvement consumers will
eliminate the mindset-congruency effect.
Method and Procedure
One hundred fifty-eight student and community volunteers were recruited through a large
public university. Eighteen were removed, as they had not heard of the target brand prior to the
study (N = 140, 51% female, Mage = 22 years). In a central location on the university campus, we
set up a trade table for a well-known, high-end tea brand, TWG. Prior to completing our study,
participants had received $5.00 for taking part in an ostensibly unrelated study. Upon receiving
payment, participants were informed that TWG was promoting a new line of teas for the
upcoming season, and they were asked to stop by the trade booth as they went on their way.
TWG was selected because a pretest (N = 32) showed that it elicited considerable variance on the
focal brand closeness variable (M = 3.52, SD = 1.89), and that this variability was not correlated
with how positively the brand was viewed by the study population (r = -.25, p = .19).
Upon approaching the TWG booth, participants were greeted by a confederate acting as a
TWG employee who provided information about the company and its products. Participants
were shown an advertisement (Web Appendix W14) that invited them to “accept our invitation
to experience our new tea collection.” They were randomly assigned to conditions that varied
26
the concreteness of the messaging in the ad (concrete versus abstract). In the concrete condition,
the advertisement used more concrete language (e.g., “allow the teabag to steep for four
minutes—no more, no less. During this time the tea leaves open, hydrate, and infuse the cup with
the essence and aroma of tea fruit and flowers”). In the abstract condition the advertisement
used more abstract language (e.g., “while the tea steeps, the ethereal essence envelops and
soothes because of each tea’s playful yet calming aromas”).
After viewing the advertisement, participants were given an opportunity to purchase a
sample pack of three of the featured teas using a “pay what you will” structure. Those who opted
to purchase the product (any amount higher than $0) paid as much as they were willing for a
TWG-branded package. Importantly, all brand payment decisions were binding such that the
branded package was always provided in exchange for the indicated amount. All participants
were then asked to complete a short feedback card that captured demographic information,
category usage (i.e., “how many cups of tea do you drink per week”, continuous), and the IOS
measure of psychological distance (Choi and Winterich 2013) as well as perceived similarity to
the spokesperson (1 not at all similar – 7 very similar) and perceived usefulness of information (1
not at all useful – very useful)
2
. Lastly, participants were asked if they would be interested in
providing their email address to receive future communication for the TWG brand as an
additional behavior-dependent variable (0 = no, 1 = yes).
Results
Mindset-congruency effect. We first wanted to ensure that the concreteness manipulation did
not inadvertently affect perceptions of psychological distance. A one-way ANOVA verified that
2
Usefulness of information was examined as an alternative mechanism and found not to mediate the effect of the
construal level brand closeness interaction on brand evaluations ( = .02, 95% CI = -.03, .08). Salesperson
similarity was also included as an exploratory moderator. Results are reported in Web Appendix W15.
27
there was no significant effect of concreteness on self–brand distance (F(1, 138) < 1).
To examine the mindset-congruency effect on consumer spending, we first regressed
purchase price on concreteness (dummy coded), brand closeness, the two-way interaction term,
and age and gender included as covariates. Results indicate significant main effects of
concreteness (B = -1.91, t(134) = -2.74, p < .01), brand closeness (B = -.77, t(148) = -2.61, p <
.05), and the two-way interaction (B = .56, t(134) = 3.10, p < .01; f 2 = .07). A floodlight analysis
revealed that the effect of concreteness was significant and negative for brand closeness scores
below 2.04 (B = -.77, t(134) = -1.98, p = .05) and significant and positive for brand closeness
scores above 4.63 (B = .68, t(134) = 1.98, p = .05). That is, for more distant brands, the amount
paid was at least 35% higher when the ad featured abstract (versus concrete) language, whereas
for more close brands, the amount paid was at least 28% higher when the ad featured concrete
(versus abstract) language.
3
Boundary condition. First, PROCESS Model 3 (Hayes 2018) was used to regress purchase
price on concreteness (dummy coded), brand closeness, category usage rate, all two-way
interactions, the three-way interaction term, and age and gender covariates. Results reveal a
significant three-way interaction (B = .10, t(130) = 2.02, p < .05; f 2 = .03). Floodlight analysis
indicates that the simple interaction effect of concreteness and brand closeness was significant
and positive for any category usage rate above 3.28 (B = .39, t(130) = 1.98, p = .05). That is, the
mindset-congruency effect was attenuated for individuals with an average category usage rate of
3.28 cups/week and below. Next, we used spotlight analyses to test the effect of concreteness
across closeness scores at two levels of category usage: light user (-1 SD) and heavy user (+1
3
A binary logistic regression yielded a similar interaction for participants’ likelihood to engage with the brand via
email (B = -3.93,
2(1) = 9.27, p < .01, see Web Appendix W16). Floodlight analyses indicate that the pattern of the
interaction matches that of the dollars spent DV.
28
SD). Supporting our mindset-congruency hypothesis, for heavy users the effect of concreteness
was significant and negative for those who perceived the brand to be psychologically distant (B
= -1.86, t(130) = -2.40, p < .05) and significant and positive for those who perceived the brand to
be psychologically close (B = 1.47, t(130) = 2.77, p < .01). In contrast, no significant effects
were found for light users (see Figure 2).
--Insert Figure 2 HERE--
Discussion
Study 4 was a field study designed to boost managerial relevance by showing behavioral
outcomes in an ecologically valid setting. Our results suggest that our core mindset-congruency
effect persists for consumer spending in a realistic and branded trade booth setting. For close
(distant) consumers, the amount paid for the brand was greater, and consumers were more likely
to engage with the brand when the message used concrete (abstract) language. The results shed
light on a boundary condition to the observed mindset-congruency effect: category involvement.
Consumers with lower category involvement (i.e. those that drink tea rarely) attend to category-
related information less, regardless of their distance to the focal brand, mitigating any benefit of
matching message concreteness to brand distance.
General Discussion
It is unlikely that consumers will ever care about as many brands as marketers would want,
so it is imperative that marketers learn to thrive within the constraints of existing brand
relationships, many of which are rather distant. Our findings suggest that marketing
communications promoting such brands are more successful if they employ high-level, abstract
language. Across all studies (see Table 2), we underscore that self–brand distance is a thread
29
weaving through many major CBR measures, including brand attachment, brand love, self–brand
connection, brand commitment, and brand identification. This in turn establishes the groundwork
for specific consumer-based strategies to extract value from pre-existing brand relationships
using the theoretical lens of social psychological distance. We show that level of psychological
distance associated with a brand relationship can trigger a favorable congruency effect when
matched with the appropriate level of construal or concreteness of a marketing message,
resulting in enhanced processing fluency, more favorable brand evaluations, higher donations,
and more spending. We also identify several theoretical and practical moderators of our effect.
Marketing Implications
We contribute to the understanding of how marketers can better manage the full spectrum of
consumer–brand relationships. It is well understood that a high level of closeness between the
consumer’s self and a brand is a desirable marketing outcome and an effective input to brand
loyalty (Escalas and Bettman 2003). However, as the current research shows, it is not only the
distance between the consumer and the brand that matters—it is the manner in which this
distance interacts with how marketers speak to consumers about brands. Our mindset-
congruency effect sheds light on a significantly overlooked aspect of brand relationships by
demonstrating how managers can better realize value from relationships in which the brand is not
close to the self.
Importantly, the flip side of this congruency effect demonstrates how marketers can better
leverage close brands. We find that the use of concrete language within marketing
communications results in more positive brand attitudes and increased trust, satisfaction, and
spending. That is, while a high level of closeness between the consumer and the brand is
beneficial, this outcome is made even more positive by tailoring the concreteness of brand
30
language to match the psychological distance implied by that relationship.
Psychological distance should thus be given due consideration as a segmentation variable.
For example, consider Walmart, which was listed by different respondents in several of our
samples as a psychologically close or psychologically distant brand. People who exhibit a close
relationship likely comprise a segment of working families who rely on Walmart’s low prices to
accommodate budgets. Conversely, those who relate to the brand along the lines of a distant
relationship are likely younger and more brand-conscious, relying on Walmart’s prices but
preferring to avoid being seen using Walmart-branded products. Our results imply that Walmart
can profit from both groups by leveraging its accumulated customer relationship management
databases pertaining to psychographics (e.g., spending and shopping habits) and demographics
(e.g., age) to customize the concreteness of their marketing messaging.
In fact, identifying segments of consumers based on their relative self–brand distance should
be fairly straightforward. For example, using what consumers write on social media, market
research firms or in-house research teams could easily develop real-time monitoring tools based
on dictionaries that reflect relative distance and then target consumer segments accordingly.
Another approach could be based on surveying consumers directly. Firms already do this
prolifically with the Net Promoter Score (NPS), which is essentially a future-looking word-of-
mouth metric. Like the IOS scale used in our studies, NPS is a single-item metric, but the former
has advantages. For example, unlike NPS, which lacks a “strong theoretical development”
(Lemon and Verhoef 2016, p. 81), IOS boasts a rich theoretical tradition, does not require
transformation (in our samples it tended to be normally distributed), does not disregard the
middle of the scale, and can be treated as a continuous measure. We were curious about whether
NPS could be used in place of IOS and conducted a high-powered online experiment (cell sizes
31
~130) that failed to find anything resembling the results we demonstrate in the studies above. We
ran an additional study (cell sizes ~100) using trust instead of psychological distance and again
the results were not promising.
Importantly, the approach implied by our research involves minimal investment. For
example, in Studies 2 and 4 we found that simple changes in how information was presented in
brand communications (e.g., Molson and TWG ads) caused distant consumers to donate and
spend more than they otherwise would have and to even spend as much as close consumers. To
illustrate, the Study 4 spotlight analyses showed that distant participants spent an average of
$2.98 after viewing an ad with abstract language, whereas close participants spent an average of
$2.79 across both information types. This result may be surprising when framed in light of
marketers’ enthusiasm for relationship building, but other scholars working in the construal-level
domain have found similar tangible advantages emerging from simple changes to message
framing (e.g., White, MacDonnell, and Dahl 2011). Compared to the resource-intensive process
of solidifying relationships, we document a comparatively low threshold for making changes that
should have positive and immediate financial impact. Of course, we are not suggesting that
marketers abandon relationship-building efforts, but different tactics may be more beneficial
with distant consumers. Future research could assess the comparative value of these two
strategies.
Our studies also identify both brand-level and segmentation moderators with relevant
implications for the application of our congruency effect. First, the recommendation implied by
our mindset-congruency effect is contingent upon the level of search versus experience attributes
that characterize the brand. While our standard congruency effect holds for brands that possess
even reasonably small levels of experience attributes (experience/search < 5.30), the effect is
32
reversed for brands characterized by predominantly search attributes (experience/search > 6.34).
For high-search brands—those where consumers can reliably evaluate the brand before purchase
(e.g., clothing, jewelry, furniture)—managers should focus on aligning close (distant) search
brands with abstract (concrete) brand communications. Second, when the brand possesses a well-
developed brand stereotype (e.g., very high levels of warmth or competence), our mindset-
congruency effect is mitigated. Such stereotypes are already prominent brand-management
considerations (Bennett et al. 2019; Malone and Fiske 2013; Packard, Moore, and McFerran
2020) and extant research (e.g., Kervyn, Fiske, and Malone 2012) suggests that only a few
exemplary brands like Coca-Cola and Campbell’s ever reach high levels—according to our data,
6.22/7 on competence and 5.22/7 on warmth—where our mindset-congruency effect is unlikely
to work. It also might be noted that a few select brands reach superior levels on both dimensions
(Aaker, Garbinsky, and Vohs 2012), but we would anticipate the same basic mitigation result:
such brands are so resolutely positioned in this “golden quadrant” (Aaker, Garbinsky, and Vohs
2012, p. 191) that they would resist updating through the types of marketing communications
examined in this paper. We would advise brand managers that using a mindset-congruency
strategy in such a situation would be wasteful.
Additionally, consumers with lower category involvement are unlikely to demonstrate a
mindset-congruency effect. For example, when consumers report a low category usage rate (i.e.,
drink tea rarely), they attend to category-related information less, regardless of their distance to
the focal brand. Here, it is important to note that while consumers are less likely to form
connections in low-involvement product categories (e.g., Reimann and Aron 2009), they still
often do (e.g., Warrington and Shim 2000), meaning that psychological distance with a brand is
not confounded with category involvement. For example, in our Study 4 we found only a small
33
correlation between the measures of category usage and self–brand distance (r = .26, p <. 01),
suggesting that consumers have separate sentiments about brands and categories.
Theoretical Contributions
We contribute to the marketing literature on consumer–brand relationships (Fournier 1998).
By laying out how the social psychological distance associated with a brand relationship is a core
dimension of numerous brand-relationship types, we perform a useful phenomena-to-construct
mapping (MacInnis et al. 2020), which allows us to develop a simplifying strategy. In contrast to
research where brand dimensions proliferate without an attendant level of clarity concerning how
to put those dimensions to optimal use, we posit that psychological distance is inherent to brand
relationships, is functionally synonymous with many concepts that scholars use to explore self–
brand linkages, and is statistically unidimensional. Furthermore, in demonstrating mindset-
congruency effects, our findings suggest that brand relationships parallel interpersonal
relationships in terms of level of psychological distance associated with the relationship partner.
Second, we contribute back to Construal Level Theory (Trope and Liberman 2010). Past
literature examining the social dimension of psychological distance has predominantly focused
on the effects of in-groups versus out-groups (e.g., Linville, Fischer, and Yoon 1996; Liviatan,
Trope, and Liberman 2008), such that in-groups are perceived as close while out-groups are
perceived as distant. In contrast, we demonstrate that the norms that constitute a given
relationship can offer subtle variations to these effects. That is, even positive, in-group
relationships can be psychologically distant if the norms that govern the relationship imply
distance (e.g., secret affair). Furthermore, we identify the search versus experience nature of the
brand as a boundary condition of Construal Level Theory that is unique to the marketing context.
We show that the mindset-congruency effect is overridden and reversed for high-search brands
34
where, over the course of a brand relationship, consumers have reached a point of information
saturation. Thus, for high-search brands, construal-incongruent information better captures
consumer attention, leading to more favorable brand evaluations.
Third, our paper provides the first empirical application of the social dimension of
psychological distance to non-human targets. While the psychological distance of inanimate
objects can be altered along temporal, spatial, and hypothetical dimensions (e.g., Trope and
Liberman 2010), the social dimension has been applied only to human targets due to an
underlying assumption that non-human entities are not truly social. However, through our
examination of brand relationships, our insights suggest that the psychological distance of such
objects can be influenced by social concepts such as ascribed relational norms. This in turn
advances the possibility for psychological distance-based construal effects that would otherwise
not be predicted by extant literature. These effects are made possible by consumers’ tendency to
anthropomorphize brands and see them akin to a relationship partner (e.g., Fournier 1998).
Future Research Directions
First, we propose a simplifying strategy that rests on a single idea: many of the somewhat
disparate, even fragmented constructs appearing in the CBR literature share a latent feature tied
to social psychological distance. Given the proliferation of brand constructs in the marketing
literature, future research may productively adopt a similar approach. That is, researchers would
benefit from taking a step back from or looking across the many measures and constructs in the
CBR literature to identify those that have unique meaning versus those that have shared
meaning, and to understand whether there are other latent lenses through which the field may
continue to consolidate and clarify. Doing so would likely improve relationship marketing
practice, make marketing spending more efficient, and reduce some of the redundancies that
35
seem apparent in the CBR literature.
Second, we focus primarily on brand-specific consequences of our mindset-congruency
effect (i.e., evaluations, spending) across our studies, but it is possible that the effect may
similarly influence aspects of consumer judgment and decision making. For example, research
could build on existing self-control (Wan and Agrawal 2011) and gift-giving studies (Baskin et
al. 2014), which report that construal level is associated with a preference for feasibility or
desirability attributes. Thus, the closeness of a consumer’s relationship with a brand may
represent a way to identify and cater to consumer attribute preferences during the decision-
making process, such that as close (distant) relationships evoke a low-level (high-level) mindset
they should lead to a greater emphasis on feasibility (desirability) attributes.
Third, our findings suggest a nuance in social-based construal-level effects in that they may
depend on the real versus fictional nature of the relationship. While extant research has shown
that the priming of specific relationship norms can influence construal for fictional relationships
(e.g., Aggarwal and Law 2005), we show that when the relationship is lived and experienced,
effects due to perceived psychological distance appear to supersede the priming effect. Thus,
future research should examine the juxtaposition of these two competing effects in order to
disentangle relationship norm predictions based on real versus fictional brands.
Fourth, we find evidence to suggest that our mindset-congruency effect is effectively
mitigated for brands that are strongly positioned. In Study 3 we examine this boundary condition
using two fundamental brand stereotypes (i.e., warmth and competence; Kervyn, Fiske, and
Malone 2012) for which the stereotype literature predicts that consumers will rely on existing
beliefs rather than new information when forming object evaluations (Sherman et al. 2005). To
further ground this finding in the branding literature, it would be worthwhile to examine whether
36
the same pattern of results extends to other brands that are superlatively positioned but on less
fundamental, non-stereotype dimensions, such as brands that are viewed as very exciting or
powerful (MacInnis and Folkes 2017; Yang and Aggarwal 2019).
Finally, although we examine a number of factors, the complexity of consumer
interactions with brands necessitates that further research explore moderating variables or
boundary conditions to our brand closeness-based congruency effect. For example, there may be
situations in which a consumer’s construal mindset does not align with the traditional predictions
of construal level theory. Consider a frequent user of the Tide brand who feels that the brand is
distant from their self-concept.
4
As a result of their use of the brand (to do laundry), they tend to
think most often about more concrete aspects of using the brand (e.g., measuring out detergent;
adding to a wash). Thus, it is possible that this consumer would tend to view this distant brand in
a more concrete as opposed to abstract manner. While we address this empirically by ruling out
any moderating role of functional versus symbolic and public versus private products, additional
research should explore this potential occurrence for distant brands.
4
We thank the Associate Editor for this suggestion.
37
REFERENCES
Aaker, Jennifer L., Susan Fournier, and S. Adam Brasel. “When Good Brands Do Bad,” Journal
of Consumer Research, 31 (1), 1–16.
Aaker, Jennifer L., Emily N. Garbinsky, and Kathleen D. Vohs (2012), “Cultivating Admiration
in Brands: Warmth, Competence, and Landing in the ‘Golden Quadrant’,” Journal of
Consumer Psychology, 22 (2), 191–94.
Aaker, Jennifer L., Kathleen D. Vohs, and Cassie Mogilner (2010), “Nonprofits Are Seen as
Warm and For-Profits as Competent: Firm Stereotypes Matter,” Journal of Consumer
Research, 37 (2), 224–37.
Aggarwal, Pankaj and Sharmistha Law (2005), “Role of Relationship Norms in Processing
Brand Information,” Journal of Consumer Research, 32 (3), 453–64.
Alba, Joseph W., John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan Sawyer, and
Stacy Wood (1997), “Interactive Home Shopping: Consumer, Retailer, and Manufacturer
Incentives to Participate in Electronic Marketplaces,” Journal of Marketing 61 (3), 38–53.
Allard, Thomas and Dale Griffin (2017), “Comparative Price and the Design of Effective
Product Communications,” Journal of Marketing, 81 (5), 16–29.
Alter, Adam L. and Daniel M. Oppenheimer (2008), “Effects of Fluency on Psychological
Distance and Mental Construal (or Why New York Is a Large City, but New York Is a
Civilized Jungle),” Psychological Science, 19 (2), 161–67.
Ang, Swee Hoon, Yih Hwai Lee, and Siew Meng Leong (2007), “The Ad Creativity Cube:
Conceptualization and Initial Validation,” Journal of the Academy of Marketing Science, 35
(2), 220–32.
Aron, Arthur, Elaine N. Aron, and Danny Smollan (1992), “Inclusion of Other in the Self Scale
38
and the Structure of Interpersonal Closeness,” Journal of Personality and Social
Psychology, 63 (4), 596–612.
Arsel, Zeynep and Scott Stewart (2015), “Identity Degrading Brands,” in Strong Brands, Strong
Relationships, Susan Fournier, Michael Breazeale, and Jill Avery, eds. New York, NY:
Routledge, 106–16.
Baskin, Ernest, Cheryl J. Wakslak, Yaacov Trope, and Nathan Novemsky (2014), “Why
Feasibility Matters More to Gift Receivers than to Givers: A Construal-Level Approach to
Gift Giving,” Journal of Consumer Research, 41 (1), 169–82.
Batra, Rajeev and Douglas M. Stayman (1990), “The Role of Mood in Advertising
Effectiveness,” Journal of Consumer Research, 17 (2), 203–14.
Bennett, Aronté Marie, Chris Malone, Kenyn Cheatham, and Naina Saligram (2019), “The
Impact of Perceptions of Politician Brand Warmth and Competence on Voting Intentions,”
Journal of Product and Brand Management, 28 (2), 256–73.
Chaudhuri, Arjun and Morris B. Holbrook (2001), “The Chain of Effects from Brand Trust and
Brand Affect to Brand Performance: The Role of Brand Loyalty,” Journal of Marketing, 65
(2), 81–93.
Choi, Woo Jin and Karen Page Winterich (2013), “Can Brands Move in From the Outside? How
Moral Identity Enhances Out-Group Brand Attitudes,” Journal of Marketing, 77 (2), 96–111.
Dillon, William R. and Sunil Gupta (1996), “A Segment-Level Model of Category Volume and
Brand Choice,” Marketing Science, 15 (1), 38–59.
Escalas, Jennifer Edson (2004), “Narrative Processing: Building Consumer Connections to
Brands,” Journal of Consumer Psychology, 14 (1-2), 168–80.
Escalas, Jennifer Edson and James R. Bettman (2003), “You Are What They Eat: The Influence
39
of Reference Groups on Consumers’ Connections to Brands,” Journal of Consumer
Psychology, 13 (3), 339–48.
Fiske, Susan T., Amy J. C. Cuddy, Peter Glick, and Jun Xu (2002), “A Model of (Often Mixed)
Stereotype Content: Competence and Warmth Respectively Follow from Perceived Status and
Competition,” Journal of Personality and Social Psychology, 82 (6), 878–902.
Fletcher, Garth J. O., Jeffry A. Simpson, and Geoff Thomas (2000), “The Measurement of
Perceived Relationship Quality Components: A Confirmatory Factor Analytic Approach,”
Personality and Social Psychology Bulletin, 26 (3), 340–54.
Ford, Gary T., Darlene B. Smith, and John L. Swasy (1990), “Consumer Skepticism of
Advertising Claims: Testing Hypotheses from Economics of Information,” Journal of
Consumer Research, 16 (4), 433–41.
Fournier, Susan (1998), “Consumers and Their Brands: Developing Relationship Theory in
Consumer Research,” Journal of Consumer Research, 24 (4), 343–73.
Fournier, Susan and Claudio Alvarez (2013), “Relating Badly to Brands,” Journal of Consumer
Psychology, 23 (2), 253–64.
Fournier, Susan, Susan Dobscha, and David Glen Mick (1998), “The Premature Death of
Relationship Marketing,” Harvard Business Review, 76 (1), 42–51.
Freeman, Karen, Patrick Spenner, and Anna Bird (2012), “Three Myths about What Customers
Want,” Harvard Business Review, retrieved February 1, 2017.
Freitas, Antonio L., Peter Gollwitzer, and Yaacov Trope (2004), “The Influence of Abstract and
Concrete Mindsets on Anticipating and Guiding Others' Self-Regulatory Efforts,” Journal of
Experimental Social Psychology, 40 (6), 739–52.
Freling, Traci H. and Lukas P. Forbes (2005), “An Empirical Analysis of the Brand Personality
40
Effect,” Journal of Product and Brand Management, 14 (7), 404–13.
Garbarino, Ellen and Mark S. Johnson (1999), “The Different Roles of Satisfaction, Trust, and
Commitment in Customer Relationships,” Journal of Marketing, 63 (2), 70–87.
Han, DaHee, Adam Duhachek, and Nidhi Agrawal (2016), “Coping and Construal Level
Matching Drives Health Message Effectiveness via Response Efficacy or Self-Efficacy
Enhancement,” Journal of Consumer Research, 43 (3), 429–447.
Havas (2020), “Meaningful Brands” (accessed December 9, 2019) https://www.meaningful-
brands.com/en
Hayes, Andrew F. (2018), Introduction to Mediation, Moderation, and Conditional Process
Analysis: A Regression-Based Approach (second edition), New York, NY: Guilford Press.
Heckler, Donna S. and Brian D. Till (2009), Creating Brands People Love, Upper Saddle River,
New Jersey: Pearson Education.
Hoch, Stephen J. and John Deighton (1989), “Managing What Consumers Learn from
Experience,” Journal of Marketing, 53 (2), 1–20.
Huang, Peng, Nicholas H. Lurie, and Sabyasachi Mitra (2009), “Searching for Experience on the
Web: An Empirical Examination of Consumer Behavior for Search and Experience
Goods,” Journal of Marketing, 73 (2), 55–69.
Ipsos (2020) “Anholt Ipsos Nation and City Brands Indices” (accessed July 25, 2020)
https://www.ipsos.com/en/2020-anholt-ipsos-city-brand-index.
Johar, Gita Venkataramani, Jaideep Sengupta, and Jennifer L. Aaker (2005), “Two Roads to
Updating Brand Personality Impressions: Trait versus Evaluative Inferencing,” Journal of
Marketing Research, 42 (4), 458–69.
Kervyn, Nicolas, Susan T. Fiske, and Chris Malone (2012), “Brands as Intentional Agents
41
Framework: How Perceived Intentions and Ability Can Map Brand Perception,” Journal of
Consumer Psychology, 22 (2), 166–76.
Kisielius, Jolita and Brian Sternthal (1986), “Examining the Vividness Controversy: An
Availability-Valence Interpretation,” Journal of Consumer Research, 12 (4), 418–31.
Labroo, Aparna A. and Vanessa M. Patrick (2009), “Providing a Moment of Respite: Why a
Positive Mood Helps Seeing the Big Picture,” Journal of Consumer Research, 35 (5), 800–09.
Lee, Angela Y. and Jennifer L. Aaker (2004), “Bringing the Frame into Focus: The Influence of
Regulatory Fit on Processing Fluency and Persuasion,” Journal of Personality and Social
Psychology, 86 (2), 205–18.
Lee, Angela Y. and Aparna A. Labroo (2004), “The Effect of Conceptual and Perceptual Fluency
on Brand Evaluation,” Journal of Marketing Research, 41 (2), 151–65.
Lemon, Katherine N. and Peter C. Verhoef (2016), “Understanding Customer Experience
Throughout the Customer Journey,” Journal of Marketing, 80 (6), 69–96.
Linville, Patricia W., Gregory W. Fischer, and Carolyn Yoon (1996), “Perceived Covariation
among the Features of Ingroup and Outgroup Members: The Outgroup Covariation Effect,”
Journal of Personality and Social Psychology, 70 (3), 421–36.
Liviatan, Ido, Yaacov Trope, and Nira Liberman (2008), “Interpersonal Similarity as a Social
Distance Dimension: Implications for Perception of Others’ Actions,” Journal of
Experimental Social Psychology, 44 (5), 1256–69.
Keller, Punam Anand, and Lauren G. Block (1997) “Vividness Effects: A Resource-matching
Perspective,” Journal of Consumer Research, 24 (3), 295-304.
MacDonnell, Rhiannon and Katherine White (2015), “How Construals of Money versus Time
Impact Consumer Charitable Giving,” Journal of Consumer Research, 42 (4), 551–63.
42
MacInnis, Deborah J. and Valerie S. Folkes (2017), “Humanizing Brands: When Brands Seem to
be Like Me, Part of Me, and in a Relationship with Me,” Journal of Consumer Psychology, 27
(3), 355–74.
MacInnis, Deborah J., Vicki G. Morwitz, Simona Botti, Donna L. Hoffman, Robert V. Kozinets,
Donald R. Lehmann, John G. Lynch Jr., and Cornelia Pechmann (2020), “Creating Boundary-
Breaking, Marketing-Relevant Consumer Research,” Journal of Marketing, 84 (2), 1–23.
Malar, Lucia, Harley Krohmer, Wayne D. Hoyer, and Bettina Nyffenegger (2011), “Emotional
Brand Attachment and Brand Personality: The Relative Importance of the Actual and the
Ideal Self,” Journal of Marketing, 75 (4), 35–52.
Malone, Chris and Susan T. Fiske (2013), The Human Brand: How We Relate to People,
Products, and Companies, San Francisco, CA: John Wiley & Sons.
Meyvis, Tom, Kelly Goldsmith, and Ravi Dhar (2012), “The Importance of the Context in Brand
Extension: How Pictures and Comparisons Shift Consumers’ Focus from Fit to Quality,”
Journal of Marketing Research, 49 (2), 206–17.
Miller, Felicia, Susan Fournier, and Chris Allen (2012), “Exploring Relationship Analogues in
the Brand Space,” in Consumer-Brand Relationships: Theory and Practice, Susan Fournier,
Michael Breazeale, and Marc Fetscherin, eds. London, UK: Routledge, 30–56.
Mitra, Kaushik, Michelle C. Reiss, and Louis M. Capella (1999), “An Examination of Perceived
Risk, Information Search and Behavioral Intentions in Search, Experience and Credence
Services,” Journal of Services Marketing, 13 (3), 208–28.
Murdock, Mitchel R. and Priyali Rajagopal (2017), “The Sting of Social: How Emphasizing
Social Consequences in Warning Messages Influences Perceptions of Risk,” Journal of
Marketing, 81 (2), 83–98.
43
Nelson, Phillip (1970), “Information and Consumer Behavior,” Journal of Political Economy, 78
(2), 311–29.
Packard, Grant, Sarah G. Moore, and Brent McFerran (2020), “Speaking to Customers in
Uncertain Times,” Sloan Management Review, 60 (2), 68–71.
Park, C. Whan, Andreas B. Eisingerich, and Jason Whan Park (2013), “Attachment–Aversion
(AA) Model of Customer–Brand Relationships,” Journal of Consumer Psychology, 23 (2),
229–48.
Park, C. Whan, Deborah J. MacInnis, and Joseph R. Priester (2009), “Research Directions on
Strong Brand Relationships,” in Handbook of Brand Relationships, Deborah J. MacInnis, C.
Whan Park, and Joseph R. Priester, eds. Armonk, NY: M.E. Sharpe, 379–91.
Pronin, Emily and Lee Ross (2006), “Temporal Differences in Trait Self-Ascription: When the
Self is Seen as an Other,” Journal of Personality and Social Psychology, 90 (2), 197–209.
Puzakova, Marina, Hyokjin Kwak, and Charles R. Taylor (2013), “The Role of Geography of
Self in ‘Filling in’ Brand Personality Traits: Consumer Inference of Unobservable Attributes,”
Journal of Advertising, 42 (1), 16–29.
Reimann, Martin and Arthur Aron (2009), “Self-Expansion Motivation and Inclusion of Brands
in Self,” in Handbook of Brand Relationships, Deborah J. MacInnis, C. Whan Park, and
Joseph R. Priester, eds. Armonk, NY: M.E. Sharpe, 65–81.
Sharma, Piyush, Bharadhwaj Sivakumaran, and Roger Marshall (2014), “Exploring Impulse
Buying in Services: Toward an Integrative Framework,” Journal of the Academy of Marketing
Science, 42 (2), 154–70.
Sherman, Jeffrey W., Steven J. Stroessner, Frederica R. Conrey, and Omar A. Azam (2005),
“Prejudice and Stereotype Maintenance Processes: Attention, Attribution, and Individuation,”
44
Journal of Personality and Social Psychology, 89 (4), 607–22.
Swaminathan, Vanitha, Karen L. Page, and Zeynep Gurhan-Canli (2007), “‘My’ Brand or ‘Our’
Brand: The Effects of Brand Relationship Dimensions and Self-Construal on Brand
Evaluations,” Journal of Consumer Research, 34 (2), 248–59.
Thomson, Matthew, Deborah J. MacInnis, and C. Whan Park (2005), “The Ties that Bind:
Measuring the Strength of Consumers’ Emotional Attachments to Brands,” Journal of
Consumer Psychology, 15 (1), 77–91.
Trope, Yaacov and Nira Liberman (2010), “Construal-Level Theory of Psychological Distance,”
Psychological Review, 117 (2), 440–63.
Trope, Yaacov, Nira Liberman, and Cheryl Wakslak (2007), “Construal Levels and
Psychological Distance: Effects on Representation, Prediction, Evaluation, and Behavior,”
Journal of Consumer Psychology, 17 (2), 83–95.
Wan, Echo Wen and Nidhi Agrawal (2011), “Carryover Effects of Self-Control on Decision
Making: A Construal-Level Perspective,” Journal of Consumer Research, 38 (1), 199–214.
Warrington, Patti and Soyeon Shim (2000), “An Empirical Investigation of the Relationship
between Product Involvement and Brand Commitment,” Psychology and Marketing, 17 (9),
761–82.
White, Katherine, Rishad Habib, and David J. Hardisty (2019), “How to SHIFT Consumer
Behaviors to be More Sustainable: A Literature Review and Guiding Framework,” Journal of
Marketing, 83 (3), 22–49.
White, Katherine, Rhiannon MacDonnell, and Darren W. Dahl (2011), “It’s the Mind-Set that
Matters: The Role of Construal Level and Message Framing in Influencing Consumer
Efficacy and Conservation Behaviors,” Journal of Marketing Research, 48 (3), 472–85.
45
Wittenbraker, John, Helen Zeitoun, and Susan Fournier (2015), “Using Relationship Metaphors
to Understand and Track Brands,” in Strong Brands, Strong Relationships, Susan Fournier,
Michael Breazeale, and Jill Avery, eds. London, UK: Routledge, 360–75.
Yang, Linyun W. and Pankaj Aggarwal (2019), “No Small Matter: How Company Size Affects
Consumer Expectations and Evaluations,” Journal of Consumer Research, 45 (6), 1369–84.
Zaichkowsky, Judith Lynne (1985), “Measuring the Involvement Construct,” Journal of
Consumer Research, 12 (3), 341–52.
Zayer, Linda Tuncay and Stacy Neier (2011), “An Exploration of Men’s Brand Relationships,”
Qualitative Market Research: An International Journal, 14 (1), 83–104.
46
Table 1: Self–Brand Distance Across a Selection of CBR Constructs
CBR Construct
(focal component)
Sources
Sample Items
Attachment-Aversion
Relationship
(Brand Self-Distance)
Park et al. 2013
I am personally connected to [personally
disconnected from] the brand; The brand is very
close to me [very far away] and [not] who I am.
Brand Attachment
(Brand Self-Connection)
Park et al. 2010
To what extent is [brand] part of you and who you
are? To what extent do you feel personally
connected to [brand]?
Brand Commitment
(Affective Commitment)
Fullerton 2005; Lee et al.
2007
[Brand] has a great deal of personal meaning to me;
I feel a strong sense of identification with [brand]; I
feel emotionally attached to [brand].
Brand Identification
Homburg et al. 2009;
Einwiller et al. 2006;
Stokburger-Sauer et al. 2012
I strongly identify with this [brand]; I feel attached
to this [brand]; Being a customer of [brand] is part
of my sense of who I am.
Brand Love
(Self–Brand Integration)
Bagozzi et al. 2017; Batra et
al. 2012
My personal identity and this brand's identity
match; Using this brand says something 'true' and
'deep' about who I am as a person.
Brand Relationship Quality
(Self-Connection or Self-
Concept Connection)
Aaker et al. 2004;
Swaminathan et al. 2007
This brand says a lot about the kind of person I
would like to be; This brand makes a statement
about what is important to me in life.
Brand Self-Expression
Carroll & Ahuvia 2006
This brand symbolizes the kind of person I really
am inside; The brand is an extension of my inner
self.
Brand Self-Relevance
Eisingerich & Rubera 2010
[Brand] means a great deal to me; I cannot imagine
life without [brand].
Ego Involvement
Beatty & Kahle 1988;
Beatty et al. 1988
I can make many connections or associations
between my use of [brand] and experiences in my
life; The brands I use say a lot about who I am.
Inclusion of Brand in Self
(IOS)
Reimann et al. 2012; Aron
et al. 1992
zipper scale
Psychological Distance
Choi & Winterich 2013
zipper scale
Self–Brand Congruence
(Actual, Ideal)
Malar et al. 2011
The personality of [brand] is consistent with how I
see myself (my actual self); The personality of
[brand] is a mirror image of the person I would like
to be (my ideal self).
Self–Brand Connection
Escalas & Bettman 2003;
Escalas 2004
I consider this brand to be 'me'; This brand reflects
who I am.
47
Table 2: Overview of Studies
Sample
Construal Variable
Psychological-Distance
Variable
DVs
Covariates
Moderators /
Mediators
Study 1:
Embedded
Construal
Undergrad
(N = 249)
International Standard:
Construal manipulation
embedded within brand
information.
Brand relationship:
Committed (close) vs.
Secret Affair (distant).
Brand
evaluations:
Attitudes, trust,
satisfaction
Age
Gender
Mediator:
Processing fluency
Replication
Study: Web
Appendix W7
MTurk
(N = 126)
“How vs. Why”:
Construal mindset
manipulation.
Brand relationship:
Committed (close) vs.
Secret Affair (distant).
Brand
evaluations:
Attitudes, trust,
satisfaction
Affect
Age
Gender
Study 2:
Donation
Study
Undergrad &
community
volunteers
(N = 156)
Charity co-brand:
Construal manipulation
embedded within brand
communications.
Measured
Donation
behavior
Age
Gender
Study 3:
Eliciting Close
vs. Distant
Brands
Undergrad
(N = 201)
International Standard:
Concreteness
manipulation embedded
within brand information.
Elicited brands via IOS
manipulation.
Brand
evaluations:
Attitudes, trust,
satisfaction
Age
Gender
*Moderators:
Search vs.
Experience
Brand Stereotypes
Study 4:
Field Study
Undergrad &
community
volunteers
(N = 140)
Trade show materials:
Concreteness
manipulation embedded
within brand
communications.
Measured
Purchase
behavior, email
engagement
Age
Gender
*Moderators:
Category
involvement
*Study 3 also included functional/symbolic, private/public brands, and political orientation (moderators included on an exploratory basis). Study 4 also included
usefulness/relevance of information (alternate mediator) and salesperson similarity (exploratory moderator).
48
Figure 1: Brand Relationship Type × Construal Level Effect for Search vs. Experience
Brands (Study 3)
Note: Error bars = +/- 1SEs.
* p < .07, ** p < .05, *** p < .01, ****p < .001
49
Figure 2: Brand Relationship Type × Construal Level Effect for Light vs. Heavy Users
(Study 5)
Note: Error bars = +/- 1SEs.
* p < .05, ** p < .01
