Respond to the following in a minimum of 350 words:
Customer satisfaction surveys are everywhere, but do they measure loyalty? Review the attached “Building, Measuring, and Profiting from Customer Loyalty” article. Briefly summarize key points from this article and discuss strategies for earning and measuring customer loyalty. (250 words)
How can marketing managers best promote consumer adoption of a new product? (100 words)
ORIGINAL EMPIRICAL RESEARCH
George F. Watson IV1 & Joshua T. Beck2 & Conor M. Henderson3 & Robert W. Palmatier4
Received: 5 September 2014 /Accepted: 23 March 2015 /Published online: 29 April 2015
# Academy of Marketing Science 2015
Abstract Achieving customer loyalty is a primary marketing
goal, but building loyalty and reaping its rewards remain on-
going challenges. Theory suggests that loyalty comprises atti-
tudes and purchase behaviors that benefit one seller over com-
petitors. Yet researchers examining loyalty adopt widely vary-
ing conceptual and operational approaches. The present inves-
tigation examines the consequences of this heterogeneity by
empirically mapping current conceptual approaches using an
item-level coding of extant loyalty research, then testing how
operational and study-specific characteristics moderate the
strategy → loyalty → performance process through meta-
analytic techniques. The results clarify dissimilarities in loy-
alty building strategies, how loyalty differentially affects per-
formance and word of mouth, and the consequences of study-
specific characteristics. Prescriptive advice based on 163
studies of customer loyalty addresses three seemingly simple
but very critical questions: What is customer loyalty? How is it
measured? and What actually matters when it comes to cus-
tomer loyalty?
Keywords Customer loyalty . Relationship marketing .
Content analysis . Meta-analysis . Word of mouth
Customer loyalty is the central thrust of marketing effort
s
(Dick and Basu 1994; Evanschitzky et al. 2012), and U.S.
firms spend dramatically to build and manage customer loy-
alty. For example, annual loyalty program outlays have grown
27% since 2010 to exceed $48 billion across 2.7 billion pro-
gram enrollees in the United States alone, yet less than half of
the 22 memberships per household are active (Berry 2013).
However, the financial returns of many loyalty-building ef-
forts fail to meet expectations (Henderson et al. 2011; Nunes
and Dréze 2006). Even though the concept of Bcustomer
loyalty^ has been debated for more 60 years (Brown 1952),
the mixed returns of loyalty efforts still stem, in part, from
divergent theoretical and operational approaches, such as the
varied use of attitudinal loyalty without behavioral loyalty or
the use of modified word-of-mouth measures as proxies for
customer loyalty (Dick and Basu 1994; Keiningham et al.
2007; Oliver 1999; Reinartz and Kumar 2002). To test the
consequences of this heterogeneity empirically, we synthesize
extant loyalty research to provide parsimonious guidance to
both academics and managers seeking to understand the strat-
egy → customer loyalty → performance process.
Although there is no consensus definition of loyalty, extant
research generally agrees that it represents a mix of attitudes
and behaviors that benefit one firm relative to its competitors
(Day 1969; Dick and Basu 1994; Melnyk et al. 2009). Within
* George F. Watson, IV
watson4g@uw.edu
Joshua T. Beck
beckjo@uc.edu
Conor M. Henderson
conorh@uoregon.edu
Robert W. Palmatier
palmatrw@uw.edu
1 University of Washington, 4295 E. Stevens Way NE, MacKenzie
Hall, Box 353226, Seattle, WA 98195-3200, USA
2 University of Cincinnati, 2925 Campus Green Dr., 106 Carl H.
Lindner Hall, Cincinnati, OH 4522-1106, USA
3 Lundquist College of Business, University of Oregon, 486 Lillis
Business Complex, Eugene, OR 97403-1208, USA
4 University of Washington, 4295 E. Stevens Way NE, 418 Paccar
Hall, Box 353226, Seattle, WA 98195-3200, USA
J. of the Acad. Mark. Sci. (2015) 43:790–825
DOI 10.1007/s11747-015-0439-4
this conceptual umbrella, researchers often selectively exam-
ine loyalty as an attitude, purchase behavior, or multidimen-
sional construct (e.g., attitudes and purchase behaviors, word
of mouth). Beyond this broad description there is significant
variation in the conceptualization and operationalization of
loyalty, which may explain heterogeneity in loyalty-related
effects. We map 163 studies published in marketing journals
since 1980, at the measurement item level, to capture their
heterogeneous research approaches. We then evaluate the de-
gree to which this heterogeneity leads to disparate empirical
results, by examining the structural relationships among loy-
alty, its antecedents, and its outcomes according to meta-
analytic data (Zablah et al. 2012). Finally, we examine possi-
ble moderators of the effect of loyalty on outcomes with a
meta-regression (Rubera and Kirca 2012).
The results of these analyses produce three main contribu-
tions. First, we reconcile the differential effects of two theo-
retical elements of customer loyalty—attitudes and behav-
iors—according to meta-analytic results. The antecedents dif-
ferentially build attitudinal and behavioral loyalty, and attitu-
dinal and behavioral loyalty differentially influence manage-
rially relevant outcomes such as word of mouth and perfor-
mance (i.e., sales, share of wallet, profit performance, and
other measurable changes). The common research practice
of using single-element measures of loyalty (i.e., only attitude
or behavior) thus leads to mixed guidance regarding the effect
of loyalty on performance. This concern is especially prob-
lematic when we note that most research (65% of studies in
our sample) examines loyalty as an end outcome, such that it
serves as a potentially misleading proxy for performance.
Second, we examine the moderating role of the measure-
ment composition (e.g., ratio of attitudinal vs. behavioral loy-
alty items, inclusion of word-of-mouth items) and study-
specific characteristics (e.g., temporal orientation) to explain
additional variance in loyalty-related effects, using a meta-
analysis of the sources of variation identified by our literature
review and item-level coding procedure. We find for example
that measures composed of combined attitudinal and behav-
ioral items are more effective than attitude-only or behavior-
only measures. Varying measurement compositions and
study-specific characteristics of loyalty also produces different
effects, depending on the type of loyalty and the outcome of
interest. These analyses help clarify the heterogeneity in loy-
alty effects that stem from conceptualizations and the study
context.
Third, we provide prescriptive guidance for researchers and
managers. In particular, we recommend strategies that build
attitudinal and behavioral loyalty, the use of loyalty items
obtained from the measures that are most predictive of perfor-
mance and word of mouth, and consideration of contextual
information (e.g., business vs. consumer markets) that can
leverage the effect of customer loyalty. We thus attempt to
add clarity to the varied conceptual and empirical findings
achieved through decades of research by answering three sim-
ple but important bquestions: What is customer loyalty? (the-
oretical), What are researchers doing? (measurement ap-
proaches), and What actually matters? (empirical results).
Theoretical domain of customer loyalty
The rich early history of customer loyalty research allowed
Jacoby and Chestnut (1978) to cite more than 50 definitions of
it. Following more recent, elaborate theoretical expositions
(Dick and Basu 1994; Oliver 1999), current theories most
often delineate attitudinal loyalty and behavioral loyalty as
customer loyalty’s primary elements (Chaudhuri and
Holbrook 2001).
Loyalty as favorable attitudes and purchase behaviors
Attitudes are the first element of customer loyalty. People are
motivated information processors who use information to
form their attitudes (Ahluwalia 2000; Moorman et al. 1993).
Attitudinal loyalty then is a Bcognition^ or Bpleasurable
fulfillment^ that favors a particular entity (Oliver 1999, p.
35; see also Chaudhuri and Holbrook 2001). Strong, loyal
attitudes result from systematic evaluations (Petty and
Cacioppo 1986) and influence many customer performance–
related behaviors (Park et al. 2010; Petty, Haugtvedt, and
Smith 1995). Strong positive attitudes induce Bdefensive
processes^ in the face of competition that cause customers to
resist competitive offers, even when they are objectively better
(Ahluwalia 2000, p. 230). Oliver (1999, p. 34) captures this
idea when he notes that loyalty persists Bdespite situational
influences and marketing efforts [that have] the potential to
cause switching behavior.^
Purchase behaviors, the second element of loyalty, are also
central in loyalty research (Ailawadi et al. 2008; De Wulf et al.
2001). Behavioral loyalty entails repeated purchases that stem
from a conation or action orientation involving a Breadiness to
act^ to the benefit of a particular entity (Oliver 1999, p. 35; see
also Chaudhuri and Holbrook 2001; De Wulf et al. 2003).
Various examinations of customer loyalty focus on measuring
behaviors, such as repeated purchase behaviors, that have ob-
vious benefits for a firm’s financial performance. This view
has given rise to stochastic models of loyalty, including the
use of recency, frequency, monetary theory; churn/retention;
and purchase sequences. Prior research also demonstrates the
benefits of behavioral loyalty. For example, Gupta et al.
(2004) find that the impact of a 1% improvement in customer
retention is five times greater than the effects of a similar
increase in margin. Yet purely behavioral approaches are often
agnostic about the psychological processes associated with
customer action. They ignore the real possibility that repetitive
purchase behavior arises from situational constraints, such as a
J. of the Acad. Mark. Sci. (2015) 43:790–825 791
lack of viable alternatives, or usage situations, such as habit
(Henderson et al. 2011). Regardless of their cause, customer
behaviors can explain financial outcomes as loyalty-based
purchase activities.
Measurement composition and study-specific
characteristics in loyalty research
Despite clear delineations between attitudes and purchase be-
haviors, theories of customer loyalty suggest both are integral
(Dick and Basu 1994; Oliver 1999). Furthermore, some re-
searchers characterize loyalty as a general orientation reflected
by non-purchase behaviors, such as advocacy (Jones et al.
2008), willingness to pay a premium (Chaudhuri and
Holbrook 2001), or continued silence in the hope that things
get better (Hirschman 1970). As a result, loyalty measures are
often fuzzy; despite its duration, literature on loyalty presents
ad hoc measures that are sometimes composed of attitudinal
items, sometimes composed of behavioral items, or both—or
even both together with items that measure ancillary con-
structs. For example, many researchers include word of mouth
(WOM) items in their operationalization of customer loyalty
(Evanschitzky et al. 2012), despite both theoretical (Dick and
Basu 1994) and empirical (de Matos and Rossi 2008;
Söderlund 2006) arguments for their separation.
In addition, researchers examine customer loyalty in vari-
ous research settings, creating research-specific measurement
characteristics that may exert influences on results. We con-
sider two key characteristics: temporal orientation and target.
Temporal orientation refers to whether loyalty is measured as
a past account or future predictions of loyal attitudes and be-
haviors. For example, a customer might be asked to recall
previous instances of being loyal (Ailawadi et al. 2008;
Davis-Sramek et al. 2009) or else estimate future intentions
to be loyal (Johnson et al. 2006; Wagner et al. 2009). The
target is the attribution customers make about to whom or
what they are loyal. For example, loyalty may be Bowned
by^ or directed toward the selling firm or a salesperson
(Palmatier et al. 2007). We consider the effects of both mea-
surement composition and study-specific characteristics in the
subsequent sections.
Conceptual model and hypotheses
To understand how loyalty may vary depending on its
operationalization, we first consider how linkages in the ante-
cedents → customer loyalty → outcomes framework vary
depending on the use of attitudinal or behavioral loyalty. By
examining attitudinal and behavioral loyalty as separate me-
diators, we can isolate relative differences in both their main
and interaction effects. Therefore, we evaluate how the theory
underlying each element supports distinct predictions. We
only include constructs in our conceptual framework if prior
studies have demonstrated at least three empirical links be-
tween the antecedent and attitudinal and behavioral loyalties,
to enable our tests of the differences through meta-analysis.
We also include measures that feature both attitudinal and
behavioral loyalties to support moderation analyses. We iden-
tified four antecedents (commitment, trust, satisfaction, and
loyalty incentives) and two outcomes (WOM and perfor-
mance) that meet these criteria. Table 1 summarizes the con-
structs included in the model, their definitions, and common
aliases. Furthermore, we evaluate other sources of heteroge-
neity identified in our literature review (measurement compo-
sition, study-specific characteristics) and probe for additional
sources of heterogeneity by coding study-level factors to test
their effects on loyalty-to-outcome linkages. Consistent with
our research questions, our hypotheses focus on comparative
differences among the links in our model.
Loyalty antecedents
Commitment, trust, satisfaction, and loyalty incentives (e.g.,
reward programs, perks, favorable treatment) have all been
positively linked to customer loyalty, but we expect them to
have differential effects on attitudinal and behavioral loyalties.
Attitudinal loyalty results from positive evaluations of a seller
based on previous exchange experience (Brakus et al. 2009;
Liu-Thompkins and Tam 2013). Drivers of loyalty that pri-
marily enhance a customer’s evaluation of the exchange
should have a stronger effect on attitudinal than on behavioral
loyalty. Conversely, behavioral loyalty results from situational
triggers and habit (Gustafsson et al. 2005; Johnson et al.
2006), which may not involve a strong attitudinal component.
Thus drivers of loyalty that primarily operate as situational
triggers in an exchange should have a stronger effect on be-
havioral than on attitudinal loyalty. Commitment, or the desire
to maintain a valued relationship (Moorman et al. 1992), trust,
which is confidence in the reliability and integrity of a seller
(Morgan and Hunt 1994), and satisfaction, which is the per-
ceived difference between prior expectations and actual per-
formance (Tse and Wilton 1988), all contribute to a customer’s
positive experience. Commitment and trust create the sense
that customers are in a pleasurable relationship rather than a
passing transaction (Palmatier et al. 2006); satisfaction pro-
vides a comparative basis (prior expectation versus actual ex-
perience) on which to develop attitudes (Geyskens and
Steenkamp 2000). Therefore, commitment, trust, and satisfac-
tion should have stronger effects on attitudinal loyalty than on
behavioral loyalty. Alternatively, loyalty incentives are addi-
tional Bextrinsic^ enticements meant to encourage repeat pa-
tronage (De Wulf et al. 2001), so they might operate as repur-
chase reminders that reduce effortful purchase considerations
and encourage habitual purchasing or as rewards for the pos-
itive behavior of repurchasing (Henderson et al. 2011). Thus
792 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
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J. of the Acad. Mark. Sci. (2015) 43:790–825 793
we expect loyalty incentives to operate primarily through be-
havioral rather than attitudinal loyalty. Finally, as the theory of
planned behavior predicts and prior research demonstrates, we
expect attitudinal loyalty to affect behavioral loyalty positive-
ly (Ajzen and Fishbein 1980; Chaudhuri and Holbrook 2001).
H1: (a) Commitment, (b) trust, and (c) satisfaction have
stronger positive effects on attitudinal loyalty than on
behavioral loyalty.
H2: Loyalty incentives have stronger positive effects on be-
havioral loyalty than on attitudinal loyalty.
H3: Attitudinal loyalty positively affects behavioral loyalty.
Loyalty outcomes
We anticipate that the evaluation- and action-based mecha-
nisms underlying attitudinal and behavioral loyalties differen-
tially influence WOM and performance outcomes. Because
attitudinal loyalty is associated with positive evaluations of a
seller, and evaluations are abundant and easy to communicate,
the effect of attitudinal loyalty should be strongest for WOM.
Behavioral loyalty instead might not include a strong, acces-
sible attitudinal component, which provides the basis for
WOM (Berger and Schwartz 2011). Therefore, the effect of
behavioral loyalty on WOM should be weaker than that of
attitudinal loyalty. For performance, we expect an opposite
pattern of effects. Attitudinal loyalty tends to be based on
conformity (Berger and Heath 2008) and may exist despite
situational constraints (e.g., financial, location) that impede
actual purchases (i.e., loyalty can be aspirational), so we ex-
pect its effect on performance to be weaker. Instead, behav-
ioral loyalty, which is based on a conation or readiness to act
and is tied directly to purchase, should have a stronger effect
on performance.
H4: The effect of attitudinal loyalty on WOM is greater than
the effect of behavioral loyalty.
H5: The effect of behavioral loyalty on performance is great-
er than the effect of attitudinal loyalty.
Role of measurement composition and study-based
characteristics
Researchers use different compositions of loyalty measures,
which should moderate the effect of loyalty on outcomes.
Dick and Basu (1994) provide a strong conceptual argument
that neither a relatively high attitude nor a behavioral inclina-
tion to purchase repeatedly are sufficient to capture customer
loyalty fully. Customers with high attitudinal loyalty go to
greater lengths to support a seller, and their cognitive biases
help them resist competitive persuasion attempts through
mechanisms such as avoidance or counterarguments
(Ahluwalia 2000; Park et al. 2010). However, these customers
also may lack the ability or opportunity to support the seller
(e.g., financial constraints). Behavioral loyalty directly in-
creases seller revenues through frequent repurchasing and
demonstrates the customer’s ability and opportunity to sup-
port the seller. However, these benefits may be short lived if
customers lack the motivation to continue their purchase be-
haviors when their environment changes. Behaviorally loyal
customers with low attitudinal loyalty also may be more likely
to exploit their relative importance and seek to extract extra
concessions from the seller. Thus, customer loyalty should
capture both behavioral and attitudinal aspects, to reflect cus-
tomers’ desire, opportunity, and ability to support the seller
financially while avoiding competitors. Because measures
that combine attitudes and behaviors capture the variance
accounted for by each aspect of loyalty, we expect combined
measures of loyalty to exert a stronger effect on performance
outcomes than either attitudinal or behavioral measures
alone.
Yet broad measures of loyalty that include WOM items
might attenuate the predictive effect of loyalty on performance
outcomes, because WOM is a socially complex phenomenon
that involves self-image concerns, consideration for others’
interests, and serendipitous accessibility (Berger and
Schwartz 2011; De Matos and Rossi 2008; Söderlund 2006).
Therefore, even though WOM and loyalty are correlated,
measures of loyalty that include WOM items may capture
unrelated or even countervailing effects (e.g., customers may
be very loyal to a condom company but unlikely to recom-
mend it), so they will be less effective at predicting overall
performance.
H6: Measures of loyalty that include both attitude and be-
havioral items have a stronger positive effect on WOM
than separate measures of attitudinal or behavioral
loyalty.
H7: Measures of loyalty that include both attitude and be-
havioral items have a stronger positive effect on perfor-
mance than separate measures of attitudinal or behav-
ioral loyalty.
H8: The positive effect of loyalty on performance appears
weaker when loyalty measures include WOM.
We also find systematic variation in the characterization of
customer loyalty across extant literature, particularly in terms
of the temporal orientation and loyalty target(s). Although
research questions or contexts might restrict researchers’
choices of forward-looking versus backward-looking loyalty
measures, we expect backward-looking loyalty measures to
exhibit a stronger effect on both objective performance and
positive WOM. First, backward-looking loyalty assessments
tend to be more accurate, because customers avoid the
794 J. of the Acad. Mark. Sci. (2015) 43:790–825
difficulty of imagining obstacles (e.g., price) that might inter-
fere with future purchase behaviors (Ajzen 2002; Zimbardo
and Boyd 1999). Second, forward-looking loyalty relies on
top-of-mind factors, whereas backward-looking loyalty bene-
fits from subtle psychological mechanisms that offer powerful
predictors of future behavior (e.g., cognitive dissonance, hab-
it, sunk costs, switching costs; Henderson et al. 2011).
Therefore, both attitudinal and behavioral loyalty should have
greater influences on WOM and firm performance when mea-
sured as backward-looking rather than forward-looking
orientation.
H9: Backward-looking loyalty exhibits a stronger effect on
(a) objective performance and (b) word of mouth than
does forward-looking loyalty.
Although targets of loyalty frequently vary, research
often ignores the potential implications of this variance.
Loyalty to the firm should be a better predictor of objec-
tive performance than loyalty to a salesperson, for several
reasons (Palmatier et al. 2007). First, loyalty to a sales-
person creates dangers, in that salespeople frequently
change positions, so the time when firms can benefit from
this loyalty is relatively short; this loyalty also might
transfer to a competitor if the salesperson switches firms.
Second, salespeople may act opportunistically as agents
of the firm and offer their favorite customers unnecessary
discounts or perks to gain personal favor (Palmatier et al.
2009). Third, the salespeople to whom the customer is
most loyal represent only a limited portion of the firm’s
total offering, such that customers usually must deal with
multiple salespeople, brands, and locations. This narrow
representation restricts the benefits that might accrue if
the same amount of loyalty were directed to the firm as
a whole. Thus, we expect firm loyalty to have a greater
impact on performance than salesperson loyalty does.
However, loyalty to a salesperson may be a better predictor
of WOM, because customers can be more confident that
others following their recommendations will enjoy a similarly
positive experience if they recommend a specific salesperson.
Customers view salespeople as more consistent or entatitive
targets than firms, which comprise multiple salespeople,
brands, and locations. Thus, when assessing an individual as
opposed to a firm, Bcustomers are quicker to form judgments,
believe the judgments more strongly, and are more likely to
act on the beliefs^ (McConnell et al. 1997, p. 759). Loyalty to
salespeople therefore should have a greater impact on positive
WOM than loyalty to a firm, because it is based on a more
consistent target.
H10a: The positive effect of customer loyalty on objective
performance increases when loyalty targets the firm
rather than the individual salesperson.
H10b: The positive effect of customer loyalty on word of
mouth increases when loyalty targets the individual
salesperson rather than the firm.
Empirical study
To determine BWhat is customer loyalty?^ we begin by de-
scribing our data collection process. Then, to investigate
BHow is loyalty measured?^ and BWhat actually matters?^
we code the composition of the measurement items that we
find in individual studies and perform a random effects meta-
analysis of reliability adjusted, r-to-Z–transformed correla-
tions between the constructs in our conceptual model. With
these meta-analytic data, we perform a structural path analysis
(Model 1), followed by a multivariate moderation analysis, or
Bmeta-regression^ (Models 2 and 3), to test our hypotheses.
Methodology
Data sample and criteria for inclusion We used several ap-
proaches to identify potential studies for inclusion in our anal-
yses. In the search process, we referred to the EBSCO data-
base and reviewed journals ranked in the highest tier
(Polonsky and Whitelaw 2006), namely, Journal of
Marketing, Journal of Marketing Research, Marketing
Science, Journal of the Academy of Marketing Science,
Journal of Consumer Research, and Journal of Retailing, dur-
ing 1980–2013. For each article of each volume of these
journals we evaluated whether the authors measured any con-
struct with Bloyalty^ in its name (e.g., Bconsumer loyalty,^
Bcustomer loyalty,^ attitudinal loyalty,^ Bbehavioral loyalty^),
with the exception of employee loyalty or similar unrelated
constructs. We then performed a more targeted search of these
and related journals, dissertations, and working papers using
the Business Source Premier EBSCO, Social Science
Research Network (SSRN), ABI/Informs, and PsychINFO
global databases. To find studies that investigated issues relat-
ed to customer loyalty, we used search terms such as Bloyalty,
^ Battitudes,^ Brepurchase,^ and related synonyms (see
Table 1) across all scholarly, peer-edited marketing and man-
agement journals and dissertations that were electronically
available. Finally, we inspected the reference lists of the major
narrative and empirical reviews of customer loyalty and relat-
ed research to identify any potentially missing studies.
The criteria for inclusion required that survey-based studies
provide the exact item wording and observational studies pro-
vide exact measurement definitions, or else reference to their
origins. So that we could examine the empirical implications
of heterogeneity across loyalty research, we included a study
if it reported a Pearson correlation coefficient or other statisti-
cal information (e.g., β, univariate F, t-statistics, χ2) that we
J. of the Acad. Mark. Sci. (2015) 43:790–825 795
could use to calculate a correlation coefficient according to the
formulas provided by Hunter and Schmidt (1990) or Peterson
and Brown (2005).
Our sample does not include studies that rely on choice
modeling to estimate the extent to which loyalty exists in a
particular context. Such studies often estimate the amount of
brand/retail patronage loyalty by detecting a brand–house-
hold-specific utility (e.g., Guadagni and Little 1983; Horsky
et al. 2006). This stream of literature is large and well under-
stood, but the estimates of brand loyalty are rarely tied to other
theoretical constructs, which precludes them from entering
our analyses.
Item-level coding We examined the exact item wording of
the measures or the citation that provided the source of the
measures to categorize the type of loyalty studied by the re-
searcher accurately. We extracted, coded, and categorized
each measurement item (i.e., questions responded to by each
study’s participants, objective measures), following a
predefined set of rules (Kolbe and Burnett 1991).
Definitions that reflect the types and elements of customer
loyalty outlined in our prior theoretical review are summa-
rized in Table 1. Guided in part by our literature review, we
deconstructed each sentence and coded the language of the
items in each study for common content (e.g., attitudes, be-
haviors, WOM, temporal orientation, target). Two coders in-
dependently evaluated each article. Fewer than 6% of the ef-
fects differed across the double coding procedures, and dis-
agreements were resolved through discussion.
With this procedure, we could examine the bundle of items
that each researcher chose to measure loyalty and thereby
determine their choice of loyalty conceptualization and the
extent to which they maintained content validity. The insights
from this content analysis stemmed from our determination of
how each sample conceptualized loyalty, using (1) only atti-
tudinal measures, (2) only behavioral measures, (3) both atti-
tudinal and behavioral measures as separate constructs, or (4)
attitudinal and behavioral measures in the same scale as a
single construct. The loyalty conceptualization thus reflects
the researchers’ choice and bundling (e.g., mean average) of
items. Table 7 in the Web Appendix provides a summary of
the final database of 163 studies and their corresponding cod-
ing decisions.
Meta-analysis To examine the differential effects of attitudi-
nal and behavioral loyalty and to maintain construct validity,
we used a subsample that included attitude-only and behavior-
only measures (i.e., no mixed or Binclusive^ loyalty mea-
sures). In addition, we included constructs only if we had at
least three effects between each construct and all other
constructs in the model in our effort to develop the input
correlation matrix (Palmatier et al. 2006). Thus, the structural
path analyses used to test H1–H5 were based on 126 studies,
151 separate samples, and 713 effects using attitude-only and
behavior-only measures of loyalty.
To this subsample, we applied meta-analytic techniques to
generate a correlation matrix of all constructs to use as input
for the structural path models (Rubera and Kirca 2012; Zablah
et al. 2012). After compiling the data, we adjusted every cor-
relation for reliability (attenuation correction) by dividing it by
the product of the square root of the reliabilities of the two
constructs (Hunter and Schmidt 2004). We transformed the
reliability-corrected correlations into Fisher’s z-coefficients,
and then performed a random-effects meta-analysis on the
Fisher z-coefficients. Following standard procedures
(Shadish and Haddock 2009), we next transformed the z-
scores back to r-correlations to obtain the revised, sample-
weighted, reliability-adjusted correlation coefficients and
95% confidence intervals with associated t-statistics (Hedges
and Olkin 1985; Zablah et al. 2012). The random-effects ap-
proach provides more realistic, less inflated estimates of aver-
age effect sizes; accounts for variability in true effect sizes
across studies; and is generalizable to a population of potential
studies (Raudenbush 2009). We addressed the potential prob-
lem of selective publication bias in several ways. First, we
computed and report the Q statistic (d.f.=n–1) test of homo-
geneity (Rosenthal 1979). Second, we tested for publication
bias with funneling and trim-and-fill analyses (Homburg et al.
2012), neither of which suggested publication bias was an
issue.
Structural path analysis Meta-analytic correlations be-
tween all constructs in the model produced by the anal-
ysis and formed into a meta-analytic correlation matrix
provided the input for the structural path analyses in
Mplus 7.11 (Zablah et al. 2012). Our conceptual model
includes commitment, trust, satisfaction, and loyalty in-
centives as antecedents and WOM and performance as
outcomes. Model 1 tests our hypotheses in the presence
of paths from all antecedents to both attitudinal and be-
havioral loyalties and with both loyalties linked to both
outcomes, such that attitudinal loyalty is modeled as an
antecedent of behavioral loyalty. All the constructs are
observed variables, antecedents may covary, and we used
the harmonic mean (n=5671) across all correlations as
the sample size (Rubera and Kirca 2012). Our choice
to use the harmonic mean provides more conservative
testing than the use of the arithmetic mean or median
because it give less weight to substantially large cumu-
lative samples sizes typical of meta-analyses, and as such
is a common and strongly recommended decision for
meta-analytic structural equation models (Pick and
Eisend 2014). We examined within-model hypothesized
differences in path coefficients by setting the correspond-
ing paths to be equal and testing for significance using
Wald Chi-square tests.
796 J. of the Acad. Mark. Sci. (2015) 43:790–825
Multivariate moderation analysis To examine the moderat-
ing effects of sources of heterogeneity, we expanded the sam-
ple that we used for the structural path analysis to include
Binclusive^ measures of loyalty. With this approach, we can
compare the effects of attitudinal-only, behavioral-only, and
various inclusive forms of customer loyalty elements with a
hierarchical linear model (HLM), in which the effects are
nested within studies. We performed our analysis by
regressing the moderator variables on correlations (meta-
regression) to account for within-study error correlation be-
tween effect sizes (Homburg et al. 2012; Rubera and Kirca
2012), coding for the specific qualities of each construct we
examine. To evaluate how the effect of loyalty on WOM and
performance varies across (1) loyalty aspects (H4–H7), (2)
other conceptual features (H8–H10; WOM inclusive, tempo-
ral orientation, and target), and (3) study-level factors that
served to test robustness (e.g., business vs. consumer markets,
common method susceptibility), we included studies with any
type of loyalty that reported an effect on either WOM or per-
formance. By including all forms of loyalty in this analysis,
we established a sample that is sufficiently large to support
tests of all moderation effects simultaneously while also ac-
counting for loyalty aspects. Thus, the multivariate HLM
moderation analyses were based on 32 (30) studies, 41 (32)
separate samples, and 68 (57) effects for WOM (perfor-
mance), using all measures of loyalty.
As a replication and robustness test, we also evaluated the
differences between loyalty operationalizations by dummy
coding an effect as equal to 1 if the loyalty measure was a
mixture of attitudes and behaviors in a single scale, consistent
with 35% of our sample, and 0 if it included only attitudes or
behaviors. In addition, as an alternative test of H4 and H5, we
captured the moderating effect of the mixture of attitudinal
and behavioral items by coding the ratio of attitudinal to total
items used in the measure of loyalty (1 = all attitudinal, 0.5 =
half attitudinal and half behavioral, 0 = all behavioral). These
two moderators reflect extant research, where (0, 0) indicates
behavioral loyalty, (0, 1) is attitudinal loyalty, and (1, ratio)
serves as a loyalty proxy with varying mixtures of attitudinal
and behavioral items, as is common.
We also evaluated other conceptual features by dummy
coding whether a loyalty effect was WOM inclusive to test
H8 (1 = measure included at least one WOM item, 0 = mea-
sure did not include any WOM items). To evaluate the mod-
erating effect of temporal orientation, we coded the ratio of
forward-looking to total loyalty items for each study effect to
test H6 (1 = all forward-looking, 0.5 = half forward- and half
backward-looking, 0 = all backward-looking). For the moder-
ating effect of the target, we dummy coded the measure for
each study effect for the loyalty target to test H10 (1 = indi-
vidual salesperson, 0 = ambiguous, −1 = the firm).
Finally, to understand and control for potential influences
of study-based features on our results, we coded and tested the
moderating effects of six study characteristics (Albers et al.
2010; Homburg et al. 2012; Sethuraman et al. 2011).
Specifically, we used a dummy to code whether a loyalty
effect was from business or consumer markets (1 = business,
0 = ambiguous, −1 = consumer), pertained to brand loyalty (1
= Bbrand^ was included in the construct name, context, or any
items, 0 = not brand-related in any way), appeared in an un-
published journal article or dissertation (1 = unpublished or
dissertation, 0 = published), and was susceptible to common
method bias (1 = used a method or sample susceptible to
common method bias, 0 = not susceptible to common method
bias). To account for the last two study features, we used
mean-centered continuous coding schemes. We evaluated
any longitudinal effects according to the four-digit year of
the study’s publication date; for journal quality, we coded
the Journal Eigenfactor Scores for the source of each loyalty
effect, such that unpublished journal and dissertation effects
were assigned the sample mean value (West and Bergstrom
2013).
Results
Item-level content analysis Table 2 shows the breakdown of
the loyalty constructs in our sample, coded at the item level,
which helps address our second research question. Overall, we
find that though many researchers maintain attitudinal loyalty
and behavioral loyalty as separate constructs (65% of studies),
a substantial set combine attitudinal and behavioral items to-
gether (35% of studies). Furthermore, many researchers ex-
amine only behavioral (43% total) or attitudinal (7%) loyalty,
but only 15% examine both attitudinal and behavioral loyal-
ties in the same study, as separate constructs. Studies with
behavior-only or attitude-only measures of loyalty, typically
labeled Bloyalty^ without qualifiers, potentially misrepresent
the effects of the antecedents on loyalty and loyalty’s impact
on outcomes, because attitudes and behaviors have divergent
effects (Baker et al. 2002; Brexendorf et al. 2010; Burton et al.
1998; Maxham and Netemeyer 2002).
Among the 35% of researchers examining customer loyalty
using both attitudinal and behavioral measures in the same
construct, more than half (18% of total sample) operationalize
loyalty with WOM as an indicator of loyalty (Table 2, Panel
A). Although WOM and loyalty are related, the prevalence of
WOM as a measure of customer loyalty conflicts with both
theoretical (Dick and Basu 1994) and empirical (De Matos
and Rossi 2008; Söderlund 2006) arguments for their separa-
tion. In addition, the use of forward- versus backward-looking
loyalty measures is unevenly divided across studies, at 59%
and 41%, respectively (Table 2, Panel B). Prior research
shows that the abstract cognitive processes of reconstructing
the past and constructing the future influence decision making
(Zimbardo and Boyd 1999), so the selection of a particular
temporal measurement may create variance in empirical
J. of the Acad. Mark. Sci. (2015) 43:790–825 797
results. The selling firm is the primary target of customer
loyalty (72%), with the remainder of the research attention
divided approximately equally between loyalty to a brand
(15%) and to an individual salesperson (13%). However, re-
searchers typically fail to qualify or acknowledge the loyalty
target (Table 2, Panel C), even though firm- and salesperson-
Table 2 Item-level content analysis
Attitudinal and behavioral
loyalty mixed 35%
(18% WOM inclusive)
Attitudinal and
behavioral loyalty
separately
15%
Behavioral
loyalty only
43%
Attitudinal
loyalty only
7%
Forward-
looking
loyalty
59%
Backward-
looking
loyalty
41%
Individual
13%
Brand
15%
Firm
72%
The underlined terms highlight the coding criteria at the item level
798 J. of the Acad. Mark. Sci. (2015) 43:790–825
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8
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(7
)
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rd
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f
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h
1
2
84
8
6
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4
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76
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0.
06
0.
4
4
0.
63
23
3.
2
6
(1
1)
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at
is
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ct
io
n
↔
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oy
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ty
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ce
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ve
s
3
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7)
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pe
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or
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an
ce
3
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1
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7
3.
94
**
*
0.
04
0.
0
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0.
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10
,5
3
3.
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(3
2)
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o
rd
o
f
m
o
ut
h
3
0
25
,1
72
0
.5
5
10
.2
9
*
**
0.
06
0.
4
6
0.
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19
71
.9
9
(2
9)
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oy
al
ty
in
ce
nt
iv
es
↔
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ti
ve
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or
m
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ce
2
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,4
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6
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0.
1
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0.
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58
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.4
4
(2
3)
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rd
o
f
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o
ut
h
1
4
87
7
5
0
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3
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85
**
*
0.
05
0.
1
3
0.
32
10
7.
4
6
(1
3)
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bj
ec
ti
ve
pe
rf
or
m
an
ce
↔
W
o
rd
o
f
m
o
ut
h
2
4
71
6
2
0
.2
6
2.
20
*
0.
12
0.
0
2
0.
47
99
0.
3
0
(2
3)
*
p
<
.1
0
;
*
*
p
<
.0
5
;
*
*
*
p
<
.0
1
N
ot
es
:M
ea
n
co
rr
el
at
io
n
s
ca
lc
u
la
te
d
u
si
n
g
r
a
n
d
o
m
ef
fe
ct
s
m
o
de
l.
R
ep
o
rt
ed
co
rr
el
at
io
n
s
w
er
e
ad
ju
st
ed
fo
r
at
te
n
u
at
io
n,
co
n
v
er
te
d
to
F
is
he
r’
s
Z
to
st
ab
il
iz
e
v
ar
ia
n
ce
,a
n
d
t
h
en
co
n
v
er
te
d
b
ac
k
to
r
fo
r
re
p
o
rt
in
g
p
u
rp
o
se
s.
C
.I
.
=
co
n
fi
d
en
ce
in
te
rv
al
J. of the Acad. Mark. Sci. (2015) 43:790–825 799
based loyalty reflect different decision processes that differen-
tially affect performance (Palmatier et al. 2007).
Meta-analysis and structural path analysis (Model
1) Table 3 contains the sample-size weighted mean meta-
analytic correlations, total Ns, number of effects, coefficient
t-values, 95% confidence intervals, and corresponding Q sta-
tistics among the antecedents, attitudinal and behavioral loy-
alty, and outcomes in our model. We used this information to
calculate our structural path analysis in Mplus 7.11. The mod-
ification indices suggested modifications to the initial hypoth-
esized model to improve fit, by allowing the conceptually
related constructs of commitment, trust, and satisfaction to
correlate with outcomes. Importantly, prior research supports
the inclusion of these links considering the well documented
influence of all three constructs on performance and word of
mouth (Morgan and Hunt 1994; Palmatier et al. 2006). These
changes resulted in acceptable fit statistics for Model 1: chi-
square (χ2 (2)) = 174.75, comparative fit index (CFI) = 0.99,
and standardized root mean residual (SRMR) = 0.04. Figure 1
shows the results of our final structural path analysis using
meta-analytic data (Model 1), with standardized beta coeffi-
cients and construct R-square values.
Examining our model in relation to our third research ques-
tion, we find that trust (βAtt=0.27 vs. βBeh=0.22, χ
2(1) =
16.41, p<.01) and satisfaction (βAtt=0.25 vs. βBeh=0.04,
χ2(1)=115.06, p<.01) have stronger positive effects on attitu-
dinal than on behavioral loyalty, in support of H1b and H1c,
respectively. However, commitment is equally powerful for
building both attitudinal and behavioral loyalty (βAtt=0.34
vs. βBeh=0.35, χ
2(1) = 2.80, p>.05), so we cannot confirm
H1a. Furthermore, loyalty incentives (βAtt=−0.08 vs. βBeh=
0.01 (n.s.), χ2(1) = 31.16, p<.01) do not have a significantly
stronger positive effect on behavioral loyalty than on attitudi-
nal loyalty, so we reject H2. Consistent with H3, attitudinal
loyalty has positive, significant impact on behavioral loyalty
(βAtt=0.10, χ
2(1) = 174.75). We also find support for H4, in
that the effect of attitudinal loyalty on WOM is stronger than
the effect of behavioral loyalty on WOM (βAtt=0.41 vs.
βBeh=0.19, χ
2(1) = 94.06). On the flipside, the effect of be-
havioral loyalty on performance is greater than that of attitu-
dinal loyalty (βAtt=0.02 (n.s.) vs. βBeh=0.27, χ
2(1) =
133.98), in support of H5.
Multivariate moderation analysis (Models 2 and 3) Table 4
shows the results of our two moderation analyses (WOM in
Model 2; performance in Model 3). By using an expanded
sample (all measures of loyalty linked to outcomes rather than
just attitude-only and behavior-only measures) for these two
paths and simultaneously controlling for other potential mod-
erators, we retested some hypotheses to increase confidence in
our findings related to our third research question, BWhat
matters?^ The positive effects of loyalty on WOM (β=0.33,
p<.05) and performance (β=−0.34, p<.05) are significantly
moderated in opposite directions by the ratio of attitudinal
items in the loyalty measure, corroborating our support for
H4 and H5. A higher percentage of attitudinal items enhances
the effect of loyalty on WOM while simultaneously suppress-
ing its effect on performance. Similarly, the positive effects of
loyalty on both WOM (β=0.31, p<.05) and performance (β=
0.11, p<.05) are significantly moderated by measures that use
a mix of attitudinal and behavioral items in the same construct
rather than attitude- and behavior-only loyalty measures, in
further support of H7 and H8. A non-significant intercept of
0.35 further reflects that the effect of loyalty on WOM is
dependent upon its conceptualization.
The positive effect of loyalty on performance decreases
(β=−0.44, p<.05) in loyalty measures that contain at least
one WOM item, which implies that WOM-inclusive loyalty
measures are less predictive of performance, in support of H6.
Commitment
Trust
Satisfaction
Loyalty
incentives
Attitudinal
loyalty
R = .542
Behavioral
loyalty
R = .402
Word of
mouth
R = .482
Performance
R = .142
Fig. 1 Results: structural path
model analysis (Model 1)
800 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
4
R
es
u
lt
s:
m
u
lt
iv
ar
ia
te
m
et
a-
re
g
re
ss
io
n
o
f
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o
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s
M
o
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er
at
o
r
v
ar
ia
b
le
s
H
y
p
o
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es
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ll
lo
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→
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M
(M
o
d
el
2
)
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ll
lo
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al
ty
→
p
er
fo
rm
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ce
(M
o
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el
3
)
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(S
E
)
α
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(S
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)
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te
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ep
t
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o
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lt
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ix
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(a
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al
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ra
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it
em
s)
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er
su
s
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re
as
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ts
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6
,
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7
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1
**
*
(0
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4
)
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1
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(
0
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)
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.
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at
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f
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ti
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al
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it
em
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ty
it
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4
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(0
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(0
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9
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th
er
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n
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at
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re
s
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.
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in
cl
u
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ve
lo
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ty
H
8
–
–
−0
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4
*
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(0
.0
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)
4
.
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em
po
ra
l
or
ie
n
ta
ti
o
n
o
f
lo
ya
lt
y:
ra
ti
o
o
f
fo
rw
ar
d
-l
o
o
k
in
g
lo
y
al
ty
to
to
ta
l
it
em
s
H
9
0
.1
3
**
*
(
0
.0
2
)
−0
.2
0
*
*
*
(0
.0
5
)
5
.
T
ar
g
et
o
f
lo
y
al
ty
:
in
d
iv
id
u
al
v
er
su
s
fi
rm
lo
y
al
ty
H
1
0
0
.1
0
(0
.0
8
)
0
.0
3
(0
.0
2
)
S
tu
d
y
–
b
as
ed
fe
at
u
re
s
6
.
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u
si
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s
v
er
su
s
c
o
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su
m
er
m
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k
et
s
0
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4
*
(0
.0
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)
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.0
6
(0
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)
7
.
B
ra
n
d
v
er
su
s
n
o
n
-b
ra
n
d
lo
y
al
ty
−0
.1
7
(0
.0
9
)
−0
.0
8
(0
.2
7
)
8
.
U
n
p
u
b
li
sh
ed
v
er
su
s
p
u
b
li
sh
ed
re
se
ar
ch
0
.2
2
(0
.1
9
)
0
.0
2
(0
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8
)
9
.
C
o
m
m
o
n
m
et
h
o
d
su
sc
ep
ti
b
il
it
y
0
.1
5
(0
.2
4
)
−0
.1
4
(0
.0
9
)
1
0
.
Y
ea
r
o
f
p
u
b
li
ca
ti
on
(m
ea
n
-c
en
te
re
d
)
0
.0
3
**
(
0
.0
1
)
0
.0
1
(0
.0
1
)
11
.
Jo
u
rn
al
q
u
al
it
y
u
si
n
g
jo
u
rn
al
ei
g
en
fa
ct
or
(m
ea
n
-c
en
te
re
d)
−0
.6
9
(1
.0
0
)
−0
.9
9
(1
.0
0
)
*
p
<
.1
0
;
*
*
p
<
.0
5
;
*
*
*
p
<
.0
1
N
ot
es
:
α
=
ra
n
d
o
m
ef
fe
ct
s
in
te
rc
ep
t,
β
=
ra
n
d
o
m
ef
fe
ct
s
re
g
re
ss
io
n
co
ef
fi
ci
en
t.
S
ig
n
if
ic
an
t
re
g
re
ss
io
n
co
ef
fi
ci
en
ts
in
d
ic
at
e
su
p
p
o
rt
fo
r
m
o
d
er
at
io
n
o
f
th
e
sp
ec
if
ie
d
re
la
ti
o
n
sh
ip
.
A
ll
m
o
d
er
at
o
rs
ra
n
si
m
u
lt
an
eo
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sl
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an
d
ar
e
sh
o
w
n
as
st
an
d
ar
d
iz
ed
co
ef
fi
ci
en
ts
C
o
d
in
g
1
.
1
=
co
n
st
ru
ct
m
ix
ed
b
o
th
at
ti
tu
d
in
al
an
d
b
eh
av
io
ra
l
it
em
s
in
si
n
g
le
m
ea
su
re
;
0
=
o
n
ly
at
ti
tu
d
in
al
o
r
o
n
ly
b
eh
av
io
ra
l
it
em
s
2
.
1
=
1
0
0%
at
ti
tu
d
in
al
it
em
s;
0
=
1
0
0
%
b
eh
av
io
ra
l
it
em
s
3
.
1
=
co
n
st
ru
ct
in
cl
u
d
es
W
O
M
it
em
(s
);
0
=
n
o
W
O
M
it
em
s
4
.
1
=
1
0
0%
fo
rw
ar
d
-l
o
o
k
in
g
it
em
s;
0
=
1
0
0%
b
ac
k
w
ar
d
-l
o
o
k
in
g
it
em
s
5
.
1
=
lo
y
al
ty
ta
rg
et
is
an
in
d
iv
id
u
al
;
0
=
ta
rg
et
w
as
am
b
ig
u
o
u
s;
−1
=
lo
y
al
ty
ta
rg
et
is
a
fi
rm
6
.
1
=
b
u
si
n
es
s-
to
-b
u
si
n
es
s
m
ar
k
et
st
u
d
y
;
0
=
am
b
ig
u
o
u
s
m
ar
k
et
st
u
d
y
;
−1
=
b
u
si
n
es
s-
to
-c
o
n
su
m
er
m
ar
k
et
st
u
d
y
7
.
1
=
Bb
ra
n
d^
w
as
in
cl
u
d
e
in
co
n
st
ru
ct
n
am
e,
co
n
te
x
t,
o
r
an
y
it
em
s;
0
=
n
o
t
b
ra
n
d
-r
el
at
ed
in
an
y
w
ay
8
.
1
=
u
n
pu
b
li
sh
ed
o
r
d
is
se
rt
at
io
n
;
0
=
p
u
b
li
sh
ed
in
p
ee
r
ed
it
ed
jo
u
rn
al
9
.
1
=
st
u
d
y
u
se
d
sa
m
p
le
/m
et
h
o
d
o
lo
g
y
su
sc
ep
ti
b
le
to
co
m
m
o
n
m
et
h
o
d
b
ia
s;
0
=
u
n
li
k
el
y
su
sc
ep
ti
b
le
to
co
m
m
o
n
m
et
h
o
d
b
ia
s
1
0
.
F
o
u
r-
d
ig
it
y
ea
r
in
w
h
ic
h
st
u
d
y
w
as
re
p
o
rt
ed
11
.
Jo
u
rn
al
ei
g
en
fa
ct
o
r
sc
o
re
s
fo
r
th
e
jo
u
rn
al
o
f
ev
er
y
lo
y
al
ty
ef
fe
ct
(h
tt
p:
//
w
w
w
.e
ig
en
fa
ct
o
r.
o
rg
/)
.
U
n
p
u
bl
is
h
ed
/d
is
se
rt
at
io
n
ef
fe
ct
s
=
sa
m
p
le
m
ea
n
J. of the Acad. Mark. Sci. (2015) 43:790–825 801
http://www.eigenfactor.org/
We also find support for H9a and H9b, focused on the tempo-
ral orientation of loyalty, because the effects of loyalty on
WOM (β=0.13, p<.05) and performance (β=−0.20, p<.05)
are significantly moderated in opposite directions by the ratio
of forward-looking items in the loyalty measure. A higher
percentage of forward-looking, relative to backward-looking,
loyalty items enhances the effect of loyalty on WOM while
simultaneously suppressing its effect on performance.
However, we did not find support for H10a and H10b, which
proposed that the effect of loyalty on outcomes would be
moderated by the target (individual versus firm) (WOM: β=
0.10, p>.10; performance: β=0.03, p>.10).
With respect to other (non-hypothesized) study fea-
tures, we find that the effect of loyalty on WOM is slight-
ly stronger in business (vs. consumer) markets (β=0.10,
p<.05), and the effect of loyalty on WOM has grown
stronger over time (β=0.03, p<.05). However, the effect
of loyalty on outcomes did not vary significantly for
brand loyalty, unpublished sources, methods susceptible
to common method bias, or journal quality. These find-
ings increase our confidence that our sample is represen-
tative and unlikely to suffer from potential inclusion or
method biases that emerge when aggregating a sample
of studies for meta-analytic research (Sethuraman et al.
2011).
Post hoc analysis (Model 4)
Researchers often conceptualize customer loyalty as a
Bfavorable correspondence between attitudes and behaviors,
^ stemming from an underlying motivation to maintain a re-
lationship with a particular entity (Dick and Basu 1994, p.102;
see also Brady et al. 2012; Sirdeshmukh et al. 2002). Our
moderation analysis (Model 2 and 3) shows that the positive
effects of loyalty on both WOM and performance are signif-
icantly and positively moderated by measures that use a mix
of attitudinal and behavioral items in the same construct
(WOM β=0.31, p<.05; performance β=0.11, p<.05); we
investigated this finding with a post hoc structural path anal-
ysis that parallels Model 1. Specifically, we modeled attitudi-
nal and behavioral loyalties as reflective indicators of a latent
construct (i.e., loyalty), with all antecedents and outcomes
linked to it according to the data we used in our first structural
path analysis. As with Model 1, we modeled all constructs as
observed variables (except for loyalty as a latent construct),
allowed the antecedents to covary, and used the harmonic
mean (n=5671) across all correlations as the model’s sample
size (Rubera and Kirca 2012).
Figure 2 contains the results of our post hoc structural
Model 4, which provides slightly improved model fit sta-
tistics: χ2(4) = 130.38, p<.05, CFI=0.99, and SRMR=
0.01. To gain insight into the nature of customer loyalty,
we compared the 95% confidence intervals of the stan-
dardized beta coefficients from Model 1 against our post
hoc model with loyalty as a latent construct. Mirroring the
results from our moderation analyses, modeling customer
loyalty as a single latent construct results in stronger stan-
dardized coefficients for performance (βLoyalty=0.51,
[0.46, 0.57]) compared with either attitudinal (βAtt=
0.02, [−0.01, 0.06]) or behavioral (βBeh=0.27, [0.24,
0.30]) loyalty alone. We find a similar pattern of results
for the effect of loyalty on WOM (βLoyalty=0.54, [0.47,
0.62]; βAtt=0.41, [0.39, 0.43]; βBeh=0.19, [0.17, 0.21]);
the model that includes both attitudinal and behavioral
elements to capture customer loyalty results in better fit
and stronger effects than models that maintain either ele-
ment separately. These findings support the concept that
firms benefit most from Btrue customer loyalty,^
Loyalty
Attitudinal
measures
Behavioral
measures
Commitment
Trust
Satisfaction
Loyalty
incentives
Word of
mouth
R = .442
Performance
R = .122
Fig. 2 Post Hoc: structural path
model analysis (Model 4)
802 J. of the Acad. Mark. Sci. (2015) 43:790–825
involving a positive cognitive state (attitudinal loyalty)
manifested as positive behavioral actions (behavioral loy-
alty) (Dick and Basu 1994; Oliver 1999; Sirdeshmukh
et al. 2002).1
Strengthening the loyalty framework for researchers
and practitioners
From this precise inventory and examination of the primary
conceptualizations of customer loyalty, we derive theoretical
and practical insights to guide marketing research and prac-
tice. By synthesizing decades of research, we (1) provide a
single conceptual definition of loyalty, (2) describe how re-
searchers should approach empirical definitions to clarify cru-
cial aspects of their treatment of loyalty and reduce measure-
ment heterogeneity, and (3) provide exemplary loyalty mea-
sures, which we summarize in Table 5. We also provide a
database of recent loyalty-related studies in marketing (Web
Appendix, Table 7) and a summary of best practices in terms
of study design and methodological choices (Table 6).
From a conceptual standpoint, customer loyalty is a collec-
tion of attitudes aligned with a series of purchase behaviors
that systematically favor one entity over competing entities.
However, empirical definitions should append a temporal as-
pect (backward-looking vs. forward-looking), because of its
influence on how loyalty gets processed psychologically and
its ultimate impact on performance outcomes. To address var-
ious combinations of empirical definitions, we offer 10 exem-
plary items, extracted from various studies that broadly cap-
ture elements (attitudes and behaviors) and study-specific
characteristics (temporal aspect, target) that influence loyalty.
We thus offer specific advice to researchers and practitioners
to help them capture the customer loyalty construct more ac-
curately and reap its benefits.
Guidance for researchers
Every loyalty study should consider four primary conceptual
guidelines. First, if researchers seek to understand how ante-
cedents create loyalty, loyalty must be measured and reported
as an attitude or behavior separately, because the antecedents
differentially build each element. Satisfaction (Model 1) has
little effect on behavioral loyalty (β=0.04, p<.05) but a strong
effect on attitudinal loyalty (β=0.24, p<.05), for example.
Other antecedents that we did not consider in the current study
plausibly should exert similarly distinct effects, and ignoring
such differences could produce misleading results that depend
more on the loyalty element measured than on the actual effi-
cacy of the loyalty-building strategy. Second, if researchers
seek to understand the effect of loyalty on objective
performance outcomes (e.g., revenue, profit), they must mea-
sure loyalty as both an attitude and a behavior, because this
composition offers the strongest effect on objective perfor-
mance (β=0.11, p<.05; Table 1, Model 3). Third, if research
aims to investigate WOM outcomes, attitudinal loyalty may
be the best predictor, because behaviors reflect potential con-
straints (e.g., size of wallet, store location) that are less impor-
tant for WOM outcomes, whereas technology and other social
shifts appear to enhance the effect of loyalty on WOM over
time (β=0.03, p<.05; Table 4, Model 2). Although re-
searchers are cognizant of fusing WOM items into their mea-
sures of loyalty, they should work to avoid doing so, accord-
ing to our empirical evidence this common practice under-
mines the linkage between loyalty and performance, as well
as the theoretical reasons for the separation (de Matos and
Rossi 2008; Dick and Basu 1994; Söderlund 2006). Fourth,
researchers studying loyalty will be better served by
employing backward-looking measures, which also help
guard against potential inflation of the link between loyalty
and WOM (β=0.13, p<.05; Table 4, Model 2). In Table 6 we
summarize best practices derived from recent meta-analyses
published in Journal of Marketing, Journal of Marketing
Research, Journal of Consumer Research, and Journal of
the Academy of Marketing Science for key research decisions
(sample, measurement, heterogeneity, and analyses), which
we used to guide our methodological approach.
Guidance for practitioners
We offer three primary practical recommendations. First,
even if assessed with just two questions (e.g., BWhat is
your attitude about X relative to its competitors?^ and
BHow often do you purchase from X instead of its
competitors?^), customer loyalty measures need to reflect
both attitudes and behaviors, because both aspects of
loyalty together have a stronger effect on objective
performance than either alone. The loyalties expressed
by a firm’s loyal customers often differ in composition:
some customers are only attitudinally loyal or only behav-
iorally loyal, and others are both simultaneously, and each
group exerts significantly different effects on outcomes
(Dick and Basu 1994). Thus, the value of loyalty for a
seller depends on not only the level of loyalty but also its
composition in the customer portfolio. A customer with
very high attitudinal loyalty could report a high net pro-
moter score (NPS), which is a popular metric among
Fortune 500 companies for measuring loyalty
(Reichheld 2003), but a lack of corresponding behavioral
loyalty may reduce the effect of this NPS on objective
performance outcomes (Keiningham et al. 2007). From
this perspective, the ultimate effect on a specific outcome
depends largely on which loyalty (attitudinal or behavior-
al) the firm considers, such that marketing investments1 We thank an anonymous reviewer for their guidance on this point.
J. of the Acad. Mark. Sci. (2015) 43:790–825 803
T
ab
le
5
S
u
m
m
ar
y
o
f
k
ey
fi
n
d
in
g
s
an
d
im
p
li
ca
ti
o
n
s
fo
r
re
se
ar
ch
er
s
an
d
p
ra
ct
it
io
n
er
s
D
ef
in
in
g
lo
y
al
ty
C
o
n
ce
p
tu
al
d
ef
in
it
io
n
C
u
st
o
m
er
lo
y
al
ty
is
a
co
ll
ec
ti
o
n
o
f
at
ti
tu
de
s
al
ig
n
ed
w
it
h
a
se
ri
es
o
f
p
u
rc
h
as
e
b
eh
av
io
rs
th
at
sy
st
em
at
ic
al
ly
fa
v
or
o
n
e
en
ti
ty
o
v
er
co
m
p
et
in
g
en
ti
ti
es
.
M
ea
su
ri
n
g
lo
y
al
ty
E
x
em
p
la
r
at
ti
tu
d
in
al
lo
y
al
ty
m
ea
su
re
s
R
ep
re
se
n
ta
ti
v
e
ar
ti
cl
es
1
.
I
pr
ef
er
[t
ar
g
et
]
o
v
er
co
m
pe
ti
to
rs
.
B
re
iv
ik
an
d
T
h
o
rb
jø
rn
se
n
(2
0
0
8
);
Y
im
et
al
.
(2
0
0
8
)
2
.
I
en
jo
y
d
o
in
g
b
u
si
n
es
s
w
it
h
[t
ar
g
et
].
3
.
I
co
n
si
d
er
[t
ar
g
et
]
m
y
fi
rs
t
p
re
fe
re
n
ce
.
4
.
I
ha
v
e
a
p
o
si
ti
v
e
at
ti
tu
d
e
to
w
ar
d
[t
ar
g
et
].
5
.
I
re
al
ly
li
k
e
[t
ar
g
et
].
E
x
em
p
la
r
b
eh
av
io
ra
l
lo
y
al
ty
m
ea
su
re
s
R
ep
re
se
n
ta
ti
v
e
ar
ti
cl
es
1
.
I
of
te
n
bu
y
p
ro
d
u
ct
s/
se
rv
ic
es
fr
o
m
[t
ar
g
et
].
B
ra
d
y
et
al
.
(2
0
1
2
);
D
e
W
u
lf
et
al
.
(2
0
0
1
)
2
.
I
on
ly
b
u
y
p
ro
d
u
ct
s/
se
rv
ic
es
fr
o
m
[t
ar
g
et
].
3
.
T
h
e
la
st
ti
m
e
I
p
u
rc
h
as
e
a
p
ro
d
u
ct
/s
er
v
ic
e,
I
b
o
u
g
h
t
fr
o
m
[t
ar
g
et
].
4
.
I
fr
eq
u
en
tl
y
b
u
y
fr
o
m
[t
ar
g
et
].
5
.
I
bu
y
m
o
st
fr
o
m
[t
ar
g
et
].
K
ey
fi
n
d
in
g
s
R
es
ea
rc
h
an
d
m
an
ag
er
ia
l
im
p
li
ca
ti
o
n
s
L
o
ya
lt
y
co
m
p
o
si
ti
on
S
ix
ty
-f
iv
e
p
er
ce
n
t
o
f
re
se
ar
ch
er
s
ad
o
p
t
th
e
se
p
ar
at
e
lo
y
al
ty
p
er
sp
ec
ti
v
e,
d
es
p
it
e
w
ea
k
er
an
d
le
ss
co
n
si
st
en
t
p
re
d
ic
ti
o
n
s
o
f
lo
y
al
ty
o
u
tc
o
m
es
.
D
is
ag
re
em
en
t
ab
o
u
t
th
e
na
tu
re
o
f
lo
ya
lt
y
re
su
lt
s
in
v
ar
y
in
g
co
n
cl
u
si
o
n
s
ab
o
u
t
lo
ya
lt
y
’s
ef
fe
ct
iv
en
es
s.
C
o
m
m
it
m
en
t,
tr
u
st
,
an
d
sa
ti
sf
ac
ti
o
n
h
av
e
st
ro
n
g
er
ef
fe
ct
s
o
n
at
ti
tu
d
in
al
th
an
b
eh
av
io
ra
l
lo
y
al
ty
,
an
d
lo
y
al
ty
in
ce
n
ti
v
es
h
av
e
a
n
eg
at
iv
e
ef
fe
ct
o
n
at
ti
tu
d
in
al
lo
y
al
ty
b
ut
a
n
o
n
si
g
n
if
ic
an
t
ef
fe
ct
o
n
b
eh
av
io
ra
l
lo
y
al
ty
.
C
o
n
si
st
en
t
w
it
h
th
is
p
er
sp
ec
ti
v
e,
an
te
ce
d
en
ts
d
if
fe
re
n
ti
al
ly
af
fe
ct
at
ti
tu
d
in
al
an
d
b
eh
av
io
ra
l
lo
y
al
ty
co
n
st
ru
ct
s
w
h
en
se
p
ar
at
ed
.
R
es
ea
rc
h
er
s
se
p
ar
at
in
g
at
ti
tu
d
in
al
an
d
b
eh
av
io
ra
l
lo
y
al
ty
m
u
st
q
u
al
if
y
th
e
fi
n
d
in
g
s
as
so
ci
at
ed
w
it
h
ea
ch
,
b
ec
au
se
th
ey
m
ay
n
o
t
g
en
er
al
iz
e
to
th
e
o
th
er
m
ea
su
re
o
f
lo
y
al
ty
.
B
eh
av
io
ra
l
lo
y
al
ty
h
as
a
si
g
n
if
ic
an
tl
y
st
ro
n
g
er
ef
fe
ct
o
n
p
er
fo
rm
an
ce
(β
=
0
.2
7
)
th
an
at
ti
tu
d
in
al
lo
y
al
ty
(β
=
0
.0
2)
,
b
u
t
b
eh
av
io
ra
l
lo
y
al
ty
ha
s
a
w
ea
ke
r
ef
fe
ct
on
W
O
M
(β
=
0
.1
9
)
th
an
at
ti
tu
d
in
al
lo
y
al
ty
(β
=
0
.4
1
).
C
o
n
cl
u
si
o
ns
ab
o
u
t
lo
y
al
ty
’s
ef
fe
ct
iv
en
es
s
d
ep
en
d
la
rg
el
y
o
n
w
h
ic
h
lo
y
al
ty
co
n
st
ru
ct
is
m
ea
su
re
d
.
M
an
ag
er
s
ex
am
in
in
g
lo
ya
lt
y
as
ei
th
er
at
ti
tu
d
es
o
r
b
eh
av
io
rs
m
ay
b
e
o
v
er
–
o
r
u
n
d
er
-p
re
d
ic
ti
n
g
lo
ya
lt
y’
s
tr
ue
ef
fe
ct
s
o
n
o
u
tc
o
m
es
,
an
d
o
v
er
–
o
r
u
n
d
er
-v
al
u
in
g
cu
st
o
m
er
s
as
a
re
su
lt
.
In
m
ix
ed
m
ea
su
re
s
o
f
cu
st
o
m
er
lo
y
al
ty
,
a
h
ig
h
er
p
er
ce
n
ta
g
e
o
f
at
ti
tu
d
in
al
it
em
s
en
h
an
ce
s
th
e
ef
fe
ct
o
f
lo
y
al
ty
o
n
W
O
M
(β
=
0
.3
3
),
w
h
il
e
si
m
ul
ta
n
eo
u
sl
y
su
p
p
re
ss
in
g
it
s
ef
fe
ct
o
n
p
er
fo
rm
an
ce
(β
=
0
.4
0
).
S
in
g
le
-c
o
n
st
ru
ct
m
ea
su
re
m
en
t
sc
al
es
o
f
ag
gr
eg
at
e
lo
y
al
ty
sh
o
u
ld
b
al
an
ce
at
ti
tu
d
in
al
an
d
b
eh
av
io
ra
l
it
em
s.
F
o
r
ex
am
p
le
,
h
o
ld
in
g
al
l
m
ea
su
re
s
at
th
ei
r
m
ea
n
in
M
o
d
el
4
,
a
1
%
in
cr
ea
se
i
n
th
e
ra
ti
o
o
f
at
ti
tu
d
in
al
it
em
s
in
a
b
eh
av
io
ra
l
lo
y
al
ty
m
ea
su
re
in
cr
ea
se
s
th
e
p
er
fo
rm
an
ce
p
re
d
ic
ti
v
en
es
s
b
y
2
5
%
(r
=
.4
0
fo
r
p
u
re
b
eh
av
io
ra
l,
r
=
.5
0
w
it
h
1
%
at
ti
tu
d
in
al
it
em
s)
.
F
ac
to
rs
th
at
le
v
er
ag
e
th
e
ef
fe
ct
iv
en
es
s
o
f
lo
y
al
ty
W
O
M
-i
n
cl
u
si
v
e
m
ea
su
re
of
cu
st
o
m
er
lo
y
al
ty
ar
e
le
ss
p
re
d
ic
ti
v
e
p
er
fo
rm
an
ce
(β
=
− 0
.4
4
).
R
es
ea
rc
h
er
s
co
n
ta
m
in
at
in
g
lo
y
al
ty
m
ea
su
re
s
w
it
h
W
O
M
m
ay
b
e
u
n
d
er
es
ti
m
at
in
g
lo
y
al
ty
’s
tr
u
e
ef
fe
ct
o
n
p
er
fo
rm
an
ce
.
M
an
ag
er
s
sh
o
u
ld
av
o
id
u
si
n
g
N
P
S
to
m
ea
su
re
tr
u
e
lo
y
al
ty
.
B
ac
kw
ar
d
-l
o
o
k
in
g
(e
v
id
en
ce
-b
as
ed
)
m
ea
su
re
s
o
f
lo
y
al
ty
ar
e
st
ro
n
g
er
p
re
d
ic
to
rs
o
f
p
er
fo
rm
an
ce
th
an
fo
rw
ar
d
lo
o
k
in
g
(e
x
p
ec
ta
ti
o
n
-b
as
ed
)
m
ea
su
re
s,
b
u
t
th
e
re
v
er
se
is
tr
u
e
fo
r
W
O
M
.
M
an
ag
er
s
an
d
re
se
ar
ch
er
s
sh
o
u
ld
ca
re
fu
ll
y
co
n
si
d
er
th
ei
r
lo
y
al
ty
m
ea
su
re
m
en
ts
ac
co
rd
in
g
to
th
ei
r
o
b
je
ct
iv
es
.
A
lt
h
o
u
g
h
fo
rw
ar
d
-l
o
o
k
in
g
m
ea
su
re
s
h
av
e
a
st
ro
n
g
er
as
so
ci
at
io
n
w
it
h
W
O
M
,
th
e
ea
se
o
f
W
O
M
in
te
n
ti
o
n
m
ay
o
v
er
st
at
e
lo
y
al
ty
’s
ef
fe
ct
.
T
h
e
ef
fe
ct
o
f
cu
st
o
m
er
lo
y
al
ty
o
n
W
O
M
h
as
be
en
in
cr
ea
si
n
g
o
v
er
th
e
p
as
t
1
0
y
ea
rs
(β
=
0
.0
3
).
E
n
ha
n
ce
m
en
ts
in
o
n
li
n
e
te
ch
n
o
lo
g
y
h
av
e
m
ad
e
it
ea
si
er
fo
r
cu
st
o
m
er
s
to
en
g
ag
e
in
W
O
M
ac
ti
v
it
ie
s.
D
ec
re
as
ed
p
er
so
n
al
re
la
ti
o
n
sh
ip
s
al
so
m
ay
in
cr
ea
se
th
e
p
re
v
al
en
ce
an
d
im
p
o
rt
an
ce
o
f
ex
ch
an
g
e-
b
as
ed
re
la
ti
o
n
sh
ip
s.
804 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
6
B
en
ch
m
ar
k
o
f
m
et
h
o
d
s
in
m
et
a-
an
al
y
se
s
p
u
b
li
sh
ed
in
m
ar
k
et
in
g
jo
u
rn
al
s
si
n
ce
2
0
1
2
a
R
ef
er
en
ce
D
at
a
S
et
d
ev
el
o
pm
en
t
E
ff
ec
t
si
ze
st
at
is
ti
c
M
o
d
er
at
o
rs
to
as
se
ss
h
et
er
o
g
en
ei
ty
in
p
ri
m
ar
y
sa
m
p
le
s
A
na
ly
si
s
ap
p
ro
ac
h
R
ob
us
tn
es
s
ch
ec
ks
M
ea
su
re
m
en
t
h
et
er
o
g
en
ei
ty
A
dd
it
io
na
l
he
te
ro
ge
ne
it
y
P
ic
k
an
d
E
is
en
d
20
1
4
1
70
sa
m
p
le
s.
D
at
ab
as
e
se
ar
ch
su
p
p
le
m
en
te
d
w
it
h
m
an
u
al
se
ar
ch
of
ke
y
jo
ur
na
ls
an
d
re
fe
re
n
ce
li
st
of
ke
y
ar
ti
cl
es
.
C
o
n
ta
ct
ed
le
ad
in
g
au
th
o
rs
fo
r
w
or
ki
ng
or
un
pu
bl
is
he
d
sa
m
pl
es
.
C
or
re
la
ti
o
n
co
ef
fi
ce
in
t
(c
on
ve
rt
ed
fr
om
ot
h
er
st
at
is
ti
cs
if
co
rr
el
at
io
n
no
t
re
po
rt
ed
)
in
ve
rs
e
va
ri
an
ce
w
ei
gh
te
d
an
d
ad
ju
st
ed
fo
r
m
ea
su
re
m
en
t
re
li
ab
il
it
y.
T
yp
e
o
f
m
ea
su
re
m
en
t
o
f
m
ai
n
co
ns
tr
u
ct
s,
in
te
nt
io
ns
vs
.
be
ha
v
io
rs
an
d
m
on
et
ar
y
v
s.
no
n-
m
o
n
et
ar
y
co
st
s.
C
h
ar
ac
te
ri
st
ic
s
of
th
e
co
nt
ex
t
(e
.g
.,
se
rv
ic
e
vs
g
oo
ds
,
B
2
C
v
s.
B
2B
)
an
d
st
ud
y
de
si
gn
(e
.g
.,
fi
el
d
vs
ex
pe
ri
m
en
ta
l
de
si
gn
,
si
ng
le
vs
.
m
u
lt
ip
le
fi
rm
s
in
sa
m
p
le
)
H
un
te
r
an
d
S
ch
m
id
t
(2
00
4
)
bi
v
ar
ia
te
an
al
y
si
s.
S
E
M
pa
th
an
al
ys
is
on
m
et
a-
an
al
y
ti
c
co
rr
el
at
io
n
m
at
ri
x
.
M
o
d
er
at
io
n
w
it
h
Q
st
at
is
ti
c
an
d
co
m
p
ar
is
o
n
of
ef
fe
ct
si
ze
s
ac
co
un
ti
ng
fo
r
sa
m
p
le
si
ze
.
R
ep
or
te
d
re
su
lt
s
fo
r
tw
o
co
m
p
et
in
g
S
E
M
m
od
el
s.
v
an
L
ae
r
et
al
.
20
1
4
1
32
sa
m
p
le
s.
S
ea
rc
h
ed
1
3
y
ea
rs
of
m
an
us
cr
ip
ts
ac
ro
ss
fi
v
e
la
n
g
ua
g
es
,
in
cl
ud
in
g
u
np
ub
li
sh
ed
st
ud
ie
s
an
d
bo
o
k
se
ct
io
ns
.
M
ea
su
re
m
en
t
sc
al
e
w
as
p
ri
m
ar
y
in
cl
u
si
o
n
cr
it
er
ia
.
C
or
re
la
ti
o
n
co
ef
fi
ce
in
t
(c
on
ve
rt
ed
fr
om
ot
h
er
st
at
is
ti
cs
if
co
rr
el
at
io
n
no
t
re
po
rt
ed
)
in
ve
rs
e
va
ri
an
ce
w
ei
gh
te
d
an
d
ad
ju
st
ed
fo
r
m
ea
su
re
m
en
t
re
li
ab
il
it
y.
T
yp
e
of
m
ea
su
re
m
en
t
sc
al
e
us
ed
fo
r
m
ai
n
co
ns
tr
u
ct
.
C
h
ar
ac
te
ri
st
ic
s
of
av
er
ag
e
su
bj
ec
t
(e
.g
.,
av
er
ag
e
ag
e
o
f
re
sp
on
d
en
t)
in
ea
ch
pr
im
ar
y
st
u
d
y.
H
un
te
r
an
d
S
ch
m
id
t
(2
00
4)
bi
va
ri
at
e
an
al
ys
is
.
C
he
ck
ed
fo
r
m
o
de
ra
ti
o
n
w
it
h
Q
-s
ta
ti
st
ic
.
R
ep
or
te
d
re
su
lt
s
fo
r
ra
w
co
rr
el
at
io
n
s,
in
v
er
se
-v
ar
ia
n
ce
w
ei
g
h
te
d
co
rr
el
at
io
n
s,
an
d
in
v
er
se
-v
ar
ia
n
ce
w
ei
g
h
te
d
w
it
h
re
li
ab
il
it
y
ad
ju
st
m
en
t
co
rr
ec
te
d
co
rr
el
at
io
n
s.
R
ub
er
a
an
d
K
ir
ca
2
01
2
1
59
sa
m
p
le
s.
S
ea
rc
h
fo
cu
se
d
on
15
jo
ur
na
ls
.
S
up
pl
em
en
te
d
w
it
h
un
pu
bl
is
he
d
w
or
k
an
d
pa
pe
rs
fo
un
d
fr
om
an
ex
am
in
at
io
n
of
re
v
ie
w
ar
ti
cl
es
’
re
fe
re
n
ce
se
ct
io
n
s.
C
or
re
la
ti
o
n
co
ef
fi
ce
in
t
(c
on
ve
rt
ed
fr
om
ot
h
er
st
at
is
ti
cs
if
co
rr
el
at
io
n
no
t
re
po
rt
ed
)
in
ve
rs
e
va
ri
an
ce
w
ei
gh
te
d
an
d
ad
ju
st
ed
fo
r
m
ea
su
re
m
en
t
re
li
ab
il
it
y.
T
yp
e
of
m
ea
su
re
m
en
t
o
f
m
ai
n
co
ns
tr
u
ct
,
th
re
e
di
ff
er
en
t
ty
p
es
of
ou
tc
om
es
,
an
d
ob
je
ct
iv
e
vs
.
su
b
je
ct
iv
e
m
ea
su
re
s.
C
h
ar
ac
te
ri
st
ic
s
of
av
er
ag
e
fi
rm
s
(e
.g
.,
ad
v
er
ti
si
n
g
in
te
n
si
ty
)
in
p
ri
m
ar
y
st
u
d
ie
s,
re
se
ar
ch
co
nt
ex
t
(e
.g
.,
in
du
st
ry
,
lo
ca
ti
on
,
y
ea
r)
,
an
d
pu
bl
ic
at
io
n
(i
.e
.,
m
ar
k
et
in
g
vs
.
m
an
ag
em
en
t
jo
ur
n
al
).
H
un
te
r
an
d
S
ch
m
id
t
(1
99
0
)
bi
v
ar
ia
te
an
al
y
si
s.
S
E
M
pa
th
an
al
ys
is
on
m
et
a-
an
al
y
ti
c
co
rr
el
at
io
n
m
at
ri
x
,
an
d
m
u
lt
il
ev
el
m
o
d
el
s
fo
r
m
o
de
ra
ti
o
n
an
al
ys
is
.
In
v
es
ti
ga
te
d
re
v
er
se
ca
us
al
it
y
us
in
g
st
ud
ie
s
th
at
re
p
or
te
d
co
rr
el
at
io
n
s
be
tw
ee
n
ou
tc
om
es
m
ea
su
re
d
at
ti
m
e
t
an
d
p
re
d
ic
to
r
at
ti
m
e
t+
1.
In
ve
st
ig
at
ed
po
ss
ib
le
b
ia
s
fr
om
o
m
it
te
d
-v
ar
ia
b
le
s
an
d
m
u
lt
ic
o
li
n
ea
ri
ty
.
Z
ab
la
h
et
al
.
20
1
2
3
23
sa
m
p
le
s.
D
at
ab
as
e
se
ar
ch
su
p
p
le
m
en
te
d
w
it
h
m
an
u
al
se
ar
ch
o
f
re
la
te
d
sp
ec
ia
li
ze
d
jo
u
rn
al
s.
L
im
it
ed
in
cl
us
io
n
to
st
ud
ie
s
co
n
du
ct
ed
in
E
n
g
li
sh
to
li
m
it
in
fl
u
en
ce
of
cu
lt
ur
e.
C
or
re
la
ti
o
n
co
ef
fi
ci
en
ts
.
E
st
im
at
ed
m
ea
n
co
rr
el
at
io
n
s
an
d
va
ri
an
ce
us
in
g
ra
nd
om
ef
fe
ct
s,
m
u
lt
il
ev
el
m
o
d
el
s
(R
au
de
nb
us
h
20
09
).
D
id
no
t
tr
an
sf
or
m
ra
w
co
rr
el
at
io
n
s
to
z-
sc
o
re
s
(H
un
te
r
an
d
S
ch
m
id
t
2
00
4)
.
N
on
e;
p
ri
m
ar
y
st
ud
ie
s
w
er
e
co
ns
is
te
n
t
in
th
ei
r
m
ea
su
re
m
en
t
ap
pr
oa
ch
.
R
es
ea
rc
h
co
nt
ex
t
(e
.g
.,
jo
bs
w
he
re
em
p
lo
y
ee
s
in
te
ra
ct
w
it
h
a
la
rg
e
nu
m
b
er
of
cu
st
o
m
er
s)
.
E
st
im
at
ed
av
er
ag
e
co
rr
el
at
io
n
co
ef
fi
ci
en
ts
an
d
va
ri
an
ce
us
in
g
ra
nd
om
ef
fe
ct
s
m
od
el
s,
th
en
S
E
M
pa
th
an
al
ys
is
o
n
es
ti
m
at
ed
co
rr
el
at
io
ns
,
an
d
m
ul
ti
le
ve
l
m
od
el
s
fo
r
m
o
de
ra
ti
on
an
al
ys
is
.
D
ro
p
pe
d
al
l
st
ud
ie
s
pu
b
li
sh
ed
in
jo
ur
na
ls
of
lo
w
er
qu
al
it
y
(b
as
ed
on
jo
ur
na
l
sc
or
e
on
S
S
C
I)
an
d
re
pe
at
ed
pa
th
an
al
ys
is
to
ch
ec
k
fo
r
co
ns
is
te
nt
re
su
lt
s.
H
o
m
bu
rg
et
al
.
20
1
2
4
31
sa
m
pl
es
.
T
ho
ro
ug
h
se
ar
ch
o
f
ar
ti
cl
es
fr
o
m
6
in
fl
ue
nt
ia
l
jo
ur
na
ls
ov
er
a
13
-y
ea
r
w
in
do
w
.
C
or
re
la
ti
o
n
(P
ea
rs
o
n’
s’
r
an
d
IC
C
)
an
d
ag
re
em
en
t
st
at
is
ti
cs
.
C
o
rr
el
at
io
n
s
tr
an
sf
o
rm
ed
to
z-
sc
o
re
s
an
d
co
rr
ec
te
d
fo
r
m
ea
su
re
m
en
t
er
ro
r
an
d
co
n
st
ru
ct
si
m
il
ar
it
y.
T
yp
e
o
f
re
li
ab
il
it
y
(P
ea
rs
o
n’
s
r
o
r
IC
C
),
fi
v
e
ch
ar
ac
te
ri
st
ic
s
of
m
ea
su
re
s
(e
.g
.,
pr
es
en
t
vs
.
pa
st
fo
cu
se
d,
ob
je
ct
iv
e
vs
.
su
b
je
ct
iv
e,
pe
op
le
vs
.
no
n
pe
rs
on
al
en
ti
ti
es
),
an
d
tw
o
ch
ar
ac
te
ri
st
ic
s
of
C
h
ar
ac
te
ri
st
ic
s
of
av
er
ag
e
in
fo
rm
an
t
(e
.g
.,
or
ga
ni
za
ti
on
al
ro
le
),
ch
ar
ac
te
ri
st
ic
s
of
av
er
ag
e
or
ga
ni
za
ti
on
(e
.g
.,
fi
rm
ag
e)
,
an
d
re
se
ar
ch
co
nt
ex
t
(e
.g
.,
in
du
st
ry
dy
n
am
is
m
).
H
un
te
r
an
d
S
ch
m
id
t
(2
00
4)
w
ei
gh
te
d
m
ea
n
w
it
h
su
bg
ro
u
p
an
al
ys
is
by
co
nt
ex
t.
W
ei
gh
te
d
re
gr
es
si
o
n
fo
r
te
st
in
g
hy
p
ot
he
si
s,
w
ei
gh
ti
n
g
by
in
ve
rs
e
of
st
an
da
rd
er
ro
rs
of
ea
ch
st
u
dy
’s
ef
fe
ct
s.
U
se
d
m
u
lt
il
ev
el
m
o
d
el
s
un
le
ss
m
is
si
ng
va
lu
es
C
om
p
ar
ed
re
su
lt
s
fr
o
m
re
li
ab
il
it
y
an
d
ag
re
em
en
t
(t
w
o
se
p
ar
at
e
in
d
ic
at
o
rs
).
In
ve
st
ig
at
e
pu
b
li
ca
ti
o
n
b
ia
s
w
it
h
fa
il
sa
fe
N
an
d
Bt
ri
m
an
d
fi
ll
^
m
et
ho
d
to
ch
ec
k
fo
r
p
ot
en
ti
al
m
is
si
n
g
st
ud
ie
s
th
at
m
ig
ht
pu
ll
do
w
n
th
e
av
er
ag
e
ef
fe
ct
si
ze
.
J. of the Acad. Mark. Sci. (2015) 43:790–825 805
T
ab
le
6
(c
o
n
ti
n
u
ed
)
R
ef
er
en
ce
D
at
a
S
et
d
ev
el
o
pm
en
t
E
ff
ec
t
si
ze
st
at
is
ti
c
M
o
d
er
at
o
rs
to
as
se
ss
h
et
er
o
g
en
ei
ty
in
p
ri
m
ar
y
sa
m
p
le
s
A
na
ly
si
s
ap
p
ro
ac
h
R
ob
us
tn
es
s
ch
ec
ks
M
ea
su
re
m
en
t
h
et
er
o
g
en
ei
ty
A
dd
it
io
na
l
he
te
ro
ge
ne
it
y
tr
ia
ng
ul
at
io
n
(e
.g
.,
ar
ch
iv
al
vs
.
cu
st
o
m
er
da
ta
).
re
qu
ir
ed
an
al
ys
is
at
st
ud
y
le
ve
l.
S
te
en
ka
m
p
an
d
G
ey
sk
en
s
2
01
2
1
28
sa
m
p
le
s
re
p
re
se
n
te
d
co
m
pa
ni
es
in
12
co
un
tr
ie
s.
C
or
re
la
ti
o
n
co
ef
fi
ci
en
t
(c
on
ve
rt
ed
fr
om
ot
h
er
st
at
is
ti
cs
if
co
rr
el
at
io
n
no
t
re
po
rt
ed
),
co
rr
ec
te
d
fo
r
n
on
-i
nd
ep
en
de
nc
e,
ou
tl
ie
rs
,
an
d
se
ve
n
st
at
is
ti
ca
l
ar
ti
fa
ct
s
(e
.g
.,
m
ea
su
re
m
en
t
er
ro
r,
ra
n
g
e
re
st
ri
ct
io
n
).
In
it
ia
l
co
rr
ec
ti
o
n
fo
r
v
ar
ia
n
ce
in
m
ea
su
re
m
en
t
ar
ti
fa
ct
s
(e
.g
.,
di
ch
o
to
m
iz
at
io
n
of
a
co
n
ti
nu
ou
s
va
ri
ab
le
),
bu
t
no
t
te
st
ed
as
m
o
de
ra
to
r.
C
h
ar
ac
te
ri
st
ic
s
of
st
u
di
es
’
co
nt
ex
t
(e
.g
.,
cu
lt
ur
al
di
m
en
si
on
s,
re
gi
on
,
co
un
tr
y,
ye
ar
)
an
d
ex
ch
an
ge
go
ve
rn
an
ce
(e
.g
.,
h
ie
ra
rc
h
ic
al
v
s.
re
la
ti
o
n
al
).
M
od
el
es
ti
m
at
ed
st
ud
y
co
rr
el
at
io
ns
as
a
fu
nc
ti
on
of
ch
ar
ac
te
ri
st
ic
s
us
in
g
G
L
S
es
ti
m
at
io
n
te
ch
ni
q
u
es
fo
r
m
et
a-
an
al
y
si
s
th
at
ac
co
un
ts
fo
r
de
pe
nd
en
ci
es
am
on
g
co
rr
el
at
io
ns
th
at
co
m
e
fr
o
m
th
e
sa
m
e
st
u
dy
.
T
es
te
d
tw
o
al
te
rn
at
iv
e
(b
ut
ov
er
la
pp
in
g)
th
eo
re
ti
ca
l
m
o
de
ls
o
f
cu
lt
u
ra
l
ef
fe
ct
s
on
T
C
E
.
U
se
d
ef
fe
ct
co
di
ng
to
re
m
ov
e
in
fl
ue
nc
e
of
un
eq
ua
ll
y
si
ze
d
gr
ou
ps
fr
om
th
e
ov
er
al
l
es
ti
m
at
ed
m
ea
n
co
rr
el
at
io
n
s.
T
es
te
d
m
o
de
ra
ti
o
n
by
es
ti
m
at
in
g
in
te
ra
ct
io
n
co
ef
fi
ci
en
ts
.
S
u
m
m
ar
y
of
be
st
p
ra
ct
ic
es
C
as
t
a
w
id
e
n
et
to
ob
ta
in
a
la
rg
e
sa
m
pl
e
o
f
st
ud
ie
s
es
ti
m
at
in
g
re
le
v
an
t
ef
fe
ct
s,
an
d
us
e
co
di
ng
to
te
st
if
d
if
fe
re
n
ce
s
in
th
e
or
ig
in
al
st
ud
ie
s’
co
nt
ex
t
an
d
pu
b
li
ca
ti
o
n
st
at
us
(e
.g
.,
re
gi
on
,
y
ea
r,
pu
b
li
ca
ti
on
st
at
us
,
jo
ur
na
l
re
pu
ta
ti
on
)
sy
st
em
at
ic
al
ly
al
te
r
ef
fe
ct
si
ze
s.
U
n
it
-l
es
s
in
d
ic
at
o
r
o
f
ef
fe
ct
si
ze
s:
co
rr
el
at
io
n
co
ef
fi
ci
en
ts
(c
o
m
p
le
x
m
u
lt
iv
ar
ia
te
m
o
d
el
s)
o
r
es
ti
m
at
ed
el
as
ti
ci
ti
es
(v
ar
ia
b
le
s
m
ea
su
re
d
w
it
h
ob
je
ct
iv
e
in
di
ca
to
rs
).
E
x
er
ci
se
ca
u
ti
o
n
w
it
h
tr
an
sf
o
rm
at
io
n
s
an
d
co
n
v
er
si
o
ns
,
ch
ec
k
if
it
in
fl
u
en
ce
s
re
su
lt
s.
T
es
t
if
h
et
er
o
g
en
ei
ty
in
op
er
at
io
n
al
de
fi
ni
ti
on
an
d
/o
r
m
ea
su
re
m
en
t
sc
al
es
o
f
k
ey
co
n
st
ru
ct
sy
st
em
at
ic
al
ly
in
fl
u
en
ce
s
ef
fe
ct
si
ze
s.
C
on
si
de
r:
A
ve
ra
ge
u
ni
t/
re
sp
on
de
n
t,
re
se
ar
ch
co
nt
ex
t,
pu
b
li
ca
ti
o
n
o
ut
le
t,
da
ta
co
ll
ec
ti
on
m
et
ho
d,
an
d
m
o
de
l
(w
h
en
u
si
ng
m
o
d
el
es
ti
m
at
es
ra
th
er
th
an
co
rr
el
at
io
n
s)
.
E
st
im
at
e
m
ea
n
ef
fe
ct
si
ze
u
si
n
g
ra
nd
om
ef
fe
ct
s
m
od
el
s.
T
es
t
if
h
et
er
o
ge
n
ie
ty
ac
ro
ss
p
ri
m
ar
y
st
u
d
ie
s
in
fl
u
en
ce
s
m
ea
n
ef
fe
ct
si
ze
.
M
et
a-
an
al
y
ti
c
S
E
M
pa
th
an
al
ys
is
if
st
ud
yi
ng
a
co
m
p
le
x
m
u
lt
iv
ar
ia
te
m
o
d
el
,
ex
pl
o
ri
n
g
sy
st
em
of
re
la
ti
on
sh
ip
s.
C
on
si
de
r
al
te
rn
at
iv
e
m
od
el
s
to
te
st
ke
y
re
la
ti
on
sh
ip
s.
T
es
t
m
od
er
at
o
rs
in
di
v
id
ua
ll
y
an
d
in
gr
ou
ps
if
m
u
lt
ic
o
ll
in
ea
ri
ty
is
an
is
su
e.
R
un
an
al
y
si
s
w
it
h
an
d
w
it
ho
ut
ac
co
un
ti
n
g
fo
r
w
ei
g
ht
s
(e
.g
.,
sa
m
p
le
si
ze
).
C
he
ck
fo
r
pu
bl
ic
at
io
n
bi
as
an
d
jo
ur
na
l
qu
al
it
y
by
te
st
in
g
it
as
a
pr
ed
ic
to
r
o
f
ef
fe
ct
si
ze
s.
U
se
fu
n
ne
l
p
lo
ts
an
d
tr
im
an
d
fi
ll
to
ch
ec
k
fo
r
i
ss
ue
s
w
it
h
sa
m
pl
e
an
d
in
cl
us
io
n.
a
T
ab
le
su
m
m
ar
iz
es
re
ce
n
tl
y
p
u
b
li
sh
ed
ar
ti
cl
es
fr
o
m
Jo
u
rn
a
l
o
f
M
ar
ke
ti
n
g,
Jo
u
rn
a
l
o
f
M
ar
ke
ti
n
g
R
es
ea
rc
h
,
Jo
u
rn
a
l
o
f
th
e
A
ca
d
em
y
o
f
M
a
rk
et
in
g
S
ci
en
ce
,
an
d
Jo
u
rn
a
l
o
f
C
on
su
m
er
R
es
ea
rc
h
.
M
a
rk
et
in
g
S
ci
en
ce
d
id
n
o
t
p
u
b
li
sh
an
y
ar
ti
cl
es
u
si
n
g
m
et
a-
an
al
y
ti
c
te
ch
n
iq
u
es
d
ur
in
g
th
is
ti
m
e
w
in
d
o
w
806 J. of the Acad. Mark. Sci. (2015) 43:790–825
may be misallocated simply because of the type of loyalty
used to evaluate the investment. Similarly, assessments of
customers’ future value may be biased by the loyalty metric
used as an intermediate indicator of future performance.
Second, our findings suggest that customer loyalty
cannot be bought using incentive strategies but can be
built with relational strategies (commitment, trust, and
satisfaction). The $48 billion spent on U.S. loyalty pro-
grams likely is not building Btrue^ loyalty (Berry 2013).
For an average customer, adding another loyalty card to
the dozens he or she already owns may be less effective
for building attitudinal and behavioral loyalties (β=
−0.08 and 0.01) than building relationships through
commitment (β=0.34 to 0.35) and trust (β=0.27 to
0.22) or improving transaction performance though sat-
isfaction (β=0.25 and 0.04) (Model 1).
Third, strategies for capitalizing on WOM should be
separate from strategies aimed at increasing customer
loyalty. Including WOM in loyalty measures detracts
from the construct’s accuracy for predicting performance
(β=−0.44, p<.05, Model 3). This point is especially
important in light of our finding that customer loyalty
has a stronger effect on WOM, but not performance, in
business markets than in consumer markets, which like-
ly reflects the greater interrelatedness in business rela-
tionships. Customers with high attitudinal loyalty likely
spread WOM (β=0.41, p<.05, Model 1) but might not
contribute much to a seller’s bottom line (β=0.02,
p>.05) though their behaviors. Therefore, managers
who take a portfolio approach to marketing invest-
ments—such that they recognize customer referral value
as separate from customer lifetime value (Petersen et al.
2009)—should invest in attitudinal loyalty only insofar
as it maximizes their overall customer portfolio lifetime
value. This implication may be especially relevant for
service settings and business-to-business markets, in
which the effect of loyalty on WOM is much stronger.
Limitations and directions for research
Typical of meta-analyses, this study has several limita-
tions. First, we attempted to include many loyalty con-
structs and samples across publication outlets, but we
may have overlooked some. Second, the constructs we
include and our results are limited to variables for
which there exist enough data for analysis. Our frame-
work is a summary of important loyalty-related con-
structs, not an exhaustive list. Our primary objective
was to ascertain the implications of heterogeneity in
extant loyalty conceptualizations and measurements,
which required a thorough examination of loyalty’s re-
lationship to a few key constructs rather than all con-
structs. Third, the heterogeneity in effect sizes that was
not accounted for by our moderation analysis suggests
that including other, unmeasured moderating factors
might influence the reported effect sizes.
Further research thus might expand the constructs in-
cluded in our customer loyalty framework to examine
how they differentially affect attitudinal loyalty, behav-
ioral loyalty, and customer loyalty. Dependence, cooper-
ation, communication, conflict, and unfairness all might
exert distinct influences on types of loyalty. Additionally,
new research may also consider whether and how vari-
ous types of financial outcomes (e.g., Tobin’s Q vs. ROI)
are differentially impacted by attitudinal, behavioral, or
combined loyalty. Clarifying these effects would provide
a richer set of options for managers to tailor their mar-
keting actions to enhance WOM and performance, given
their unique circumstances.
Conclusion
Practitioners recognize the importance of repeat patron-
age, but Bfew say they have cracked the code on build-
ing long-term loyalty^ (Weissenberg 2013). Despite ele-
gant conceptualizations (Dick and Basu 1994; Oliver
1999), academics have failed to demonstrate consistently
how loyalty builds and when it is most effective. It is
therefore no surprise that many of the promises associ-
ated with building customer loyalty remain unrealized.
We find evidence in support of the premise that this
failure stems, in part, from a systematic divergence be-
tween the conceptualizations (What is customer
loyalty?) and measurement (How is it measured?) of
loyalty. We aggregate more than three decades of loyal-
ty research to address this divergence and explicate
when differences between theory and practice influence
the strategy → loyalty → performance process (What
actually matters?).
Our results offer clear evidence in support of con-
struct divergence. Although loyalty is primarily concep-
tualized as the alignment of attitudes and behaviors,
items used to measure loyalty often include extraneous
constructs (Table 2). In addition, study-specific charac-
teristics get incorporated into conceptualizations and/or
operationalizations of loyalty, often with little or no dis-
cussion of their potential effects. We have assessed the
moderating effect of several aspects of loyalty across
163 studies published in marketing journals since 1980
that measure loyalty as an attitude, a behavior, or both
to determine when loyalty is most effective for
predicting performance outcomes.
Acknowledgments The authors thank the Marketing Science Institute
(MSI) for their feedback and publication of a working paper of this
research.
J. of the Acad. Mark. Sci. (2015) 43:790–825 807
A
p
p
en
d
ix
T
ab
le
7
A
lp
h
ab
et
ic
al
li
st
of
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u
d
ie
s
R
ef
er
en
ce
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n
p
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li
sh
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o
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em
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it
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rn
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ig
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us
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ic
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ip
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u
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1
808 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
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d
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u
rn
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o
f
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et
ai
li
n
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u
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li
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e
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6
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lo
d
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t,
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ff
re
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.(
1
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o
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9
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at
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d
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n
d
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ar
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1
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0
2
.
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u
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sh
ed
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eh
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ra
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es
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o
le
s,
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m
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.,
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li
e
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.
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h
n
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d
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ir
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.
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o
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al
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il
d
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al
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el
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ip
s:
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x
te
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n
,^
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u
rn
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lo
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u
si
n
es
s
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h
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8
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p
ri
l)
,
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5–
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1
.
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u
b
li
sh
ed
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eh
av
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ra
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se
v
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h
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ip
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),
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el
at
io
ns
h
ip
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ar
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et
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g
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te
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n
s
in
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ra
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f
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h
ai
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k
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ar
ke
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ar
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ar
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n
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li
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e
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d
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k
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g
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ir
m
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2
C
Y
es
0
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0
4
0
5
7
J. of the Acad. Mark. Sci. (2015) 43:790–825 809
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
en
ce
U
n
p
ub
li
sh
ed
L
o
y
al
ty
p
er
sp
ec
ti
v
e
T
em
p
o
ra
l
o
ri
en
ta
ti
o
n
T
ar
g
et
M
ar
k
et
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o
m
m
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n
m
et
h
o
d
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ia
s
su
sc
ep
ti
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il
it
y
?
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u
rn
al
q
u
al
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y
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ig
en
fa
ct
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r)
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o
w
d
en
,
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n
a
L
ay
-H
w
a
(2
0
11
),
BE
n
g
ag
in
g
th
e
S
tu
d
en
t
as
a
C
u
st
o
m
er
:
A
R
el
at
io
n
sh
ip
M
ar
k
et
in
g
A
p
p
ro
ac
h
,^
M
a
rk
et
in
g
E
d
u
ca
ti
o
n
,
2
1
(S
ep
te
m
b
er
),
2
11
–
2
2
8
.
P
u
b
li
sh
ed
In
cl
u
si
v
e
B
ac
k
w
ar
d
-l
o
o
k
in
g
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ir
m
B
2
C
Y
es
0
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0
0
0
0
0
B
ra
d
y,
M
ic
h
ae
l
K
.,
C
la
y
M
.
V
o
o
rh
ee
s,
an
d
M
ic
h
ae
l
J.
B
ru
sc
o
(2
0
1
2
),
B
S
er
v
ic
e
S
w
ee
th
ea
rt
in
g
:
It
s
A
n
te
ce
d
en
ts
an
d
C
u
st
o
m
er
C
o
ns
eq
u
en
ce
s,
^
Jo
u
rn
a
lo
fM
a
rk
et
in
g
,7
6
(M
ar
ch
),
81
–
9
8
.
P
u
b
li
sh
ed
A
g
g
re
g
at
e
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
B
Y
es
0
.0
1
2
3
3
7
B
re
iv
ik
,
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in
ar
,
an
d
H
el
g
e
T
ho
rb
jø
rn
se
n
(2
0
0
8
),
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o
n
su
m
er
B
ra
n
d
R
el
at
io
n
sh
ip
s:
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n
In
v
es
ti
g
at
io
n
o
f
T
w
o
A
lt
er
n
at
iv
e
M
o
d
el
s,
^
Jo
u
rn
a
l
o
f
th
e
A
ca
d
em
y
o
f
M
a
rk
et
in
g
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ci
en
ce
,
3
6
(D
ec
em
b
er
),
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4
3
–
4
7
2
.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
,
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eh
av
io
ra
l
F
o
rw
ar
d
-l
o
o
k
in
g
B
ra
n
d
B
2
C
Y
es
0
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0
5
4
0
3
B
re
x
en
d
o
rf
,
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im
O
li
v
er
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il
ke
M
ü
h
lm
ei
er
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o
rs
te
n
T
o
m
cz
ak
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n
d
M
ar
ti
n
E
is
en
d
(2
0
1
0
),
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h
e
Im
pa
ct
o
f
S
al
es
E
n
co
u
n
te
rs
o
n
B
ra
n
d
L
o
y
al
ty
,^
Jo
u
rn
a
l
o
f
B
u
si
ne
ss
R
es
ea
rc
h
,
6
3
(N
o
v
em
be
r)
,
11
4
8–
11
5
5
.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
,
B
eh
av
io
ra
l
F
o
rw
ar
d
-l
o
o
k
in
g
B
ra
n
d
B
2
C
Y
es
0
.0
0
9
2
0
3
C
an
d
i,
M
ar
in
a
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0
1
0
),
BB
en
ef
it
s
o
f
A
es
th
et
ic
D
es
ig
n
as
an
E
le
m
en
t
o
f
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ew
S
er
vi
ce
D
ev
el
op
m
en
t,
^
Jo
u
rn
a
l
o
f
P
ro
d
u
ct
In
n
o
v
a
ti
o
n
M
a
n
a
g
em
en
t,
2
7
(O
ct
o
b
er
),
1
0
4
7
–
1
0
6
4
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
B
Y
es
0
.0
0
3
9
5
7
C
as
es
,
A
n
n
e-
S
o
p
h
ie
,
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h
ri
st
o
p
h
e
F
o
u
rn
ie
r,
P
ie
rr
e-
L
o
u
is
D
u
b
o
is
,
an
d
Jo
h
n
F.
T
an
n
er
Jr
.
(2
0
1
0
)
BW
eb
S
it
e
S
p
il
l
O
v
er
to
E
m
ai
l
C
am
p
ai
g
n
s:
T
h
e
R
o
le
o
f
P
ri
v
ac
y,
T
ru
st
,
an
d
S
h
o
p
p
er
s’
A
tt
it
u
d
es
,^
Jo
u
rn
a
l
o
f
B
u
si
n
es
s
R
es
ea
rc
h,
6
3
(S
ep
te
m
b
er
–
O
ct
o
b
er
),
9
9
3
–
9
9
9.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
0
9
2
0
3
C
as
ta
ld
o
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an
d
ro
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ra
n
ce
sc
o
P
er
ri
n
i,
N
ic
o
la
M
is
an
i,
an
d
A
n
to
ni
o
T
en
ca
ti
(2
0
0
9
),
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h
e
M
is
si
n
g
L
in
k
B
et
w
ee
n
C
o
rp
o
ra
te
S
oc
ia
l
R
es
p
o
n
si
b
il
it
y
an
d
C
o
n
su
m
er
T
ru
st
:
T
h
e
C
as
e
o
f
F
ai
r
T
ra
d
e
P
ro
d
u
ct
s,
^
Jo
u
rn
a
l
o
f
B
u
si
ne
ss
E
th
ic
s,
8
4
(J
an
u
ar
y
),
1
–
1
5
.
P
u
b
li
sh
ed
A
g
g
re
g
at
e
F
o
rw
ar
d-
lo
o
k
in
g
B
ra
n
d
B
2
C
Y
es
0
.0
1
3
9
5
0
C
h
au
d
h
u
ri
,
A
rj
u
n
,
an
d
M
o
rr
is
B
.
H
o
lb
ro
o
k
(2
0
0
1
),
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h
e
C
h
ai
n
of
E
ff
ec
ts
fr
o
m
B
ra
n
d
T
ru
st
an
d
B
ra
n
d
A
ff
ec
t
to
B
ra
n
d
P
er
fo
rm
an
ce
:
T
h
e
R
o
le
o
f
B
ra
n
d
L
o
y
al
ty
,^
Jo
u
rn
a
l
o
f
M
a
rk
et
in
g
,
6
5
(A
p
ri
l)
,
8
1
–
9
3
.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
,
B
eh
av
io
ra
l
F
o
rw
ar
d
-l
o
o
k
in
g
B
ra
n
d
B
2
C
N
o
0
.0
1
2
3
3
7
C
h
eb
at
,
Je
an
-C
h
ar
le
s,
M
.
Jo
se
p
h
S
ir
g
y,
S
te
p
h
an
G
rz
es
k
o
w
ia
k
(2
0
1
0
),
BH
o
w
C
an
S
h
o
p
p
in
g
M
al
l
M
an
ag
em
en
tB
es
tC
ap
tu
re
M
al
l
Im
ag
e?
^
Jo
u
rn
a
l
o
f
B
u
si
ne
ss
R
es
ea
rc
h
,6
3
(2
0
1
0
),
7
3
5
–
74
0
.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
,
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
0
9
2
0
3
C
h
it
tu
ri
,
R
av
in
d
ra
,
R
aj
ag
o
p
al
R
ag
h
u
n
at
h
an
,
an
d
V
ij
ay
M
ah
aj
an
(2
0
0
8
),
BD
el
ig
h
t
b
y
D
es
ig
n
:
T
h
e
R
o
le
o
f
H
ed
o
n
ic
V
er
su
s
U
ti
li
ta
ri
an
B
en
ef
it
s,
^
Jo
u
rn
a
lo
fM
a
rk
et
in
g
,7
2
(M
ay
),
4
8
–
6
3
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
F
o
rw
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
1
2
3
3
7
C
o
ll
is
h
aw
,
M
ar
y
A
n
n
,
L
in
d
a
D
ye
r,
an
d
K
at
h
le
en
B
o
ie
s
(2
0
0
8
),
BT
h
e
A
u
th
en
ti
ci
ty
o
f
P
o
si
ti
v
e
E
m
o
ti
o
n
al
D
is
p
la
y
s:
C
li
en
t
R
es
p
o
n
se
s
to
L
ei
su
re
S
er
v
ic
e
E
m
p
lo
y
ee
s,
^
Jo
u
rn
a
lo
fL
ei
su
re
R
es
ea
rc
h
,
4
0
(J
an
u
ar
y
),
2
3
–
4
6
.
P
u
b
li
sh
ed
In
cl
u
si
v
e
B
ac
k
w
ar
d-
lo
o
k
in
g
In
d
iv
id
u
al
B
2
C
Y
es
0
.0
0
0
8
7
0
810 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
en
ce
U
n
p
ub
li
sh
ed
L
o
y
al
ty
p
er
sp
ec
ti
v
e
T
em
p
o
ra
l
o
ri
en
ta
ti
o
n
T
ar
g
et
M
ar
k
et
C
o
m
m
o
n
m
et
h
o
d
b
ia
s
su
sc
ep
ti
b
il
it
y
?
Jo
u
rn
al
q
u
al
it
y
(E
ig
en
fa
ct
o
r)
C
ro
n
in
Jr
.,
J.
Jo
se
p
h
,
an
d
M
ic
ah
el
K
.
B
ra
d
y,
an
d
G
.
T
o
m
as
M
.
H
u
lt
(2
0
0
0
),
BA
ss
es
si
n
g
th
e
E
ff
ec
ts
o
f
Q
u
al
it
y,
V
al
u
e,
an
d
C
u
st
o
m
er
S
at
is
fa
ct
io
n
on
C
o
ns
u
m
er
B
eh
av
io
ra
l
In
te
n
ti
o
n
s
in
S
er
v
ic
e
E
n
v
ir
o
n
m
en
ts
,^
Jo
u
rn
a
lo
fR
et
a
il
in
g
,7
6
(J
un
e)
,1
9
3
–
21
8
.
P
u
b
li
sh
ed
In
cl
u
si
v
e
F
o
rw
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
0
2
8
9
9
C
ro
n
in
Jr
.,
J.
Jo
se
p
h
,
an
d
S
te
v
en
A
.
T
ay
lo
r
(1
99
2
),
BM
ea
su
ri
ng
S
er
v
ic
e
Q
u
al
it
y
:A
R
ee
x
am
in
at
io
n
an
d
E
x
te
n
si
o
n
,^
Jo
u
rn
a
lo
f
M
a
rk
et
in
g
,
5
6
(J
u
ly
),
5
5
–
6
8
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
F
o
rw
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
1
2
3
3
7
D
av
is
-S
ra
m
ek
,
B
et
h
,
C
o
rn
el
ia
D
ro
g
e,
Jo
h
n
T
.
M
en
tz
er
,
an
d
M
at
th
ew
B
.
M
y
er
s
(2
0
0
9
),
BC
re
at
in
g
C
o
m
m
it
m
en
t
an
d
L
o
y
al
ty
B
eh
av
io
r
A
m
o
n
g
R
et
ai
le
rs
:
W
h
at
ar
e
th
e
R
o
le
s
o
f
S
er
v
ic
e
Q
u
al
it
y
an
d
S
at
is
fa
ct
io
n
?^
Jo
u
rn
a
l
o
f
th
e
A
ca
d
em
y
o
f
M
a
rk
et
in
g
S
ci
en
ce
,
3
7
(D
ec
em
b
er
),
4
4
0
–
4
5
4
.
P
u
b
li
sh
ed
A
tt
it
ud
in
al
,
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
B
Y
es
0
.0
0
5
4
0
3
D
aw
es
,
Jo
h
n
(2
0
0
9
),
BT
h
e
E
ff
ec
t
o
f
S
er
vi
ce
P
ri
ce
In
cr
ea
se
s
on
C
u
st
o
m
er
R
et
en
ti
o
n
th
e
M
o
d
er
at
in
g
R
o
le
o
f
C
u
st
o
m
er
T
en
u
re
an
d
R
el
at
io
n
sh
ip
B
re
ad
th
,^
Jo
u
rn
a
l
o
f
S
er
vi
ce
R
es
ea
rc
h,
11
(F
eb
ru
ar
y
),
2
3
2
–
2
4
5
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
N
o
0
.0
0
2
7
2
9
D
e
W
u
lf
,
K
ri
st
o
f,
G
ab
y
O
d
ek
er
k
en
-S
ch
rö
d
er
,
an
d
D
aw
n
Ia
co
b
u
cc
i
(2
0
0
1
),
BI
n
v
es
tm
en
ts
in
C
o
n
su
m
er
R
el
at
io
n
sh
ip
s:
A
C
ro
ss
-C
o
u
n
tr
y
an
d
C
ro
ss
-I
n
d
u
st
ry
E
x
p
lo
ra
ti
o
n
,^
Jo
u
rn
a
l
of
M
a
rk
et
in
g
,
6
5
(O
ct
ob
er
),
3
3
–
5
0
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
1
2
3
3
7
D
em
o
u
li
n
,
N
at
h
al
ie
,
an
d
P
ie
tr
o
Z
id
d
a
(2
0
0
9
).
BD
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v
er
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o
f
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u
st
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m
er
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d
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d
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ti
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im
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ar
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th
e
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ro
ce
ry
R
et
ai
l
M
ar
k
et
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u
rn
a
l
of
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et
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5
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ep
te
m
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er
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9
1–
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0
5
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u
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li
sh
ed
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eh
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io
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ac
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es
0
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9
9
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en
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h
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h
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n
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er
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d
in
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us
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er
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at
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ct
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d
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o
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ty
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n
E
m
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ir
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o
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st
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es
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h
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te
rn
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ti
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a
l
Jo
u
rn
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of
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rm
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ti
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n
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n
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u
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2
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9
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u
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li
sh
ed
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cl
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0
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rö
g
e,
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o
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ia
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ap
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o
u
te
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tt
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e
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h
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g
e:
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en
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er
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s
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er
ip
h
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ro
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g
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h
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h
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ar
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er
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s
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co
m
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ar
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e
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d
v
er
ti
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n
g,
^
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u
rn
a
l
o
f
M
a
rk
et
in
g
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es
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h
,
2
6
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ay
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3
–
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04
.
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u
b
li
sh
ed
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tt
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ud
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al
,
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eh
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io
ra
l
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o
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ch
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aj
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u
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.
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g
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d
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ag
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ra
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d
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e
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s
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u
lt
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le
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ra
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h
ic
R
et
ai
l
M
ar
ke
ts
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u
rn
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o
f
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et
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,
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ec
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r)
,
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9
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2
2
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u
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sh
ed
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eh
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l
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o
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n
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ar
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ac
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s
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o
n
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u
en
ce
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o
f
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er
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th
e
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o
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ty
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ro
g
ra
m
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o
m
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an
y,
^
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u
rn
a
l
o
f
th
e
A
ca
d
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y
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a
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ci
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te
m
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er
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5
–
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3
8
.
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u
b
li
sh
ed
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cl
u
si
v
e
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d
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o
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k
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g
F
ir
m
B
2
C
Y
es
0
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0
5
4
0
3
J. of the Acad. Mark. Sci. (2015) 43:790–825 811
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
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n
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er
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e
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o
m
m
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ti
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y
?
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u
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al
it
y
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ig
en
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r)
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v
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o
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e
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n
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er
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0
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h
e
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el
at
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en
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e
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er
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e
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ip
s,
^
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u
rn
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l
o
f
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u
si
n
es
s
R
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h
,
5
9
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o
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1
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u
b
li
sh
ed
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u
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v
e
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0
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ra
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s
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d
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e
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at
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en
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u
rn
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o
f
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ve
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u
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l
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le
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er
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th
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ar
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u
rn
a
l
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a
rk
et
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9
.
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u
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li
sh
ed
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e
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ir
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0
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ri
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.,
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le
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ip
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h
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n
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er
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ai
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le
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rn
a
l
o
f
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er
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l
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g
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n
d
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le
s
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3
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9
5
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u
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li
sh
ed
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eh
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l
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0
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an
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ar
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0
),
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er
st
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d
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e
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u
st
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er
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er
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e
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ro
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s:
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n
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at
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o
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th
e
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if
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re
n
ce
s
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et
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n
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w
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er
s
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d
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ta
y
er
s,
^
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u
rn
a
l
o
f
M
a
rk
et
in
g
,
6
4
(J
ul
y
),
p
65
–
8
7
.
P
u
b
li
sh
ed
In
cl
u
si
v
e
B
ac
k
w
ar
d
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o
k
in
g
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ir
m
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2
C
Y
es
0
.0
1
2
3
3
7
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ar
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ar
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o
,
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ll
en
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d
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ar
k
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.
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h
n
so
n
(1
9
9
9
),
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he
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if
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re
nt
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o
le
s
o
f
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at
is
fa
ct
io
n
,
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ru
st
,
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d
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o
m
m
it
m
en
t
in
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u
st
o
m
er
R
el
at
io
n
sh
ip
s,
”
Jo
u
rn
a
l
o
f
M
a
rk
et
in
g
,
6
3
(A
p
ri
l)
,
7
0
–
8
7
.
P
u
b
li
sh
ed
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tt
it
ud
in
al
,
B
eh
av
io
ra
l
F
o
rw
ar
d
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o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
1
2
3
3
7
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ar
n
ef
el
d
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n
a,
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n
d
re
as
E
gg
er
t,
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ab
ri
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a
V
.H
el
m
,a
n
d
S
te
p
h
en
S
.
T
ax
(2
0
1
3
),
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ro
w
in
g
E
x
is
ti
n
g
C
u
st
o
m
er
s’
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ev
en
u
e
S
tr
ea
m
s
T
h
ro
u
g
h
C
u
st
o
m
er
R
ef
er
ra
l
P
ro
g
ra
m
s,
^
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u
rn
a
l
o
f
M
a
rk
et
in
g
,
7
7
(J
u
ly
),
1
7
–
3
2
.
P
u
b
li
sh
ed
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tt
it
ud
in
al
,
B
eh
av
io
ra
l
F
o
rw
ar
d
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o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
1
2
3
3
7
G
el
b
ri
ch
,
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at
ja
(2
0
11
),
BI
H
av
e
P
ai
d
L
es
s
T
h
an
Y
o
u
!
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h
e
E
m
o
ti
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al
an
d
B
eh
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io
ra
l
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o
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se
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en
ce
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o
f
A
d
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ta
g
ed
P
ri
ce
In
eq
u
al
it
y,
^
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u
rn
a
l
o
f
R
et
a
il
in
g
,
8
7
(J
u
n
e)
,
2
0
7
–
2
2
4
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
F
o
rw
ar
d
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o
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k
in
g
F
ir
m
B
2
C
Y
es
0
.0
0
2
8
9
9
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il
ly
,
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ar
y
C
.,
an
d
B
et
sy
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.
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el
b
(1
9
8
2
),
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st
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u
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h
as
e
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o
n
su
m
er
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ro
ce
ss
es
an
d
th
e
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o
m
p
la
in
in
g
C
o
n
su
m
er
,”
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u
rn
a
l
o
f
C
o
ns
u
m
er
R
es
ea
rc
h
,
9
(D
ec
em
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er
),
3
2
3
–
3
28
.
P
u
b
li
sh
ed
B
eh
av
io
ra
l
B
ac
k
w
ar
d
-l
o
o
k
in
g
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ir
m
B
2
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Y
es
0
.0
1
2
0
9
1
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il
-S
au
ra
,
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en
e,
an
d
M
ar
ia
E
u
g
en
ia
R
u
iz
-M
o
li
n
a
(2
0
11
),
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o
–
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st
ic
s
S
er
v
ic
e
Q
u
al
it
y
an
d
B
u
y
er
–
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u
st
o
m
er
R
el
at
io
ns
h
ip
s:
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h
e
M
o
d
er
at
in
g
R
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le
o
f
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ec
h
n
o
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y
in
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2
B
an
d
B
2
C
C
o
n
te
x
ts
,^
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er
vi
ce
s
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d
u
st
ri
es
Jo
u
rn
a
l,
3
1
(M
ay
),
11
0
9
–
11
2
3
.
P
u
b
li
sh
ed
A
g
g
re
g
at
e
B
ac
k
w
ar
d
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o
o
k
in
g
F
ir
m
B
2
C
Y
es
0
.0
0
2
5
5
6
G
o
tt
li
eb
,
B
.
H
.,
G
re
w
al
,
D
.,
&
B
ro
w
n
,
S
.
W
.
1
9
9
4
.
C
o
n
su
m
er
sa
ti
sf
ac
ti
o
n
an
d
p
er
ce
iv
ed
q
u
al
it
y
:
C
om
p
le
m
en
ta
ry
o
r
di
v
er
g
en
tc
o
n
st
ru
ct
?
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u
rn
al
o
f
A
p
p
li
ed
P
sy
ch
o
lo
g
y,
7
9
:8
7
5–
88
5
.
P
u
b
li
sh
ed
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812 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
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J. of the Acad. Mark. Sci. (2015) 43:790–825 813
T
ab
le
7
(c
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ti
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)
R
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814 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
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o
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ff
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ti
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e?
^
Jo
u
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a
l
o
f
M
a
rk
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in
g
R
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h
,
3
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o
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er
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7
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4
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u
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li
sh
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ud
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o
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ee
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Y
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en
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d
L
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ty
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ep
ar
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en
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o
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ar
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ex
as
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er
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ty
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n
p
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cl
u
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o
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d
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k
in
g
F
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m
B
2
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Y
es
0
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0
4
0
5
7
J. of the Acad. Mark. Sci. (2015) 43:790–825 815
T
ab
le
7
(c
o
n
ti
n
ue
d
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R
ef
er
en
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n
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ed
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e
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ig
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im
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ai
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s,
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u
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a
l
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rg
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ay
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te
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ti
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rm
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u
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te
rn
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ti
o
n
a
l
Jo
u
rn
al
of
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in
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8
7
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9
7
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b
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sh
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it
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4
7
816 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
en
ce
U
n
p
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ed
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er
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T
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o
m
m
o
n
m
et
h
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s
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?
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u
rn
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al
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ax
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9
–
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sh
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ar
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at
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m
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g
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ar
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ci
en
ce
In
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ar
ti
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,
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u
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er
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ir
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at
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ip
s:
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o
w
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tt
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t
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ty
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el
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er
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re
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re
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ce
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r
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s,
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ep
u
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e
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te
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o
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d
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h
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g
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el
at
io
n
sh
ip
B
re
ad
th
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u
rn
a
l
of
M
a
rk
et
in
g
R
es
ea
rc
h,
5
0
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eb
ru
ar
y
),
12
5
–
1
4
2
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u
b
li
sh
ed
B
eh
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io
ra
l
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o
rw
ar
d
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o
o
k
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0
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it
ta
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ik
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ak
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ra
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ct
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u
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te
n
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d
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ep
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e
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eh
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io
r:
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v
es
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h
ar
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s,
^
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u
rn
a
l
of
M
a
rk
et
in
g
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es
ea
rc
h,
3
8
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eb
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ar
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1
3
1
–
1
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2
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u
b
li
sh
ed
B
eh
av
io
ra
l
B
ac
k
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o
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0
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2
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o
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si
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ts
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ed
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al
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e
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d
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m
er
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o
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ty
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en
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er
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^
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sy
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o
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n
d
M
a
rk
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g
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8
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ec
em
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er
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5
4
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11
7
6
.
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u
b
li
sh
ed
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cl
u
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v
e
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o
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d
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o
k
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ra
n
d
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2
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Y
es
0
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0
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9
3
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o
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el
is
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u
n
t
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0
0
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),
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o
w
ar
d
U
n
d
er
st
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d
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e
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n
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sy
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o
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o
f
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ip
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ar
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ep
ar
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o
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ar
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n
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n
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ud
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al
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l
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0
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5
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o
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eg
o
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he
V
al
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t
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er
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at
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ct
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d
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er
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ce
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a
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et
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g
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ci
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ce
,
2
5
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te
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ct
o
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er
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4
3
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u
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li
sh
ed
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ra
l
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u
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l
o
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g
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7
3
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ar
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4
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u
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ra
l
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k
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0
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ip
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g
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u
rn
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,
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ly
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0
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u
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li
sh
ed
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ra
l
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0
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ra
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le
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0
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o
n
su
m
er
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v
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at
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ra
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h
e
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u
rn
a
l
o
f
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ra
n
d
M
a
n
a
g
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en
t,
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ar
ch
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2
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5
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u
b
li
sh
ed
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tt
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ud
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k
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ra
n
d
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2
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Y
es
0
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0
0
0
0
0
J. of the Acad. Mark. Sci. (2015) 43:790–825 817
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
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n
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m
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ig
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r)
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R
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at
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n
sh
ip
s:
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re
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m
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e
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ca
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g
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e
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u
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l
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se
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h
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ie
r
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n
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u
rn
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l
o
f
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o
n
su
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er
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h
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ar
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2
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u
b
li
sh
ed
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io
ra
l
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ac
k
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o
k
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g
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m
B
2
C
Y
es
0
.0
1
2
0
9
1
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al
m
at
ie
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o
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er
t
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.,
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is
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ch
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p
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),
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st
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er
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ty
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an
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in
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th
e
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en
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d
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er
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ed
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o
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ty
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u
rn
a
l
o
f
M
a
rk
et
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g
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4
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ay
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9
9.
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u
b
li
sh
ed
In
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u
si
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o
0
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2
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e
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us
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er
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ra
ti
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e
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ip
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u
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u
b
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ed
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l
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0
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3
7
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al
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d
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R
el
at
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ip
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ar
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u
rn
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e
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a
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,
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s
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ip
s:
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l
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li
sh
ed
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l
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ar
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.
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n
d
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h
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en
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e
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u
rn
a
l
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f
M
a
rk
et
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,
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l
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1
2
3
3
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ar
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818 J. of the Acad. Mark. Sci. (2015) 43:790–825
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J. of the Acad. Mark. Sci. (2015) 43:790–825 819
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f
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n
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sy
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5
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820 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
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R
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0
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5
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te
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d
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u
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f
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k
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u
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l
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2
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7
J. of the Acad. Mark. Sci. (2015) 43:790–825 821
T
ab
le
7
(c
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n
ti
n
ue
d
)
R
ef
er
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ay
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9
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al
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u
rn
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o
f
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el
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ip
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at
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u
rn
a
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s,
^
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u
rn
a
l
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f
B
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R
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er
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n
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al
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at
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w
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sy
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n
d
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2
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te
rn
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l
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u
rn
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822 J. of the Acad. Mark. Sci. (2015) 43:790–825
T
ab
le
7
(c
o
n
ti
n
ue
d
)
R
ef
er
en
ce
U
n
p
ub
li
sh
ed
L
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ty
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er
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ti
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e
T
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ar
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et
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o
m
m
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n
m
et
h
o
d
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ia
s
su
sc
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ti
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it
y
?
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u
rn
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q
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al
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y
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ct
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u
rn
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ip
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t:
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u
tc
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m
es
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u
rn
a
l
of
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er
so
n
a
l
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li
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g
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d
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le
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p
ri
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,
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6
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–
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.
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b
li
sh
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- Building, measuring, and profiting from customer loyalty
Abstract
Theoretical domain of customer loyalty
Loyalty as favorable attitudes and purchase behaviors
Measurement composition and study-specific characteristics in loyalty research
Conceptual model and hypotheses
Loyalty antecedents
Loyalty outcomes
Role of measurement composition and study-based characteristics
Empirical study
Methodology
Results
Post hoc analysis (Model 4)
Strengthening the loyalty framework for researchers and practitioners
Guidance for researchers
Guidance for practitioners
Limitations and directions for research
Conclusion
References