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8/18/2019 Revisiting the Satisfaction–Loyalty Relationship
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Journal of Retailing 89 (3, 2013) 246–262
Revisiting the Satisfaction–Loyalty Relationship: Empirical Generalizationsand Directions for Future Research
V. Kumar a,∗, Ilaria Dalla Pozza a,b, Jaishankar Ganesh c
a Center for Excellence in Brand & Customer Management, J. Mack Robinson School of Business, Georgia State University, Atlanta, GA 30303-3989, United Statesb IPAG Business School, Paris, France
c School of Business, Rutgers University, Camden, NJ, United States
Abstract
This extensive literaturereviewhighlights the state of the art regarding the relationship between customer satisfaction and loyalty, both attitudinaland behavioral. In particular, it brings to light several issues that should be carefully considered in analyzing the efficacy of customer satisfaction in
explaining and predicting customer loyalty. In fact, for many years companies all around the world have heavily invested in customer satisfaction
in the hope of increasing loyalty, and hence, consequently, profitability. But after having gone through a detailed analysis, it is clear that this link
it is not as strong as it is believed to be and customer satisfaction is not enough to explain loyalty. In fact, the major findings of this review are
captured in the form of a few empirical generalizations. We generalize that, while there is a positive relationship between customer satisfaction
and loyalty, the variance explained by just satisfaction is rather small. Models that encompass other relevant variables as moderators, mediators,
antecedent variables, or all three are better predictors of loyalty than just customer satisfaction. Further, the satisfaction–loyalty relationship has
the potential to change over time. Similar weaker findings are uncovered and the study offers specific guidelines on who, when, and how much to
satisfy. Finally, suggestions for future research to explore this domain are offered.
© 2013 New York University. Published by Elsevier Inc. All rights reserved.
Keywords: Customer satisfaction; Loyalty; Word-of-Mouth; Customer lifetime value; Retention; Generalizations
Introduction
While having a satisfied customer base is a laudable goal
that is not to be questioned, its impact on loyalty and per-
formance outcomes is not as obvious. In reality, the question
concerning the efficacy of the satisfaction–loyalty link is
much more nuanced than if a simple yes, it exists, or no, it
doesn’t. Researchers (Kamakura et al. 2002; Rust, Zahorik,
and Keiningham 1995) have for long suggested that companies
should not blindly follow the path of only focusing on customer
satisfaction in the hope of improving loyalty. Specifically, these
studies have pointed out the necessity of considering the cost of
We would like to thank the seminar participants at various universities in
the U.S., France, and Italy and Yashoda Bhagwat for their valuable suggestions
during the preparation of this manuscript. We thank Renu for copyediting the
manuscript.∗ Corresponding author. Tel.: +1 404 413 7590; fax: +1 832 201 8213.
E-mail addresses: vk@gsu.edu, dr vk@hotmail.com (V. Kumar),
ilaria.dalla pozza@devinci.f r (I.D. Pozza), Jganesh@camden.rutgers.edu
(J. Ganesh).
a customer satisfaction improvement when deciding whether or
not to make customer satisfaction investments (Kamakura et al.
2002). A meta-analysis conducted by Szymanski and Henard
(2001) finds that satisfaction explains less than 25 percent of
the variance in repeat purchase. More precisely, the associa-
tion between customer satisfaction and loyalty is highly variable
depending on the industry, customer segment studied, the nature
of the dependent and independent variables, and the presence of
numerous factors that serve as mediators, moderators, or both
to the relationship.
For instance, while several studies report of a positive sig-
nificant relationship between satisfaction and loyalty, Verhoef
(2003), examining the effect of satisfaction along with other
variables on defection and customer share development, found
no significant direct effect for satisfaction. Only affective com-
mitment and loyalty program membership were found to have a
significant positivedirect effect on customer retention.However,
satisfaction comes into play when moderated by relationship
age. Results also vary according to the way loyalty is mea-
sured (intentions vs. actual behavior). For instance, Seiders et al.
(2005) find that customer satisfaction has a strong positive effect
0022-4359/$ – see front matter © 2013 New York University. Published by Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.jretai.2013.02.001
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 247
on repurchase intentions, but found no direct effects on repur-
chase behavior.
Researchers have also indicated the presence of moderators
in the satisfaction–loyalty relationship. For instance, Homburg
and Giering (2001) in linking customer satisfaction to loy-
alty recognized that the link is not universally strong for all
segments. The authors find significant moderating effects of
customer characteristics: among them, age, variety seeking, and
income seem important variables. Mittal and Kamakura (2001)
findthat in theautomotive industry satisfactionratings arehigher
for women than men. Others have pointed out that satisfaction
is not the main driver of loyalty. Agustin and Singh (2005)
conducted their study in the retail clothing and airline indus-
tries and found that relational trust and value are the strongest
determinants of loyalty intentions, rather than customer sat-
isfaction. Similarly, Ngobo (1999) and Anderson and Mittal
(2000) f ound variability of the satisfaction–loyalty link across
industries.
Deftly summarizing more than two decades of academic
research on this issue, Mittal and Frennea (2010) offer strate-gic insights and critical guidelines to managers that, among
other things, identify the differences across customer groups
and segments and the varying impact of customer satisfac-
tion on behavior across industries. While Mittal and Frennea
(2010) do point out the presence of customer segment differ-
ences, theydo not systematically address moderators, mediators,
and other predictors of loyalty that could potentially reduce
the relevance of customer satisfaction. Luo and Homburg
(2007) on the other hand explore the moderating impact of
market concentration on the relationships between customer
satisfaction and future advertising and promotion effective-
ness as well as a firm’s human capital performance. Whilethey state that satisfaction increases customer loyalty and
influences future purchase intentions and behaviors they do
not directly examine this relationship. They do not provide
empirical generalizations regarding the relationship between
customer satisfaction and loyalty. Despite a plethora of stud-
ies examining the impact of satisfaction on a firm’s customer
base in multiple contexts using other moderating and mediat-
ing variables (Biong 1993 – B2B; Bowen and Chen 2001 –
hospitality; Keh and Lee 2006 – services; Vesel and Zabkar
2009 – DIY programs; Söderlund 2002 – prepurchase famil-
iarity; Suh and Yi 2006 – product involvement; Yi and La
2004 – expectations) there still exists a void in terms of gen-
eralizable empirical findings (Garbarino and Johnson 1999)relating the various attitudinal and behavioral measures of
loyalty and the role of customer, relational, and marketplace
characteristics in understanding the satisfaction–loyalty rela-
tionship.
Helping to fill this void, Gupta and Zeithaml (2006) iden-
tify and develop empirical generalizations on three links: the
relationship between unobservable metrics (customer satis-
faction) and financial performance, the relationship between
unobservable constructs and observable constructs (satisfac-
tion and retention), and the impact of observable constructs
on financial performance (relationship between retention and
profitability). Gupta and Zeithaml (2006) develop empirical
generalizations by considering eleven studies expressing loy-
alty as observable actual behavior (retention or repurchase rather
than repurchase intentions). The focus of our study is on exam-
ining the relationship between customer satisfaction and loyalty
– using both attitudinal and behavioral measures. In Gupta
and Zeithaml’s (2006) words, we focus both on relationships
between perceptual customer metrics (customer satisfaction and
attitudinal loyalty) and on relationships between unobserva-
ble metrics and behavioral metrics (customer satisfaction and
behavioral loyalty) in order to provide a more comprehensive
review.
The primary objective of this study is to provide a compre-
hensive review and draw empirical generalizations addressing
these critical issues that impact the satisfaction–loyalty link. In
particular, this study examines the following questions: What
do we really know about the customer satisfaction–loyalty link?
Is customer satisfaction a good predictor of loyalty? Is it really
worth investing in customer satisfaction in an effort to improve
loyalty? The generalizations are based on studies that span mul-
tiple retail and service sectors including banking and financialservices, hospitality, insurance, pharmaceuticals, telecommuni-
cations, automotive, and retail grocery. Our conclusion is that
the customer satisfaction–loyalty main effect is indeed weak and
that customer satisfaction, by itself, can hardly change customer
loyalty in a significant way. In fact, the systematic presence
of moderators, mediators, and other predictors of loyalty intro-
duce a high variability in the findings, thus reducing the role
of satisfaction. So, does it really make sense for companies to
continue to adopt the conventional paradigm? In a resource con-
strained environment, should companies continue to invest in
customer satisfaction in the traditional sense, in the hope that
customer loyalty and profits will follow? Should companiescontinue to look at the link between satisfaction and loyalty
in isolation or should they examine the relationship in a broader
context?
The next section presents a literature review on the relation-
ship between customer satisfaction and loyalty. The literature
review and the associated analysis of the empirical findings will
be conducted separately for attitudinal and behavioral loyalty.
First, we will look at the direct relationship between satisfaction
and loyalty (direction, shape, variance explained). Then, we will
investigate the moderators, mediators, and other predictors of
loyalty, after controlling for the effect of satisfaction. Based on
past research findings, we draw empirical generalizations that
offer consistent explanations to these complex relationships. Inthe final section we examine research addressing the broader
phenomenon of customer-oriented strategy and customers dif-
ferences in terms of the value they bring to the firm as measured
by the lifetime value (Gupta et al. 2006) and draw insights on
who to satisfy and how much and when to satisfy. For instance,
companies should be engaged in proactive strategies that enable
them to target their resources first toward satisfying the high
value customers while minimizing investments targeted at non-
profitable or less profitable customers, thus bringing profitability
and a stronger focus on costs to bear at the outset of the decision-
making process. We conclude by highlighting directions for
future research.
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248 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
Customer satisfaction–loyalty relationship: literature
review and generalizations
Attitudinal loyalty measured as intention
Attitudinal loyalty can be expressed as the likelihood to rec-
ommend, the likelihood to repurchase, or depending on the
context, the likelihood to visit/repurchase from the retailer
again (Agustin and Singh 2005; Anderson and Mittal 2000;
Anderson and Sullivan 1993; Bloemer and de Ruyter 1998;
Chandrashekaran et al. 2007; Cronin, Michael, and Hult 2000;
Gustafsson and Johnson 2004; Homburg and Furst 2005;
Homburg and Giering 2001; Johnson, Herrmann, and Huber
2006; LaBarbera and Mazursky 1983; Lam et al. 2004; Liang
and Wang 2004; Mittal, Kumar, and Tsiros 1999; Mittal, Ross,
and Baldasare 1998; Ngobo 1999; Seiders et al. 2005). These
“likelihoods” are measured as intentions based on self-reported
surveys. Literature is replete with research addressing the sat-
isfaction and attitudinal loyalty relationship. Fig. 1 and Table 1
present a summary of the results.The studies presented in Table 1 are organized according to
how the constructs were measured, that is, using single item or
multi-item scales. For each study, Table 1 indicates the direc-
tion of the relationship (positive, negative or not significant),
the R2 and the shape of the relationship where reported (lin-
ear, concave or convex, asymmetric nonlinear, with increasing
or decreasing returns). Based on these results, we identify the
following generalizations (Bass and Wind 1995):
G1: Overall, there is a positive relationship between customer satisfaction
and loyalty intentions.
It is important to note that while Szymanski and Henard
(2001) report their findings based on a meta-analysis of nine
studies on customer satisfaction and repeat purchase, they do
acknowledge that further analysis is necessary because, “few
correlations are available in the literature to report on these asso-
ciations and so a few studies reporting different effect sizes in
the future could alter conclusions (24).” Further Szymanski and
Henard (2001) state that studying the relationship between sat-
isfaction, loyalty, retention, and other variables using research
excluded from their meta-analysis could be insightful, inter-
esting, and valuable. Hence, G1 is grounded on an extensive
literature base, which studies additional variables, and both con-
firms and extends their findings.
Interestingly, there is one study (Homburg and Furst 2005)
which stands apart from G1 by finding a nonsignificant
relationship between satisfaction and loyalty. However, this non-
significant result is valid only for overall satisfaction but not for
transactional satisfaction (positively related to intentions). The
explanation may rely in the setting investigated. The study takes
place in a complaint management setting and it seems reason-
able to think that satisfaction (as transactional satisfaction is
expressed) recorded after an interaction with customer service
is dominant in affecting loyalty. Moreover, the sample size of
the study is relatively small.
Regarding the variance explained in loyalty, it is not nor-
mally possible to isolate the unique contribution of customersatisfaction since other variables are introduced in the study as
moderators, mediators or other predictors. The only exception
is presented by Anderson and Sullivan (1993), who indicate
an R2 of .19 with the only variable being customer satisfac-
tion (Table 1). In general, the R2 always refers to the overall
model encompassing other variables. For instance, Agustin and
Singh (2005) report an R2 between .43 and .51 by including
trust and value in the model, while Cronin, Michael, and Hult
(2000) report an R2 of .94 by including service value and service
quality (Table 1). Similarly, Seiders et al. (2005) indicate an R2
of .42 by including involvement and convenience. While the
results addressing the shape of the relationship between sat-isfaction and intentions are varied, it is safe to state that the
majority of the studies report a linear relationship (Bloemer and
de Ruyter 1998; Bolton and Drew 1991; Cronin, Michael, and
Hult 2000; Garbarino and Johnson 1999; Homburg and Furst
2005; Homburg and Giering 2001; LaBarbera and Mazursky
Fig. 1. Relationship between multiple item customer satisfaction measures and loyalty intentions.
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 249
Table 1
Summary of satisfaction–loyalty intention findings.
Customer satisfaction
Single item Multiple items
Overall satisfaction Overall satisfaction ACSI/SCSB Transactional
satisfaction
Attribute satisfaction
Loyalty
intentions
Single item Linear
• LaBarbera and
Mazursky (1983) (+)
• Mittal, Kumar, and
Tsiros (1999) (+)
R2: .37–.50
• Shankar, Smith, and
Rangaswamy (2003) (+)
R2: .39–.50
• Baumann, Burton, and
Elliott (2005) (+)
R2: .47–.72
• Keiningham et al.
(2007) (+)
•
Anderson andSullivan (1993) (+)
R2: .19 (only CS)
Linear
• Olsen (2002) (+)
• Gustafsson and
Johnson (2004) (+)
R2: .38
• Chandrashekaran
et al. (2007) (+)
Decreasing returns
• Oliva, Oliver, and
MacMillan (1992) (+)
R2: .33
Concave/convex
• Jones and Sasser
(1995) (+)
Asymmetric nonlinear
• Mittal, Ross, and
Baldasare (1998) (+)
Multiple item Linear
• Bloemer and de
Ruyter (1998) (+)
Linear
• Garbarino and
Johnson (1999) (+)
• Cronin, Michael, and
Hult (2000) (+)
R2: .94
• Lam et al. (2004) (+)
• Homburg and Furst
(2005) (ns)
Increasing returns
• Anderson and
Mittal (2000) (+)
Linear
• Homburg and
Furst (2005) (+)
Linear
• Homburg and
Giering (2001) (+)
• Liang and Wang
(2004) (+)
• Seiders et al. (2005)
(+)
R2: .42
Nonlinear, quadratic• Ngobo (1999) (+)
R2: 0.57
Decreasingreturns
• Agustin and
Singh (2005) (+)
R2: .43–.51
(+), (−) and ns (non significant) refer to the direction of the association between customer satisfaction and the dependent variable.
1983; Lamet al.2004;Liangand Wang2004;Mittal, Kumar, and
Tsiros 1999; Olsen 2002; Seiders et al. 2005; Shankar, Smith,
and Rangaswamy 2003).
The exceptions to the linear relationship findings include
the studies conducted by Jones and Sasser (1995), Ngobo
(1999), Mittal, Ross, and Baldasare (1998), Oliva, Oliver, and
MacMillan (1992), Anderson and Mittal (2000), and Agustinand Singh (2005). For instance, Anderson and Mittal (2000),
using the ACSI, find that the link between customer satis-
faction and repurchase intention is asymmetric and nonlinear
with increasing returns. The line becomes steeper on each
end, where the line rises into the delight or extreme dissat-
isfaction zone. In the middle there is a flattening zone, a
zone of apathy where changes in customer satisfaction result
in minor changes in loyalty (Anderson and Mittal 2000; J.D.
Power and Associates 2007). As a consequence, whencustomers
are delighted (Berman 2005; Jones and Sasser 1995; Oliver,
Rust, and Varki 1997; Reichheld 1996; Rust and Oliver 2000;
Schneider and Bowen 1999), they tend to ignore competing
brands, while a decrease in satisfaction below a certain threshold
has a greater impact on repurchase intentions than an equiva-
lent increase in the flattening zone (Anderson and Mittal 2000)
(Fig. 2).
On the contrary, Agustin and Singh (2005) highlight the
simultaneity in curvilinear effects of loyalty determinants such
as transactional satisfaction, trust, and relational value, the latterexpressed as an evaluation of price paid. In particular, trans-
actional satisfaction has a positive linear effect but a negative
quadratic effect. That is, as satisfaction increases, its impact
on loyalty decreases. Decreasing returns are supported also by
Oliva, Oliver, and MacMillan (1992). Some other authors have
analyzed the variation of the shape of the relationship on the
basis of industry characteristics.
For instance, Jones and Sasser (1995) find that in highly
competitive industries the shape of the relationship is convex,
while in less competitive industries it is concave. Similarly,
Ngobo (1999) finds that the nonlinear relationship varies
according to the industry (quadratic negative relationship with
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Fig. 2. Shape of the relationship between satisfaction and repurchase intentions
(Anderson and Mittal 2000).
decreasing returns for an insurance company, two thresholdmodel with decreasing returns for the camera and bank industry,
linear relationship for the retailer). Two studies, such as those
of Jones and Sasser (1995) and Ngobo (1999) are considered
enough to define an empirical generalization related to nonlinear
relationships affected by the type of industry (Bass and Wind
1995, p. 2). In addition, other authors have argued that industry
type impacts the association between customer satisfaction
and behavior (Keiningham et al. 2007; Verhoef 2003). Ittner
and Larcker (1998) find that the value relevance of customer
satisfaction measures varies across industries. This leads us to
the following generalization:
G2: The type of industry affects the specific shape of the nonlinear
relationship.
Moderators in the relationship between customer
satisfaction and loyalty intentions
The relationship between customer satisfaction and loyalty
intentions is strongly affected by the presence of moderators
(Baron and Kenny 1986) that can strengthen or weaken the asso-
ciation. This explains why satisfied customers defect, since other
variables intervene in affecting the strength of the relationship.
In fact, Reichheld (1996) notes that 65–85 percent of customers
who defect, report before defection, that they were satisfied or
very satisfied. Customers can express different levels of loyaltyintentions while holding similar levels of customer satisfaction
(Reichheld 1996).
According to Seiders et al. (2005), moderators have been
divided into customer, relational, and marketplace variables.
Referring to customer-related moderators, past research has
found positive moderator effects with satisfaction strength, and
age, and negative effects with variety seeking behavior, and
income (Chandrashekaran et al. 2007; Homburg and Giering
2001). In particular, Homburg and Giering (2001) find a sig-
nificant moderating effect of customer characteristics such as
age, variety seeking behavior, and income. That is, young cus-
tomers tend to be less loyal, while variety seeking behavior
markedly weakens the relationship. As a consequence, in highly
competitive environments that allow for several choices, if
switching costs are not severe, we can expect a weaker relation-
ship due to the natural inclination of the customer to try different
alternatives. Regarding income, past research has found that in
the automotive industry it negatively moderates the relationship:
that is, a greater availability of economic resources broadens the
customer’s range of alternative options, thus reducing loyalty.
At similar levels of customer satisfaction, customers with higher
incomes display less loyalty toward the company (Homburg and
Giering 2001).
Relational moderators are variables that can depict the rela-
tionship between the customer and the company; customers can
be variedly interested in forming a relationship with the com-
pany, thus exhibiting a major or minor propensity in investing
resources to strengthen it (Garbarino and Johnson 1999). In
some situations, relational variables can strengthen the associa-
tion between satisfaction and loyalty (Agustin and Singh 2005;
Baumann,Burton, andElliot 2005; Bloemer andde Ruyter1998;
Oliva, Oliver, and MacMillan 1992). For instance, Oliva, Oliver,and MacMillan (1992) find that when transaction costs are suffi-
ciently high, a consumer may remain loyal even under moderate
dissatisfaction. This means that high levels of transaction costs
can entangle the customer in a not fully satisfactory relationship.
On a similar line, Bloemer and de Ruyter (1998) point out the
importance of “elaboration”, an indicator of the customer moti-
vation to evaluate a store, while Baumann, Burton, and Elliot
(2005) identify “the length of the relationship” and Agustin and
Singh (2005) the “value” as elements that strengthen the rela-
tionship. On the contrary, Garbarino and Johnson (1999) find
that for customers reporting high levels of relationship value,
satisfaction is less important than “trust” and “commitment” inaffectingloyalty. Finally, Chandrashekaranet al. (2007) find“the
length of the relationship” as not being influential in determining
loyalty.
Among marketplace moderators we have switching costs,
the type of product, the level of competition, and the kind of
medium (online vs. offline) used by customers to have negative
effects (Baumann, Burton, and Elliot 2005; Jones and Sasser
1995; Olsen 2002; Shankar, Smith, and Rangaswamy 2003).
Customersperform theireconomic transactions in differentenvi-
ronments and marketplaces that can affect the relationship.
Notably, the Internet has radically changed the way customers
relate to a company, ultimately affecting their satisfaction and
loyalty. For instance, Shankar, Smith, and Rangaswamy (2003)find that overall satisfaction has a stronger positive impact on
loyalty online than offline. The Internet has actually created
less loyal customers. In fact, rather than repurchasing the same
product over time, Internet consumers are more likely to look
at every purchase as a fresh start, counting on the impres-
sive quantity of information and choices coming from the web.
Due to the higher competition exacerbated by the Internet and
the customers’ empowerment, satisfaction acquires much more
importance in affecting loyalty online than offline. Addressing
a different marketplace moderator (type of product), Szymanski
and Henard (2001) find that the correlation between satisfaction
and repeat purchasing is lower on average when products rather
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 251
than services are the focus of the study. Olsen (2002) also finds
that the relationship varies across products, while Baumann,
Burton, and Elliott (2005) find a very small negative moder-
ating effect of switching costs on intentions in the short term.
This leads us to:G3: The relationship between customer satisfaction and loyalty intentions
is moderated by customer, relational, and marketplace characteristics.
Interestingly, these factors show a more mixed effect (positive and negative) depending on the specific variable used in the analysis.
Also, the impact of customer satisfaction on loyalty inten-
tions changes over time. A satisfied customer can state some
intentions today that may differ from her intentions tomorrow,
because of the influence of the moderators in the intervening
period. Customers might discover a new competitor’s product
or, more simply, their memory about the positive experience
might decay over time (Mazurski and Geva 1989;Mittal, Kumar,
and Tsiros 1999). Mazurski and Geva (1989) find that the rela-
tionship becomes weaker as time goes by and the time lag
between customer satisfaction and loyalty increases. In addi-
tion, the drivers of customer satisfaction can also change over
time. For instance, Mittal, Kumar, and Tsiros (1999) find that
when customers buy a car, their initial satisfaction is mainly
driven by the experience with the dealer service. However, dur-
ing later consumption periods, when they get to experience the
product more, satisfaction with the product prevails. To con-
clude, customers value different attributes over time, implying
that different kinds of investment are required over the customer
lifecycle to improve the overall satisfaction or the total customer
experience. To make matters a bit murkier, in most cross sec-
tional studies, customer satisfaction and loyalty are measured at
the same time with common method bias potentially influencing
the responses(Agustin andSingh 2005). Unfortunately, a lack of longitudinal research investigating the impact of customer sat-
isfaction on loyalty makes it difficult to judge conclusively the
long-term effect of the relationship. Thus, we generalize that:
G4: The satisfaction–loyalty relationship has the potential to change over
the customer lifecycle.
Role of mediators and other predictors of loyalty intentions
Past research has shown that customer satisfaction does not
always have a direct effect on loyalty, but often works through
mediators. In particular, Agustin and Singh (2005), Garbarino
and Johnson (1999) and Liang and Wang (2004) identified trust,commitment, and relational value, to be potential mediators.
Most of these studies also introduce other relevant predictors of
loyalty intentions, some of which have shown stronger explana-
tory power than satisfaction in determining loyalty. In particular,
past studies (Agustin and Singh 2005; Baumann, Burton, and
Elliot 2005; Cronin, Michael, and Hult 2000; Lam et al. 2004;
Mittal, Kumar, and Tsiros 1999) have examined the role of
trust, relational value, switching costs, length of the relation-
ship, affective attitude, service quality, service value, and prior
intentions in predicting loyalty intentions. In fact, these stud-
ies address a critical need in the satisfaction–loyalty literature
for more holistic models explain the outcome variable better.
Agustin and Singh (2005)and Cronin, Michael, andHult (2000),
express the need to collectively include more predictors to
explain loyalty, since, from a managerial standpoint,establishing
initiatives to improve only one variable – customer satisfaction,
is an incomplete strategy. This leads us to conclude that:
G5: Holistic models that encompass other relevant variables as a
moderator, mediator, as antecedent variables, or all three are better
predictors of loyalty than models with just customer satisfaction.
Attitudinal loyalty measured as Word-of-Mouth (WOM)
WOM hasreceived a lot of attention as an alternative measure
of loyalty. For instance, Aaker (1991) noted that the real value
of those customers most loyal to an entity stems more from their
impact on other customers in the marketplace than from their
individual purchase behavior.
Notably, Reichheld (2003) states that the only number a com-
pany needs to grow is the net promoter score (NPS), the net
number of customers willing to recommend the company. Even
though this statement has been largely disproved by recent aca-demic literature, the remarkable impact the NPS has created on
the business environment is proof of the importance imputed to
WOM as an alternative measure of loyalty.
WOM can be positive or negative. Positive WOM may
include making recommendations about a product or service,
and informing others of the quality of an offer. Customers
who spread favorable WOM about a company can become the
company’s best salespeople. On the contrary, negative WOM
includes expressing disappointment about a negative experi-
ence or product or a complaint. Customers spreading negative
WOM can poison the company’s reputation and can actively
seek for other more valuable alternatives (Wangenheim 2005).Today, communities of angry customers can easily express
their complaints about a bad experience by simply posting
on the web (examples are consumerreview.com, dpreview.com,
failingenterprise.com). While before companies were pretty
much immuneto negative WOM coming from angry consumers,
today, the Internet has given the customers an unprecedented
power in attacking companies’ reputation.
Memorable is the extreme behavior of a customer, Jeremy
Dorosin, who, in 1995 bought an expensive Starbucks espresso
machine for $299 (www.starbucked.com). The machine turned
out to be defective almost immediately. The replacement
machine was also found to be defective. Dorosin complained to
Starbucks regional offices, but never got a satisfactory answer.As a consequence, he started to purchase ads on Wall Street
Journal to complain about the company. This got the attention
of the national media with appearances on popular television
shows talking about his bad experience. While this might be an
extreme behavior, it is an example that reiterates the power of
WOM, one that companies cannot afford to ignore (J.D. Power
and Associates 2007).
Customer satisfaction is considered an antecedent of WOM.
Researchhas shown thatpositiveWOM from satisfied customers
lowers the cost of attracting new customers and enhances the
firm’s overall reputation, while that from dissatisfied customers
has the opposite effect (Anderson, Fornell, and Mazvancheryl
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252 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
2004; Fornell 1992). The studies on the relationship between
customer satisfaction and WOM are mostly cross-sectional, with
WOM being a self-reported measure of past behavior. Brown
et al. (2005) is an exception where they measure WOM three
months after reporting customer satisfaction; but it is still a self-
reported measure of past behavior.
Table 2 summarizes the results of the relationship between
satisfaction and WOM (after controlling for other possible
variables), identifying the direction, shape, and the variance
explained. While most studies have examined the effect of pos-
itive WOM (Brown et al. 2005; Verhoef, Franses, and Hoekstra
2002; Wangenheim and Bayon 2003), a few consider simulta-
neously both positive and negative WOM effects, and investigate
whether dissatisfied angry customers have a higher propensity
to report negative experiences to others as compared to satis-
fied customers’ propensity to report positive recommendations
(Anderson 1998; Bowman and Narayandas 2001). Wangenheim
(2005) provides an interesting twist, where satisfaction with the
current provider is related to negative WOM about the previous
provider.For the most part, the shape of the relationship curve is lin-
ear (Brown et al. 2005; Lam et al. 2004; Verhoef, Franses,
and Hoekstra 2002), with Anderson (1998) and Bowman and
Narayandas (2001) reporting the existence of a U-shaped rela-
tionship. Specifically, Anderson (1998) showed that extremely
satisfied and dissatisfied customers are more vociferous than
merely satisfied customers and that, extremely dissatisfied cus-
tomers engage in greater WOM than highly satisfied customers.
In the middle lies a big portion of “passive” and complacent
customers, merely satisfied customers, who normally do not
speak about their experiences, good or bad, but are susceptible
to competitive actions. Fig. 3 presents the results of the analy-sis, indicating direction, shape, and number of studies in each
category.
Moderators, mediators, and other predictors of WOM
The relationship between customer satisfaction and WOM
is characterized by the presence of moderators and mediators.
While customer satisfaction has a positive effect on customer
referral, other variables seem to predict WOM better. Among
other variables, past research (Brown et al. 2005; Lam et al.
2004; Verhoef, Franses, and Hoekstra 2002; Wangenheim 2005)
has found commitment, trust, payment equity, product involve-
ment, and market mavenism to be better predictors of WOM.For instance, Verhoef, Franses, and Hoekstra (2002) f ound that
affective commitment is a better predictor of WOM than satis-
faction. Similarly, in a meta-analysis, de Matos and Rossi (2008)
found that commitment is themost relevantantecedent of WOM.
Also, among moderators, Brown et al. (2005) found that cus-
tomer commitment weakens the relationship, while Anderson
(1998) showed the existence of differences between countries
of origin.
The influence of commitment on the satisfaction–WOM rela-
tionship is intriguing. It is interesting to note that a variable
that serves to express the strength of customers’ relationship
with the firm, actually contribute to weakening the effect of
satisfaction on WOM (Brown et al. 2005). Commitment comes
across as a critical variable since it both mediates and moderates
the relationship, while satisfaction assumes a more central role
in explaining the referral activity in low commitment situations
(Brown et al. 2005). Similarly, Bowman and Narayandas (2001)
show that the more satisfied customers are with the final out-
come of a complaint, the less likely they are to engage in WOM
activity. This leads us to generalize that:
G6: While customer satisfaction is positively related to Word-of-Mouth,
models with related variables such as commitment, trust, and product
involvement serve as better predictors of WOM .
Customer satisfaction and behavioral loyalty
Often, companies are more interested in observing customer
behavior, rather than intentions, since it can be directly linked to
revenues and profitability (Bemmaor 1995; Chandon, Morwitz,
and Reinartz 2005; Jamieson and Bass 1989). Table 3 presents
a summary of the research in this area that have used several
different measures of behavioral loyalty including retention (orthe complementary metric – defection/churn), lifetime duration,
usage,share of wallet andcross buying. Retention, lifetime dura-
tion and usage reflectthe lengthand thedepth of therelationship,
while cross buying and share of wallet provides an indication of
its breadth (Bolton, Lemon, and Verhoef 2004).
Behavioral loyalty measured in terms of relationship length
and depth – customer retention, lifetime duration, and usage
In examining retention, defection, and usage behaviors, it is
important to note that thebehavioral variables are recorded some
time after the customer satisfaction survey (Bolton 1998; Boltonand Lemon 1999; Capraro, Broniarczyk, and Srivastava 2003;
Gustafsson, Johnson, and Ross 2005; Ittner and Larcker 1998;
Mittal and Kamakura 2001; Seiders et al. 2005). For instance,
Mittal and Kamakura (2001), in an automotivesetting,record the
new brand acquired by the customer after a customer satisfac-
tion survey, while Bolton, Kannan, and Bramlett (2000) record
the number of customer transactions and monitor whether the
customer has canceled the service during the year following
the survey. Different measures of customer behavior are used
in contractual versus noncontractual settings. In particular, for
contractual settings (such as financial, telecommunication, and
health insurance), measures of retention or defection/churn are
used since it is relatively straightforward to observe termina-tion of the customer-provider relationship. On the contrary, in
noncontractual settings (such as retail and automotive), where
defection cannot be easily detected (Reinartz and Kumar 2000,
2002), metrics such as repurchase behavior, number of repur-
chase visits, and dollar spent are used.
Table 4 and Fig. 4 present a classification of the studies
based on how customer satisfaction and the dependent variable
are measured. These exhibits also report the direction of the
relationship and, when possible, the shape of the relationship.
While these studies mostly predict a positive relation-
ship between satisfaction and measures of behavioral loyalty,
regarding the shape of the relationship, the results are not
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 253
Table 2
Summary of satisfaction–WOM findings.
Customer satisfaction
Single item Multiple items
Overall Transactional Overall Transactional Attribute
satisfaction
ACSI/SCSB
WOM
Posit. WOM • Wangenheim and
Bayon (2003) (+)
Linear
• Lam et al. (2004)
(+)
Linear
• Brown et al.
(2005)
R2: .29 (+)
• Verhoef, Franses,
and Hoekstra (2002)
R2: .37 (+)
• Wangenheim and
Bayon (2003) (+)
Negat. WOM • Wangenheim
(2005) (+)
R2: .26–.59
Posit. and
Negat. WOM
U shaped
• Bowman and
Narayandas (2001)
U shaped
• Anderson (1998)
R2
: .03–.1 (only CS)(+), (−) and ns (non significant) refer to the direction of the association between customer satisfaction and the dependent variable.
conclusive. While some studies report a nonlinear and asymmet-
ric association (Ittner and Larcker 1998; Mittal and Kamakura
2001), others (Bolton and Lemon 1999; Gustafsson, Johnson,
and Ross 2005; Perkins-Munn, Lerzan Aksoy, and Keiningham
2005), find a linear relationship. More interestingly, the kind
of setting (contractual vs. noncontractual) does not consistently
predict a positive or negative relationship.
Role of moderators in the relationship between satisfaction
and behavioral loyalty
Here again, the satisfaction–behavioral loyalty relationship
is affected by the presence of moderators (customer, relational,
marketplace, or all three). In particular, among customer mod-
erators, past research has found positive effects for age, income,
and gender, and negative effects for level of education, and num-
ber of children, with marital status, and competitor knowledge
being not significant (Capraro, Broniarczyk, and Srivastava
2003; Mittal and Kamakura 2001; Seiders et al. 2005). More
specifically, Mittal and Kamakura (2001) find that the relation-
ship between satisfaction and repurchase behavior for cars to be
stronger for women than for men, and stronger for older than for
younger consumers. Moreover, subjects with more education
tend to have lower levels of retention than those with a high
school education. Also, consumers with one or more child in thehousehold have lower tolerance than those without any children.
Interestingly, there have been very few studies examining
the moderating role of marketplace variables in the relationship
between satisfaction and behavioral loyalty. Of these, most
have found little or no moderating effect of these variables.
Fig. 3. Direction and shape of the satisfaction–WOM relationship.
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254 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
Table 3
Satisfaction – retention, lifetime, and usage relationship shapes and direction.
Study Dependent variable and
direction of the
relationship
Shape of the
relationship or
technique used
Industry Percent of the variance
explained
Bolton (1998) Duration of the
provider–customer
relationship (+)
Proportional
hazard regression
Cellular telephone industry
(contractual setting)
8 percent (only CS)
Bolton and Lemon (1999) Actual usage level (+) Linear Continuous service providers
(contractual setting)
12 percent with other
predictors
Bolton, Kannan, and
Bramlett (2000)
Retention (+) (renewal
of the membership) and
number of transaction in
the following year (+)
Logistic
regression and
tobit model
Financial services (credit
card) (contractual setting)
Capraro, Broniarczyk,
and Srivastava (2003)
Defection (−) Hierarchical
logistic regression
Choice of health insurance
plan at a large University
(contractual setting)
8 percent (only CS)
25 percent with other
predictors
Gustafsson, Johnson, and
Ross (2005)
Churn (−) Linear Financial services (credit card
membership) (contractual
setting)
50 percent with other
predictors
Ittner and Larcker (1998) Retention rate in the
following year (+)
Linear Telecommunication industry
with one year contract
Adjusted R2 from 1.3
percent to 4.9 percent
with relationship age
Mittal and Kamakura
(2001)
Repurchase behavior of
a new car (+)
Nonlinear Automotive (noncontractual) 11.25 percent with other
predictors
Perkins-Munn, Lerzan
Aksoy, and
Keiningham (2005)
Actual repurchase (+) Linear Truck industry;
pharmaceutical
(noncontractual)
15 percent with other
predictors
Seiders et al. (2005) Number of repurchase
visits and repurchase
spending in the
following 52 weeks (ns)
Linear Retail chain of upscale
women’s apparel
(noncontractual)
From 10 percent to 13
percent with other
predictors
Verhoef (2003) Retention (ns) Probit model Insurance products
(contractual)
17 percent with other
predictors
(+), (−) and ns (non significant) refer to the direction of the association between customer satisfaction and the dependent variable.
Table 4Summary of satisfaction – retention, lifetime, and usage relationship findings.
Customer satisfaction
Single item Multiple items
Overall satisfaction Overall satisfaction Attribute satisfaction Relative satisfaction
Behavioral
loyalty
Retention/occurrence of the repurchase Nonlinear increasing
returns
• Mittal and
Kamakura (2001) (+)
Diminishing returns
• Ittner and Larcker
(1998) (+)
Linear
• Perkins-Munn,
Lerzan Aksoy, and
Keiningham (2005)
(+)
• Bolton, Kannan, and
Bramlett (2000) (+)
Churn Linear
• Gustafsson,
Johnson, and Ross(2005) (−)
• Capraro,
Broniarczyk, and
Srivastava (2003) (−
)• Verhoef (2003) (ns)
Duration of the relationship • Bolton (1998) (+)
Usage
Minutes of
usage
Linear
• Bolton and Lemon
(1999) (+)
Number of
repurchase
visits
Linear
• Seiders et al. (2005)
(ns)
Amount of
spending
Linear
• Seiders et al. (2005)
(ns)
Number of
transactions
• Bolton, Kannan, and
Bramlett (2000) (+)
(+), (−) and ns (non significant) refer to the direction of the association between customer satisfaction and the dependent variable.
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 255
Fig. 4. Direction and shape of the satisfaction–retention, lifetime, and usage relationship.
For instance, research (Mittal and Kamakura 2001; Seiders
et al. 2005) has found very little effect of other marketplace
variables such as the area of customer’s residence and com-
petitive intensity in moderating this relationship. However,
Seiders et al. (2005) did report a positive moderating effect
of convenience in the relationship between satisfaction for a
retailer and behavioral loyalty. More studies are needed to
verify the moderating role of the marketplace variables.
Among relational moderators, studies (Bolton 1998; Bolton,
Kannan, and Bramlett 2000; Gustafsson, Johnson, and Ross
2005; Seiders et al. 2005; Verhoef 2003) have found that churn,
relationship age, membership in a loyalty program, and levelof involvement to have positive effects. It is worth noting that
in the two studies in which satisfaction has no direct effect on
behavior, satisfaction turns out to be significant by interacting
with other variables, for instance, with relationship age (Verhoef
2003), involvement, and household income (Seiders et al. 2005).
This leads us to the generalization that:
G7: While customer satisfaction is mostly positively related to behavioral
loyalty measures, by itself, it does not always result in higher
likelihoods of retention, longer lifetime duration, and higher levels of
usage. Customer, relational, and marketplace variables play a
significant moderating role.
Role of other predictors in explaining behavioral loyalty
Past research studies have shown that other predictors of loy-
alty are significant and can have a stronger explanatory power
than satisfaction (Capraro, Broniarczyk, and Srivastava 2003;
Ittner and Larcker 1998). Among significant predictors, we have
relationship age (Ittner and Larcker 1998), prior churn or prior
customer tendency to switch provider (Gustafsson, Johnson, and
Ross 2005), likelihood to repurchase (Perkins-Munn, Lerzan
Aksoy, and Keiningham 2005), commitment, loyalty product
membership, type of product (Verhoef 2003), level of involve-
ment (Seiders et al. 2005), knowledge about competitive offers,
and switching risk (Capraro, Broniarczyk, and Srivastava 2003).
An interesting finding is presented in Capraro, Broniarczyk, and
Srivastava (2003), where it is shown that customer knowledge
of competitive alternatives account for about twice as much
variance in explaining customer defection as satisfaction and
perceived switching risk. In fact, it appears that consumers are
more likely to stay with a brand, even one that has disappointed
them in the past, if they have no information of alternatives. On
the contrary, an in-depth knowledge of alternate offers provides
customers an incentive to switch. Likewise, Bolton, Kannan,
and Bramlett (2000) argue that members of loyalty programs
weigh re-patronage intentions more heavily than nonmembers,
thus indicating a direct relationship between reward programmembership and behavioral loyalty. Further, they argue that
members of loyalty programs reveal stronger ties to the service
organization than nonmembers.
Of the variables shown by past studies as predictors of behav-
ioral loyalty, purchase and ego involvement can be considered
as important antecedents to brand loyalty. Purchase involvement
can best be understood as the cost, effort or investment in a pur-
chase (Mittal and Lee 1989). It is the outcome of an individual’s
interaction with a product and the purchase situation (Beatty,
Kahle, and Homer 1988). Ego involvement has been defined
as the importance of the product to the individual and to the
individual’s self concept, values and ego (Beatty, Kahle, and
Homer 1988). Ego involvement is similar to enduring involve-ment defined as an ongoing concern for a particular product
class and relatively independent of purchase situations (Bloch
and Richins 1983; Richins and Bloch 1986).
Beatty, Kahle, and Homer (1988) conceptualized and empir-
ically tested an involvement–commitment model, showing that
ego involvement leads to purchase involvement, which in turn
leads to brand commitment. Other research has empirically
supported the purchase involvement–brand commitment rela-
tionship (Mittal and Lee 1989). Dick and Basu (1994) advance
the proposition that higher ego involvement is likely to lead
to customer loyalty. Other researchers have similarly suggested
that ego or enduring involvement leads to higher brand loyalty
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256 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
Table 5
Summary of satisfaction – share of wallet findings.
Customer satisfaction
Single item Multiple items
Overall satisfaction Change in overall
satisfaction
ACSI/SCSB Attribute
satisfaction
Share of wallet
Objective Cubic
• Keiningham et al.
(2003) (+)
R2: .07 (only CS)
Nonlinear
• Cooil et al. (2007) (+)
Linear
• Mägi (2003) (+)
R2: .21–.29
Linear
• Perkins-Munn
et al. (2005) (+)
R2: .07
Self-reported Decreasing returns
• Bowman and
Narayandas (2001) (+)
Linear
• Perkins-Munn et al.
(2005) (+)
R2: .14 Linear
• Keiningham et al.
(2007) (+)
Partially self-reported • Verhoef (2003)
(ns)
(+), (−) and ns (non significant) refer to the direction of the association between customer satisfaction and the dependent variable.
attitudes or intentions (Zaichkowsky 1985), and in a services
context that involvement tends to lead to stronger loyalty to
the service provider (Ganesh, Arnold, and Reynolds 2000;
Longfellow and Celuch 1992). Keiningham et al. (2007) ques-
tion that any single attitudinal measure alone, such as customer
satisfaction, could best determine future customer behavior. In
their study of three different industries, the authors argue for the
use of a multiple indicator instead of a single predictor model to
predict customer retention.
Table 4, that summarizes the studies relating customer
satisfaction and behavioral loyalty, clearly indicates that the per-
centage of variance explained in behavioral loyalty increaseswhen adding variables such as switching risk and knowledge
(Capraro, Broniarczyk, and Srivastava 2003), previous churn
and commitment (Gustafsson, Johnson, and Ross 2005), affec-
tive commitment, participation in a loyalty program (Verhoef
2003), involvement, relationship age, relationship program par-
ticipation (Seiders et al. 2005), prior usage and price (Bolton and
Lemon 1999), likelihood to purchase and brand image (Perkins-
Munn, Lerzan Aksoy, and Keiningham 2005), age, gender and
education (Mittal and Kamakura 2001). When customer satis-
faction is considered the sole predictor of behavioral loyalty, the
varianceexplained is lower (Bolton 1998;Capraro, Broniarczyk,
and Srivastava 2003). Hence:
G8: Models that encompass along with satisfaction other relevant predictor
variables such as past customer tendency to switch provider,
relationship age, commitment, loyalty program membership, level of
involvement, switching risk are better predictors of behavioral loyalty
than models with just customer satisfaction.
Other measures of behavioral loyalty: share of wallet and
cross buying
Recently, academic and practitioners have started to focus
their attention on share of wallet as a better metric to detect
customer behavior. In fact, research has shown that customers
increasingly hold polygamous loyalty to brands (Bennett and
Rundle-Thiele 2005; Cooil et al. 2007; Rust, Lemon, and
Zeithaml 2004b; Uncles, Dowling,and Hammond 2003;Uncles,
Ehrenberg, and Hammond 1995). Customers divide their spend-
ing among different brands in a category and are continuously
influenced by competition in their choices (Yim and Kannan
1999). For instance, some customers may just change their
spending pattern with a company rather than completely stop
doing business with it, by shifting some of their share of wallet
to another brand. Therefore, companies are expending substan-
tial effort in understanding the spending patterns of customers
rather than their defection. Once again, satisfaction is consid-
ered as a strong antecedent of share of wallet. Table 5 andFig. 5 present a classification of the studies addressing this
relationship.
Table 5 classifies past studies based on the way customer
satisfaction and share of wallet are measured. While customer
satisfaction is measured using traditional methods, share of
wallet can be a self-reported measure, a partially self-reported
measure, or a measure recorded in the company’s database
(objective). When share of wallet is a self-reported measure and
is recorded in cross-sectional studies, it may be correlated to sat-
isfaction as a result of common method bias. The self-reported
measures of share of wallet is similar to the use of repurchase
intentions questions commonly contained in a customer satis-
faction questionnaire (Keiningham, Perkins-Munn, and Evans2003).
In the only study that allows isolating the single contribu-
tion of customer satisfaction (Keiningham, Perkins-Munn, and
Evans 2003) the variance explained is only 7 percent. In gen-
eral, the explained variance is rather small and it ranges from 7
percent to 29 percent, when other variables are introduced. The
shape of the relationship varies from linear (Mägi 2003; Perkins-
Munn, Lerzan Aksoy, and Keiningham 2005) to nonlinear(Cooil
et al. 2007), nonlinear with decreasing returns (Bowman and
Narayandas 2001), and cubic in Keiningham, Perkins-Munn,
and Evans (2003). This latter study states that the greatest pos-
itive impact occurs at the upper extreme levels of satisfaction.
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 257
Fig. 5. Direction and shape of the satisfaction–share of the wallet relationship.
Moreover, the functional form of the relation varies by customer
segments.
Moderators play a major role in the relationship and other
variables can be significantpredictorsthan satisfaction. Here, we
encounter customer and relational moderators more frequently
than marketplace variables. For instance, Cooil et al. (2007) find
that income, and length of the relationship negatively moder-
ate the relationship between change in satisfaction and change
in share of wallet. That is, customer with high income and a
lengthy relationship are less likely to reduce their level of spend-
ing with the company. Furthermore, Mägi (2003), in his study
of the grocery setting, found that “the interest of the customer in
comparing different shopping alternatives on price (price sensi-
tivity)” has a negative moderatingeffect on the relationship. Also“the interest of a customer in establishing a personal relationship
with service personnel” has a negative moderating effect on the
relation. In other words, shoppers who value a personal relation
with store personnel are less likely to decrease their share of
shopping as a consequence of a decrease in satisfaction. Also,
Bowman and Narayandas (2001) find support for the positive
moderating effect of prior loyalty, and volume of purchase.
Among significant predictors, research reveals that in a gro-
cery setting, customers who own a card of competing chains
and are prone to compare price, and tend to reduce their share
of wallet (Mägi 2003). Further, Verhoef (2003) reports that
commitment, direct mailing, and the participation in a loyaltyprogram, positively affect share of wallet. In addition, Bowman
and Narayandas (2001) find that the level of loyalty directly
affect share of wallet, while Perkins-Munn, Lerzan Aksoy, and
Keiningham (2005) indicate repurchase intentions as a signifi-
cantpredictor. The findings presentedabove leadus to generalize
that:G9: Here again, while customer satisfaction is positively related to share of
wallet, models that include other relevant moderator and predictor
variables explain share of wallet behavior better than models that rely
only on customer satisfaction.
Conventional wisdom states that customer satisfaction
impacts cross buying. In other words, higher the satisfaction
with a firm’s product, greater is the probability that the customer
will buyotherproducts/services from the firm. However interest-
ingly, the empirical studies that exist on the effect of satisfaction
on cross buying, report contrasting findings. Verhoef, Franses,
and Hoekstra (2001, 2002) find no significant direct effect
of satisfaction on cross-buying. However, Verhoef, Franses,
and Hoekstra (2001) find satisfaction to have an effect on
cross-buying when moderated by relationship length. Similarly,
Verhoef, Franses, and Hoekstra (2002) find that a change in sat-
isfaction level between two points in time positively affects the
change in number of services purchased; but, satisfaction itself
has no significant direct effect. The variance explained in the
two studies is 15 percent and 8 percent, respectively.
Loveman(1998), in a retailbanking setting, finds that averagecustomer satisfaction with the branch is significantly positively
correlated with average cross-sell, which expresses the average
number of services purchased per household. In a bank setting,
Hallowel (1996) reports that overall division satisfaction is pos-
itively related to the division-reported cross sell rates, which
record the percentage of customer households with multiple
accounts (account cross sell) or multiple services (service cross
sell). In these particular situations, the level of aggregation used
(in Loveman’s study the branch level and in Hallowel’s study
the division level) may have influenced the results. In fact, in
their comparison of two models for the sales–advertising rela-
tionship at the individual and aggregate level, Bass and Leone(1986) find that a model of the same form estimated at a higher
level of aggregation is characterized by an increased coefficient
for the independent variable (advertising, in this situation).Thus,
we conclude that:G10: The relationship between customer satisfaction and cross buying is
inconclusive, with the level of aggregation used to analyze the data
potentially impacting the strength of the relationship.
So what do we know for sure about the customer
satisfaction–loyalty relationship?
This extensive literature review has highlighted the state of
the art regarding the relationship between customer satisfaction
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258 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
and loyalty, both attitudinal and behavioral. In particular, it
has brought to light several issues that should be carefully
considered in analyzing the efficacy of customer satisfaction in
explaining and predicting customer loyalty. In fact, for many
years companies all around the world have heavily invested
in customer satisfaction in the hope of increasing loyalty, and
hence, consequently, profitability. Academics have conducted
several studies on the satisfaction–loyalty relationship some-
times with contrasting findings. After having gone through
the above analysis, the major findings of this review and the
accompanying empirical generalizations include:
1. Overall, there is a positive relationship between customer
satisfaction and loyalty.
2. However, the variance explained by just satisfaction is rather
small – around 8 percent.
3. Holistic models that encompass other relevant variables as
moderators, mediators, antecedent variables, or all three are
better predictors of loyalty than models with just customer
satisfaction.
4. Inclusion of these variables increases the variance explained,
on an average,to 34 percent (54 percent for attitudinal loyalty
and 15 percent for behavioral loyalty, respectively).
5. The satisfaction–loyalty relationship has the potential to
change over the customer lifecycle.
6. While customer satisfaction has a positive relationship with
WOM, other related variables such as commitment, trust, and
product involvement serve as better predictors of WOM.
7. Customer satisfaction, by itself, does not always result in
retention, lifetime duration and usage. Customer, relational
and marketplace variablesoften play a significantmoderating
role.8. The relationship between customer satisfaction and cross
buying is characterized by contrasting findings. The level of
aggregation used to analyze the data may impact the strength
of the relationship.
The preceding review and analysis indicate that customer
satisfaction is often times a necessary but not a sufficient con-
dition to predict loyalty. Our empirical generalizations are also
supported by the findings on the customer satisfaction–loyalty
link discussed in service-profit-chain research (Bowman and
Narayandas 2004; Heskett et al. 1994; Heskett, Sasser, and
Schlesinger 1997; Kamakura et al. 2002; Loveman 1998; Rucci,Kirn, and Quinn 1998). The service profit chain (SPC) frame-
work states that exceptional customer service results in greater
customer satisfaction and retention, which in turn results in
higher profitability.
Heskett et al. (1994) theoretically support the notion that the
relationship between customer satisfaction and loyalty is non lin-
ear with increasingreturns. Heskett et al. (1997) find that the link
between customer satisfaction and loyalty, although positive, is
theweakest of all in the service profit chain, and that the relation-
ship between them is not constant. TheSPC proposed by Heskett
et al. (1994) became rather popular as it is demonstrated by the
numerous case studies reported by academics and practitioners
(Loveman 1998; Rhian and Cross 2000; Rucci, Kirn, and Quinn
1998).
In an interesting application of the SPC to business markets,
Bowman and Narayandas (2004) find that the experience of the
account manager and the client satisfaction with a competitor
enhance the relation between customer satisfaction and the Share
of Customer Wallet (SCW). Customer size decreases the respon-
siveness of SCW to satisfaction. SCW is influenced by overall
customer satisfaction and the relation shows increasing returns,
thus supporting the notion of customer delight. Kamakura et al.
(2002) using structural equation models, simultaneously test
for all the links of the chain, investigating also for mediating
effects. Customer satisfaction itself is not an unconditional guar-
antee of profitability and some firms may remain unprofitable
despite high levels of satisfaction due to a high investment in
customer satisfaction. Moderators are not investigated but the
authors advocate their inclusion in the model. Specifically, the
authors find a positive relationship between customer percep-
tions of personnel and equipment with consumers’ behavioral
intentions (intentions to recommend).
If customer satisfaction is not enough – what needs to be
done?
A more holistic view of the relationship: Customer satis-
faction is not enough to fully explain loyalty; other variables
need to be included in the relationship model to depict a more
complete picture. In particular, it is clear from the review that
variables such as customer perceived value, switching costs, and
relational variables such as trust, commitment, relationship age,
loyalty program membership, and level of customer involve-
ment, seems to be the most desirable candidates for inclusionin the model. While it is clear that these additional variables are
critical in customer satisfaction studies, their specific role in the
overall model indeed varies depending on the circumstances and
context. Past research has shown these variables to alternatively
be predictors of loyalty, antecedents to satisfaction, and act as
moderators, mediators, or both in the satisfaction–loyalty rela-
tionship. The decision to include one or more of these variables
in a holistic model is very much context specific.
Who to satisfy? One of the main paradigms of customer
relationship management stresses the fact that customers are
indeed heterogeneous. However, companies still invest in cus-
tomer satisfaction in the same way for the entire customer base.
In particular, customers are different in terms of the future value,or profitability, they can bring to a company. A truly customer
oriented approach optimizes customer selection (Kumar and
Petersen 2005), that is, allocation of resources to the most prof-
itable customers for the company. When allocating financial
resources, the most resources should be assigned to the most
profitable (or potentially profitable) customers.
The future value of a customer can be efficiently mea-
sured through the customer lifetime value (CLV) metric, whose
superiority over other metrics (such as past profitability or
RFM models) in defining future customer profitability has
already been well demonstrated in the literature (Kumar 2008).
CLV is generally defined as the present value of all future
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V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262 259
profits coming from a customer during his/her life or rela-
tionship with a firm. It is similar to the discounted cash flow
applied in finance (Gupta and Zeithaml 2006). CLV is gen-
erally applied at the individual customer or segment level
and it is a forward-looking metric since makes a projection
over the future by incorporating sophistication in modeling
(Gupta et al. 2006; Gupta and Zeithaml 2006). Given this, it
makes sense to allocate resources first to customers with a
high CLV. These customers are also the most attractive for
competitors. Clearly, the most profitable customers should be
satisfied first in order to strengthen their relationship and to
keep them away from the temptations of competitive offer-
ings.
How much and when to satisfy? The use of the CLV can
also provide several important insights on the maximum level
of investment that should be allocated to each customer. In fact,
this is set by the future customer profitability as measured by
CLV. In other words, a company should not invest in a customer
an amount of resources greater than his/her expected level of
future profitability. However, in order to decide when to invest,a company should also look at the current level of customer sat-
isfaction for each customer and at the shape of the relationship
between customer satisfaction and loyalty. For instance, in pres-
ence of decreasing returns in the relationship, a company should
pay attention before deciding to invest in highly satisfied cus-
tomersto further secure their loyalty andhope for higher returns.
The definition of the shape of the relationship plays a major role
in the cost/benefit relationship.
Directions for future research
Our approach depicts a customer satisfaction strategythat starts with future customer profitability considerations
(CLV), with the end goal of undertaking different investments,
efforts/expenditures incurred to exceed expectations or cause
delight, for customers segments according to their profitability.
However, the satisfaction–loyalty relationship is not generally
investigated for different levels of customer profitability both
before and after a customer satisfaction investment (i.e., efforts
to improve customer service) (Homburg, Koschate, and Hoyer
2005). In a recent study, Kumar et al. (2009) elaborate on the
weakness of thesatisfaction–loyalty link, as it is currently imple-
mented by companies, to present an alternate path that reverse
the logic, the profitability–loyalty–satisfaction chain. The new
paradigm starts the customer relationship management strategywith customer profitability and the idea that customers with dif-
ferent profitability should be rewarded and satisfied differently.
A systematic analysis of the relationship between satisfaction
andloyalty for thedifferent levels of profitability is much needed
in the literature.
The need to better investigate the link between satisfaction
and profitability as expressed by CLV is also supported by
the consideration that recent research has clearly demonstrated
that loyalty is not appropriately measured (Reinartz and Kumar
2002) and that CLV is the best measure for predicting profitabil-
ity of the company (Gupta,Lehmann,and Stuart 2004; Rust et al.
2004a). According to the above premises, a direct investigation
of the satisfaction–CLV link that discards loyalty could be a
promising avenue for future research. The presence of mediators
suggests that researchers need to clearly examine how customer
satisfaction affects financial performance. For instance, a recent
paper (Luo, Homburg, and Wieseke 2010) shows that customer
satisfaction led to improved analyst recommendations and those
in turn led to better financial performance. Such insights do not
necessarily imply a reduced role for satisfaction, but rather the
role of satisfaction needs to be better understood.
Further, we have seen that customer satisfaction itself may
not be enough to explain loyalty. However, relationships among
other relevantvariables maychange over time. There is an urgent
need for longitudinal studies in customer satisfaction that can
capture these changing relationships over time. For instance,
Garbarino and Johnson (1999) demonstrate that whereas satis-
faction mediates the relationship between trust and loyalty for
transactional exchanges, the mechanism is different for rela-
tional exchanges. In the latter case, trust mediates the effect of
satisfaction on loyalty intentions andtherefore the effect of satis-
faction in affecting loyalty becomes less central. In other words,antecedents of loyalty for customers with a relational orienta-
tion are different from the antecedents of transaction-oriented
customers.
However, this study is cross sectional, so we cannot under-
stand the dynamics and the interrelations among variables over
time. In fact, did satisfaction contribute to the formation of trust
and commitment over time? What role does satisfaction play
not only on loyalty but also on trust and commitment over time,
during the evolution of the relationship? It may be possible
that, as relationships evolve and go through different phases, the
dynamics among variables changeas well as therole of customer
satisfaction on all the other variables. According to the results of the literature review, we may expect that early in the relationship
customer satisfaction is more relevant, while, when the relation-
ship gets firm, greater importance is attributed to commitment
and trust. In this particular situation, the use of models with lon-
gitudinal data that can capture variation both cross-sectional and
over time can be extremely useful. Researchers need to develop
theory to understand when and under what conditions the link
will be systematically stronger or weaker.
The importance of time in customer satisfaction studies has
been highlighted also by other authors, since measures made at
different points in time may drive to different conclusions. For
instance, Mazurski and Geva (1989) find that satisfaction and
loyalty intentions are highly correlated when measured in thesame survey at the same time. However, for the same persons,
customer satisfaction is not correlated with intentions measured
after two weeks. In this particular situation, time plays an impor-
tant role since the effect of customer satisfaction seems to decay
over time. Hence, longitudinal studies are required to answer
such critical questions.
A third importantissueis related to theway customer satisfac-
tion is measured. While an attribute based measure of customer
satisfaction can be useful for managers to identify areas of
future intervention and improvement, it does not lends itself
toward the delivery of a holistic experience for the customer
that involves “sense, feel, think, act and relate”. According to the
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260 V. Kumar et al. / Journal of Retailing 89 (3, 2013) 246–262
principles of experiential marketing (Schmitt 1999), marketers
should touch upon higher levels of the customer experience and
start thinking of an operationalization of customer satisfaction
that encompasses not only physical product characteristics or
concrete aspects of the service, but also intangible elements of
the customer experience that can satisfy higher order needs such
as self-esteem, socialization,or both.Future researchshould also
investigate these aspects and delineate more precise measures of
satisfaction that encompass intangible aspects of an experience
leading to satisfaction.
In an interesting study of online markets, Shankar, Smith,
and Rangaswamy (2003) f ound that overall satisfaction had a
stronger positive impact on loyalty online than offline and that
loyalty is higher online than offline. As the relevance of the
Internet in developing and strengthening customer relationships
increases, andcustomers aremore andmore used to makingtheir
transactions online, a better understanding of these dynamics in
the online setting would be advocated. Are the dynamics of cus-
tomer satisfaction and loyalty the same online and offline? More
research in this direction is certainly needed to shed light on agrowing phenomenon that is marking the Marketing discipline
in the 21st century.
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