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: [email protected], dr [email protected] (V. Kumar),

ilaria.dalla [email protected] r (I.D. Pozza), [email protected]

(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

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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.



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.




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




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




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

http://www.consumerreview.com/

http://www.dpreview.com/

http://www.failingenterprise.com/

http://www.starbucked.com/

http://www.starbucked.com/

http://www.failingenterprise.com/

http://www.dpreview.com/

http://www.consumerreview.com/




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




Table 2

Summary of satisfaction–WOM findings.



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.




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


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


8 percent (only CS)


predictors

Gustafsson, Johnson, and

Ross (2005)

Churn (−) Linear Financial services (credit card

membership) (contractual

setting)


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)


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)


predictors


Table 4Summary of satisfaction – retention, lifetime, and usage relationship findings.



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


(ns)

Amount of

spending

Linear


(ns)

Number of

transactions

• Bolton, Kannan, and

Bramlett (2000) (+)





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




Table 5

Summary of satisfaction – share of wallet findings.



Overall satisfaction Change in overall

satisfaction

ACSI/SCSB Attribute

satisfaction

Share of wallet

Objective Cubic


(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


(2007) (+)

Partially self-reported • Verhoef (2003)

(ns)


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.




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




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




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




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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