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Commitment, satisfaction, andcustomer loyalty: a theoreticalexplanation of the ‘satisfaction trap’Xiaobo Wu a , Haojun Zhou a & Dong Wu aa School of Management , Zhejiang University , Hangzhou ,People's Republic of ChinaPublished online: 18 Apr 2011.
To cite this article: Xiaobo Wu , Haojun Zhou & Dong Wu (2012) Commitment, satisfaction, andcustomer loyalty: a theoretical explanation of the ‘satisfaction trap’, The Service IndustriesJournal, 32:11, 1759-1774, DOI: 10.1080/02642069.2010.550043
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Commitment, satisfaction, and customer loyalty: a theoreticalexplanation of the ‘satisfaction trap’
Xiaobo Wu, Haojun Zhou∗ and Dong Wu∗
School of Management, Zhejiang University, Hangzhou, People’s Republic of China
(Received 6 October 2010; final version received 16 December 2010)
This paper examines the relationship between satisfaction and customer loyalty andthe moderating effects of affective and calculative commitment. The results suggestthat while customer loyalty increases monotonically with the enhancement ofsatisfaction, the marginal effect of satisfaction on customer loyalty actuallydecreases. Calculative commitment positively moderates the curvilinear relationshipof satisfaction and customer loyalty, i.e. enhancing calculative commitment couldintensify the relationship between satisfaction and customer loyalty. Affectivecommitment has no significant moderating effect on the relationship of satisfactionand customer loyalty, but it can directly enhance the customer loyalty. The researchfindings can help us to explain the phenomenon of the ‘satisfaction trap’.
Keywords: satisfaction; loyalty; affective commitment; calculative commitment
Introduction
The relationship between satisfaction and customer loyalty has always been a research
mystery (Oliver, 1999). Many scholars in the field of services marketing believed that if
firms could fully satisfy consumers by offering them products or services beyond their
expectations, they would naturally win customer loyalty. Based on this logic, the goal
of maximizing customer satisfaction has been earnestly pursued by product and service
providers (Oliver, 1999). However, many firms despite giving substantial resources to
maximize customer satisfaction have found that there was no corresponding improvement
in customer loyalty. Jones and Sasser (1995) commented that merely satisfying customers
that have the freedom to make choices is not enough to keep them loyal. Oliver (1999)
pointed out that the relation between satisfaction and customer loyalty is asymmetric.
Although loyal consumers are most typically satisfied, satisfaction does not universally
translate into loyalty. Reichheld and Teal (1996) labelled this phenomenon the ‘satisfac-
tion trap’.
The asymmetry between satisfaction and customer loyalty motivated researchers to
explore the formation of customer loyalty from other perspectives. Drawing on the
concept of commitment from the field of organizational behaviour, marketing scholars
Morgan and Hunt (1994) proposed the concept of relationship marketing. They argued
that the commitment and trust formed during the consuming process are the key determi-
nants of customer loyalty. Since then, relationship marketing has developed as a unique
field of research, and relationship commitment has become the central concept in this
field of research (Fullerton, 2003; Gilliland & Bello, 2002). Relationship commitment
ISSN 0264-2069 print/ISSN 1743-9507 online
# 2012 Taylor & Francis
http://dx.doi.org/10.1080/02642069.2010.550043
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∗Corresponding author. Email: [email protected]
The Service Industries Journal
Vol. 32, No. 11, August 2012, 1759–1774
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refers to the consumers’ psychological attachment to the products or services of a
company (Gilliland & Bello, 2002), and it can effectively enhance customer loyalty (Full-
erton, 2003; Gilliland & Bello, 2002; Gustafsson, Johnson, & Roos, 2005). Relationship
marketing emphasizes the importance of establishing, developing, and maintaining the
customers’ relationship commitment to companies (Morgan & Hunt, 1994).
The relationship marketing perspective contributes to explaining the formation of
customer loyalty, and it also partly explains how the ‘satisfaction trap’ comes about.
When a company focuses merely on satisfaction rather than on customer relations, the
benefits from pursuing satisfaction will be greatly discounted (Pritchard, Havitz, &
Howard, 1999). However, it is unsatisfactory to explain separately the formation of
customer loyalty from the perspectives of services marketing and relationship marketing.
Supposing that satisfaction and commitment impact customer loyalty independently
and that the relationship of satisfaction and customer loyalty is linear, as most of the exist-
ing literature does (Gustafsson et al., 2005; Lin & Wang, 2006), why is the relation of sat-
isfaction and customer loyalty asymmetric (Oliver, 1999)? Even if the power of
satisfaction to improve customer loyalty is weak, the relationship between satisfaction
and customer loyalty is statistically symmetrical. This sparked us to consider (1)
whether there is a nonlinear relationship between satisfaction and customer loyalty and
(2) whether there is an interactive effect of satisfaction and commitment on customer
loyalty.
Although most previous empirical literature assumed a linear relationship between sat-
isfaction and customer loyalty, some recent studies have indicated that there is a nonlinear
relationship between satisfaction and customer loyalty (Agustin & Singh, 2005; Mittal &
Kamakura, 2001). The existing literature is still unable to give a clear answer to the second
question, yet a few studies provide some clues. For instance, Oliver (1999) argued that the
process whereby satisfaction transforms into loyalty is much like that of a caterpillar trans-
forming into a butterfly. The process of the caterpillar transforming into a butterfly
requires an appropriate ‘climate’, similarly for satisfaction to translate into customer
loyalty needs synergistic factors. Ahluwalia, Burnkrant, and Unnava (2000) pointed out
that commitment can act as a buffer, when customers respond to any negative news
concerning the companies. Their research indicated that the role of commitment is
similar to that of a ‘climate’ which may contribute to the conversion of satisfaction into
customer loyalty. In other words, increased commitment can intensify the relationship
between satisfaction and customer loyalty.
We developed hypotheses based on the existing theoretical and empirical literature in
services marketing and relationship marketing, to examine the curvilinear relationship
between satisfaction and customer loyalty and to examine how the interaction of commit-
ment and satisfaction influences this curvilinear relationship. Examining these two ques-
tions may not only gives a theoretical explanation for the phenomenon of the ‘satisfaction
trap’, but also have the potential to provide an opportunity to merge these two fields of
research so as to build a more comprehensive understanding of the formation of customer
loyalty (Fullerton, 2005; Gruen, Summers, & Acito, 2000).
Literature review and hypothesis development
Small changes in customer loyalty (e.g. 5%) can yield disproportionately large changes in
profitability (e.g. 25–100%) (Reichheld & Teal, 1996; Reichheld, Markey, & Hopton,
2000). Therefore, developing, maintaining and improving customer loyalty become the
firms’ central marketing activities (Dick & Basu, 1994). In the past two decades,
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researchers have explained customer loyalty mainly from two theoretical perspectives.
The first is that of services marketing (Zeithaml, 2000), where it is argued that satisfaction
based on the value of the product or service is the key determinant of customer loyalty
(Cronin & Taylor, 1992; Zeithaml, 2000; Zeithaml, Berry, & Parasuraman, 1996). The
second is that of relationship marketing (Sheth & Parvatiyar, 2002), which argues that
the relationship commitment is the key determinant of customer loyalty (Gundlach,
Achrol, & Mentzer, 1995; Morgan & Hunt, 1994). The literature relevant to these two
theoretical perspectives has developed almost independently, so that customer loyalty is
explained from either one perspective or the other. As neither could perfectly explain cus-
tomer loyalty, integrating these two theoretical perspectives may help us better explain and
predict customer loyalty (Fullerton, 2005).
The curvilinear relationship of satisfaction and customer loyalty
Many scholars in the field of services marketing believe that satisfaction is the key factor
in determining customer loyalty. Their fundamental argument is that customer loyalty will
be naturally intensified by an increase in satisfaction (Cronin & Taylor, 1992; Zeithaml,
2000). In the most recent literatures, researchers have assumed a linear relationship
between satisfaction and customer loyalty. For instance, Gustafsson et al. (2005), Lin
and Wang (2006) assumed a positive linear relationship between satisfaction and customer
loyalty. However, the linear relationship between them is contradicted by Oliver’s ‘satis-
faction-loyalty asymmetry’ (Oliver, 1999). Satisfaction does not universally translate into
loyalty (Oliver, 1999). This has led some researchers to argue that there is a nonlinear
relationship between satisfaction and customer loyalty (Agustin & Singh, 2005; Mittal,
Ross, & Baldasare, 1998; Oliver, 1999).
In a large-scale study of automobile customers, Mittal and Kamakura (2001) found
that there is a strong nonlinear relationship between satisfaction and repurchase behaviour.
Although satisfaction has a stable and positive effect on repurchase behaviour, this posi-
tive effect marginally declines with the increase in satisfaction. Oliva, Oliver, and
MacMillan (1992) and Gomez, McLaughlin, and Wittink (2004) also found a similar cur-
vilinear relationship between satisfaction and customer loyalty in their empirical studies.
Agustin and Singh (2005) tried to give a theoretical explanation for this curvilinear
relationship. Based on Maslow’s (1943) theory of a hierarchy of needs and Herzberg’s
(1966) theory of motivation-hygiene, they conceptualized trust as a ‘motivator’ and satis-
faction as a ‘hygiene’. Although satisfaction meets customer needs at the lower order,
it cannot meet customer needs at the higher order. Although satisfaction has a stable
and positive effect on customer loyalty, this effect in fact marginally declines with
the increase in satisfaction. Based on the above argument, we propose the following.
H1: Customer loyalty increases monotonically with the enhancement of satisfaction, yet themarginal effect of satisfaction on customer loyalty decreases.
The direct effects of affective and calculative commitment
The relationship marketing literature recognizes another potential driver of customer loyalty:
namely, relationship commitment (Gustafsson et al., 2005). Drawing on the organizational
behaviour literatures (Meyer & Allen, 1997), marketing scholars have variously defined
commitment as a desire to maintain a relationship (Moorman, Zaltman, & Deshpande,
1992), as the sacrifice or potential for sacrifice if a relationship ends (Anderson & Weitz,
1992), and as the absence of competitive offerings (Gundlach et al., 1995). These
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various aspects of commitment create a ‘stickiness’ that keeps customers loyal to a brand
or company even when satisfaction may be low. The various definitions suggest that
relationship commitment is a multidimensional construct. In the recent literature, marketing
researchers have generally suggested that relationship commitment is composed of
two dimensions: affective commitment and calculative commitment (Gustafsson et al.,
2005; Johnson, Herrmann, & Huber, 2006; Ruyter, Moorman, & Lemmink, 2001).
Gilliland and Bello (2002) have suggested that relationship commitment is composed
of calculative commitment and loyalty commitment while Fullerton (2003) has used
the terms affective commitment and continuance commitment. In essence, loyalty com-
mitment equals affective commitment; continuance commitment equals to calculative
commitment.
Affective commitment is a ‘hotter’, or more emotional, factor that develops through
the reciprocity or personal involvement that a customer has with a company, which
results in a higher level of trust and commitment (Fullerton, 2003; Gustafsson et al.,
2005). When customers come to like (or, in some cases, love) brands or service providers,
they are showing affective commitment (Fullerton, 2003). A lot of empirical studies have
demonstrated that affective commitment has a positive effect on customer loyalty. For
instance, Ruyter et al. (2001) found that the higher the customers’ affective commitment,
the higher the loyalty to suppliers in high-tech industries. In an experimental study, Full-
erton (2003) found that the higher the customers’ affective commitment, the lower their
switching intentions and the more they were willing to pay. Johnson et al. (2006) found
that, in a longitudinal study, the positive effect of affective commitment on customer
loyalty increases over time, which accentuates its importance to customer loyalty. There-
fore, we can safely assume that affective commitment does have a positive effect on cus-
tomer loyalty.
Calculative commitment is the colder, or more rational, economically based depen-
dence on product benefits because of a lack of choice or of high switching costs (Gustafs-
son et al., 2005; Johnson et al., 2006; Ruyter et al., 2001). Calculative commitment is a
neutral or even negative psychological state (Fullerton, 2003). When a customer rationally
weighs alternatives and switching costs, and finds no better alternatives or the switching
costs too high, that customer has to stay with the current choice. Although calculative
commitment is a very different psychological state compared to affective commitment,
calculative commitment will make customers more loyal through lack of alternative
choices or through high switching costs. This positive impact of calculative commitment
on customer loyalty has also been supported by a few empirical studies (Gustafsson et al.,
2005; Ruyter et al., 2001). For instance, Gustafsson et al. (2005) found that calculative
commitment has a stable and positive effect on telecommunications customers’ loyalty.
Therefore, we assume that calculative commitment does have a positive effect on custo-
mer loyalty.
Affective commitment is a positive psychological state. It is reasonable to speculate
that the sense of satisfaction is strong when a customer uses a favourite product or
service. So we assume that affective commitment has a positive effect on satisfaction.
By contrast, calculative commitment is a neutral or even negative psychological state.
So we speculate that calculative commitment has no significant effect on satisfaction.
H2a: Affective commitment has a positive effect on customer loyalty.
H2b: Calculative commitment has a positive effect on customer loyalty.
H2c: Affective commitment has a positive effect on satisfaction.
H2d: Calculative commitment has no significant effect on satisfaction.
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The moderating effects of affective and calculative commitment
Both calculative commitment and affective commitment create a ‘stickiness’ that keeps
customers loyal to a product or service (Gustafsson et al., 2005). The ‘stickiness’ can
be generated from trust, and it can also result from lack of other options or from high
switching costs. No matter what the cause of ‘stickiness’, it is likely to intensify the
relationship between satisfaction and customer loyalty. In other words, at the given
level of satisfaction, increasing customers’ affective or calculative commitment can inten-
sify the relationship between satisfaction and customer loyalty.
Although the moderating effects of affective and calculative commitment on the
relationship between satisfaction and customer loyalty have not been fully researched, a
few studies indicate that there are potential moderating effects. For instance, Aydin,
Ozer, and Arasil (2005) found that the mobile phone users’ switching cost (an element
of calculative commitment) has a positive moderating effect on the relationship between
satisfaction and customer loyalty. Similarly, Yang and Peterson (2004) found that when
customer satisfaction is above the average level, the switching costs have a significantly
positive moderating effect on that relationship. Ahluwalia et al. (2000) identified the
affective commitment of the consumer towards the brand as a moderator of negative
news concerning a company. In other words, at the given level of satisfaction, if customers’
affective commitment to the company is high, the response to negative news will be greatly
reduced. Therefore, the probability of remaining loyal to the company will be higher. This
implies that affective commitment is likely to moderate positively the relationship between
satisfaction and customer loyalty. Therefore, we propose the following.
H3a: Affective commitment positively moderates the curvilinear relationship between satis-faction and customer loyalty.
H3b: Calculative commitment positively moderates the curvilinear relationship between sat-isfaction and customer loyalty.
Methodology
Research setting and samples
We tested our hypotheses in the context of China’s booming 3G business. The 3G business
is the current focus of competition among mobile communication operators (China
Telecom, China Mobile, China Unicom) in China. The success or failure of 3G business
will largely determine the future competitive position of the mobile communication oper-
ators. These operators are using a variety of ways to attract 3G users and to enhance the
loyalty of their existing customers. This provides us with an excellent research setting
in which to test our theoretical hypotheses.
We used a questionnaire survey to collect data. We pre-tested the questionnaire before
conducting a large-scale survey, in order to clarify the meaning of items and to ensure the
high reliability of the scales. The large-scale survey was conducted from May to August in
2010. In order to avoid possible measurement error through personal sentiment to their
companies, we did not deliver questionnaires to staff in China Telecom, China Mobile,
or China Unicom. Respondents had to have used the 3G services for at least one month.
A total of 609 3G users were approached for participation in the survey. Of these, 349
people responded favourably, giving us a response rate of 60.6%. We deleted a few ques-
tionnaires due to missing data or lacking experiences for 3G use (below 1 month). In the
end, 305 questionnaires (87.4% of the respondents) contained complete data of 3G users
who had used 3G services for at least 1 month and could be used in the analysis. The demo-
graphic characteristics of the sample are described in Table 1.
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Measures
To ensure the content validity of the measures, we developed scales used in this study from
previous literature. A few items were slightly modified to fit the research setting. Satisfac-
tion items were based on seven-point semantic differential scales. All remaining scale
items used seven-point Likert scales, anchored between ‘strongly disagree’ to ‘strongly
agree’. Table 2 provides the scale items of the constructs.
Customer loyalty
Experience indicates that measuring loyalty is difficult. Researchers have used both atti-
tudinal and behavioural measures to measure this variable (Oliver, 1999; Zeithaml,
2000). From an attitudinal perspective, customer loyalty has been viewed as a specific
desire to continue a relationship with a service provider (Czepiel & Gilmore, 1987).
From a behavioural view, customer loyalty is defined as repeat patronage (Neal, 1999).
Because the repeat patronage of 3G services is difficult to measure, we measured customer
loyalty from an attitudinal perspective. We adapted the customer loyalty scale from a con-
tinuance intention scale (Bhattacherjee, 2001b; Thong, Hong, & Tam, 2006) and a loyalty
intentions scale (Johnson et al., 2006).
Satisfaction
The measure of satisfaction was adapted from Spreng, MacKenzie, and Olshavsky (1996)
overall satisfaction scale. This scale captures respondents’ satisfaction levels along a
Table 1. Demographic characteristics of the sample.
Characteristics Number Percentage (%) Cumulative percentage (%)
GenderMale 132 43.3 43.3Female 173 56.7 100.0
AgeUnder 20 22 7.2 7.221–25 132 43.3 50.526–30 56 18.4 68.931–35 45 14.8 83.635–40 17 5.6 89.2Above 40 33 10.8 100.0
Income/month (RMB)Under 2000 131 43.0 43.02000–4000 57 18.7 61.64000–6000 63 20.7 82.36000–8000 17 5.6 87.98000–10,000 14 4.6 92.5Above 10,000 23 7.5 100.0
Time for 3G use1–6 month 144 47.2 47.27–12 month 77 25.2 72.5Above 1 year 84 27.5 100.0
Provider of 3G servicesChina Telecom 116 38.0 38.0China Mobile 175 57.4 95.4China Unicom 14 4.6 100.0
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Table 2. Survey measures, loadings, and average variance extracted.
Constructs Survey measures Loading AVESources ofmeasures
Perceivedusefulness
1. I find 3G services useful in my dailylife
0.853 0.803 Davis (1989),Hung et al.(2003), Thonget al. (2006)
2. Using 3G services make it easier toget along with my daily life
0.920
3. Using 3G services help me performmany things more conveniently
0.914
Perceived easeof use
1. Learning to use 3G services is easyfor me
0.860 0.785 Davis (1989),Hung et al.(2003), Thonget al. (2006)
2. It is easy for me to become skillful atusing 3G services
0.915
3. My operation with 3G services is clearand understandable
0.882
Perceived cost 1. The fee that I have to pay for the use of3G services is not too high (reversed)
0.889 0.738 Hung et al. (2003),Kim et al.(2007), Wu andWang (2005)
2. The fee that I have to pay for the use of3G services is reasonable (reversed)
0.823
3. I am pleased with the fee that I have topay for the use of 3G services(reversed)
0.863
Affectivecommitment
1. I take pleasure in being a customer ofthe company
0.811 0.746 Gustafsson et al.(2005)
2. The company is the operator that takesthe best care of their customers
0.898
3. I have feelings of trust towards thecompany
0.880
Calculativecommitment
1. I would suffer economically if I switchto other 3G services providers
0.824 0.728 Gilliland and Bello(2002),Gustafsson et al.(2005)
2. It would be costly to change my phonenumber if I switch to other 3G servicesproviders
0.853
3. The switching cost would be high if Iswitch to other 3G services providers
0.881
Satisfaction How do you feel about your overallexperience of 3G services use
1. 1 ¼ ‘Very dissatisfied’, 7 ¼ ‘Verysatisfied’
0.916 0.837 Bhattacherjee(2001b), Sprenget al. (1996)2. 1 ¼ ‘Absolutely terrible’, 7 ¼
‘Absolutely delighted’0.912
3. 1 ¼ ‘Very displeased’, 7 ¼ ‘Verypleased’
0.916
Customerloyalty
1. I intend to continue using 3G servicesin the future
0.840 0.642 Bhattacherjee(2001b),Johnson et al.(2006), Thonget al. (2006)
2. I would recommend other people touse 3G services
0.789
3. I seldom consider switching to another3G services provider
0.749
4. I will always try to use 3G services inmy daily life
0.825
Agree–disagree scale: 1 ¼ ‘strongly disagree’, 7 ¼ ‘strongly agree’.
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seven-point scale anchored between three semantic differential adjective pairs: ‘very dis-
satisfied/very satisfied’, ‘very displeased/very pleased’, and ‘absolutely terrible/absolutely
delighted’. This scale was appropriate because an affect such as satisfaction is best
measured along bipolar evaluative dimensions (Ajzen & Fishbein, 1977).
Affective and calculative commitment
We adapted the affective and calculative commitment scales from prior studies (Gilliland &
Bello, 2002; Gustafsson et al., 2005). The affective commitment measures refer to the plea-
sure in being a customer of the company, the feelings of trust towards the company, and
whether the company takes care of its customers. Calculative commitment refers explicitly
to the economic consequences of ending the relationship (e.g. the switching cost).
Control variables
We controlled for two demographic variables (Venkatesh, Brown, Maruping, & Bala,
2008) and three variables of customer perception that have been found to be significantly
related to customer loyalty (Bhattacherjee, 2001a; Davis, Bagozzi, & Warshaw, 1989; Wu
& Wang, 2005). Age was measured in year intervals. Gender was measured as a dichot-
omous variable, coded 0 for male and 1 for female. To make the relationship between
focus variables more robust, we controlled perceived usefulness, perceived ease of use,
and perceived cost of use all of which had been found in previous studies to be signifi-
cantly related to both satisfaction and customer loyalty. The scales of perceived useful-
ness, perceived ease of use, and perceived cost were adapted from the measures used in
previous empirical studies (Davis, 1989; Hung, Ku, & Chang, 2003; Kim, Chan, &
Gupta, 2007; Thong et al., 2006; Wu & Wang, 2005).
Analytical procedures
We used hierarchical regression analysis to test our hypotheses. Hierarchical regression is
one of the most useful tools for testing interaction effects because it allows a researcher to
base variables’ order of entry on their causal priority (Cohen, Cohen, West, & Aiken,
2003). For variable measured by multiple items, we calculated the mean of the items as
the value for corresponding variable. Except for the dichotomous variable gender, all
the independent variables were mean-centred to reduce multicollinearity (Cohen et al.,
2003). The interaction term was the product of the centred scores of corresponding
variables.
Results
Reliability and discriminant validity
We used principal components analyses to operationalize latent variables from the survey
measures. The criterion for establishing reliability is that the measurement loadings should
exceed 0.707 to ensure that at least half of the variance in the observed variable is shared
with the latent variable. This reliability criterion is referred to as communality or, in the
case of the standardized results we report here, average variance extracted (AVE)
(Fornell & Larcker, 1981). As we show in Table 2, the AVE criterion is met for each
of the constructs, which supports the reliability of the measures.
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Table 3 presents descriptive statistics and correlations for the study variables. Here, we
focus on the correlations involving the latent constructs. To ensure the discriminant val-
idity of the constructs, Fornell and Larcker (1981) argue that the AVEs of any two con-
structs should be greater than their squared correlation. When the latent variable
correlations in Table 3 are squared (not shown), none exceeds the AVE of the constructs.
This supports the discriminant validity of the constructs.
Results of hierarchical regression analyses
Table 4 presents the results of hierarchical regression in which customer loyalty is
the dependent variable. There are four estimated models. Model 1 is the baseline model,
which includes all the control variables. Model 2 is used to test H2a and H2b. The
results show that these two hypotheses are both supported by the empirical data, which indi-
cates that both affective commitment and calculative commitment have a positive effect on
customer loyalty. However, the effect of affective commitment (b ¼ 0.244, p , 0.001) on
customer loyalty is much stronger than that of calculative commitment (b ¼ 0.095, p ,
0.05). Model 3 is used to test H1. The results show that the coefficient of first-order term
of satisfaction is significantly positive (b ¼ 0.513, p , 0.001), and the coefficient of quad-
ratic term of satisfaction is significantly negative (b ¼ 20.070, p , 0.01). By analysing
the axis of symmetry of the quadratic curve, the value of satisfaction is seen to be 3.66
(centred). This supports H1 is supported. Customer loyalty monotonically increases with
enhancement of satisfaction, but the marginal effect of satisfaction on customer loyalty
decreases. Model 4 is used to test H3a and H3b. Model 4 contains only the first-order inter-
action terms of satisfaction and commitment: ‘satisfaction × affective commitment’ and
‘satisfaction × calculative commitment’. Since the main effects of satisfaction include
the quadratic term, therefore the higher-order interaction may exist. Based on Model 4,
we further examined the significance of the higher-order interaction terms of satisfaction
and commitment: ‘satisfaction squared × affective commitment’ and ‘satisfaction
squared × calculative commitment’. The regression analysis shows that they are not sig-
nificant. So the final model (Model 4) does not include those two higher-order interaction
terms. The results indicate that H3b is supported by empirical data, but H3a is not. In other
words, calculative commitment has a positive moderating effect on the curvilinear relation-
ship of satisfaction and customer loyalty (b ¼ 0.083, p , 0.01). But affective commitment
has no significant moderating effect on the curvilinear relationship of satisfaction and cus-
tomer loyalty.
Table 5 presents the results of hierarchical regression in which satisfaction is the
dependent variable. There are three estimated models. Model 5 is the baseline model,
including all control variables. Model 6 is used to test H2c, and model 7 is used to test
H2d. The results show that these two hypotheses are both supported by the empirical
data, which means that affective commitment has a positive effect on satisfaction (b ¼
0.192, p , 0.001), but calculative commitment has no significant effect on satisfaction.
Explanation of interaction effect
Figure 1 depicts the interactive effect of calculative commitment and satisfaction on cus-
tomer loyalty (estimated by model 4). In the figure, all the values of the independent vari-
ables are set to zero (centred data) except for calculative commitment and satisfaction. The
value of the dichotomous variable gender does not affect the shape of the curved surface of
the interaction effect. But it does affect the intercept of the curved surface. When the value
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Table 3. Means, standard deviations, correlations, and scale reliabilities.
Variables Mean SD 1 2 3 4 5 6 7 8 9
1. Gender 0.57 0.502. Age 3.01 1.44 20.268∗∗
3. Perceived usefulness 5.15 1.40 0.278∗∗ 20.109 (0.875)4. Perceived ease of use 4.72 1.32 0.014 20.080 0.418∗∗ (0.862)5. Perceived cost 4.25 1.40 0.041 20.206∗∗ 20.117 20.287∗∗ (0.820)6. Affective commitment 4.20 1.39 20.079 0.135∗ 0.207∗∗ 0.178∗∗ 20.351∗∗ (0.825)7. Calculative commitment 4.38 1.44 0.009 0.064 0.270∗∗ 0.182∗∗ 20.143∗ 0.403∗∗ (0.812)8. Satisfaction 4.57 1.03 0.138∗ 20.010 0.341∗∗ 0.270∗∗ 20.401∗∗ 0.396∗∗ 0.167∗∗ (0.902)9. Customer loyalty 4.45 1.30 0.096 0.140∗ 0.400∗∗ 0.340∗∗ 20.437∗∗ 0.465∗∗ 0.336∗∗ 0.627∗∗ (0.810)
Internal reliabilities (Cronbach’s a reliability) for the overall constructs are given in parentheses on the diagonal.∗p , 0.05.∗∗p , 0.01.
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is set to 1 (female), the curved surface will shift upward 0.127. The ranges of calculative
commitment and satisfaction are ‘mean + 2 std. deviation’ (the probability of the sample
data fall outside the range is 5%). From the figure we can clearly see that at the given level
of satisfaction, the greater the calculative commitment, the higher the customer loyalty,
and the greater the slope of the curve in the surface. Therefore, enhancing calculative com-
mitment can intensify the relationship of satisfaction and customer loyalty.
These findings can help us to explain some interesting phenomena. For example, it
helps to explain why Chinese users are not allowed to keep their phone number when
they switch their mobile communication operators. Although the technology to keep
their phone number when they switch their mobile communication operators may not
be an issue, the operators intentionally or unintentionally increase the user’s switching
cost. It may also help to explain why operators are enthusiastically developing a variety
of virtual networks which serve different user groups (e.g. a university or a company).
Although these operators need to give a considerable discount on the fee, the users’
switching costs (an important element of calculative commitment) are greatly increased.
If a user’s friends, colleagues or classmates are all using the virtual network X which is
provided by operator A, then switching to the virtual network Y which is provided by oper-
ator B will be costly (high switching costs) even if Y is better than X. In this case, even if
customers feel dissatisfied with the virtual network X which is provided by operator A,
they will most likely continue to use it.
Discussion
This study examined the relationship between satisfaction and customer loyalty and the
moderating effects of affective and calculative commitment on this relationship. The
results indicate that customer loyalty monotonically increases with the enhancement of
Table 4. Results of hierarchical regression of commitment, satisfaction and customer loyalty.
Variables Model 1 Model 2 Model 3 Model 4
Constant 4.366 (0.098)∗∗∗ 4.319 (0.092)∗∗∗ 4.450 (0.086)∗∗∗ 4.459 (0.084)∗∗∗
Gender 0.152 (0.134) 0.234 (0.127) 0.135 (0.113) 0.127 (0.110)Age 0.117 (0.046)∗ 0.094 (0.043)∗ 0.107 (0.038)∗∗ 0.128 (0.039)∗∗
Perceived usefulness 0.286 (0.051)∗∗∗ 0.212 (0.049)∗∗∗ 0.132 (0.044)∗∗ 0.142 (0.044)∗∗
Perceived ease of use 0.122 (0.054)∗ 0.113 (0.051)∗ 0.092 (0.045)∗ 0.125 (0.045)∗∗
Perceived cost 20.318 (0.047)∗∗∗ 20.236 (0.046)∗∗∗ 20.109 (0.043)∗ 20.092 (0.043)∗
Affective commitment 0.244 (0.049)∗∗∗ 0.142 (0.045)∗∗ 0.134 (0.044)∗∗
Calculative commitment 0.095 (0.045)∗ 0.096 (0.040)∗ 0.094 (0.039)∗
Satisfaction 0.513 (0.060)∗∗∗ 0.553 (0.060)∗∗∗
Satisfaction squared 20.070 (0.027)∗∗ 20.118 (0.030)∗∗∗
Satisfaction × affectivecommitment
0.045 (0.035)
Satisfaction × calculativecommitment
0.083 (0.029)∗∗
R2 0.337 0.419 0.549 0.571F 30.450∗∗∗ 30.659∗∗∗ 39.883∗∗∗ 35.443∗∗∗
△R2 0.082 0.129 0.022△F 20.998∗∗∗ 42.315∗∗∗ 7.524∗∗∗
Dependent variable: customer loyalty. The regression coefficients are unstandardized. Standard errors are inparentheses.∗p , 0.05.∗∗p , 0.01.∗∗∗p , 0.001.
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satisfaction, but the marginal effect of satisfaction on customer loyalty decreases. Calcu-
lative commitment positively moderates the curvilinear relationship of satisfaction and
customer loyalty. This study provides interesting findings and will help us to explain
the phenomenon of ‘satisfaction-loyalty asymmetry’ and of the ‘satisfaction trap’. They
also have practical implications for firms wishing to improve their customer loyalty.
Theoretical contribution
First, customer loyalty monotonically increases with the enhancement of satisfaction, but
the marginal effect of satisfaction on customer loyalty decreases. Because of the
Table 5. Results of hierarchical regression of affective, calculative commitment and satisfaction.
Variables Model 5 Model 6 Model 7
Constant 4.488 (0.082)∗∗∗ 4.455 (0.079)∗∗∗ 4.485 (0.082)∗∗∗
Gender 0.152 (0.112) 0.209 (0.108) 0.156 (0.112)Age 20.024 (0.038) 20.037 (0.037) 20.026 (0.038)Perceived usefulness 0.184 (0.042)∗∗∗ 0.145 (0.041)∗∗∗ 0.176 (0.043)∗∗∗
Perceived ease of use 0.044 (0.045) 0.042 (0.043) 0.042 (0.045)Perceived cost 20.269 (0.039)∗∗∗ 20.210 (0.040)∗∗∗ 20.266 (0.039)∗∗∗
Affective commitment 0.192 (0.039)∗∗∗
Calculative commitment 0.030 (0.037)R2 0.258 0.314 0.259F 20.760∗∗∗ 22.690∗∗∗ 17.389∗∗∗
△R2 0.056 0.002△F 24.307∗∗∗ 0.656
Dependent variable: satisfaction. The regression coefficients are unstandardized. Standard errors are inparentheses.∗p , 0.05.∗∗p , 0.01.∗∗∗p , 0.001.
Figure 1. Interaction effect of calculative commitment and satisfaction on customer loyalty.
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decreasing marginal effect of satisfaction on customer loyalty, a company that puts sub-
stantial resources into enhancing customer satisfaction may find no corresponding
improvement in customer loyalty. The curvilinear relationship between satisfaction and
customer loyalty could partially explain the phenomenon of ‘satisfaction-loyalty asymme-
try’ and the ‘satisfaction trap’.
Secondly, despite the fact that affective and calculative commitment are both dimen-
sions of relationship commitment, their effects on customer loyalty work very differently.
Although affective commitment has no significant moderating effect on the relationship
between satisfaction and customer loyalty, it has direct positive effects on both satisfaction
(b ¼ 0.192, p , 0.001) and customer loyalty (satisfaction is controlled, b ¼ 0.142, p ,
0.01). In other words, increasing affective commitment can directly enhance customer
loyalty, whereas it cannot intensify the relationship between satisfaction and customer
loyalty. Although calculative commitment has no significant effect on satisfaction and
has only a small effect on customer loyalty (b ¼ 0.096, p , 0.05), it significantly moder-
ates the relationship between satisfaction and customer loyalty (b ¼ 0.083, p , 0.01). In
other words, increasing calculative commitment has no impact on customer satisfaction,
whereas it can significantly intensify the relationship between satisfaction and customer
loyalty.
Affective commitment has a significant direct effect but no moderating effect. The
effect of affective commitment on customer loyalty is similar to that of satisfaction on cus-
tomer loyalty. Therefore, it seems that affective commitment is a ‘supplement’ to satisfac-
tion. Changing the level of affective commitment will shift the position of the curve of
satisfaction and customer loyalty. Calculative commitment has a weak direct effect but
a significant moderating effect. The effect of calculative commitment seems to be an
‘intensifier’ that can intensify the relationship between satisfaction and customer
loyalty. Changing the level of calculative commitment will change the shape of the
curve of satisfaction and customer loyalty. These findings contribute to our understanding
of the phenomenon of the ‘satisfaction trap’. When affective commitment and/or calcula-
tive commitment are at a low level, a high level of satisfaction does not necessarily trans-
form into customer loyalty (Figure 1, the right front side of the curved surface). Thus, the
phenomenon of the ‘satisfaction trap’ appears.
Managerial implications
These research findings have important practical implications for companies wishing to
improve customer loyalty. The results demonstrate that when the level of customers’
affective commitment and/or calculative commitment is low, satisfaction cannot be
effectively transformed into loyalty. If customers have little trust in the company, or
have the freedom to make choices with low switching costs, satisfied customers may
yet be disloyal customers. Neglecting customers’ affective and calculative commitment
in the blind pursuit of customer satisfaction is likely to cause a company to fall into the
‘satisfaction trap’. The potential to improve customer satisfaction, affective commit-
ment, and calculative commitment may be very different, dependent on the different
initial levels of these factors. Given a firm’s marketing resources, it is rational to
balance the allocation of resources between services marketing (by improving the
value of products or services to enhance satisfaction) and relationship marketing
(through the development and maintenance of customer relations to enhance affective
and calculative commitment). This should result in the maximization of customer
loyalty.
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Limitations and directions for future research
We acknowledge several limitations of this study. First, we tested the theoretical hypoth-
eses in the setting of 3G business in China. Although the hypotheses are based on previous
theoretical and empirical studies, the external validity of the research findings needs to be
further examined by samples from different contexts. Secondly, satisfaction, commitment
and customer loyalty may change over time. The cross-sectional data used in this study
limited us to examining the dynamic mechanism of the interplay between the variables.
Getting longitudinal data may help us to better reveal relationships between the variables
and to enhance the inference of causality. Finally, although the study shows that affective
and calculative commitment has important implications to customer loyalty, we have not
investigated how to improve customers’ affective and calculative commitment. To explore
the determinants of customers’ affective and calculative commitment and the interplay
mechanism between them maybe a promising research direction.
Conclusions
The results demonstrate that although customer loyalty monotonically increases with the
enhancement of satisfaction, the marginal effect of satisfaction on customer loyalty in fact
decreases. Affective commitment has direct positive effects both on satisfaction and on
customer loyalty, whereas it has no significant moderating effect on the relationship
between them. Therefore, enhancing affective commitment can shift the curve of relation-
ship of satisfaction and customer loyalty upwards. Although calculative commitment has
no effect on satisfaction and merely a small effect on customer loyalty, it does significantly
moderate the relationship between satisfaction and customer loyalty. That is, enhancing
calculative commitments could intensify the relationship between satisfaction and custo-
mer loyalty. Therefore, calculative commitment is the synergic factor that enables satisfac-
tion to translate into customer loyalty (Oliver, 1999). These findings contribute to our
understanding of the phenomenon of the ‘satisfaction trap’. Neglecting customers’
relationship commitment, blind pursuit of customer satisfaction is likely to induce a
company falling into ‘satisfaction trap’. These research findings therefore have practical
implications for companies wanting to improve their customers’ loyalty. Future research
needs to further examine the external validity of the findings of this study and to explore
the determinants of customers’ affective and calculative commitment.
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