International Journal of
Strategic Innovative Marketing
Vol. 02 (2015) DOI: 10.15556/IJSIM.02.02.001
Relationship quality and consumer
loyalty in high-tech services: The dual
role of continuance commitment
Apostolos N. Giovanis1, a
1Department
of Business Administration, Technological Educational Institute of Athens, Egaleo,
Athens, Greece
a)Corresponding
author: [email protected]
Abstract: The purposes of this paper is first to empirically measure the direct effects of
relationship quality (RQ) components on customer loyalty, and second to examine the
moderating effects of continuance commitment on these relationships in the context of
high-tech consumer services. This paper extends the relationship commitment paradigm by
testing a contingency model to assess the impact of satisfaction, trust, and affective
commitment on customer loyalty, under different levels of continuance commitment. Data
was collected from a survey of 460 customers of mobile telecommunication service providers
and analyzed using partial least squares methodology. Findings clearly indicate that the
impact of all RQ components on customer loyalty is statistically significant but also varies
according to the level of continuance commitment. Relevant theoretical and managerial
implications are presented at the end of the paper.
Keywords: Satisfaction, Trust, Commitment, Loyalty, Relationship marketing, High-tech
services.
1. Introduction
The latest marketing paradigm is about satisfying customer needs profitably [1].
Also, loosing customer is costly. It can cost up to 5 times more to acquire a new
customer than keep an existing customer, while old customers bring more profit
than new ones [2, 3]. As a result, the establishment and development of mutually
beneficial relationships with customers is of paramount importance for service
providers. Therefore, relationship marketing is currently considered as an important
part of service providers’ strategic planning [4], and research has shown that
relationship quality can even replace service quality and customer satisfaction as
the key superior performance driver [e.g. 4, 5].
On the other hand, various recent studies have questioned the linear effects of
strong relationships on customer loyalty manifestations, raising the issue of the
“dark side” of relationships [6, 7]. For example, several previous studies have shown
that the effects of satisfaction and trust on loyalty are affected by several moderators
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RELATIONSHIP QUALITY AND CONSUMER LOYALTY IN HIGH-TECH SERVICES: THE DUAL ROLE OF
CONTINUANCE COMMITMENT
such as switching costs [8], attraction of alternatives [9, 10], or commitment [6].
Also, others have proved that the relationship between affective commitment and
loyalty is moderated by continuance commitment, which is merely determined by
switching cost perceptions and lack of viable alternatives for customers [11, 12, 13].
However, findings regarding the role of continuance commitment, which reflects the
constrained-based part of relationships [12], where consumers believe they cannot
end a relationship because of economic; social, or psychological costs, are
inconclusive. Also, the nature of relationships between relationship quality
components and loyalty is not clear yet [8].
The objective of this paper is to understand better the role of RQ components for
customer loyalty. This study examines the impact of factors that make customers
want-to-be loyal to their service providers, such as satisfaction; trust, and affective
commitment, and the impact of factors that make customers have-to-be loyal to
their service providers, such as continuance commitment. In that research
framework, both the direct effects of all RQ components on customer loyalty and the
moderating effects of continuance commitment on the relationships among the three
“want-to-be” RQ components and loyalty are investigated.
The paper is structured as follows. First, the relevant literature is reviewed, then
the conceptual framework and appropriate research hypotheses are developed;
third, the research methodology is presented; fourth, results are analyzed and
discussed, and finally theoretical and managerial implications; limitations and
suggestions for further research are provided.
2. Conceptual Background and Research Hypotheses
2.1. Customer loyalty and relationship quality
Customer loyalty (CL) is the most important goal of relationship marketing actions
since it results in increased profits; better acquisition rates from positive referrals,
and reduced acquisition and retention costs [14]. CL is defined as a “deeply held
commitment to rebuy or re-patronize a preferred product or service consistently in the
future, thereby causing repetitive same-brand or same brand-set purchasing, despite
situational influences and marketing efforts having the potential to cause switching
behavior” [15, p.392].
Given the benefits of customer loyalty for service providers, it is useful to
elaborate on the way that it is synthesized. According to Sanchez-Franco et al. [16]
customer loyalty is a composite concept combining both RQ and customers’
behavioral intentions to retain their service provider, while RQ represents the
outcome of the interaction between the firm and its customers.
RQ refers to customers’ perceptions of how well a relationship fulfil customers’
expectations, predictions, goals, and desires [17]. Hennig-Thurau and Klee [18,
p.751] postulate that RQ is “the degree of appropriateness of the relationship to fulfil
the needs of the customer associated with the relationship”. Although, there is no
clear consensus regarding the dimensions comprising the construct [19, 20], most
researchers converge on the idea that the three most common such dimensions are
relationship satisfaction (RSAT), trust (TR) and commitment (COM) [20, 21, 22, 23].
Relationship satisfaction express customer’s affective and emotional state
towards a relationship and its definition refers to the favorable affective response of
customers who find all past service encounters with their service providers
rewarding; fulfilling, and stimulating [24, 25]. However, satisfaction per se does not
automatically lead to purchase intention that, in turn, leads to loyalty [4].
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Trust is the second component of RQ and is defined as a service provider’s
characteristic that inspires confidence in customers within a relationship [26]. Since
trust is very important for building healthy relationships, it is important to consider
how it develops. Based on McKnight et al. [27], there are three related, but distinct
dimensions of trust: competence; integrity and benevolence. Competence refers to
the customer’s belief that the service provider has the knowledge; expertise, and
skills required to fulfill his/her needs [28]. Integrity refers to the application of a set
of moral and ethical principles, acceptable to both customer and service provider,
which are predictable and reliable and which lead to equity [29]. Integrity reflects
several service providers’ behaviors including honesty; predictability; credibility, and
dependability [16]. Finally, benevolence refers to the extent to which one party
believes that a second party has intentions and motives beneficial to the first party.
At the center of benevolence is one firm’s willingness to help the customer. The three
dimensions of trust are not interchangeable [16], and according to the study of
Sanchez-Franco et al. [16], the concept of trust is considered as an index composed
by its causal indicators. For that reason, trust is modelled as a higher-order
formative construct having competence, integrity and benevolence as its subcomponents [30].
Relationship commitment is the third dimensions of RQ and refers to the
customer’s belief that the relationship with the service provider is important enough
to exert maximum effort at maintaining that relationship in the future [31]. Several
researchers split commitment in two sub-dimensions, affective and continuance [32,
33]. Affective commitment (ACOM) refers to a customer’s free will to maintain the
relationship with the provider [32], while continuance commitment (CCOM) is the
state of attachment to a partner in recognition of the sacrificed benefits and losses
incurred if the relationship ends [34].
The development of relationships with customers leads to higher retention rates
in the long term [20]. Several previous studies in different service settings have
validated the direct links between all RQ’s dimensions and customer loyalty
manifestations [35, 12, 36, 37, 38, 39, 16, 40, 13, 33, 41, 42]. Kotler and Keller [1]
argue that customer satisfaction is the key to customer retention. Several theoretical
and empirical evidences validated the strong association between satisfaction and
customer retention and/or customer loyalty. In theory, several authors speculate
about the contribution of satisfaction to customer loyalty. For example, Oliver [15]
suggests that satisfaction is an affective antecedent of loyalty. Furthermore,
researchers often consider satisfaction as affecting the likelihood of repurchasing or
reusing the service of a provider. Trust is an essential factor of relationship
development and positively affects customer retention, especially when the risks of
service usage are perceived as significant [16]. For example, Sanchez-Franco et al.
[16] directly linked trust to loyalty in a high-tech consumer service context and find
this relationship to be significant. Commitment is also expected to positively affect
loyalty, given that customers who are committed to their relationship with their
service provider are likely to continue using and recommend the service [12, 13, 16].
Based on these findings the following hypotheses are posited:
H1: Relationship satisfaction positively affects customer loyalty
H2: Trust positively affects customer loyalty
H3: Affective commitment positively affects customer loyalty
H4: Continuance commitment positively affects customer loyalty
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RELATIONSHIP QUALITY AND CONSUMER LOYALTY IN HIGH-TECH SERVICES: THE DUAL ROLE OF
CONTINUANCE COMMITMENT
2.2. The moderating effects of continuance commitment
Previous research has shown that continuance commitment is directly related to
customer loyalty. However, various researchers suggest that there is a need to
consider the role of continuance commitment in the effect of other RQ components
on customer loyalty [43, 12, 13].
The moderating role of continuance commitment or switching cost, which is one
of its proxies [12], on the satisfaction-loyalty and trust-loyalty relationships, has
received considerable attention in the past. However, relevant empirical findings are
inconclusive and the nature of the above relationships is not clear yet [8]. There are
some studies that find positive moderating effects [e.g. 44, 45, 13], while the
majority of studies reveal negative moderating effects [e.g. 46, 47, 48]. Finally, there
are few studies that find no significant moderating effect [e.g. 49, 50]. Fullerton [11]
and Bansal et al. [12] also suggest that when continuance commitment is low, the
impact of affective commitment on loyalty is high. In contrast, when continuance
commitment is high, due to high switching costs or lack of alternative options, the
effect of affective commitment on loyalty is low. Based on the above discussion on
the moderating effects of continuance commitment the following hypotheses are
proposed:
H5: Continuance commitment negatively moderates
relationship satisfaction and customer loyalty
the
relationship
between
H6: Continuance commitment negatively moderates the relationship between trust
and customer loyalty
H7: Continuance commitment negatively moderates
affective commitment and customer loyalty
the
relationship
between
Based on the review of the aforementioned studies, a model describing the
proposed relationships among the five constructs analyzed in this study is shown in
Figure 1.
Figure 1. Proposed model.
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3. Research Methodology
3.1. Research setting
High-tech, continuous purchasing services, such as mobile telecommunication
services, provides an appropriate research setting to test the proposed theoretical
hypotheses, since the objective of such service providers is to increase their
effectiveness through customer loyalty enhancement and for these purpose they are
using CRM actions to cultivate strong relationships with their customers and exploit
several marketing mechanisms to raise switching barriers [51, 13]. As such, the
population of the study is contract customers of mobile telecommunication service
providers living in Athens, the capital of Greece.
3.2. Survey instrument
The scales used to operationalize the concepts of the proposed model were adopted
from different sources to suit the study. Items for relationship satisfaction were
adopted from Sanchez-Franco et al. [16]. The trust scale that measures its three
sub-dimensions of competence, integrity, and benevolence was retrieved from the
study of McKnight et al. [27]. Affective and continuance commitment were
operationalized using the scales of Han et al. [38]. Finally, customer loyalty was
assessed using the scale developed by Aydin et al. [48] for the mobile
telecommunication sector. The items of the loyalty scale express repurchase
intention, resistance to switching due to superior competitors’ offer, and willingness
to recommend the service provider to others. All items were measured on a sevenpoint Likert scale ranging from 1 “strongly disagree” to 7 “strongly agree”. A pilot test
of the final survey instrument with thirty mobile telecommunication services’
customers indicated an acceptable level of all scales’ reliability, since all Cronbach
alpha coefficients ranged higher than 0.70. Additionally the correlation patterns
using the resulted correlation matrix provided evidence for scales’ convergent and
discriminant validity [52].
3.3. Data collection and sample profile
To reach participants a convenient sampling method was used. Twenty appropriately
trained students were selected on the basis of some background experience in
research and data collection to approach 30 individuals each. Data collection was
done in the service providers’ points of sales (POS). In order to reach as much as
possible of the heterogeneous population, avoid location-based bias, and ensure a
wide spread of potential respondents, six POS in three different areas of Athens,
with different socioeconomic profiles were selected for data collection. Fieldwork was
conducted in January and February 2014, over a period of four weeks, and the data
was collected during the POS operating hours, at different times of the day and days
of the week to reduce date and time bias. Research participation was purely
voluntary.
A total of 600 questionnaires were distributed and 480 were collected. However,
20 were rejected because of incomplete data, resulting in 460 usable responses,
yielding a net response rate of about 76%. Among the 460 respondents, 51% were
females and 23% were in the 18-24 age-group; 25% in the 25-34 age-group; 17% in
the 35-44 age-group; 17% in the 45-54 age-group, and 18% were older than 55. In
terms of educational level, 55% of respondents held a university degree; 18% had
some college education; and 26% had secondary school education. 29% of research
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RELATIONSHIP QUALITY AND CONSUMER LOYALTY IN HIGH-TECH SERVICES: THE DUAL ROLE OF
CONTINUANCE COMMITMENT
participants were private employees; 28% were public servants; 18% were
businessmen/self-employed and 24% were non-employed.
Using the Armstrong and Overton procedure [53], nonresponse bias was
evaluated by comparing early respondents with late respondents for all constructs
considered in this study. No significant differences were recorded at the 0.05 level of
significance.
3.4. Data analysis method
The method of Partial Least Squares (PLS-SEM) analysis [54], an implementation of
Structural Equation Modelling (SEM) with SmartPLS 2.0 (M3) [55], was applied to
test the measurement model and the proposed hypotheses. There are three main
reasons to use the PLS technique. First, PLS path modelling has less strict
requirements on sample size and residual distributions than covariance-based SEM
techniques [56]; second, PLS can handle formatively measured constructs and
moderated relationships more easily and effectively than covariance SEM [57] and
third, PLS-PM is used for plausible causality exploration (prediction) rather than for
confirmatory causality testing [58]. The aim of this study is much closer to the
causality exploration, since it is related to the prediction of customers’ future
intentions based on the optimal linear relationships between the proposed latent
constructs. Also, since the proposed model includes a formative construct (trust)
and investigates the impact of moderating variables (continuance commitment), PLS
is considered more appropriate for this study than covariance-based SEM
techniques.
4. Results
4.1. Assessment of the measurement model
The test of the measurement model involves the estimation of item’s reliability,
internal consistency reliability, convergent validity, and discriminant validity of firstorder reflective constructs, which indicate the strength of measures used to test the
proposed model [59]. The factor loadings of all items, as shown in Table 1, exceed
0.82 (> 0.7) showing strong item’s validity [54].
All latent constructs’ reliability was examined using the Cronbach’s Alpha and
Composite Reliability (CR) measures [60]. Gefen and Straub [54] suggest that a value
of 0.70 provide adequate evidence for internal consistency. As shown in Table 1,
Cronbach’s alpha and CR of all reflective measures included in the study exceed
0.70 suggesting that all measures were good indicators of their respective
components.
The average variance extracted (AVE), which indicates the amount of variance
that is captured by the construct in relation to the variance due to measurement
error, was used to assess convergent validity. As depicted in Table 2, AVE values for
all constructs exceed 0.73, which is much higher than the recommended cutoff
value of 0.50 [54], suggesting satisfactory convergent validity.
Discriminant validity was assessed by comparing the square root of AVE
extracted from each construct with the correlations among constructs. The initial
run of PLS revealed that, in accordance with the results of Sanchez-Franco [16],
integrity and benevolence are not distinct from one another and for this reason they
combined to form the integrity_benevolence factor (INT_BEN). The findings of the
new run of PLS provided strong evidence of discriminant validity among all first
INTERNATIONAL JOURNAL OF STRATEGIC INNOVATIVE MARKETING
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order constructs. As seen in Table 2, the square roots of AVE for all first-order
constructs are higher than their shared variances [60].
Table 1. Psychometric properties of the constructs
Construct
Items
Loadings
t-stat
RSat1
0.95
144.84
RSat2
0.96
202.28
RSat3
0.96
143.15
RSat4
0.90
64.07
TInt1
0.89
82.00
TInt2
0.88
64.16
TInt3
0.83
39.63
TInt4
0.84
46.12
TBen1
0.80
38.46
TBen2
0.83
47.19
TBen3
TCmp1
0.87
0.92
71.47
98.28
TCmp2
0.94
128.48
TCmp3
0.93
102.95
TCmp4
0.90
82.73
ACom1
0.87
71.38
ACom2
0.92
89.50
ACom3
0.91
83.88
ACom4
CCom1
0.87
0.87
61.86
46.76
CCom2
0.82
26.75
CCom3
0.92
109.52
CL1
0.88
70.79
CL2
0.91
99.45
CL3
0.86
55.58
CL4
0.86
62.12
Relationship
Satisfaction
TrustIntegrity
Benevolence
Trust-Competence
Affective
Commitment
Continuance
Commitment
Customer Loyalty
Cronbach’s
alpha
0.96
Composite
Reliability
0.97
Average Variance
Extracted
0.89
0.94
0.95
0.72
0.94
0.96
0.85
0.92
0.94
0.80
0.84
0.90
0.75
0.90
0.93
0.77
Table 2. Constructs’ inter-correlations and descriptive statistics
Construct
1
2
3
4
5
1 Relationship Satisfaction
0.94
2 Trust Competence
0.74
0.92
3 Trust Integrity_Benevolence
0.58
0.68
0.85
4 Affective Commitment
0.54
0.44
0.62
0.92
5 Continuance Commitment
0.13
0.20
0.42
0.50
0.87
6 Customer Loyalty
0.67
0.63
0.68
0.63
0.47
6
0.88
MV
SD
4.90
1.62
5.52
1.18
5.24
1.14
3.62
1.64
3.40
1.57
4.99
1.43
A commonly-used method of approximating second-order factors is the repeated
indicator approach [61], where the second-order factor is directly measured by using
items of all its lower-order factors. The trust construct is modeled as a formative
second-order construct. According to Wetzels et al. [62], PLS-PM, can also be used
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RELATIONSHIP QUALITY AND CONSUMER LOYALTY IN HIGH-TECH SERVICES: THE DUAL ROLE OF
CONTINUANCE COMMITMENT
for hierarchical models with formative constructs, by reversing the direction of the
relationships between the higher and the lower-order constructs.
The measurement quality of the formative second-order factors was tested
following the suggestions by Chin [63] and Diamantopoulos and Winklhofer [64].
First, the correlations among the first-order constructs were examined. As shown in
Table 2, the correlation between competence and integrity_benevolence is 0.68.
Although this correlation is relatively high, it indicates that trust is better
represented as a formative rather than reflective second-order construct, since the
latter usually exhibit extremely high correlations (≥ 0.8) among their first-order
factors [65].
Furthermore, all first-order trust-related components were found to have
significant path coefficients in forming customer perception about trust. Results
suggest that integrity_benevolence (β = 0.57; t = 23.21) is the most important trust
component followed by competence (β = 0.52; t = 21.39). Also, the variance inflation
factors (VIF) were computed for these two first-order trust dimensions to assess
multicollinearity. VIF values above 10 would suggest the existence of excessive
multicollinearity and raise doubts about the validity of the formative measurement
[64]. The VIF values for the first-order dimensions of trust is less than 2 meaning
that multicollinearity is not a concern for the trust construct.
4.2. Assessment of the structural model
The PLS-SEM method was also used to confirm the hypothesized relations between
constructs in the proposed model. The significance of the paths included in the
proposed model was tested using a bootstrap resample procedure. Smart-PLS
software was used to conduct the PLS-SEM analysis [55]. Table 3 shows the model’s
standardized path coefficients and their statistical significance which is used for the
validation of all hypothesized relationships and the assessment of the structural
model.
With regards to the direct effects of the four RQ components on customer loyalty,
the results for the non-moderated model (Model 1) (see Table 3) show support for
hypotheses H1, H2; H3, and H4, as trust (β = 0.34; t = 6.00), relationship satisfaction
(β = 0.31; t = 6.48), continuance commitment (β = 0.24; t = 7.38), and affective
commitment (β = 0.13; t = 3.60) emerge as significant predictors of customer loyalty.
The proposed model accounted for 65% of the variance in customer loyalty and the
relatively high value of coefficient of determination (R2) indicate that sizeable
portions of the variance in the dependent variable is explained by the chosen
independent variables.
To test hypotheses H5 to H7, proposing that continuance commitment moderates
the effect of RQ components on customer loyalty, three separate moderation
analyses were conducted including one interaction at a time into the structural
model. This was based on Ping’s suggestion of a stepwise procedure of one
interaction at a time because the detection of more than one or two significant
interactive terms is difficult [66]. The results of Model 2 indicates that continuance
commitment has a significant moderating effect (β = -0.16; t = -5.86) on the
relationship between relationship satisfaction and customer loyalty. According to
this, the total effect of relationship satisfaction on customer loyalty decreases in
customers with high continuance commitment, due to its moderating effect. For
them, the total effect of relationship satisfaction on customer loyalty will become
0.22 (0.38-0.16), while for customers with low continuance commitment becomes
INTERNATIONAL JOURNAL OF STRATEGIC INNOVATIVE MARKETING
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0.38. Thus, continuance commitment has a moderation effect on the relationship
satisfaction-customer loyalty link, as predicted by H5.
Table 3. Structural model’s results
Model 1
Independent
Variables
Model 2
Model 3
Model 4
β
t-stat
β
t-stat
β
t-stat
β
t-stat
Relationship Satisfaction (RSAT)
0.31
6.48
0.38
6.64
0.33
5.75
0.34
5.84
Trust
0.34
6.00
0.27
4.76
0.31
5.30
0.32
5.68
Affective Commitment (ACOM)
0.13
3.60
0.12
2.98
0.13
2.96
0.12
2.71
Continuance Commitment (CCOM)
0.24
7.38
0.29
7.52
0.27
6.67
0.27
6.60
-0.16
-5.86
-0.07
-2.59
-0.07
-2.66
RSAT * CCOM
TRUST * CCOM
ACOM * CCOM
R
2
0.65
0.68
0.66
0.66
In the same manner, the fact that parameters of TRUST*CCOM (β = -0.07; t = 2.59) and ACOM*CCOM (β = -0.07; t = -2.66) in Models 3 and 4 respectively are
significant, provide evidence that H6 and H7 are supported. Thus, continuance
commitment has also moderating effects in the trust-customer loyalty and the
affective commitment-customer loyalty relationships, since for customers with high
continuance commitment, the effect of trust and affective commitment on loyalty is
less that the effect for customers with low continuance commitment. For customers
with high continuance commitment the total effect of trust and affective
commitment on loyalty becomes 0.24 (0.31-0.07) and 0.05 (0.12-0.07) respectively,
compared with the relevant values of 0.31 and 0.12 for customers with low
continuance commitment.
Finally, since the explanatory power of the moderated structural models is
higher than that of the non-moderated model, we can conclude that customer
loyalty is a function of satisfaction, trust, affective commitment, continuance
commitment and its moderating effects.
5. Discussion, Limitations and Directions for Research
The purpose of this paper was to explain customer loyalty in a high-tech,
continuous purchasing service context using four RQ components as its main
determinants and to examine the existence of possible moderating effects of
continuance commitment on the relationships between trust, satisfaction and
affective commitment and customer loyalty. The findings provided support for all
proposed hypotheses and revealed that continuance commitment plays a double role
in the relationship quality-customer loyalty link determination.
Based on the empirical study’s findings, the most important determinants of
customer loyalty is trust and satisfaction followed by the two aspects of
commitment. The fact that the importance of affective commitment is very low is
attributed to the fact that it is overrode by continuance commitment due to high
switching costs and lack of attractive alternative options for customers [12]. These
results agree with previous studies [12, 48, 36, 13, 33, 41].
Regarding the moderating effects of continuance commitment on the relationship
between RQ components and customer loyalty, it was found that these effects are
lower for customers with high continuance commitment. This means that
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RELATIONSHIP QUALITY AND CONSUMER LOYALTY IN HIGH-TECH SERVICES: THE DUAL ROLE OF
CONTINUANCE COMMITMENT
continuance commitment reduces customers’ sensitivity to satisfaction, trust, and
affective commitment. These results are consistent with those of Aydin et al. [48]
and Fullerton [11], but are different with those of Wu et al. [13], where the
interactive effect of continuance commitment with satisfaction was found to be
positive, and Bansal et al. [12], where the interactive effect between continuance
commitment and affective commitment was found insignificant. Bansal et al. [12]
admitted that although there is a solid theoretical background related to the validity
of this hypothesis, they attributed their findings on the nature of the field study and
the service context where customers presented very low values of continuance
commitment. The findings also show that the moderating effect of continuance
commitment on trust and affective commitment are much lower than that of
satisfaction, meaning that continuance commitment affects more seriously the
short-term results of relationships, represented by satisfaction, than those of longterm results, represented by trust and affective commitment. However, even these
small interaction effects should be taken under consideration since they can be
meaningful under extreme moderating conditions [56].
The key managerial implication of these findings for mobile service providers is
that since trust and satisfaction are important for increasing customer loyalty, they
need to invest in actions that will enhance them. The investment on market
research, the development and delivery of valuable services and the use of
responsive two-way customer communication will probably contribute to the
enhancement of satisfaction, trust, and affective commitment [67]. On the other
hand, service providers need to identify the customers that base their relationships
on switching costs or on the lack of better options, because for them, satisfaction;
trust, and affective commitment are less important, due to the role of high
continuance commitment. Mobile service providers have to understand how
continuance commitment work and develop appropriate retention programs to
protect and enhance their customer relationships.
This study is not without limitations. However these limitations offer
opportunities for further research. First, the convenient sampling strategy followed
for data collection does not ensure the full generalization of results. The proposed
model can be used for further research using a random sampling approach that
provides a more representative sample. Second, the industry-specific sample may
also affect the generalizability of findings. Therefore, it is necessary to test the
proposed model in other relevant service settings before the interrelationships
among RQ components and customer loyalty are fully clarified. Third, the findings of
this research were based on a cross-sectional study. This reduces the ability of the
study to reflect the temporal changes in the research constructs. As a result,
longitudinal studies are needed to clarify the effects of temporal changes.
As far as future research recommendations are concerned, certain variables
could be incorporated into the proposed framework to enhance its predictive
performance and to provide better understanding of the customer’s decision-making
process. For example, a future research effort could consider the interaction of
certain brand-related concepts, such as brand identity and brand identification,
with relationship quality components’ assessment [68], as they all contribute in the
delivery of a customized and sustainable brand experience that, in turn, promotes
customer loyalty and service brand performance [69]. Moreover, future research can
replicate this study in different types of services; and across different countries that
are characterized by a different culture in order to identify cultural differences or
similarities in the proposed interrelationships. Finally, since relationship quality is
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11
shown to affect largely customer loyalty, the study of all relevant constructs in
different relationship stages will provide new insights for service companies on how
to best manage their relationships with customers over time.
Appendix 1: Questionnaire’s items per construct
Relationship
satisfaction
I am satisfied with my decision to contract the services of my service provider
My choice to contract my service provider was a wise one
I am happy with my decision to contract my service provider
I think I did the right thing by deciding to contract this service provider
Integrity
My service provider is truthful in its dealings with me
I would characterize my current service provider as honest
My service provider would keep its commitments to me
My service provider is sincere and genuine
Benevolence
I believe that my current service provider would act in my best interest
If I required help, my current service provider would do its best to help me
My service provider is interested in my well-being, not just its own
Competence
My service provider is competent and effective in its work
My service provider performs its role of providing online services very well
Overall, my current service provider is a capable and proficient Internet
service provider
In general, my service provider is very knowledgeable
Affective
commitment
I
I
I
I
Continuance
commitment
I have received more benefits in my SP than in other service providers
Compared with my service provider, it would be too costly for me to visit
other service providers
It is more convenient for me to use my service provider than at other service
providers
Customer
Loyalty
I
I
I
I
feel
feel
feel
feel
like “part of the family” at my service provider
“emotionally attached” to my service provider
happy being a customer of my service provider
a strong sense of belonging to my service provider
would considerer my service provider as my first choice to buy new services
would say positive things about my service provider to other people
would recommend my service provider to someone who seeks my advice
will continue using my service provider even if others’ prices are lower
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