Uddin, Mohammed Belal; Akhter, Bilkis
Article
Determinants of customer satisfaction of banking
industry in Bangladesh
Pakistan Journal of Commerce and Social Sciences (PJCSS)
Provided in Cooperation with:
Johar Education Society, Pakistan (JESPK)
Suggested Citation: Uddin, Mohammed Belal; Akhter, Bilkis (2012) : Determinants of customer
satisfaction of banking industry in Bangladesh, Pakistan Journal of Commerce and Social Sciences
(PJCSS), ISSN 2309-8619, Johar Education Society, Pakistan (JESPK), Lahore, Vol. 6, Iss. 2, pp. 242-256
This Version is available at:
https://hdl.handle.net/10419/188055
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Pak. J. Commer. Soc. Sci.
2012 Vol. 6 (2), 242-256
Determinants of Customer Satisfaction of Banking
Industry in Bangladesh
Mohammed Belal Uddin (corresponding author)
Assistant Professor, Department of Accounting & Information Systems
Comilla University, Kotbari, Comilla – 3503, Bangladesh
Email: belal_137@yahoo.com
Bilkis Akhter
Assistant Professor, Department of Accounting & Information Systems,
University of Dhaka, Dhaka – 1000, Bangladesh
Email: bilkis_akhter@ yahoo.com
Abstract
This study aims to investigate, through the development and operationalized constructs of
service quality, service charge, perceived value, and customer satisfaction; customer
satisfaction and its determinants of the banking industry in Bangladesh. An exploratory
factor analysis and structural equation modeling was used to analyze data. Measurement
model and structural model indicate that service quality and fair service charge both have
positive direct impact on customer satisfaction in a mass service industry (i.e., banking
industry). It was further observed that they also have indirect influence on customer
satisfaction through perceive value, i.e. perceived value has mediating role between
quality, charge fairness and satisfaction. Bank managers are recommended to formulate
operations and marketing strategies that focus on desires of customers to enhance level of
satisfaction.
Keywords: Service quality, Service charge, Perceived value, Customer satisfaction,
Banking industry.
1. Introduction
In modern economics, service sector plays significant role side by side manufacturing
and other sectors. Banking sector performs its activities economically and socially in a
country. Service managers of such service factory are more concerned about their quality
of service and client satisfaction (Olorunniwo et al., 2006). The central bank of
Bangladesh is Bangladesh Bank. The financial system as well as financial sector of
Bangladesh is dominated by commercial banks. The banking system includes of four
government owned commercial banks, thirty private commercial banks, nine foreign
commercial banks, and five specialized development banks. Some new private
commercial banks will enter in the market very soon. Bangladesh Bank is the supreme
authority of financial sector, and it regulates all banks and non-bank financial institutions.
In Bangladesh, commercial banks provide some products and service to their clients
(website of Bank Asia). Banking services include mobile banking, SME banking, internet
banking, SMS banking, credit card, ATM services, foreign currency account, locker
Uddin and Akhter
243
service, and loan and advances (term loan, car loan, education loan, housing loan, micro
group credit, micro credit enterprise, etc.). They also offer corporate banking, loan
syndication, real-time online banking for corporate clients. Service quality, service
charges, perceived value and customer satisfaction are the key sources of success in any
service factory (Olorunniwo and Hsu, 2006). Issues that affect service quality and
customer satisfaction have operational and marketing orientations. To understand the
dimensions of service quality and for measurement of customer satisfaction it is
important to know under which typology commercial banks are belong. In this regard, the
classification given by Schmenner (1986) is important. Schmenner divided services under
four quadrants based on labor intensity and customer interaction. Labor intensity is the
ratio of labor cost to the machinery and equipment value. On the other hand, customer
interaction is defines as, the joint measure of customer contact and customization of
services. Under this categorization, commercial banking services belong to mass service
category. In commercial banking sector, there are high labor intensity and low
customization of services. Mass service also includes retailing, wholesaling, schools,
traditional long-distance ground trucking. Another three quadrants of services are: service
factory (airlines, hotels, trucking, resorts and recreation), service shop (hospital,
restaurant, auto and their services), and professional service (accounting firms, audit
firms, medical clinics, law firms).
The improvement of service quality, perceived value, and satisfaction ensure customer
loyalty (Kuo et al., 2009; Lai et al., 2009; Wu and Liang, 2009). Since the studies
regarding service quality, perceived vale, and customer satisfaction issues in banking
industry is limited and there is no available measurement scales for service quality and
customer satisfaction, especially in Bangladesh, this study efforts to propose the
measurement scales for factors affecting customer satisfaction and for customer
satisfaction itself. The objectives of this study are firstly, to recognize the influencing
factors of customer satisfaction and post-purchase intentions. Secondly, to examine the
interrelationship between customer satisfaction and influencing factors of satisfaction
such as service quality, service charge, and perceived value. The result of this study has
managerial and academic implications. Managers of commercial banking service
providers can use the findings as sources of reference to manage their business and
improve their service quality, and academicians can use the finding for application of
service marketing field and further extension of this topic or related topics.
The rest of the paper is structured as follows. The next section provides the theoretical
discussion and hypotheses of the study. The following two sections outline research
methodology and offer statistical analysis and major findings of the study. The last
section presents discussion, theoretical and managerial implications, limitations of this
study, and guidelines for further study.
2. Theoretical background and hypotheses development
2.1.1 Service quality
The gap between customers’ expectation and real performance of a service is termed as
service quality (Parasuraman et al., 1985; 1988). Parasuraman et al., (1988) developed
the SERVQUAL model as mentioning five dimensions such as tangibility,
responsiveness, reliability, assurance, and empathy. In 1992, Cronin and Taylor proposed
the alternative method, referred to as SERVPERF. They argued that, to assess service
Determinants of Customer Satisfaction
244
quality, perception of customers regarding the performance of service provides better
results than using SERVQUAL. Along with other researchers in 1994, Parasuraman et al.
also mentioned that measurement method using SERVPERF is better than using
SERVQUAL, though SERVQUAL can provide better diagnostic results of service
quality. The dimensions (i.e. tangibility, responsiveness, reliability, knowledge, and
accessibility) of service quality for mass service as well as banking service will be
dominant.
2.1.2 Service charge
In finance service charge is termed as the amount of payment requested by the seller of
services. Service charge as well as price is determined by several factors such as
willingness of the buyer to pay, willingness to accept, costs, markup, legal environment,
intensity of competition price substitute products, etc. Price fluctuations in many service
industries results in price-performance and the level of price-performance stability
moderates the relationship between performance potential and successive performance
and satisfaction judgments (Voss et al., 1998). The perceived price fairness related to
different level intangible services has direct or indirect effect on customer loyalty in case
of banks, auto repair and maintenance shops, and (gasoline) filling stations (Lien and Yu-
Ching, 2006).
2.1.3 Perceived value
Perceived value is customers’ psychological assessment regarding the product and
service about the utility of that product or service comparing with expectation. Recently
value perceptions have been focused by marketing researchers and managers to explain
customer satisfaction and loyalty (Lin and Wang, 2006). To assess value perception
customers consider perceived benefits relative to sacrifice (Lee et al., 2007). Except
monetary sacrifice perceived value assessment includes social psychological perspective
and non monetary costs such as search cost, transaction cost, negotiation cost, and
consumption of time (Kuo et al., 2009; Chen and Tsai, 2008).
2.1.4 Customer satisfaction
Customer satisfaction is the authentic expression of the status of satisfaction will differ
from person to person and product/service to product/service and is an appraisal of how
products and services of a company meet up or exceed customer anticipation. Satisfaction
is the consequence of a number of both psychological and physical factors which
associate with satisfaction behaviors. Kotler (2000) defined satisfaction as: “a person’s
feeling of pleasure or disappointment resulting from comparing a product’s perceived
performance (or outcome) in relation to his or her expectations”. Organizations can
accomplish customer satisfaction by satisfying their customers’ needs and wants (La
Barbera and Mazursky, 1983). Customer Satisfaction is customers’ collective conception
of a firm’s service performance (Johnson and Fornell, 1991).
2.2 Relationship among the variables
With the consumption of any product or service customers have some benefits
expectation based on their advance sacrifice of resources. Perceived value is the appraisal
of the expected benefits with actual performance of the products or services. Several
scholars examined association between service quality and perceived value in their
studies and found positive relationship between them (Hutchinson et al., 2009; Kuo et al.,
Uddin and Akhter
245
2009; Lai et al., 2009; Turel and Serenko, 2006; Wu and Liang, 2009). They found high
service quality is correlated with high perceived value. And experience about service
quality positively and significantly persuade perceived value of a customer (Chen and
Chen, 2010). On the other hand, According to the satisfaction model customer
satisfaction is influenced by service quality. When customers get expected service
quality, it leads to higher satisfaction (Hutchinson et al., 2009). Service quality is the
determinant of customer satisfaction (Cronin and Taylor, 1992) and by ensuring good
service quality; service providers can enrich customer satisfaction (Kuo et al., 2009).
Service quality has direct positive influence on customer satisfaction and post-purchase
intentions (Gerpott et al., 2001; Kim et al., 2004; and Lin and Wang, 2006). So, we posit:
H1: Service quality has positive effect on perceived value in banking services.
H2: Service quality has positive effect on customer satisfaction in banking services.
Customers are always cost concern. Reduction of outlays related with purchasing
process, is one the way to enhance perceived value (Chen and Hu, 2010). Customer value
is a function of service quality and service charge. It provides a competitive advantage
when firms take cost-cutting imitative to ensure customer value (Spiteri and Dion, 2004).
Real price competitiveness is an important determinant of customer value. Price
satisfaction increases the value perception and there is a direct relationship between price
and value (Ralston, 2003). Price has an impact on customer buying behavior and value
perception. Price/service charge needs special consideration to assess value perception of
customers, not generalized along with other factors (Lockyer, 2005). Again, Customer
satisfaction is affected by the price/service charge awareness (Iyer and Evanschitzky,
2006; Varki and Colgate, 2001). Price level, value for money and special offers may
result in both satisfaction and dissatisfaction and price fairness, price perceptibility and
price processibility may result in dissatisfaction for customers (Zielke, 2008). In addition
to the various levels of product/service price, a mixture of price awareness dimensions
have potentiality to intimidate the customers’ satisfaction (Diller, 2000; Matzler and
Pramhas, 2004; Matzler et al., 2006). Perceptions of customers about price/service charge
fairness have been major concern due to huge interest of mass people (Xia et al., 2004,
Martin et al., 2009). Therefore, we propose:
H3: Fair service charge has positive effect on perceived value in banking services.
H4: Fair service charge has positive effect on customer satisfaction in banking services.
Customer satisfaction is positively influenced by perceived value. The extent of
satisfaction depends on extent of perceived value and higher level of perceived value lead
to higher level of customer satisfaction (Kuo et al., 2009; Turel and Serenko, 2006).
Customer satisfaction tends to positive post purchase behavior, thus, satisfaction plays a
mediating role in the relationship of perceived value and behavioral intentions (Lin and
Wang, 2006). Among the determinants of satisfaction perceived value is the important
one (Chen and Chen, 2010) and perceived value plays mediating role between service or
product quality and customer satisfaction (Chen and Tsai, 2008). Service quality and fair
service charge both have significant, direct effects on perceived value. Then, perceived
value influences on customer satisfaction that lead to positive behavioral intentions, i.e.
customer loyalty (Lai et al., 2009)
H5: Perceived value has positive effect on customer satisfaction in banking services.
Determinants of Customer Satisfaction
246
3. Research methodology
3.1 Scale development
The scale as well as the questionnaire was designed according to the existing literature
and experts’ opinions. To design attitude rating scale of respondents we have reviewed
management, marketing, and operations management literature. Some items were directly
adopted from previous survey instrument to operationalize the constructs in this study.
Few new items also included in different constructs to get good response from data
collection through survey. The questionnaire has two parts. The first part was intended to
understand the personal information of respondents using nominal scale. The second part
consists the perceptions of respondents regarding the constructs of the model. All
constructs were measured using multiple items by a seven point Likert-type scale (1=
strongly disagree, 2= disagree, 3= moderately disagree, 4= neutral, 5= moderately agree,
6= agree, and 7= strongly agree).
3.2 The Sample
Total 400 questionnaires were distributed to the general people who have at least one
bank account. The questionnaire was distributed on random basis. And 335 responses
were received, of which 322 were complete and usable (response rate is 84 percent,
whereas, effective response rate is approximately 96 percent). Sixty-one percent (61
percent) respondents were men and 39 percent were women. 62 percent respondents were
up to thirty years, 13 percent were thirty-one to forty years, 16 percent were forty-one to
fifty years, and 9 percent were above fifty years old. 26 percent respondents were
involved in government service, 30 percent were in private service, 26 percent were in
business, and 18 percent were housewife respondents. 36 percent respondents completed
up to college level education, 46 percent completed graduation, and 18 percent completed
post-graduation. The frequency distribution for monthly income was as follows: 54
percent up to Taka 20,000 (Taka is the Bangladeshi currency unit), 27 percent between
Taka 20,001 and Taka 30,000, 13 percent between Taka 30,001 and Taka 40,000, 4
percent between Taka 40,001 and 50,000, and 2 percent above Taka 50,000. The
Uddin and Akhter
247
summary statistics of the survey are shown in Table 1. In order to control common
method biases, it was assured to respondents that there was no right or wrong answers
and they should provide answer as honestly as possible and no information will be shared
with other person or organization. It has been also assured that the respondents’ identity
will not be disclosed, i.e. as like answers to be anonymous and the information of this
survey will be used for researchers’ academic purpose.
Table 1: Summary Statistics of Questionnaire Survey
Constructs
No. of
items
Mean
SD
*
Sources of scale
Service quality
6
5.135
1.404
Olorunniwo et al., 2006;
Olorunniwo and Hsu, 2006
Service charge
3
6.278
0.963
Kim and Lee, 2010; Zielke,
2008
Perceived value
3
5.658
1.154
Chen and Tsai, 2008;
Cronin et. al., 2000;
Hutchinson et al., 2009; Lai
et al., 2009
Customer
satisfaction
5
5.622
1.076
Lin and wang, 2006;
Olorunniwo et al., 2006
SD* = standard deviation
4. Analyses and Results
AMOS 17.0 was used as the analysis instrument and structural equation modeling (SEM)
was employed in this study to test proposed model and hypotheses. Maximum likelihood
method was adopted for parameter estimation. Measurement model and structural model
test were used to test fitness of the model. The exploratory factor analysis (EFA) was
performed to understand underlying relationship of factors. A Bartlett sphericity test was
performed to verify whether the data were appropriate for factor analysis. A KMO
(Kaiser–Meyer-Olkin) value of 0.856 and significance level of .000 were obtained using
Bartlett’s sphericity test, which implies that the inter-correlation matrix contains
sufficient common variance to make factor analysis worthwhile. For EFA, the Principal
Component Analysis (PCA), with varimax rotation and eigenvalue greater than 1 was
used. As a conservative heuristic, a cut-off point as 0.50 (suppress absolute value less
than 0.50) was imposed in factor analysis that enhance the total reliability of the
questionnaire. We restricted the number of factors to four as the theoretical background
of this study has total four underlying factors. Table-2 shows the rotated factor loadings
and their respective eigenvalue and cronbach alpha values. It is notable that all calculated
alpha values are above the widely recognized rule of thumb of 0.70 (Nunnally, 1978),
that expresses a good internal consistency among items within each construct.
Determinants of Customer Satisfaction
248
Table 2: Result of factor analysis
No.
Service
quality
Service
charge
Perceived
value
Customer
satisfaction
Sq1
Sq2
Sq3
Sq4
Sq5
Sq6
0.912
0.926
0.932
0.904
0.924
0.782
Sc1
Sc2
Sc3
0.883
0.846
0.823
Pv1
Pv2
Pv3
0.845
0.899
0.775
Cs1
Cs2
Cs3
Cs4
Cs5
0.755
0.653
0.759
0.782
0.758
Eigenvalue
6.648
3.275
1.737
1.354
Cumulative
percentage of
explained
variance
39.107
58.372
68.587
76.553
Cronbach alpha
0.961
0.875
0.834
0.836
Overall cronbach alpha is 0.895.
4.1 Measurement model
To have a more rigorous interpretation of customer satisfaction, confirmatory factor
analysis (CFA) was conducted. The CFA model or Measurement model was applied to
identify and determine the relationships of variables in the model. To evaluate the
goodness-of-fit of model several measures of indices are used as suggested by Hair et al.
(1998), Iacobucci (2010), Schumacker (1992): Chi-square/degrees of freedom (χ√/df)
ratio, root mean-square error of approximation (RMSEA), goodness of fit index (GFI),
normed fit index (NFI), comparative fit index (CFI), incremental fit index (IFI). As
Table-3 shows χ√/df = 1.886, RMSEA = 0.056, GFI = 0.944, NFI = 0.961, CFI = 0.981,
and IFI = 0.981. All measures fulfill the suggested values. Therefore, CFA model can be
said as a good fit model.
Uddin and Akhter
249
Table-3: Goodness of Fit Statistics for Measurement Model and Structural Model
Suggested
values
Measurement
model values
Structural
model
values
Absolute measures
χ√/df
RMSEA
GFI
<3
<0.06
>0.90
1.886
0.053
0.944
1.727
0.048
0.948
Incremental fit
measures
NFI
CFI
IFI
>0.90
>0.90
>0.90
0.961
0.981
0.981
0.965
0.985
0.985
The measurement model was further evaluated for reliability and validity, after achieving
the well fit indices. The amount of variance in an item because of underlying construct is
indicated by item reliability. Standardized loading greater than 0.70 demonstrate item
reliability, but standardized loadings ≥ 0.50 are also acceptable (Chin, 1998; Hair et al.,
1998). For construct reliability, value ≥ 0.70 is required that intends to the degree to
which an observed variable reveals an underlying factor. Table-4 presents the item
reliability and construct reliability results. Standardized loadings ranged from 0.555 to
0.949 indicating good item reliability. All values of construct reliability were above the
threshold value (i.e. 0.70) indicating high level of reliability for all the constructs.
Determinants of Customer Satisfaction
250
Table-4: Measurement Model Results
Constructs and variables
Standardized
loadings
t
-
statistics
Construct
reliability
(CR)
Average
variance
extracted
(AVE)
Service quality
Sq1
Sq2
Sq3
Sq4
Sq5
Sq6
0.911
0.942
0.949
0.872
0.892
0.764
21.187**
22.439**
22.739**
19.655**
20.496**
16.642**
0.95
0.79
Service charge
Sc1
Sc2
Sc3
0.921
0.896
0.702
20.574**
19.836**
14.056**
0.88
0.72
Perceived value
Pv1
Pv2
Pv3
0.789
0.889
0.713
16.091**
18.370**
14.070**
0.84
0.64
Customer
satisfaction
Cs1
Cs2
Cs3
Cs4
Cs5
0.750
0.792
0.735
0.555
0.704
13.764**
15.185**
14.149**
7.838**
14.066**
0.83
0.51
**Indicates significance at p< 0.01 level.
CR= (∑ Standardized loadings) √ ∕ [(∑ Standardized loadings) √ + ∑ (measurement indicator
error)]
AVE = ∑ (Standardized loadings√) ∕ [∑ (Standardized loadings√) + ∑ (measurement indicator
error)]
After being assured that a scale instrument provides necessary levels of reliability, this
study stepped to scale validity. Convergent validity and discriminant validity were tested
under construct validity in this study. Convergent validity assesses the degree to which
dimensional measures of the same concept are correlated. To assess convergent validity
average variance extracted (AVE) is used (Fornell and Larcker, 1981; Hair et al., 1998).
Representation of unobserved constructs by items is truly denoted as higher as the
average variance extracted is higher. For unobserved construct the average variance
extracted (AVE) should be more than 0.50 (Hair et al., 1998). Table-4 shows the average
variance extracted (AVE) values for constructs ranged from 0.51 to 0.79 exceeded the
Uddin and Akhter
251
threshold value 0.50, supportive evidence for convergent validity. Moreover, in a CFA
setting, t-statistics related to factor loadings is assessed to measure convergent validity
(Rao and Troshani, 2007). All items offer good measures to their respective latent
construct because of all t-statistics values are statistically significant at 0.01 level and
confirmed convergent validity of the constructs. Average variance extracted (AVE) is
also used to assess discriminant validity (Fornell and Larcker, 1981). The role of thumb is
that the average variance extracted (AVE) values should be greater than corresponding
squired inter-construct correlation estimates (SIC) in the model. Table-5 shows the
average variance extracted (AVE) estimates in the diagonal values and corresponding
squired inter-construct correlation estimates (SIC) values, supportive evidence for
discriminant validity. For example, average variance extracted (AVE) estimate for
service charge was 0.72 and corresponding squired inter-construct correlation estimates
(SIC) values were 0.08, and 0.36 for perceived value and customer satisfaction
respectively, an indication of discriminant validity.
Table-5: Squared correlations between constructs
Service
quality
Service
charge
Perceived
value
Customer
satisfaction
Service
quality
0.79*
Se
rvice
charge
0.03
0.72*
Perceived
value
0.06
0.08
0.64*
Customer
satisfaction
0.19
0.36
0.14
0.51
*
*Diagonal elements are average variance extracted (AVE)
4.2 Structural model
Table-3 shows the common model-fit indices, recommended values and results of the test
of structural model fitness. As shown in Table-3, comparison of all fit indices with their
corresponding recommended values (Hair et al., 1998; Iacobucci, 2010; Schumacker,
1992) the evidence of a good model fit was exposed. Given the good fit of the model, the
estimated path coefficients of the structural model were then examined to evaluate the
hypotheses.
Determinants of Customer Satisfaction
252
Table-6: Path analysis of structural model
Casual path
Hypotheses
Path
coefficient
t
-
statistics
Results
Service
quality
perceived value
H1
0.206**
3.444
Supported
Service quality
customer satisfaction
H2
0.303**
5.401
Supported
Service charge
perceived value
H3
0.260**
4.309
Supported
Service charge
customer satisfaction
H4
0.523**
8.552
Supported
Perceived value
customer satisfaction
H5
0.159**
2.710
Supported
**indicates significance at p< 0.01 level
Table-6 depicted the empirical results of structural model by path analysis. The path
coefficients along with hypotheses and t-values of the latent constructs are visualized in
Figure-2, where hypotheses were drawn in the solid lines. The empirical results support
all hypotheses (i.e., H1, H2, H3, H4 and H5). The empirical results found significant
positive relationship among service quality, fair service charge, perceived value and
customer satisfaction. It is notable that there is a direct and indirect effect of service
quality on customer satisfaction. On the other hand, fair service charge has also
significant direct and indirect effect on customer satisfaction.
Figure-2: Outcome of Hypothesized Structural Model
5. Conclusions and implications
The scales developed for service quality, service charge, perceived value, and customer
satisfaction were tested using a diversified data set collected by a questionnaire survey in
Bangladesh. Structural equation modeling (SEM) including measurement model and
structural model was employed in this study to test proposed model and hypotheses. The
Uddin and Akhter
253
results demonstrate that service quality and fair service charge both have direct positive
influence on customer satisfaction. They have also indirect role on customer satisfaction
through perceived value. This study contributes in the branch of consumer behavior in
terms of theory development and managerial implications especially in banking industry
in a developing country like Bangladesh.
This study finds service quality and fair service charge both have significant positive
impact on customer satisfaction in banking industry of Bangladesh. This result is
consistent with finding of other scholars (Cronin and Taylor, 1992; Hutchinson et al.,
2009; Iyer and Evanschitzky, 2006; Varki and Colgate, 2001). Usually, service quality is
the important predictor of customer satisfaction, but this study establishes service charge
fairness has great impact on customer satisfaction simultaneously with service quality.
This result has managerial implications. In order to successfully operate the banking
business managers should emphasize the quality and charge fairness. It is a complex
process to make customers satisfied and maintenance of satisfaction and that require
investment of tangible and intangible resources. Thus, the positive effect of quality, and
fairness of service charge, makes customers satisfied. Managers should have planning to
ensure service quality, competitive service charges.
Again, the empirical results show perceived value has the mediating role between service
quality, service charge and customer satisfaction. It implies that quality and charge
fairness both have indirect impact on customer satisfaction through perceived value,
which is similar to the other studies (Kuo et al., 2009; Lai et al., 2009; Turel and Serenko,
2006). This result also offers implications for banking industry in Bangladesh. Managers
should know what customers want and how they become satisfied. From a managerial
perspective, service quality, fair service charge and perceived value is an important
influencing factor on customer satisfaction. Firms should understand the importance of
quality assurance, charge fairness, and value of the service to customers. Perceived value
is influenced by service quality and charge fairness. At the same time, they have positive
direct influence on customer satisfaction. Therefore, bank managers should develop a
systematic assessment program to monitor service quality, perceived value and
satisfaction of customer. Bank clients should be informed about the activities of bank
management regarding customer satisfaction issues. Banks can communicate with their
present and prospective clients by website, leaflet and poster, advertisement, seminar and
conference, etc.
A limitation of this study is focusing on only mass service. Another limitation is not a big
data set (n = 322) and only focuses on one sector (commercial banking). Future study
should utilize this methodology for several industries in mass service to confirm the
model identified for customer satisfaction. Finally, further study should address the
customer satisfaction issues on other typology of service such service factory, service
shop, and professional service.
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