2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
Research Limitations/ Implications
The data is collected from just one district in Tamil Nadu. Further testing of the
proposed conceptual model across different sections of customer is needed to
determine the generalisability of this study s findings.
Practical Implications
The online health providers shall concentrate these factors to give better service to
the people. This paper gives a suggestion to the government to have more number of
online health centres that saves time and cost of the people.
Keywords: Customer trust, customer commitment, customer retention, Online Health Care
System, Structural Equation Modelling
INTRODUCTION
In the past two decades there are plenty of studies taken place in e-commerce and
customer retention. But in the burgeongoing digital world, studies in Online Health Care
System (OHCS) received very less attention from the researchers. OHCS can be a best
substitute for a country like India having very huge population and less number of
health professionals. OHCS will cut down the cost, and reduce the geographical
barriers. Increasingly, therefore, OHCS is unavoidable in this ever expanding internet era.
In this realm needless to emphasise that customer retention is paramount factor for
ensuring profitability and performance. Based on this seminal idea this research first
focuses on the predictor of customer trust and customer satisfaction. Second, it focuses on
unravelling the relationship among customer trust, customer satisfaction, customer
commitment and customer retention using Structural Equation Modelling (SEM).
CONCEPTUAL UNDERPINNINGS INFORMATION QUALITY (IQ)
Information quality is measuring the quality of the e-commerce information. (Huan-
Ming Chuang and Chwei-Jen Fan, 2011). The quality of accurate information and
its presentation about the services offered by a service provider (Nusair and Kandampully,
2008). E-commerce information quality dimensions are accuracy, Reliability,
completeness, interpretability and ease of understanding (Wang and Strong 1996). In
OHCS information quality delivered by service provider place a important role in customer
trust. Information quality is to deliver confidence and inspire trust in the OHCS
transactions. (Huan-Ming Chuang and Chwei-Jen Fan, 2011). Seyed et al, (2011) revealed
that information quality is the best predicting factor for trust attitude. OHCS information
quality leads to higher level of satisfaction (Bliemel and Hassanein, 2007).
RESPONSIVENESS
Parasuraman et al. (1985) e x p l a i n e d t h a t R e s p o n s i v e n e s s r e f e r s t o t h e
w i l l i n g n e s s o f service providers to help customers and provide prompt service. Online
health care service needs immediate response (Wu, 2000, Thae Min Lee 2005) from the
service providers because it deals with human life. When problems occurred people always
expect from the service provider to handle the problems successfully (Parasuraman et
244
3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
al., (2005).It is important to the service provider to give prompt communication and timely
support (Kim et al., 2004) in the case of any questions or problems of the customers
(Semeijn et al, 2005, Moorman et al. 1993, Clement and Selvam 2009). This will lead to
reduce the distrust of the customer. OHCS responsiveness is positively related to customer
trust (Chao-Min Chiu et. al 2008, Kineta Hung et al., 2011).
USER INTERFACE (UI)
Gummerus et al., (2004) define the user interface as the channel through which
customers are in contact with the OHCS system provider. The quality of user interface
have impact on customer satisfaction (Park and Kim, 2003). Alam and Yasin [2009]
echoed that when the customer feel better instructiveness thorough good user interface, it
will guide to make the customers satisfied. User interface have direct impact on customer
trust and customer satisfaction (Gummerus, et al., 2004)
PERCEIVED USEFULNESS (PU)
Perceived Usefulness is defined as the degree to which a person believes that
using a particular system would enhance his or her transaction performance (Davis,
1989). Perceived usefulness has positive influence on customer satisfaction (Flavian and
Carlos 2006). Perceived usefulness has significant impact on trust in OHCS. Cyr
(2008) illustrated that perceived usefulness has a significant effect on customer loyalty
intention.
SECURITY
OHCS have all the medical and personal data of the customer. Hence security is the
major concern for OHCS trust. The customer does not have panic about the confidentiality
while giving data about the ill. Because, security is closely associated with trustfulness of
online health providers. OHCS should encompass with low risk and high safety (Zhilin
Yang 2004). Security positively influences e-satisfaction (Szymanski and Hise 2000).
The perceived lack of security on in online health system is major a block (Balfour et
al., 1998). The main barrier to the development of online health care system is lack of
security as perceived by the customers.
CUSTOMER SATISFACTION
Customer satisfaction is a measure which eases the organisation with abundant
information about customer retention, customer satisfaction helps the organisation to
develop a successful policies to bring good service to the customer (Shah Ankit, 2011). In
health care sector, organisations should focus on customer satisfaction to fulfil the
emotional and psychological needs of the customers (Pairot, 2008). In OHCS satisfied
customers have the will give repeat business to the organisation compare to the dissatisfied
customers (Soheila ghane et al. 2011). Customer satisfaction is a predictor of customer
retention (Janet Sim et al, 2006, Zeithaml, 2000, Garbarino and Johnson, 1999). It leads
to the organisation to build loyalty in the mindset of the customers and they pay less
attention to the competitors (Kotler, 2000). Yu (2007) analysed the impact of customer
satisfaction on customer retention, customer cost and customer profitability. Customer
245
4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
satisfaction have positive relationship with customer retention (Gummesson, 1987). Higher
satisfaction will lead to have long-term relationship with the organisation (Seyed et al, 2011).
Park and Kim (2003) and Chiou (2004) argued that customer commitment is a result of customer
satisfaction.
CUSTOMER TRUST
A consumer's willingness to rely on the service provider and take action in
circumstances where such action makes the customer vulnerable to the service provider.
(Jarvenpaa and Tractinsky, 1999). In electronic commerce customer trust is also defined that
confidence in the reliability on a person or a system (Meng, 2004). Customer trust is closely
related to customer satisfaction and it is measured as antecedent of customer satisfaction
(Yau, 2007). Customer trust is a paramount for customer satisfaction (Gummerus, et. Al,
2004). In OHCS customer trust have direct impact on customer satisfaction. (Seyed et al,
2011 Flavian and Carlos 2006). Customer trust in health care is the key factor for organisational
performance (Gounaris et al. 2005). Trust is a latent construct for retention (Reichheld 1993;
Ranaweera and Prabu, 2003). Pavlou and Fygenson (2006) illustrated through their research
that trust plays a important role in driving customer repurchase intention. Gummesson,
(1987), Teichert and Rost (2003), Garbarino and Johnson, (1999), Yau, (2007) and
Zeithaml, (2000) found that trust has positive relationship with customer retention and
also it is a key element of customer retention. Trust has strong relationship with customer
commitment (Yau, 2007).
CUSTOMER COMMITMENT
Commitment is defined as a psychological attachment or an affective attachment
which produced an enduring wish to uphold long-term relationships. (Fullerton, 2005, Morgan
and Hunt, 1994). Moorman et al., (1993) explained that commitment means that customer in a
relationship feels motivated to some extent to do business with service provider.
Commitment is positively related to repurchase intentions (Fullerton, 2005). Commitment in
an e-commerce goes beyond satisfaction and commitment is a crucial predictor of retention
(Gustafsson et al. 2005 and Wilson, et al, 1995). Health care services performance
depends on the relationship with the customer. Commitment guides to maintain long-
term relationships between the services provider and the customer (Wilson et al. 1995). In
OHCS committed customers give positive feedback about the service providers to others
(Gustafsson et al. 2005). Gummesson, (1987) and Zeithaml, (2000) reveals that commitment
have positive effects on customer behavioural intention and retention.
CUSTOMER RETENTION
Zeithaml, (2000) Retention referred to a service provider s capability adapt the
‟
existing customers into repeat customers to ensure long-term relationship. “Deeply
held commitment to rebuy or repatronize a preferred product or service consistently in the
future, despite situational influences and marketing efforts having the potential to cause
switching behaviour” (Oliver, 1999). In service industry cost of acquiring new customer is
higher than retaining a current customer (Anonymous, 1997, Reichheld and Sasser (1990).
Customer retention is directly affecting profitability (Kotler (2000), Zeithaml, (2000), Ross
(1995) and performance of the service provider. (Yau, 2007)
246
5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
Author Factors affecting Retention
Hennig-Thurau and Klee, (1997) Customer satisfaction and Relationship
quality
Garbarino and Johnson, (1999) Trust and Commitment
Lee et al. (2001), Ranaweera and Prabhu, Switching cost
(2003)
STRUCTURAL MODEL AND HYPOTHESISES
H1-1 – Responsiveness has a positive impact on customer trust
H1-2 – Security has a positive impact on Customer trust
H1-3 – User Interface has a direct impact on Customer trust
H1-4 – Information Quality has a positive impact on trust
H1-5 – Perceived usefulness has direct impact on customer trust
H2-1 – Responsiveness has a positive impact on customer satisfaction
H2-2 – Security has a positive influence on Customer satisfaction
H2-3 – User Interface has a direct impact on Customer satisfaction
H2-4 – Information Quality has a positive impact on satisfaction
H2-5 – Perceived usefulness has direct impact on customer satisfaction
H3 – Customer trust has a direct impact on customer satisfaction
H4 – Customer trust has a positive impact on customer commitment
H5 – Customer trust has influence on customer retention
H6 – Customer satisfaction has a direct influence on customer commitment
H7 – Customer satisfaction has positive impact on customer retention
H8 – Customer commitment has a direct impact on customer retention
Figure1: Proposed Model
247
6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
SAMPLING AND DATA COLLECTION
Pilot study has been conducted on 54 respondents who have taken treatment from
the online health care centre for the past one year in Coimbatore city, Tamilnadu. The
respondents were asked to fill up the questionnaire when they came for second
consultation. The size of the population for this study was unknown. It has been suggested that
a sample of between 200 and 1,000 respondents for populations of 10,000 or more is
preferable (Alreck and Settle, 1985). After considering such resources budget, time and
accessibility to respondents, this survey targeted 461 respondents in order to provide
sufficient power for the statistical analyses.
The population for the study is the patients of online health care patients in
Coimbatore city and the stratified random sampling technique was used for choosing the sample
size of the study. There are 5 hospital are providing OHCS through 13 centres in Coimbatore
city. In the first stage of sampling (using stratified sampling method), one top ranked centre
from each hospital in terms of size of the patients base chosen. Accordingly, from the
chosen set of 5 top OHCS centres, the administrators of and doctors of centres were
approached for obtaining details of the patients who have taken the OHCS from each centre
in the past one year. The list, thus obtained from administrators and doctors of the centre from
each hospital 341, 902, 719, 570 and 997 numbers of patients respectively.
In the second stage (using simple random sampling method), from this
parsimonious list of patients provided by the administrator and doctors, using random table,
50% from each of the above mentioned total was drawn and arrived at the sample size of
170, 401, 360, 285 and 498 in each hospitals respectively. These 1714
(170+401+360+285+498) patients were then approached for collecting responses for the
study through questionnaire. Subsequently, 825 out of 1714 patients approached gave their
consent for responding, after many contacts established in person and over phone. Upon
conducting interviews with these favourably inclined patients, the sample size for this
investigation turned out to be 83, 73, 91, 113, and 101 from each hospital respectively,
constituting a final sample size of 461 in numbers and thereby yielding a response rate of
55.87%.
Table1: Sample Profile
Variable Measure Frequency
Male 298
Gender Female 163
Less than 25 143
25-35 107
36-45 159
Age 45-60 41
Above 60 11
School level 275
Education Graduate 126
Level
Post graduate 49
None 11
Urban 162
Location Rural 299
248
7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
DATA ANALYSIS
To prove the theoretical model, Structural Modelling Equation (SEM) is
used for data analysis. Two step modelling approach will give improved reliability
and reduce problems of inter correlation of constructs in the path model (Anderson
and Gerbing, 1988). In the first step, Exploratory Factor Analysis (EFA) was used
to explore the factors from the variables and Confirmatory Factor Analysis (CFA)
was deployed to verify the fitness of the factors.
EXPLORATORY FACTOR ANALYSIS (EFA) RESULTS
Kaiser Meyer Olkin measure of sampling adequacy refers the sample size for
the study is good enough to perform the factor analysis. The value is 0.768. The table
below shows that the factor loadings were above 0.5 which underlines the convergent
validity of the factors. Based on the results of EFA, nine factors were formed and
they were named as Perceived Usefulness (PU), Information quality (IQ), User
Interface (UI), Security, Responsiveness, Customer Trust, Customer Satisfaction,
Customer Commitment and Customer Retention.
Table2: Exploratory Factor Analysis (EFA) Results
Factor
Variables Extraction Loadings Factor
Through OHCS I Can get variety of information .523 .684 PU
The OHCS is easy to use .691 .752 UI
The information on the OHCS is easy to
understand .645 .734 UI
I am able to get the required information through
OHCS at any time .498 .629 Responsiveness
The OHCS provides prompt attention to my
request and questions .615 .579 Responsiveness
The OHCS has mechanism to ensure the safe
transmission of its customers information .566 .552 Security
The OHCS facilitates to get my health information .502 .586 IQ
The OHCS is trustworthy .645 .716 Trust
The OHCS insists the confidence in its customers .570 .657 Trust
The OHCS provides the relevant the services .490 .534 IQ
information
I have a personal attachments with this OHCS .492 .567 Commitment
It is easy to complete the transaction on the
OHCS .511 .578 IQ
I am confident that OHCS does not misuse any
information about me .572 .639 Security
OHCS provides good quality of information
through online .554 .614 PU
249
8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
I am committed to my relationship
with OHCS .571 .663 Commitment
OHCS will Save time .519 .536 PU
The information available in the OHCS is visually
appealing .520 .702 UI
I OHCS service provider fulfils my needs .534 .583 Trust
The OHCS does not behave opportunistically .515 .544 Trust
The OHCS has technical capacity to ensure that
the data I send cannot be modified by others .659 .711 Security
Changing my preference from the OHCS requires
major rethinking. .524 .682 Commitment
Information provided by OHCS is easy
to .548 .655 IQ
understand
The payment through OHCS is safe .526 .577 Security
I continue to use OHCS .542 .555 Retention
I feel that the risk associated with OHCS is low .522 .582 Security
OHCS will save money .495 .527 PU
I definitely recommend OHCS to my friends .512 .600 Retention
In the future I will continue to use OHCS .540 .627 Retention
Using the OHCS makes it easier to get the
information needed .498 .512 PU
Using the OHCS requires a lot of skills .723 .541 UI
The OHCS has Flexibility .546 .560 Responsiveness
I find that using the OHCS is useful for collecting
information .574 .607 PU
The OHCS is effective in resolving my problems .673 .583 Responsiveness
I prefer to use traditional health care system rather
than OHCS. .572 .584 Retention
The OHCS is effective in handling complaints .499 .711 Responsiveness
Overall I am satisfied with the performance of
OHCS .639 .757 Satisfaction
After identifying the factors the reliability check were done through using Cronbach s ‟
alpha and the reliability coefficients of the factors were higher than the cut-off level of 0.70
(Nunnally, 1978) which shows the internal consistency. The following table shows the
reliability analysis results.
250
9. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
Table3: Reliability Coefficient of the Factors
No. Factor Cronbach’s α
1. Perceived Usefulness 0.74
2. Information Quality 0.78
3. User Interface 0.82
4. Security 0.72
5. Responsiveness 0.71
6. Customer Trust 0.85
7. Customer Satisfaction 0.71
8. Customer Commitment 0.79
9. Customer Retention 0.77
MEASUREMENT MODEL
The related fit indicators of the measurement model were achieved the acceptable level.
This explains the measurement model factors have established discriminant validity.
Except customer satisfaction which has only one variable in the factor. The following table
will show the measurement model fit statistics
Table4: Fit indices of Measurement model
Fit statistics Acceptable level Obtained level
Chi-Square - 1483
Df - 938
Chi-Square significance P ≤ 0.05 < 0.01
Chi-Square/ df 3 1.58
GFI > 0.90 0.91
AGFI >0.90 0.91
NFI > 0.90 0.93
RFI > 0.90 0.92
CFI > 0.90 0.94
TLI >0.90 0.94
RMSEA < 0.05 0.01
RMR <0.02 0.01
251
10. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
HYPOTHESES EXAMINATION
For hypothesises examination and to know the direct and indirect effect of the related
factors is shown in the below table.
Table5: Results of Hypothesises examination
Hypothesis Model fit Coefficient (β) P
H1-1 Responsiveness › Customer trust 0.158 < .01
H1-2 Security › Customer trust 0.232 < .01
H1-3 User interface › Customer trust 0.422 0.05
H1-4 Information quality › Customer trust 0.241 < .01
H1-5 Perceived usefulness › Customer trust 0.211 0.04
H2-1 Responsiveness › Customer satisfaction 0.238 0.01
H2-2 Security › Customer satisfaction 0.144 0.02
H2-3 User interface › Customer satisfaction 0.212 < .01
H2-4 Information quality › Customer satisfaction 0.442 0.05
H2-5 Perceived usefulness › Customer satisfaction 0.132 < .01
H3 Customer trust › Customer satisfaction 0.399 < .01
H4 Customer trust › Customer commitment 0.354 0.03
H5 Customer trust › Customer retention 0.305 < .01
H6 Customer satisfaction › Customer commitment 0.280 < .01
H7 Customer satisfaction › Customer retention 0.142 0.02
H8 Customer commitment › Customer retention 0.343 < .01
Table6: Structural Model Fit Indices
Fit Statistics Value
Chi-Square 2270
Df 929
Goodness of fit index(GFI) 0.92
Adjusted Goodness of Fit Index (AGFI) 0.91
Normed Fit Index (NFI) 0.90
Relative Fit Index (RFI) 0.89
Comparative Fit Index (CFI) 0.88
Incremental Fit Index (IFI) 0.91
Tucker Lewis Index (TLI) 0.01
Root mean Square Error of Approximation ( RMSEA) 0.02
The above table shows the examination of SEM results shows that the
influence of responsiveness, security, user interface, information quality and perceived
usefulness on customer trust. It explains: Responsiveness (γ = 0.158, P < .01), Security (γ
= .232, P < .01), User interface (γ = 0.422, P = .05), Information quality (γ = 0.241, P <
.01) and Perceived usefulness (γ = 0.211, P = .04) has positive impact on customer trust.
Therefore, Hypothesises H1-1, H1-2, H1-3, H1-4, and H1-5 are supported.
252
11. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
This research also found that the impact of responsiveness, security, user
interface, information quality and perceived usefulness on customer satisfaction.
Responsiveness (γ = 0.238, P = .01), Security (γ = .144, P = .02), User interface (γ =
0.212, P < .01), Information quality (γ = 0.442, P = .05) and Perceived usefulness (γ = 0.132,
P < .01) has positive impact on customer trust. Based on the empirical results, the
Hypothesises H2-1, H2-2, H2-3, H2-4, and H2-5 are supported. After analysing the
factor influence on customer trust and customer satisfaction the researchers analysed the
cause and effect relationship between customer trust, customer satisfaction, customer
commitment and customer retention. This analysis shown that customer trust have impact
on customer satisfaction (β = 0.399, P = <0.01). Customer trust have positive influence on
customer commitment (β = 0.354, P = 0.03). Customer trust have direct impact on
customer retention (β = 0.305, P = <0.01). Customer satisfaction have direct impact on
customer commitment (β = 0.280, P = <0.01). Customer commitment have positive impact
on customer retention. As a result of this H3, H4, H5, H6, H7 and H8 has been supported.
Figure 2: Structural Model
DISCUSSION
The SEM result shows that responsiveness, security, and information quality are the
most important predictors of customer trust. Comparatively, User interface and perceived
usefulness have influence on customer trust. This situation happened, due to the
respondents are more familiar to the traditional health care system. This indicate that the
online health service providers should focus more on user interface and also should create
awareness about OHCS to create trust in customer mind.
From the empirical research, the researcher found the factors affecting
customer satisfaction. Responsiveness, security, user interface and perceived usefulness are
highly influence than information quality. This happened because of their education and
location.
The researcher also found that the relationship between customer trust, customer
satisfaction, customer commitment and customer retention.
253
12. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
MANAGERIAL IMPLICATIONS
Since the results suggest that responsiveness, security, information quality,
perceived usefulness and user interface are the determinants of customer trust, the service
providers has to concentrate on these factors to seed customer trust. Also the mentioned
constructs contributed to customer satisfaction stemming from customer trust. This
research has facilitated the health care service providers to understand the predictors of
customer trust, customer satisfaction, customer commitment and customer retention. It is
very imperative that the traditional health service providers have to adopt the suggested
model in OHCS in due course. The marketer may follow this model to retain their
customers rather than appointing strategic planners to do the task. The government can
also adopt OHCS effectively in rural areas so as to reduce the prevalent health barriers.
CONCLUSION, LIMITATIONS AND FUTURE DIRECTIONS
The purpose of this study is to create a conceptual model framework and empirically
prove the model. This study also found the direct and indirect effect of the related factors.
The measurement model and structural model were found to be fit through the scores
obtained in fit indices. Hence it is important that the customers of OHCS need these factors
from the service providers to return to the service. Also this model can be applied in other
service sectors. This research was done in only a small geographical area in India. The
sample size chosen for the study is relatively small. Further the study can be extended with
some more demographic variables, new geographical area, more sample size and
also include organisational performance in the model.
REFERENCES
1. Alam, S. S. and N. M., Yasin, (2009), An investigation into the antecedents of customer
satisfaction of online shopping, The Australian and New Zealand Marketing Academy
Conference (ANZMAC), Melbourne, Australia
2. Alreck, P. and Settle, R. (1985), The Survey Research Handbook, 45, US: Irwin.
3. Anderson, J. C., and Gerbing, D. W. (1988), Structural equation modelling in practice:
A review and recommended two-step approach. Psychological Bulletin, 103(3) 411-423.
4. Anonymous (1997), Assessing brand loyalty in the Netherlands, Strategic Direction,
135, 6-8.
5. Balfour, A., Farquhar, B. and Langmann, G. (1998), The consumer needs in global
electronic commerce, Electronic Markets, 8 (2), 9-12.
6. Bliemel, M., and Hassanein, K. (2007), Consumer satisfaction with online
health information retrieval: A model and empirical study. E-Service Journal, 5(2), 53–84.
7. Chao-Min Chiu and Chen-Chi Chang, Hsiang-Lan Cheng and Yu-Hui Fang (2008),
Determinants of customer repurchase intention in online shopping, Online Information
Review 33 4, 2009 761-784
8. Chiou, (2004), The antecedents of consumers loyalty toward internet service providers,
Information and Management, 41, 685-695.
9. Clement Sudhahar J, Selvam M, (2008), Customer Loyalty Management in Banking, I
edition, Pallavi Publications.
10. Cyr, D. (2008), Modeling Website design across cultures: Relationships to trust,
satisfaction and e-loyalty, Journal of Management Information Systems, 24, 4:47-72.
254
13. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
11. Davis, F.D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of
information technology, MIS Quarterly 13(3), 319-342.
12. Flavian and Carlas, (2006), The role played by perceived usability, satisfaction and
consumer trust on website loyalty. Information and Management, 43 (1), 1- 14
13. Fullerton, G. (2005), The impact of brand commitment on loyalty to retail service
brands. Canadian Journal of Administrative Sciences, 22(2), 97-113.
14. Garbarino, E. and M. S. Johnson (1999), The Different roles of satisfaction, trust, and
commitment in customer relationships. Journal of Marketing 63(2), 70-87.
15. Gounaris, S., Dimitriadis, S. and Stathakopoulos, V. (2005), Antecedents of perceived
quality in the context of Internet retail stores. Journal of Marketing Management, 21(7),
669-682.
16. Gummerus, J., V. Liljander, M. Pura, A. Van Riel, (2004), Customer loyalty to content-
based Web sites: The case of an online health-care service, Journal of Services
Marketing, 18(3), 175–186.
17. Gustafsson, A., Johnson, M. D. and Roos, I. (2005), The effects of customer satisfaction,
relationship commitment dimensions, and triggers on customer retention, Journal of
Marketing, 69(4)210-218.
18. Hennig-Thurau, T. and Klee, A. (1997), The impact of customer satisfaction and
relationship quality on customer retention: A critical reassessment and model
development, Psychology and Marketing, 14(8) 737-764.
19. Huan-Ming Chuang and Chwei-Jen Fan (2011), The mediating role of trust in the
relationship between e-retailer quality and customer intention of online shopping,
African Journal of Business Management, 5(22), 9522-9529
20. Inamullah khan, (2012), Impact of customer satisfaction and customers retention on
customer loyalty, International Journal of Scientific and Technology Research, 1(2),
106-110.
21. Janet Sim, Brenda Mak, and David Jones, (2006), A model of customer satisfaction and
retention for hotels, Journal of Quality Assurance in Hospitality and Tourism, 7,(3/4),
1-23.
22. Jarvenpaa, S. L. and N. Tractinsky. (1999), Consumer trust in an internet store: A cross-
cultural validation, Journal of Computer Mediated Communication, 5(2), 1-35.
23. Kim, H. W., Xu, Y., and Koh, J. (2004), A comparison of online trust building factors
between potential customers and repeat customers. Journal of the Association for
Information Systems, 5(10), 392–420.
24. Kineta Hung, Stella Yiyan Li, and David K. Tse (2011), Interpersonal Trust and
Platform Credibility in a Chinese Multi brand Online Community Journal of
Advertising, 40(3), 103–116.
25. Lee, M.K.O. and E. Turban. (2001), A trust model for consumer internet shopping,
International Journal of Electronic Commerce, 6(1), 75-92.
26. Meng, Bingchun. (2004), Infrastructure, service and trust: Assessing the environment of
commerce in China. College Park, PA: Pennsylvania State University.
27. Moorman, C., R. Deshpande, and G. Zaltman, (1993), Factors affecting trust in market
research relationships, Journal of Marketing, 57, 81-101.
28. Morgan, RM and Hunt, SD (1994), „The commitment-trust theory of relationship
marketing , Journal of Marketing, 58, 20-38.
‟
29. Nunnally, J. (1978), Psychometric theory. 2nd Edition. New York: McGraw-Hill.
255
14. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
30. Nusair, K. and Kandampully, J. (2008), The antecedents of customer satisfaction with
online travel services: a conceptual model. European Business Review, 20(1), 4-19.
31. Oliver, R. L. (1999), Whence customer loyalty? Journal of Marketing, 63(4), 33-44.
32. Pairot R. (2008), Members Satisfaction Of Fitness Service Quality: A case study of
‟
California Wow Xperience public company limited, Presented In Partial Fulfilment Of
The Requirements For The Master Of Arts Degree In Business English For International
Communication At Srinakharinwirot University, 2008.
33. Parasuraman, A., Zeithaml, V.A. and Malholtra, A. (2005), E-S-QUAL: a multiple-item
scale for assessing electronic service quality, Journal of Service Research, 7 3, 213-35.
34. Parasuraman, A., Zeithaml, V. and Berry, L.L. (1985), A conceptual model of service
quality and its implications for future research, Journal of Marketing, 49, 41-50.
35. Park, C. and Y. Kim, (2003), Identifying Key Factors Affecting Consumer Purchase
Behavior in an Online Shopping Context, International Journal of Retail and
Distribution Management, 31(1), 16–29.
36. Pavlou, P.A. and Fygenson, M. (2006), Understanding and predicting electronic
commerce adoption: an extension of the theory of planned behavior, MIS Quarterly, 30 (1),
115-43.
37. Ranaweera, C. and Prabhu, J. (2003), The Influence of Satisfaction, Trust and Switching
Barriers on Customer Retention in a Continuous Purchasing Setting, International
Journal of Service Industry Management, 14(4) 374-395.
38. Reichheld, F. F. (1993), Loyalty-based management. Harvard Business Review 71(2),
64-73
39. Reichheld, F. F. and Sasser, W. E. (1990), Zero defections: quality comes to services,
Harvard Business Review, 68(5), 105-111
40. Ross Joel E., (1995), Total Quality Management: Text, Cases, and Readings, 2nd
Edition, St. Lucie Press, USA
41. Semeijn, J., van Rie, A.C.R. l, van Birgelen, M.J.H. and Streukens, S. (2005), E-services
and offline fulfilment: how e-loyalty is created, Managing Service Quality, 15(2), 182-
94.
42. Seyed Fathollah Amiri Aghdaie, Amir Piraman, Saeed Fathi, (2011) An analysis of
factors affecting the consumer s attitude of trust and their impact on internet purchasing
‟
behavior, International Journal of Business and Social Science, 2 (23) , 147-158
43. Shah Ankit, (2011), Factors influencing online banking customer satisfaction and their
importance in improving overall retention levels: An Indian Banking Perspective,
Information and Knowledge Management, 1(1) 45-54
44. Soheila ghane, M. Fathian, M. R. Gholamian (2011), Full relationship among
e- satisfaction, e-trust, e-service quality, and e-loyalty: The case of Iran e-banking, Journal
of Theoretical and Applied Information Technology, 33(1) 1-6.
45. Szymanski, D., R. Hise (2000). E-Satisfaction: An initial examination. Journal of
Retailing 76(3) 309-322.
46. Teichert, T. and K. Rost (2003). Trust, involvement profile and customer retention
modelling, effects and implications. International Journal of Technology Management
26(5/6), 621-639.
47. Thae Min Lee, (2005), The Impact of Perceptions of Interactivity on Customer Trust and
Transaction Intentions in Mobile Commerce, Jour nal of Electronic Commerce
Research, 6(3), 165-180
256
15. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
6510(Online), Volume 4, Issue 1, January- February (2013)
48. Wang, R., and Strong, D. (1996), Beyond accuracy: What data quality means to data
consumers, Journal of Management Information Systems, 12(4), 5–34.
49. Wilson, D. T., Soni, P.K. and O Keeffe, M. (1995) Modelling Customer Retention as
‟
a Relationship Problem, Pennsylvania: Institute for the Study of Business Markets,
Research Report, 13.
50. Wu, G., (2000) The role of perceived interactivity in interactive ad processing, Doctoral
Dissertation, The University of Texas at Austin.
51. Yau Suk Ching Eppie (2007). Factors affecting customer retention in internet banking
among Hong Kong professionals and business practitioners, Doctoral Dissertation,
Newcastle Graduate School of Business ,University of Newcastle.
52. Yu, (2007). An Empirical Investigation on the Economic Consequences of Customer
Satisfaction. Total Quality Management and Business Excellence 18(5), 555-569.
53. Zeithaml, V. A. (2000). Service quality, profitability, and the economic worth of
customers: What we know and what we need to learn. Journal of the Academy of
Marketing Science, 28(1), 67-85.
54. Zhilin Yang, Minjoon Jun, Robin T. Peterson. (2004). Measuring customer perceived
online service quality Scale development and managerial implications International
Journal of Operations and Production Management 24(11), 1149-1174.
55. Navin Kumar Kohli, “Performance Evaluation and Benchmarking of Sopus for Quality
Services to Consumers” International Journal of Management (IJM), Volume 2, Issue 2,
2011, pp. 7 - 12, ISSN Print: 0976-6502, ISSN Online: 0976-6510, Published by IAEME.
56. Dr .Anukrati Sharma, “A Study on E – Commerce and Online Shopping: Issues and
Influences” International journal of Computer Engineering & Technology (IJCET), Volume
4, Issue 1, 2013, pp. 364 - 376, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375,
Published by IAEME.
257