Predictors of customer retention in online health care system ohcs


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Predictors of customer retention in online health care system ohcs

  1. 1. INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online), Volume 4, Issue 1, January- February (2013)ISSN 0976-6502 (Print)ISSN 0976-6510 (Online)Volume 4, Issue 1, January- February (2013), pp. 243-257 IJM© IAEME: Impact Factor (2013): 6.9071 (Calculated by GISI) © PREDICTORS OF CUSTOMER RETENTION IN ONLINE HEALTH CARE SYSTEM (OHCS) - STRUCTURAL EQUATION MODELLING (SEM) APPROACH 1 2 R. Thiru Murugan , Dr. J Clement Sudhahar 1 Ph. D Scholar, School of Business Leadership and Management, Karunya University, Coimbatore, Tamil Nadu, India. 2 Professor in Marketing Area , School of Business Leadership and Management, Karunya University, Coimbatore, Tamil Nadu, India ABSTRACT Purpose The purpose of this study is to identify the predictors of customer trust and customer satisfaction, and to empirically test the relationship among customer trust, customer satisfaction, customer commitment and customer retention in online health care system in Indian context. Design/ Methodology and approach This paper stems from a conceptual framework grounded on the theory concerning customer trust and retention. The predictors of customer trust, customer satisfaction, customer commitment and customer retention in online health care are identified through literature support. After conceptual underpinnings, a questionnaire was developed and survey conducted among the patients of the Hospitals who use online health care system. To empirically test this, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were carried out. These analyses done through SPSS and AMOS software. Findings The study shows that Perceived usefulness, Information quality, Responsiveness, Security and User interface are predictors of customer trust and customer retention. It a l s o empirically prove the relationship between customer trust, customer satisfaction, customer commitment and customer retention happening through Online Health Care System in India. 243
  2. 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 theproposed conceptual model across different sections of customer is needed todetermine the generalisability of this study s findings.Practical Implications The online health providers shall concentrate these factors to give better service tothe people. This paper gives a suggestion to the government to have more number ofonline health centres that saves time and cost of the people.Keywords: Customer trust, customer commitment, customer retention, Online Health CareSystem, Structural Equation ModellingINTRODUCTION In the past two decades there are plenty of studies taken place in e-commerce andcustomer retention. But in the burgeongoing digital world, studies in Online Health CareSystem (OHCS) received very less attention from the researchers. OHCS can be a bestsubstitute for a country like India having very huge population and less number ofhealth professionals. OHCS will cut down the cost, and reduce the geographicalbarriers. Increasingly, therefore, OHCS is unavoidable in this ever expanding internet era.In this realm needless to emphasise that customer retention is paramount factor forensuring profitability and performance. Based on this seminal idea this research firstfocuses on the predictor of customer trust and customer satisfaction. Second, it focuses onunravelling the relationship among customer trust, customer satisfaction, customercommitment 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 andits 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). InOHCS information quality delivered by service provider place a important role in customertrust. Information quality is to deliver confidence and inspire trust in the OHCStransactions. (Huan-Ming Chuang and Chwei-Jen Fan, 2011). Seyed et al, (2011) revealedthat information quality is the best predicting factor for trust attitude. OHCS informationquality 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 ew i l l i n g n e s s o f service providers to help customers and provide prompt service. Onlinehealth care service needs immediate response (Wu, 2000, Thae Min Lee 2005) from theservice providers because it deals with human life. When problems occurred people alwaysexpect from the service provider to handle the problems successfully (Parasuraman et 244
  3. 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 timelysupport (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 toreduce the distrust of the customer. OHCS responsiveness is positively related to customertrust (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 whichcustomers are in contact with the OHCS system provider. The quality of user interfacehave impact on customer satisfaction (Park and Kim, 2003). Alam and Yasin [2009]echoed that when the customer feel better instructiveness thorough good user interface, itwill guide to make the customers satisfied. User interface have direct impact on customertrust and customer satisfaction (Gummerus, et al., 2004)PERCEIVED USEFULNESS (PU) Perceived Usefulness is defined as the degree to which a person believes thatusing a particular system would enhance his or her transaction performance (Davis,1989). Perceived usefulness has positive influence on customer satisfaction (Flavian andCarlos 2006). Perceived usefulness has significant impact on trust in OHCS. Cyr(2008) illustrated that perceived usefulness has a significant effect on customer loyaltyintention.SECURITY OHCS have all the medical and personal data of the customer. Hence security is themajor concern for OHCS trust. The customer does not have panic about the confidentialitywhile giving data about the ill. Because, security is closely associated with trustfulness ofonline health providers. OHCS should encompass with low risk and high safety (ZhilinYang 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 etal., 1998). The main barrier to the development of online health care system is lack ofsecurity as perceived by the customers.CUSTOMER SATISFACTION Customer satisfaction is a measure which eases the organisation with abundantinformation about customer retention, customer satisfaction helps the organisation todevelop a successful policies to bring good service to the customer (Shah Ankit, 2011). Inhealth care sector, organisations should focus on customer satisfaction to fulfil theemotional and psychological needs of the customers (Pairot, 2008). In OHCS satisfiedcustomers have the will give repeat business to the organisation compare to the dissatisfiedcustomers (Soheila ghane et al. 2011). Customer satisfaction is a predictor of customerretention (Janet Sim et al, 2006, Zeithaml, 2000, Garbarino and Johnson, 1999). It leadsto the organisation to build loyalty in the mindset of the customers and they pay lessattention to the competitors (Kotler, 2000). Yu (2007) analysed the impact of customersatisfaction on customer retention, customer cost and customer profitability. Customer 245
  4. 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). Highersatisfaction 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 customersatisfaction.CUSTOMER TRUST A consumers willingness to rely on the service provider and take action incircumstances where such action makes the customer vulnerable to the service provider.(Jarvenpaa and Tractinsky, 1999). In electronic commerce customer trust is also defined thatconfidence in the reliability on a person or a system (Meng, 2004). Customer trust is closelyrelated 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 organisationalperformance (Gounaris et al. 2005). Trust is a latent construct for retention (Reichheld 1993;Ranaweera and Prabu, 2003). Pavlou and Fygenson (2006) illustrated through their researchthat trust plays a important role in driving customer repurchase intention. Gummesson,(1987), Teichert and Rost (2003), Garbarino and Johnson, (1999), Yau, (2007) andZeithaml, (2000) found that trust has positive relationship with customer retention andalso it is a key element of customer retention. Trust has strong relationship with customercommitment (Yau, 2007).CUSTOMER COMMITMENT Commitment is defined as a psychological attachment or an affective attachmentwhich produced an enduring wish to uphold long-term relationships. (Fullerton, 2005, Morganand Hunt, 1994). Moorman et al., (1993) explained that commitment means that customer in arelationship feels motivated to some extent to do business with service provider.Commitment is positively related to repurchase intentions (Fullerton, 2005). Commitment inan 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 performancedepends on the relationship with the customer. Commitment guides to maintain long-term relationships between the services provider and the customer (Wilson et al. 1995). InOHCS committed customers give positive feedback about the service providers to others(Gustafsson et al. 2005). Gummesson, (1987) and Zeithaml, (2000) reveals that commitmenthave 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. “Deeplyheld commitment to rebuy or repatronize a preferred product or service consistently in thefuture, despite situational influences and marketing efforts having the potential to causeswitching behaviour” (Oliver, 1999). In service industry cost of acquiring new customer ishigher 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. 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 HYPOTHESISESH1-1 – Responsiveness has a positive impact on customer trustH1-2 – Security has a positive impact on Customer trustH1-3 – User Interface has a direct impact on Customer trustH1-4 – Information Quality has a positive impact on trustH1-5 – Perceived usefulness has direct impact on customer trustH2-1 – Responsiveness has a positive impact on customer satisfactionH2-2 – Security has a positive influence on Customer satisfactionH2-3 – User Interface has a direct impact on Customer satisfactionH2-4 – Information Quality has a positive impact on satisfactionH2-5 – Perceived usefulness has direct impact on customer satisfactionH3 – Customer trust has a direct impact on customer satisfactionH4 – Customer trust has a positive impact on customer commitmentH5 – Customer trust has influence on customer retentionH6 – Customer satisfaction has a direct influence on customer commitmentH7 – Customer satisfaction has positive impact on customer retentionH8 – Customer commitment has a direct impact on customer retention Figure1: Proposed Model 247
  6. 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 fromthe online health care centre for the past one year in Coimbatore city, Tamilnadu. Therespondents were asked to fill up the questionnaire when they came for secondconsultation. The size of the population for this study was unknown. It has been suggested thata sample of between 200 and 1,000 respondents for populations of 10,000 or more ispreferable (Alreck and Settle, 1985). After considering such resources budget, time andaccessibility to respondents, this survey targeted 461 respondents in order to providesufficient power for the statistical analyses. The population for the study is the patients of online health care patients inCoimbatore city and the stratified random sampling technique was used for choosing the samplesize of the study. There are 5 hospital are providing OHCS through 13 centres in Coimbatorecity. In the first stage of sampling (using stratified sampling method), one top ranked centrefrom each hospital in terms of size of the patients base chosen. Accordingly, from thechosen set of 5 top OHCS centres, the administrators of and doctors of centres wereapproached for obtaining details of the patients who have taken the OHCS from each centrein the past one year. The list, thus obtained from administrators and doctors of the centre fromeach hospital 341, 902, 719, 570 and 997 numbers of patients respectively. In the second stage (using simple random sampling method), from thisparsimonious 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 of170, 401, 360, 285 and 498 in each hospitals respectively. These 1714(170+401+360+285+498) patients were then approached for collecting responses for thestudy through questionnaire. Subsequently, 825 out of 1714 patients approached gave theirconsent for responding, after many contacts established in person and over phone. Uponconducting interviews with these favourably inclined patients, the sample size for thisinvestigation 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 of55.87%. 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. 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) isused for data analysis. Two step modelling approach will give improved reliabilityand reduce problems of inter correlation of constructs in the path model (Andersonand Gerbing, 1988). In the first step, Exploratory Factor Analysis (EFA) was usedto 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 forthe study is good enough to perform the factor analysis. The value is 0.768. The tablebelow shows that the factor loadings were above 0.5 which underlines the convergentvalidity of the factors. Based on the results of EFA, nine factors were formed andthey were named as Perceived Usefulness (PU), Information quality (IQ), UserInterface (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. 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 SatisfactionAfter 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 thereliability analysis results. 250
  9. 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 0.77MEASUREMENT MODELThe 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 tablewill 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. 10. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)HYPOTHESES EXAMINATIONFor hypothesises examination and to know the direct and indirect effect of the relatedfactors 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 theinfluence of responsiveness, security, user interface, information quality and perceivedusefulness 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. 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, userinterface, 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, theHypothesises H2-1, H2-2, H2-3, H2-4, and H2-5 are supported. After analysing thefactor influence on customer trust and customer satisfaction the researchers analysed thecause and effect relationship between customer trust, customer satisfaction, customercommitment and customer retention. This analysis shown that customer trust have impacton customer satisfaction (β = 0.399, P = <0.01). Customer trust have positive influence oncustomer commitment (β = 0.354, P = 0.03). Customer trust have direct impact oncustomer retention (β = 0.305, P = <0.01). Customer satisfaction have direct impact oncustomer commitment (β = 0.280, P = <0.01). Customer commitment have positive impacton customer retention. As a result of this H3, H4, H5, H6, H7 and H8 has been supported. Figure 2: Structural ModelDISCUSSION The SEM result shows that responsiveness, security, and information quality are themost important predictors of customer trust. Comparatively, User interface and perceivedusefulness have influence on customer trust. This situation happened, due to therespondents are more familiar to the traditional health care system. This indicate that theonline health service providers should focus more on user interface and also should createawareness about OHCS to create trust in customer mind. From the empirical research, the researcher found the factors affectingcustomer satisfaction. Responsiveness, security, user interface and perceived usefulness arehighly influence than information quality. This happened because of their education andlocation.The researcher also found that the relationship between customer trust, customersatisfaction, customer commitment and customer retention. 253
  12. 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 serviceproviders has to concentrate on these factors to seed customer trust. Also the mentionedconstructs contributed to customer satisfaction stemming from customer trust. Thisresearch has facilitated the health care service providers to understand the predictors ofcustomer trust, customer satisfaction, customer commitment and customer retention. It isvery imperative that the traditional health service providers have to adopt the suggestedmodel in OHCS in due course. The marketer may follow this model to retain theircustomers rather than appointing strategic planners to do the task. The government canalso 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 empiricallyprove 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 scoresobtained in fit indices. Hence it is important that the customers of OHCS need these factorsfrom the service providers to return to the service. Also this model can be applied in otherservice sectors. This research was done in only a small geographical area in India. Thesample size chosen for the study is relatively small. Further the study can be extended withsome more demographic variables, new geographical area, more sample size andalso include organisational performance in the model.REFERENCES1. Alam, S. S. and N. M., Yasin, (2009), An investigation into the antecedents of customersatisfaction of online shopping, The Australian and New Zealand Marketing AcademyConference (ANZMAC), Melbourne, Australia2. 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 globalelectronic commerce, Electronic Markets, 8 (2), 9-12.6. Bliemel, M., and Hassanein, K. (2007), Consumer satisfaction with onlinehealth 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 InformationReview 33 4, 2009 761-7848. 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, Iedition, 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
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