Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2021 Vol: 25 Issue: 3

Customers Perceptions of Online Retailing Service Quality and Their Loyalty

Vikas Gautam, Department of Marketing & Business Strategy, ICFAI Business School

Vikram Sharma, Department of Marketing, ICFAI Business School


Computer-assisted services have developed in abundance and contributing substantially towards swift progression of Internet espousal. The main objective of the current study was to examine the effects of E-Service Quality (website design, reliability, privacy/security, and customer service), Customer Perceived Value, Corporate Image, Customer Satisfaction, Trust, and Commitment, on customer Loyalty. After theoretical considerations, a research model was developed and empirically tested. 243 eligible responses were collected via an offline questionnaire survey. Higher order Structural Equation Modeling results confirmed significant effects of customer satisfaction, corporate image, and commitment on customer loyalty by explaining 79.70 % of variance. Whereas, trust proved to be insignificant predictor of customer loyalty. Commitment was not found having significant mediating effect in the relationship between customer satisfaction and loyalty. On the other hand, corporate image has an indirect positive effect on customer loyalty through customer satisfaction. Moreover, electronic service quality has a direct and indirect positive effect on customer satisfaction through customer perceived value. Drawing from the results, this research provides empirical support that customer loyalty can be influenced by e-service quality, customer perceived value, customer satisfaction, commitment, and corporate image in online retail context.


E-Service Quality, Customer Perceived Value, Customer Satisfaction, Commitment, Corporate Image, Trust, Customer Loyalty.


Past few decades have witnessed a rapid growth in Electronic-commerce market. This growth has resulted in drawing attention of social science researchers across the globe towards electronic marketing activities. Every organization dreamt of having large loyal customer base, thus all global e-commerce companies are developing strategies to achieve customer loyalty. According to Parasuraman & Grewal (2000), inducing loyalty among customers demand ingredients like; superior quality products, superior value and flawless after-sales service. As well, customer delight is as indispensable key to achieve customer loyalty (Oliver, 1999). The service quality provides significant information and feeling to prospects and customers in virtual shopping context, as it does in case of brick and mortar retail store. Several management scholars have confirmed tangible and intangible benefits for the companies resulting from customer’s loyal behavior. Reichheld (1996) identified positive word of mouth and product recommendations as intangible benefits, whereas repeat purchases as tangible benefits. In addition, loyal customer saves money for the company and maintains long term relationships. Customer satisfaction is very important to ensure loyalty (Anderson & Srinivasan, 2003; Oliver, 1999), whereas customer perceived value is considered a critical component of customer satisfaction (Cronin et al., 2000). Perceived customer value is understood as what benefits a customer receive in relation to the costs incurred in the transaction.

Parasuraman & Grewal (2000) found evidence for strong relationship between customers’ perceived value and purchase/repurchase intentions. In virtual retail context, customers continue to put their regular and continued efforts to find better value for their money across other websites, because it is not guaranteed that satisfied customers will always be loyal (Anderson and Srinivasan, 2003). Customer perceived value has been playing a vital role in predicting buying behavior and achieving sustainable competitive advantage (Parasuraman, 1997).

In offline retail context, service quality is vital for ensuring customer satisfaction (Cronin et al., 2000). This relationship between service quality and customer satisfaction stood valid in virtual retail settings also (Hsu, 2006). In online retail settings, marketers need to focus on few important factors such as website design, reliability, security, and customer value to satisfy customers and achieve organizational success (Aladwani & Palvia, 2002). Hence, the investigation of the relationships of these factors and customer satisfaction will yield useful insights. Electronic service quality is at the core for predicting customer satisfaction and loyalty (Zhou et al., 2019). Moreover, customer perceived value moderates the relationship between customer satisfaction and loyalty in e-commerce context (Chang et al., 2009). Authors validated the significant influence of electronic service quality on customer satisfaction and loyalty. Service quality along with corporate image affects satisfaction strongly and further enhances the loyalty (Kuo & Ye, 2009).

Extending the reach of customer loyalty, Yieh et al., (2007) identified determinants of customer loyalty like; perceived price fairness, perceived product quality, customer satisfaction, trust. Earlier, Yu et al., (2005) applied Customer Satisfaction Index Model suggested by Fornell et al., (1996) among Lexus automobile users in Taiwan and listed the predictors of customer loyalty namely; customer expectation, perceived quality, perceived value, overall customer satisfaction, and customer complaints. Customer loyalty has been studied extensively across globe and listed a number of determinants such as; good management, image and customer services, trust in a store, trust in a salesperson, brand reputation, customer satisfaction etc. (Al-Awadi, 2002; Macintosh & Lockshin, 1997; Selnes, 1993). In services sector, trust is considered an important factor as customers need to believe the sellers about the promise they made with them. It is a well-known fact that intangible nature of services poses a challenge for marketers to fulfill the promises made by service organizations.

The future of buyer–seller relationships depends on the commitment made by the allies. According to Masud & Daud (2019), commitment positively strengthens customer satisfaction and augments customer loyalty. Therefore, these constructs are in limelight for better understanding by the researchers. It becomes a very challenging task for researchers to conclude the complete set of antecedents of customer loyalty in different contexts. This gives further scope to dig more about customer loyalty and its antecedents. Therefore, the current study intended to explore the customer loyalty construct in electronic-commerce sector of India.

The online retail context is quite challenging for marketers while ascertaining customer loyalty because of absence of touch and feel. The online service environment is not direct when compared with a physical service environment (Cox and Dale, 2001). This poses a challenge in identifying a best combination of e-service quality attributes. Technology is boon or bane at the same time. Therefore, online retail context becomes an important area for research to increase customer loyalty for capturing changes due to inbuilt dynamism of it.

The main objective of this study was to test the impacts of e-service quality, customer satisfaction, customer value, corporate image, trust and commitment on customer loyalty. Moreover, this study had other objectives as under

1. To study the mediating effect of Customer Perceived Value in the relationship between E-Service Quality and Customer Satisfaction.

2. To study the mediating effect of Commitment in the relationship between Customer Satisfaction and Loyalty.

3. To study the mediating effect of Customer Satisfaction in the relationship between Corporate Image and Customer Loyalty.

Motivated by the studies conducted by (Joo et al., 2017; Oghuma et al., 2016; Hsu et al., 2015; Jung & Chung, 2012; Thong et al., 2006; Lin et al., 2005; Bhattacherjee, 2001), this study employed expectation–confirmation model suggested by Bhattacherjee (2001) to investigate the factors affecting customer loyalty in online retail context. Expectation–confirmation model is extensively used in the field of marketing to study customers’ satisfaction and customer loyalty / repurchase intentions.

This article is organized as follows: the following sections review the literature on the electronic service quality, customer value, customer satisfaction, commitment, corporate image, trust and customer loyalty, and propose the conceptual model and associated hypotheses guiding this research. Next sections describe the research methodology and present the findings of data analysis. The last two sections discuss the contributions of this study, implications, limitations and future scope of the study.

The Review of Literature

E-Service Quality

E-Service Quality is defined as the extent to which a website (virtual marketplace) facilitates efficient and effective shopping, purchasing, and delivery of products (Zeithaml et al., 2001). According to Zeithaml et al., (2002), internet has been proved as highly significant marketing channel for marketing products to target customers. In the present scenario, all marketers are trying to opt for Omni-channel strategy. Marketing research experts across globe had found that web presence is something unavoidable for all the firms. Yang and Jun, (2002) argued that delivering high electronic service quality has become very important in addition to offering low prices and being present across the web. An elemental understanding of factors impacting satisfaction of online customers is of great substance to the proponents of electronic commerce (McKinney et al., 2002).

As a result, marketing researchers are focusing on the service quality of electronic commerce in terms of two most important aspects namely; attracting new customers and retaining the existing customers (Jun et al., 2004). Customers’ needs fulfillment is the ultimate goal of any organization and Grewal et al., (2004) compared online and offline shopping contexts and found that online shopping is able to meet customers’ need more efficiently and effectively. The similar results were confirmed by Monsuwe et al., (2004) in their empirical study. Therefore, the never ending customers’ expectations are posing serious challenges for the online marketers in terms of service quality standards. Customers’ expectations levels were found higher for those customers, who shop online in comparison to the customers who shop in offline mode (Lee and Lin, 2005). Electronic-service quality is linked positively with customer satisfaction, repurchase intention, and word of mouth for online consumers (Blut, 2016).

Customer Perceived Value

According to Yu et al., (2014), customer perceived value is recorded higher in those instances when they receive high value in comparison to spending less in terms of money, time and effort. In others words, quality of service pushes customer perceived value. Furthermore, higher customer perceived value leads to higher customer satisfaction. According to Ryu & Han (2010), customer perceived value plays the role of mediating variable in the relationship between service quality and customer satisfaction.

In case of medical services, customer value perceptions are directly influenced by perceived service quality (Choia et al., 2004). Lindgreen and Wynstra (2005) identified two dimensions for customer value namely; the value of products and value of the buyer-seller relationships. According to Chircu and Mahajan (2006), customer perceived value in online shopping context can be ensued by reducing transaction costs and consequently the customer value will add to competitive advantage by increasing company’s performance.

Customer Satisfaction and Loyalty

Customer expectations and performance of the product in the marketplace are the criteria used by marketers to assess customer satisfaction. Customer satisfaction is an abstract psychological state resulting when the emotion adjoining discontinued expectations is tied with the consumer's preceding feelings about the consumption practice (Oliver, 1997). Kotler (2000) defined customer satisfaction as “A feelings of pleasure or disappointment resulting from comparing a product's perceived performance (or outcome) in relation to his or her expectations”. Various social science and psychology researchers from different contexts studied the customer satisfaction in both online and offline shopping contexts. Mckinney et al., (2002) identified two different sources of customer satisfaction in case of electronic commerce namely; satisfaction with the website's performance in delivering information and satisfaction with the quality of content mentioned in the information.

Anderson and Srinivasan (2003) defined customer satisfaction with reference to electronic commerce as “contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm”. Parasuraman et al., (1988) studied customer satisfaction and concluded that service quality caused customer satisfaction. Further studies were conducted to find whether service quality had any link to customer loyalty or not. Bloemer and Kasper (1995) posited that customer satisfaction is a necessary precondition for loyalty but is not sufficient on its own to automatically lead to repeat purchases (an action of customer loyalty). Zeithaml, et al., (1996) found a significant link between service quality and behavioral intentions (or customer loyalty). Authors confirmed service quality as an antecedent to behavioral intentions. In case of electronic commerce, website quality has been found impacting consumers’ attitudes and behaviors. Product information and website design are important predictors of customer satisfaction in online shopping context (Szymanski & Hise, 2000).

According to Li & Zhang (2002) enhanced website quality can help customers in smooth and convenient transactions and prompt them to revisit the online store repeatedly. Later, Anderson and Srinivasan (2003) examined the electronic commerce and defined customer loyalty as "the customer's favorable attitude toward an electronic business resulting in repeat buying behavior". Authors argued that a dissatisfied customer is more likely to explore for information on alternative options and more likely to capitulate to competitor overtures than is a satisfied customer.

Corporate Image

Corporate image is of tremendous importance to service firms (Gronroos, 1982). Further, author concluded that corporate image is the outcome of assessment of services made by the consumers. Zimmer and Golden (1988) defined corporate image as “The overall impression left on the minds of customers, as a gestalt and as an idiosyncratic cognitive configuration”. Initially, research in the area of corporate image was focusing only on the retailers and the manufacturers. No researchers considered consumer’s assessment of service as indicator of corporate image. A thorough understanding of the components of corporate image may help organizations in improving their competitive performances (Nguyen & LeBlanc, 1998).

It has been found in the literature that customer satisfaction is the main predictor of customer loyalty in tangible as well as intangible product categories. But Andreassen and Lindestad (1998) found that corporate image surpassed customer satisfaction as main predictor of customer loyalty in complex and uncommonly purchased services contexts. These findings enforced researchers across the globe to think differently by challenging the traditional view that loyalty can be thought only if satisfaction is guaranteed. Further, various researchers like; Bloemer and de Ruyter (1998); Koo (2003); McAlexander, et al., (2003); Hart and Rosenberger (2004) proposed that customer satisfaction does not disappear at all from the picture but it may act as a mediator in the relationship between corporate image and customer loyalty.


Commitment is defined as “The belief that an ongoing relationship is so important that the partners are willing to work at maintaining the relationship and are willing to make short-term sacrifices to realize long-term benefits” (Bowen and Shoemaker, 1998, p. 15). Authors further clarified about short-term sacrifices in terms of monetary or non-monetary, whereas long-term benefits would be repeat purchases and positive word-of-mouth by consumers. Commitment is of two types namely; affective commitment and calculative commitment (Mathieu & Zajac, 1990). According to Konovsky & Cropanzano, (1991) Affective commitment is defined as “it is non-instrumental and is motivated by a generalized sense of positive regard for, and attachment to, the brand or supplier, and the desire to continue the relationship because he or she likes the brand or supplier and enjoys the relationship”. Affective commitment serves as a psychological barrier to switching (Johnson et al., 2001). Authors explained calculative commitment as more rational and economical in nature. It means high switching costs force customers to stay loyal to companies. Furthermore, authors found mediation effects of affective and calculative commitments in the relationship between customer satisfaction and loyalty. McAlexander et al., (2003) noted that for customers who demonstrate a sturdy relationship with a company, commitment and trust supersede satisfaction as predictors of loyalty.


Trust can be defined as “the willingness to rely on an exchange partner in whom one has confidence” (Moorman et al., 1993, p. 82). Accordingly, trust deals with the belief that one party is able to fulfill the promise made to other party with the willingness to do. Trust is considered to reduce uncertainty among consumers by managing risks helping them to make best choice with ease (Morrison & Firmstone, 2000). Garbarino & Johnson (1999) argued that trust will be the crucial factor in ascertaining customer commitment in tough times and helping in maintaining long-term relationships. Literature has a good number of evidence about causal relationship between trust and customer loyalty. There exists a negative relationship between trust and probability that future relationship between parties will end (Morgan & Hunt, 1994). Shemwell & Cronin (1995) argued that gender plays a significant role in relationships between consumer and service providers with reference to trust and commitment. In their findings, authors found that females place more emphasis on these in comparison to males. In addition, this study found differences among types of service providers. There exists a direct relationship between trust in salesperson and intentions to purchase with reference to store loyalty model (Macintosh & Lockshin, 1997).

The Relationships among E-Service Quality, Customer Perceived Value, Customer Satisfaction, Corporate Image, Commitment, Trust, and Loyalty

Various empirical studied in the past had confirmed strong positive influence of e-service quality on customer satisfaction (c.f. Amin, 2016; Kao & Lin, 2016; Su et al., 2016; Carlson & O’Cass, 2010). Higher customer satisfaction may be achieved with the help of high level of e-service quality and subsequently increased customer loyalty can be achieved (Amin, 2016). Blut et al., (2016) built a framework of e-service quality and its subsequent impacts on customer satisfaction, repurchase intention and word-of- mouth outcomes. Higher customer perceived value leads to higher customer satisfaction (Yu et al., 2014). Customer satisfaction and trust positively affect customer loyalty in supplier-customer relationship (Singh and Sirdeshmukh, 2000). According to Yieh et al., (2007), trust has a positive impact on customer loyalty.

Authors confirmed these results in automobile service and repair industry. In the information age, benevolence-based trust is considered as a central element of loyalty in tourism context (Devece et al., 2015). Corporate image surpassed customer satisfaction as main predictor of customer loyalty (Andreassen & Lindestad, 1998). Corporate image moderates the relationship between corporate social responsibility and customer loyalty of insurance customers in Taiwan (Lee, 2019). The robust relationship between customers and company augments loyal behavior. In this context, commitment and trust supersede satisfaction as predictors of customer loyalty (McAlexander et al., 2003). According to Alonso-Dos-Santos et al., (2020), customer satisfaction, use, and trust are most critical in attaining customer loyalty for mobile banking users.

Conceptual Framework and Hypotheses Development

This study proposed a research model to explain the online shopping behavior of consumers. The study model includes constructs like; e-service quality (web site design, reliability, security and customer service), customer value, customer satisfaction, corporate image, commitment, trust and customer loyalty in Figure 1. The conceptual framework of this study was inspired by expectation–confirmation model suggested by Bhattacherjee (2001) to investigate the factors affecting customer loyalty in online retail context.

Figure 1 Proposed Study Model

Hypotheses Development

Based on conceptual framework and literature review, the following study hypotheses were formulated:

H1: E-service quality positively affects Customer Value.

H2: E-service quality positively affects Customer Satisfaction.

H3: Customer Value positively affects Customer Satisfaction.

H4: Corporate Image positively affects Customer Satisfaction.

H5: Customer Satisfaction positively affects Commitment.

H6: Corporate Image positively affects Customer Loyalty

H7: Customer Satisfaction positively affects Customer Loyalty.

H8: Commitment positively affects Customer Loyalty.

H9: Trust positively affects Customer Loyalty.

H10: Customer Value will have a mediating effect on the relationship between E-service Quality and Customer Satisfaction.

H11: Commitment will have a mediating effect on the relationship between Customer Satisfaction and Customer Loyalty.

H12: Customer Satisfaction will have a mediating effect on the relationship between Corporate Image and Customer Loyalty.

Research Methodology

To examine the desired relationships among study constructs namely; e-service quality, customer value, customer satisfaction, corporate image, commitment, trust and customer loyalty, we collected primary data from the targeted respondents through field survey method. The focus of the research instrument was to understand the views of respondents about study constructs with reference to online retail sector in India.

Sampling Design and Data Collection

The population for the present study included consumers residing in Gurugram, The Millenium City, covered under National Capital Region of India. Gurugram is an Information Technology hub of India. Majority of Multinational Companies have their corporate offices located in this city. As a result, people from all over the country and also form different nations reside here. This feature makes it highly qualified for conduction of perception based surveys. Survey had screening question for potential respondent about familiarity online shopping companies and selected respondent might have used electronic commerce companies for their shopping. In order to achieve study objectives, the primary data was collected by using convenience sampling method. This paper pencil based field survey was conducted in offline mode. The target respondents were approached outside malls, markets of 14, 29, and 31 sectors in Gurugram.

The measurement instrument used for the data collection was a structured questionnaire with close-ended questions, which consisted of one second order construct namely; e-service quality measured with four first order constructs lie; website design, customer service, security, reliability, and nine first order constructs to measure the study variables namely; customer value, customer satisfaction, commitment, corporate image, trust and customer loyalty.

The field survey helped in collecting 273 duly filled questionnaires from the respondents. After elimination of incomplete questionnaires (30), wherein excessive amounts of important data were missing, we were left with final 243 sample size.

Sample Size Justification

To assess the structural relationships among the study constructs, Structural Equation Modeling (SEM) with AMOS 20.0 (Covariance based SEM) with maximum likelihood estimation was employed in this study. Even though SEM is considered in case of testing robustness of study model, there is some specific sample size requirement. In Structural Equation Modeling as a rule of thumb, any number above 200 (critical sample size) is understood to provide sufficient statistical power for data analysis (Hoelter, 1983; Hoe, 2008). In case of current study, a sample size of 243 is considered sufficient for testing model fit and study hypotheses.

Measurement Scales

In order to measure the study constructs, we have borrowed already established scales from literature. To measure second order construct e-service quality, we have borrowed 25 items scale (Website Design = 13 items; Customer Service = 5 items; Reliability = 3 items, Security = 4 items) developed by Wolfinbarger & Gilly (2002). Customer perceived value construct was measured by 4 items scale developed by Zeithaml (1998). Customer satisfaction construct was measured by 3 items scale developed by Anderson & Srinivasan (2003). Commitment construct was measured by 3 items scale developed by Kumar et al., (1995). Corporate image construct was measured by 3 items scale developed by Johnson et al., (2001). Trust construct was measured by 3 items scale developed by Doney & Cannon (1997) and subsequently modified by Snehota & Soderlund (1998). Customer loyalty construct was measured by 3 items scale developed by Zeithaml et al., (1996). All the study variables except corporate image were measured on 7-point Likert’s scale (1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neither Disagree Nor Agree, 5 = Somewhat Agree, 6 = Agree, 7 = Strongly Agree). Corporate image was measured on 7 point Likert’s scale (1 = Strongly Unfavorable, 2 = Unfavorable, 3 = Somewhat Unfavorable, 4 = Neither Unfavorable Nor Favorable, 5 = Somewhat Favorable, 6 = Favorable, 7 = Strongly Favorable) in Figure 2.

Figure 2 Confirmatory Factor Analysis Model

Data Analysis

We have analyzed data to achieve the objectives of the study such as; general sample description, and calculation of Cronbach’s alpha values to check reliability of the measurement scales used in the study with the help of IBM SPSS 20.0, confirmatory factor analysis to test the model fit, and higher order structural equation modeling using IBM AMOS 20.0 to estimate the complete study model and test study hypotheses.

General Sample Description

It can be seen from the above Table 1 that out of 243 sample respondents, 49.8 percent of the respondents were of the age group 21 to 25 years, 23.9 percent of 26-35 years, 14.0 percent of 36 to 45 years, 6.20 percent above 45 years.

Table 1 Sample Profile
S.N. Variable Levels Number Percentage
1 Age Below 20 15 6.2
20-25 121 49.8
26-35 58 23.9
36-45 34 14.0
46-60 10 4.1
Above 60 5 2.1
2 Gender Male 161 66.30
Female 82 33.70
3 Marital Status Married 89 36.60
Unmarried 154 63.40
4 Educational Qualification Pre-Intermediate 2 0.80
Intermediate 7 2.90
Graduate 83 34.20
Post Graduate 151 62.10
5 Employment Status Self-Employment 22 9.1
Salaried/Wage Earner 90 37.0
Business 14 5.8
Professional 33 13.6
Student 81 33.3
Others 3 1.2
6 Monthly Household Income (In Rs.) Below 10000 13 5.3
10000-25000 35 14.4
26000-50000 46 18.9
51000-75000 39 16.1
Above 75000 110 45.3

Majority of respondents were males with 66.30 percent and females respondents were of less percentage of 33.70 percent. The majority of the respondents were unmarried (63.40 percent), as percentage of married was 36.60 percent. There were more post graduate respondents (62.10 percent) than graduate and others. Moreover, the occupational variables showed the percentage of salaried/wage earner, student, professional, self employment, business and others were 33.00 percent, 33.30 percent, 13.60 percent, 9.10 percent, 5.80 percent, and 1.20 percent respectively. In the field survey it was also found that the respondents came from different income backgrounds; a major part of them (45.30 percent) earned greater than Rs.75, 000 monthly (household) but below Rs.25000 were 19.70 percent. For normality, the univariate skewness and univariate kurtosis of the observed variables were calculated. The maximum univariate skewness observed in the dataset is -0.917, and the maximum univariate kurtosis observed is 0.872. West et al., (1996) suggested that normality may be a problem when the numerical values of univariate skewness and kurtosis cross 2 and 7, respectively.

Since there is no mechanism in place to completely remove any type of bias in responses, efforts are made to reduce the biasness to the manageable level. The present study employed Harman’s single factor test (Harman, 1976) to detect common method variance. In this test, CMV is a concern if a single dimension accounts for the majority of the variance, then there may be high chances of presence of common method variance problem (Podsakoff et al., 2003). In the data analysis, none of factors explained more than 50 percent of the variance. Hence, this dataset didn’t show problem related to common method variance.

Reliability Analysis

To test reliability of the measurement scales, we followed the criteria (Cronbach’s alpha value > 0.70) suggested by Nunnally & Bernstein, (1994). It can be seen from the above table 2 that all the Cronbach’s alpha values range from 0.816 to 0.974, thus it can be concluded that the measurement scales used in the study met the reliability criteria.

Table 2 Reliability Analysis Results
S.N. Name of Construct Cronbach’s Alpha
1 Website Design 0.894
2 Customer Service 0.872
3 Reliability 0.821
4 Security / Privacy 0.902
             E-service Quality 0.947
5 Customer Value 0.847
6 Customer Satisfaction 0.860
7 Corporate Image 0.816
8 Commitment 0.852
9 Trust 0.856
10 Customer Loyalty 0.896
Overall 0.974

Confirmatory Factor Analysis Results

Confirmatory Factor Analysis model with first and second order constructs had a total of 122 distinct parameters and 1081 distinct sample moments. A minimum was achieved with chi-square value 1763.522 (df = 959, p < .000). All parameters of the study were practicable and standard errors in acceptable limits. Statistical significance of parameter estimates was established as test-statistic (t-value) in each case was greater than threshold limit of 2.58. The study model showed a good fit exhibited by numerous goodness-of-fit indices. Ratio of minimum discrepancy (CMIN = 1763.522) to degrees of freedom (DF =959) was 1.839 (good if < 3), Goodness of Fit Index (GFI) was 0.893 (good if >0.90), Incremental Fit Index (IFI) was .912 (good if >0.90), Tucker-Lewis Index (TLI) was .924 (good if >0.90), Comparative Fit Index (CFI) was 0.937 (good if > 0.90), Root Mean Square Residual (RMR) was 0.041 (good if < 0.05), Root Mean Square Error of Approximation (RMSEA) was 0.042 (good if < 0.08), PCLOSE = .547 (good if close to 1), ECVI = 8.296. All indices exceeded the recommended threshold levels (Browne & Cudeck, 1993; Bagozzi & Yi, 1988). Therefore present study model was confirmed.

Convergent and Discriminant Validity

It is highly recommended in the literature that convergent and discriminant validities of measurement constructs should be ensured. In the current study, convergent validity was checked by reviewing factor loadings, Average Variance Extracted (AVE) and Composite Reliability (CR) as suggested by Hair et al. (2010). It can be concluded from the above table 3 that all factor loadings and composite reliability surpassed the requirement of 0.70 criteria. Moreover, the average variances extracted (AVEs) in the case of all four constructs were all above the 0.50 level (Bagozzi and Yi, 1988; Fornell and Larcker, 1981), thus indicating high levels of convergence among the items in measuring their respective constructs. To assess discriminant validity, we followed the procedure suggested by Fornell & Larcker (1981) and Hair et al., (2010). The procedure states that the square root of AVE should be greater than correlation among the constructs. We found strong evidences in our analysis that the square root of AVE was greater than correlation among the constructs. Thus, discriminant validity among the constructs was established.

Table 3 Results
CPV 0.849 0.585 0.765            
CMT 0.855 0.664 0.574 0.815          
TRS 0.858 0.601 0.497 0.489 0.775        
CLT 0.895 0.632 0.502 0.435 0.465 0.795      
CRI 0.819 0.532 0.372 0.489 0.447 0.475 0.729    
CUS 0.862 0.676 0.344 0.417 0.477 0.432 0.405 0.822  
ESQ 0.932 0.774 0.218 0.455 0.472 0.448 0.437 0.452 0.880

Structural Equation Modeling Results (Hypotheses Testing)

The parameters estimates from current study model are evident from structural model of SEM in terms of path coefficients.

Direct Relationships

It can be concluded from the above Table 4 that 8 study hypotheses were supported at 5% level of significance. We found positive significant effects of second order construct Electronic Service Quality on Customer perceived value (β = 0.892, p < 0.000); Electronic Service Quality on Customer Satisfaction (β = 0.137, p = 0.013); Customer perceived value on Customer Satisfaction (β = 0.543, p < 0.000); Corporate Image on Customer Satisfaction (β = 0.628, p < 0.000); Corporate Image on Customer Loyalty (β = 0.485, p < 0.000); Customer Satisfaction on Customer Loyalty (β = 0.690, p < 0.000); Customer Satisfaction on Commitment (β = 0.814, p < 0.000); Commitment on Customer Loyalty (β = 0.505, p <0.000); Trust on Customer Loyalty (β = 0.171, p < 0.131).

Table 4 Path Coeffecients of Direct Relationhips
S.N. Hypothesis Relationship β S.E. C.R. p-value R2
1 H1 CPEV ˂˗ ESQ 0.892 0.093 9.812 < 0.000 0.796
2 H2 CUST <- ESQ 0.137 0.061 2.262 0.013   0.855
3 H3 CUST <- CPEV 0.543 0.134 3.501 < 0.000
4 H4 CUST <- CRIM 0.628 0.052 9.205 < 0.000
5 H5 CMMT <- CUST 0.814 0.098 9.205 < 0.000 0.626
6 H5 CULY <- CRIM 0.485 0.060 7.155 < 0.000   0.797
7 H6 CULY <- CUST 0.690 0.134 5.872 < 0.000
8 H8 CULY <- CMMT 0.505 0.074 7.154 < 0.000
9 H9 CULY <- TRST 0.171 0.152 1.125 0.131

Mediation Analysis

In the present study, four objectives were related to test for the mediation effects among various constructs. We have used the model mentioned below to examine the proposed mediation effects Baron & Kenny (1986) suggested a method to test for mediation effects among study constructs. In our study, we tested the mediating effects by using this method. The mediation analysis was conducted with the help of structural equation modeling and results are presented in the given Table 5.

Table 5 Path Coefficients
Relationship Direct without Mediator Direct with Mediator Indirect
CUST <--- ESQ 0.653 (0.000) CPEV
0.452 (0.001)
0.397 (0.016) Partial Mediation
CULY <--- CUST 0.643 (0.000) CMMT
0.666 (0.000)
0.177 (0.056)
No Mediation
CULY<--- CRIM 0.366 (0.000) CUST
0.125 (0.087)
0.426 (0.010)
Full Mediation

It is quite evident from the above Table 5 that customer satisfaction had fully mediated relationship between corporate image and customer loyalty (β = 0.426, p= 0.010). We found evidence of partial mediation of the customer perceived value in the relationship between electronic service quality and customer satisfaction (β=0.397, p= 0.016). But we did not find mediation effect of commitment in the relationship between customer satisfaction and loyalty (β = 0.177, p=0.056). All these study hypotheses were tested at 5% level of significance.

Discussion and Conclusion

The main objective of this study was to test the effects of e-service quality, customer satisfaction, and customer perceived value, corporate image, trust and commitment on customer loyalty in online retail settings. The study model yielded a superior fit and explained a good amount of variance in customer loyalty (R2 = 0.797). Study findings supported all nine hypotheses indicating direct paths. Firstly, the effect of electronic-service quality on customer satisfaction was found significant and positive. These results are consistent with the findings of Chen and Yang (2015). The conceptualization of electronic service quality used in the present research proved to have a better ability to envisage consumer behavior than other generally used scales like eTailQ, WebQual and E-S-Qual (Blut et al., 2015). Moreover, we found evidence of partial mediation effect of customer perceived value on the relationship between electronic-service quality and customer satisfaction. Therefore, electronic-commerce companies should focus on improving electronic service quality in terms of customer friendly website design, reliable services, secure consumer personal information and prompt and empathetic customer service. According to Zeithaml (1988), perceived customer value is the ratio of perceived benefits to perceived costs. Therefore, this study strongly suggested that online retail stores should try to increase the benefits (like; quality of goods and services, image value, competitive price, fast delivery etc.) and reduce the costs (like; times and search costs, monetary, energy etc.). Also the results confirmed positive significant effects of customer satisfaction, commitment and corporate image on customer loyalty. Besides, a significant impact of trust on customer loyalty was also confirmed.

Secondly, this study proposed an integrative model to explain the user's online shopping behavior which was based on established relationships among electronic-service quality, customer perceived value, customer satisfaction, commitment, corporate image, trust, and customer loyalty. Unlike past studies, we consider that satisfaction is not the only factor with a considerable influence on loyalty: loyalty is also explained by the corporate image, commitment and trust a customer has in the firm.

Study results confirmed impacts of customer satisfaction, corporate image and commitment on customer loyalty. E-Service Quality is crucial in attaining e-satisfaction, which subsequently leads to achieve e-Loyalty (Amin, 2016). Customer satisfaction is ensured through best management of e-service quality in content-driven e-service web sites (Carlson & O’Cass, 2010). In internet banking context, e-satisfaction was found to be predicted by e-service quality dimensions (Zavareh et al., 2012).

This study confirmed significant impact of commitment on customer loyalty. These results are consistent with the findings of study conducted in telecom settings by Izogo (2017).

The current study couldn’t establish significant impact of trust on customer loyalty. In the relationship between corporate image and customer loyalty, we found full significant mediating impact of customer satisfaction. It means, alone corporate image may not help e-commerce firms to ensure customer loyalty, but they need to constantly focus on customer satisfaction, which can be assured by providing high service quality. On the other hand, commitment did not mediate the relationship between customer satisfaction and loyalty. It means that satisfied customer will definitely exhibit loyalty towards the firm.

Implications of the Study

The results of this study hold implications for both academicians and managers. In terms of academics, the results of the study demonstrated the need to include constructs like corporate image and commitment in addition to electronic service quality, customer-perceived value, and customer satisfaction in the integrated model to explain customer loyalty. These constructs can explain that myopic focus on electronic service quality may not assure customer loyalty, but focus on building positive corporate image and commitment by firms for best serving customers is also very important in present highly competitive markets. Further, this study also proved efficacy of expectation–confirmation model in explaining customer loyalty with the help of commitment, corporate image in addition to service quality, customer perceived value and satisfaction.

In the study, results confirmed positive significant direct effect of customer satisfaction on loyalty. Moreover, customer perceived value partially mediated relationship between electronic service quality and customer satisfaction. Therefore, to ensure customer satisfaction, managers need to focus on all service quality attributes in case of electronic commerce like; design of websites, protection of personal and financial information of customers, reliable services in terms of order placement, delivery and entire transaction process, and most important attribute namely customer service, because, there is high probability of service failure in case of online service transactions.

Managers need to understand how electronic service quality is formed and how important each attribute and factor of electronic service quality is to warrant customer satisfaction and trust, which in the end can help to retain online customers. Managers can improve the service quality of online stores based on the results of this research and combine it with the recent market trends. Corporate image and commitment were found significant predictors of customer loyalty in our study. Therefore, it is suggested that managers should put their committed efforts to sustain corporate image of the firm in the eyes of the customers.

Limitations and Future Scope of the Study

There are various limitations of this study that should be considered when interpreting its results and recommendations are made. First limitation is related to data used to test the integrated model and study hypotheses. The results of the study are based on cross-sectional primary data, which may lead to measurement error (it is very difficult to avoid random error). It is highly suggested to conduct longitudinal study in order to understand changes in loyal behavior of customers for streamlining marketing strategies. Respondents for this study were expected to respond to close-ended questions. This restricts the exploratory aspect of research. Similar types of studies should be conducted across different cultural settings to check for effect of cultural aspects. Further, future studies may relate customer loyalty with the profitability of the firms in online retail context.


  1. Al-Awadi, A. (2002). A proposed model of customer loyalty in retailing sector based on the Kuwaiti experience. Total Quality Management and Business Excellence, 13(7), 1035–1046.
  2. Aladwani, A.M., & Palvia, P.C. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information & Management, 39(6), 467–476.
  3. Alonso-Dos-Santos, M., Soto-Fuentes, Y., & Valderrama-Palma, V.A. (2020). Determinants of mobile banking users’ loyalty. Journal of Promotion Management, 1-19.
  4. Amin, M. (2016). Internet banking service quality and its implication on e-customer satisfaction and e-customer loyalty. International Journal of Bank Marketing, 34(3), 280–307.
  5. Anderson. R.E., & Srinivasan, S.S. (2003). "E-satisfaction and e-loyalty: A contingency framework," Psychology and Marketing. 20(2), 123-138.
  6. Andreassen, T.W. & Lindestad, B. (1998). Customer loyalty and complex services: The impact of corporate image on quality, customer satisfaction and loyalty for customers with varying degrees of service expertise. International Journal of Service Industry Management, 9(1), 7–23.
  7. Baron, R.M., & Kenny, D.A., (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
  8. Bagozzi, R.P. & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  9. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351-370.
  10. Bloemer, J.M.M., & de Ruyter, K. (1998). On the relationship between store image, store satisfaction and store loyalty. European Journal of Marketing, 32(5/6), 499–513.
  11. Bloemer, J.M., & Kasper, H.D. (1995). The complex relationship between consumer satisfaction and brand loyalty. Journal of Economic Psychology, 16(2), 311–330.
  12. Blut, M. (2016). E-service quality: development of a hierarchical model. Journal of Retailing. 92 (4), 500–517.
  13. Blut, M., Chowdhry, N., Mittal, V., & Brock, C. (2015). E-service quality: a meta-analytic review. Journal of Retailing, 91 (4), 679–700.
  14. Bowen, J.T., & Shoemaker, S. (1998). Loyalty: A strategic commitment. Cornell Hotel and Restaurant Administration Quarterly, 39(1), 12–25.
  15. Browne, M.W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230--258.
  16. Carlson, J., & O’Cass, A. (2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviors in content-driven e-service web sites. Journal of Services Marketing, 24(2), 112–128.
  17. Chang, H.H., Wang, Y., & Yank, W. (2009). The impact of e-service quality, customer satisfaction and loyalty on e-marketing: Moderating effect of perceived value. Total Quality Management and Business Excellence, 20(4), 423–443.
  18. Chen, L., & Yang, W. (2015). Perceived service quality discrepancies between telecommunication service provider and customer. Computer Standards & Interfaces, 41(3), 85–97.
  19. Chircu, A.M., & Mahajan, V. (2006). Managing electronic commerce retail transaction costs for customer value. Decision Support Systems, 42(2), 898-914.
  20. Choi, Kui-Son, Woo-Hyun Cho, Sunhee Lee, Hanjoon Lee, & Chankon Kim. (2004). The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: a South Korean study. Journal of Business Research, 57, 913-921.
  21. Cox, J., & Dale, B.G. (2001). Service quality and e-commerce: An exploratory analysis. Managing Service Quality, 11(2), 121–132.
  22. Cronin, J.J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218.
  23. Cronin, J.J., & Taylor, S.A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68.
  24. Devece, C., Garcia-Agreda, S., & Ribeiro-Navarrete, B. (2015). The value of trust for travel agencies in achieving customers' attitudinal loyalty. Journal of Promotion Management, 21(4), 516-529.
  25. Doney, P.M., & Cannon, J.P. (1997). An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61(2), 35–51.
  26. Fornell. C. Johnson. M.D. & Anderson. E.W. Cha, J. & Bryant. B.E. (1996) American customer satisfaction index: nature, purpose and findings. Journal of Marketing, 60(4), 7-18.
  27. Fornell & Larcker. D.F. (1981). Structural equation models with unobservable variables and measurement errors: Algebra and statistics. Journal of Marketing Research, 18, 382-388.
  28. Grewal, D., Iyer, G.R., & Levy, M. (2004). Internet retailing: enablers, limiters and market consequences. Journal of Business Research, 57(7), 703-713.
  29. Grönroos, C. (1982). Strategic management and marketing in the service sector. Helsingfors, Finland: Swedish School of Economics.
  30. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. & Tatham, R.L. (2010). Multivariate data analysis, 6th Edition. Upper Saddle River, New Jersey: Pearson Prentice Hall.
  31. Harman, H.H., 1976. Modern factor analysis. University of Chicago Press.
  32. Hart, A.E., & Rosenberger, P.J., III (2004). The effect of corporate image in the formation of customer loyalty: An Australian replication. Australasian Marketing Journal, 12(3), 88–96.
  33. Hoe, S.L. (2008). Issues and procedures in adopting SEM technique. Journal of Applied Quantitative Methods, 3(1), 76-83.
  34. Hoelter, D.R. (1983). The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods and Research, 11, 325-344.
  35. Hsu, M.H., Chang, C.M., & Chuang, L.W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35, 45-56.
  36. Hsu, H. (2006). An empirical study of web site quality, customer value, and customer satisfaction based on e-shop. The Business Review, 5(1), 190–193.
  37. Izogo, E.E. (2017). Customer loyalty in telecom service sector: The role of service quality and customer commitment. TQM Journal, 29(1), 1–20.
  38. Johnson, M.D., Gustafsson, A., Andreassen, T.W., Lervik, L., & Cha, J. (2001). The evolution and future of national customer satisfaction index models. Journal of Economic Psychology, 22(2), 217–245.
  39. Joo, Y.J., Park, S., & Shin, E.K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83-90.
  40. Jun, M., Yang Z. & Kim D.S. (2004). "Customers' perceptions of online retailing service quality and their satisfaction". International Journal of Quality & Reliability Management. 21(8). 817-840.
  41. Jung, C.H., & Chung, Y.S. (2012). Determinants of the user’s satisfaction and continued usage intention in IPTV services. Journal of Digital Convergence, 10, 137-146.
  42. Kao, T.W.D., & Lin, W.T. (2016). The relationship between perceived e-service quality and brand equity: A simultaneous equations system approach. Computers in Human Behavior, 57, 208–219.
  43. Koo, D.M. (2003). Inter-relationships among store images, store satisfaction, and store loyalty among Korea discount retail patrons. Asia Pacific Journal of Marketing and Logistics, 15(4), 42–71.
  44. Kotler, P. (2000), Marketing Management. 10th edition. New Jersey, Prentice-Hall.
  45. Kumar, N., Scheer, L.K., & Steenkamp, J.B.E.M. (1995). The effects of perceived interdependence on dealer attitudes. Journal of Marketing Research, 32(3), 348–356.
  46. Kuo, Yen-Ku, & Ye, Kung-Don (2009). The causal relationship between service quality, corporate image and adults’ learning satisfaction and loyalty: A study of professional training programmes in a Taiwanese vocational institute. Total Quality Management and Business Excellence, 20(7), 749–762.
  47. Lam, S.Y., Shankar, V., Erramilli, M.K., & Murthy, B. (2004). Customer value, satisfaction, loyalty, and switching costs: An illustration from a business-to-business service context. Journal of the Academy of Marketing Science, 32, 293–311.
  48. Lee, C.Y. (2019). Does corporate social responsibility influence customer loyalty in the Taiwan insurance sector? The role of corporate image and customer satisfaction. Journal of Promotion Management, 25(1), 43-64.
  49. Lee G.G-, Lin H.F. (2005), "Customer perceptions of e-service quality in online shopping". International Journal of Retail and Distribution Management. 33(2), 161-176.
  50. Li, N., & Zhang, P. (2002). Consumer online shopping attitudes and behavior: An assessment of research. AMCIS 2002 proceedings, 74.
  51. Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42, 683-693.
  52. Lindgreen, A., & Wynstra. F. (2005). Value in business markets: What do we know? Where are we going?” Industrial Marketing Management, 34(7). 732- 748.
  53. Macintosh, G., & Lockshin, L. (1997). Retail relationships and store loyalty: A multi-level perspective. International Journal of Research in Marketing, 14, 487–497.
  54. Masud, H., & Daud, W.N.W. (2019). Human resource management practices and organizational commitment: Research methods, issues, and future directions. Review of Integrative Business and Economics Research, 8, 217–226.
  55. Mathieu, J.E., & Zajac, D.M. (1990). A review and meta-analysis of the antecedents, correlates, and consequences of organizational commitment. Psychological Bulletin, 108(2), 171–194.
  56. McAlexander, J.H., Kim, S.K., & Roberts, S.D. (2003). Loyalty: The influences of satisfaction and brand community integration. Journal of Marketing Theory and Practice, 11(4), 1–11.
  57. McKinney, V., Yoon. K. & Zahedi, F.M. (2002), The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296-315.
  58. Monsuwe, T.P.Y. Dellaert. B.G.C. & Ruyter. K.D. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1). 102-121.
  59. Moorman, C., Deshpande´, R., & Zaltman, G. (1993). Factors affecting trust in market research relationships. Journal of Marketing, 57, 81–101.
  60. Morrison, D.E., & Firmstone, J. (2000). The social function of trust and implications for e-commerce. International Journal of Advertising, 19, 599–623.
  61. Morgan, R., & Hunt, S. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 59, 20–38.
  62. Nguyen, N., & LeBlanc, G. (1998). The mediating role of corporate image on customers’ retention decisions: An investigation in financial services. The International Journal of Bank Marketing, 16(2), 52–65.
  63. Nunnally, J. C., & Bernstein I.bH. (1994). Psychometric theory. New York: McGraw-Hill.
  64. Oghuma, A.P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33, 34-47.
  65. Oliver. R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research. 17, 416-469.
  66. Oliver, R.L. (1997). Customer satisfaction: A behavioral perspective on the consumer. New York: McGraw-Hill.
  67. Oliver, R.L. (1999). Whence consumer loyalty? Journal of Marketing, 63 (Special issue), 33–44.
  68. Parasuraman, A. (1997). Reflections on gaining competitive advantage through customer value. Journal of the Academy of Marketing Science, 25(2), 154–161.
  69. Parasuraman, A., & Grewal, D. (2000). The impact of technology on the quality-value-loyalty chain: A research agenda. Journal of Academic of Marketing Science, 28, 168–174.
  70. Parasuraman, A., Zithaml, V.A., & Berry, L.L. (1988). SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
  71. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. & Podsakoff, N.P. 920030. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879-903.
  72. Reichheld, F.F. (1996). The loyalty effect. Boston, MA: Harvard Business School Press.
  73. Ryu, K., & Han, H. (2010). Influence of the quality of food, service, and physical environment on customer satisfaction and behavioral intention in quick-casual restaurants: Moderating role of perceived price. Journal of Hospitality & Tourism Research, 34(3), 310-329.
  74. Selnes, F. (1993). An examination of the effect of product performance on brand reputation, satisfaction and loyalty. European Journal of Marketing, 27(9), 19–35.
  75. Shemwell, D.J. & Cronin, J.J. (1995). Trust and commitment in customer/service-provider relationships: An analysis of differences across service types and between sexes. Journal of Customer Service in Marketing & Management, 1(2), 65–75.
  76. Singh, J., & Sirdeshmukh, D. (2000). Agency and trust mechanisms in consumer satisfaction and loyalty judgements. Journal of the Academy of Marketing Science, 28(1), 150–167.
  77. Snehota, I., & Soderlund, M. (1998). Relationship marketing – what does it promise and what does it deliver? An empirical examination of repeat purchase customers. In P. Andersson (Ed.), Marketing: Research and practice, Proceedings of the 27th EMAC conference, Stockholm (pp. 311–330).
  78. Su, L., Swanson, S.R., & Chen, X. (2016). The effects of perceived service quality on repurchase intentions and subjective well-being of Chinese tourists: The mediating role of relationship quality. Tourism Management, 52, 82–96.
  79. Szymanski, M. David & Richard T. Hise (2000). E-Satisfaction: An Initial Examination. Journal of Retailing, 76(3), 309-322.
  80. Thong, J.Y., Hong, S.J., & Tam, K.Y. (2006). The effects of postadoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64, 799-810.
  81. West, S.G., Finch, J.F., & Curran, P.J. (1995). Structural equation models with non-normal variables: problems and remedies. In: Hoyle RH, editor. Structural equation modeling: Concepts, issues and applications. Newbery Park, CA: Sage, pp. 56–75.
  82. Wolfinbarger. M.F. & Gilly. M.C. (2002). .comQ: dimensionalizing, measuring and predicting quality of the e-tailing experience. Working paper. Marketing Science Institute. Cambridge, MA. 1-51.
  83. Yang, Z. & Jun. M. (2002). Consumer perception of e-service quality: from Internet purchaser, and non-purchaser perspectives. Journal of Business Strategies. 19(1). 19-42.
  84. Yang. Z. & Peterson. R.T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing. 21(10), 799-822.
  85. Yieh, K., Chiau, Y., & Chiu, Y. (2007). Understanding the antecedents to customer loyalty by applying structural equation modelling. Total Quality Management and Business Excellence, 18(3), 267–284.
  86. Yu, H.S., Zhang, J.J., Kim, D.H., Chen, K.K., Henderson, C., Min, S.D., & Huang, H. (2014). Service quality, perceived value, customer satisfaction, and behavioral intention among fitness center members aged 60 years and over. Social Behavior and Personality: An International Journal, 42(5), 757-767.
  87. Yu, C.J., Wu, L., Chiao, Y., & Tai, H. (2005). Perceived quality, customer satisfaction, and customer loyalty: The case of Lexus in Taiwan. Total Quality Management and Business Excellence, 16(6), 707–719.
  88. Zavareh, F.B., Ariff, M.S.M., Jusoh, A., Zakuan, N., Bahari, A.Z., & Ashourian, M. (2012). E-service quality dimensions and their effects on e-customer satisfaction in internet banking services. Procedia—Social and Behavioral Sciences, 40(40), 441–446.
  89. Zeithaml. V.A., Parasuraman. A., & Malhotra. A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science 30(4), 362-375.
  90. Zeithaml, V.A., Parasuraman, A., & Malhotra, A. (2001). A conceptual framework for understanding e service quality: implication for future research and managerial practice, MSI Working Paper Series, Cambridge, MA, 1-49.
  91. Zeithaml. V.A., Berry, L.L. & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60, 31-46.
  92. Zimmer, M.R., & Golden, L.L. (1988). Impressions of retail stores: A content analysis of consumer images. Journal of Retailing, 64(3), 265–293.
  93. Zhou, R., Wang, X., Shi, Y., Zhang, R., Zhang, L., & Guo, H. (2019). Measuring e-service quality and its importance to customer satisfaction and loyalty: an empirical study in a telecom setting. Electronic Commerce Research, 19(3), 477-499.
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