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\title{Service Quality, Satisfaction and Loyalty on Online Marketing: An Empirical Investigation}
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             \author[1]{Gajendra  Sharma}

             \affil[1]{  Kathmandu University}

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\date{\small \em Received: 16 December 2016 Accepted: 3 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


The purpose of this study is to empirically investigate the impacts of satisfaction on e-service quality, trust and on e-loyalty in online marketing. The theoretical background used in this study was social exchange theory. Online survey from students and faculty members of Kathmandu University, Nepal was conducted in this study. The analytical results indicate that e-service quality, customer service, and trust have strong direct effect on satisfaction and impacts of indirect effects with e-loyalty are more significant. Findings also indicated that satisfaction was a significant variable that mediated the relationships between service quality and customer loyalty.

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\keywords{service quality, satisfaction, loyalty, online marketing.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
he Internet has changed people's purchase habits and provides convenience for searching information in online platform. The Internet makes easier to market different products and services. Online marketing offers to consumers a shopping experience different from physical-based marketing such as convenience, search cost, delivery, and price \hyperref[b46]{(Palmer 2000)}. In this connection, online marketing is continued to grow \hyperref[b4]{(Chen and He 2003)}. Privacy and security have been recognized as the major barriers preventing web users from online marketing, leading to the decrease of consumers' perceptions of online service quality. In order to encourage repeat purchases and establish customer loyalty, \hyperref[b45]{Zeithaml et al. (2002)} suggested companies transfer the focus of e-marketing to eservice. Majority of studies on customer satisfaction, however, have largely focused on traditional business channels. Only moderate effort has been devoted to service satisfaction in the online market perspective \hyperref[b10]{(Choi et al. 2000}; Ho and Wu 1999, Gajendra and Li 2013). The unique characteristics of Internet-based services focus in the area of human-computer interactions. Studies have shown that e-service quality, e-satisfaction and e-trust are key factors for establishing e-loyalty (Reichheld and Schefter 2000). These factors have direct and indirect effects on e-loyalty. We observe a gap in the literature that there is not a complete view on all of effects that e-trust, e-satisfaction, and e-service quality have on each other and on e-loyalty. \hyperref[b2]{Boshoff (2007)} examined the relationship between e-quality and e-loyalty and proposed that the E-S-QUAL scale should have six dimensions. Marimonet al.  {\ref (2010)} applied the E-S-QUAL instrument to an analysis of the relationship between loyalty and purchasing in an e-market and expanded \hyperref[b2]{Boshoff's (2007)} model by adding new construct.\par
Customer loyalty is considered as the foundation of competitive advantage and has strong influence on organization's performance  {\ref (Rust et al. 2000)}. A number of studies showed that companies can generate more profit in an online as well offline environment through retaining their current customers rather than to attain previous ones \hyperref[b24]{(Hogan et al. 2003}). Moreover, it was observed that loyal customers were less interested to change the company because of price and they also engage in positive word-of-mouth communication and refer it to other users. Higher customer loyalty in service industries will lead to better efficiency. Several researches had focused on inquiring the association of customer loyalty with its antecedents (McMullan \& Gilmore 2008; \hyperref[b25]{Ibanez et al. 2006;}\hyperref[b30]{Liu et al. 2005)}. A number of practitioner studies have provided empirical support for a relationship. Due to the unique characteristics of online marketing \hyperref[b11]{(Collier \& Bienstock 2006;}\hyperref[b43]{Wolfinbarger \& Gilly 2003)}, the conceptualization and measurement of e-service quality encompasses the two facets of customer services and information systems and includes all factors related to the process of service delivery. \hyperref[b16]{Fassnacht and Koese (2006)} argue that e-service is a higher-order construct consisting of environment quality, product and services delivery quality as well as outcome quality. 
\section[{II. Literature Review and Hypothesis Development a) Website Design and E-Satisfaction}]{II. Literature Review and Hypothesis Development a) Website Design and E-Satisfaction}\par
Peterson (1997) reports that good website design in online marketing is about high-quality organization and easy search. This includes providing consumers' uncluttered screens, simple search paths and fast presentations. Moreover, each of these elements of site design could impact e-satisfaction levels in the variety of a more pleasurable marketing experience being a more satisfying one. Marketing is thought to be pleasant and satisfying to consumers when the transaction sites are fast, uncluttered, and easy-to-navigate. The main function of a business website is to inform consumers about the company and its products or services (Hallerman2009). Analysis of website performance in terms of design criteria and other associated factors became an important area of investigation \hyperref[b41]{(Tarafdar and Zhang 2008)}. Appropriate website design eventually enables organizations complete their online marketing activities with greater success.\par
Hence, designing an effective website that attracts and retains internet users has become an important mission. \hyperref[b9]{Cho and Park (2001)} conducted an empirical study of 435 samples of internet users to examine the e-satisfaction index for internet marketing. They found that the customer satisfaction is assessed using the quality of website design. Previous study found that website design factors are strong predictors of customer quality judgments, satisfaction, and loyalty for internet marketers (Wolfmbarger and Gilly 2003). A good strategy includes knowing what to emphasize on a website, presenting with consistency, and using up-todate technology \hyperref[b40]{(Tan et al. 2009)}. Website design can influence customers' perceived image of company, and attract customers to conduct purchasing online easily with good navigation and useful information on the website. Website should offer appropriate information and multiple functions to customers. Website design can be considered as a multidimensional construct, and define it as the customer's assessment of website attributes based upon their experience with the website. Several studies indicate positive relationship between the website quality and e-satisfaction  {\ref (Szymanski and Hise 2001;}\hyperref[b5]{Chen et al. 2002;} {\ref Cheungand Lee 2002)}. We expect that each of the dimensions of website quality will have a positive impact on e-satisfaction. H1: Website design is directly associated with esatisfaction in online marketing. 
\section[{b) Customer Service and E-Satisfaction}]{b) Customer Service and E-Satisfaction}\par
Previous study conducted by \hyperref[b29]{Lin (2005)} showed that customer service could satisfy customers while it was investigated that satisfied customers tended to be loyal customer \hyperref[b35]{(Oliver 1999)}. A high quality of customer service has resulted in e-satisfaction and finally created repeated purchasing behavior in online environment. Customer satisfaction distinguishes the variance between the customer's expectations and the customer's perceptions. When e-selling will be a growing trend in shopping, the online market will become highly competitive, so one way online markets can become more competitive is by offering better customer service. Online retailers should determine and deliver the quality service to customers in order to create satisfaction. Perceived value revolves around price and convenience. Customers expect a lower price on the Internet in general \hyperref[b27]{(Koch 2003)}. Given the price sensitivity of online consumers, the ease of finding prices may contribute to the customer value. Customer service is defined as a consumer's perception of the net benefits gained in exchange for costs incurred in obtaining the desired benefit  {\ref (Chen \& Dubinsky 2003)}. Moreover, Heinonen (2006) defined customer service as the perceived outcome of the trade-off of benefit and sacrifice of technical, functional, temporal, and spatial dimensions. Some research has found that customer service has a significant direct impact on e-satisfaction  {\ref (Chi et al. 2008;} {\ref Ha \& Janda 2008)}. H2: Customer service is positively related with esatisfaction in online marketing. 
\section[{c) Trust and E-Satisfaction}]{c) Trust and E-Satisfaction}\par
Researchers in the marketing area have considered trust as one of the key constructs of relationship marketing  {\ref (Ribbinket al. 2004}). Trust has been defined as the degree of confidence or certainty the customer has in exchange options. E-trust will therefore be defined as the degree of confidence customers have in online exchanges, or in the online exchange channel (Reichheld and Schefter 2000). Stewart (1999) claimed that the failure of the Internet marketing is largely attributable to the lack of trust consumers have in the electronic channels. So, customer trust is considered as another important antecedent of satisfaction. Razzaque and Boon (2003), for instance, found a significant effect of trust on satisfaction in the context of channel relationship. Customer's trust plays a primary role in maintaining long-term relationships between customers and online service providers \hyperref[b8]{(Chiu et al. 2009)}. Customers are unlikely to transact through the website which lacks trust, because of fear of vendor opportunism. The dimensions of trust are responsiveness, system availability and contact. Trust toward online companies is often regarded as a key factor of online marketing growth, online success and competitiveness  {\ref (Gounaris et al. 2005)}. Trust in e-service is related to the buying and payment process, the reliability of the website, privacy and securities issues, order fulfillment, service delivery, after sales service and the status of the company. Customers' trust to online companies is critical for online companies' success. 
\section[{H3: Trust directly and positively influences e-satisfaction in online marketing d) E-Satisfaction as a Mediator}]{H3: Trust directly and positively influences e-satisfaction in online marketing d) E-Satisfaction as a Mediator}\par
Zeithaml and Bitner (2000) defined e-satisfaction as the customers' assessment of a product or service in terms of whether that product or service has met their needs and expectations in online platform. Satisfaction has been shown to be positively related to loyalty and this effect also occurs in online environment. Shankar et al.  {\ref (2003)} indicated that the effect of satisfaction on loyalty is stronger online than offline. Satisfied customers tend to have higher usage of service, possess stronger repurchase intention, and are often keen to recommend the product or service to their acquaintances. The process of measuring antecedents of e-satisfaction has been found to be both difficult and somewhat controversial  {\ref (Szymanski and Henard 2001)}. Researchers have employed various approaches, including attribute-level performance, prior experiences with the service, frequency of service usage, and expectation-disconfirmation approaches, to measure e-satisfaction  {\ref (Shankar et al . 2000)}.  {\ref Szymanski and Hise (2000)} have employed the cumulative approach to identify five facets of e-satisfaction. These are: shopping convenience, product offerings, site design, financial security, and product information. The first four factors significantly impact on e-satisfaction with online shopping.  {\ref Wolfinbarger and Gilly (2002)}, through focus group interviews, a content analysis, and an online survey, and have uncovered four contributors to the online retailing experience: Website design, reliability, privacy/security, and customer service. E-satisfaction was investigated completely by Ribbink et al  {\ref (2009)}. The results of their investigation showed that when a customer is satisfied with an online service provider, is inclined to repeat his purchase by the same provider and this increases the trust between them. 
\section[{e) E-Service Quality and E-Satisfaction}]{e) E-Service Quality and E-Satisfaction}\par
Service quality literature indicated that perceptions of high service quality and high service satisfaction resulted in a very high level of purchase intentions. According to  {\ref Parasuraman, Berry and Zeithmal (1985)}, a perception of service quality is a result of a comparison between what consumers consider the service should be and their perceptions about the actual performance offered by the service provider. Service quality and overall satisfaction implicitly include issues such as price perception, which is usually only felt rather than objectively measurable. A majority of studies view them as antecedents of esatisfaction (Gajendra and Li 2013), i.e. satisfaction is conceptualized as a mediator of the relationship between quality and loyalty. Service quality as defined by Santos (2003) described service quality as the customers' overall judgment of the excellence of service offering. Service quality is also affected by the ability of an organization to satisfy customers' needs, according to their expectation level (Yoo\& Park 2007). E-service quality can help firms to differentiate themselves by offering enhanced satisfaction, encouraging repeat purchases and building loyalty \hyperref[b45]{(Zeithaml, Parasuraman and Malhotra, 2002)}. Each service element in ecommerce offers an opportunity for such differentiation. Eservice quality may also affect customers' emotional responses such as liking, joy, pride, dislike and frustration. Customer service quality has a direct relationship with customer satisfaction in the context of online marketing. Ribbink et al.  {\ref (2009)} articulates that the importance of attributes of online customer's satisfaction is dependent on technology readiness. Research on the antecedents to e-service adoption also suggests that e-service experience has impact on customers' perception and evaluation of e-service quality.\par
H4: E-service quality is positively related with esatisfaction in online environment. 
\section[{f) E-Satisfaction and E-Loyalty}]{f) E-Satisfaction and E-Loyalty}\par
Customer e-loyalty is an important issue in the competitive environment of e-marketing. Different studies show that e-loyalty is influenced by esatisfaction, e-trust and e-service quality. Customer loyalty has been defined as "a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior" \hyperref[b35]{(Oliver, 1999)}. The concept of e-loyalty extends the traditional loyalty concept to online consumer behavior.  {\ref Cyr et al. (2007)} defined e-loyalty as intention to revisit a website or to make a transaction from it in the future. Strauss et al.\par
(2009) define e-loyalty as "A customer's favorable attitude toward an e-commerce website that predisposes the customer to repeat buying behavior". Numerous researchers have tried to find relevant antecedents and their role in creating e-loyalty. E-service quality, e-satisfaction, corporate image, word-of-mouth communication, perceived value have been proposed as antecedents of loyalty (Wieringa and Verhoef 2007; Patterson and Smith 2003) and these variables are also taken as motivating force of competitive advantage and corporate success. Satisfaction is created when an individual believes that the other person has useful indices besides profitability \hyperref[b17]{(Flavian et al 2006)}. Satisfaction is one of the signs of loyalty of customer. \hyperref[b28]{Lewis and Soureli (2006)} defined that satisfaction is "security that at first means that behavior is guided from the goal of one person to another one and second it is based on qualification of a business to keep its commitments". According to Ribbinket al. (2009), satisfaction is the trust of a loyal customer and of great importance when an online trading is occurred. H5: Loyalty in online environment directly effects esatisfaction The first part of the block is relating to the motivating factors for e-satisfaction in online environment. The variables are website design, customer service and trust. In this case these variables act as dependent variables and e-satisfaction acts as independent variable. In addition, e-service quality acts as dependent variable on e-satisfaction. The next variable 'loyalty on online marketing' or e-loyalty acts as independent variable and e-satisfaction or satisfaction in online marketing as dependant variable. 
\section[{III. Theoretical Background: Social Exchange Theory}]{III. Theoretical Background: Social Exchange Theory}\par
Social exchange theory is a social psychological and sociological perspective that explains social change and stability as a procedure of negotiated exchanges between parties. Social exchange theory states that all human relationships are formed by the use of a subjective cost-benefit analysis and the comparison of alternatives. The social exchange context argues that people calculate the overall worth of a particular relationship by subtracting its costs from the rewards it provides \hyperref[b33]{(Monge 2003)}. Social exchange theory was introduced in the 1960s by George Homans. After Homans founded the theory, a number of theorists such as Richard Emerson, John Thibaut, Harold Kelley and Peter Blau continued to inscribe about the theory. Homans's primary concern within this field was focusing on the behavior of individuals when interacting with one another. He believed characteristics such as power, conformity, status, leadership and justice within social behavior was important to explain within the theory (Cook and Rice 2001). Emerson states that social exchange theory is an approach in sociology that is described for simplicity as an economic analysis of noneconomic social situations \hyperref[b15]{(Emerson 1976}). Social exchange includes both a concept of a relationship, and some belief of a shared obligation in which both parties perceive responsibilities to each other. Another common form of exchange is negotiated exchange which focuses on the negotiation of rules in order for both parties to essential differences. This theory can be applied to various social backgrounds such as intimate relationships or work locations. The study discovers the different factors involved when an individual decides to establish an online relationship (Rivka and Azy 2009). Overall the study followed the social exchange theory's idea that people are attracted to those who grant them rewards. Individuals will try to understand the source or cause of feelings produced by social exchange. In this way, emotions become attributed to the object that caused them. Individuals interpret and exchange their feelings with respect to social relationships (e.g. partners, groups, networks). Positive emotions produced by exchange will increase commonality in these relationships, while negative emotions will decrease solidarity. 
\section[{IV. Methodology a) Measures}]{IV. Methodology a) Measures}\par
Table \hyperref[tab_1]{1} provides the name of variables, their acronym and description. There are altogether 6 construct variables and 24 items to be measured for data analysis. All scales consisted of 5-point likert questions, ranging from "1 as strongly disagrees" to "5 as strongly agree". As shown in the research model (Fig. \hyperref[fig_0]{1}) the variables in left block are website design, customer service and trust. E-satisfaction acts as a mediating variable. The variable in the right block is Eservice quality. Another variable loyalty on online marketing acts as independent variable on esatisfaction. In the table acronym of each variable, their description and indicator items were presented. Each construct contains 4 indicator items and altogether there are 20 indicators. The item in each variable was selected from literature review. 
\section[{Website design E-satisfaction E-service quality}]{Website design E-satisfaction E-service quality}\par
Customer Pretest of the questionnaires was conducted with 25 users to check the reliability and clarity of questionnaires. Pretest was performed for screening of questions i.e. select those which have clear meaning and understandable. The pilot test was performed with 30 IT experts. Some questions were modified as per the suggestion of users to avoid confusions and to make reliable survey. Altogether 541 participants were requested for survey participation. The responses were received from 378 participants. Thus the response rate is 69.87\%. Out of them 9 responses were discarded due to incomplete and invalid answers. Consequently, remaining 369 responses were used for data analysis. The survey contains 20 questionnaires and it takes 10 minutes to answer. Each participant received a small gift for answering survey questionnaires. The e-mail addresses of each participant were collected from IT department of the University. The survey link was sent to each participant. Each item of a questionnaire was rated on a five point likert scale from "strongly agree" to "strongly disagree". Neutral was given the score of 3. Of all respondents, 56.5\% were male, 43.5\% were female. The age varies from 22 to 58. The average age is 25. 
\section[{V. Results}]{V. Results} 
\section[{a) Measurement Model}]{a) Measurement Model}\par
Structural equation modeling (SEM) using Smart PLS 2.0 was used to analyze measurement and psychometric properties of the measure for constructs. After a refinement of the survey instrument utilized in our initial tests, all constructs reported high reliability (composite reliability > 0.8, AVE> 0.7). Thus, the measurements fulfill convergent validity requirements. Based on the above described tests our measurement model (Table \hyperref[tab_0]{2}) is validated and we have demonstrated that all measures in this study have adequate convergent and discriminant validity. In order to access the construct validity and reliability, a test on Cronbach's alpha was conducted for \hyperref[b21]{Hair et al. (1998)} stated that the threshold value of Cronbach's alpha should be 0.60. Communality is the sum of the squared factor loadings for all factors for a given variable. Communalities report the percentage of variance within each variable that is explained by the resulting factors. The value is above 70\% which shows the adequate fit. Variance extracted of 0.5 or higher indicates adequate convergent validity. The value of AVE was obtained above 0.5 in our result. The value of construct reliability 0.7 or higher suggests good reliability. The internal consistency reliability (ICR) should be above 0.707. Coefficient of determination (R 2 ) is received from F-statistics. Internal reliability was evaluated by the composite reliability of each latent variable. Composite reliabilities of all constructs should be above 0.70 threshold \hyperref[b1]{(Barclay et al. 1995)}. In our result the value of composite reliability is above 0.70. The redundancy has noofficial value for analysis but higher value is preferred. 
\section[{b) Confirmatory Factor Analysis Model}]{b) Confirmatory Factor Analysis Model}\par
Out of 20 items, 3 items were deleted due to lower factor loading less than 0.6. In Website design (WD) the third item (WD3) deleted. In Customer service (CS) construct fourth item (CS4) was removed. Similarly in E-service quality (EQ) construct the second item (EQ2) was eliminated due to low factor loading. The result of CFA is presented in Table \hyperref[tab_2]{4}. Reliability of construct is how individuals respond and validity means what is supposed to measure. Individual item reliability can be checked by examining the factor loading of each item on its corresponding latent variable. The loading of all items should be higher than 0.707 \hyperref[b1]{(Barclay et al. 1995)}. However, survey data highly depends upon the opinion of participants, so some fluctuation in result may take place. According to Manly (1994) loading above 0.6 is usually considered high and below 0.4 is low. If all measurement items are strongly significant with a value of over 0.60, then it will be a good model fit and all construct variables are valid. The proposed research model shows a good construct fit as all factor loadings are above 0.6. The research model is statistically significant and well constructed.   The factor loadings are in acceptable range and the t-values are significant at the .01 level. If the square root of the AVE is greater than all of the inter-construct correlations, it is an evidence of sufficient discriminant validity \hyperref[b7]{(Chin 1998)}. In order to further access validity of measurement instruments, a cross loading table was constructed. It can be observed that each item loading in the table is much higher on its assigned construct than on the other constructs, supporting adequate convergent and discriminant validity. Chin (1998) suggests that, covariance based estimates such as reliability and AVE are not applicable for evaluating formative constructs. Instead, the path weights of indicators need to be examined to check if they significantly contribute to the emergent construct.\par
Service Quality, Satisfaction and Loyalty on Online Marketing: An Empirical Investigation EQ 0.32 0.25 0.49 0.52 EL 0.31  \hyperref[tab_4]{6} presents the value of latent variable correlations and Square root of AVE. An AVE is used to assess the convergent and discriminant validity of the constructs. The AVE helps to measure the amount of variance that a construct captures from its indicators relative to the amount due to measurement error. In order to assess the convergent validity, AVE of the stipulated construct should be greater than 0.50 and the value of square root of the AVE should be greater than .707 \hyperref[b18]{(Fornell and Larcker 1981)}.  \hyperref[tab_6]{7} presents the summary of hypothesis result results of research model. All t-statistics will be significant at p < 0.001. If the probability value (p value) is less than the significance level, the null hypothesis is rejected. If the T value is greater than 2.63, then the path is significant at p<0.01. T value in between 2.63 and 1.96 is significant at p<0.05. Likewise, T value below 1.96 is not significant (P<0.01). The key objective of this study is to provide a more comprehensive understanding of the role of eservice quality, e-loyalty, website design, trust and customer service on e-satisfaction. The empirical results indicate that all the five hypotheses are supported. Analysis of data from 369 participants shows that website design (H1), customer service (H2), trust (H3) and e-service quality (H4) is found to have a positive and significant relationship with e-satisfaction. Esatisfaction (H5) is found to have a positive and significant relationship with loyalty. These are significant findings in that these backgrounds are able to explain a large part of the variance of e-satisfaction. 
\section[{VI. Discussion}]{VI. Discussion} 
\section[{a) Summary of the Results}]{a) Summary of the Results}\par
E-service quality, e-satisfaction and e-loyalty are critical determinants of the success of online marketing. Accordingly, there is a rise of research on these constructs. E-loyalty brings high rate of customer retention and reduced cost for recruiting new customers which leads to long-term profitability to the online service provider. The purpose of this study is to propose a comprehensive model of the e-satisfaction development process by conceptualizing that e-loyalty is influenced by e-satisfaction and e-satisfaction is effected by e-trust, customer service and e-service quality. This study tests all of the direct and indirect impacts that these factors can have on each other and in turn on customer e-satisfaction, and presents a comprehensive model on their relationship which goes beyond what previous researches have studied. The quality of e-services has a direct and an indirect impact on both e-satisfaction and e-trust. It means that the better e-service quality, the more customer esatisfaction and e-trust of the internet marketing services. In addition, e-trust not only has a direct impact on e-satisfaction but also has an indirect influence through e-loyalty. Since an online transaction is perceived to be associated with higher risk, trust has Volume XVII Issue II Version I Year ( ) been considered as a critical component in online marketing context. Therefore, online providers should recognize that to build e-satisfaction and e-loyalty, there has to be a prior development of e-trust. In the Internetbased market, customers in actual fact expect correct services, accurate transactions and records, and prompt delivery either electronically or physically. Customer services are also one of the important antecedents of satisfaction  {\ref (Anderson et al. 1994)}. Company employees should have adequate knowledge to respond customer questions, properly handle problems that arise, understand customer specific needs, and address complaints in an cordial manner. In contrast to the findings of  {\ref Szymanski and Hise (2000)}, the study found that a product or service offering is a significant indicator of overall satisfaction in online platform. The finding that security and privacy do not significantly impact on overall satisfaction was consistent with results discovered by  {\ref Wolfinbarger and Gilly (2000)} in their study of service quality dimensions of electronic marketing.\par
Improving e-service quality is the main key to long-term advantage in the electronic age. Therefore, understanding, measuring and managing e-service quality has become an indispensable issue for ensuring customers' satisfaction and loyalty as well as profitability of service industries.This study proved that esatisfaction can affect attitudes and intensions towards reusing of e-service. As an antecedent of online repurchase, marketers should pay enough attention to improve e-services quality and adopt with customers' needs and preferences. Building customer loyalty in emarketing is difficult; it requires online companies to differentiate themselves from their competitors. Practitioners should consider focusing more on customer interface design as a marketing strategy. Also, the argument that trust is positive affect satisfaction and that is supported in the finding. However, it has previous study been supported that trust plays significant role to gain success in e-marketing. Trust can easily motivated by conducting trustworthy, secured, private, responsiveness, and personalized for users \hyperref[b34]{(Norizan et al., 2010)}. Finally, the argument that e-satisfaction affects positively on e-loyalty and that is supported by the findings. However,  {\ref Norizan et al., (2001)} indicated that satisfaction has positive relationship with customer loyalty including repurchase intention and positive word of mouth. 
\section[{b) Theoretical and Managerial Implications}]{b) Theoretical and Managerial Implications}\par
Our findings have both managerial and research implications. From a managerial perspective, online marketers' can establish early warning systems based on continuously measuring customer perceptions, so that management can take appropriate remedial action when any of these dimensions is perceived as falling below an acceptable level.\par
Moreover, e-marketers can use the scale items developed in this study to target their online marketing activities with regard to competitors to identify their comparative strengths and weakness from the standpoint of customers. The managers should be aware that the most important dimension of e-service recovery in terms of enhancing customer satisfaction is trust. Managers should therefore ensure that all problems and returns are effectively handled through company websites. This is the most critical point in seeking to restore customer confidence after a service failure. Moreover, managers should note that the contact' dimension has no effect on loyalty. These results had implication for practice and social exchange theory. The study findings demonstrate that there is a need to incorporate constructs other than e-satisfaction, corporate image, and perceived value for customer loyalty in order to extend social exchange theory. Seiders et al. (  {\ref 2005}) also concluded that relational, marketplace and customer characteristics moderate the relationship between satisfactions and repurchase behavior. From a research perspective, our study provides an early conceptualization of the relevant antecedents of e-satisfaction.\par
Our findings provide a foundation for the further study of this important topic along with both theoretical and empirical dimensions. E-service quality and trust are also key points that require consideration. A prompt response with customers' inquires, making a policy of responding to customers as individuals, using helpful, friendly people and caring about customers are important in online marketing to enhance the e-service quality which provides e-satisfaction. Moreover, convincing customers that they are getting high value from the provider is an important objective. Our study shows that customer service is a contributing factor to esatisfaction. Offering a wide variety of products and services, carefully evaluating price competition, and making the purchase from the website to be easier and efficient are the means to increase customers' value. Esatisfaction is confirmed to be a predecessor of customer value. The continuous improvement in esatisfaction will lead to the increase of customer. Satisfied customers are more likely to purchase repeatedly and speak positive word of-mouth. The managerial implication here is that the online marketing can improve e-satisfaction and the reflection of improved e-satisfaction quality will increase customer loyalty which combined together in making customers satisfied, and then enhance their purchase intentions. 
\section[{c) Limitations}]{c) Limitations}\par
This study has two major limitations. First, the sample collected from academic community may not be representative of the general population of online marketers. The analytical results presented here thus may have limited generalizability. Second, since this study only considered online marketing aspect of internet platform, this study was limited to examining the causalrelationships among e-satisfaction, e-service quality, customer service, trust and e-loyalty. In future study, other significant variables, such as company reputation and profitability of the online marketing may be added into the hypothesized research model. Moreover, future studies may use different sampling method to collect data, for example, randomly selecting respondents from a list of customers of a specific emarketer. 
\section[{VII. Conclusions}]{VII. Conclusions}\par
According to the results, the most important effective factor on loyalty of the customers in e-services including online marketing is e-satisfaction. Attaining customer satisfaction with high quality services, fulfillment of the expectations of the customers at the best form being pioneer in presenting new services to facilitate customers' affairs will promise the survival of online business. Overall satisfaction of customers plays a significant role in determining repurchase intentions, recommendations, and price sensitivity. Moreover, as some satisfaction determinants directly affect behavioral intentions or indirectly influence behavioral intentions through overall satisfaction, management should focus on these prominent attributes. Identifying satisfaction antecedents will not only help management learn how to take effective measures to improve overall satisfaction, but facilitate efforts to devote limited company resources into the optimum locations and in turn, achieve favorable behavioral intentions. E-satisfaction quality may not only determined by website quality but it depends upon consumer trust, service quality and customer service. In some online marketing services, the quality of associated logistics and customer service components may also determine a customer's overall perception of satisfaction. It may be important for future research to test the developed propositions in other types of e-services which are more strongly associated with experiential use. Therefore, future work should examine the customer profile dependency of components of e-satisfaction.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
    \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.14166666666666666\textwidth}P{0.2833333333333333\textwidth}P{0.2125\textwidth}P{0.14166666666666666\textwidth}P{0.07083333333333333\textwidth}}
WD\tabcellsep 1.00\tabcellsep \tabcellsep \tabcellsep \\
CS\tabcellsep 0.74\tabcellsep 1.00\tabcellsep \tabcellsep \\
TR\tabcellsep 0.71\tabcellsep 0.70\tabcellsep 1.00\tabcellsep \\
ES\tabcellsep 0.69\tabcellsep 0.72\tabcellsep 0.78\tabcellsep 1.00\end{longtable} \par
 
\caption{\label{tab_0}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.15488888888888888\textwidth}P{0.03588888888888889\textwidth}P{0.6384444444444444\textwidth}P{0.020777777777777777\textwidth}}
Construct\tabcellsep Acronym\tabcellsep Description\tabcellsep Items\\
Website design\tabcellsep WD\tabcellsep Variable indication website quality for online marketing\tabcellsep 4\\
Customer\tabcellsep CS\tabcellsep Includes variables related to customer value in online\tabcellsep 4\\
service\tabcellsep \tabcellsep platform\tabcellsep \\
Trust\tabcellsep TR\tabcellsep Variables indicating trust in online marketing\tabcellsep 4\\
E-satisfaction\tabcellsep ES\tabcellsep Variables including user satisfaction in online environment\tabcellsep 4\\
E-service\tabcellsep EQ\tabcellsep Includes variables relating to quality of online services\tabcellsep 4\\
quality\tabcellsep \tabcellsep \tabcellsep \\
E-loyalty\tabcellsep EL\tabcellsep Variables including loyalty on online marketing\tabcellsep 4\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.3987654320987654\textwidth}P{0.45123456790123456\textwidth}}
Items used for\tabcellsep principal\\
construct\tabcellsep Factor loading\\
WD1\tabcellsep 0.91\\
WD2\tabcellsep 0.93\\
WD4\tabcellsep 0.88\\
CS1\tabcellsep 0.82\\
CS2\tabcellsep 0.85\end{longtable} \par
 
\caption{\label{tab_2}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.013144329896907217\textwidth}P{0.03614690721649485\textwidth}P{0.05586340206185567\textwidth}P{0.0306701030927835\textwidth}P{0.5663015463917526\textwidth}P{0.04490979381443299\textwidth}P{0.10296391752577319\textwidth}}
\tabcellsep AVE\tabcellsep Composite Reliability\tabcellsep R Square\tabcellsep Cronbach's Alpha\tabcellsep Communality\tabcellsep Redundancy\\
WD\tabcellsep 0.872\tabcellsep 0.874\tabcellsep \tabcellsep 0.881\tabcellsep 0.897\tabcellsep \\
CS\tabcellsep 0.812\tabcellsep 0.812\tabcellsep 0.131\tabcellsep 0.838\tabcellsep 0.751\tabcellsep 0.135\\
TR\tabcellsep 0.810\tabcellsep 0.875\tabcellsep 0.063\tabcellsep 0.894\tabcellsep 0.843\tabcellsep 0.411\\
ES\tabcellsep 0.649\tabcellsep 0.853\tabcellsep \tabcellsep 0.910\tabcellsep 0.733\tabcellsep \\
EQ\tabcellsep 0.715\tabcellsep 0.922\tabcellsep 0.319\tabcellsep 0.891\tabcellsep 0.710\tabcellsep 0.171\\
EL\tabcellsep 0.814\tabcellsep 0.862\tabcellsep 0.313\tabcellsep 0.902\tabcellsep 0.787\tabcellsep 0.054\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep CS3\tabcellsep \tabcellsep 0.83\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep TR1\tabcellsep \tabcellsep 0.86\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep TR2\tabcellsep \tabcellsep 0.83\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep TR3\tabcellsep \tabcellsep 0.91\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep TR4\tabcellsep \tabcellsep 0.84\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep ES1\tabcellsep \tabcellsep 0.87\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep ES2\tabcellsep \tabcellsep 0.80\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep ES3\tabcellsep \tabcellsep 0.82\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep ES4\tabcellsep \tabcellsep 0.90\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EQ1\tabcellsep \tabcellsep 0.86\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EQ3\tabcellsep \tabcellsep 0.91\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EQ4\tabcellsep \tabcellsep 0.83\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EL1\tabcellsep \tabcellsep 0.89\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EL2\tabcellsep \tabcellsep 0.84\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EL3\tabcellsep \tabcellsep 0.82\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep EL4\tabcellsep \tabcellsep 0.87\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Table 5 provides the result of path coefficient to}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{test the research model. Path coefficients are}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{standardized versions of linear regression weights which}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{can be used in evaluating the possible causal linkage}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{between statistical variables in the structural equation}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{modeling approach. The standardization involves}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{multiplying the ordinary regression coefficient by the}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{standard deviations of the corresponding explanatory}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep variable.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.8189781021897811\textwidth}P{0.031021897810218975\textwidth}}
WD 0.855\tabcellsep \\
CS 0.337 0.881\tabcellsep \\
TR 0.276 0.351 0.930\tabcellsep \\
ES 0.343 0.251 0.314 0.865\tabcellsep \\
EQ 0.342 0.441 0.322 0.246 0.798\tabcellsep \\
EL 0.320 0.342 0.218 0.348 0.401\tabcellsep 0.882\end{longtable} \par
 
\caption{\label{tab_4}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.45535714285714285\textwidth}P{0.06071428571428571\textwidth}P{0.3339285714285714\textwidth}}
WD\tabcellsep CS\tabcellsep TR ES EQ EL\\
WD\tabcellsep \tabcellsep \\
CS\tabcellsep \tabcellsep \\
TR\tabcellsep \tabcellsep \\
ES\tabcellsep \tabcellsep \\
Table\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.39301075268817204\textwidth}P{0.2193548387096774\textwidth}P{0.23763440860215052\textwidth}}
Hypothesis\tabcellsep T-Statistic\tabcellsep Support\\
H1:WD?ES\tabcellsep 11.54 **\tabcellsep Supported\\
H2: CS?ES\tabcellsep 9.42 **\tabcellsep Supported\\
H3: CS?INT\tabcellsep 10.26 **\tabcellsep Supported\\
H4: CS?TR\tabcellsep 6.91 **\tabcellsep Supported\\
H5: ITD?CS\tabcellsep 8.40 **\tabcellsep Supported\\
\multicolumn{2}{l}{** p<0.01, t-value significant}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 7 :}\end{figure}
 			\footnote{© 2017 Global Journals Inc. (US)} 			\footnote{( )2017EService Quality, Satisfaction and Loyalty on Online Marketing: An Empirical Investigation} 			\footnote{( ) 2017 © 2017 Global Journals Inc. (US) 1} 			\footnote{© 2017 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{bibitemlist}
 			 		 	 
\end{document}
