People’s purchasing behaviour’s have altered as a result of the Internet, which also makes it easier to do online information searches. Different items and services can be more easily marketed thanks to the internet. With regard to convenience, search costs, delivery, and pricing, online marketing offers consumers a buying experience that is distinct from that provided by physical-based marketing (Palmer 2000). Online marketing has thus far continued to expand (Chen and He 2003). Users’ opinions of the quality of online services have decreased as a result of the primary hurdles to online marketing that have been identified as privacy and security. Zeithaml et al. (2002) recommended businesses shift the emphasis of e-marketing to e-service in order to promote repeat purchases and build consumer loyalty. However, the majority of customer satisfaction research have mostly concentrated on conventional commercial channels.

Literature review and hypothesis development

Website Design and E-Satisfaction

According to Peterson (1997), effective website design for internet marketing is around superior organization and simple search. This includes offering users clean screens, easy search interfaces, and quick presentations. Additionally, each of these site design components may influence e-satisfaction levels in a variety of ways, such as making marketing more enjoyable and gratifying. Consumers perceive marketing to be enjoyable and fulfilling when transaction sites are quick, uncluttered, and simple to use. A business website’s primary purpose is to tell customers about the business and its goods and services (Hallerman, 2009). An important topic of research has been the evaluation of website performance in relation to design criteria and other supporting variables (Tarafdar and Zhang 2008). Eventually, appropriate website design enables businesses to complete their online.

H1: Website design is directly associated with satisfaction in online marketing.

Customer Service and E-Satisfaction

Customer service may please customers, according to a previous study by Lin (2005), and it has been shown that happy customers are more likely to become devoted ones (Oliver 1999). A high level of customer care has led to e-satisfaction and, ultimately, to recurrent buying habits in an online setting. Customer satisfaction makes a distinction between the discrepancy between the customer’s expectations and perceptions. The online market will become more competitive as e-selling becomes a popular trend in retail, so one method for online marketplaces to become more competitive is by providing superior customer service. Customers should receive high-quality service from online shops in order to increase satisfaction. Price and convenience play a major role in perceived value. Customers generally anticipate paying less while shopping online (Koch 2003).

H2: Customer service is positively related with satisfaction in online marketing.

Trust and E-Satisfaction

One of the fundamental elements of relationship marketing, according to marketing researchers (Ribbinket et al. 2004), is trust. The degree of certainty or confidence a consumer has in their exchange options has been referred to as trust. Accordingly, the definition of e-trust will be the level of trust that consumers place in online exchanges or the online exchange channel (Reichheld and Schefter 2000).

According to Stewart (1999), consumer mistrust of electronic channels is a major factor in the failure of Internet marketing. Customer trust is thus regarded as another crucial factor in satisfaction. For instance, Razzaque and Boon (2003) discovered a substantial link between trust and happiness in the context of channel relationships.

Trust from customers is crucial for continuing business.

H3: Trust directly and positively influences e-satisfaction in online marketing

E-Satisfaction as a Mediator

Zeithaml and Bitner (2000) defined e-satisfaction as the customer’s assessment of a good or service in terms of whether it satisfied their wants and expectations on an online platform. It has been established that customer pleasure and loyalty are strongly associated, and this relationship holds true online as well. Shankar et al. (2003) claim that satisfaction affects loyalty more strongly online than it does offline. Customers who are happy with the product or service are more likely to recommend it to their friends, are more likely to use the product or service frequently, and are more likely to make another purchase. The technique used to measure precursors

E-satisfaction research has been shown to be difficult and somewhat disputed (Szymanski and Henard 2001).Many techniques have been employed.

E-Service Quality and E-Satisfaction

The literature on service quality suggested that very high levels of purchase intentions were a direct result of perceptions of excellent service quality and high customer satisfaction. A comparison between what consumers believe the service should be and their impressions of the actual performance provided by the service provider yields a perception of service quality, according to Parasuraman, Berry, and Zeithmal (1985). The implicit inclusion of problems like price perception, which is typically only felt rather than objectively quantified, in measures of service quality and overall happiness. According to the vast majority of studies, they serve as antecedents to satisfaction (Gajendra and Li 2013), meaning that satisfaction is thought of as a mediator between loyalty and quality. Santos’ (2003) definition of service quality stated that it is the consumers' overall assessment.

H4: E-service quality is positively related with e-satisfaction in online environment.

E-Satisfaction and E-Loyalty

Customer e-loyalty is a crucial issue in the cutthroat world of online marketing. According to numerous research, e-loyalty is influenced by e-trust, e-satisfaction, and e-service quality. According to Oliver (1999), the definition of customer loyalty is "a strongly held commitment to repurchase or patronize 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."

E-loyalty is the extension of conventional loyalty to online customer behavior. E-loyalty was described by Cyr et al. (2007) as the desire to return to a website or conduct business there in the future. E-loyalty is described by Strauss et al. (2009) as "A customer's positive attitude toward an e-commerce website that predisposes the customer to repeat business."

H5: Loyalty in online environment directly effects satisfaction

Figure 1 Research model

Figure 1 shows the logical framework or Research model of this study. The conceptual model has Been developed based on extensive literature review.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.

Theatrical background social exchange theory

A social psychological and sociological perspective known as “social exchange theory” analyzes social development and stability as a process of negotiated exchanges between parties. According to the social exchange theory, every human interaction is the result of weighing the relative costs and benefits of many options. According to the social exchange perspective, people determine a relationship’s overall value by deducting its expenses from its benefits (Monge, 2003). In the 1960s, George Homans established the concept of social trade.

Many theorists continued to write on the theory after Homans established it, including Richard Emerson, John Thibaut, Harold Kelley, and Peter Blau.

Homans’ main interest in this area was paying attention to how people behave when they are interacting.

Research Methodology

Measurement model

Table 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. 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 e-Satisfaction. 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.

Table 1: Variables of research model

Construct          Acronym                            Description                                                 Items

Online survey and Data Collection

 This study was conducted from March to Aug 2025, in India. The participants were faculty members And Masters as well as doctorate level students of IT Department of Indian Universities. The survey Questionnaires were prepared from literature review. Pre-test 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 liker 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.

Results Analysis

Measurement model

Structural equation modelling (SEM) using Smart PLS 2.0 was used to analyse measures and psychometric properties of the measure for constructs.

After a refinement of the survey instrument utilized in our initial tests, 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 fulfil convergent validity requirements. Based on the above described tests our measurement model (Table 2) is validated and we have demonstrated that all measures in this study have adequate convergent and discriminant validity.

Table 2 : Inter-variable correlation matrix

1            2            3            4         5         6

 In order to access the construct validity and reliability, a test on Cronbach’s.

Table 3 shows the overall results of research model. Internal Consistency is an indication of how well.The items for a construct are correlated. Internal Consistency can be measured in terms of Cronbach’s alpha. That provides a lower bound of internal Consistency and composite reliability, which is a more Accurate measure of internal consistency. The overall Cronbach’s alpha is 0.84 and varies in between 0.74 to 0.95

Table 3: Overview of the results

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 (R2) 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 (Barclayet al. 1995). In our result the value of composite reliability is above 0.70. The redundancy has noofficial value for analysis but higher.

Confirmatory Factor Analysis Model

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 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 (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 significantly.

Table 4 : Results of confirmatory factor analysis

Table 5 provides the result of path coefficient to test the research model. Path coefficients are standardized versions of linear regression weights which can be used in evaluating the possible causal linkage between statistical variables in the structural equation modeling approach. The standardization involves multiplying the ordinary regression coefficient by the standard deviations of the corresponding explanatory variable. Higher the coefficient, higher the relationship withvariables. The values of path coefficients in the table are satisfactory. Thus the research model was well constructed. It provides validity and reliability of  research model in this study.

Table 5: Path coefficients

WD CS TR ES EQ EL

WD

CS

TR

ES     0.32    0.25    0.49 0.52

EQ                                                  0.32

EL

Table 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.

Table 6: Latent variable correlations and Square root of AVE

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 (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.

Test Of Hypothesis

Table 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.

Summary of hypothesis test results

Hypothesis        T-Statistic       Support          

                          H1:WD   ES        11.54”           supported

                          H2:CS     ES         9.42”            supported

                          H3:CS     INT       10.26”          supported 

                          H4:CS     TR         6.91”            supported  

                          H5:ITD    CS         8.40”            supported

^** p<0.01, t- value significant

The key objective of this study is to provide a more comprehensive understanding of the role of e-service 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 e 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.

Discussion

Summary of the results

Service excellence, online satisfaction, and online loyalty are crucial factors in determining the success of online marketing. As a result, study on these constructs is increasing. E-loyalty increases client retention rates and lowers the cost of acquiring new consumers, which boosts an online service provider’s long-term profitability. The aim of this study is to conceptualize that e-loyalty is influenced by e-satisfaction, and e-satisfaction is effected by e-trust, customer service, and e-service quality, in order to offer a comprehensive model of the e-satisfaction development process. This study examines all of the effects—direct and indirect—that these variables may have on one another and, ultimately, on customer satisfaction. It also develops a detailed relationship model for these variables as been regarded as a crucial element in the context of online marketing. Online service providers should understand that e-trust must first grow in order to foster e-satisfaction and e-loyalty. Customers actually demand correct services, accurate transactions and records, and timely delivery, whether it be electronically or physically, in the Internet-based market.

Theoretical and Managerial Implications

One of the key predictors of satisfaction is customer service (Anderson et al. 1994).Employees of the company should be sufficiently knowledgeable to reply to client inquiries, handle problems as they emerge, comprehend the unique demands of customers, and politely handle complaints. The study discovered that a product or service offering is a key indication of overall happiness in an online platform, which is contrary to the findings of Szymanski and Hise (2000). b)Theatri Our findings have consequences for both management and research. Online marketers can create early warning systems based on ongoing consumer perception measurements from a managerial standpoint, allowing management to respond appropriately when any of these aspects is seen as a problem. Additionally, e-marketers can use the scale items created in this study to focus their online marketing efforts on competitors in order to determine their relative strengths and weaknesses from the perspective of customers. The management need to be aware that trust is the key component of e-service recovery when it comes to raising client happiness. Therefore, managers should make sure that all issues and refunds are successfully addressed via business websites. The most important aspect of trying to win back customers’ trust following a service failure.

Limitations

This study has two significant flaws. First of all, the sample drawn from the academic community could not be representative of all online marketers. Thus, the generalizability of the analytical findings reported here may be limited. Second, because this study solely examined the online marketing component of the internet platform, it was restricted to looking at the Causal connections between online satisfaction, online service quality, online customer support, and online truste-loyalty. To come Study, additional important factors like firm reputation and financial success of online marketing be incorporated into the proposed research model. Future research may also employ various sample techniques. A technique for gathering data, such as randomly choosing respondents from a database of a certain company’s clients.

Conclusion

The findings show that e-satisfaction is the most significant element in influencing customer loyalty to e-services, including online marketing. Online businesses will be certain to survive if they can satisfy customers with high-quality services, meet customers’ expectations in the best way possible, and be innovators in introducing new services to ease customers’ affairs. Customers’ overall satisfaction is a key factor in determining their propensity for repeat purchases, referrals, and price sensitivity. Management should concentrate on these salient characteristics because some satisfaction variables directly affect behavioral intentions or indirectly influence behavioral intentions through overall satisfaction. Finding the factors that contribute to satisfaction will not only assist management learn how to improve overall satisfaction, but also make it easier to use the limited corporate resources in the best possible ways.

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