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Taherdoost, H. E-service Satisfaction Model. Encyclopedia. Available online: (accessed on 10 December 2023).
Taherdoost H. E-service Satisfaction Model. Encyclopedia. Available at: Accessed December 10, 2023.
Taherdoost, Hamed. "E-service Satisfaction Model" Encyclopedia, (accessed December 10, 2023).
Taherdoost, H.(2021, December 22). E-service Satisfaction Model. In Encyclopedia.
Taherdoost, Hamed. "E-service Satisfaction Model." Encyclopedia. Web. 22 December, 2021.
E-service Satisfaction Model

Customer satisfaction is regarded as one determining factor in the success of businesses. Therefore, customer satisfaction is considered one of the most critical features that determine the success of activities conducted by online businesses for cross-border e-commerce. 

customer satisfaction e-commerce satisfaction e-service satisfaction e-service satisfaction model (ESM)

1. Introduction

Today, the Internet is recognized as an indispensable tool to transfer data and deliver products or services in relationships among different businesses or in their relationships with their customers. Technological information and communication developments (ICT) have encouraged organizations and individuals to move toward Internet-based relationships [1]. The Internet, as a communication tool, provides the ability to transfer any type of information in an environment that is almost friendly. Many businesses, today, employ the Internet as a favorable platform to present customer services. Customer satisfaction is highly emphasized in a great number of companies because of its critical role in the continuous improvement of businesses [2]. Therefore, it is necessary to consider and evaluate its accomplishment level. The performance of business processes in both product and service organizations is determined by customer satisfaction as one key factor [3].
Organizations can achieve their goals and objectives through reliance on customer satisfaction [4], since the future profitability of firms is highly dependent on their customer satisfaction. Customer satisfaction, as Chen [5] articulates, is one key factor that leads customers to repeat their experience in using an e-service. In addition, it is demonstrated [6] that it is more profitable to make current customers loyal in comparison to attracting new customers. Therefore, it can be concluded that customer satisfaction in using an e-customer is a key factor in achieving customer loyalty objectives [7]. Since customer satisfaction is one determinant factor to specify the failure or success of a business, antecedents and outcomes of customer satisfaction [8], specifically in information systems [9][10], have been previously investigated in other studies. Customer satisfaction, as a multi-dimensional factor, comprises different aspects, such as technology, behavior, and marketing [1][11]. Businesses need to understand the needs of their customers and assign their resources to respond to those needs. In this case, organizations will be led toward continuous improvement [12]. Some instruments are, indeed, developed by scholars to evaluate users’ satisfaction, relying on the features of the system and information [13][14][15][16]; however, there is still a necessity to develop tools to measure and analyze customer satisfaction [17]. In addition, despite the application of current tools for the assessment of web-based services, including e-commerce [18][19][20], there is still an increasing demand to modify existing instruments to provide more accurate tools [21]. However, a set of broadly accepted elements that lead to customer satisfaction is not still provided [22]. To evaluate the factors and processes that influence the success or failure of information technology and identify its value from the customers’ perspective, various studies have been conducted on data warehousing [23], general computing [15], and decision support systems [24]. Based on the research by Hoffman and Bateson [25], customers share their experience of using an online service with nine other individuals; thus, one unsatisfactory experience can prevent other potential customers from using the e-commerce, which will eventually lead to the failure of the e-commerce.

Despite the significant importance of customer service satisfaction assessment, a specific and comprehensive method is not provided for managers of companies to measure it. For doing this, it is necessary to define terms such as customer satisfaction, identifying its dimensions and eventually the way of its conceptualization and measurement.

In this study, comprehensive exploratory analysis was employed to collect all the characteristics of customer satisfaction and adapt the comprehensive model of the e-service satisfaction model (ESM), while reviewing the theoretical base of customer satisfaction. The structure of this paper is as follows. The definition of customer satisfaction and its specifications are presented, and its dimensions are determined with the help of prior review articles in the first section. Then, the aspects of satisfaction and the related factors are determined through employing findings of researches that have been conducted on e-commerce users’ satisfaction, aiming to develop a survey instrument to assess the customer satisfaction with e-commerce. These factors are then categorized and reduced using exploratory factor analysis. Finally, an e-commerce satisfaction survey is developed. E-commerce satisfaction experts analyzed the validity of the content survey, and exploratory factor analysis was employed to ensure the validity of the instrument through discriminate and constructive tests.

The most significant feature of the present study is to provide an integrated theoretical service instrument by considering a range of factors, such as distinctive features of electronic commerce, aspects of satisfaction about e-commerce, and features that are common in e-commerce and traditional commerce that influence e-commerce satisfaction and application of the ESM in the context of e-commerce as a subsection of e-service. Contrary to different definitions of satisfaction that have been previously presented in the field of information systems, this article defines satisfaction is as the extent of users’ belief in meeting their needs and expectations.

2. Customer Satisfaction Measurement Development

The conceptual model of e-service satisfaction [26] that was applied in the context of e-commerce is shown in Figure 1. To extract different items that form the constructs of an e-service satisfaction model and to develop the proposed survey, a comprehensive literature review was performed.
Figure 1. E-service satisfaction model (ESM).

3. Pilot Study and Data Collection

By conducting a pilot study on 35 postgraduate students in Malaysia [27], the response rate, heterogeneity, and understandability of the questions were examined. Based on the answers of the majority of the students, the questionnaire was easy to understand and 15–20 min was enough to complete it. To collect data, the survey website was presented to 2075 users of e-service in Malaysia. The response rate was around 18.5%, which was close to the one of Abreu and Oliveira [28] (16%), Taherdoost and Madanchian [26], and Fryrear [29]. After eliminating improper responses, to investigate the effect of the nonresponse bias on the results, the data that were collected using the questionnaire were divided into two main categories, early and late responses (50 responses were considered to be early, and the remaining 50 responses were considered to be late) [30]. The mean and standard deviation for the first 50 responses were, respectively, 3.9833 and 0.513 and for the last 50 responses, 3.9863 and 0.486, respectively. The T-test between groups was 0.119 for the sign of 0.741. The data that were collected from the sample can be generalized to the broader population. It should be noted that according to the collected data about the demographic information of participants, the majority of the respondents were female (58% females compared to 42% males). In addition, the age range of the participants was as follows: half the participants were aged 20–29 years and labeled as the young generation, and one-third of the respondents were aged 30–39 years. All respondents were e-commerce users at least once, and two-thirds of the respondents were using e-commerce more than once daily. Furthermore, almost half of the respondents were those users who have been dependent on using e-commerce for different purposes for more than 5 years.

4. Reliability Assessment

Cronbach’s alpha is regarded as the fittest measure to evaluate reliability [31]. According to the results for the Cronbach’s alpha range (0.921 to 0.927), the constructs have good reliability. In addition, based on the results of the KMO and Bartlett tests that were employed to investigate the adequacy of sampling, the value of KMO was 0.872. Thus, its value was greater than 0.60, which is mainly regarded as the conventional cut-off point, and the Bartlett test showed a significant value. Therefore, the observed correlations between variables were mutual in terms of their variance, and it seems that data were properly factored.
The results of the PCA applied with SPSS software version 25 showed that the eigenvalues of all nine factors were greater than 1. This was also supported by the Scree test. As shown in Figure 2, the curve of the line ended at the ninth factor, and it was evident on the Scree plot [32]. Therefore, according to a study that was conducted by Straub and Gefen [33], all factors should be considered for further analysis. In addition, any extracted new factor that consists of an eigenvalue greater than 1 is not included in the analysis.
Figure 2. Scree plot.
Due to the loading factor being above 0.40, all nine components remained in the survey instrument, and the baseline criteria in research in the field of information systems are met by both discriminant and convergent validity.

5. Validating the E-Service Satisfaction Model

To test the conceptual model, SEM, a second-generation multivariate modeling technique, was applied. The procedure has two important characteristics: illustration of the common processes that are investigated after a range of structural equations (i.e., regression) and modeling of those relationships to provide a more obvious conception of the theory. In addition, confirmatory factor analysis and path analysis were integrated [34] to form SEM, which, in turn, performed multiple regression analyses and evaluated whether the model is fit by relying on statistical methods, such as the chi-square test [35]. Furthermore, to provide a better judgment about the model’s fit, there were several goodness-of-fit indexes. In addition, path analysis was employed using SPSS AMOS version 24 to evaluate and test the proposed model.
Analyzing the results of CFA led to a dataset that was used in this case, aiming to fit the structural equation model with the values of the sample. Figure 3 shows the output of analyzing the structural equation modeling using SPSS AMOS version 24.
Figure 3. Results of structural equation modeling for the model.
Based on the estimated path coefficients, customer satisfaction affects all aspects positively. The coefficients of training, performance, user-friendliness, trust, usability, security, quality, and design were all significant.
Furthermore, the goodness-of-fit index (GFI) and the adjusted goodness-of-fit index (AGFI) were both calculated to identify the overall degree of the model fit. The calculated values were 0.91 and 0.87, respectively. According to previous studies, these values should be greater than 0.90 [36] and preferably greater than 0.80 [37], respectively. Thus, the model fit on the sample data is acceptable. However, the value of the normed fit index (NFI), which hat is a measure of the fit for the suggested model versus the null model [38], and the comparative fit index (CFI), as a criterion of the overall fit [39] of this model, were 0.92 and 0.97, respectively. Thus, due to the higher values compared with the recommended thresholds (0.9 for both indexes presented by Fornell and Larcker [40] and Bentler [41], respectively), the proposed model is completely reliable.

6. Recommendations

In this paper, the definition of customer satisfaction and its specifications were presented and its dimensions were determined with the help of previous review articles. Then, the aspects of satisfaction and the related factors were used based on the findings on e-commerce and e-service satisfaction in different studies to form a survey instrument that is practical for the evaluation of customer satisfaction with e-commerce. To achieve this, 36 aspects of customer satisfaction were extracted from studies that were previously conducted in the information systems field, and then, their aspects were categorized and reduced using exploratory factor analysis. Consequently, eight main factors were determined as constructs to measure how customers are satisfied with e-commerce, as articulated in ESM. As a result, an e-commerce satisfaction survey was developed. For this purpose, related items for each factor were investigated through a literature review, a new questionnaire was created, 12 experts helped to validate the content of the survey, the content-validity survey was conducted with the aid of 12 experts, the statistical significance level to calculate CVR was considered as 0.05, Cronbach’s alpha was calculated since it is considered the fittest internal consistency measure, factor analysis was performed since it is regarded as a statistical method to verify the construct validity by using principal component analysis with the method of varimax rotation, and subsequently structural equation modeling and path analysis were applied with the aid of SPSS AMOS version 24. The final survey instrument after performing the above-mentioned steps comprised solely 28 main items. In addition, it was approved that eight constructs, namely training, performance, user-friendliness, trust, usability, security, quality, and design, have a direct and significant influence on e-commerce satisfaction.

With the rapid improvement of e-commerce, customer satisfaction has been introduced as an important managerial aspect that needs to be assessed by the service provider in customers’ behavior. Performance that includes delivery, flexibility, availability, ease of use, fulfillment, processing, and being functional in practice; trust that mainly includes reliability, assurance, and credibility; usability that comprises web usability and efficiency; user-friendliness that is originally made of convenience and ease of use; a design that includes aesthetic design, navigation, customization, the appearance of the website, site attraction, site presentation, and layout and structure; security; quality and training are the most critical characteristics that determine the level of e-commerce satisfaction  and that should be considered to offer a higher level of customer satisfaction with e-commerce. Findings indicated that performance, trust, usability, user-friendliness, design, training, security, and quality are the most important characteristics of e-commerce satisfaction that should be taken into consideration in order to have high customer satisfaction with e-commerce. Therefore, the e-service satisfaction model (ESM), which is presented in Figure 2, was verified and approved through a systematic statistical procedure and can be applied to evaluate customer satisfaction with e-commerce and e-service environments. However, the e-service satisfaction model (ESM) could be considered for the evaluation and assessment of customer satisfaction in other electronic version services, too, including e-banking, e-business, e-ticketing, e-gaming, and e-finance.

Although the conclusions of this research may be more attractive for service-centered firms, IT experts, e-service users, and other audiences can also find suitable information in sub-clusters of each section to assess service maturity. Therefore, identifying the customer expectation and filling the gaps will help to identify the level of customer satisfaction, and the findings of this research will be helpful for e-service policymakers as well as its users to improve customer satisfaction. The findings of this research lead to a platform that is beneficial for e-commerce providers to understand how to satisfy e-commerce users. The generated knowledge can be used by e-commerce service providers as a platform for how to increase the customer satisfaction with their service. Although attracting new customers is crucial for all marketing and sales managers, in some cases, strategies to make loyal and regular customers receive more attention among researchers and practitioners because the cost is one-fifth [82]. With careful strategy implementation by policymakers, agencies, and system developers, high-quality and secure e-services can be successfully implemented to increase customer satisfaction.


  1. Alawneh, A.; Al-Refai, H.; Batiha, K. Measuring user satisfaction from e-Government services: Lessons from Jordan. Gov. Inf. Q. 2013, 30, 277–288.
  2. Bournaris, T.; Manos, B.; Moulogianni, C.; Kiomourtzi, F.; Tandini, M. Measuring users satisfaction of an e-government portal. Procedia Technol. 2013, 8, 371–377.
  3. Kenett, R.S.; Salini, S. Modern analysis of customer satisfaction surveys: Comparison of models and integrated analysis. Appl. Stoch. Model. Bus. Ind. 2011, 27, 465–475.
  4. Hussain, R.; Al Nasser, A.; Hussain, Y.K. Service quality and customer satisfaction of a UAE-based airline: An empirical investigation. J. Air Transp. Manag. 2015, 42, 167–175.
  5. Chen, S.-C. The customersatisfaction–loyaltyrelationinaninteractivee-servicesetting: The mediators. J. Retail. Consum. Serv. 2012, 19, 202–210.
  6. Boulter, J. How to Build Profitable Customer Relationships. 2014. Available online: (accessed on 22 April 2021).
  7. Gummerus, J.; Liljander, V.; Pura, M.; van Riel, A. Customer loyalty to content-based Web sites: The case of an online health-care service. J. Serv. Mark. 2004, 18, 175–186.
  8. Ramasubbu, N.; Mithas, S.; Krishnan, M. High tech, high touch: The effect of employee skills and customer heterogeneity on customer satisfaction with enterprise system support services. Decis. Support Syst. 2008, 44, 509–523.
  9. Delone, W.H.; McLean, E.R. Information Systems Success: The Quest for the Dependent Variable. Inf. Syst. Res. 1992, 3, 60–95.
  10. Montesdioca, G.P.Z.; Maçada, A.C.G. Measuring user satisfaction with information security practices. Comput. Secur. 2015, 48, 267–280.
  11. Stamenkov, G.; Dika, Z. Bank employees’ internal and external perspectives on e-service quality, satisfaction and loyalty. Electron. Mark. 2016, 26, 291–309.
  12. Lee, Y.-C.; Wang, Y.-C.; Lu, S.-C.; Hsieh, Y.-F.; Chien, C.-H.; Tsai, S.-B.; Dong, W. An empirical research on customer satisfaction study: A consideration of different levels of performance. SpringerPlus 2016, 5, 1577.
  13. Bailey, J.E.; Pearson, S.W. Development of a tool for measuring and analyzing computer user satisfaction. Manag. Sci. 1983, 29, 530–545.
  14. Baroudi, J.J.; Orlikowski, W.J. A short-form measure of user information satisfaction: A psychometric evaluation and notes on use. J. Manag. Inf. Syst. 1988, 4, 44–59.
  15. Doll, W.J.; Torkzadeh, G. The measurement of end-user computing satisfaction. MIS Q 1988, 12, 259.
  16. Ives, B.; Olson, M.H.; Baroudi, J.J. The measurement of user information satisfaction. Commun. ACM 1983, 26, 785–793.
  17. Legris, P.; Ingham, J.; Collerette, P. Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 2003, 40, 191–204.
  18. Cho, N.; Park, S. Development of electronic commerce user-consumer satisfaction index (ECUSI) for Internet shopping. Ind. Manag. Data Syst. 2001, 101, 400–406.
  19. Huang, J.-H.; Yang, C.; Jin, B.-H.; Chiu, H. Measuring satisfaction with business-to-employee systems. Comput. Hum. Behav. 2004, 20, 17–35.
  20. Muylle, S.; Moenaert, R.; Despontin, M. The conceptualization and empirical validation of web site user satisfaction. Inf. Manag. 2004, 41, 543–560.
  21. Tojib, R.D.; Sugianto, L.-F.; Sendjaya, S.A. Conceptual Model for B2E Portal User Satisfaction. In Proceedings of the International Conference on Business and Information, Singapore, 1 June 2006.
  22. Al-Kasasbeh, M.; Dasgupta, S.; Al-Faouri, A. Factors affecting e-service satisfaction. Commun. IBIMA 2011, 1–12.
  23. Chen, L.-D.; Soliman, K.S.; Mao, E.; Frolick, M.N. Measuring user satisfaction with data warehouses: An exploratory study. Inf. Manag. 2000, 37, 103–110.
  24. McHaney, R.; Cronan, T.P. Computer simulation success: On the use of the end-user computing satisfaction instrument: A comment. Decis. Sci. 1998, 29, 525–535.
  25. Hoffman, D.K.; Bateson, J.E.G. Services Marketing: Concepts, Strategies, & Cases, 4th ed.; Cengage Learning: Boston, MA, USA, 2016.
  26. Taherdoost, H.; Madanchian, M. Developing and validating a theoretical model to evaluate customer satisfaction of e-services. In Advances in Business Strategy and Competitive Advantage; IGI Global: Hershey, PA, USA, 2020; pp. 46–65.
  27. Taherdoost, H. Determining sample size; how to calculate survey sample size. Int. J. Econ. Manag. Syst. 2017, 2, 237–239.
  28. Abreu, B.R.A.; De Oliveira, L.K. The Potential of Response Rate in Online Transportation Surveys. Procedia Soc. Behav. Sci. 2014, 162, 34–41.
  29. Fryrear, A. Survey Response Rates; SurveyGizmo: Boulder, CO, USA, 2015.
  30. Mat Roni, S. Introduction to SPSS. 2014: School of Business; Cowan University: Joondalup, Australia, 2014.
  31. Robinson, W.S. Ecological Correlations and the Behavior of Individuals. Int. J. Epidemiol. 2009, 38, 337–341.
  32. Taherdoost, H. Understanding of e-service security dimensions and its effect on quality and intention to use. Inf. Comput. Secur. 2017, 25, 535–559.
  33. Straub, D.S.; Gefen, D.D.; Boudreau, M.-C. Validation guidelines for IS positivist research. Commun. Assoc. Inf. Syst. 2004, 13, 24.
  34. Swanson, R.A.; Holton, E.F. Research in Organizations: Foundations and Methods in Inquiry; Berrett-Koehler Publishers: San Francisco, CA, USA, 2005.
  35. Singh, J.; Wilkes, R.E. When Consumers Complain: A Path Analysis of the Key Antecedents of Consumer Complaint Response Estimates. J. Acad. Mark. Sci. 1996, 24, 350–365.
  36. Bagozzi, R.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94.
  37. Etezadi-Amoli, J.; Farhoomand, A.F. A structural model of end user computing satisfaction and user performance. Inf. Manag. 1996, 30, 65–73.
  38. Bentler, P.; Bonett, D. Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 1980, 88, 588.
  39. Gerbing, D.W.; Anderson, J.C. Monte Carlo evaluations of goodness of fit indices for structural equation models. Sociol. Methods Res. 1992, 21, 132–160.
  40. Fornell, C.; Larcker, D. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388.
  41. Bentler, P.M. Comparative fit indexes in structural models. Psychol. Bull. 1990, 107, 238–246.
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