Research Article: 2020 Vol: 24 Issue: 1
Isar Kiani, St. John Fisher College
Fariba Latifi, Lakehead University
Fatemeh Aliakbari, University of Houston
This paper presents the findings of a study exploring the effects of five service quality dimensions - content usefulness, content efficiency, service stability, service responsiveness and provider reliability – relationship between customer satisfaction, intention to make electronic purchases, and proposed in past research. For the analysis, we have used regressions to estimate path coefficients of a previously proposed model; the path diagram fitting variables was generated. Our findings indicate that customer satisfaction has the strongest effect on electronic purchasing intention among Iranian electronic shoppers. The path diagram also shows that provider reliability, content usefulness and content effectiveness affect electronic purchasing intention and customer satisfaction, while service responsiveness and service stability doesn’t show significant relation with research dependent variables.
E-Service, Customer Satisfaction, Electronic Businesses, Content Usefulness, Content Efficiency, Service Stability, Service Responsiveness, Provider Reliability.
Companies will not reach their performance expectations unless they can deliver their products or services to the market and ensure that their levels of sales are retained and experience growth. Almost every commercial company is engaged with selling products, services, or even ideas. Costly marketing campaigns, product quality procedures and other cost consuming systems are even better reasons to ensure that existing customers keep coming back. A returning customer often represents a successful interaction with that customer. Consequently, companies increasingly realize customers as their most important assets. Faced with globalization of the marketplace, more sophisticated customers and the necessity to deliver to the market faster and more efficiently, companies are launching different initiatives in order to gain customer satisfaction and maintain their competitiveness in the market. Studies have found returning customers to provided higher profit margins to companies than those that are first time clients. Therefore, customer retention is an important issue on the mind of practitioners, as well an interesting topic for researcher to study. As the result of studying consumer retention a significant body of literature has been developed that focuses on customer satisfaction. A customer’s satisfaction with a previous experience has been suggested to have a strong relationship with further purchase intentions. Consequently, companies are using many different tools to make their customers more satisfied. Return policies, guaranty or warranty policies, better trained sales crew, convenient locations and other systems are some of the actions that companies have used, or are using, to get themselves more satisfied clients. On the other hand, researchers have conducted numerous studies to understand what goes on in customers’ brains and hearts to analyze their shopping behavior and the effect of each of these tools on their satisfaction.
With the fast-growing number of internet users and online shoppers, the customer satisfaction literature found its way from traditional shopping studies into the literature of electronic retailing. Practitioners use their previous knowledge to satisfy their customers in the new and demanding virtual environment. The logic for many actions revolves around the belief that online customers who subscribe to electronic portals are not much different from traditional customers. While this assumption may be somewhat accurate, the change in customers’ behavior in electronic businesses from traditional stores suggests that more to be going on. Past research has suggested that individuals may experience changes in their personality as customers when they subscribe to electronic purchasing (Laroche et al., 2005). Therefore, it can be expected that in the internet environment customers’ expectations change and their satisfactions rely on different factors. Challenges that companies face have extended themselves into the virtual marketplace as well and similar to the traditional marketplace, electronic commerce is gradually recognizing service quality as an important aspect of business. Therefore, understanding the factors though which customers evaluate service quality of businesses helps businesses in better investing in processes that are most important. Consequently, relationship marketing and customer retention have emerged as important areas in marketing, since they directly influence a company’s most important assets: its customers. Businesses are well aware that their long-term and loyal customers make more frequent purchases and are less costly to serve, whereas replacing existing customers with new ones can turn out to be very expensive.
High profile failures in e-service during later 1990s (e.g., fulfillment problems at ToysRUs.com, capacity issues at AOL) have led to the understanding that poor design of processes and low e-service quality can result in massive dissatisfaction among customers. Subsequently, researchers and practitioners alike have embarked on the path to understand various dimensions that constitute e-service quality and their relationship to customer satisfaction. This trend has become even more popular as more and more businesses understand the benefits of providing services electronically; benefits such as higher efficiencies, lower costs, and higher accessibility to markets that seemed to be beyond a company’s reach in the past. Since then, many ambitious attempts have been made to replicate and implement quality improvement initiatives of the likes of Xerox, Motorola, Ford, and General Motors for electronic businesses. Customers’ perception of e-service quality has been found to be a complex construct with numerous factors as its constituents. Some researchers have undertaken extended studies to understand how this perception lends itself into ultimate evaluation that customers develop regarding a business. What seems to be undermined in existing literature is the focus on dimensions that represent customer retention rather than customer attraction. In fact, most past research has focused on aspects of e-service quality that result in customer attraction. However, our aim is to focus on recurring customers and the influence of their perception of e-service quality on their subsequent behavior.
In this paper, we have built on extant literature on e-service quality in order to better understand factors that contribute to customer retention. Building on past research (Joo & Sohn, 2008), we have proposed a model for the effect of e-service quality on customer satisfaction and customers’ intention for subsequent purchase as an index for customer retention. This paper is organized as follows. First, we review extant literature that has focused on the notions of customer satisfaction and e-service quality. Then, we propose a theoretical model that relates the various dimensions of customers’ perception of e-service quality to customer satisfaction and ultimately, customer’s intention to make a subsequent purchase. We then proceed with explaining the theoretical framework and the hypotheses for the research. We use structural equation modeling to test for the hypotheses that have been proposed, concluding with results, implications, limitations, and discussing avenues for future research.
Companies understand that their ability to survive they need to retain their customers and strive to expand their customer base (Dabholkar et al., 2000). They also realize that achieving this objective will not be at reach without satisfied customers (Cronin et al., 2000). Extant research has found a direct link between customer satisfaction and the level of service quality that is provided to them by a company (e.g., Bolton & Drew, 1991; Boulding et al., 1993). Hence, the past decade has witnessed an increase in attention from practitioners and researchers alike to better understand service quality, its antecedents, and consequences. Higher service quality has been linked to business performance, lower costs, higher customer satisfaction, and stronger customer loyalty. Therefore, understanding constituents of service quality and their measurement have been the focus of interest (e.g., Joo & Sohn, 2008; Parasuraman et al., 1988; Zeithaml et al., 2002).
Service quality has been defined as a function of differences between expectation and performance along the quality dimensions. Five main dimensions for service quality have been identified: reliability, responsiveness, tangibility, assurance and empathy which capture access and understanding the customer (Parasuraman et al., 1988). There have been calls for further research on the effects of quality, value and customer satisfaction on customer behavioral intentions in service environment. Results from past studies suggest that the indirect effect of the service quality and value constructs enhanced their impact on behavioral intention (Cronin et al., 2000). The relationship between service quality and customer satisfaction has been extensively studied in the past. Expectations, perceived performance desires, desired congruency, and expectation disconfirmation have been found to influence customers’ perception of service quality and customer satisfaction in traditional services with physical transactions taking place between customers and agents (Spreng & Mackoy, 1996). Customers can also develop their perception of service quality based on the value that they perceive they get from a transaction. Perceived value which involves the comparison between benefits and sacrifices has been found to be significantly influenced when technical aspects of service quality improve (Sweeny et al., 1997).
More recently, researchers have showed more interest in the objective service performance rather than the more subjective perception of service quality, emphasizing post-consumption attitudes. This approach avoids inclusion of expectations that exist prior to a purchase being made (e.g., O’Neil et al., 2003). However, measures of service quality and service performance have revealed themselves to be well representations for service quality (O’Nail et al., 2003). With the increasing trend of electronic commerce, service in the electronic environment (e-service) has become the focus of interest. This has resulted in conceptual models being proposed for e-service quality dimensions that represent idiosyncrasies of electronic commerce (Santos, 2003). Many companies are making increase use of the web to deliver the products or services to their customers (Joo & Sohn, 2008). Despite difficulties encountered as the result of the internet bubble burst, the number of online businesses still continues to grow (Wolfinbarger & Gilly, 2003). This increasing trend has given rise to a range of issues such as service customization, customer relationship management, and integration of channels of interaction as important elements to perform successfully on the Web platform. While e-business processes have become realities in the world of business, theory and research have yet to be fully developed. However, low re-purchasing by customers has been an obstacle for electronic retailers (Joo & Sohn, 2008).
There are differences for perceived service quality across different types of services. Factors that influence customers’ perception of service quality for services with more reliance on technology (e.g., IT based services) can be different from those in more traditional businesses where the service involves direct interaction between the parties involve (Zhu et al., 2002). Customers that perceive services provided by a company to be useful are often more satisfied and develop a stronger tendency to return to the same company for their needs in the future (Bhattacherjee, 2001). There is little consensus amongst researchers when it comes to defining e-service or e-service experience. E-service has been used to define web-based service or interactive services that are delivered on the internet (Joo & Sohn, 2008; Parasurman et al., 1988). The lack of consensus has led to the use of different terminologies and construct definitions across conducted studies (e.g., Harris & Goode, 2004; Joo & Sohn, 2008; Parasurman et al., 1988; Szymanski & Hise, 2000; Wolfinbarger & Gilly, 2003). It has been suggested that a distinction between e-commerce and e-service be made. The distinction has been suggested on the basis that e-service can be offered simultaneous with the process of e-commerce or it can be delivered alone, unconditionally or with a service contract (Voss, 2003). For the purpose of this study, we build on the definition provided by Joo & Sohn (2008) which defines e-service as deeds, efforts or performances whose delivery is mediated by information technology.
As mentioned previously, service quality is an important determinant of customer satisfaction which in turn influences a company’s competitive position. Therefore, similar to other types of business, success or failure of e-commerce also depends on the level of service quality that is provided to customers (Yang, 2001). This has resulted in an increasing interest to study service quality with a focus on electronic businesses to better understand the factors that contribute to them or result in their deterioration (Yoo & Donthu, 2001). Consequently, multiple classifications have been proposed for service quality for electronic businesses (e.g., Francis & White, 2002; Trocchia & Janda, 2003; Wollfinbarger & Gilly, 2003, Yang et al., 2004). Dimensions of e-service quality have been proposed to fall into incubatic and active dimensions. Incubatic dimensions include proper design of a website, use of technology to provide consumers’ ease of access, and attractions of a website. The active dimensions, on the other hand, include those such as good support, fast speed, and attentive maintenance that a website can provide to its customers. Together, these dimensions have been found to influence increasing hit rates, stickiness and customer retention in e-businesses (Santos, 2003).
Past studies have provided evidence for similarity of dimensions that constitute customer satisfaction across culturally diverse settings (Brady & Robertson, 2001). Therefore, researchers have been able to apply the same theoretical models to test for variances in consumer behavior in response to different levels of service quality in cross-cultural settings. The majority of past research on service quality has focused on more traditional business models and therefore the area of service quality in electronic businesses (e-service) and its effect on customer satisfaction remains relatively untapped. A customer’s online buying experience consists of wide range of processes from information search, product evaluation, decision making, engaging in transaction, delivery, returns and customer service. Yet, the focus of most research on electronic retailing (e- tailing) has been on the customers’ interface with websites (e.g., Loiacono et al., 2007; Webb & Webb, 2004; Yoo & Dontha, 2001). However, the appropriateness of assessing website quality in place of e-service quality has strongly been debated against (Zeithaml et al., 2002). It is generally accepted among researchers that e-service quality is composed of five dimensions: reliability of contents provider, content efficiency, content usefulness, service responsiveness, and service ability (Joo & Sohn, 2006). Reliability of contents provider refers to the degree of faith that customers have in the provider. Content efficiency involves ratio of perceived quality to the prices that are offered for products or services on the electronic portal. Content usefulness describes the degree of ease by which customers utilize contents of the web portal. Service responsiveness represents the level of acceptable feedback that is provided by the electronic business. Service stability refers to the degree of stability of the system and degree to which quality of transactions is guaranteed.
The objective of this study is to understand the factors that constitute customer satisfaction that result from an interaction experience and their influence on customers’ subsequent decision to return to the web retailer for a future purchase. Therefore, in our study we have tested relationship between different dimensions of service quality and two dependent variables: customer satisfaction and intention to buy (future purchase). Our study has built on the dimensions proposed by Joo & Sohn (2008) for two reasons. First, their study is more recent and therefore they have built their study a more comprehensive set of the literature. Second, their focus on service quality has been for electronic businesses, which is similar to the setting that we intend to study. Table 1 illustrates the measurement variables of our survey, which are selected according to Joo & Sohn’s (2008) study.
|Table 1 JOO & Sohn’s (2008) Measurement Variables|
|Quality Factor||Latent Variables|
|Content Usefulness|| Easiness of using contents
Effectiveness of using contents
Overall completion of content
|Content Efficiency|| Rating of price given quality
Rating of quality given price
|Service Stability|| Maintenance of a stable system
Control for the private information and transaction data
|Service Responsiveness|| Providing Continuous service
Solving customer’s inconvenience
Easiness of searching contents
Completion of providing site
|Provider’s Reliability|| Perceived value of the provider
Reputation of the provider
Superiority of a competitor
Expected growth of the provider
Table 2 demonstrates a comparison of considered models in literature including the model used in the current study.
|Table 2 Comparison of Reviewed Models|
|Model||Focus on Service Quality Rather Than Website Quality||Focus on Customer Satisfaction||Focus on Customer Retention||Information Technology Consideration||B2C Environment||Providing Measurement||Finding Quality Dimensions||Guiding Quality Improvement||Practical Study|
|Cronin et al. (2000)||Ï||Ï||Ï||Ï||Ï|
|Parasuraman et al. (1988)||Ï||Ï||Ï||Ï||Ï|
|Loiacono et al. (2002)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
|Yoo & Donthu (2001)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
|Trocchia et al. (2003)||Ï||Ï||Ï||Ï|
|Yang et al. (2004)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
|Brady & Robertson (2001)||Ï||Ï||Ï||Ï||Ï||Ï|
|Spreng & Mackoy (1996)||Ï||Ï||Ï|
|Sweeney et al. (1997)||Ï||Ï||Ï||Ï||Ï|
|Dabholkar et al. (2000)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
|Zhu et al. (2002)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
|Webb & Webb (2004)||Ï||Ï||Ï||Ï||Ï||Ï|
|Joo & Sohn (2008)||Ï||Ï||Ï||Ï||Ï||Ï||Ï|
Using the dimensions proposed by Joo & Sohn (2008), we have developed a structural equation model (SEM) with relationship that has been hypothesized as eleven hypotheses. Based on the selected latent variables the hypotheses of the study are formulated as follows:
H1: Higher level of provider reliability would positively influence customer satisfaction regarding the purchase experience from electronic portal
H2: Higher Content efficiency would positively influence customer satisfaction regarding the purchase experience from electronic portal
H3: Content usefulness would positively influence customer satisfaction regarding the purchase experience from electronic portal
H4: Service responsiveness would positively influence customer satisfaction regarding the purchase experience from electronic portal
H5: Service stability would positively influence customer satisfaction regarding the purchase experience from electronic portal
H6: Provider reliability would positively influence intention to return for purchase from electronic portal
H7: Content efficiency would positively influence intention to buy to return for purchase from electronic portal
H8: Content usefulness would positively influence intention to buy to return for purchase from electronic portal
H9: Service responsiveness would positively influence intention to buy to return for purchase from electronic portal
H10: Service stability would positively influence intention to buy to return for purchase from electronic portal
H11: Customer satisfaction would positively influence intention to buy to return for purchase from electronic portal
The sample for this research consisted of random selection of 183 users from electronic businesses in Iran. The selection of users from Iran is not expected to influence our findings, as past research has found no significant difference for perceived service quality across culturally diverse settings (Brady & Robertson, 2001). To conduct this study, we presented each of the participants with a survey that corresponded to the measurement variables presented in Table 1. E-commerce platforms experienced by respondents included: bekhan.com, iiketab.com, digikala.com, fara.ir, ihome.ir, takhfifan.com, Andro, netbarg.ir, snapp.ir, reyhoon.ir, fidibo.ir, bamilo.ir, zoodfood.com, rajatrains.com, iranair.com, adinehbook.com, and alopyek.ir. Each question was formatted on a 9-point Likert scale. From the participants in our study, 130 subjects (71%) were male and the rest of them were female; 99% had academic educations; 135 subjects have done more than one purchase. Thicket and book were most occupied by internet purchasers with 28.4 and 24.3 respectively.
To analyze the results from the surveys, we used path analysis and multiple linear regressions to define direct and indirect effects among model’s variables. In order to test the proposed hypothesizes, we carried out the survey for the digital content users in Iran. First, confirmatory factor analysis was conducted to establish the links between measurement variables and the latent variables that have been illustrated in Table 1. We tested for reliability of the research instrument using Cronbach’s Alpha (α). Our result show that the value of α for every factor is more than 0.7 (total α was about 94%); which affirms the reliability of relationship among measurement variables and the latent variables.
We then proceeded with multiple linear regressions to estimate path coefficients of the proposed structural equation model as illustrated in Figure 1. Since the sample size is not large, this can be considered as a suitable approach. Our analysis reveals six paths out of eleven to be significant at 5% level Figure 1. Our findings indicate that except for two of the variables –‘service responsiveness’ and ‘service reliability’- all latent variables have a positive relationship with customer satisfaction. These variables have direct and indirect effect on customer’s intention to return for a subsequent purchase. Direct and indirect effects can be identified in Figure 1. Table 3 includes the relationship between factors and Table 4 demonstrates the acceptance or rejection of hypotheses as a result of total effects and summarizes the findings. Figure 1 shows the graphic scheme of the proposed Structural Equation Model (SEM):
|Table 3 Casual Relationship Between Factors|
|From Variable||To Variable||Mediator Variable||Direct Effect||Indirect effect||Total Effect|
|Provider’s Reliability||Intention to Buy||-||0.278||-||0.393|
|Provider’s Reliability||Intention to Buy||Customer Satisfaction||-||0.115|
|Content Usefulness||Intention to Buy||-||0.186||-||0.364|
|Content Usefulness||Intention to Buy||Customer Satisfaction||-||0.178|
|Customer Satisfaction||Intention to Buy||-||0.404||-||0.404|
|Content Efficiency||Intention to Buy||Customer Satisfaction||-||0.097||0.097|
|Provider’s Reliability||Customer Satisfaction||-||0.284||-||0.284|
|Content Efficiency||Customer Satisfaction||-||0.239||-||0.239|
|Content Usefulness||Customer Satisfaction||-||0.441||-||0.441|
|Table 4 Hypothesis Acceptance or Rejection Results|
|No.||Hypothesis||Acceptance or Rejection|
|H1||provider reliability would affect positively on Customer satisfaction||Accepted|
|H2||Content efficiency would affect positively on Customer satisfaction||Accepted|
|H3||Content usefulness would affect positively on Customer satisfaction||Accepted|
|H4||Service responsiveness would affect positively on Customer satisfaction||Rejected|
|H5||Service stability would affect positively on Customer satisfaction||Rejected|
|H6||Provider reliability would affect positively on intention to buy||Accepted|
|H7||Content efficiency would affect positively on intention to buy||Accepted|
|H8||Content usefulness would affect positively on intention to buy||Accepted|
|H9||Service responsiveness would affect positively on intention to buy||Rejected|
|H10||Service stability would affect positively on intention to buy||Rejected|
|H11||Customer satisfaction would affect positively on intention to buy||Accepted|
Effect of Provider’s Reliability on Customer Satisfaction and Intention to Buy
As shown in Table 3, the path coefficient between ‘provider’s reliability’ and ‘customer satisfaction’ was 0.284 and the path coefficient between ‘provider’s reliability’ and ‘intention to return for a subsequent purchase’ was 0.393. The result can be associated with customers trust. Customers’ confidence in the service provider is often considered as a key factor in Customer satisfaction and intention to return for a subsequent purchase. Therefore, it has been suggested that customers perceive less risk when making their purchase from better know service providers, or those with better corporate reputation (Yang, 2004), perhaps due to their higher reliability. Reliability refers to a company’s image in customers’ perception and can be enhanced through actions such as clarity about provider’s history and identity, indicating past activities of the provider, advertising and brand management, and clarifying policies of the provider (Gronroos, 1984; Jarvenpaa & Tractinsky, 1999).
Effect of Content Efficiency on Customer Satisfaction and Intention to Buy
Our data analysis shows a path coefficient about 0.239 between ‘content efficiency’ and ‘customer satisfaction’, and a path coefficient about 0.097 between ‘content efficiency’ and ‘intention to return for a subsequent purchase’. ‘Content efficiency’ is considered to represent cost and quality sensitivity. Obviously, price is a key factor in a purchase which influences perceived service value (Cronin et al., 2000; Santos, 2003; Sweeny et al., 1997). Companies can take several actions to increase their level of ‘content efficiency’. Such actions include: offering products and services at sensible price and qualities, and optimize delivery of purchases (Chen & Popovich, 2003).
Effect of Content Usefulness on Customer Satisfaction and Intention to Buy
The path analysis in this study reveals that the path coefficient between ‘content usefulness’ and ‘customer satisfaction’ is about 0.364. Also, the path coefficient between ‘content usefulness’ and ‘intention to buy’ (intention to return for subsequent purchase) is about 0.441, which shows the strongest relationship among variables. This factor demonstrated customer’s main aim of purchasing e-service. The importance of ‘content usefulness’ in ‘customer satisfaction’ has been acknowledged in past research (Bhattacherjee, 2001; Santos, 2003; Sweeny et al., 1997; Dabholkar et al., 2000). Electronic businesses can enhance their ‘content usefulness’ through taking steps such as: providing products which have less risks, or increasing the ease by which the product or service is used.
Effect of Service Responsiveness and Service Stability on Customer Satisfaction and Intention to Buy
According to results from path analysis no significant coefficient was found between ‘service responsiveness’ and ‘service stability’ and dependent variables. To understand this result we reviewed the electronic portals that were most frequently visited by the respondents in our study. Our follow up analysis revealed that electronic portals with highest reputation attracted the largest number of visitors and generated most electronic transactions. For instance, the majority of respondents had made purchases from four electronic portals: rajatrains.com (45%), bekhan.com (33%), iranair.com (10%), and adinehbook.com (10%). We argue that, a main reason for which this relationship was not supported can be the higher reputation of websites which leads to strong maintenance of their websites.
Effect of Customer Satisfaction and Service Stability on Intention to Buy
As evident in Table 3, ‘customer satisfaction’ is most important factor predicting ‘intention to buy’. This result is conformed to Brady and Robertson’s (2001) study which suggests the ‘service quality’ influences ‘customer satisfaction’, ultimately leading to ‘intention to buy’.
Extroversion and International Perspective
Further analysis of the results and investigation of data showed very little tendencies among e-commerce platforms included in our data to reach beyond Iranian borders and extend their services to international customers. Additionally, with respondent being mainly Iranian and placing orders from within Iran, there was no evidence of whether international outreach would influence intention to buy in any form.
Effects of Technology Context
Additional comments provided by respondents reveals a general dissatisfaction with the technology infrastructure that enables e-commerce platforms. However, our findings do not provide any evidence as to whether this dissatisfaction is transferred to the e-commerce platforms. Overall, customers seem to be adequately capable of drawing a distinction between the service provided by the e-commerce platform and the quality of technology, isolating the service from technology in developing their intent to purchase.
Effects of Central Government and Policies
There is not sufficient evidence to demonstrate whether government policies and restrictions influence customer satisfaction with respect to e-commerce services received, and consequently, the intent to purchase. As e-commerce regulations were relatively underdeveloped at the time of the study, most regulations remained common with traditional commerce platforms.
Our findings in our study show that while there are substantial similarities between the Iranian context and other previously studied contexts when it comes to customer satisfaction and intent to purchase on e-commerce platforms, there are also certain differences. These differences can mainly be due to contextual idiosyncrasies of the Iranian context. Most importantly, we find that customer satisfaction is directly and positively related to the intent to purchase. However, while factors such as provider reliability, content efficiency and content usefulness all influenced customer satisfaction and subsequently the intention to buy, we found that other factors such as service responsiveness and service stability did not play a role. Our further investigation of the data found that factors such as technology platform, government policies and breadth of a platform’s international operations did not play major roles in either customer satisfaction or the intent to make a purchase.
We acknowledge that our study and its findings are not without limitations. A limitation of our research is the relatively small sample size (n=183). Considering that electronic commerce is a relatively novel concept in the Iranian market, the number of product categories and the available sample has been quite constrained. The small sample size is a direct consequence of this limitation. Nevertheless, our findings demonstrate a good degree of consistency with past findings which support our theory and analysis. For future research, we suggest that researchers conduct studies that focus on determining the effect of each element of selling process on Customer satisfaction, and finding differences between effective quality dimensions on Customer satisfaction between famous websites and new emerging websites (Jarvenpaa & Todd, 1997).
A main limitation of this study is its reliance on respondents that are highly educated. While the use of student and college educated samples is a generally accepted practice in the field of marketing, it undoubtedly restricts the generalizability of findings. The use of e-commerce platforms is not a practice that is limited merely to highly educated individuals. In the Iranian context, however, the rate of penetration for the use of internet among the broader population, while increasing, still remains rather limited compared to more educated populations. As the use of internet becomes more widespread, a need for a study to capture broader consumer attitudes and behaviors with respect to internet platforms of commerce is greater felt. This provides an opportunity for future research and to extend generalizability of findings in this area Gianni & Franceschini, 2003).
Bhattacherjee, A. (2001). An empirical study of the antecedents of electronic commerce service continuance, Decision Support Systems, 32, 201-214.
Bolton, R.N. & Drew, J.H. (1991). A multistage model of customers’ assessment of service quality and value, Journal of Consumer Research, 17(March), 375-384.
Boulding, W., Kalra, A., Staelin, R., & Zeithaml, V.A. (1993). A dynamic process model of service quality: from expectations to behavioral outcomes, Journal of Marketing Research, 30(February), 7-27.
Brady, M.K. & Robertson, C.J. (2001). Searching for a consensus on the antecedent role of service quality and satisfaction: an exploratory cross-national study, Journal of Business Research, 51, 53-60.
Chen, I.J. & Popovich, K. (2003). Understanding customer relationship management (CRM): People, process and technology, Business Process Management Journal, 9(5), 672-688.
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 environment, Journal of Retailing, 76(2), 193-218.
Dabholkar, P.A., Shepherd, C.D. & Thorpe, D.I. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study, Journal of Retailing, 79(2), 131-139.
Gianni, G. & Franceschini, F. (2003). A new model to support the personalized management of a quality e-commerce service, International Journal of Service Industry Management, 14(3), 331-346.
Gronroos, C. (1984). A service quality model and its marketing implications, European Journal of Marketing, 18(4), 36-44.
Harris, L.C. & Goode, M.M.H. (2004). The four levels of loyalty and the pivotal role of trust: a study of online service dynamics, Journal of Retailing, 80, 139-158.
Jarvenpaa, S.L. & Todd, P.A. (1997). Consumers reactions to electronic shopping on the World Wide Web, International Journal of Electronic Commerce, 1(2), 59-88.
Jarvenpaa, S.L. & Tractinsky, N. (1999). Consumer trust in an internet store: a cross-cultural validation, Journal of Computer Mediated Communication, 5(2), www.ascus.org/jcmc/vol5/issue2/jarvenpaa.html.
Joo, Y.G. & Sohn, S.Y. (2008). Structural equation model for effective CRM of digital content industry, Expert Systems with Applications, 34, 63-71.
Laroche, M., Yang, Z., McGougall, G.G.G., & Bergeron, J. (2005). Internet versus bricks-and-mortar retailers: an investigation into intangibility and its consequences, Journal of Retailing, 81(4), 251-267.
Loiacono, E.T., Watson, R.T. & Goodhue, D.L. (2002). WEBQUAL: a measure of website quality. In K. Evans and L. Scheer (Eds.), 2002 Marketing educators’ conference: Marketing Theory and Applications, 13, 432-437.
O’Neil, M., Palmer, A. & Wright, C. (2003). Disconfirming user expectations of the online service experience: inferred versus direct disconfirmation modeling, Internet Research: Electronic Networking Application and Policy, 13(4), 281-296.
Parasuraman, A., Zeithaml, V.A. & Berry, L.L. (1988). SERVQAL: a multiple-item scale for measuring consumer perception of service quality, Journal of Retailing, 64(1), 12-40.
Santos, J. (2003). E-service quality: a model of virtual service quality dimensions, Managing Service Quality, 13(3), 233-246.
Spreng, P.A. & Mackoy, R.D. (1996). An empirical examination of a model of perceived service quality and satisfaction, Journal of Retailing, 722, 201-214.
Sweeny, J.C., Soutar, G.N. & Johnson, L.W. (1997). Retail service quality and perceived value, Journal of Customer Services, 4(1), 39-46.
Szymanski, D.M. & Hise, R.T. (2000). E-Satisfaction: an initial examination, Journal of Retailing, 76(3), 309-322.
Trocchia, P.J. & Janda, S. (2003). How do consumers evaluate Internet retail service quality? Journal of Service Marketing, 17(3), 243-253.
Voss, C.A. (2003). Rethinking paradigms of service-service in a virtual environment, International Journal of Operations & Production Management, 23(1), 88-104.
Webb, H.W. & Webb, L.A. (2004). SiteQual: an integrated measure of Web site quality, The Journal of Enterprise Information Management, 17(6), 430-440.
Wolfinbarger, M. & Gilly, M.C. (2003). eTailQ: dimensionalizing measuring and predicting etail quality, Journal of Retailing, 79, 183-198.
Yang, Z. (2001). Customer perceptions of service quality in internet-based electronic commerce, Proceeding of the 30th EMAC Conference, 8-11 May, Bergen.
Yang, Z., Jun, M. & Peterson, R.T. (2004). Measuring customer perceived online service quality, scale development and managerial implications, International Journal of Operations & Production Management, 24(11), 1149-1174.
Yoo, B. & Donthu, N. (2001). Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL), Quarterly Journal of Electronic Commerce, 2(1), 31-46.
Zeithaml, V.A., Parasuraman, A. & Malhotra, A. (2002). Service quality delivery through websites: a critical review of extent knowledge, Journal of the Academy of Marketing Science, 30, 362-410.
Zhu, F.X., Wymer, W.J. & Chen, I. (2002). IT-based services and service quality in customer banking, International Journal of Service Industry Management, 13(1), 69-90.