1-Introduction
The global nature of technology, low cost, access to millions of people, rapid growth, and the capabilities of the infrastructure supporting the Internet (especially the Web) have led to different interests for individuals, organizations and society, with the development of information technology and inclusive The use of it in the daily lives of individuals, especially with the spread and spread of the use of the Internet, is increasingly felt to be necessary to explore and develop patterns related to the behavior of individuals in the field of cyberspace. Among the phenomena that have grown in recent years with the growth of information technology, the sale of goods and services through companies and online stores has gradually found its place as a new way to fit Lifestyle of today. With the growth and expansion of such stores, the need to develop appropriate behavioral patterns are also felt. Initially, when Amazon's online store began selling goods over the internet, customers were hesitant about whether to buy their products online over the internet but today, with the growth and development of using the Internet in various areas, online shopping has been part of the lives of individuals (Hong & Kim, 2012). Online sales for more than a decade, with a growth rate of 25%, have had the fastest growth in the sales channel, and the Internet has become a favorite medium for consumers who want more purchasing power (Jeon & Kim, 2015). The results of a research in the United States show that the online sales of online stores will reach $ 262 billion in 2013, to $ 370 billion in 2017 (Chang & Tseng, 2014). Simultaneously with creating conditions for the development of online product sales, Internet Shops Provider Online Sales Services and also companies that sell their goods directly through the Internet have also increased. By increasing the number of online stores, these stores compete with each other to gain a greater share of the customer market, which can lead to a reduction in the profitability and survival of these stores (Brown & Jayakody, 2009). In such a context, one of the concepts that can be vital for online stores, along with the attraction of new customers, intent is re-purchase from the store (Kim et al., 2012). The intention of re-purchase expresses personal judgment as to the repetition of the purchase from the same company. The reason why customers decide to supply the same company with the products and services they need is due to previous experiences with the company's capabilities and capabilities (Ariffin et al., 2016). Given the difficulty and cost of attracting new customers for companies, organizations must invest in maintaining their current customers and encouraging them to re-purchase from the organization for profitability or, at least, survival in competitive environments. This is more important in online environments because customers can easily access competing websites and learn about the services they provide. Therefore, customers can easily purchase their competitor's websites in case of equal conditions. Therefore, the design and implementation of strategies that can convince customers to perform re-purchase are important for online stores. Therefore, considering the importance of recognizing and investigating effective factors on the intention of online re-purchase, in this research, using a combination of research model (qualitative and quantitative), we will identify and investigate the factors affecting online customer loyalty.
2-Online Loyalty
Research in e-commerce has shown that loyalty is crucial for long-term growth and profitability in electronic markets (Rust and Oliver 2000, Srinivasan et al., 2002)
Electronic loyalty is the customer's preferred mentality toward electronic retailers who reciprocate his buying behaviors (Eskandari, 2010). Zhang (2008), based on the definition of Armstrong and Hagel, 1996, defined loyalty to the website as the same person repeating visits to the same website. Kayer et al. (2008) considered online loyalty to revisit a website with a view to purchasing it in the future. Online repurchase intentions represent the customer's self-reported likelihood of engaging in further repurchase behavior. Consequently, online shopping consumers will depend heavily on experience quality in which experience quality can be obtained only through prior purchasing experience (Razak et al., 2014). Attracting electronic customers is more difficult than ordinary customers, since access to electronic databases for Internet users is only possible by pressing a button. The results of the research show that the two consequences of loyalty behavior are oral-to-mouth advertising and the desire to pay more. E-loyal customers not only generate business profits but also lead to an increase in market share (Srinivasan, Anderson & Ponnavolu, 2003). Because e-loyalty reduces customers' sensitivity to prices, and they also recommend visiting the company's website to other individuals, which will ultimately lead to an increase in online transactions. Therefore, in order to maintain the competitive advantage and loyalty of e-customers, companies need to achieve appropriate and effective strategies in the area of electronic customer loyalty. Tafalie et al. (2013) in their research, a meta-analysis of online futures, cited the benefits of online customer loyalty in Table 1. In fact, organizations that have succeeded in gaining the loyalty of their customers in the online environment can provide them with a great deal of loyalty to them.
Table 1: The benefits and results of customer loyalty
| Source | The results of online loyalty |
| Donio et al. (2006) | Loyalty Customer Loyalty |
| Ponnavolu (2000) | Allocate the cost of purchasing from the online store |
| Wang, Pallister, and Foxall (2006a, 2006b) | Frequency of purchase from online sales |
| Ponnavolu (2000) | Increase the number of hits from the store |
| Choi et al. (2006) | Less sensitivity to price |
| Srinivasan, Anderson et Ponnavolu (2002) | Reduce search for other online stores |
| Srinivasan, Anderson et Ponnavolu (2002) | Posing mouth to mouth |
| Srinivasan, Anderson et Ponnavolu (2002); Ponnavolu (2000) | Willingness to pay more |
| Ponnavolu (2000); Huang, 2008 | Increased tendency to purchase from the site |
| Shankar, Smith, and Rangaswamy (2003) | Increased overall satisfaction |
| Liang et al. (2008) | Increased cross-selling |
| Liang et al. (2008) | Keep customer |
Reichheld and Schaffer (2000) know that the pillars of electronic loyalty make customer support, deliver products and services at the right time and in exchange for reasonable pricing and transparent and trustworthy procedures for the privacy of individuals.
3-Research Methodology
The present study is a composite method. Combined methodology is one of the new methodologies in social and behavioral sciences that is based on paradigm convergence and the integration of dominant paradigms in the social sciences, so that the emergence of a new paradigm has led to more than theoretical controversy and method. In the research method, a combination of several methods is used and follows two views of quantization and qualitative. In this research, both quantitative and qualitative methods have been used with equal value. The method used in the qualitative section included the method of thematic analysis. In the quantitative part, the research is based on the purpose of development and according to the analysis of the descriptive-correlation data, analyzed by structural equation modeling. For the qualitative method, the depth interviewing tool will be used and for this purpose, the first protocol for the interview is to be made around the answer to the research questions by the researcher. A questionnaire was used for quantitative testing and testing of a model that was adapted from the previous method. The questionnaires were formed based on theoretical studies on variables that were discovered in the previous step.
4-Qualitative Research Section
In the qualitative part of the research, the method of analytic analysis was used. Thematic analysis is a method for determining, analyzing and expressing the existing patterns within the data (Braun & Clarke, 2006). This method organizes the data and describes it in detail. But it can go beyond this and interpret different aspects of the topic of research (Thomas, 2003). The theme is the most abstract level of data, whose shaping and selection depends heavily on research structures (Ryan & Bernard, 2003). In this section, data from in-depth interviews with 18 university students in Tehran, who had a history of Internet shopping from two Digikala and Bamilo stores, included 73 free codes and 29 descriptive codes and 8 main themes is presented in Table 2.
Table 2: Free codes and descriptive code and the main theme
| Axial Code (Main Theme) | Descriptive Code | Free code |
| Variety of cart products |
Varied products | Variety |
| See other goods | ||
| Comprehensive products | ||
Right to Choose | Purchase fits the budget | |
| Power of choice | ||
Quality products | Find quality goods | |
| Ensure quality | ||
| Qualitatively good | ||
| Web design |
Search facility | Search facility |
| Easily search | ||
| Reasonable information | ||
Information quality | Information category | |
| Possibility to compare goods and brands | ||
| Compare prices | ||
| Site information | ||
| Get informed about prices | ||
| Compare goods and services | ||
| Accuracy of information | ||
| Provide needed information | ||
| Get info about goods | ||
Navigation
| Commodity Classification | |
| Possibility to identify goods | ||
Intuitive design
| Attractive design | |
| Update the site | ||
| Being User-friendly | ||
| Visual appeal | ||
| Other users comments | Get opinions from others | |
| Confirm others | ||
| Warranty |
After sales services
| after sales services |
| Custody after sales | ||
Warranty
| Warranty | |
| Possibility to return the goods | ||
| Ability to switch | ||
| Warranty purchase | ||
| Ability to see the goods | Possibility to return the goods | |
| Ability to switch | ||
| Reputation | Popularity | Being in love |
| Reputation | being famous | |
| Website reputation | ||
| Good record | Honesty of the company | |
| Site validation | ||
| Upgrade | Advertising | Having ads |
| Advertising by sending an image | ||
| Dialogue via SMS | ||
| Good price | The price is right | |
| The price is cheaper | ||
| The right fee | ||
| Discount | Relevant Deals | |
| Special offer | ||
| Special sales | Festivals of sale | |
| Sales | ||
| Delivery | Precise Delivery Order | Get the item ordered |
| Receipt based on order terms | ||
| Urgent delivery | Fast delivery of goods | |
| Delivered at promised time | ||
| Delivery of goods at home | ||
| Delivered with convenient packaging | Non-impact packaging | |
| suitable packaging | ||
| Satisfaction | Good shopping experience | Enjoyable shopping experience |
| Easy Shopping | ||
| Satisfaction with the purchase process | Comfortable shopping | |
| Satisfaction with how to buy | ||
| Satisfaction with the purchase | Satisfaction with previous purchasing experience | |
| The correctness of the purchase decision from the store | ||
| Service Satisfaction | Satisfaction with how to respond and keep track of the store | |
| Satisfaction with the delivery of services | ||
| Trust | Assurance | Being sure of the store's performance |
| Trust information | Ensuring website information | |
| Trust on site products | The assurance of the provided products | |
| Originality of products | ||
| Confidentiality | Secure purchase | |
| Maintain user information | ||
| Keep purchasing information |
A review of the history of research suggests that in most of the previous studies, online satisfaction and trust variables were considered as intermediary variables that had an impact on online re-purchase (& Váques, 2017 Ribbink et al, 2004 ; Phong & Dai Trang, 2018; López-Miguens). Therefore, in this study, we decided to consider these variables as intermediary variables. On the other hand, considering that the purpose of the present study was to investigate both effective factors before and after purchase and the interviews were based on this basis, therefore, according to the main themes derived from research and research backgrounds Previous variables were website design, store reputation, promotion, promotion and variety of goods as pre-purchase factors and delivery variables and warranties as post-purchase factors. Therefore, taking into account these factors, the initial pattern of the final analysis of the present research was presented in Figures 1 and 2, and this was the basis for analysis in the quantitative part of the research.
Figure 1: Primary research pattern from thematic analysis
Figure 2: Conceptual Model of Research
5-Quantitative research part
After conducting the interviews and analyzing them in the coding method and presenting the model by the method of thematic analysis and presenting the research model, in the second part of the research for testing the designed model, in the quantitative analysis method, structural equation modeling was used. In the quantitative part, students of Azad University in Tehran who had a history of Internet shopping from two Digikala and Bamilo stores were selected as the statistical population of the study. A questionnaire was used to obtain the required data for analysis. The 5-factor Likert spectrum was "completely disagreeable" to "fully agree" as a measure of questions. Questionnaire questions were extracted from previous research and results from the qualitative section of the research. After the questionnaire was prepared, 25 questionnaires were pre-tested among the members of the statistical society to verify the reliability of the questionnaire. Cronbach's alpha method was used to test the reliability. Results indicated that the Cronbach's alpha coefficient for all variables and the total questionnaire was higher than 0.7 (Cronbach's alpha values calculated for variables, as well as the entire questionnaire with the explanatory questions of variables in Table 3 Presented). On the other hand, the content validity of the questionnaire was confirmed by experts. In this way, the questionnaire had the necessary reliability and reliability for distribution in the statistical society.
Table 3: Cronbach's alpha coefficients
| Cronbach's alpha coefficients | Questions | Variables |
| 0.906 | 1-4 | Web Design |
| 0.704 | 5-7 | Reputation |
| 0.773 | 8-11 | Upgrade and advance |
| 0.725 | 12-14 | Variety of goods |
| 0.754 | 15-17 | Warranty |
| 0.709 | 18-20 | Delivery |
| 0.723 | 21-24 | Satisfaction |
| 0.720 | 25-28 | Trust |
| 0.805 | 29-32 | Loyalty |
| 0.934 | 1-32 | Total |
To test the research hypotheses, structural equation modeling was used. Modeling structural equations is one of the statistical modeling techniques. In this research, for testing the conceptual model of research, Partial Least Squares (PLS) method, which is a Variance-Oriented Path Modeling Technique, allows for the analysis of theory and measures simultaneously (Fornell & Larcker, 1981). Unlike covariance-centric methods, this method can be used for small volume samples as well as for cases where the distribution of variables is not normal. Data analysis and hypothesis testing were performed by Smart Pls software. The Bootstrapping method (with 200 resamples) was used to examine the significance of factor loads and correlation coefficients (Ramayah & Rahbar, 2013).
6-Measurement model evaluation
The Convergent validity and Discriminant validity tests were used to test the model (Teo et al., 2015). Leung et al. (2013) have defined Convergent validity as "the ability of tools to produce the same results, despite the use of different methods." The three main criteria with which the convergent validity for the measurement model can be measured are (Fornell & Larcker, 1981; Teo et al. 2015):
- Factor loads calculated in the measurement section of the model are greater than 0.5 (0.5 <).
- Calculated values for Composite Reliability (CR) for all constructs (here are hidden variables) must be greater than the standard value of 0.7 (0.7 <).[1]
- Average Variance Extracted (AVE), for research structures should be higher than the standard value of 0.5 (0.5 <).
Table 4: Factor load values for each construct marker, AVE and CR
CR
| AVE | Meaningful number (t) | Factor load | Marker mark | Structure |
0.89001 | 0.669719 | 20.53158 | 0.845219 | q1 | Web Design |
| 10.9897 | 0.748894 | q2 | |||
| 16.81735 | 0.832809 | q3 | |||
| 19.314 | 0.842656 | q4 | |||
0.833423 | 0.625621 | 16.922 | 0.817552 | q5 | Reputation |
| 15.76831 | 0.812772 | q6 | |||
| 13.21792 | 0.740185 | q7 | |||
0.841966 | 0.573257 | 17.59154 | 0.812975 | q8 | Promotion and promotion of the brand |
| 6.644681 | 0.643597 | q9 | |||
| 10.10223 | 0.814726 | q10 | |||
| 16.94913 | 0.744381 | q11 | |||
0.829887 | 0.619597 | 21.76266 | 0.804974 | q12 | Product diversity |
| 14.0548 | 0.81264 | q13 | |||
| 20.09968 | 0.741907 | q14 | |||
0.831216 | 0.622164 | 10.24691 | 0.835162 | q15 | Warranty |
| 17.23757 | 0.729457 | q16 | |||
| 9.273894 | 0.798052 | q17 | |||
0.756358 | 0.509995 | 12.88992 | 0.726509 | q18 | Delivery |
| 5.042237 | 0.770097 | q19 | |||
| 14.29048 | 0.639625 | q20 | |||
0.835874 | 0.56033 | 16.98569 | 0.761499 | q21 | Satisfaction |
| 10.41163 | 0.77052 | q22 | |||
| 12.11236 | 0.710038 | q23 | |||
| 13.77065 | 0.750724 | q24 | |||
0.851953 | 0.589973 | 13.28816 | 0.756904 | q25 | Trust |
| 14.12006 | 0.782818 | q26 | |||
| 16.09329 | 0.76568 | q27 | |||
| 17.54457 | 0.766759 | q28 | |||
| 0.63132 | 14.80631 | 0.794438 | q29 | Loyalty |
| 19.38402 | 0.795257 | q30 | |||
| 14.60059 | 0.811233 | q31 | |||
| 20.53158 | 0.776927 | q32 |
The results of the review of the Convergent validity criteria showed that:
- All calculated values for factor loads the observed indexes are greater than the minimum value of 0.5. Therefore, the tool used in the research has a convergent validity.
- All computed value for combined reliability (CR) is greater than the minimum criterion considered 0.7. Therefore, in this criterion, the tool used in the research has a convergent validity.
- The calculated value for the average of the variance (AVE) for all the research variables is greater than the minimum criterion considered 0.5. Therefore, it can be said that in terms of this criterion, the tool used in the research has a convergent validity.
In total, considering the standard values and the calculated values, we can say that the research measurement model has a convergent validity. To investigate discriminant validity, the Cross Loadings Load Table, which can be calculated from the output of the SmartPC software, was used. For this work, the correlation of each marker with all other structures of the model was calculated that the correlation values should be more than the other structures for the selected structure of the researcher. Therefore, each marker should exhibit the highest correlation with its structure only and has the least correlation with other structures.
Table 5: Convergent validity study through cross loadings loads
| Upgrade | Trust | Repurchase | Satisfaction | Reputation | Delivery | Variety | Design | Warranty | |
| 0.263365 | 0.294509 | 0.365333 | 0.366924 | 0.287971 | 0.227942 | 0.268859 | 0.845219 | 0.223151 | q1 |
| 0.120452 | 0.21608 | 0.243038 | 0.306908 | 0.157524 | 0.190275 | 0.218453 | 0.748894 | 0.197999 | q2 |
| 0.062514 | 0.214772 | 0.273367 | 0.318084 | 0.195442 | 0.203583 | 0.168412 | 0.832809 | 0.212816 | q3 |
| 0.207164 | 0.341932 | 0.38568 | 0.382463 | 0.296963 | 0.305952 | 0.301849 | 0.842656 | 0.249722 | q4 |
| 0.318111 | 0.525513 | 0.493451 | 0.43342 | 0.817552 | 0.282339 | 0.414333 | 0.297719 | 0.399099 | q5 |
| 0.328164 | 0.546585 | 0.526789 | 0.436005 | 0.812772 | 0.315104 | 0.227673 | 0.237469 | 0.345011 | q6 |
| 0.263009 | 0.517189 | 0.51471 | 0.486403 | 0.740185 | 0.366352 | 0.43841 | 0.166245 | 0.393157 | q7 |
| 0.812975 | 0.457116 | 0.496531 | 0.521658 | 0.359038 | 0.388706 | 0.393912 | 0.145757 | 0.337584 | q8 |
| 0.643597 | 0.266956 | 0.346875 | 0.343152 | 0.267622 | 0.18069 | 0.232427 | 0.105343 | 0.19391 | q9 |
| 0.814726 | 0.425231 | 0.396271 | 0.356354 | 0.268042 | 0.318626 | 0.298676 | 0.15404 | 0.173351 | q10 |
| 0.744381 | 0.339681 | 0.407765 | 0.351584 | 0.249786 | 0.322172 | 0.303487 | 0.235088 | 0.175089 | q11 |
| 0.293423 | 0.39797 | 0.444496 | 0.500715 | 0.47661 | 0.310629 | 0.804974 | 0.199128 | 0.490093 | q12 |
| 0.346694 | 0.370708 | 0.441808 | 0.526874 | 0.317447 | 0.354088 | 0.81264 | 0.280461 | 0.39362 | q13 |
| 0.341151 | 0.383999 | 0.38784 | 0.452578 | 0.277708 | 0.278593 | 0.741907 | 0.2264 | 0.250962 | q14 |
| 0.251885 | 0.519001 | 0.502439 | 0.415878 | 0.377511 | 0.298618 | 0.346599 | 0.253492 | 0.835162 | q15 |
| 0.232437 | 0.340633 | 0.408624 | 0.451327 | 0.381488 | 0.238386 | 0.421801 | 0.166171 | 0.729457 | q16 |
| 0.229537 | 0.431075 | 0.504859 | 0.463559 | 0.379493 | 0.329613 | 0.386024 | 0.218079 | 0.798052 | q17 |
| 0.293809 | 0.351327 | 0.371752 | 0.439041 | 0.285206 | 0.726509 | 0.316221 | 0.210474 | 0.23231 | q18 |
| 0.263192 | 0.420661 | 0.445421 | 0.481098 | 0.390034 | 0.770097 | 0.311215 | 0.194052 | 0.291541 | q19 |
| 0.338274 | 0.331542 | 0.346222 | 0.365019 | 0.174261 | 0.639625 | 0.223598 | 0.221707 | 0.264989 | q20 |
| 0.356066 | 0.467277 | 0.562838 | 0.761499 | 0.459789 | 0.469841 | 0.452541 | 0.350209 | 0.423042 | q21 |
| 0.383099 | 0.487682 | 0.578919 | 0.77052 | 0.463829 | 0.55587 | 0.461748 | 0.489126 | 0.441277 | q22 |
| 0.438503 | 0.438689 | 0.53812 | 0.710038 | 0.39252 | 0.345733 | 0.526838 | 0.163586 | 0.431661 | q23 |
| 0.415638 | 0.495206 | 0.641144 | 0.750724 | 0.396419 | 0.42535 | 0.444634 | 0.247594 | 0.382998 | q24 |
| 0.348436 | 0.756904 | 0.567351 | 0.419302 | 0.502401 | 0.368853 | 0.371148 | 0.296074 | 0.410382 | q25 |
| 0.455538 | 0.782818 | 0.616787 | 0.482798 | 0.500916 | 0.390251 | 0.32973 | 0.244289 | 0.352415 | q26 |
| 0.357901 | 0.76568 | 0.617925 | 0.495111 | 0.541251 | 0.403663 | 0.411037 | 0.263768 | 0.481434 | q27 |
| 0.383539 | 0.766759 | 0.668431 | 0.536479 | 0.515059 | 0.425362 | 0.384768 | 0.225082 | 0.442836 | q28 |
| 0.433213 | 0.600342 | 0.794438 | 0.651291 | 0.464233 | 0.457414 | 0.506469 | 0.264892 | 0.513243 | q29 |
| 0.450552 | 0.605437 | 0.795257 | 0.690958 | 0.4803 | 0.42668 | 0.44977 | 0.36459 | 0.529911 | q30 |
| 0.400107 | 0.651236 | 0.811233 | 0.581395 | 0.533927 | 0.458278 | 0.347505 | 0.281621 | 0.437164 | q31 |
| 0.466314 | 0.70443 | 0.776927 | 0.539739 | 0.581766 | 0.394542 | 0.412356 | 0.343659 | 0.426942 | q32 |
The cross loadings load table (Table 5) indicates the discriminant validity of the research model. Since correlation values for markers have the highest correlation with their structure, they have less correlation with other structures.
7-Structural part of the model
Research hypotheses are based on the relationships between hidden variables that can be expressed in terms of the structural part of the model, so the structural part of the model is used to examine the research hypothesis. The conceptual model tested by SmartPlus software is presented in Fig. 3.
Figure 3: The tested model of research in SmartPlus software
The numbers written on the lines represent the beta coefficients derived from the regression equation between the variables, which is the same coefficient of the path. The numbers inside each circle represent the value of the coefficient of determination (R2) whose predictor variables are entered into that circle through the arrows. The value of the determination coefficient represents the percentage of variations in the dependent variable, which is explained by the predictor variables. The results indicate that a total of 065.7% of changes in online customer satisfaction and 60.1% of online trust changes are caused by independent variables. Also, 0.768% of the changes related to the customer loyalty variable are generated by online trust and trust. The results of testing the hypotheses of the research are also given in Table 6.
Table 6: Results of the test of research hypotheses
| Relations between variables | Factor load | T value | Result |
| Satisfaction --- Website design | 0.142 | 2.067 | Accept the hypothesis |
| Trust --- Website Design | 0.06 | 0.814 | Reject the hypothesis |
| Satisfaction --- Fame | 0.159 | 2.063 | Accept the hypothesis |
| Trust --- Fame | 0.393 | 4.748 | Accept the hypothesis |
| Satisfaction - upgrade and advance | 0.176 | 2.671 | Accept the hypothesis |
| Trust --- Upgrade and advance | 0.190 | 2.360 | Accept the hypothesis |
| Satisfaction --- variety of goods | 0.251 | 2.901 | Accept the hypothesis |
| Trust --- variety of goods | 0.045 | 0.532 | Reject the hypothesis |
| Satisfaction --- warranty | 0.175 | 2.254 | Accept the hypothesis |
| Trust --- Warranty | 0.204 | 2.496 | Accept the hypothesis |
| Satisfaction --- delivery | 0.261 | 3.605 | Accept the hypothesis |
| Trust --- Delivered | 0.169 | 2.367 | Accept the hypothesis |
| Intent on online re-purchase --- Satisfaction | 0.444 | 6,996 | Accept the hypothesis |
| Intent on online re-purchase --- Trust | 0.525 | 7.224 | Accept the hypothesis |
The test results showed that, apart from the two hypotheses concerning the effect of website design on trust and variety of goods on online trust, the rest of the research opportunity is confirmed.
8-Conclusion and Suggestions
With regard to the growth of information technology and the increase of access to the Internet, online stores have become a new way of retail and distribution, and have gradually become more and more customer-oriented as a new way of providing Find goods. Therefore, considering the growth and development of these stores, it is necessary to consider the concepts of marketing and management in this new context. Therefore, the purpose of this study was to investigate the factors before and after purchase on the intention to re-purchase customers in online stores. . The results of the data analysis showed that both after-purchase and pre-purchase factors have a positive effect on consumer re-purchase through online satisfaction and trust. Considering the fact that online stores are a relatively new phenomenon in the field of retail trade in Iran, therefore, marketing strategies in these stores should be taken into consideration more and more. Considering the results of this research, the pre-purchase factors and both after-sales agents are positively influenced by the customer's re-purchase intention through customer trust and online satisfaction. Therefore, it is suggested that online stores through brand development and promotion through various tools such as advertising, supplying products with high variety and reasonable prices, etc., as a well-known brand in the minds of customers, also, the proper design of the website and the complete introduction of the products along with the review and review of the views of the consumer product to help make decisions and select the right customers and build trust in customers in the field of privacy and personal information of users and provide personal capabilities. Properly tailored to users based on their user profiles, there are some issues that should be addressed by online store managers. Performing the process of delivery of goods in the shortest possible time and delivery of goods with convenient packaging and low cost of providing guarantees for the return of goods in case of a problem in the received goods is also one of the issues that should be considered by online store managers.
[1]Some researchers, for example (Bagozzi & Yi, 1988) have proposed a benchmark of 0.6.