Ease of use has been defined as the quality of a user’s experience when interacting with a website, often measured by the ease with which the user can obtain the desired information ([81]). [104] stated that ease of use is an important predictor of website success. [101] confirmed this in their study, indicating that it will lead to an increase in online transactions. In the same context, [23] concluded in his study that website success is driven by ease of use and the ability to easily navigate a website. Besides, [27] stated that a good design must provide not only beauty and appeal, but also high levels of ease of use.

[33] stated that ease of use leads to high levels of customer satisfaction and loyalty towards the website and, consequently, website success. According to [90], ease of use is considered an important element in a website’s success. This is consistent with the results of research by [108], which found that ease of use positively affects repurchase intention. It implies that ease of use has a large direct impact on users’ intention to keep using the website, highlighting the significance of ease of use in promoting e-commerce website success. Accordingly, the following hypothesis is proposed:

H1: Ease of use significantly affects e-commerce website success.

With the increase in online transactions, the role of customer reviews as a source of information is becoming increasingly important. Customer reviews are comments that customers leave on the seller’s website; they often concern product quality and performance ([32]). They are significant for online buyers because they are submitted by other consumers like them, making them more trustworthy than seller-created information ([15]). Customer reviews on e-commerce platforms are highly important, as they play a crucial role in helping potential customers evaluate both the reliability of the seller and the quality of the products. These reviews offer significant perspectives on the experiences of other individuals about both the products and the retail companies. Consequently, it helps buyers make informed and safe purchasing decisions ([111]). In this context, [93] stated that consumers trust information provided by customer reviews more than that provided by marketers about the product for a simple reason that e-tailers rarely provide negative aspects about the product, while customers provide honest reviews and candid opinions about the product or service.

Customer reviews are a key source of information for online shoppers, as they can use it to make more informed purchases ([24]). [113] argue that customer reviews are more reliable when they are written by actual consumers who have used the item before. It represents a reflection and a record of customers’ opinions, assessments, and experiences. Consequently, their influence and importance in e-commerce have increased ([9]). Therefore, the researcher promotes this factor to be one of the proposed factors that affect e-commerce website success. Consequently, the following hypothesis is proposed:

H2: Customer reviews significantly affect e-commerce website success.

The exponential growth in the number of online retailers in recent years has heightened the importance of the customer service quality they provide. The customer service of websites primarily aims to assist online customers in completing transactions effectively and efficiently as interactions between buyers and sellers occur without direct, face-to-face contact ([61]). [66] stated that customer service quality is a complete evaluation by customers of the services provided by the site. [77] defined customer service quality as the customer’s attitude formed through a long-term evaluation of a website’s service performance. [87] argued that to establish customer loyalty, organizations have to change their focus from the aspect of exchange and transactions to the aspect of providing high-quality customer service.

[76] concluded in their study that the quality of customer service provided has a significant impact on customer satisfaction. [35] emphasized that the greater a website’s ability to facilitate communication with its customers, the higher their satisfaction levels and the more frequently they revisit the site. [48] further asserted that the quality of customer service plays a significant role in influencing customers’ online purchasing decisions. [36] noted that high-quality customer service enhances customers’ purchase intentions. Additionally, [112] reported that customer service quality is a key determinant of whether customers will make future purchases on the website. Therefore, providing high-quality online service can foster a positive perspective among consumers, which in turn leads to website success. Based on the above justification, the following hypothesis is proposed:

H3: Customer service quality significantly affects e-commerce website success.

With the development of technology, e-customers view security and privacy as important indicators of e-commerce service satisfaction. This statement is supported by a study conducted by [35], who noted that e-user satisfaction is strongly related to security and privacy practices. [30] argues that a secure website is necessary for e-commerce to thrive. In the same context, [98] stated that security in online transactions and the protection of personal information is very crucial in an online purchasing system. They argue that the more secure the website, the higher the customer satisfaction.

Security has been defined as protection from conditions or events that could cause a threat to data or Internet resources, whether through destruction, modification of data, fraud, or misuse. [62] defined privacy as the ability of the website to secure customers’ personal data from being violated. [97] noted that e-customers’ privacy concerns stem from issues such as the unauthorized use of credit card information, the potential sale of their data to third parties, the sharing of personal details with other businesses without consent, and the risk of data being accessed by hackers or identity thieves.

[29] found that the ability to provide a secure site will have a better chance at convincing a potential customer to buy from that site than other sites. [67] stated that effectively addressing security and privacy concerns is a key factor influencing customers’ willingness to make repeat purchases from the website. Therefore, security and privacy positively influence revisiting the website. Based on this explanation, the following hypothesis is proposed:

H4: Security and privacy significantly affect e-commerce website success.

Content quality is widely recognized as a fundamental dimension of website characteristics. [94] referred to it as the “king dimension” of any website, emphasizing its role as the primary source of value for customers. [1] stated that content quality involves providing authentic, unbiased, and error-free information that enables customers to gain a complete understanding of the product and avoids any misleading content. [49] defined it as the provision of information that helps consumers make well-informed decisions when shopping online. [110] found that quality content positively influences consumer trust and increases user engagement. In a study by [45], the authors concluded that content quality is a key factor in evaluating overall website quality and has the greatest influence on customer satisfaction.

[8] noted that well-organized content enhances customers’ purchase intentions, as the provided information largely shapes their decisions. A study by [88] examined the factors influencing consumers’ decisions to shop online and found that the availability of valuable website content plays a significant role in shaping purchase decisions. The findings of [37] also confirmed the positive effect of content quality on purchase intention.

In summary, consumers’ purchasing decisions in online marketplaces are greatly influenced by the quality of content provided. High-quality content fosters trust and confidence among customers, enabling them to make well-informed decisions that meet their needs while reducing the likelihood of dissatisfaction with their purchases ([31]). Accordingly, the following hypothesis is proposed:

H5: Content quality significantly affects e-commerce website success.

Visual appeal refers to the effective design and presentation of product information, including layout elements such as font size, color, videos, animations, and imagery ([105]). Various factors contribute to the visual appeal of a website, such as the number and dimensions of online images, the choice of background, consistent typography, and color usage ([11]). Professionally designed websites can attract consumers, foster positive attitudes, and enhance their satisfaction.

In one study conducted by [69], they found that visually attractive websites enhance users’ satisfaction with online purchases. Similarly, [106] noted that visual appeal has a significant influence on online buying behavior. In line with this, [21] found that websites with aesthetically pleasing interfaces provide better online shopping experiences, build trust, and strengthen relationships with online vendors.

[91] stated that the visual appearance presented to users plays a crucial role in determining the website’s success in attracting a large number of visitors. In the same context, [3] noted that a visually appealing website attracts consumers and increases their satisfaction. Furthermore, [75] emphasizes the importance of factors such as colors, graphics, and animations as relevant dimensions of e-consumer satisfaction.

In summary, a well-designed website can influence customers’ online purchasing decisions by enhancing their attitudes and building trust. Accordingly, in the e-commerce context, website aesthetics are expected to play a significant role in the website’s success. Therefore, the researcher proposes the following hypothesis:

H6: Visual appeal significantly affects e-commerce website success.

In today’s business world, customization has become a strategic tool for businesses looking to enhance customer satisfaction and foster customer loyalty ([5]). With the vast amount of customer data available, companies can tailor their products and services to meet the specific needs and preferences of their customers ([56]). [103] defined website customization as providing tailored content and services that are specifically designed by leveraging consumer databases and modifying offerings to align with consumer preferences, while [7] defined site customization as the extent to which suppliers either customize the website to appeal to users or enable the users themselves to customize the content. Similarly, [20] defined customization as a system that uses information technology and flexible processes to deliver individually customized products and services at a cost near that of mass-produced items.

[109] stated that customization can positively influence customer attitudes towards the website, increase emotional attachment, and foster repeat business. [86] added that when a company takes the time to personalize interactions, whether in customer service, marketing, or product offerings, it signals that they are attentive to the individual’s preferences and needs. Consequently, this level of attention results in higher satisfaction levels, as customers feel a stronger connection to the website. [80] added that when customers feel understood and valued, they are more likely to return to the website. Similarly, [22] found that product and service customization increased customer satisfaction and user retention rates in online retail. [99] stated that customized products and services led to greater convenience and customer satisfaction, as they did not have to spend time searching for products or services that suited their needs. Based on these findings, this study hypothesizes:

H7: Customizing products and services significantly affect e-commerce website success.

The success of e-commerce websites is a multidimensional concept, assessed using various indicators such as user satisfaction, sales volume, customer retention, repeat purchases, repeat visits to the website, and revenue growth ([63]). According to [74], a successful e-commerce site is one that not only can meet consumers’ needs but also fulfills user expectations. [64] emphasize that success indicators should focus on website effectiveness, noting that a site may be technically sound yet still fail if it does not achieve its intended purpose.

[48] add that the biggest challenge encountered by every online retailer is to convert the website visitor into a buyer. This can only be achieved through the website’s success in convincing the visitor to make an actual purchase. [4] also confirmed that the success of a website stems primarily from its suitability to the needs of the consumer and its distinction from other websites. [79] described website success as a website’s ability to effectively communicate its intended messages to consumers and visitors. On the other hand, [102] have focused on consumers’ perceptions of the perceived value of website features to judge the success of an e-commerce website. [115] concluded in their study that achieving online customer satisfaction is a crucial factor in the success of an e-commerce website.

In short, a successful website should deliver to its audience whatever they are looking for and include detailed information about the products and services. In addition, it aims to meet customers’ needs, preferences, and expectations to achieve their satisfaction. If it fails to do this, then the website may no longer be characterized as a successful one.

A pilot study was conducted to verify the tool’s feasibility and usability before proceeding with the main research. Thirty questionnaires were distributed to respondents to be filled out, and to refine the data gathering instrument. [70] explains that the pilot test serves as a formal process for validating a questionnaire using a small sample. In the same context, [114] stated that the pilot stage enables researchers to identify and correct unforeseen issues before carrying out a full-scale survey. This stage also aims to confirm the instrument’s validity and reliability following any necessary adaptations. According to [60], when the data collection tool is well-designed, valid, reliable, and grounded in an appropriate literature review and statistical assumptions, the results can be generalized to a larger population. In this regard, the questionnaires were sent to 30 respondents to be tested and refined. Accordingly, some questions were adjusted to avoid misunderstandings.

Validity is the extent to which the questionnaire accurately measures what it is supposed to measure ([12]). [28] defined validity as: “the ability of a scale or instrument to measure what is intended to be measured.” In the same context, [95] stated that an instrument is a valid measure if it successfully measures the phenomenon. In this respect, content validity was employed in this study to ensure that the instrument was appropriate and that the items were clearly understood. To further verify the instrument’s validity, the factor analysis was conducted using SPSS. The results of the factor analysis are presented in Table 1.

Table 1. Rotated component matrix results.

Extraction Method: Principal Component Analysis. Source: Primary collected data.

The results of the factor analysis indicate that the questionnaire demonstrates satisfactory factor validity, as the items accurately reflect the variables being measured. Accordingly, the instrument is fully representative of its intended constructs, and the statements are generally clear and understandable.

The internal consistency reliability of the measurement model was evaluated using Cronbach’s alpha. The rule of thumb for assessing Cronbach’s alpha is that the values should be higher than 0.7 ([42]). In addition, [38] stated that if the value of construct reliability is about/more than 0.7, it can be stated as reliable. [46] add that Cronbach’s Alpha coefficients of 0.7 to 0.8 are good. Furthermore, [78] stated that for research purposes, a Cronbach’s alpha of 0.70 or higher is sufficient.

Table 2 indicates that Cronbach’s Alpha values for all variables exceed 0.7, demonstrating strong internal consistency reliability of the measurement model and confirming the overall validity of the instrument.

After ensuring the validity and reliability of the questionnaire, the researcher started stage two, which involves the distribution of 400 surveys to a sample of the Alexandria governorate population who have used e-commerce sites for purchasing for at least one year.

The researcher determined the estimated sample size by using the [18] and [89]. This sample size is based on a 95 percent confidence level assumption, with a standard deviation of 0.5, and a 5 percent error. The result of the calculation was a sample size of 384. The questionnaire was distributed among 400 respondents. 46 responses were removed as the answers were not completed or the questionnaire was not returned. Thus, 354 responses were qualified for the final data analysis with a response rate of 88.50%.

Additionally, the researcher used the Kaiser-Meyer-Olkin (KMO) test to evaluate the adequacy of the sample for analysis. As presented in Table 3, the KMO value was 0.827, indicating that the sample size is sufficient.

Table 2. Reliability testing result.

Source: Primary collected data.

Table 3. KMO and Bartlett’s test.

Source: Primary collected data.

5.4.1. Demographic Profile and Respondents’ Information

Table 4 presents the descriptive statistics of the respondents’ demographic characteristics.

Table 4. Respondents’ demographic characteristics.

Source: primary data collected.

Table 4 shows that, among the sample pool of 354, the majority were male (53.39%), while the remaining were female (46.61%). In terms of age, the majority of respondents (45.48%) were aged 26 - 40, followed by 18 - 25 (31.64%), then 41 - 60 (16.67%), and those aged 60 and above (6.21%). About income, most of the respondents earn a monthly income between 5000 and less than 12,000 L.E., which formed (41.24%) of the total respondents. 30.79% earn a monthly income between 12,000 L.E. and less than 20,000 L.E., while 19.21% earn a monthly income between 20,000 L.E. and less than 28,000 L.E. Only 8.76% of respondents earn a monthly income of 28,000 L.E. and above. Regarding educational level, the results in the table show that more than half of the respondents have higher education; they account for 57.34% of the total, while those with intermediate education account for 42.66%. Considering respondents’ online Purchasing experience, the largest proportion (43.22%) had between one and less than three years of experience, followed by (35.88%) with three to less than five years, while (20.9%) reported more than five years of experience.

5.4.2. Ensuring the Normality of Collected Data

A normality test is a statistical method used to determine whether a sample dataset follows a normal distribution, which is crucial for choosing suitable analytical techniques. [40] emphasized the importance of testing for normality, noting that violation of this assumption may result in the incorrect rejection of the proposed model. In this study, the Shapiro-Wilk test was employed to examine the normality of the collected data. Previous studies by [72] and [55] have identified the Shapiro-Wilk test as one of the most powerful methods for assessing normality. As presented in Table 5, the Shapiro-Wilk test results indicated that the data were normally distributed, as the p-values ranged from 0.093 to 0.213, all exceeding the 0.05 threshold. Therefore, the normality assumption was not violated.

Table 5. Normality tests: Shapiro-Wilk test.

Source: Primary data processed

5.4.3. Ensuring the Absence of Common Method Bias

In quantitative research, common method bias (CMB) is a common occurrence. CMB occurs when survey data from a single source is collected at the same time. [58] stated that common method bias can occur when both the independent and dependent variables are measured within one survey using the same response method. In addition, Common method bias arises if any of the variables account for a high covariance among all the variables ([2]). The presence of common method bias may seriously compromise a study’s validity. [43] stated that common method bias exists if the principal constructs are significantly and highly correlated (Total variance extracted by one factor exceeds 0.50).

Table 6. Total variance explained Harman’s single-factor test for common method bias.

Extraction Method: Principal Component Analysis.

The findings reported in Table 6 indicate that common method bias does not represent a serious threat to this study. The variance explained by a single factor is 22.819%, falling below Harman’s recommended threshold of 50% ([43]). This outcome confirms that the collected data are not affected by common method bias. Consequently, the research model is reliable, and the data are considered adequate for further statistical analysis.

In the following sections, the collected data will be analyzed using SPSS software. This procedure includes descriptive analysis to summarize and concisely outline the key features of the collected data and highlight the underlying patterns and trends within it. Also, correlation analysis will be used to discover the strength and direction of the relationship between all independent variables and the dependent variable. In addition, multiple regression analysis will be conducted to assess the relative importance of factors strongly influencing e-commerce website success, and these factors will then be used to build a linear prediction equation to predict the success of e-commerce websites in attracting and retaining customers.

5.5.1. Descriptive Analysis

Table 7. Descriptive analysis.

The findings presented in Table 7 indicate that Ease of Use recorded the highest mean among all proposed website characteristics. This result suggests that Egyptian users perceive ease of navigation and simplicity of transaction completion as the most influential factors in the success of e-commerce websites. The relatively high mean score reflects strong agreement among respondents regarding its importance. In contrast, Customization reported the lowest mean among the proposed variables. This finding implies that respondents perceive the personalization feature as having less influence compared to other website characteristics.

In addition, the descriptive analysis showed that content quality, security & Privacy, customer reviews, and customer service showed relatively strong evaluations, highlighting the importance of content accuracy, data protection, and customer service staff interaction in shaping Egyptian user perceptions of e-commerce platforms.

Furthermore, the descriptive analysis revealed that the standard deviations of the study variables ranged from 0.631 to 0.803, indicating a good degree of homogeneity and relative agreement among Egyptian users regarding the influence of the variables under study on the success of e-commerce websites. These findings confirm the stability of the sample’s trends and the absence of sharp differences in opinions, thus enhancing the reliability of the results and their suitability for subsequent statistical analyses.

5.5.2. Correlation Analysis

Correlation analysis is a statistical technique used to examine and measure the relationship between two variables by determining both the strength and direction of their association. In this study, Pearson correlation coefficients were used to assess the strength and direction of relationships between the dependent variable (e-commerce website success) and each proposed independent variable. The findings of the correlation analysis are presented in Table 8.

Table 8. Results of correlation analysis (r).

**Correlation is significant at the 0.01 level (2-tailed).

The correlation analysis reveals that all independent variables are strongly and positively associated with the dependent variable. Ease of use demonstrates the strongest relationship with e-commerce website success (r = 0.839). This is followed by content quality (r = 0.792) and security and privacy (r = 0.736), while customization shows the weakest, yet still significant, correlation (r = 0.473).

5.5.3. Multicollinearity Test

Multicollinearity exists when two or more independent variables in a regression model are correlated. If there is a high degree of correlation, it is difficult to determine the contribution of each independent variable because their effects will be confounded. ([39]). In addition, the presence of multilinearity leads to impractical explanations of the results. As a result, it becomes difficult to accurately determine the individual effects of each independent variable on the dependent variable ([41]). Furthermore, multicollinearity can lead to misinterpretation of the dependent variable in the prediction model, making the model unreliable ([34]; [47]).

To assess collinearity in the collected data, a multicollinearity test was performed to calculate VIF values. According to [42] and [50], VIF values below 5 indicate no collinearity problems in the results. The results of the multicollinearity test are shown in Table 9.

Table 9. Results of multicollinearity test.

Dependent Variable: E-commerce Website Success.

The results of the multicollinearity test indicate that no significant collinearity exists among the independent variables included in the study. The tolerance values ranged from 0.279 to 0.427, all exceeding the acceptable threshold of 0.20 as suggested by [71].

In addition, the Variance Inflation Factor (VIF) values were found to range between 2.341 and 3.584, which are well below the recommended maximum limit of 5 proposed by [42].

Accordingly, the regression model can be considered appropriate for examining the relationships among the study variables. The absence of multicollinearity among the independent variables further supports the reliability of the model’s predictive capability, thereby justifying the continuation of the regression analysis.

5.5.4. Multiple Regression Analysis

Regression analysis is employed to examine the effect of the proposed independent variables on the dependent variable. This statistical technique is used to investigate the predictive relationship between multiple independent variables and the dependent variable. In addition, regression analysis determines the extent to which each independent variable contributes to explaining variations in the dependent variable while controlling for the effects of other variables in the model. Furthermore, regression analysis provides important indicators such as beta coefficients, the coefficient of determination (R²), and significance levels, which help evaluate the explanatory power of the research model and identify the most influential predictors of the dependent variable.

In this study, multiple regression analysis was applied to assess the impact of website characteristics—namely ease of use, customer reviews, customer service, security and privacy, content quality, visual appeal, and customization—on e-commerce website success. The results of this analysis are listed in Tables 10-12, respectively.

Table10. Model summary.

a. Predictors: (Constant), Customization, Content Quality, Customer reviews, Visual appeal, Security & Privacy, Ease of use, Customer Service. b. Dependent Variable: E-commerce Website Success.

Table 10 provides a summary of the linear regression model, including key statistics such as R, R², adjusted R², the standard error, and the Durbin-Watson test.R value represents the strength of the relationship between the independent variables (collectively) and the dependent variable. The result of 91.8% indicates a very strong positive correlation, suggesting that variations in the dependent variable are strongly associated with changes in the independent variables.R² value of 0.842 suggests that the model’s independent variables explain 84.2% of the variation in the dependent variable.To evaluate the robustness and consistency of the model, the Durbin-Watson test was employed to detect the presence of autocorrelation between residuals in a regression model. Ideally, Values between 1.5 and 2.5 are commonly accepted as the threshold for assuming no autocorrelation ([59]). In this study, the Durbin-Watson value is 1.865, placing it well within the acceptable range. Thus, the model can be considered stable and reliable.

Table11. ANOVA.

a. Predictors: (Constant), Customization, Content Quality, Customer reviews, Visual appeal, Security Privacy, Ease of use, Customer Service. b. Dependent Variable: E-commerce Website Success.

The ANOVA results show that the regression model is statistically significant, as the value of (F) reached 321.190 at a significance level (Sig.) of 0.000. This indicates that the independent variables, when considered jointly, significantly influence the dependent variable. Accordingly, the overall model demonstrates adequate explanatory power and statistical significance that make it appropriate for testing the study’s hypotheses and making predictions.

Table 12. Results of multiple linear regression analysis.

a. Dependent Variable: E-commerce Website Success.

The findings presented in Table 12 indicate that all predictor variables are statistically significant, demonstrating their positive effect on e-commerce website success. Accordingly, all the proposed hypotheses are accepted.The standardized coefficients (β) reveal that ease of use is the most influential predictor of e-commerce website success (β = 0.234), followed by content quality (β = 0.217), while customization represents the weakest predictor (β = 0.098).Moreover, the positive standardized regression coefficients (β) indicate that e-commerce website success—the dependent variable—moves in the same direction as the independent variables move.The unstandardized regression coefficients show that a one-unit increase in ease of use leads to a 0.192 increase in e-commerce website success. Similarly, a one-unit rise in content quality results in a 0.183 increase in website success, whereas a one-unit increase in customization contributes to a 0.086 increase in website success.Based on these results, the multiple linear regression equation can be expressed as follows:

Y = a + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇

where a is a constant;

Y is the dependent variable (e-commerce website success);

X₁, X₂, X₃, X₄, X₅, X₆, and X₇ are the independent variables, which are (Ease of use, Customer reviews, Customer Service, Security & Privacy, Content Quality, Visual appeal, and Customization), respectively.

β₁, β₂, β₃, β₄, β₅, β₆, and β₇ are the standardized coefficients for the previously mentioned independent variables.

So, the general regression model can be reformulated as follows:

Y = 0.153 + 0.234 ease of use + 0.153 customer reviews + 0.172 customer service + 0.198 security & privacy + 0.217 content quality + 0.126 visual appeal + 0.098 customization

5.5.5. Stepwise Regression Analysis

Stepwise regression is a statistical technique used in research to build an optimal regression model by selecting the most significant independent variables that explain the dependent variable. In addition, it helps identify the most influential variables while excluding those that contribute little to the model. Consequently, it improves the explanatory power of the model, reduces multicollinearity, and enhances predictive accuracy by retaining only statistically significant variables in the final regression equation. According to [96], the stepwise regression technique is used to reduce the list of explanatory variables to a concise collection of the most useful variables.

Therefore, to assess the most important variables that strongly influence e-commerce website success and to develop a linear prediction equation based on the candidate variables, stepwise regression analysis was conducted. The results of this analysis are presented in Table 13.

Table 13. Results of stepwise regression analysis.

a. Dependent Variable: E-commerce Website Success.

The results of the stepwise regression analysis indicate that all independent variables included in the model have a positive effect on the e-commerce website success variable.The significance level (Sig.) for all variables was 0.000, indicating that the model is statistically significant and providing support for all proposed hypotheses.The standardized regression coefficients (β) show that ease of use is the strongest predictor of e-commerce website success (β = 0.193), followed by content quality (β = 0.167), then security & privacy (β = 0.135), then customer service (β = 0.121), and finally customer reviews (β = 0.109).These results reflect the relative importance of variables in explaining the variance in e-commerce website success. Ease of use appears to be the most influential factor compared to the others, while customer reviews ranked last in terms of influence, although they remained statistically significant.Based on this information, the regression model derived from the stepwise analysis can be expressed as follows:E-commerce website success = 0.118 + 0.193 ease of use + 0.167 content quality +.135 security & privacy + 0.121 customer service + 0.109 customer reviews.

This study contributed to the literature and added new knowledge by proposing a model that explores the role of website characteristics on e-commerce website success, which has not been previously studied in the e-commerce literature. Hence, the model can be considered unprecedented.

To our knowledge, no study has combined the proposed factors into a single model. Previous studies have examined some factors individually, but this study used an integrated methodology, evaluating several factors simultaneously.

This study could mark the beginning of an interesting discussion in the e-commerce context on whether the proposed factors have a relationship with site profit. In addition, the findings will further open avenues for future research to explore more factors that can help marketers/practitioners.

This study reveals the most important website characteristics that lead to e-commerce website success. Therefore, managers of such sites should pay more attention to these characteristics to ensure the success of their websites, thereby reducing the time, effort, and cost associated with paying attention to other factors that are not useful.

This research assists in finding a forecasting model for e-commerce website success to attract consumers. This enables companies operating in this field to improve their online marketing efforts and increase sales and profitability in an increasingly competitive digital marketplace.

This research provides valuable insights into the success factors of e-commerce websites. The results of this research thus provide important contributions to e-retailers and e-commerce marketers in ensuring the success of their online platforms.

This study highlights the mechanisms of e-commerce websites’ success from the consumer’s perspective, helping marketing managers in the e-retail sector to formulate effective strategies that enhance customer satisfaction and foster long-term relationships with their clients. Additionally, the findings of this study assist website designers in focusing on key design features, thereby strengthening the competitive advantage of these websites.

The findings also provide helpful information for businesses looking to improve their website. Consequently, the results of this study can be used as a guide for e-commerce companies to face existing challenges and compete effectively in the online market.

Understanding these factors helps e-commerce managers create successful websites and build stronger customer relationships. In addition, it helps them reallocate their resources in a way that maintains existing customers and attracts more of them.