Pa ge 1 Pa ge 40 American Journal of Multidisciplinary Research and Innovation (AJMRI) Exploratory Study of the Impact of the Trending Web Store Characteristics on Returning Products: Context of the Saudi Online Shopping Khaled Shaban1*, Bushra Mohammed Al Sulayyim2 Volume 4 Issue 6, Year 2025 ISSN: 2158-8155 (Online), 2832-4854 (Print) DOI: https://doi.org/10.54536/ajmri.v4i6.3601 https://journals.e-palli.com/home/index.php/ajmri Article Information ABSTRACT Received: July 08, 2024 Accepted: September 10, 2024 Published: November 20, 2025 This study examines the complicated world of customer behaviour in online shopping, focusing on Product Detail Pages (PDPs), traditional vs online purchase preferences, and product return issues. The research emphasises the significance of product displays pages (PDPs) in delivering product information, including size, colour, pricing, return policies, and user reviews. These factors strongly influence consumer purchases. Online shopping is becoming more popular, yet many people prefer traditional purchasing. This preference is largely motivated by return policy concerns and a desire to check products, particularly in specific categories, physically. The study emphasises the need for precise product descriptions, clear return processes, and controlling the supply chain quality in decreasing product returns. The correlation study showed a statistically significant relationship between gender, income, occupation, and internet shopping habits. These results have shown demographic aspects that influence consumer behaviour. A complete study of participant responses using the one-sample t-test may reveal online shopping engagement. Keywords Information System, Online Shopping, Product Returns, Saudi Arabia 1 Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia 2 Department of Computer Informatics, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia * Corresponding author’s e-mail: k.shaban@seu.edu.sa INTRODUCTION The visual representation of a web store has a significant impact on the development of consumer loyalty in online retail, similar to its impact on customer loyalty in brick- and-mortar retail establishments (Soni, 2021). Kalia et al. (2017) found that the design features of the web store include many key elements, including simplicity of access, convenience, and substitutability of personal examination (Kalia, 2017). The inclusion of aesthetical characteristics and the presentation of information systems on websites have been widely recognised as a fundamental element of online retail service excellence (Soni, 2021). Theoretical frameworks have been developed to explain online shopping consumer behaviour, which includes a range of workable propositions for assessing consumers’ propensity to engage in online shopping (Nachar, 2019). The present exploratory research examines the impact of current web store attributes on the occurrence of product returns, specifically within the way of the online shopping environment in Saudi Arabia. The research has explored insights into the fundamentals that lead to product returns in the specific market by analysing the essential features and characteristics of prominent online retailers. Alzahrani et al. (2019) stated that the use of e-commerce among small and medium-sized enterprises (SMEs) in Saudi Arabia has seen prominent growth (Alzahrani, 2019). This may be considered in reducing the impact of returning products (Alzahrani, 2019). This study is of great importance in comprehending customer behaviour and preferences within the domain of online buying, providing useful implications for e-commerce platforms and enterprises operating in Saudi Arabia. Florido-Benítez (2023) stated that online companies are now adjusting to the changing circumstances and actively pursuing digital strategies to address the issues they face in e-business-to-business (B2B) e-commerce (Florido-Benítez, 2023). Despite the prevalence of conventional shopping among Saudi consumers, internet shopping is seeing significant growth due to its convenience and competitive pricing (Griffis et al., 2012). Furthermore, the results of this research have the potential to facilitate the adoption of customised approaches that target the reduction of product returns and the improvement of overall customer satisfaction within the online shopping landscape in Saudi Arabia. This research analysed product detail pages and return policies as key drivers in online customer behaviour. It evaluates PDPs’ product information and returns policy clarity. The study also examines how technology may improve online purchasing and minimise returns. LITERATURE REVIEW Theoretical Background The study has explored that how e-commerce highlights the impact of the theory of reasoned action on the decision-making process of purchase. The Engel Kollet Blackwell (EKB) framework, as explained by Thomas and Babu (2021), presents a comprehensive model that defines a five-step progression undertaken by consumers in their decision-making process leading to purchase (Thomas & Babu, 2021). The process starts with the first stage of issue detection, which is then followed by the subsequent stage of information system search and concludes with the stage of alternative appraisal. The fourth stage of the buying channel involves Pa ge 41 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 4(6) 40-47, 2025 decision-making, which is significantly impacted by the information system stage in the input phase. This step enables consumers to obtain sufficient data and compare it with other rivals or alternatives. The information stage encompasses many crucial factors that are associated with consumer purchase choices. Copeland et al. (2020) have exposed a range of factors that exert an effect on consumer purchasing choices in online retail (Copeland & Zhao, 2020). These factors, which may operate independently or in conjunction with one another, include elements such as pricing, product specifications, return policies, logistical considerations, and the social validation provided by customer ratings and evaluations (Copeland & Zhao, 2020). Rahman et al. (2023) stated that online marketing has emerged as a prominent strategy in this digital age, allowing companies to reach a wider audience and expand their customer base beyond geographical limitations (Rahman, 2023). With increasing global interconnectedness, the impact of internet marketing is expanding, effectively crossing geographical divides and allowing cross- border e-commerce transactions on a global scale (Rahman, 2023). AL Hilal et al. (2023) stated that the Kingdom of Saudi Arabia is now seeing a substantial surge in the expansion of e-commerce, therefore generating noteworthy prospects (AL Hilal, 2023). The e-commerce user base in Saudi Arabia had a significant increase, growing from 18.22 million individuals in 2017 to 33.6 million individuals in 2024 (AL Hilal, 2023). According to a recent survey conducted by PwC in the Middle East area, the Global Consumer Insights Survey (PwC Middle East 2021) has shown that a majority of consumers, namely 67%, have exhibited a heightened inclination towards digital platforms compared to their previous behaviour (M. El Khoury et al., 2023). Namoun et al. (2022) explored in the study proposed a mobile commerce acceptance model in which the characteristics of performance expectation, cost, and effort expectancy were identified as major determinants for user adoption (Namoun et al., 2022). Florido-Benítez et al. (2023) explored that different factors have contributed to the occurrence of product returns within online shopping in Saudi Arabia (Florido-Benítez, 2023). According to Chaleshtari et al. (2022) stated that a key determinant pertains to disparities seen between the product description and the tangible object acquired. The dependence of customers on precise product information systems is substantial, and any discrepancy in this regard results in discontent, ultimately leading to product returns (Chaleshtari et al., 2022). Moreover, the prevalence of size and fit concerns in online fashion transactions is noteworthy since shoppers often have difficulties in properly assessing the visual appearance and proper fit of an item prior to purchase (Chaleshtari et al., 2022). Abdellatif et al. (2019) found that the occurrence of delivery delays or the receipt of damaged items upon arrival may also serve as catalysts for product returns since consumers have a legitimate expectation for rapid and undamaged deliveries (Abdellatif & Alshibani, 2019). Cultural preferences and regional expectations may affect consumer behaviour, as seen by the distinct preferences and sensitivities observed among Saudi Arabian customers, which in turn impact their choices about product returns (Mabkhot et al., 2022). Product Detail Page (PDP) Lee et al. (2017) stated that a product detail page (PDP) refers to a webpage found on an electronic commerce platform that offers a comprehensive information system regarding a particular product (Lee & Wang, 2017). This information system encompasses various aspects, including but not limited to size, colour, price, return policy, shipping details, evaluations, reviews, product imagery, and other pertinent information that may significantly influence a customer’s decision to make a purchase (Hudders et al., 2021). According to Heitmann et al. (2020), it has been shown that the inclusion of product specifications with visual representations such as photos or videos has a significant impact on consumer purchase choices (Heitmann et al., 2020). The product information system encompasses many components, such as specs, features, and reviews, which serve the purpose of addressing client inquiries and facilitating product comparisons with other options (Wang et al., 2019). A significant majority of consumers, surpassing 85%, engage in the practice of examining product evaluations on the internet prior to finalising their buying choices (Wang et al., 2019). Arrafi et al. (2021) observed that the ease of placing orders with a single click, comprehensive product information, unrestricted product availability, and prompt delivery times are significant determinants that contribute to impulsive online purchasing (Arrafi & Ghabban, 2021). This has shown that the product detailing page makes it easy for the customers to select the products. Traditional vs. Online Shopping The prevalence of online purchasing has seen a notable surge in popularity over the 21st century. The reason for its widespread appeal may be attributed to the prioritisation of convenience in contemporary society (Lokhande). According to Rahman and Mannan’s (2018) investigation on customer purchasing patterns in Bangladesh, it was observed that customers exhibit a disinclination towards e-commerce enterprises that possess inadequate or ambiguous return policies (Rahman & Mannan, 2018). Consequently, they tend to prefer patronising conventional brick-and-mortar establishments that provide more favourable return policies (Rahman & Mannan, 2018). In their recent study, Aryani et al. (2021) examined consumer behaviour in the context of the COVID-19 pandemic (Aryani et al., 2021). The authors highlight a notable preference among some customers for conventional buying over online purchasing, citing the ability to physically inspect, touch, and browse things as a key factor influencing Pa ge 42 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 4(6) 40-47, 2025 this choice (Aryani et al., 2021). According to research conducted by Harris et al. (2017) in the United Kingdom, customer preferences for online or conventional shopping are influenced by the specific categories of items they want to acquire (Harris et al., 2017). Harris et al. (2017), for instance, the act of purchasing groceries and convenience items via online platforms may not be as appealing to a significant portion of customers (Harris et al., 2017). It has been observed by Soni et al. (2021) that web store has played an important role in producing online retail customer loyalty in the same manner as they influenced customer loyalty in traditional retail stores (Soni, 2021). Wang et al. (2023) found that online shopping from web stores was most preferred through livestream buying, which has emerged as a new kind of e-commerce, gathering more customer interest compared to traditional online purchasing methods (Wang & Oh, 2023). It has been observed that rise in online purchase is a manifestation of changing customer tastes in the current period. Although influence of trends on the e-commerce industry, it is essential for companies to possess a comprehensive awareness of the intricate elements that impact customer decision-making. MATERIALS AND METHODS Research Strategy and Approach The research used a quantitative research methodology in order to accomplish research aims. The study of product return relied on quantitative market research methods with the help of surveys (Copeland & Zhao, 2020). The approach described is extensively used in several disciplines, including sociology and economics, to gather data for the purpose of enhancing quality. Empirical research has used a quantitative approach to investigate the determinants of consumer purchasing behaviour in online retail settings (Aldousari, 2016.). Similarly, a research methodological approach was taken by AlGhamdi et al. (2012) in the study investigating factors influencing Saudi customer’s purchasing decisions in the online retail market (AlGhamdi et al., 2012). Sampling Rashid et al. (2019) found that the primary analysis method used for data collection was the utilisation of a pre-tested questionnaire with Likert scale items consisting of five points (Rashid et al., 2019). Rashid et al. (2019) found that the questionnaire was developed on the Google Form platform. Rashid et al. (2019) stated that the participants were sent the questionnaire by electronic mail and also through other social media platforms such as WhatsApp and Twitter (Rashid et al., 2019). Cochran et al. (1977) found that the sampling technique has been proposed for analysing the quantitative data with the help of a survey, and questionnaires have been adopted based on the research (Cochran, 1977). The target population consisted of those participants who were engaged in online shopping in Saudia Arab. Also, the sample size for the study was 600 to evaluate how well the online shopping websites’ characteristics were useful as per their requirements. Data Analysis The data obtained was promptly stored in a Google survey and afterwards exported to the SPSS (Statistical Package for the Social Sciences) software for further analysis. By asking participants who did shop online more than twice, the results illustrate that 88.8% Yes and 11.2 No. Therefore, the bridge, as designed, divides the participants into two groups. The reasons for the bridge are to understand why participants do not shop online and what the key factors are that participants prefer to shop in traditional stores. Also, to understand in more detail for the group how to shop online. Table 1: Correlation Analysis Age range Education Marital status Monthly income - In Saudi Riyals Gender Age range Pearson Correlation 1 .301** -.164** 0.077 -.161** Sig. (2-tailed) 0 0 0.06 0 N 600 600 600 600 600 Education Pearson Correlation .301** 1 -0.052 0.004 0.043 Sig. (2-tailed) 0 0.202 0.932 0.296 List of Countries Pearson Correlation 0.031 -0.076 .189** .302** .213** Sig. (2-tailed) 0.447 0.064 0 0 0 Marital status Pearson Correlation -.164** -0.052 1 -0.034 .653** Sig. (2-tailed) 0 0.202 0.403 0 Monthly income - In Saudi Riyals Pearson Correlation 0.077 0.004 -0.034 1 -0.041 Sig. (2-tailed) 0.06 0.932 0.403 0.318 Gender Pearson Correlation -.161** 0.043 .653** -0.041 1 Sig. (2-tailed) 0 0.296 0 0.318 Pa ge 43 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 4(6) 40-47, 2025 The correlation matrix has shown insights into the interrelationships among different variables within a research study with a sample of 600 participants in the Kingdom of Saudi Arabia. It was found that there exists a positive connection (r = 0.301, p < 0.01) between age and education, indicating that as individuals get older, their level of education tends to rise. The data demonstrates a significant negative relationship between marital status and both age (r = -0.164, p < 0.01) and education (r = -0.052, p < 0.05). This suggests that people who were married often exhibit a small tendency to be younger and possess lower educational attainment. The relationship between monthly income and age has a mild positive correlation (r = 0.077, p < 0.06), indicating that when age increases, there was a little tendency for monthly income to also increase. On the other hand, the association between monthly income and education have shown minimal and negative (r = 0.004, p > 0.05), suggesting that there was no significant association between education level and monthly income. It has been also observed that there exists a substantial correlation between gender and marital status (r = 0.653, p < 0.01), suggesting a relationship between the feminine gender and being married. The results have contributed to a more comprehensive comprehension of the complex relationship between demographic factors, hence illuminating probable trends and correlations among age, educational attainment, married status, monthly income, and gender among the examined population residing in Saudi Arabia. Table 2: Frequency Analysis Statistics I a m 1 8 ye ar s o ld o r o ld er an d I a gr ee to pa rt ic ip at e in th is qu es tio nn ai re . A ge ra ng e E du ca tio n Li st o f C ou nt rie s M ar ita l s ta tu s Yo ur m on th ly in co m e - I n Sa ud i R iy al s O cc up at io n G en de r tra di tio na l_ Sh op pi ng pr od uc t d es cr ip tio n Re tu rn P ro du ct Sa tis fa ct io n_ of _C us to m er N 600 600 600 600 600 600 600 600 600 555 555 555 Mean 1.00 2.03 1.96 4.67 3.76 2.27 1.07 1.95 3.8116 1.9894 1.8776 1.8036 Table 3: Regression Analysis Statistics M od el R R S qu ar e A dj us te d R Sq ua re St d. E rr or o f th e E st im at e Change Statistics D ur bi n- W at so n R S qu ar e C ha ng e F C ha ng e df 1 df 2 Si g. F C ha ng e 1 .555a .308 .303 .05005 .308 61.266 4 550 .000 .344 The table shows the sample size and mean values for various variables in a questionnaire, including age range, education level, countries, marital status, monthly income in Saudi Riyals, occupation, gender, traditional shopping behavior, product description, return of products, and customer satisfaction. The data shows that the majority of participants are 18 years or older, have an average education level of 2.03, are from a wide variety of countries, have a higher likelihood of being married or committed, and have an average monthly income of 3.76. The data also provides a comprehensive summary of participants’ views and experiences regarding product descriptions, returns, and customer satisfaction. The summary of the model demonstrates the efficacy of the regression model in forecasting the dependent variable. The statistical analysis indicates that the entire model is highly significant (F = 61.266, p < 0.001), indicating that there is a considerable contribution from at least one predictor variable in explaining the variability seen in the dependent variable. The R Square coefficient of determination, with a value of 0.308, indicates that Pa ge 44 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 4(6) 40-47, 2025 about 30.8% of the variance in the dependent variable can be explained by the independent variables. The Adjusted R Square value, which takes into account the number of predictors and accounts for the complexity of the model, is 0.303. This suggests that, even after controlling for the number of variables, the model maintains a significant level of explanatory ability. The Durbin-Watson statistic, which has a value of 0.344, is within the permissible range, suggesting the absence of severe autocorrelation in the residuals. The statistical analysis reveals that the incorporation of predictors into the model has resulted in a noteworthy augmentation in the R Square value (R Square Change = 0.308, F Change = 61.266, df1 = 4, df2 = 550, p < 0.001), hence providing more evidence in favor of the model’s overall explanatory power. The regression model is statistically robust, with the predictors contributing significantly to explaining the variability in the dependent variable Table 4: ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression .614 4 .153 61.266 .000b Residual 1.378 550 .003 Total 1.991 554 Table 5: Coefficients Model Unstandardized Coefficients Standardized Coefficients t P-Value B Std. Error Beta 1 (Constant) 1.066 .072 14.727 .000 Traditional_Shopping -.004 .007 -.023 -.646 .519 product_description .071 .016 .162 4.486 .000 Satisfaction_of_Customer .229 .015 .544 14.973 .000 Shopping_Online_customers .091 .015 .221 5.891 .000 The ANOVA findings evaluate the statistical significance of the regression model in elucidating the variability seen in the dependent variable. The regression model, together with its predictors, demonstrates statistical significance (F = 61.266, p < 0.001), suggesting that the whole regression model well accounts for the variability seen in the dependent variable. The regression model’s sum of squares is 0.614, with a corresponding 4 degrees of freedom, leading to a mean square value of 0.153. The residual sum of squares is determined to be 1.378, accompanied by a total of 550 degrees of freedom, resulting in a mean square value of 0.003. The F-statistic and ANOVA findings support the statistical significance of the regression model in explaining observed variability in the dependent variable, demonstrating its ability to accurately capture meaningful associations. The coefficients table offers valuable information about the individual contributions of each predictor variable to the regression model for the dependent variable. The coefficient of the constant term is 1.066, with a standard error of 0.072. It is highly statistically significant (t = 14.727, p < 0.001), suggesting a significant intercept in the model. In relation to the predictors, the conventional shopping behavior has a statistically insignificant and negative correlation with the dependent variable (β = -0.023, p = 0.519), suggesting that this component does not make a meaningful contribution to the prediction. On the other hand, there are notable differences between the variables “product_description” (β = 0.162, p < 0.001) and “Satisfaction_of_Customer” (β = 0.544, p < 0.001) in terms of their positive and statistically significant relationships with the dependent variable. This implies that there is a positive correlation between the quality of product descriptions and consumer happiness, resulting in higher values of the dependent variable. Furthermore, there is a noteworthy and statistically significant correlation (β = 0.221, p < 0.001) seen in the “Shopping_Online_ customers” group. This suggests that individuals who partake in online shopping are inclined to display greater values of the dependent variable. The aforementioned results underscore the significance of proficient product descriptions, consumer contentment, and online purchasing conduct in shaping the anticipated result. Discussion The influential predictors that have a substantial impact on the dependent variable within the context of this research. Although conventional purchasing behavior does not seem to have a significant impact, the importance of product descriptions, customer happiness, and engagement in online shopping becomes evident (Sartor, 2019). The use of concise and persuasive product descriptions, in conjunction with an elevated level of customer satisfaction, has a favorable impact on the anticipated result. The increasing impact of internet buying behavior highlights the changing terrain Pa ge 45 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 4(6) 40-47, 2025 of customer preferences. In order to increase the total customer experience and favorably affect the examined outcome variable, it is essential for businesses to emphasize the improvement of product descriptions, the nurturing of customer happiness, and the adaptation to the rising prevalence of online purchasing (Otto et al., 2020). These insights provide essential information that may be used in strategic decision-making and customized interventions aimed at successfully addressing consumer behavior and preferences. According to Statista, in recent years, there has been a shown in global retail e-commerce sales, with projections indicating substantial growth to reach a staggering $6542 billion by the year 2023 (Statista, 2019). Despite the implementation of different strategies aimed at nurturing consumer purchasing motivation, the issue of product returns remains a prevalent concern throughout several nations (Lin, 2020). The percentage of product returns observed within the online fashion and clothing sector may be attributed to many factors, including faults and damage, excessive consumer expectations, inadequate fit, and indecisiveness among customers (“Retail right - national: ,” 2023). Shumba et al. (2023) stated that in online product purchases, it is important for the service provider to give a precise and comprehensive description of the product; however, this has enabled the customers to assess and evaluate their quality expectations preceding to making a purchase (Shumba & Ferreira, 2023). According to Frei et al. (2020), the issue of product returns extends beyond its economic implications, as it has significant environmental consequences. As per the findings of a study conducted by Vogue Business (Frei et al., 2020). The findings of the survey indicate that a proportion of respondents expressed a preference for traditional shopping methods, such as the opportunity to analyse products personally. However, online fraudulent activities and a need for further product information have been included. Rahman et al. (2023) stated that in contemporary society, the practice of online shopping serves to fulfil fundamental necessities, including but not limited to water, food, and clothing, while also operating in a non-discriminatory manner, devoid of any evaluation or consideration of the goods or services purchased (Rahman & Hossain, 2023). Medhi et al. (2022) explored that traditional retail structures, such as shopping malls or online web stores, were reducing the concept of traditional shopping (Medhi & Rashid, 2022). Zhu et al. (2023) stated that the efficiency issues that have been observed in traditional supply chains persist in e-commerce supply chains as well. These issues arise due to decentralised decision-making among various entities involved, resulting in the twofold marginalisation of utility and a subsequent decrease in overall system efficiency (Zhu et al., 2023). The preference for traditional shopping focus on deliberation before buying as this has explored the significance of in-person interactions. Roamno et al., (2021) observed that, traditional purchasing emphasises consideration before purchase(Romano et al., 2021). Product range is valued moderately to highly, that has shown a preference for diversity that has resulted in many reasons for not buying online have shown that how complicated customer incentives are (Boyer et al., 2021). These results help organisations provide customers’ different interests and behaviour. With the help of these restraints helps organisations interact with customers and boost customer satisfaction and loyalty. Moreover, preference for traditional shopping and hesitancy towards online shopping suggests that those who favor in-person experiences harbor reservations about buying online (Pang et al., 2021). To enhance comprehension of the formulation of attitudes and intents towards online purchase in order to facilitate the development of new online shopping platforms and technologies by businesses. These associations offer actionable insights for marketers to tailor strategies and enhance the overall customer purchasing journey based on consumer behavior patterns (Riley & Klein, 2021). CONCLUSION The research highlights the significance of PDPs that are designed to be easily navigable by users, return policies that are transparent and easily understandable, and the use of technology to improve the overall online shopping experience and reduce the occurrence of product returns. Moreover, the research highlights the need to implement customised strategies to accommodate diverse customer inclinations and demographic characteristics. The study uses a quantitative strategy employed in many questionnaire-based consumer behaviour studies. It is recommended for firms to allocate resources towards the development of streamlined and customer-centric return procedures. Enhancing customer satisfaction and loyalty may be achieved through the implementation of several strategies, such as the establishment of transparent return rules, the adoption of user-friendly processes, and the provision of prompt and efficient customer service. These measures aim to streamline the return process, ensuring a hassle-free experience for customers. The research suggests that despite the increasing popularity of internet buying, conventional purchasing preferences continue to have a certain level of impact. 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