The Impact of Sustainable Logistics Service Quality in E-shopping Applications on Consumer Purchase Decision-Making Stages: A Survey Study in the Arab Gulf

The Impact of Sustainable Logistics Service Quality in E-shopping Applications on Consumer Purchase Decision-Making Stages: A Survey Study in the Arab Gulf | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Impact of Sustainable Logistics Service Quality in E-shopping Applications on Consumer Purchase Decision-Making Stages: A Survey Study in the Arab Gulf Naglaa Elgammal, Mohammed Mostafa Refaat Moharam, Dalia Hassan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8254418/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to examine the impact of sustainable logistics service quality (SLSQ) on consumer purchase decision-making stages in e-shopping apps in the Arab Gulf, employing a survey-based methodology with 390 participants who were selected based on a convenience sample. Despite the global research interest of this research area, most of the literature that is available focuses on the Western or Asian markets and few empirical studies have been undertaken in the Arab Gulf, where unique cultural, regulatory, and infrastructural conditions shape consumer demands and logistics operations. The results of this study collectively demonstrate a strong and consistent relationship between (SLSQ) and the stages of consumer purchase decision-making in e-shopping applications across the Arab Gulf region, with moderating factors such as environmental concern and attitudes toward governmental sustainability plans. The high means observed for green packaging and smart returns/reverse logistics indicate that consumers in the Gulf prioritize tangible and convenient sustainability features that directly impact their experience. On the other hand, aspects such as carbon footprint management received relatively lower attention, likely due to their abstract or less visible nature to the average online shopper. Nationality and education level had the most consistent and significant effects across all decision-making stages, suggesting that cultural background and educational attainment shape consumers’ understanding and appreciation of sustainable logistics. Age, profession, and income also influenced most stages, indicating that lifestyle and economic capability play key roles in shaping e-shopping decisions Business and commerce/Business and management Social science/Business and management Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Business and commerce/Information systems and information technology Sustainable Logistics Service Quality (SLSQ) E-shopping Applications Consumer Purchase Decision-Making Stages Arab Gulf Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction After the rush of e-shopping applications rolled out across the Arab Gulf, consumer demands changed exponentially to convenience, speed, and efficiency. What was once a long, tedious process, now transformed into merely going online to transact business from the comfort of one`s home. In the midst of this online world, the idea of sustainable logistics service quality (SLSQ) that includes environmentally friendly, economically sound, and socially responsible logistics practices have emerged as a paradigm shift (Arabelen & Kaya, 2021 ) Green logistics includes several different processes, such as best routing, green packaging, improving fuel efficiency, using environmentally friendly vehicles, reverse logistics, recycling materials, reducing waste and relying on renewable energy sources in logistics operations, all focused on reducing environmental impacts but not at the expense of efficiency of operations (Deckert, 2023 ; Murphy & Poist, 2000 ; Seroka-Stolka, 2014 ). When it comes to e-shopping uses, providing higher service quality with green logistics has the potential to influence customer purchasing behavior significantly (Blanco & Sheffi, 2024 ), particularly in the GCC region, where e- shopping is also developing well. According to Huma et al. ( 2020 ) logistics service quality (LSQ) is consumers' perception of delivery performance concerning their expectations. Therefore, LSQ becomes a critical variable to influence satisfaction, loyalty, and intention to buy again. Delivery time, information accuracy, order condition, reverse logistics, and ease of payment have been identified as the most essential dimensions impacting customers' satisfaction and loyalty in online buying, especially for digitally-suave generations such as Generation Z (Do et al., 2023 ; Mallieswari et al., 2023 ). Some studies have agreed that there is a stronger likelihood of customers remaining with retailers that display environmentally friendly fulfillment processes and would even pay extra for environmentally friendly fulfillment services. (Asawawibul et al., 2025 ; Karina et al., 2024 ; Oh et al., 2022 ) Despite this global research interest, most of the literature that is available focuses on the Western or Asian markets and few empirical studies have been undertaken in the Arab Gulf, where unique cultural, regulatory, and infrastructural conditions shape consumer demands and logistics operations. GCC countries are actively adopting Industry 4.0 and smart logistics technologies such as automation, IoT monitoring, and blockchain, but their contribution to sustainable quality of service and influencing purchasing behavior remains poorly explored. Conversely, last-mile services of different degrees of digital efficiency or sustainability are provided by UAE-based and other Gulf countries' logistics firms such as Quiqup and Shipa Delivery, but very few studies have examined consumers' reactions towards these types of services. This study aims to bridge these gaps by examining the impact of SLSQ on consumer purchase decision-making stages in e-shopping apps in the Arab Gulf, employing a survey-based methodology. Literature review Empirical literature demonstrates that logistics practices based on sustainability positively impact consumers' buying intentions. Kale (2025) confirmed that green delivery and carbon-free shipping are major driving factors behind consumers' purchasing decisions, with well-off consumers being more willing to pay more for eco-friendly delivery. Likewise, in Indonesia, a study on the users of "Bhinneka" online shopping platform showed that purchase decisions are strongly affected by LSQ, affirming the importance of reliability and safety in logistics (Dianavera & Aminoto, 2020). Generational dynamics also influence expectations. In Generation Z, delivery quality, speed, flexibility, tracking, and greener options were essential in forming purchase decisions and loyalty, capturing this group's increased digital literacy and ethical sensitivity (Radyi et al., 2024). This is consistent with European evidence, where procurement managers increasingly emphasize sustainability in logistics choice, but with limited compliance leverage over suppliers (Large et al., 2013). Green performance of online shopping logistics also plays a deciding role. Green transport tactics used in online purchases minimize congestion and emissions, along with building trust among consumers, particularly within urban last-mile delivery networks (Nica, 2015). A bibliometric analysis ensures that the subject is heavily evolving, especially on circular economy, omnichannel retail, and city logistics topics (Rita & Ramos, 2022). Kasap (2025) studied sustainable e-commerce approaches and two factors (green logistics and digitalization) influenced consumer purchasing behavior across several countries, with the US and Brazil being most sensitive to sustainable delivery choices. Kawa and Pierański (2021) established links between environmental logistics feature and satisfaction levels for customers. Their research revealed that environmentally friendly delivery options, green packaging choices, and carbon-neutral delivery options all served to influence consumer choices and consumer loyalty strongly in the choice of platforms used for shopping. They mention that green logistics is no longer a peripheral offering but has become a core process to e- shopping. Chinese empirical evidence has demonstrated that delivery quality is the strongest determinant of customer satisfaction and can directly impact repeat purchase intentions and loyalty (Choi, 2019). The empirical research on online logistics service reviews confirmed that elements of delivery (punctuality, reliability, and empathy) combined with reassuring online review information influence buying intentions (Li & Chen, 2021). A bibliometric analysis of SLSQ also revealed expanding global interest since 2013, emphasizing the role AI, blockchain and big data play in improving green logistics while not negatively impacting customer satisfaction (Abdelaziz & Munawaroh, 2024). Meanwhile, studies conducted in the countries of Saudi Arabia, Mexico, Colombia and Ecuador have confirmed that online buying decisions are affected by SLSQ, brand reputation, perceived product attributes, and perceived value (Al Hamli & Sobaih, 2023; Gu et al., 2021; Larios-Gómez et al., 2021). In India, eco-friendly last-mile logistics impacted consumers' intentions to buy natural foods and organic products through e-commerce by mediating trust as an intermediary between logistics performance and purchase intention (Gupta et al., 2024; Maurya et al., 2023; Naz et al., 2024). Likewise, as evidence shows, delivery stability and sustainability are evidently influencing factors of cross-border e-commerce satisfaction in Korea, with green delivery moderating impacts that enhance trust and loyalty (Oh et al., 2022). However, there are still obstacles. A Brazilian research study pointed to the fact that consumers are open to green delivery, but speed and price remain dominating factors, which means that sustainability remains secondary in actual purchase trade-offs (Nogueira et al., 2021). As per the Italian case study, customers are willing to pay a little extra for eco-friendly delivery as long as the environmental benefits, such as reforestation, are clearly communicated (Biancolin & Rotaris, 2024). Green packaging, as well as logistics, were found to be equally important through consumer trust in the buying behavior of customers in a study conducted in Pakistani e-commerce in South Asia (Ahmad et al., 2024). Also, more current comparative research on global e-commerce leaders, such as Amazon and Alibaba, shows that companies achieve a competitive edge and customer satisfaction alongside sustainability by controlling their own distributed logistics (Florido-Benítez, 2024). In Vietnam, however, Nguyen et al. (2019) made it clear that the biggest determinants of customer satisfaction are customer service, order quality, information quality, and delivery quality. This means that aside from receiving their products on time, being able to have problems solved and having personalized attention goes a long way to encourage customer loyalty regarding online shopping (Santarek et al., 2022). Verma (2024) suggested that shoppers from the GCC region care about e-retailing sites post-COVID. Cultural considerations go a long way in this regard. The results reflect that shoppers from within the region want logistics partners who demonstrate environmental sustainability but still provide efficiency in their services. Then, combining logistics quality and consumer mindset, studies show that green delivery and dynamic pricing have mixed impacts: while speed and quality have a strong impact on repurchase, green logistics alone does not automatically lead to repeat purchases without complementary value drivers (Asawawibul et al., 2025; Lin et al., 2023; Zheng et al., 2022). Another interaction effect between logistics quality and membership systems shows how loyalty programs paired with efficient delivery can strengthen consumer loyalty (El Moussaoui et al., 2023; Lin et al., 2023). Logistics 4.0 interlinks Internet of Things (IoT), artificial intelligence, blockchain, and big data technologies for end-to-end real-time transparency, predictive scheduling of deliveries, and sustainable supply chain practices (Perotti et al., 2022; Sun et al., 2022). These technologies, as discussed in research, enhance consumer confidence in terms of heightened visibility, transparency, and responsiveness of purchasing decision-driving logistic activities (Büyüközkan & Göçer, 2025; Farajpour et al., 2022; Parhi et al., 2022). Another study suggests that intelligent logistics systems improve the quality attributes of service like reliability, flexibility, and customization as per customers' preferences and green and optimized manners (Kolasińska-Morawska et al., 2022; Shee et al., 2021). Within the setup of e-commerce platforms, digital logistics solutions like automated warehouses and AI-based scheduling of deliveries reduce delays and operational mistakes by far, increasing customer satisfaction (Szeredi et al., 2024). Logistics 5.0, characterized by people-centered technology integration, intelligent automation, and sustainability-oriented principles, lies at the center of consumer purchase decision influence on online shopping use cases (Andres et al., 2024; Trstenjak et al., 2022). Empirical evidences show that dimensions of logistics service quality, such as delivery timeliness, accuracy, reverse logistics, and return convenience, significantly influence customer satisfaction, trust, and repurchase intentions and serve as determinants of e-commerce buying behavior (Li et al., 2023; Szeredi et al., 2024). Besides, consumer perception of logistics transparency via attributes like traceability, online ratings of delivery performance, and logistics carrier reputation are shown to strengthen purchase intentions and minimize perceived risks (Bolatan, 2021; Hsu et al., 2024). Although much research has indeed been carried out on sustainable logistics service quality across various global contexts, there remains an enormous empirical space to comprehend the precise mechanisms through which sustainable logistics activities influence consumer purchase decision-making in Arab Gulf e-shopping markets. While earlier studies have discussed sustainability-logistics relationships in different contexts like Asia, Europe, and the Americas, very few have explored specifically how cultural, economic, and technological factors unique to the Arab Gulf region act as mediators between SLSQ dimensions and e-shopping purchasing behavior of consumers. The need to fill this critical gap in the literature justifies this current study of mine of three Gulf countries, namely, Bahrain, the UAE, and the Sultanate of Oman. Theoretical Framework The Study Model Theory provides the theoretical framework for this study The SLSQ encompasses five key dimensions: green or environmentally friendly packaging, eco-friendly product transportation, effective carbon footprint management, smart returns that consider environmental policies, and recycling options for both products and packaging. (Morashti, An & Jang, 2022, Kader et al, 2023). According to the Study Model Theory, these five key dimensions of the SLSQ influence the five decision-making stages of eco-friendly shopping apps, namely, Need recognition, Information search, Evaluation of alternatives, actual purchase, and finally, post-purchase (Prashar & Kalotra, 2023, Panwar et al. 2019). This relationship is highlighted in Fig. 1 below. Among the most well-known examples of e-shopping platforms that have started to give the idea of sustainable logistics more consideration in their operations, especially in the Arabian Gulf region, are Noon, Trendyol, Shein, Temu, AliExpress, Ounass, Talabat, and Amazon. These applications have strived to enhance their systems to minimize the environmental impact of transportation, storage, and packing activities after realizing that attaining environmental efficiency is just as vital as speed of delivery or quality of service. Some of these applications, for instance, have utilized eco-friendly delivery bikes and electric cars to reduce emissions, while others have employed smart systems or solar-powered distribution centres to consolidate orders into fewer shipments, thereby cutting down on fuel use and the number of logistics trips. Utilizing recyclable or biodegradable materials, these applications are eager to adopt eco-friendly packaging techniques. As a result, these applications show how the e-commerce ecosystem is increasingly incorporating sustainability. In this context, the Study Model Theory (Figure No.1) assumes that e- shopping app users' assessment of the SLSQ offered and available through these apps may influence the decision-making stages through these apps. Many studies have found that environmental concern had an impact on purchasing intentions and behaviour towards green products. Therefore, researchers believe that this variable can be taken into account and can influence the relationship between the SLSQ offered through e-shopping apps and the decision-making stages through these apps. (Echchad & ghaith 2022, Kumar, et al, 2022, Nguyen et al. 2021, Zheng et al. 2021). Based on observations, the researchers also included a variable that influences the relationship between the decision-making stages through e-shopping apps and the SLSQ provided by these apps: attitude toward government sustainable development plans. Study Questions & Hypothesis: RQ1: What are the favorite e-shopping applications for Gulf consumers? RQ2: Which SLSQ dimensions do Arab Gulf e-shopping consumers prioritize most? RH1: The SLSQ in e-shopping applications has a positive effect on all stages of the consumer decision-making process. RH2: The consumer environmental concern has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages. RH3: The consumer attitude toward sustainable development plans has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages. RH4: There are statistically significant differences in purchasing behavior from e-shopping applications that provide sustainable logistics services, attributed to the demographic variables of the consumer (age, gender, educational level, profession, Nationality, income). Methodology This is a descriptive study using the survey methodology, focusing on three Gulf countries, namely, the UAE, Bahrain, and the Sultanate of Oman, targeting consumers aged 18+ who start the youth stage and can make independent purchasing decisions. The authors used Raosoft (http://www.raosoft.com/samplesize.html), which is a free website used to calculate the study sample size. The authors determined the margin of error as 5% and the confidence level of 95%. The final calculated sample result was 385. The collected study sample size was 390 participants, in the period starting from March 20 to May 26, 2025. The study objectives were explained, particularly the SLSQ, at the beginning of the online survey. To ensure that the target sample uses various electronic applications for purchasing, we began with a direct question about the extent of their use of these applications. If they use most of them, the respondent continues to fill out the questionnaire. If they do not use them, they receive a thank-you message and are asked to close the questionnaire. For this reason, the authors depended on the convenience sample of the GCC participants who use the e-shopping applications and their age starting from 18years old. The researchers made sure to inform the participants of the study's objectives and to emphasize the confidentiality of their personal data and that the study results would only be used for scientific research purposes, thus confirming the involvement of informed consent. The following table shows the characteristics of the sample taken: Table (1) Description of the study sample Table 1 presents the description of the study sample. It shows that (55.6%) of the total study sample (390 participants) were male, while (65.9%) were young people aged between 18 and 35 years. The majority (52.3%) of the study sample were domiciled in the UAE; (29.2%) in Oman, and (18.5%) in Bahrain. The variation in sample size among the Gulf countries in this study is natural and consistent with established scientific standards in social research. The United Arab Emirates recorded the largest number of participants (202), followed by Oman (114), and Bahrain (72). This variation is attributed to several methodological and demographic factors, most notably the difference in population size among these countries, which makes larger countries more likely to provide a wider range of participants. The study employed purposive sampling, which means that sample size is influenced by the nature of voluntary participation, which varies from one society to another. Despite this variation, all resulting sample sizes are considered sufficient and appropriate for meeting the requirements of statistical analysis and ensuring adequate representation of the characteristics of each country's population. Education was generally of a high degree, since 68.7% of the respondents had higher education qualifications. Government Employee category was extensive (43.1%), with a considerable number of respondents (34.9%) earning less than $1000 per month. Most of the respondents were in the lower to middle income brackets. The profile of the sample indicates a well-educated, young digitally active population, who are working in the government sector and earning a low income. Confirmatory factor analysis Confirmatory Factor Analysis (CFA) was performed to examine the validity and reliability of the study constructs and to ensure the adequacy of the measurement model before proceeding to the structural model analysis (SEM). Table (2) Results of Confirmatory Factor Analysis for the Study Variables Indicator / Measure SLSQ Consumer Purchase Decision-Making Stages Environmental Concern & Attitudes towards Governmental Plans Cronbach’s α 0.819–0.866 0.809–0.850 0.841–0.863 Self-Validity 0.905–0.931 0.899–0.922 0.917–0.929 Standardized Loadings 0.736–0.946 0.819–0.923 0.398–0.852 Factor Correlations 0.801–0.960 0.832–0.975 0.789 Factor Variances 0.899–1.447 0.512–0.989 0.164–0.419 Error Variances 0.158–0.602 0.135–0.302 0.136–0.870 CMIN/DF 2.417 2.417 2.785 RMSEA 0.078 0.078 0.072 GFI 0.930 0.930 0.945 CFI 0.958 0.958 0.963 NFI 0.963 0.963 0.971 TLI 0.910 0.910 0.913 The results of Table 2 indicates that all Cronbach’s α values exceeded the acceptable threshold (0.70), confirming high internal consistency. Standardized loadings were within acceptable ranges, reflecting strong representation of observed indicators for latent constructs. Model fit indices (CMIN/DF, RMSEA, CFI, GFI, NFI, TLI) showed good levels of fit, confirming the adequacy of the measurement model for Structural Equation Modeling (SEM). Therefore, the CFA results support the construct reliability and validity of the proposed model, indicating that it is appropriate for further path analysis. Figures (2),(3) and (4) illustrate the Confirmatory Factor Analysis (CFA) model for the study variables as estimated using AMOS, showing the relationships between latent and observed variables, along with the standardized factor loadings for each path. Results The results indicate that Amazon is the most used application among respondents, with a usage rate of 91%, followed by Shein at 38%, Talabat at 31%, Timu at 21%, and Noon at 23%. Other applications, such as AliExpress, Ounass, and Trendyol, were used less frequently, with rates ranging between 8% and 13%, while the least used applications, including 6th Street, TM Done, and Namshi, ranged from 1% to 4%. These findings reflect a trend among consumers to use multiple applications to meet their diverse shopping needs. Regarding Prioritization of SLSQ Dimensions within the study sample, Table (3) shows that Green Packaging emerged as the top priority ( M = 4.05, SD = 0.967), followed by Smart Returns/Reverse Logistics ( M = 3.94, SD = 1.055). Moderate importance was assigned to Recycling Options ( M = 3.89, SD = 1.164) and Green Transportation ( M = 3.84, SD = 1.133), while Carbon Footprint Management ranked lowest ( M = 3.74, SD = 1.211). These findings suggest that tangible, visible sustainability features (packaging and returns) are prioritized over less perceptible factors (carbon management) among Gulf consumers. Table (3) Prioritization of SLSQ Dimensions Dimension Mean Std. Deviation Coefficient of Variation (CV) Rank Green Packaging 4.05 0.967 23.909 1 Smart Returns / Reverse Logistics 3.94 1.055 26.771 2 Green Transportation 3.84 1.133 29.494 3 Recycling Options 3.89 1.164 29.894 4 Carbon Footprint Management 3.74 1.211 32.414 5 To test the presence of a statistically significant correlation between the dimensions and variables of the study (SLSQ, Consumer Purchase Decision-Making stages, Environmental concern and Attitudes towards governmental sustainable development plans) in order to determine the nature and direction of the relationship between the study variables, Pearson's correlation coefficient was calculated between each of the independent variables, the dependent variable, and the moderated variables, as shown in Table No (4) below: Table No. (4) Correlation analysis between research variables Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. Green Packaging 1 2. Green Transportation .80** 1 3. Carbon Footprint Mgmt .73** .87** 1 4. Smart Returns/Reverse Log. .77** .81** .87** 1 5. Recycling Options .76** .81** .87** .90** 1 6. Environmental Concern .36** .35** .33** .35** .31** 1 7. Attitudes towards GSDP .45** .33** .32** .40** .36** .50** 1 8. Need Recognition .51** .55** .55** .56** .53** .46** .40** 1 9. Information Search .49** .54** .59** .60** .58** .48** .38** .74** 1 10. Evaluation of Alternatives .48** .55** .56** .58** .53** .48** .37** .79** .81** 1 11. Actual Purchase .51** .53** .56** .60** .54** .49** .40** .78** .85** .89** 1 12. Post Purchase .48** .53** .53** .59** .52** .50** .44** .76** .78** .85** .88** 1 13. SLSQ (Overall) .87** .93** .94** .94** .94** .37** .40** .58** .61** .58** .59** .57** 1 14. CPD Stages (Overall) .54** .59** .61** .63** .59** .52** .43** .88** .91** .94** .96** .92** .64** 1 Note : ** p < 0.01; GSDP = Governmental Sustainable Development Plans; SLSQ = Sustainable Logistics Service Quality; CPD = Consumer Purchase Decision-Making Source: Results of the statistical analysis of the program SPSS. The correlation matrix in Table (4) demonstrated significant positive relationships among all study variables (p < 0.01). SLSQ exhibited a strong correlation with Consumer Purchase Decision-Making (r = 0.637), indicating that enhanced SLSQ promotes more conscious consumer decisions. SLSQ dimensions (Green Packaging, Green Transportation, Carbon Footprint Management, Smart Returns/Reverse Logistics, and Recycling Options) showed strong inter-correlations (r = 0.73–0.94), confirming their integrated contribution to overall service quality. Moderate to high correlations emerged between SLSQ and both Environmental Concern and Attitudes towards Governmental Sustainable Development Plans, suggesting that environmentally aware consumers value highly sustainable logistics. Consumer Purchase Decision-Making sub-dimensions displayed very strong inter-correlations (r = 0.74–0.95), with SLSQ correlating significantly with all decision stages (r = 0.57–0.61), confirming that SLSQ influences each phase of consumer decision-making. To test the first main hypothesis (RH1), which states that there is a statistically significant effect for the SLSQ on the Consumer Purchase Decision-Making stages among users of the Arab Gulf, Figure (5) illustrates the structural model of the paths of the SLSQ and Consumer Purchase Decision-Making stages: Table 5 shows the results of the path analysis test for the SLSQ and Consumer Purchase Decision-Making stages: Table (5) Results of Path Analysis Test for (SLSQ) and Consumer Purchase Decision-Making stages Direct path Path coefficient value Morale level SLSQ Need Recognition .583 *** SLSQ Information Search .607 *** SLSQ Evaluation of Alternatives .583 *** SLSQ Actual Purchase .591 *** SLSQ. Post Purchase .573 *** *** indicates that the calculated value is significant at a 1% significance level. Source: Results of the statistical analysis of program AMOS. Table (5) above shows the results of the path analysis that indicate that SLSQ has a significant positive impact on all stages of Consumer Purchase Decision-Making. The path coefficients range from 0.573 to 0.607, and all are statistically significant at the 1% level (p < 0.01). Regarding RH:2, it examines whether consumer environmental concern has a moderating effect on the relationship between consumer evaluation of the SLSQ in E-shopping Applications and consumer purchasing decision-making stages, as shown in Table (6): Table (6) The moderating effect of the consumer evaluation of the SLSQ in E-shopping Applications and consumer satisfaction Independent Variable R2-chng F Df2 Significance or P-value SLSQ in E-shopping Applications* environmental concern .0002 .1442 386 .7044 *Significant at 0.05 ** Significant at 0.01 At the same time, and based on Table 6, the study model theory aimed to test whether the variable consumer environmental concern (environmental concern) moderates the relationship between the consumer evaluation of SLSQ and consumer satisfaction with e-shopping applications. The results of the analysis, based on a sample of 390 respondents, revealed that the overall model was statistically significant (F = 129.26, p < 0.001) with a coefficient of determination R² = 0.50, indicating that approximately 50% of the variance in consumer satisfaction (Y) is explained by the model. Examining the regression coefficients: The independent variable (SLSQ in E-shopping Applications) had a positive and significant effect on consumer satisfaction (B = 0.49, t = 3.22, p = 0.00, 95% CI [0.19, 0.78]). The moderator (environmental concern) also showed a positive and significant direct effect (B = 0.45, t = 3.02, p = 0.00, 95% CI [0.16, 0.75]). However, the interaction term (SLSQ in E-shopping Applications × environmental concern) was not significant (B = -0.01, t = -0.38, p = 0.70, 95% CI [-0.09, 0.06]). The test of the highest-order unconditional interaction showed a non-significant change in explained variance (ΔR² = 0.00, F = 0.14, p = 0.70), confirming that the interaction term does not contribute meaningfully to the model. The results of the RH:2 can be presented as shown in Figure (6). RH3 proposed that the consumer attitude toward sustainable development plans has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages. To test this hypothesis, Model (1) of the process macro (version 4.2) developed by Hayes (2022) was employed. According to Table (7) the results indicated that the overall model was statistically significant (F = 107.30, p < 0.001) with an R² value of 0.45, meaning that approximately 45% of the variance in consumer satisfaction was explained by the model. Table (7) The moderation affects the consumer attitude toward sustainable development plans Independent Variable R2-chng F Df2 Significance or P-value SLSQ in E-shopping Applications* attitude toward sustainable development plans .01 8.29 386.00 .0000 *Significant at 0.05 ** Significant at 0.01 Although the direct effects of both the independent variable (SLSQ in E-shopping Applications) and the moderator (attitude toward sustainable development plans) were not statistically significant (p = 0.93 and p = 0.52, respectively), the interaction term (SLSQ in E-shopping Applications × attitude toward sustainable development plans) was found to be significant (B = 0.11, p = 0.00), indicating a meaningful moderating effect. Moreover, the change in explained variance due to the interaction was also significant (ΔR² = 0.01, F = 8.29, p = 0.00). Further analysis of the conditional effects showed that the effect of consumer evaluation of SLSQ on consumer satisfaction increased with higher levels of consumer attitude toward sustainable development plans. Specifically: At low levels of attitude toward sustainable development plans (3.75), the effect was B = 0.39 (p < 0.001). At the mean level of attitude toward sustainable development plans (4.43), the effect increased to B = 0.46 (p < 0.001). At the high level of attitude toward sustainable development plans (5.00), the effect further increased to B = 0.52 (p < 0.001). These findings indicate that consumer attitude towards sustainable development plans strengthens the positive relationship between consumer evaluation of (SLSQ) and consumer purchasing decision-making stages. Therefore, Hypothesis H3 is supported, confirming that consumer attitude towards sustainable development plans (attitude towards sustainable development plans) plays a significant moderating role in enhancing the impact of consumer evaluation of SLSQ on consumer purchasing decision-making stages. The results of the third hypothesis can be presented as shown in Figure (7) Regarding RH4, Table (8) presents the influence of demographic factors (gender, age, nationality, education level, profession, and average monthly income) on different stages of the consumer decision-making process: Need Recognition, Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase Behavior. Table (8) T-test & Anova Research Variables Gender T (Sign.) Age F (Sign.) Nationality F (Sign.) Education Level F (Sign.) Profession F (Sign.) Average Monthly Income F (Sign.) Need Recognition 0.875 (0.382) 2.467 (0.086) 7.339 (0.001**) 3.437 (0.033*) 2.275 (0.061) 1.780 (0.150) Information Search 3.529 (0.000***) 5.058 (0.007**) 9.090 (0.000***) 5.766 (0.003**) 4.578 (0.001**) 3.733 (0.011*) Evaluation of Alternatives 2.088 (0.037*) 4.174 (0.016*) 12.719 (0.000***) 4.294 (0.014*) 3.385 (0.010*) 4.490 (0.004**) Actual Purchase 1.852 (0.065) 5.031 (0.007**) 11.281 (0.000***) 4.081 (0.018*) 3.829 (0.005**) 3.303 (0.020*) Post Purchase 1.569 (0.118) 3.983 (0.019*) 7.895 (0.000***) 4.170 (0.016*) 2.674 (0.032*) 4.198 (0.006**) Gender shows significant differences only in Information Search (p < 0.001) and Evaluation of Alternatives (p < 0.05), suggesting that males and females differ in how they gather and compare information before purchase. For the other stages, Need Recognition, Actual Purchase, and Post-Purchase, no significant gender differences are observed (p > 0.05). Age significantly affects Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p < 0.05), indicating that younger and older consumers may follow different decision-making patterns. No significant effect is observed for Need Recognition (p = 0.086). Nationality has a strong and consistent effect on all decision-making stages (p < 0.01 or p < 0.001), highlighting that UAE, Bahraini, and Omani consumers differ in how they recognize needs, search for information, evaluate alternatives, make purchases, and behave post-purchase. Education level significantly influences all stages (p < 0.05), indicating that higher or advanced educational attainment shapes consumers' approach to decision-making. Profession significantly affects Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p < 0.05), showing that employment status (government, private sector, unemployed, freelance) impacts consumer decisions. Income significantly influences Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p < 0.05), suggesting that consumers' financial capability affects their decision-making approach. In brief, most demographic variables significantly affect consumer decision-making stages, especially Nationality and Education Level. Gender has limited impact, affecting only Information Search and Evaluation of Alternatives. Therefore, the hypotheses are generally accepted, except for gender in some stages. Discussion The results of the study provide compelling evidence of the role of SLSQ in the consumer's purchase decision-making process across the Arab Gulf region. The high level of correlation found (r = 0.637, p < 0.01) between SLSQ and stages of consumer decision making, lends support to previous research (Dianavera and Aminoto, 2020 ) in Indonesia and Choi, 2019 , in China, confirming it as a fundamental aspect of logistics excellence in e- shopping success regardless of geographical considerations. However, it must be noted that the context of the research within the Arab Gulf states indicates different priorities amongst the consumers in that region. The analysis of the priorities established that green packaging (M = 4.05) and smart returns / reverse logistics (M = 3.94) were the valued SLSQ dimensions amongst Gulf consumers, whilst the management of the carbon footprint was at the lowest rating (M = 3.74). The implications of this finding are important when compared to similar research which has been undertaken in Europe where the environmental factors of logistics have been given more attention (Biancolin and Rotaris, 2024 ; Large et al, 2013 ) as it demonstrates that the Gulf consumers prefer tangible experiential benefits associated with the sustainability dimension of shopping experience rather than the abstract concepts of environmental impact. This is supported by Kawa and Pierański ( 2021 ) who noted that the effectiveness of green logistics is based on the visibility parameter of consumer-facing benefits, yet did seem to suggest the premise that environmentally aware consumers tend to attach the greatest importance to carbon reduction technology. The fact that consumers in this study have some preference for visible aspects of sustainability can be said to reflect the relatively early stages of sustainability awareness of the region contrary to the more mature Western markets, in which Verma ( 2024 ) found some parallels with e-retailer attitudes post-COVID in the GCC region. The path analysis showed that, overall, SLSQ had significant effects at all five of the stages of consumer decision making (β = 0.573–0.607, p < 0.01) confirming and extending earlier studies that were concerned primarily with post purchase satisfaction. This reaffirms the findings of Do et al. (2020) in their study dealing with Generation Z consumers, and extends Nguyen et al.'s ( 2019 ) linear relationships to show that sustainable logistics also affects the cognitive processes at the earlier stages of decision-making, such as need recognition (β = 0.583) and information search (β = 0.607), and not just at the post purchase evaluative stage. The consistency of these effects at all stages suggests that SLSQ operated as an overall decision heuristic rather than being a stage-specific consideration, providing further antagonism to piecemeal approaches to the implementation of green logistics. The good inter-correlations shown by the SLSQ dimensions (r = 0.73–0.94) is confirmation of the holistic approach suggested by Kawa and Pierański ( 2021 ) and the realities of Arabelen and Kaya's (2021) description of LSQ as having an integrated nature, from which companies cannot selectively implement green initiatives but have to make the changes comprehensively. Perhaps the most theoretically interesting finding concerns the moderating variables. Contrary to expectations based on an extensive literature review (Gupta et al., 2024 ; Maurya et al., 2023 ; Naz et al., 2024 ; Ahmad et al., 2024 ), environmental concern did not significantly moderate the SLSQ-decision association (ΔR² = 0.0002, p = 0.70). This finding contrasts with studies in India and Pakistan where eco-consciousness was shown to significantly mediate the effectiveness of green logistics. And in this context, the finding directly challenges the generalizability of the Theory of Planned Behavior across culture-bound contexts. It would appear from these findings that in the Gulf area, personal environmental values have, as yet, not sufficiently developed to a state where they can predict behavioural modification. This may be due, in part, to the region's long-term economic dependence on fossil fuel resources and its relatively recent engagement with sustainability discourse. Furthermore, this differs from Kale's (2025) research in Western markets in which wealthy customers are prepared to pay high prices for green delivery. On the other hand, attitudes towards government sustainability plans positively moderate the SLSQ-decision relationship (B = 0.11, p < 0.001), with the conditional effects increasing the level of attitude identification from (B = 0.39) to (B = 0.52). This discovery illustrates the peculiar divergence in consumer behavior in the Gulf: reliance by consumers on institutional (government) legitimacy and national discourses about sustainability instead of their own individual environmental ethics. It agrees with Verma ( 2024 ) who emphasized the role of cultural considerations in e-retail within the GCC and extends Large et al. ( 2013 ) findings with respect to the dynamics of sustainability regulations, showing that government endorsement acts as a trust-enhancing mechanism in collectivist cultures. Finally, the importance of national planning (UAE Vision 2030, Bahrain Vision 2030, Oman Vision 2040) in influencing consumer responses appears to indicate that sustainability policies driven from the top down effectively create a more trenchant resonance than do grassroots environmental movements within the Gulf context. The demographic analysis indicated that, consistently across all stages of decision making, the most determining factors were nationality and level of education (p < 0.001), thus supporting the findings by Radyi et al. ( 2024 ) on generational influence of purchase propensity while also adding a critical cultural dimension, which is often poorly addressed in research on global e- shopping. The significant nationality effects across UAE, Bahraini and Omani consumers seem to indicate intra-regional differences that warrant further investigation and which may be indicative of differences in infrastructure maturity, regulatory regimes and exposure to sustainability messaging. The limited gender effects (significant only for information search and evaluation) are contrary to certain Western emergent findings but support the increased gender-neutrality of digital consumption in modernizing Gulf countries. Conclusion The results of this study collectively demonstrate a strong and consistent relationship between SLSQ and the stages of consumer purchase decision-making in e-shopping applications across the Arab Gulf region. The high means observed for green packaging and smart returns/reverse logistics indicate that consumers in the Gulf prioritize tangible and convenient sustainability features that directly impact their experience. On the other hand, aspects such as carbon footprint management received relatively lower attention, likely due to their abstract or less visible nature to the average online shopper. These findings suggest that while Gulf consumers appreciate sustainability, they are primarily motivated by visible and functional eco-friendly practices that improve service quality and convenience. The confirmatory factor analysis (CFA) confirmed the robustness and reliability of the study constructs, with all factor loadings, Cronbach’s alpha values, and model fit indices falling within acceptable thresholds. This ensured that the measurement model accurately reflects the theoretical relationships among SLSQ, consumer decision-making stages, and moderating factors such as environmental concern and attitudes toward governmental sustainability plans. The path analysis further revealed that SLSQ has a significant positive effect on all five decision-making stages, emphasizing that improving sustainable logistics can directly enhance consumers’ awareness, evaluation, and satisfaction processes. Regarding the moderating variables, the results indicated that environmental concern did not significantly moderate the relationship between SLSQ and consumer decision-making, suggesting that personal concern alone is not a decisive factor in shaping purchase behavior. However, attitudes toward governmental sustainable development plans demonstrated a significant moderating effect, indicating that trust in governmental efforts and national sustainability initiatives strengthens the positive impact of SLSQ on consumer satisfaction. This highlights the importance of institutional credibility and national sustainability campaigns in fostering environmentally conscious consumption patterns. The analysis of demographic variables revealed important behavioral differences. Nationality and education level had the most consistent and significant effects across all decision-making stages, suggesting that cultural background and educational attainment shape consumers’ understanding and appreciation of sustainable logistics. Age, profession, and income also influenced most stages, indicating that lifestyle and economic capability play key roles in shaping e-shopping decisions. In contrast, gender had a limited impact, affecting only the information search and evaluation stages, implying that both men and women share similar attitudes toward sustainable e-shopping once purchase intentions are made. In summary, the findings affirm that the adoption of sustainable logistics practices not only enhances the operational quality of e-shopping platforms but also strengthens consumer trust and loyalty. Companies operating in the Gulf region should therefore focus on visible sustainability dimensions such as eco-friendly packaging and return systems, while also supporting national environmental initiatives to increase consumer engagement. Moreover, marketing strategies should be tailored to the demographic characteristics of target markets, particularly in terms of nationality, education, and income, to maximize the effectiveness of sustainability-driven value propositions. Future Studies This study’s findings demonstrate the significance of further investigating how the SLSQ via e-shopping applications relates to consumer purchasing decision-making processes, thereby paving the way for many future studies. Further studies could investigate how aspects of sustainable logistics—like eco-friendly packaging, green transport, and smart delivery models—affect consumer satisfaction with e-shopping applications. To comprehend the impact of differences in logistics infrastructure and consumer cultures on consumer involvement with sustainable services, it is essential to conduct comparative studies across various countries and regions. Additionally, it is necessary to explore how mediating or moderating factors—like environmental awareness, risk perception, and application features or visual identity—affect the strength of the connection between sustainable logistics and purchasing behavior. Additional research might investigate how consumers accept sustainable delivery and return policies and how these affect long-term repeat purchases. It is also advisable to utilize methods of artificial intelligence to create models that forecast consumer satisfaction in relation to logistics services, using operational and environmental data. The suggested research paths will help to better elucidate how sustainable logistics contributes to e- shopping engagement and loyalty. Recommendations E- shopping companies operating in the Arab Gulf must strategically focus on visible sustainability dimensions. Investment must be directed to environmentally friendly packaging innovations and easy-to-use reverse logistics systems. This has a direct impact on the consumer's decision-making process. Companies such as Amazon, Noon and Talabat must create explicit sustainability communication strategies that underscore concrete benefits instead of hypothetical carbon numbers. The introduction of smart return systems equipped with real-time tracking systems and convenient drop-off points will improve consumer confidence. In the marketing campaigns, special emphasis should be placed on governmental sustainability projects (Vision 2030, UAE Green Agenda), as governmental endorsement plays a pronounced moderating role in consumer response. Multi-tier pricing structures that permit premium sustainable delivery alternatives could lead to higher willingness-to-pay from educated higher-income segment groups but maintain competition on standard services. Gulf governments must set about establishing standardized structures for green logistics certification. This will assist in transparency and consumer faith in companies. Public campaigns that emphasize initiatives for national sustainability goals could help to maximize the moderating effect found in this study. Incentives could be provided in the form of tax incentives awarded to companies who switch their fleets of vehicles to electric cars or the use of renewable-energy-based warehouses. Eventually regulations should be introduced mandating minimum recycling rates for packaging materials, and institutionalizing the desirable sustainability practices identified in this study across the E- shopping industry. Longitudinal studies investigating how sustainability priorities change with the maturity of Gulf markets would provide temporal insights. Comparative research involving how different digital domestic platforms (Noon, Shein) compared to traditional retailers' e- shopping properties could elucidate platform-oriented optimization strategies. Research as to why environmental concern is not a moderating force in the Gulf context through qualitative interviews examining the cultural hierarchies of value would increase theoretical understanding. Studies involving logistics 4.0 and 5.0 technologies (IoT, blockchain, AI) relative to the Gulf implementations would address the gap identified in the literature on smart logistics adoption. Finally, experimental designs testing specific green initiatives (e.g. reforestation contributions per order) would help identify causal mechanisms and optimal sustainability communication strategies, building on Biancolin and Rotaris' (2024) findings from Italy in the cultural context of the Gulf. Limitation Finally, the limitations of this study lie in its application to only three Gulf countries—the UAE, Bahrain, and Oman—which are the countries of origin for the researchers and where they were able to distribute the online questionnaire. This implies the importance of conducting future, more comprehensive studies covering all Gulf countries. Declarations Funding information: None. Conflict of interest statement: The authors declare that they have no conflict of interest. Ethical approval The authors received the Gulf University Research Ethics Committee's approval to conduct this experiment on December 24, 2024 (Approval number is GU- 24-5-2025). All data collection and analysis procedures were carried out in strict adherence to the Declaration of Helsinki (2013) and ICH-GCP guidelines for ethical research practices involving digital content. Data availability statement: The data that supports the findings of this study are available on request from the corresponding author. The data is not publicly available due to privacy or ethical restrictions. Author contribution : Author 1 wrote conclusion and the final revision, author 2 wrote introduction, questions, hypotheses, and methodology. Regarding author 3, she was responsible for literature review and the theoretical framework. Author 4 wrote the results section and prepared all figures. Author 5 conducted the study survey and statistical analysis. Finally, Author 6 reviewed the manuscript, wrote the recommendation, enhancing the conclusion and the discussion, and proofreading the total manuscript. Research Fund There is no funding for this research. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8254418","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":566713223,"identity":"67c137a9-d5e1-4ddf-b58e-2e17fdbda871","order_by":0,"name":"Naglaa 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hypothesis\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8254418/v1/60077c91089be1415c822e31.png"},{"id":106094260,"identity":"3137d716-88a3-4f7c-9426-10ddcc22ddaa","added_by":"auto","created_at":"2026-04-03 11:41:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8254418/v1/3417370c-476f-49e2-b732-225e5d2d4677.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Sustainable Logistics Service Quality in E-shopping Applications on Consumer Purchase Decision-Making Stages: A Survey Study in the Arab Gulf","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAfter the rush of e-shopping applications rolled out across the Arab Gulf, consumer demands changed exponentially to convenience, speed, and efficiency. What was once a long, tedious process, now transformed into merely going online to transact business from the comfort of one`s home.\u003c/p\u003e \u003cp\u003eIn the midst of this online world, the idea of sustainable logistics service quality (SLSQ) that includes environmentally friendly, economically sound, and socially responsible logistics practices have emerged as a paradigm shift (Arabelen \u0026amp; Kaya, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Green logistics includes several different processes, such as best routing, green packaging, improving fuel efficiency, using environmentally friendly vehicles, reverse logistics, recycling materials, reducing waste and relying on renewable energy sources in logistics operations, all focused on reducing environmental impacts but not at the expense of efficiency of operations (Deckert, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Murphy \u0026amp; Poist, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Seroka-Stolka, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). When it comes to e-shopping uses, providing higher service quality with green logistics has the potential to influence customer purchasing behavior significantly (Blanco \u0026amp; Sheffi, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly in the GCC region, where e- shopping is also developing well.\u003c/p\u003e \u003cp\u003eAccording to Huma et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) logistics service quality (LSQ) is consumers' perception of delivery performance concerning their expectations. Therefore, LSQ becomes a critical variable to influence satisfaction, loyalty, and intention to buy again. Delivery time, information accuracy, order condition, reverse logistics, and ease of payment have been identified as the most essential dimensions impacting customers' satisfaction and loyalty in online buying, especially for digitally-suave generations such as Generation Z (Do et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mallieswari et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome studies have agreed that there is a stronger likelihood of customers remaining with retailers that display environmentally friendly fulfillment processes and would even pay extra for environmentally friendly fulfillment services. (Asawawibul et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Karina et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oh et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite this global research interest, most of the literature that is available focuses on the Western or Asian markets and few empirical studies have been undertaken in the Arab Gulf, where unique cultural, regulatory, and infrastructural conditions shape consumer demands and logistics operations. GCC countries are actively adopting Industry 4.0 and smart logistics technologies such as automation, IoT monitoring, and blockchain, but their contribution to sustainable quality of service and influencing purchasing behavior remains poorly explored. Conversely, last-mile services of different degrees of digital efficiency or sustainability are provided by UAE-based and other Gulf countries' logistics firms such as Quiqup and Shipa Delivery, but very few studies have examined consumers' reactions towards these types of services.\u003c/p\u003e \u003cp\u003eThis study aims to bridge these gaps by examining the impact of SLSQ on consumer purchase decision-making stages in e-shopping apps in the Arab Gulf, employing a survey-based methodology.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eEmpirical literature demonstrates that logistics practices based on sustainability positively impact consumers' buying intentions. Kale (2025) confirmed that green delivery and carbon-free shipping are major driving factors behind consumers' purchasing decisions, with well-off consumers being more willing to pay more for eco-friendly delivery. Likewise, in Indonesia, a study on the users of \"Bhinneka\" online shopping platform showed that purchase decisions are strongly affected by LSQ, affirming the importance of reliability and safety in logistics (Dianavera \u0026amp; Aminoto, 2020).\u003c/p\u003e\n\u003cp\u003eGenerational dynamics also influence expectations. In Generation Z, delivery quality, speed, flexibility, tracking, and greener options were essential in forming purchase decisions and loyalty, capturing this group's increased digital literacy and ethical sensitivity (Radyi et al., 2024). This is consistent with European evidence, where procurement managers increasingly emphasize sustainability in logistics choice, but with limited compliance leverage over suppliers (Large et al., 2013).\u003c/p\u003e\n\u003cp\u003eGreen performance of online shopping logistics also plays a deciding role. Green transport tactics used in online purchases minimize congestion and emissions, along with building trust among consumers, particularly within urban last-mile delivery networks (Nica, 2015). A bibliometric analysis ensures that the subject is heavily evolving, especially on circular economy, omnichannel retail, and city logistics topics (Rita \u0026amp; Ramos, 2022).\u003c/p\u003e\n\u003cp\u003eKasap (2025) studied sustainable e-commerce approaches and two factors (green logistics and digitalization) influenced consumer purchasing behavior across several countries, with the US and Brazil being most sensitive to sustainable delivery choices. Kawa and Pierański (2021) established links between environmental logistics feature and satisfaction levels for customers. Their research revealed that environmentally friendly delivery options, green packaging choices, and carbon-neutral delivery options all served to influence consumer choices and consumer loyalty strongly in the choice of platforms used for shopping. They mention that green logistics is no longer a peripheral offering but has become a core process to e- shopping.\u003c/p\u003e\n\u003cp\u003eChinese empirical evidence has demonstrated that delivery quality is the strongest determinant of customer satisfaction and can directly impact repeat purchase intentions and loyalty (Choi, 2019). The empirical research on online logistics service reviews confirmed that elements of delivery (punctuality, reliability, and empathy) combined with reassuring online review information influence buying intentions (Li \u0026amp; Chen, 2021). A bibliometric analysis of SLSQ also revealed expanding global interest since 2013, emphasizing the role AI, blockchain and big data play in improving green logistics while not negatively impacting customer satisfaction (Abdelaziz \u0026amp; Munawaroh, 2024). Meanwhile, studies conducted in the countries of Saudi Arabia, Mexico, Colombia and Ecuador have confirmed that online buying decisions are affected by SLSQ, brand reputation, perceived product attributes, and perceived value (Al Hamli \u0026amp; Sobaih, 2023; Gu et al., 2021; Larios-Gómez et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn India, eco-friendly last-mile logistics impacted consumers' intentions to buy natural foods and organic products through e-commerce by mediating trust as an intermediary between logistics performance and purchase intention (Gupta et al., 2024; Maurya et al., 2023; Naz et al., 2024). Likewise, as evidence shows, delivery stability and sustainability are evidently influencing factors of cross-border e-commerce satisfaction in Korea, with green delivery moderating impacts that enhance trust and loyalty (Oh et al., 2022). However, there are still obstacles. A Brazilian research study pointed to the fact that consumers are open to green delivery, but speed and price remain dominating factors, which means that sustainability remains secondary in actual purchase trade-offs (Nogueira et al., 2021).\u003c/p\u003e\n\u003cp\u003eAs per the Italian case study, customers are willing to pay a little extra for eco-friendly delivery as long as the environmental benefits, such as reforestation, are clearly communicated (Biancolin \u0026amp; Rotaris, 2024). Green packaging, as well as logistics, were found to be equally important through consumer trust in the buying behavior of customers in a study conducted in Pakistani e-commerce in South Asia (Ahmad et al., 2024). Also, more current comparative research on global e-commerce leaders, such as Amazon and Alibaba, shows that companies achieve a competitive edge and customer satisfaction alongside sustainability by controlling their own distributed logistics (Florido-Benítez, 2024).\u003c/p\u003e\n\u003cp\u003eIn Vietnam, however, Nguyen et al. (2019) made it clear that the biggest determinants of customer satisfaction are customer service, order quality, information quality, and delivery quality. This means that aside from receiving their products on time, being able to have problems solved and having personalized attention goes a long way to encourage customer loyalty regarding online shopping (Santarek et al., 2022).\u003c/p\u003e\n\u003cp\u003eVerma (2024) suggested that shoppers from the GCC region care about e-retailing sites post-COVID. Cultural considerations go a long way in this regard. The results reflect that shoppers from within the region want logistics partners who demonstrate environmental sustainability but still provide efficiency in their services.\u003c/p\u003e\n\u003cp\u003eThen, combining logistics quality and consumer mindset, studies show that green delivery and dynamic pricing have mixed impacts: while speed and quality have a strong impact on repurchase, green logistics alone does not automatically lead to repeat purchases without complementary value drivers (Asawawibul et al., 2025; Lin et al., 2023; Zheng et al., 2022). Another interaction effect between logistics quality and membership systems shows how loyalty programs paired with efficient delivery can strengthen consumer loyalty (El Moussaoui et al., 2023; Lin et al., 2023).\u003c/p\u003e\n\u003cp\u003eLogistics 4.0 interlinks Internet of Things (IoT), artificial intelligence, blockchain, and big data technologies for end-to-end real-time transparency, predictive scheduling of deliveries, and sustainable supply chain practices (Perotti et al., 2022; Sun et al., 2022). These technologies, as discussed in research, enhance consumer confidence in terms of heightened visibility, transparency, and responsiveness of purchasing decision-driving logistic activities (Büyüközkan \u0026amp; Göçer, 2025; Farajpour et al., 2022; Parhi et al., 2022). Another study suggests that intelligent logistics systems improve the quality attributes of service like reliability, flexibility, and customization as per customers' preferences and green and optimized manners (Kolasińska-Morawska et al., 2022; Shee et al., 2021). Within the setup of e-commerce platforms, digital logistics solutions like automated warehouses and AI-based scheduling of deliveries reduce delays and operational mistakes by far, increasing customer satisfaction (Szeredi et al., 2024).\u003c/p\u003e\n\u003cp\u003eLogistics 5.0, characterized by people-centered technology integration, intelligent automation, and sustainability-oriented principles, lies at the center of consumer purchase decision influence on online shopping use cases (Andres et al., 2024; Trstenjak et al., 2022). Empirical evidences show that dimensions of logistics service quality, such as delivery timeliness, accuracy, reverse logistics, and return convenience, significantly influence customer satisfaction, trust, and repurchase intentions and serve as determinants of e-commerce buying behavior (Li et al., 2023; Szeredi et al., 2024). Besides, consumer perception of logistics transparency via attributes like traceability, online ratings of delivery performance, and logistics carrier reputation are shown to strengthen purchase intentions and minimize perceived risks (Bolatan, 2021; Hsu et al., 2024).\u003c/p\u003e\n\u003cp\u003eAlthough much research has indeed been carried out on sustainable logistics service quality across various global contexts, there remains an enormous empirical space to comprehend the precise mechanisms through which sustainable logistics activities influence consumer purchase decision-making in Arab Gulf e-shopping markets. While earlier studies have discussed sustainability-logistics relationships in different contexts like Asia, Europe, and the Americas, very few have explored specifically how cultural, economic, and technological factors unique to the Arab Gulf region act as mediators between SLSQ dimensions and e-shopping purchasing behavior of consumers. The need to fill this critical gap in the literature justifies this current study of mine of three Gulf countries, namely, Bahrain, the UAE, and the Sultanate of Oman.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eTheoretical Framework\u003c/h2\u003e\n \u003cp\u003eThe Study Model Theory provides the theoretical framework for this study The SLSQ encompasses five key dimensions: green or environmentally friendly packaging, eco-friendly product transportation, effective carbon footprint management, smart returns that consider environmental policies, and recycling options for both products and packaging. (Morashti, An \u0026amp; Jang, 2022, Kader et al, 2023). According to the Study Model Theory, these five key dimensions of the SLSQ influence the five decision-making stages of eco-friendly shopping apps, namely, Need recognition, Information search, Evaluation of alternatives, actual purchase, and finally, post-purchase (Prashar \u0026amp; Kalotra, 2023, Panwar et al. 2019). This relationship is highlighted in Fig.\u0026nbsp;1 below.\u003c/p\u003e\n \u003cp\u003eAmong the most well-known examples of e-shopping platforms that have started to give the idea of sustainable logistics more consideration in their operations, especially in the Arabian Gulf region, are Noon, Trendyol, Shein, Temu, AliExpress, Ounass, Talabat, and Amazon. These applications have strived to enhance their systems to minimize the environmental impact of transportation, storage, and packing activities after realizing that attaining environmental efficiency is just as vital as speed of delivery or quality of service. Some of these applications, for instance, have utilized eco-friendly delivery bikes and electric cars to reduce emissions, while others have employed smart systems or solar-powered distribution centres to consolidate orders into fewer shipments, thereby cutting down on fuel use and the number of logistics trips. Utilizing recyclable or biodegradable materials, these applications are eager to adopt eco-friendly packaging techniques. As a result, these applications show how the e-commerce ecosystem is increasingly incorporating sustainability. In this context, the Study Model Theory (Figure No.1) assumes that e- shopping app users' assessment of the SLSQ offered and available through these apps may influence the decision-making stages through these apps.\u003c/p\u003e\n \u003cp\u003eMany studies have found that environmental concern had an impact on purchasing intentions and behaviour towards green products. Therefore, researchers believe that this variable can be taken into account and can influence the relationship between the SLSQ offered through e-shopping apps and the decision-making stages through these apps. (Echchad \u0026amp; ghaith 2022, Kumar, et al, 2022, Nguyen et al. 2021, Zheng et al. 2021). Based on observations, the researchers also included a variable that influences the relationship between the decision-making stages through e-shopping apps and the SLSQ provided by these apps: attitude toward government sustainable development plans.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Questions \u0026amp; Hypothesis:\u003c/h3\u003e\n\u003cp\u003eRQ1: What are the favorite e-shopping applications for Gulf consumers?\u003c/p\u003e\n\u003cp\u003eRQ2: Which SLSQ dimensions do Arab Gulf e-shopping consumers prioritize most?\u003c/p\u003e\n\u003cp\u003eRH1: The SLSQ in e-shopping applications has a positive effect on all stages of the consumer decision-making process.\u003c/p\u003e\n\u003cp\u003eRH2: The consumer environmental concern has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages.\u003c/p\u003e\n\u003cp\u003eRH3: The consumer attitude toward sustainable development plans has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages.\u003c/p\u003e\n\u003cp\u003eRH4: There are statistically significant differences in purchasing behavior from e-shopping applications that provide sustainable logistics services, attributed to the demographic variables of the consumer (age, gender, educational level, profession, Nationality, income).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis is a descriptive study using the survey methodology, focusing on three Gulf countries, namely, the UAE, Bahrain, and the Sultanate of Oman, targeting consumers aged 18+ who start the youth stage and can make independent purchasing decisions. The authors used Raosoft (http://www.raosoft.com/samplesize.html), which is a free website used \u0026nbsp; to calculate the study sample size. The authors determined the margin of error as 5% and the confidence level of 95%. The final calculated sample result was 385. The collected study sample size was 390 participants, in the period starting from March 20 to May 26, 2025.\u003c/p\u003e\n\u003cp\u003eThe study objectives were explained, particularly the SLSQ, at the beginning of the online survey. To ensure that the target sample uses various electronic applications for purchasing, we began with a direct question about the extent of their use of these applications. If they use most of them, the respondent continues to fill out the questionnaire. If they do not use them, they receive a thank-you message and are asked to close the questionnaire. For this reason, the authors depended on the convenience sample of the GCC participants who use the e-shopping applications and their age starting from 18years old.\u003c/p\u003e\n\u003cp\u003eThe researchers made sure to inform the participants of the study\u0026apos;s objectives and to emphasize the confidentiality of their personal data and that the study results would only be used for scientific research purposes, thus confirming the involvement of informed consent.\u003c/p\u003e\n\u003cp\u003eThe following table shows the characteristics of the sample taken:\u003c/p\u003e\n\u003cp\u003eTable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003e(1) Description of the study sample\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"746\" height=\"802\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the description of the study sample. It shows that (55.6%) of the total study sample (390 participants) were male, while (65.9%) were young people aged between 18 and 35 years. The majority (52.3%) of the study sample were domiciled in the UAE; (29.2%) in Oman, and (18.5%) in Bahrain. The variation in sample size among the Gulf countries in this study is natural and consistent with established scientific standards in social research. The United Arab Emirates recorded the largest number of participants (202), followed by Oman (114), and Bahrain (72). This variation is attributed to several methodological and demographic factors, most notably the difference in population size among these countries, which makes larger countries more likely to provide a wider range of participants. The study employed purposive sampling, which means that sample size is influenced by the nature of voluntary participation, which varies from one society to another. Despite this variation, all resulting sample sizes are considered sufficient and appropriate for meeting the requirements of statistical analysis and ensuring adequate representation of the characteristics of each country\u0026apos;s population.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Education was generally of a high degree, since 68.7% of the respondents had higher education qualifications. Government Employee category was extensive (43.1%), with a considerable number of respondents (34.9%) earning less than $1000 per month. Most of the respondents were in the lower to middle income brackets. The profile of the sample indicates a well-educated, young digitally active population, who are working in the government sector and earning a low income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConfirmatory Factor Analysis (CFA) was performed to examine the validity and reliability of the study constructs and to ensure the adequacy of the measurement model before proceeding to the structural model analysis (SEM).\u003c/p\u003e\n\u003cp\u003eTable (2) \u003cem\u003eResults of Confirmatory Factor Analysis for the Study Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator / Measure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSLSQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsumer Purchase Decision-Making Stages\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental Concern \u0026amp; Attitudes towards Governmental Plans\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.819\u0026ndash;0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.809\u0026ndash;0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.841\u0026ndash;0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Validity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.905\u0026ndash;0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.899\u0026ndash;0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.917\u0026ndash;0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.736\u0026ndash;0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.819\u0026ndash;0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.398\u0026ndash;0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor Correlations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.801\u0026ndash;0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.832\u0026ndash;0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor Variances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.899\u0026ndash;1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.512\u0026ndash;0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.164\u0026ndash;0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eError Variances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.158\u0026ndash;0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.135\u0026ndash;0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.136\u0026ndash;0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMIN/DF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of Table 2 indicates that all Cronbach\u0026rsquo;s \u0026alpha; values exceeded the acceptable threshold (0.70), confirming high internal consistency. Standardized loadings were within acceptable ranges, reflecting strong representation of observed indicators for latent constructs. Model fit indices (CMIN/DF, RMSEA, CFI, GFI, NFI, TLI) showed good levels of fit, confirming the adequacy of the measurement model for Structural Equation Modeling (SEM). Therefore, the CFA results support the construct reliability and validity of the proposed model, indicating that it is appropriate for further path analysis.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eFigures (2),(3) and (4) illustrate the Confirmatory Factor Analysis (CFA) model for the study variables as estimated using AMOS, showing the relationships between latent and observed variables, along with the standardized factor loadings for each path.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe results indicate that Amazon is the most used application among respondents, with a usage rate of 91%, followed by Shein at 38%, Talabat at 31%, Timu at 21%, and Noon at 23%. Other applications, such as AliExpress, Ounass, and Trendyol, were used less frequently, with rates ranging between 8% and 13%, while the least used applications, including 6th Street, TM Done, and Namshi, ranged from 1% to 4%. These findings reflect a trend among consumers to use multiple applications to meet their diverse shopping needs.\u003c/p\u003e\n\u003cp\u003eRegarding Prioritization of SLSQ Dimensions within the study sample, Table\u0026nbsp;(3) shows that Green Packaging emerged as the top priority (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.967), followed by Smart Returns/Reverse Logistics (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.94, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.055). Moderate importance was assigned to Recycling Options (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.89, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.164) and Green Transportation (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.84, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.133), while Carbon Footprint Management ranked lowest (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.74, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.211). These findings suggest that tangible, visible sustainability features (packaging and returns) are prioritized over less perceptible factors (carbon management) among Gulf consumers.\u003c/p\u003e\n\u003cp\u003eTable (3) \u003cem\u003ePrioritization of SLSQ Dimensions\u003c/em\u003e\u003c/p\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient of Variation (CV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen Packaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmart Returns / Reverse Logistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen Transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecycling Options\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Footprint Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo test the presence of a statistically significant correlation between the dimensions and variables of the study (SLSQ, Consumer Purchase Decision-Making stages, Environmental concern and Attitudes towards governmental sustainable development plans) in order to determine the nature and direction of the relationship between the study variables, Pearson\u0026apos;s correlation coefficient was calculated between each of the independent variables, the dependent variable, and the moderated variables, as shown in Table No (4) below:\u003c/p\u003e\n\u003cp\u003eTable No. (4) \u003cem\u003eCorrelation analysis between research variables\u003c/em\u003e\u003c/p\u003e\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Green Packaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Green Transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Carbon Footprint Mgmt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Smart Returns/Reverse Log.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Recycling Options\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.90**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Environmental Concern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Attitudes towards GSDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. Need Recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9. Information Search\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.74**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10. Evaluation of Alternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.79**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11. Actual Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12. Post Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13. SLSQ (Overall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14. CPD Stages (Overall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.96**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; GSDP\u0026thinsp;=\u0026thinsp;Governmental Sustainable Development Plans; SLSQ\u0026thinsp;=\u0026thinsp;Sustainable Logistics Service Quality; CPD\u0026thinsp;=\u0026thinsp;Consumer Purchase Decision-Making\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Results of the statistical analysis of the program SPSS.\u003c/p\u003e\n\u003cp\u003eThe correlation matrix in Table\u0026nbsp;(4) demonstrated significant positive relationships among all study variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SLSQ exhibited a strong correlation with Consumer Purchase Decision-Making (r\u0026thinsp;=\u0026thinsp;0.637), indicating that enhanced SLSQ promotes more conscious consumer decisions. SLSQ dimensions (Green Packaging, Green Transportation, Carbon Footprint Management, Smart Returns/Reverse Logistics, and Recycling Options) showed strong inter-correlations (r\u0026thinsp;=\u0026thinsp;0.73\u0026ndash;0.94), confirming their integrated contribution to overall service quality. Moderate to high correlations emerged between SLSQ and both Environmental Concern and Attitudes towards Governmental Sustainable Development Plans, suggesting that environmentally aware consumers value highly sustainable logistics. Consumer Purchase Decision-Making sub-dimensions displayed very strong inter-correlations (r\u0026thinsp;=\u0026thinsp;0.74\u0026ndash;0.95), with SLSQ correlating significantly with all decision stages (r\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.61), confirming that SLSQ influences each phase of consumer decision-making.\u003c/p\u003e\n\u003cp\u003eTo test the first main hypothesis (RH1), which states that there is a statistically significant effect for the SLSQ on the Consumer Purchase Decision-Making stages among users of the Arab Gulf, Figure (5) illustrates the structural model of the paths of the SLSQ and Consumer Purchase Decision-Making stages:\u003c/p\u003e\n\u003cp\u003eTable 5 shows the results of the path analysis test for the SLSQ and Consumer Purchase Decision-Making stages:\u003c/p\u003e\n\u003cp\u003eTable (5) \u003cem\u003eResults of Path Analysis Test for (SLSQ) and Consumer Purchase Decision-Making stages\u003c/em\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDirect path\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath coefficient value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMorale level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeed Recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Search\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvaluation of Alternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActual Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** indicates that the calculated value is significant at a 1% significance level.\u003c/p\u003e\n\u003cp\u003eSource: Results of the statistical analysis of program AMOS.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(5) above shows the results of the path analysis that indicate that SLSQ has a significant positive impact on all stages of Consumer Purchase Decision-Making. The path coefficients range from 0.573 to 0.607, and all are statistically significant at the 1% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRegarding RH:2, it examines whether consumer environmental concern has a moderating effect on the relationship between consumer evaluation of the SLSQ in E-shopping Applications and consumer purchasing decision-making stages, as shown in Table\u0026nbsp;(6):\u003c/p\u003e\n \u003cp\u003eTable (6) \u003cem\u003eThe moderating effect of the consumer evaluation of the SLSQ in E-shopping Applications and consumer satisfaction\u003c/em\u003e\u003c/p\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2-chng\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance or P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ in E-shopping Applications* environmental concern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.1442\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e386\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.7044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*Significant at 0.05 ** Significant at 0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eAt the same time, and based on Table 6, the study model theory aimed to test whether the variable consumer environmental concern (environmental concern) moderates the relationship between the consumer evaluation of SLSQ and consumer satisfaction with e-shopping applications. The results of the analysis, based on a sample of 390 respondents, revealed that the overall model was statistically significant (F = 129.26, p \u0026lt; 0.001) with a coefficient of determination R\u0026sup2; = 0.50, indicating that approximately 50% of the variance in consumer satisfaction (Y) is explained by the model. Examining the regression coefficients:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe independent variable (SLSQ in E-shopping Applications) had a positive and significant effect on consumer satisfaction (B\u0026thinsp;=\u0026thinsp;0.49, t\u0026thinsp;=\u0026thinsp;3.22, p\u0026thinsp;=\u0026thinsp;0.00, 95% CI [0.19, 0.78]).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe moderator (environmental concern) also showed a positive and significant direct effect (B\u0026thinsp;=\u0026thinsp;0.45, t\u0026thinsp;=\u0026thinsp;3.02, p\u0026thinsp;=\u0026thinsp;0.00, 95% CI [0.16, 0.75]).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eHowever, the interaction term (SLSQ in E-shopping Applications \u0026times; environmental concern) was not significant (B = -0.01, t = -0.38, p\u0026thinsp;=\u0026thinsp;0.70, 95% CI [-0.09, 0.06]).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe test of the highest-order unconditional interaction showed a non-significant change in explained variance (\u0026Delta;R\u0026sup2; = 0.00, F\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;=\u0026thinsp;0.70), confirming that the interaction term does not contribute meaningfully to the model. The results of the RH:2 can be presented as shown in Figure (6).\u003c/p\u003e\n\u003cp\u003eRH3 proposed that the consumer attitude toward sustainable development plans has a moderating effect on the relation between consumer evaluation of the SLSQ in e-shopping Applications and consumer purchasing decision-making stages.\u003c/p\u003e\n\u003cp\u003eTo test this hypothesis, Model (1) of the process macro (version 4.2) developed by Hayes (2022) was employed. According to Table\u0026nbsp;(7) the results indicated that the overall model was statistically significant (F\u0026thinsp;=\u0026thinsp;107.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an R\u0026sup2; value of 0.45, meaning that approximately 45% of the variance in consumer satisfaction was explained by the model.\u003c/p\u003e\n\u003cp\u003eTable (7) \u003cem\u003eThe moderation affects the consumer attitude toward sustainable development plans\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2-chng\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance or P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLSQ in E-shopping Applications* attitude toward sustainable development plans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e386.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*Significant at 0.05 ** Significant at 0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAlthough the direct effects of both the independent variable (SLSQ in E-shopping Applications) and the moderator (attitude toward sustainable development plans) were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.93 and p\u0026thinsp;=\u0026thinsp;0.52, respectively), the interaction term (SLSQ in E-shopping Applications \u0026times; attitude toward sustainable development plans) was found to be significant (B\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.00), indicating a meaningful moderating effect. Moreover, the change in explained variance due to the interaction was also significant (\u0026Delta;R\u0026sup2; = 0.01, F\u0026thinsp;=\u0026thinsp;8.29, p\u0026thinsp;=\u0026thinsp;0.00). Further analysis of the conditional effects showed that the effect of consumer evaluation of SLSQ on consumer satisfaction increased with higher levels of consumer attitude toward sustainable development plans. Specifically:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAt low levels of attitude toward sustainable development plans (3.75), the effect was B\u0026thinsp;=\u0026thinsp;0.39 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAt the mean level of attitude toward sustainable development plans (4.43), the effect increased to B\u0026thinsp;=\u0026thinsp;0.46 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAt the high level of attitude toward sustainable development plans (5.00), the effect further increased to B\u0026thinsp;=\u0026thinsp;0.52 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings indicate that consumer attitude towards sustainable development plans strengthens the positive relationship between consumer evaluation of (SLSQ) and consumer purchasing decision-making stages.\u003c/p\u003e\n\u003cp\u003eTherefore, Hypothesis H3 is supported, confirming that consumer attitude towards sustainable development plans (attitude towards sustainable development plans) plays a significant moderating role in enhancing the impact of consumer evaluation of SLSQ on consumer purchasing decision-making stages. The results of the third hypothesis can be presented as shown in Figure (7)\u003c/p\u003e\n\u003cp\u003eRegarding RH4, Table\u0026nbsp;(8) presents the influence of demographic factors (gender, age, nationality, education level, profession, and average monthly income) on different stages of the consumer decision-making process: Need Recognition, Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase Behavior.\u003c/p\u003e\n\u003cp\u003eTable (8) \u003cem\u003eT-test \u0026amp; Anova\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResearch Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender T (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge F (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNationality F (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEducation Level F (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfession F (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Monthly Income F (Sign.)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeed Recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.875 (0.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.467 (0.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.339 (0.001**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.437 (0.033*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.275 (0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.780 (0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Search\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.529 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.058 (0.007**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.090 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.766 (0.003**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.578 (0.001**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.733 (0.011*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvaluation of Alternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.088 (0.037*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.174 (0.016*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.719 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.294 (0.014*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.385 (0.010*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.490 (0.004**)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActual Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.852 (0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.031 (0.007**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.281 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.081 (0.018*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.829 (0.005**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.303 (0.020*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost Purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.569 (0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.983 (0.019*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.895 (0.000***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.170 (0.016*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.674 (0.032*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.198 (0.006**)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGender shows significant differences only in Information Search (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Evaluation of Alternatives (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that males and females differ in how they gather and compare information before purchase. For the other stages, Need Recognition, Actual Purchase, and Post-Purchase, no significant gender differences are observed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eAge significantly affects Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that younger and older consumers may follow different decision-making patterns. No significant effect is observed for Need Recognition (p\u0026thinsp;=\u0026thinsp;0.086).\u003c/p\u003e\n\u003cp\u003eNationality has a strong and consistent effect on all decision-making stages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 or p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting that UAE, Bahraini, and Omani consumers differ in how they recognize needs, search for information, evaluate alternatives, make purchases, and behave post-purchase.\u003c/p\u003e\n\u003cp\u003eEducation level significantly influences all stages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that higher or advanced educational attainment shapes consumers\u0026apos; approach to decision-making.\u003c/p\u003e\n\u003cp\u003eProfession significantly affects Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing that employment status (government, private sector, unemployed, freelance) impacts consumer decisions.\u003c/p\u003e\n\u003cp\u003eIncome significantly influences Information Search, Evaluation of Alternatives, Actual Purchase, and Post-Purchase behavior (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that consumers\u0026apos; financial capability affects their decision-making approach.\u003c/p\u003e\n\u003cp\u003eIn brief, most demographic variables significantly affect consumer decision-making stages, especially Nationality and Education Level. Gender has limited impact, affecting only Information Search and Evaluation of Alternatives. Therefore, the hypotheses are generally accepted, except for gender in some stages.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the study provide compelling evidence of the role of SLSQ in the consumer's purchase decision-making process across the Arab Gulf region. The high level of correlation found (r\u0026thinsp;=\u0026thinsp;0.637, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between SLSQ and stages of consumer decision making, lends support to previous research (Dianavera and Aminoto, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Indonesia and Choi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, in China, confirming it as a fundamental aspect of logistics excellence in e- shopping success regardless of geographical considerations. However, it must be noted that the context of the research within the Arab Gulf states indicates different priorities amongst the consumers in that region. The analysis of the priorities established that green packaging (M\u0026thinsp;=\u0026thinsp;4.05) and smart returns / reverse logistics (M\u0026thinsp;=\u0026thinsp;3.94) were the valued SLSQ dimensions amongst Gulf consumers, whilst the management of the carbon footprint was at the lowest rating (M\u0026thinsp;=\u0026thinsp;3.74). The implications of this finding are important when compared to similar research which has been undertaken in Europe where the environmental factors of logistics have been given more attention (Biancolin and Rotaris, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Large et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) as it demonstrates that the Gulf consumers prefer tangible experiential benefits associated with the sustainability dimension of shopping experience rather than the abstract concepts of environmental impact. This is supported by Kawa and Pierański (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who noted that the effectiveness of green logistics is based on the visibility parameter of consumer-facing benefits, yet did seem to suggest the premise that environmentally aware consumers tend to attach the greatest importance to carbon reduction technology. The fact that consumers in this study have some preference for visible aspects of sustainability can be said to reflect the relatively early stages of sustainability awareness of the region contrary to the more mature Western markets, in which Verma (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found some parallels with e-retailer attitudes post-COVID in the GCC region.\u003c/p\u003e \u003cp\u003eThe path analysis showed that, overall, SLSQ had significant effects at all five of the stages of consumer decision making (β\u0026thinsp;=\u0026thinsp;0.573\u0026ndash;0.607, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) confirming and extending earlier studies that were concerned primarily with post purchase satisfaction. This reaffirms the findings of Do et al. (2020) in their study dealing with Generation Z consumers, and extends Nguyen et al.'s (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) linear relationships to show that sustainable logistics also affects the cognitive processes at the earlier stages of decision-making, such as need recognition (β\u0026thinsp;=\u0026thinsp;0.583) and information search (β\u0026thinsp;=\u0026thinsp;0.607), and not just at the post purchase evaluative stage. The consistency of these effects at all stages suggests that SLSQ operated as an overall decision heuristic rather than being a stage-specific consideration, providing further antagonism to piecemeal approaches to the implementation of green logistics. The good inter-correlations shown by the SLSQ dimensions (r\u0026thinsp;=\u0026thinsp;0.73\u0026ndash;0.94) is confirmation of the holistic approach suggested by Kawa and Pierański (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the realities of Arabelen and Kaya's (2021) description of LSQ as having an integrated nature, from which companies cannot selectively implement green initiatives but have to make the changes comprehensively.\u003c/p\u003e \u003cp\u003ePerhaps the most theoretically interesting finding concerns the moderating variables. Contrary to expectations based on an extensive literature review (Gupta et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maurya et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Naz et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), environmental concern did not significantly moderate the SLSQ-decision association (ΔR\u0026sup2; = 0.0002, p\u0026thinsp;=\u0026thinsp;0.70). This finding contrasts with studies in India and Pakistan where eco-consciousness was shown to significantly mediate the effectiveness of green logistics. And in this context, the finding directly challenges the generalizability of the Theory of Planned Behavior across culture-bound contexts. It would appear from these findings that in the Gulf area, personal environmental values have, as yet, not sufficiently developed to a state where they can predict behavioural modification. This may be due, in part, to the region's long-term economic dependence on fossil fuel resources and its relatively recent engagement with sustainability discourse. Furthermore, this differs from Kale's (2025) research in Western markets in which wealthy customers are prepared to pay high prices for green delivery.\u003c/p\u003e \u003cp\u003eOn the other hand, attitudes towards government sustainability plans positively moderate the SLSQ-decision relationship (B\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the conditional effects increasing the level of attitude identification from (B\u0026thinsp;=\u0026thinsp;0.39) to (B\u0026thinsp;=\u0026thinsp;0.52). This discovery illustrates the peculiar divergence in consumer behavior in the Gulf: reliance by consumers on institutional (government) legitimacy and national discourses about sustainability instead of their own individual environmental ethics. It agrees with Verma (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who emphasized the role of cultural considerations in e-retail within the GCC and extends Large et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) findings with respect to the dynamics of sustainability regulations, showing that government endorsement acts as a trust-enhancing mechanism in collectivist cultures. Finally, the importance of national planning (UAE Vision 2030, Bahrain Vision 2030, Oman Vision 2040) in influencing consumer responses appears to indicate that sustainability policies driven from the top down effectively create a more trenchant resonance than do grassroots environmental movements within the Gulf context.\u003c/p\u003e \u003cp\u003eThe demographic analysis indicated that, consistently across all stages of decision making, the most determining factors were nationality and level of education (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), thus supporting the findings by Radyi et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on generational influence of purchase propensity while also adding a critical cultural dimension, which is often poorly addressed in research on global e- shopping. The significant nationality effects across UAE, Bahraini and Omani consumers seem to indicate intra-regional differences that warrant further investigation and which may be indicative of differences in infrastructure maturity, regulatory regimes and exposure to sustainability messaging. The limited gender effects (significant only for information search and evaluation) are contrary to certain Western emergent findings but support the increased gender-neutrality of digital consumption in modernizing Gulf countries.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study collectively demonstrate a strong and consistent relationship between SLSQ and the stages of consumer purchase decision-making in e-shopping applications across the Arab Gulf region.\u003c/p\u003e \u003cp\u003eThe high means observed for green packaging and smart returns/reverse logistics indicate that consumers in the Gulf prioritize tangible and convenient sustainability features that directly impact their experience. On the other hand, aspects such as carbon footprint management received relatively lower attention, likely due to their abstract or less visible nature to the average online shopper. These findings suggest that while Gulf consumers appreciate sustainability, they are primarily motivated by visible and functional eco-friendly practices that improve service quality and convenience.\u003c/p\u003e \u003cp\u003eThe confirmatory factor analysis (CFA) confirmed the robustness and reliability of the study constructs, with all factor loadings, Cronbach\u0026rsquo;s alpha values, and model fit indices falling within acceptable thresholds. This ensured that the measurement model accurately reflects the theoretical relationships among SLSQ, consumer decision-making stages, and moderating factors such as environmental concern and attitudes toward governmental sustainability plans. The path analysis further revealed that SLSQ has a significant positive effect on all five decision-making stages, emphasizing that improving sustainable logistics can directly enhance consumers\u0026rsquo; awareness, evaluation, and satisfaction processes.\u003c/p\u003e \u003cp\u003eRegarding the moderating variables, the results indicated that environmental concern did not significantly moderate the relationship between SLSQ and consumer decision-making, suggesting that personal concern alone is not a decisive factor in shaping purchase behavior. However, attitudes toward governmental sustainable development plans demonstrated a significant moderating effect, indicating that trust in governmental efforts and national sustainability initiatives strengthens the positive impact of SLSQ on consumer satisfaction. This highlights the importance of institutional credibility and national sustainability campaigns in fostering environmentally conscious consumption patterns.\u003c/p\u003e \u003cp\u003eThe analysis of demographic variables revealed important behavioral differences. Nationality and education level had the most consistent and significant effects across all decision-making stages, suggesting that cultural background and educational attainment shape consumers\u0026rsquo; understanding and appreciation of sustainable logistics. Age, profession, and income also influenced most stages, indicating that lifestyle and economic capability play key roles in shaping e-shopping decisions. In contrast, gender had a limited impact, affecting only the information search and evaluation stages, implying that both men and women share similar attitudes toward sustainable e-shopping once purchase intentions are made.\u003c/p\u003e \u003cp\u003eIn summary, the findings affirm that the adoption of sustainable logistics practices not only enhances the operational quality of e-shopping platforms but also strengthens consumer trust and loyalty. Companies operating in the Gulf region should therefore focus on visible sustainability dimensions such as eco-friendly packaging and return systems, while also supporting national environmental initiatives to increase consumer engagement. Moreover, marketing strategies should be tailored to the demographic characteristics of target markets, particularly in terms of nationality, education, and income, to maximize the effectiveness of sustainability-driven value propositions.\u003c/p\u003e\n\u003ch3\u003eFuture Studies\u003c/h3\u003e\n\u003cp\u003eThis study\u0026rsquo;s findings demonstrate the significance of further investigating how the SLSQ via e-shopping applications relates to consumer purchasing decision-making processes, thereby paving the way for many future studies. Further studies could investigate how aspects of sustainable logistics\u0026mdash;like eco-friendly packaging, green transport, and smart delivery models\u0026mdash;affect consumer satisfaction with e-shopping applications. To comprehend the impact of differences in logistics infrastructure and consumer cultures on consumer involvement with sustainable services, it is essential to conduct comparative studies across various countries and regions.\u003c/p\u003e \u003cp\u003eAdditionally, it is necessary to explore how mediating or moderating factors\u0026mdash;like environmental awareness, risk perception, and application features or visual identity\u0026mdash;affect the strength of the connection between sustainable logistics and purchasing behavior. Additional research might investigate how consumers accept sustainable delivery and return policies and how these affect long-term repeat purchases. It is also advisable to utilize methods of artificial intelligence to create models that forecast consumer satisfaction in relation to logistics services, using operational and environmental data. The suggested research paths will help to better elucidate how sustainable logistics contributes to e- shopping engagement and loyalty.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eE- shopping companies operating in the Arab Gulf must strategically focus on visible sustainability dimensions. Investment must be directed to environmentally friendly packaging innovations and easy-to-use reverse logistics systems. This has a direct impact on the consumer's decision-making process. Companies such as Amazon, Noon and Talabat must create explicit sustainability communication strategies that underscore concrete benefits instead of hypothetical carbon numbers. The introduction of smart return systems equipped with real-time tracking systems and convenient drop-off points will improve consumer confidence. In the marketing campaigns, special emphasis should be placed on governmental sustainability projects (Vision 2030, UAE Green Agenda), as governmental endorsement plays a pronounced moderating role in consumer response. Multi-tier pricing structures that permit premium sustainable delivery alternatives could lead to higher willingness-to-pay from educated higher-income segment groups but maintain competition on standard services. Gulf governments must set about establishing standardized structures for green logistics certification. This will assist in transparency and consumer faith in companies. Public campaigns that emphasize initiatives for national sustainability goals could help to maximize the moderating effect found in this study. Incentives could be provided in the form of tax incentives awarded to companies who switch their fleets of vehicles to electric cars or the use of renewable-energy-based warehouses. Eventually regulations should be introduced mandating minimum recycling rates for packaging materials, and institutionalizing the desirable sustainability practices identified in this study across the E- shopping industry.\u003c/p\u003e \u003cp\u003eLongitudinal studies investigating how sustainability priorities change with the maturity of Gulf markets would provide temporal insights. Comparative research involving how different digital domestic platforms (Noon, Shein) compared to traditional retailers' e- shopping properties could elucidate platform-oriented optimization strategies. Research as to why environmental concern is not a moderating force in the Gulf context through qualitative interviews examining the cultural hierarchies of value would increase theoretical understanding. Studies involving logistics 4.0 and 5.0 technologies (IoT, blockchain, AI) relative to the Gulf implementations would address the gap identified in the literature on smart logistics adoption. Finally, experimental designs testing specific green initiatives (e.g. reforestation contributions per order) would help identify causal mechanisms and optimal sustainability communication strategies, building on Biancolin and Rotaris' (2024) findings from Italy in the cultural context of the Gulf.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eFinally, the limitations of this study lie in its application to only three Gulf countries\u0026mdash;the UAE, Bahrain, and Oman\u0026mdash;which are the countries of origin for the researchers and where they were able to distribute the online questionnaire. This implies the importance of conducting future, more comprehensive studies covering all Gulf countries.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received the Gulf University Research Ethics Committee's approval to conduct this experiment on December 24, 2024 (Approval number is GU- 24-5-2025). All data collection and analysis procedures were carried out in strict adherence to the Declaration of Helsinki (2013) and ICH-GCP guidelines for ethical research practices involving digital content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The data that supports the findings of this study are available on request from the corresponding author. The data is not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e: Author 1 wrote conclusion and the final revision, author 2 wrote introduction, questions, hypotheses, and methodology. Regarding author 3, she was responsible for literature review and the theoretical framework. Author 4 wrote the results section and prepared all figures. Author 5 conducted the study survey and statistical analysis. Finally, Author 6 reviewed the manuscript, wrote the recommendation, enhancing the conclusion and the discussion, and proofreading the total manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Fund\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelaziz, S., \u0026amp; Munawaroh, M. (2024). Unveiling the landscape of sustainable logistics service quality: A bibliometric analysis. \u003cem\u003eJurnal Optimasi Sistem Industri\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(2), 227-265. \u003c/li\u003e\n\u003cli\u003eAhmad, M. B., Shaukat, F., Rizvi, F., \u0026amp; Zulqarnain, M. (2024). 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Organic foods purchase behavior among generation Y of Bangladesh: The moderation effect of trust and price consciousness. \u003cem\u003eFoods\u003c/em\u003e, VOL.10(10).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable Logistics Service Quality (SLSQ), E-shopping Applications, Consumer Purchase Decision-Making Stages, Arab Gulf","lastPublishedDoi":"10.21203/rs.3.rs-8254418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8254418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to examine the impact of sustainable logistics service quality (SLSQ) on consumer purchase decision-making stages in e-shopping apps in the Arab Gulf, employing a survey-based methodology with 390 participants who were selected based on a convenience sample. Despite the global research interest of this research area, most of the literature that is available focuses on the Western or Asian markets and few empirical studies have been undertaken in the Arab Gulf, where unique cultural, regulatory, and infrastructural conditions shape consumer demands and logistics operations.\u003c/p\u003e \u003cp\u003eThe results of this study collectively demonstrate a strong and consistent relationship between (SLSQ) and the stages of consumer purchase decision-making in e-shopping applications across the Arab Gulf region, with moderating factors such as environmental concern and attitudes toward governmental sustainability plans. The high means observed for green packaging and smart returns/reverse logistics indicate that consumers in the Gulf prioritize tangible and convenient sustainability features that directly impact their experience. On the other hand, aspects such as carbon footprint management received relatively lower attention, likely due to their abstract or less visible nature to the average online shopper.\u003c/p\u003e \u003cp\u003eNationality and education level had the most consistent and significant effects across all decision-making stages, suggesting that cultural background and educational attainment shape consumers\u0026rsquo; understanding and appreciation of sustainable logistics. Age, profession, and income also influenced most stages, indicating that lifestyle and economic capability play key roles in shaping e-shopping decisions\u003c/p\u003e","manuscriptTitle":"The Impact of Sustainable Logistics Service Quality in E-shopping Applications on Consumer Purchase Decision-Making Stages: A Survey Study in the Arab Gulf","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 10:39:19","doi":"10.21203/rs.3.rs-8254418/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d421b77-6759-486d-a05b-99e297f94875","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60316342,"name":"Business and commerce/Business and management"},{"id":60316343,"name":"Social science/Business and management"},{"id":60316344,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":60316345,"name":"Social science/Environmental studies"},{"id":60316346,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-04-02T23:23:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 10:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8254418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8254418","identity":"rs-8254418","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}