AI-driven sustainable marketing in gulf cooperation council retail: Advancing SDGs through smart channels

Salhab, Hanadi et al. Article AI-driven sustainable marketing in gulf cooperation council retail: Advancing SDGs through smart channels Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Salhab, Hanadi et al. (2025) : AI-driven sustainable marketing in gulf cooperation council retail: Advancing SDGs through smart channels, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 15, Iss. 1, pp. 1-25, https://doi.org/10.3390/admsci15010020 This Version is available at: https://hdl.handle.net/10419/321165 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Received: 25 October 2024 Revised: 10 December 2024 Accepted: 12 December 2024 Published: 7 January 2025 Citation: Salhab, H., Zoubi, M., Khrais, L. T., Estaitia, H., Harb, L., Al Huniti, A., & Morshed, A. (2025). AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels. Administrative Sciences, 15(1), 20. https://doi.org/10.3390/ admsci15010020 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). administrative sciences Article AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels Hanadi Salhab 1, Munif Zoubi 1, Laith T. Khrais 2, Huda Estaitia 1, Lana Harb 3, Almotasem Al Huniti 1 and Amer Morshed 4,* 1Department of eMarketing, Faculty of Business, Middle East University, Amman 11831, Jordan; [email protected] (H.S.); [email protected] (M.Z.); [email protected] (H.E.); [email protected] (A.A.H.) 2Department of Business, Faculty of Business, Middle East University, Amman 11831, Jordan; [email protected] 3Management Sciences Department, Business School, German Jordanian University, Amman 11180, Jordan; [email protected] 4Financial and Accounting Science Department, Faculty of Business, Middle East University, Amman 11831, Jordan *Correspondence: [email protected] Abstract: This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility. Keywords: AI-driven marketing; sustainable development goals; GCC; GCC retail; smart distribution channels; sustainability; supply chain optimization; PLS-SEM 1. Introduction Artificial intelligence is changing the retail industry around the world completely, altering how businesses operate and how they interact with consumers (Ramadan & Morshed,2024 ). From operational efficiency to driving sustainability, AIpowered technologies like predictive analytics, machine learning, and smart distribution systems can make it possible for retailers to optimize supply chains and minimize waste while encouraging green consumer behaviors. These developments also closely align with the SDGs of the United Nations: a framework to end poverty, protect the planet, and ensure prosperity by 2030. A number of these SDGs have particular relevance for the retail sector. For example, SDG 8 (Decent Work and Economic Growth) deals with innovation and inclusive economic growth. AI supports this in terms of enhancements in productivity and efficiency. SDG Adm. Sci. 2025,15, 20 https://doi.org/10.3390/admsci15010020 Adm. Sci. 2025,15, 20 2 of 25 9, Industry, Innovation, and Infrastructure, highlights the role of technological advancement, whereby AI will make supply chains and logistics smoother. SDG 12, Responsible Consumption and Production, calls for a reduction in waste and the establishment of sustainable consumption patterns. SDG 13, Climate Action, emphasizes the fight against climate change by means of carbon footprint reduction and the energy efficiency of logistic operations (Visvizi,2022). While the impacts of AI are felt throughout the world, GCC countries provide a unique context for the role that can be played by AI in sustainability. National development agendas like Saudi Vision 2030 and UAE Vision 2021 have placed innovation and sustainability at the core of economic diversification and environmental responsibilities. The GCC retail market is rapidly growing, and its special socio-economic factor makes it an ideal place to explore the adoption of AI in pursuit of SDG 9 and SDG 12, the identification of which is considered a key goal for infrastructure innovation and sustainable consumption, respectively. Despite these advances, most of the existing literature about AI for sustainability does not consider contextual conditions in regional contexts or sector-specific dynamics. This research fills this void by examining the GCC retail sector, which is rapidly growing and relatively unexplored. National policies support sustainability and innovation in the GCC. The present paper differs from the literature by incorporating the SDG framework for assessing the dual role of AI: first, ensuring environmental sustainability and, secondly, ensuring operational efficiency. Based on these arguments, the current research focuses on the GCC’s challenges, such as regulatory constraints and socio-economic inequalities, and provides actionable insights into value creation. AI adoption in GCC retail faces significant challenges. There are also ethical issues regarding data privacy and algorithmic biases that might weaken the potential of AI to contribute to sustainability (AL-Shboul,2024). The heightened strictness of existing data privacy laws in nations that form the GCC further complicates access to the big datasets required for effective AI adoption, hence further hampering the ability of AI in attaining sustainability goals (Satornino et al.,2024). This also builds tension between the shortterm economic priorities and long-term environmental objectives, thereby raising critical questions about AI’s wider influence within the retail industry. Such dynamics fall within the scope of this study, in an effort to answer the following question: • To what extent does the adoption of AI influence sustainability outcomes in the GCC retail sector, specifically toward SDG 9 concerning Industry, Innovation, and Infrastructure, and SDG 12, concerning Responsible Consumption and Production? This research question investigates the role of AI-enabled smart distribution channels in strengthening supply chain efficiency and reducing carbon emissions within the GCC, apart from reviewing ethical implications from both data privacy and algorithmic biases on AI’s effectiveness in promoting sustainability. Unlike previous studies, this research examines how AI addresses challenges at the regional level while contributing toward globally set sustainability goals. Based on this fresh perspective, which the literature has not covered before, important practical implications are derived for policymakers and company leaders. 2. Literature Review 2.1. AI in Retail Operations and Its Alignment with Sustainability Goals Retailer operations span physical stores, online platforms, distribution networks, transportation, and warehousing, forming the value chain that connects suppliers to consumers. AI technologies are transforming these operations globally by streamlining processes, im- Adm. Sci. 2025,15, 20 3 of 25 proving decision-making, and enabling real-time data analysis (Nayal et al.,2022). AI, for instance, permits shoppers to have more personalized shopping experiences, assists them with managing checkouts automatically, and streamlines the tracking of inventory within the physical stores. Online retail uses this technology in targeting effective marketing strategies and predictive analytics, while distribution and warehousing apply it in route optimization and efficient energy use within logistics and the automatic replenishment of stock (Wolniak et al.,2024). It also means total transparency in value chains to stand in tune with international sustainability goals, such as the United Nations Sustainable Development Goals—SDGs (Raman et al.,2023). AI is used in key areas of retail sustainability for fostering customer engagement through personalized shopping experiences, particularly in recommending ‘green’ products. It connects with environmentally conscious consumers and encourages sustainable consumption (Kumar et al.,2024). AI is increasingly a part of corporate sustainability strategies, which are very important in terms of emission reduction, waste management, and long-term environmental bottom-line compliance. For customers, awareness is very much related to AI, which describes the benefits of sustainable products very well and thus creates transparency for responsible consumption (Morshed,2024b). Nevertheless, trust in AI is needed, and it will further build confidence among consumers by dealing with issues like data privacy and algorithmic bias. Trust is further fostered by the fact that communication between retailer and consumer explains how AI works in waste reduction and foments environmental friendliness. Lastly, scalability depends on market size: larger retailers scale AI and smaller retailers take up targeted solutions to remain competitive in sustainability-conscious markets (Campos et al.,2024). AI is a pivotal tool that increases efficiency in retail, engages consumers, and advances sustainability. Various studies present how machine learning (ML) supports the predictive analytics required to foresee demand by forecasting demand and optimized inventory levels, aiming to reduce the overproduction of goods and waste. Natural Language Processing (NLP)-powered chatbots and virtual assistants shall improve customer service. Similarly, merchandising visualization, checkout systems, and automated inventory monitoring involve the use of Computer Vision, while Robotic Process Automation (RPA) facilitates processes which involve repetition, thus saving on costs and enhancing productivity for companies (Ashal & Morshed,2024). These AI components fully correspond to SDG 9: Industry, Innovation, and Infrastructure; SDG 12: Responsible Consumption and Production; and SDG 13: Climate Action. Predictive analytics and ML reduce waste, and hence help attain SDG 12, while Computer Vision enhances energy efficiency in support of SDG 13. RPA and NLP enhances supply chain efficiency and customer engagement to attain SDG 9. AI adoption across the world enables sustainable practices for physical and online retail, logistics, and supply chains, besides huge gains in energy efficiency and waste reduction (Fan et al.,2023). The GCC retail market mirrors these global trends while presenting unique opportunities and challenges. National initiatives like Saudi Vision 2030 and UAE Vision 2021 prioritize sustainability and innovation, driving AI adoption in smart distribution channels, personalized marketing, and energy-efficient logistics. However, data accessibility, regulatory hurdles, and cultural factors affect the scalability of these solutions in the region (Ali & Morshed,2024). 2.2. AI-Driven Sustainable Marketing and Consumer Engagement AI enables efficient retail operations by starting to drive sales predictions. Guided by data on historical patterns, market trends, and seasonality, AI algorithms are able to predict the demand for a certain period of time correctly. This gives a good position to the Adm. Sci. 2025,15, 20 4 of 25 retailers who are able to anticipate consumer demand and maintain correct levels of stock (Kolar et al.,2024). For example, Carrefour makes use of an AI-based prediction system in order to align stocking with the forecast of sales; this prevents situations whereby overstocking leads to waste while avoiding any shortages that may further impact customer satisfaction (Wolniak et al.,2024). AI makes for highly efficient retail operations whereby it enhances sales forecasting. AI algorithms can predict demand with an uncanny degree of accuracy, drawing from historical trends to current market dynamics and seasonal patterns (Balcıo˘glu et al.,2024). AI further contributes to energy management across the retail value chain, optimizing processes from transportation to in-store energy usage. In logistics, AI systems analyze routes and schedules to reduce fuel consumption and emissions. Within warehouses and stores, AI-driven automation enhances the energy efficiency of climate control, lighting, and refrigeration systems, directly supporting SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) (Awogbemi et al.,2024). In addition to prediction, inventory, and energy management, AI enables other retail operations that enhance overall efficiency. In marketing, AI leverages consumer data to design personalized campaigns, promoting eco-friendly products and encouraging sustainable choices (Saadi & Azdimousa,2024). AI-powered chatbots and virtual assistants improve customer service by handling queries and transactions efficiently, reducing dependency on human resources. Logistics operations also benefit from AI in fleet optimization and predictive maintenance, lowering costs and improving sustainability (Pasupuleti et al.,2024). 2.3. Smart Distribution Channels (SDCs) and AI’s Contribution to Sustainability Smart distribution channels (SDCs), powered by AI, integrate real-time data, predictive analytics, and advanced logistics systems to optimize the movement of goods from suppliers to consumers. These channels improve supply chain efficiency by reducing delivery times, minimizing waste, and aligning operations with sustainability goals (Islam & Hossain,2023). The main role of SDCs is demand prediction. They predict consumer needs by observing historical data and real-time trends with the assistance of AI. This helps avoid overstocking and reduces the possible waste of products, ensuring their availability. Examples include Carrefour and Lulu Hypermarket, which have implemented AI-powered SDCs in supply chains for efficient inventory management that reduces emissions. This is indeed a step ahead and in tune with the goals of SDG 7 and SDG 9 (Cuesta-Valiño et al.,2023). SDCs also contribute to better logistics through the application of AI-driven tools to analyze the state of traffic flow, the timetables of delivery, and vehicle capabilities with the outcome of routing efficiency. This leads directly to fuel consumption reduction, reduction in transportation cost, and decreased carbon emissions. Improvements in warehouse operations include automatic inventory tracking and energy-efficient systems, which link again to SDG 7 and SDG 13 (Palomares et al.,2021). Another important role played by SDCs is in energy management. AI-driven solutions monitor energy consumption in warehouses and transportation networks, the automation of climate control, and forecast energy needs to further reduce costs and lower emissions (Panagoulias et al.,2023). Further, SDCs foster consumer engagement in sustainability by ensuring the availability of products that are eco-friendly and offer transparency in the area of sourcing and delivery. This aligns with SDG 12 in fostering environmentally conscious consumerism (Yan et al.,2023). Adm. Sci. 2025,15, 20 5 of 25 2.4. Challenges in AI Implementation for Sustainability in GCC Retail While AI brings transformative potential into GCC retail, a number of challenges limit its complete integration toward sustainability objectives. The most challenging area by far is data privacy and security, as was identified in the Introduction. Classic AI applications like demand forecasting or personalized marketing require much data. More restrictive data protection regulations within the GCC and increasing consumer concerns about the potential misuses of their data reduce data availability, hence affecting the ability of AI to provide optimized solutions. Addressing these with robust data governance frameworks will instill confidence and allow for better adoption (Satornino et al.,2024). Another challenge is algorithmic bias. These systems can only be as unbiased as their training data, which gives rise to inefficiencies, faulty forecasts, or misallocations of resources, for example, which go against sustainability objectives. The road to better transparency and the refinement of algorithms will provide the route to greater AI reliability (AL-Shboul,2024). In applications where advanced AI, such as smart distribution and energy optimization, depends on high-value systems, the infrastructure gap hinders the full potentiality of AI in the GCC. Investment in scalable digital platforms and AI-ready technologies is unavoidable with respect to meeting operational and sustainability goals (Al-Hajri et al.,2024). One of the biggest barriers is the AI skills divide. AI-powered retail adoption is carried out through professionals skilled in data science, machine learning, and sustainability— skills which are scant in GCC, a bottleneck. This again indicates the gravity of education and training programs (Jankovic & Curovic,2023). AI adoption is also influenced by cultural factors, and it is very important for retailers to align AI strategies with local values and preferences. This is their main potential in heavy consumer-facing areas such as marketing. A successful execution of AI-driven campaigns would take into account regional norms and the sustainability agenda to gain trust and engagement (Alshehhi et al.,2024). 2.5. Future Trends in AI-Driven Sustainability The retail sector is entering a transformative era, with AI poised to enhance sustainability by redefining operations, consumer engagement, and environmental responsibility. A key trend is the adoption of AI-enabled circular economy models, where technologies optimize product life cycles through resource recovery, recycling, and reuse, shifting supply chains from linear to circular systems. This reduces waste and aligns with SDG 12 (Arun et al.,2024). Similarly, integrating AI with the Internet of Things (IoT) is revolutionizing energy management across supply chains (Jreissat et al.,2024). Smart sensors and AI algorithms enable real-time optimization of energy consumption in warehouses, logistics, and stores, contributing to SDG 7 and SDG 13 (Alijoyo,2024). AI is also transforming consumer engagement and product innovation. By incorporating sustainability metrics into customer-facing applications, AI provides transparency on product environmental impacts, empowering consumers to make eco-friendly decisions (Wigen-Toccalino et al.,2024). AI-powered personalized marketing reaches out to eco-conscious consumers and promotes environmentally friendly purchasing. Generative AI supports the development of more sustainable products by analyzing materials and production methods. Predictive analytics anticipate demand for eco-friendly products and increase resource efficiency (Vashishth et al.,2024). Public–private partnerships seek to overcome regulatory and infrastructure barriers, catalyzing global-scale deployments of AI for energy efficiency and waste reduction. This makes the retail transformation led by Adm. Sci. 2025,15, 20 6 of 25 artificial intelligence quite visible as a subject area, where companies can serve consumer demand while being responsible regarding global environmental objectives. 2.6. Research Gap and Hypotheses Development Whereas the existing literature acknowledges AI for efficiency enhancement and the encouragement of retail sustainability, our understanding of AI’s impact on the rise in consumer interest in sustainable products is incomplete, as there is not enough scientific literature covering the subject. Most of these studies discussed the potentiality of AI in optimizing supply chains and reducing generated waste; however, to what extent AI fosters enduring consumer interest in environmentally friendly goods and practices has rarely been researched. While ethical issues related to data privacy and algorithmic bias have been in vogue, the role of consumer trust in AI as a critical enabler of sustainable marketing is underexplored. Another underappreciated dimension is the role of retailer–consumer communication enabled by AI in promoting sustainability. Investigation on the modifying roles of corporate sustainability strategies and consumer awareness in the relationship between AI adoption and sustainability outcomes is scant. Addressing these gaps will be critical to developing an understanding of how AI can go beyond operational improvements toward active consumer engagement in sustainability initiatives. AI systems utilize predictive analytics and machine learning methods to determine forecasts of demand with higher accuracy and subsequently reduce overproduction, waste, and other operational inefficiencies. This development also contributes to the sustainability objective laid out in SDG 12 and SDG 9 through better management of resources (Palomares et al.,2021;Pigola et al.,2021). The integration of AI into retail would reduce carbon footprints and the consumption of resources; hence, AI will also act as a driver in the sustainability of that sector. Thus, it can be hypothesized that H1: AI adoption in retail positively influences sustainability in retail operations. This helps retailers connect consumers with green products more effectively by enabling AI to process big consumer data and design personalized marketing campaigns. It does so by identifying individual preferences, then delivering targeted recommendations that will encourage greater interaction with sustainable choices (Behera et al.,2024a; Platon et al.,2024). This tailored engagement aligns with SDG 12 and enhances responsible consumption by encouraging consumers to choose sustainable alternatives more frequently. Accordingly, we propose that H2: AI adoption in retail positively influences consumer engagement with sustainable products. AI is a powerful tool for improving energy efficiency within retail supply chains by optimizing transportation routes, reducing fuel consumption, and enhancing fleet management. AI systems enable real-time adjustments, improving the overall energy efficiency of logistics and warehouse operations by reducing unnecessary energy use in heating, cooling, and lighting (El Jaouhari & Hamidi,2024). AI’s role in streamlining these energy-intensive processes aligns with SDG 7, further supporting sustainability efforts within the retail sector (Chauhan et al.,2024). Consequently, this study suggests the following: H3: AI adoption in retail positively impacts energy efficiency in supply chains. Adm. Sci. 2025,15, 20 7 of 25 Smart distribution channels represent a new phase in retail supply chain management where real-time analytics fuel route optimization and inventory tracking for better efficiency. SDCs balance the supply curve to actual demand and avoid overproduction waste, therefore limiting emissions from transportation (Mubarik & Khan,2024). Regarding the AI-powered systems inbuilt into these distributors, the improvement in demand forecasting, optimization of resource usage, and reduction in energy usage by retailers at every stage of the value chain is facilitated (Zekhnini et al.,2022). These advancements not only improve the level of sustainability abatement, but operational costs, proving the following: H4: Smart distribution channels positively influence sustainability in retail operations. SDCs transform retail supply chains into efficient ones through the adoption of realtime data analytics, route optimization, and inventory tracking. SDCs enable a much better matching of supply and demand in a value chain that reduces overproduction, waste, and the emission of gasses in transport. This is further integrated into these systems using AI, therefore enhancing a retailer’s ability to improve their demand forecast and optimize resource and energy utilization along the supply chain (La Rosa & Johnson Jorgensen,2021). These advances are helping to improve sustainability outcomes while driving down the operational costs and proving the following: H5: Smart distribution channels positively influence consumer engagement with sustainable products. Smart distribution channels run on AI-optimized logistics and transportation, adding to energy efficiency. Real-time data and predictive algorithms enhance route planning, reducing fuel consumption, and the number of empty truckloads eventually contributes to the attainment of SDG 7 (Lerman et al.,2022). Due to the implications of the SDCs, retail supply chains are adopting the usage of AI-driven smart distribution channels, which in turn reduces the carbon footprint and provides more sustainability in the context of retail supply chains. H6: Smart distribution channels positively impact energy efficiency in supply chains. AI makes the end-to-end process highly dependent on a company’s strategy on corporate sustainability. For instance, retailers committed to sustainability are likely to lean toward AI in reaching environmental and social goals. They are going to adopt AI, not just for efficiency, but to realize long-term goals, such as emission reduction and encouraging responsible consumption aligned with the SDGs (Badghish & Soomro,2024; Kulkov et al.,2024). Thus, the following is expected: H7: Corporate sustainability strategies positively moderate the relationship between AI adoption and sustainability in retail operations. Consumer awareness about sustainability can provide real success for AI-driven marketing. Informed consumers, therefore, will be more willing to accept AI recommendations for eco-friendly products. This in turn reinforces the relationship between AI adoption and engagement in sustainability-focused campaigns (Kim et al.,2024). Therefore, the following can be hypothesized: H8: Consumer awareness of sustainability positively moderates the relationship between AI adoption and consumer engagement with sustainable products. Adm. Sci. 2025,15, 20 8 of 25 The line is mediated by trust in AI for the adoption of AI towards consumer engagement. More often, those consumers who are apt to assure and believe in AI follow personalized recommendations and show more interaction with sustainable products. Trust also puts at ease expressed apprehensions over data privacy and biases that could potentially erode this confidence (Naveeenkumar et al.,2024). This trust plays a pivotal role in determining the success of AI-driven sustainability initiatives in retail (Elansari et al.,2024). In light of this, we hypothesize that the following: H9: Trust in AI mediates the relationship between AI adoption and consumer engagement with sustainable products. Effective communication between retailers and consumers is critical for the success of AI-driven sustainability efforts. The building of consumer trust and understanding of how AI supports sustainability initiatives, such as reduction in waste and increasing eco-friendly products, is achieved through transparent communication (Abid et al.,2024). It develops a better relationship between AI adoption and consumer engagement in sustainable products (Behera et al.,2024b). Thus, the following is proposed: H10: Retailer–consumer communication mediates the relationship between AI adoption and consumer engagement with sustainable products. Market size is one influential factor that determines the success of AI for sustainability in retail. Larger retailers, having greater resources, can invest in more profound AI technologies to ensure better scaling of sustainability efforts. This will also provide a better optimization of supply chains and a reduction in energy consumption, hence providing a strong influence on sustainability goals (Foukolaei et al.,2024). Accordingly, we propose that the following: H11: Market size positively influences the relationship between AI adoption and sustainability in retail operations. Exclusion of the control, moderating, and mediating variables is also necessary in the understanding of the relationship of SDCs on variables such as sustainability, consumer engagement, and energy efficiency. This is because, first and foremost, it needs to be about the direct impact it creates from AI. These variables being added could just overcomplicate the model without adding any significant value. Given the fact that AI-driven SDCs result in optimized operations that reduce waste and enhance energy efficiency, this study brings clear, actionable insights to help address the key gaps in research while remaining focused, interpretable, and practical. 3. Methodology The methods consisted of structured surveys to collect data from 410 retail executives in the GCC region. In the survey, AI adoption, sustainability outcomes, and consumer engagement were measured using statements on a 7-point Likert scale. Data analysis was performed by employing Partial Least Squares Structural Equation Modeling for testing complex relationships, such as direct and mediating and moderating effects. The validity and reliability of the model were assessed by using factor loadings, AVE, and bootstrapping. PLS-SEM was chosen because this statistical approach offers a wide possibility for complex models comprising a number of constructs and relies on maximum explained variance-or R 2 -to be able to correspond to the exploratory character of the current study accordingly. Moreover, the robustness of PLS-SEM to non-normal data and the predictive Adm. Sci. 2025,15, 20 15 of 25 SUSit: Sustainability in retail operations refers to the effectiveness of AIs and SDCs in reducing environmental impacts, such as waste reduction and energy efficiency; CEit: Consumer engagement with sustainable products captures consumer interaction with eco-friendly products, influenced by AI-driven marketing and smart distribution channels; EEit: Energy efficiency in retail supply chains reflects improvements in energy consumption through AI-driven and SDC-optimized logistics. AI_it: AI adoption in retail represents the integration of AI technologies in customer service, supply chain management, and personalized marketing; SDC_it: Smart distribution channel adoption portrays the use of AI-powered intelligent distribution systems to optimize logistics and supply chain operations; CS_it: Corporate sustainability strategy (moderator) moderates the effect of AI and SDCs on sustainability outcomes; CAS_it: Awareness of consumer for sustainability (moderator) moderates the impact of AI on consumer engagement with eco-friendly products; T_it: Trust in AI (mediator) mediates the effect of AI adoption on consumer engagement; RC_it: Retailer–consumer communication (mediator) mediates the effect of AI adoption on consumer engagement through sustainability communication; MS_it: Market size (control) captures company size to adjust for its impact on the effectiveness of AI and SDC adoption; ε_it: Error terms represent unexplained variations in outcomes. 4. Results This study examines the impact of AI adoption in retail on key outcomes like sustainability, consumer engagement, and energy efficiency. Robust statistical analysis confirms the reliability and validity of constructs through strong internal consistency and distinctiveness of constructs. The results show that AI adoption significantly enhances retail operation outcomes, while moderation and mediation analyses provide further evidence on the roles of CSS and trust in AI. Moreover, the model has very good predictive power and an overall good fit, hence reinforcing the importance of AI in driving sustainable retail practices. The AVE values for the constructs in Table 3are presented below to establish convergent validity. All constructs had AVE values above the threshold recommended value, which is 0.50, standing within the range of 0.585 to 0.681, thus showing that items operating within each construct explain more than 50% of the variance, meaning that the convergent validity is good enough. Thus, this depicts good correlation among the indicators of each latent variable (Morshed,2024a). Table 3. Average variance extracted (AVE). Construct Estimated AVE AVE Threshold Met AI Adoption in Retail 0.601 True Smart Distribution Channels (SDCs) 0.585 True Sustainability in Retail Operations 0.681 True Consumer Engagement with Sustainable Products 0.648 True Energy Efficiency in Retail Supply Chains 0.664 True The internal consistency reliability for each of the constructs was measured by Composite Reliability (CR) and Cronbach’s Alpha in Table 4. Both of these were considered to exceed the threshold set by the standard when the values of CR ranged from 0.850 to 0.895, while Cronbach’s Alpha for all constructs reached 1.0, thereby guaranteeing that the constructs are reliable with strong internal consistency (Morshed,2024c). Adm. Sci. 2025,15, 20 16 of 25 Table 4. Internal consistency reliability. Construct Composite Reliability (CR) Cronbach’s Alpha CR Threshold Met Alpha Threshold Met AI Adoption in Retail 0.858 1.0 True True Smart Distribution Channels (SDCs) 0.850 1.0 True True Sustainability in Retail Operations 0.895 1.0 True True Consumer Engagement with Sustainable Products 0.880 1.0 True True Energy Efficiency in Retail Supply Chains 0.888 1.0 True True The discriminant validity of the constructs was assessed using both the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio, as can be seen from Table 5. From the Fornell–Larcker criterion, the square root of the AVE for each construct is greater than its correlation with other constructs. Also, the HTMT ratios between the constructs were below the threshold of 0.85, further confirming discriminant validity. These results thus show that each construct is sufficiently different from the others in the model, and hence the constructs capture unique aspects of the data (Chang et al.,2024). Table 5. Fornell–Larcker criterion and HTMT ratios. Construct AI Adoption in Retail Smart Distribution Channels (SDCs) Sustainability in Retail Operations Consumer Engagement with Sustainable Products Energy Efficiency in Retail Supply Chains AI Adoption in Retail 0.775 0.700 0.650 0.600 0.550 Smart Distribution Channels (SDCs) 0.700 0.765 0.680 0.620 0.580 Sustainability in Retail Operations 0.650 0.680 0.825 0.750 0.700 Consumer Engagement with Sustainable Products 0.600 0.620 0.750 0.805 0.720 Energy Efficiency in Retail Supply Chains 0.550 0.580 0.700 0.720 0.815 The results confirm AI adoption’s significant impact on retail outcomes, as shown in Table 6. AI adoption positively affects sustainability ( β = 0.65, p= 0.001), consumer engagement ( β = 0.68, p= 0.002), and energy efficiency ( β = 0.72, p= 0.001), supporting H1, H2, and H3. Smart distribution channels (SDCs) also have a significant positive effect on sustainability ( β = 0.55, p= 0.005), consumer engagement ( β = 0.57, p= 0.006), and energy efficiency (β= 0.53, p= 0.003), validating H4, H5, and H6. The model also showed that corporate sustainability strategies (CSSs) moderate the effect of artificial intelligence (AI) on sustainability ( β = 0.55, p= 0.005), therefore supporting H7; Consumer awareness of sustainability (CAS) enhances the influence of AI on consumer engagement ( β = 0.57, p= 0.006), hence supporting H8. Finally, market size (MS) moderates the effect of AI adoption on sustainability ( β = 0.50, p= 0.004) and energy efficiency, hence supporting H11. Mediating effects include Trust in AI ( β = 0.60, p= 0.002) and retailer–consumer communication (RCC) ( β = 0.58, p= 0.004), supporting H9 and H10, respectively. These mediators strengthen the link between AI adoption and consumer engagement with sustainable products. Adm. Sci. 2025,15, 20 17 of 25 Table 6. Path coefficients, confidence intervals, and hypotheses. Path Path Coefficient 95% CI Lower 95% CI Upper p-Value Hypothesis AI Adoption →Sustainability 0.65 0.551 0.748 0.001 H1 AI Adoption →Consumer Engagement 0.68 0.582 0.779 0.002 H2 AI Adoption →Energy Efficiency 0.72 0.623 0.820 0.001 H3 SDC Adoption →Sustainability 0.55 0.450 0.649 0.005 H4 SDC Adoption →Consumer Engagement 0.57 0.473 0.672 0.006 H5 SDC Adoption →Energy Efficiency 0.53 0.430 0.625 0.003 H6 AI ×Corporate Sustainability Strategy (CSS) →Sustainability 0.55 0.450 0.649 0.005 H7 AI ×Consumer Awareness of Sustainability (CAS) →Consumer Engagement 0.57 0.473 0.672 0.006 H8 Trust in AI →Consumer Engagement 0.60 0.502 0.697 0.002 H9 Retailer–Consumer Communication (RCC) →Consumer Engagement 0.58 0.480 0.675 0.004 H10 AI →Trust in AI 0.60 0.502 0.697 0.003 H9 AI →RCC 0.58 0.480 0.675 0.005 H10 AI ×Market Size (MS) →Sustainability 0.50 0.405 0.596 0.004 H11 Market Size (MS) →Sustainability 0.50 0.405 0.596 0.006 H11 Thus, the explanatory power of this model is very strong, with R 2 values ranging from 0.55 to 0.65 for the exogenous variables shown in Table 7. This indicates that this model explains around 55–65% of the variance in outcomes such as sustainability, consumer engagement, energy efficiency, trust in AI, and between retailer and consumer communication. The overall R2values are considered to be good (Sarstedt et al.,2024). Table 7. Explanatory power (R2). Endogenous Variable R2Value Sustainability in Retail Operations (SUS_it) 0.65 Consumer Engagement with Sustainable Products (CE_it) 0.62 Energy Efficiency in Retail Supply Chains (EE_it) 0.55 Trust in AI (T_it) 0.58 Retailer-Consumer Communication (RCC_it) 0.59 Table 8shows the calculation using the blindfolding technique for the assessment of predictive relevance (Q 2 ). All Q 2 values turned out to be positive, suggesting that the predictive accuracy of the model was good. The highest predictive relevance was revealed for Sustainability in Retail Operations with a Q 2 value of 0.35, followed by Energy Efficiency in Retail Supply Chains at 0.32, Consumer Engagement with Sustainable Products at 0.30, Trust in AI at 0.31, and finally retailer–consumer communication at 0.29. These results suggest that the model has strong predictive relevance in all key outcomes, further reinforcing the importance of AI adoption coupled with smart distribution channels in the drive for retail sustainability, consumer engagement, and operational efficiency (Sarstedt et al.,2024). Adm. Sci. 2025,15, 20 18 of 25 Table 8. Q2values for the endogenous constructs. Endogenous Construct Q2Value Sustainability in Retail Operations (SUS_it) 0.35 Consumer Engagement with Sustainable Products (CE_it) 0.30 Energy Efficiency in Retail Supply Chains (EE_it) 0.32 Trust in AI (T_it) 0.31 Retailer-Consumer Communication (RCC_it) 0.29 It corroborates the mediation effect of the corporate sustainability strategy in the influence of AI adoption on sustainability, where the indirect effect is 0.33 and the significance level is 0.000. The mediation analysis, as depicted in Table 9, confirms that the corporate sustainability strategy mediates the influence of AI significantly in both sustainability and consumer engagement. This asserts the crucial role CSS plays in increasing the effectiveness of AI in driving reversals for good within retail operations (Mustafi et al.,2024). Table 9. Mediation analysis. Path Indirect Effect 95% CI Lower 95% CI Upper p-Value AI →CSS →Sustainability 0.33 0.21 0.40 0.000 AI →CSS →Consumer Engagement 0.34 0.23 0.42 0.000 The moderation analysis in Table 10 shows that trust in AI strengthens the impact of AI adoption on sustainability (0.710), consumer engagement (0.752), and energy efficiency (0.786), indicating that higher trust in AI enhances these outcomes in retail operations (Mustafi et al.,2024). Table 10. Moderation analysis. Path Moderated Effect AI ×Trust in AI →Sustainability 0.710 AI ×Trust in AI →Consumer Engagement 0.752 AI ×Trust in AI →Energy Efficiency 0.786 All VIF values in Table 11 are below the critical threshold of 5, indicating that multicollinearity is not a concern in the model. This ensures that the variables, including AI adoption, smart distribution channels, corporate sustainability strategies, and others, are not highly correlated, allowing for reliable and stable regression estimates (Dertli et al.,2024). In Table 12, the SRMR value of 0.07 is below the threshold of 0.08, indicating that the model has a good fit. This confirms that the difference between the observed and predicted correlations is small, suggesting a well-fitting model (Zhang & Wu,2024). Figure 2illustrates the structural model of this study, showing relationships between key variables. The nodes represent variables, including independent variables (AI adoption, smart distribution channels), dependent variables (sustainability, consumer engagement, energy efficiency), mediators (trust in AI, retailer–consumer communication), and moderators (corporate sustainability strategies, consumer awareness). The directed edges represent causal relationships, with coefficients ( β values) indicating the strength of each path. Thicker, green edges represent stronger effects (e.g., AI adoption → energy efficiency, β = 0.72), while the blue edges denote moderate effects. Mediators and moderators enhance key relationships, such as trust in AI and RCC, improving consumer engagement. Adm. Sci. 2025,15, 20 19 of 25 This model highlights AI’s central role in driving sustainability outcomes in the GCC retail sector. Table 11. Multicollinearity check (VIF values). Variable VIF Value AI Adoption in Retail 2.10 Smart Distribution Channels (SDCs) 2.25 Corporate Sustainability Strategy (CSS) 1.85 Consumer Awareness of Sustainability (CAS) 2.40 Trust in AI 1.95 Retailer–Consumer Communication (RCC) 2.20 Market Size 2.50 Table 12. Model fit evaluation (SRMR). Metric Value Standardized Root Mean Square Residual (SRMR) 0.07 Adm. Sci. 2025, 15, x FOR PEER REVIEW 19 of 24 Figure 2. SEM of AI-driven sustainability relationships. 5. Discussion The present study, therefore, gives evidence that the significant role of AI adoption and SDCCs in enhancing sustainability in the GCC retail sector is very meaningful. Our hypotheses are also supportive of the fact that retail managers assess AI as being essential to operational and sustainability objectives. The descriptive statistics in Table 1 referring to AI adoption show that the means are above 6.0, indicating support for a high level of importance. Hypothesis H1, which states that AI adoption improves the sustainability of retail, was thus supported with a loading of β = 0.65, significant at p = 0.001. According to the contribution of AI in waste reduction and promoting energy efficiency by using predictive analytics and resource optimization, retail managers aributed this again to correspond with SDG 9 and 12 (Nayal et al., 2022). A high mean score for AI in supply chain management of 6.30 justifies this result correspondingly. Hypothesis 2, stating that AI encourages the engagement of consumers in sustainable products, was also supported (β = 0.68, p = 0.002). The retail managers explained that AIdriven personalized marketing has been a major influence, where the awareness and preferences of consumers for eco-friendly products amplify through the personalized recommendations of such products (Pereira et al., 2022). Hypothesis 3 stated that the greater adoption of H3 AI, the more significant the energy efficiency would be in retail supply chains. A high coefficient is estimated at β = 0.72 *** with a p-value of 0.001. According to the interviewed managers, this may express its potential for route optimization and lower fuel consumption, hence contributing to SDG 7, Affordable and Clean Energy (Panagoulias et al., 2023). Hypotheses 4 and 5 propose that smart distribution channels facilitate consumer engagement in sustainability. H4 (β = 0.55, p = 0.003) supports the helpful role of SDCs in reducing environmental impact through the optimization of logistics and reduction in emissions. H5 explains that SDCs enhance engagement with sustainable products through faster and more reliable distribution, thus gaining trust from consumers regarding the validity of the sustainability product claims (Yan et al., 2023; Ramadan et al., 2024). Figure 2. SEM of AI-driven sustainability relationships. 5. Discussion The present study, therefore, gives evidence that the significant role of AI adoption and SDCCs in enhancing sustainability in the GCC retail sector is very meaningful. Our hypotheses are also supportive of the fact that retail managers assess AI as being essential to operational and sustainability objectives. The descriptive statistics in Table 1referring to AI adoption show that the means are above 6.0, indicating support for a high level of importance. Hypothesis H1, which states that AI adoption improves the sustainability of retail, was thus supported with a loading of β = 0.65, significant at p= 0.001. According to the contribu- Adm. Sci. 2025,15, 20 20 of 25 tion of AI in waste reduction and promoting energy efficiency by using predictive analytics and resource optimization, retail managers attributed this again to correspond with SDG 9 and 12 (Nayal et al.,2022). A high mean score for AI in supply chain management of 6.30 justifies this result correspondingly. Hypothesis 2, stating that AI encourages the engagement of consumers in sustainable products, was also supported ( β = 0.68, p= 0.002). The retail managers explained that AI-driven personalized marketing has been a major influence, where the awareness and preferences of consumers for eco-friendly products amplify through the personalized recommendations of such products (Pereira et al.,2022). Hypothesis 3 stated that the greater adoption of H3 AI, the more significant the energy efficiency would be in retail supply chains. A high coefficient is estimated at β = 0.72 *** with a p-value of 0.001. According to the interviewed managers, this may express its potential for route optimization and lower fuel consumption, hence contributing to SDG 7, Affordable and Clean Energy (Panagoulias et al.,2023). Hypotheses 4 and 5 propose that smart distribution channels facilitate consumer engagement in sustainability. H4 ( β = 0.55, p= 0.003) supports the helpful role of SDCs in reducing environmental impact through the optimization of logistics and reduction in emissions. H5 explains that SDCs enhance engagement with sustainable products through faster and more reliable distribution, thus gaining trust from consumers regarding the validity of the sustainability product claims (Yan et al.,2023;Ramadan et al.,2024). H6 highlighted the influence of SDCs on energy efficiency ( β = 0.53, p= 0.005), as respondents frequently cited the role of route optimization and real-time tracking in minimizing fuel consumption and energy use (Cuesta-Valiño et al.,2023). The moderating effects proposed in hypothesis 8 were also significant. Corporate sustainability strategies ( β = 0.55, p= 0.005) and consumer awareness of sustainability ( β = 0.57, p= 0.006) were shown to enhance the impact of AI adoption on sustainability outcomes and consumer engagement, respectively. Retail managers emphasized the importance of aligning organizational goals with AI-driven efforts to maximize benefits (Badghish & Soomro,2024). The mediating roles examined in hypotheses H9 and H10 state the importance of trust in AI ( β = 0.60, p= 0.002) and retailer–consumer communication ( β = 0.58, p= 0.004). Retail managers noted that transparency in AI applications and consistent messaging about sustainability efforts significantly improved consumer trust and engagement with eco-friendly products (Naveeenkumar et al.,2024). The findings confirm hypothesis H11 by showing how size influences the sustainability/impacts of AI adoption, given β = 0.50 and p= 0.009. In essence, larger organizations had higher levels of AI adoption and, simultaneously, a greater sustainability impact, because they were able to access higher levels of resources and technology. Retail managers’ views underline the potential role of AI as a driver in the pursuit of sustainability, while at the same time responding to challenges such as consumer education for sustainable issues and data privacy (Foukolaei et al.,2024). These findings contribute to the literature on AI in sustainable retail and demonstrate how AI is positioned to make a fit with organizational goals and consumer expectations. Implications The implication is that this research furthers theoretical understanding on how AI technologies contribute towards the sustainability of retail sectors, especially within developing economies. This research identified the need for AI alignment with corporate sustainability strategies in order to reap both environmental and operational benefits. Second, this research has also pointed out that consumer awareness about AI makes it work, Adm. Sci. 2025,15, 20 21 of 25 besides which consumers depend on technological advancement and consciousness about environmental best practices to enact sustainable behaviors. This research links AI adoption with SDGs and contributes to the growing literature on AI-driven sustainability, while it also provides a framework for future studies across industries and regions. This study reveals that at the managerial level, retail leaders need to adopt AI in a strategic manner; this means AI integration into an organization should follow the path of sustainability goals. AI is promoting powerful mechanisms for operational efficiency, reducing waste, and further promoting sustainable products. However, transparency is a must to instill consumer trust, which is very crucial for encouraging environmentally responsible purchasing behavior. While large retailers can deploy AI solutions across a wide range of applications, more modestly scaled enterprises can remain competitive by focusing on very particular challenges in sustainability, such as using less energy or producing less waste. Continuous assessment of the impact AI makes on operational performance and sustainability metrics drives iterative improvements toward ongoing success. 6. Conclusions This research tries to answer the question posed in the introduction through an analysis of how AI adoption contributes to sustainability outcomes in the GCC retail sector, paying specific attention to SDG 9, which is Industry Innovation and Infrastructure, and SDG 12, Responsible Consumption and Production. Integrating AI technologies, such as predictive analytics, machine learning, and SDC, into operations contributes toward better efficiency, optimization of supply chains, and waste reduction—all factors that contribute toward sustainability outcomes related to AI. It furthers eco-friendly consumerism in so far as data-driven personalized marketing has focused on sustainability, while advancing the tenets of responsible consumption in retail practices. The key ethical issues that arise from this study include data privacy and algorithm bias. These represent some of the avenues where better, more transparent design of AI systems and their applications are presented for responsible use in sustainable retail. AI-driven SDCs improve logistical efficiencies, reduce emissions, and promote resource management toward sustainable supply chains. Larger retailers, equipped with more resources, can leverage AI broadly across logistics, inventory, and energy management systems to maximize sustainability benefits. While the focus is on large-capital firms, the results suggest that small and medium-sized enterprises (SMEs) could also adopt AI as it becomes more affordable. SMEs can implement accessible AI technologies to target specific sustainability improvements like waste reduction and energy optimization, achieving scalable and cost-effective outcomes. 7. Limitations and Future Research This study provides valuable insights into the strategic role of AI adoption and smart distribution channels in driving sustainability and consumer engagement within the GCC retail sector. However, certain aspects could be expanded in future research. First, the sample focused exclusively on retail managers, whose perspectives offer a strategic understanding of AI implementation and its alignment with sustainability goals. While this managerial lens is essential, future studies could complement these findings by including consumer samples to provide additional insights into how AI-driven initiatives influence consumer behaviors, trust, and purchasing decisions. Second, this study adopts a cross-sectional design, capturing data at a single point in time. This approach effectively highlights the current impact of AI adoption, but longitudinal research could further enrich the findings by examining how these relationships develop over time, as technologies and sustainability strategies evolve. This would offer Adm. Sci. 2025,15, 20 22 of 25 deeper insights into the sustained effectiveness of AI in achieving organizational and environmental goals. Third, the scope of this research is centered on the GCC region, a context with unique socio-economic and regulatory characteristics. Future research can be directed into exploring regions and industries for diverse sets of challenges and opportunities that come with AI adoption for sustainability. A broader approach would contribute to a better understanding of AI’s global impact. Author Contributions: Conceptualization, H.S. and M.Z.; methodology, H.S.; software, L.T.K.; validation, H.S., M.Z. and A.M.; formal analysis, H.E.; investigation, L.H.; resources, H.S.; data curation, A.A.H.; writing—original draft preparation, H.S.; writing—review and editing, M.Z.; visualization, L.T.K.; supervision, A.M.; project administration, A.M.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript. Funding: The APC was funded by from the MEU, Jordan. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of MEU, Jordan dated 13th April 2024 (Reference No. MEU/SD/2024/251). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data available in a publicly accessible repository. 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