Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 3046-3066 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 12 March 2025; Revised: 16 May 2025; Accepted: 19 May 2025; Published: 29 May 2025 * Correspondence: nguyentransy@ftu.edu.vn Consumer’s brand loyalty in the fashion shopping industry on e-commerce platforms: Understanding the role of artificial intelligence efforts Sy Nguyen Tran1*, Tuan Ngo Anh2, Nhu Le Ngoc Quynh3 1,2,3Foreign Trade University, Campus 2 – Hochiminh, Vietnam; nguyentransy@ftu.edu.vn (S.N.T.). Abstract: This study investigates the influence of AI-driven marketing efforts on brand loyalty in the fashion e-commerce industry. The objective of the research is to understand how factors such as interaction, information, accessibility, customization, and entertainment contribute to the effectiveness of marketing strategies through brand experience, brand relationship, and brand trust, ultimately enhancing brand loyalty. To test the proposed hypotheses, a quantitative research design was employed using the Stimulus–Organism–Response (SOR) framework. Primary data were collected through surveys conducted with 383 consumers. The findings reveal that two out of the five AI-driven marketing factors - customization and information - have the most significant impact on brand experience, brand relationship, and brand trust. Among these mediators, brand experience emerges as the most influential driver of brand loyalty. The study concludes that AI-driven marketing efforts play a crucial role in building and reinforcing brand loyalty among online consumers, particularly within the context of e-commerce platforms. Practical implications are provided for enhancing customer service quality and maintaining effective customer relationships by integrating AI marketing tools into strategic marketing initiatives. Keywords: Artificial intelligence, Brand experience, Brand loyalty, Brand relationship, Brand trust, E-commerce, Fashion industry. 1. Introduction For almost any consumer goods industry, electronic commerce (e-commerce) platforms like Amazon, Alibaba, etc., have become an indispensable sales channel in the market [1] while the fashion industry is one of the sectors accelerating digital transformation, bringing traditional offline services online by Landim, et al. [2]. In Southeast Asian countries such as Indonesia, Singapore, Malaysia, Thailand, Vietnam, and others, Shopee and Lazada were the most popular e-commerce platforms by Sanny, et al. [3]. The rapid development of e-commerce platforms cannot be discussed without highlighting the importance and contributions of AI as AI assists organizations in carrying out tasks related to marketing efforts and enhancing customer experience by Madanchian [4]. Research on AI in e-commerce has been going on for the past three decades by Boukrouh and Azmani [5]. AI (Artificial intelligence) in e-commerce can be understood as the application of artificial intelligence methods, systems, tools, or algorithms to facilitate activities associated with the online buying and selling of goods and services. Organizations that implement e-commerce systems supported by AI are likely to achieve substantial sales and increased customer satisfaction according to Thandekkattu and Kalaiarasi [6]. AI is applied in e-commerce through various aspects such as intelligent logistics, recommendation engines, and optimal pricing applications [7] Specialized AI tools enhance the creativity of designers and effectively meet the diverse demands of customers in the fashion industry [8]. Businesses and brands can gain a competitive advantage by utilizing AI as a live- streaming tool by Zhou, et al. [9] – as discussed in the study in China, where the live-streaming industry is experiencing rapid growth. https://orcid.org/0009-0007-9585-7670 https://orcid.org/0009-0001-3391-6838 https://orcid.org/0009-0006-8035-3421 3047 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate A report by Deloitte projected that the global market for conversational AI is anticipated to reach 14 billion USD by 2025 [10]. Technologies such as artificial intelligence (AI), advanced analytics, and in-store applications are increasingly being adopted to optimize processes, promote sustainability, and enhance customer experience in the fashion industry. Ramos, et al. [11] found that AI significantly contributes to sustainability in the fashion sector through efficient supply chain management, innovative design processes, and waste control. Moreover, integrating AI into business models today and in the near future can lead to a cumulative cash flow increase of 118% by 2030 [11]. There are numerous studies on solutions aimed at improving the quality of online shopping services on e-commerce platforms to enhance customer satisfaction and stimulate purchase intentions. The findings suggest that both information security and website performance have a direct and positive impact on e-service quality [12]. Additionally, responsiveness, compensation, and contact positively affect e-recovery. E-recovery, in turn, has a positive effect on e-loyalty, which significantly influences online repurchase behavior [12]. AI is also effectively utilized in chatbot marketing efforts (CMEs), as evidenced by the study that shows that CMEs have a significant direct effect on communication quality and an indirect influence on relationship quality and customer reactions by Cheng and Jiang [13]. Due to its convenience and flexibility, online shopping has become an indispensable consumer need. Online shopping offers various advantages over offline shopping, such as enhanced convenience, greater personalization at lower costs, a wider variety of products, broader reach, faster transactions, and increased flexibility [14]. By leveraging digital platforms, brands can directly engage with customers, communicate their messages, and gather valuable data for high-level strategic decisions [14]. However, digital platforms contain vast amounts of available information, and e-commerce platforms are filled with countless alternatives. As a result, achieving brand loyalty from consumers is challenging for brands and involves numerous concerns [14]. By collecting survey data from 1,133 individuals from Generation Z in Vietnam, a study demonstrated that brand trust has a significant positive impact on brand loyalty according to Nguyen, et al. [15]. A study conducted on Tokopedia e-commerce in Yogyakarta by a group of authors from Indonesia also agrees that brand trust is a crucial factor in the development of brand loyalty [16]. In a separate study, which surveyed consumers in two major cities in Vietnam (Hanoi and Ho Chi Minh City), it was found that brand experience influences brand loyalty through customer satisfaction [17]. Another study, based on surveys conducted through mail questionnaires in the three largest cities in Vietnam, also found that brand experience influences e- loyalty through the mediation of e-satisfaction [18]. A study indicates that different types of brand relationships influence customer brand loyalty, with the intensity and direction of this impact varying under different conditions and circumstances by Khamitov, et al. [19]. In the field of e-commerce, the integration of AI has become an undeniable reality. AI is utilized for advertising, conducting transactions, and maintaining customer relationships. AI can foster virtual online friendships between brands and customers [20]. The application of AI-powered chatbots can be seen as a representative of the brand in managing customer relationships [20]. The customer experience can be influenced by AI, as positive experiences lead to greater satisfaction and enjoyment with the brand utilizing AI-driven services by Trivedi [21]. Brands can achieve differentiation and competitive advantage by enhancing the customer experience through the use of AI according to Kumar, et al. [22]. There is a substantial amount of research on AI efforts in customer support chatbot technologies, with many findings demonstrating that AI chatbots can indeed contribute to creating positive brand experiences for customers. However, AI applications in other areas, such as AI livestreamers, which not only offer interactive support but also provide high entertainment value, remain underexplored. Furthermore, most studies focus on industries like restaurant services, hospitality, or banking services, while AI applications in the fashion sector on e-commerce platforms have not been extensively researched. According to a report from the e-commerce data platform e-commerce, in the first half of 2024, e-commerce in Vietnam continued to experience remarkable growth. The report indicates that the top five e-commerce platforms—Shopee, Lazada, Tiki, Sendo, and Tiktok Shop—saw sales growth of 3048 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate over 50% compared to the same period in 2023. The report also highlights that the beauty and fashion sectors continue to lead in both sales and volume across all five aforementioned platforms. Fashion brands such as Gumac and HNOSS have gained significant success in applying digital marketing strategies to boost sales on e-commerce platforms in the Vietnamese market [23]. As brands intensify their sales efforts on e-commerce platforms in Vietnam, there is a growing need for research that delves into the impact of AI initiatives on customer loyalty to brands, particularly in the context of customers using e-commerce for shopping. From a theoretical perspective, based on previous research on the relationship between AI efforts and brand loyalty, this study aims to fill the gap in prior studies and provide meaningful contributions to the literature on brand loyalty in e-commerce platforms. This research presents new insights into how AI efforts influence customer experience and trust in fashion brands, while also offering a deeper understanding of brand relationships, which have been less emphasized in previous studies. This perspective views the brand relationship as a component of brand efforts that can enhance brand loyalty success through the impact of AI initiatives. This study was conducted to explore the following research questions (RQs). RQ1: How do AI efforts influence brand experience, brand trust, and brand relationship? RQ2: How do brand experience, brand trust, and brand relationship influence brand loyalty? From the two research questions, this study aims to understand the role of AI efforts on brand experience, brand trust, and brand relationship and through those, affect brand loyalty within the fashion industry through e-commerce. This study makes three contributions. First, there are numerous AI efforts that influence brand experience, brand trust, and brand relationship which marketing managers and policy planners can leverage to enhance the effectiveness of digital marketing. Second, the three factors—brand experience, brand trust, and brand relationship—have varying levels of impact on brand loyalty, particularly, the impact of brand relationship is meticulously examined in this study. Third, this study helps brands design strategies to leverage AI efforts that can strengthen brand loyalty through their impact on brand experience, brand trust, and brand relationship within the fashion industry on e-commerce platforms in Vietnam. This article is structured into seven interconnected sections. The first section introduces the study, outlining the background, novelty, research questions, objectives, and the urgency of the research. The second section presents theoretical perspectives and empirical evidence from previous studies, which form the theoretical foundation for constructing the conceptual framework and developing the hypotheses. The third section details the methodology, including variable measurements, sample characteristics, and data collection methods. The fourth section presents the findings, which include respondent demographics and an analysis of the relationships between the research variables. The fifth section offers a discussion, providing an in-depth analysis and comparison of the research results with existing theories and studies. The sixth section concludes with a summary of the results and discussions. Finally, the seventh section highlights the theoretical and managerial implications of the study, along with its limitations. 2. Literature Review and Hypotheses Development 2.1. Stimulus–Organism–Response Theory This study adopted the stimulus-organism-response (SOR) framework as its central theoretical model. The SOR framework illustrates how environmental stimuli (stimulus) influence individuals' internal processes (organism) and ultimately shape their behaviors (response), highlighting the sequence of these interactions. It has been widely applied in marketing research to understand consumer behavior by Kim and Lee [24]. The SOR theory has proven to be particularly useful in predicting consumer reactions to new technologies. For instance, Kim, et al. [25] employed the SOR model to analyze consumer behaviors and responses to virtual reality tourism experiences. Additionally, various researchers have used the SOR framework to investigate online consumer behavior according to Islam, et al. [26] and Zhu, et al. [27]. In line with this approach, Wu, et al. [28] assessed the impact of 3049 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate atmospheric cues within travel apps on tourists' emotional and behavioral responses. In the present study, the SOR framework is applied to explore how AI efforts (stimuli) influence brand experience, brand relationship, and brand trust (organism), which then contribute to brand loyalty (response). We specifically examine five AI-related factors as stimuli, each with varying levels of impact on the organism variables—brand experience, brand relationship, and brand trust. Ultimately, we consider brand loyalty as the response factor shaped by these internal processes. In the analysis of stimulus factors, our study provides insights drawn from previous research on the five AI effort factors: Interaction, information, accessibility, entertainment, and customization. We consider these AI effort factors as “key drivers of e-commerce”. At the same time, these AI efforts influence brand loyalty through the organism mechanism. The relationship or impact of the three organism factors on brand loyalty will be analyzed in subsequent sections. 2.2. AI Marketing Efforts A seminal study on AI marketing efforts demonstrates that AI-driven marketing has the potential to impact customer decision-making and responses according to Chen, et al. [29]. Additionally, AI marketing communication is viewed by consumers as unavoidable and generally acceptable. Earlier, another study also highlighted that marketers should consider the contributions of AI in shaping how companies manage their customer relationships [30]. In the context of the relationship between customers and brands, automated and AI-enabled solutions are expected to emerge, assisting customers in negotiating with firms. In the study by Cheng and Jiang [13] they outline that AI marketing efforts encompass five dimensions: interaction, information, accessibility, customization, and entertainment. Their research primarily focuses on AI-driven chatbots Cheng and Jiang [13]. In another study, the impact of chatbot marketing efforts (CMEs) was explored through a quantitative survey focused on luxury brands in South Korea. The research highlighted five essential marketing strategies: entertainment, interaction, trendiness, customization, and problem-solving [31]. Our study builds upon the research of Cheng and Jiang [13] and since it focuses on the fashion industry within e-commerce platforms, the AI marketing efforts in our study encompass five dimensions: interaction, information, accessibility, customization, and entertainment. The first factor is interaction, which refers to the communication between customers and the AI applications used by brands, such as chatbots [31] voice-based assistants by Maedche, et al. [32] and interactive generative AI—utilizing AI for customer care in emotionally charged interactions according to Huang and Rust [33]. The second factor is information, which refers to the AI's role in providing customers with information about products, services, or the brand itself. AI chatbots are regarded as customer service and support tools, designed to respond to frequently asked questions with predefined information [34]. The credibility of information influences chatbot adoption, which in turn affects the determination of purchase intention [35]. This suggests that enhancing the usefulness of information can shape customer engagement with chatbots. The third factor is accessibility, which involves evaluating and addressing customer information promptly, in a timely manner through AI technology. Accessibility indicates that by providing customer service 24/7, AI can enhance service quality, which influences brand image and performance by Sultan and Wong [36]. In the study on the use of chatbots on Facebook by brands, chatbots can predict consumer responses and support marketing communications due to their accessibility feature [37]. The fourth factor is entertainment, which may be explored in varying depth depending on the research context. For instance, in the study by Ho and Chow [38] on chatbots in the banking sector, the authors argue that banking customers rarely perceive financial activities or transactions as fun or enjoyable, and therefore, this factor was excluded by Ho and Chow [38]. In contrast, the study titled "Why people use chatbots" emphasizes that, beyond productivity—where chatbots assist users in obtaining quick and efficient help or information—entertainment was also identified as a key motivating factor for users by Brandtzaeg and Følstad [39]. Over a decade ago, a study also identified entertainment as one of the five factors contributing to the growing interest in luxury fashion brands 3050 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate [40]. The study suggests that when customers enjoy entertainment, brand trust is built, which significantly contributes to a luxury brand’s profitability. The last factor is customization, which Godey, et al. [41] describes as the degree to which AI-powered chatbot marketing efforts can provide tailored support to meet the specific needs of customers by Godey, et al. [41]. Recent research on AI-powered personalization methods suggests that providing highly relevant and tailored content, product suggestions, and user experiences can significantly improve customer engagement, satisfaction, and loyalty according to Raji, et al. [42]. 2.3. Brand Relationship (BR) Brand relationship is defined as the ability of a brand to establish a positive connection with consumers by providing meanings within a psycho-socio-cultural context [43]. Brand relationship refers to consumers' commitment to the long-term use of inanimate products they regularly purchase [44]. Social relationship norms can guide the interactions and relationships between consumers and brands by Ghani and Tuhin [45]. Although many brands use attachment relationships to build their marketing strategies, quality is an important aspect of a brand relationship for consumers by Schreuder, et al. [46]. By providing reliable products and services with excellent quality, a brand can strengthen its brand relationship with customers [46]. 2.4. Brand Experience (BE) The concept of brand experience captures the essence of brand building more than brand equity, brand value, brand associations, brand attitudes, and brand personality by Schmitt [47]. Customers expect values that can engage their senses and touch their hearts, and ultimately, they seek to be provided with an experience [47]. The factors that precede brand experience encompass event marketing, brand interactions, brand-related stimuli, storytelling, trust, and perceived usefulness. Meanwhile, customer satisfaction and brand loyalty are identified as the primary outcomes of brand experience [48]. Brand experience is a crucial factor that organizations should focus on to build stronger brand value in today’s technological era; brands can leverage customer-brand interactions to enhance engaging brand experiences for customers [49]. 2.5. Brand Trust (BT) Brand trust refers to consumers' readiness to depend on a brand, driven by the expectation that it will produce favorable results by Lau and Lee [50]. Brand trust is considered a key factor in fostering long-term customer relationships, thereby having a positive impact on brand loyalty [51]. Trust offers several key benefits: It reflects the consumer's willingness to rely on the brand's ability to deliver the functions it promises according to Chaudhuri et al. [54]. Building and maintaining brand trust is one of the key factors determining the success of brands in competing in the global market [52]. Customers who trust a brand are willing to pay a premium for their preferred brand because they believe it offers qualities such as uniqueness, functionality, and, most importantly, quality, which competitors cannot provide. 2.6. Brand Loyalty (BL) Brand loyalty is defined as the consistent preference for a particular product or service, or more specifically, it can be understood as the tendency of consumers to pay for products under the same brand name and value them highly [53]. Brand loyalty is categorized into four types: captive, convenience- seekers, contented, and committed by Rowley [54]. High levels of loyalty among customers provide businesses with a number of specific competitive advantages [55]. Customers with high brand loyalty have a strong belief in their choices and are typically less sensitive to price. Brand loyalty is a measure of customer relationship to a brand by Laroche, et al. [56]. Brand loyalty can also be defined as the dedication to repeatedly purchase a favored product or service in the future, regardless of any marketing efforts aimed at shaping customer behavior [57]. 3051 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate 2.7. AI Marketing Efforts and Brand Relationship For many decades, numerous studies on brand relationships (BR) have been conducted. The research by Fetscherin and Heinrich [58] built upon and synthesized 392 previous articles to clarify how consumer-brand relationship research has evolved over time. This study highlighted and illustrated the interdisciplinary aspects of the brand-customer relationship. In 2016, the research by Jeon and Baeck [59] emphasized the importance of brands fostering strong relationships with their customers. It presented a comprehensive conceptualization of this relationship, which includes four key indicators of strength: commitment, self-connection, intimacy, and satisfaction. As customer satisfaction rises, the bond between customers and the brand is strengthened [60]. Additionally, in the realm of entertainment, if consumers consistently have a positive interaction with AI shopping assistants during their purchase decisions, they are likely to build trust in both these tools and the brand providing such virtual services by Klaus and Zaichkowsky [61]. The study by Gautam and Sharma [62] also indicates that social media marketing efforts, such as customization or entertainment, can influence the customer- brand relationship (CBR). Additionally, recent research on conversational AI emphasizes that AI-based conversational interfaces can significantly influence consumer-brand relationships [63]. AI efforts related to chatbots or shopping assistants (encompassing five factors) are closely linked to the development of the customer-brand relationship. AI-driven factors assist brands in building or enhancing customer-brand relationships through predictive capabilities programmed to personalize and satisfy consumers. Based on the aforementioned line of research, hypothesis H1 is proposed to further extend the previous literature. H1: AI efforts such as interaction (H1a), information (H1b), accessibility (H1c), customization (H1d), and entertainment (H1e) have a positive and direct influence on brand relationship (BR). 2.8. AI Marketing Efforts and Brand Experience A study on a coffee shop operated by robot baristas indicated that the four types of brand experiences—sensory, affective, behavioral, and intellectual—contribute to enhancing brand satisfaction according to Hwang, et al. [64]. Therefore, to attract more consumers in this industry, businesses must offer an exceptional brand experience and promote a variety of experiences [64]. A study also highlights that metaverses, enabled by new technologies such as augmented and virtual reality and artificial intelligence, offer vast opportunities to play, work, learn, connect, or shop [65]. Through this, the study demonstrates the potential of the metaverse to create a richer, more engaging, and immersive customer experience [65]. The fashion brand Zara has applied AI technology by creating digital designs in a virtual world, launching a "Meta Collection" called Lime Glam, which allows customers to fully express themselves, thereby enhancing their shopping experience [66]. The fashion and beauty industries are particularly vulnerable to the challenges of online shopping, as customers often want to try products before purchasing them [66]. A consumer's experiential value is influenced by the use of AR (Augmented Reality) try-on features when shopping for cosmetic products online. AR technology helps entertain and educate customers while also assisting them in evaluating products [67]. Therefore, brands investing in AR technology can enhance the brand experience for customers. Nike, for example, has introduced a virtual solution to visualize every available size in its collections. The products appear in the space in the size selected by the customer, thus preventing the purchase of the wrong size and the subsequent return of the product. Based on the aforementioned line of research, hypothesis H2 is proposed to further extend the previous literature. H2: AI efforts such as interaction (H2a), information (H2b), accessibility (H2c), customization (H2d), and entertainment (H2e) have a positive and direct influence on brand experience (BE). 2.9. AI Marketing Efforts and Brand Trust The findings by Shahzad, et al. [68] revealed that the quality of AI-chatbot services significantly influences customer e-brand loyalty through the mediating role of trust. The adoption of artificial intelligence voice assistants, such as Siri, determines the impact of consumer trust on brand loyalty for 3052 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate AI-supported devices by Hasan, et al. [69]. The influence of AI virtual assistants on brand trust in retail brands is a result of a study utilizing the trust commitment theory by Jham, et al. [70]. The study by Ameen, et al. [71] on how the integration of AI in shopping can enhance the AI-enabled customer experience also highlights the mediating role of brand trust in shaping this experience. The trust- commitment theory, which emphasizes the importance of trust and commitment in the relationship- building process between buyers and sellers by Morgan and Hunt [72] is also referenced in this research. By implementing personalized AI services in the coffee industry, brands can strengthen customer loyalty through the mediating role of brand trust. Avatars are AI-powered tools that are transforming how consumers engage with brands. AI-generated avatar technology fosters emotional and cognitive trust in users' subconscious [73]. Emotional trust in an avatar has a more significant influence on consumers' trust in the brand, ultimately affecting their purchase intention by Lee et al. [80]. Based on the aforementioned line of research, hypothesis H2 is proposed to further extend the previous literature. H3: AI efforts such as interaction (H3a), information (H3b), accessibility (H3c), customization (H3d), and entertainment (H3e) have a positive and direct influence on brand trust (BT). 2.10. Brand Relationship and Brand Loyalty The study by Giovanis and Athanasopoulou [74] discovered that two out of the three factors of brand relationship (brand satisfaction and brand commitment) impact brand loyalty in the broadband services market. Another study by Quaye, et al. [75] based on a sample of 351 South African retail bank customers, emphasizes the critical roles of brand relationship quality and brand trust in fostering brand loyalty. Additionally, the study highlights that brand trust and brand relationship act as two mediating factors in the relationship between customer advocacy and brand loyalty. A study conducted in major cities in Serbia, India by Anantharaman, et al. [76] revealed that factors such as the length of the consumer-brand relationship, brand satisfaction, and trust are key contributors to loyalty [76]. These findings emphasize that cultivating strong brand relationships is crucial for businesses aiming to enhance customer loyalty and long-term engagement. Moreover, a study by Chatzi, et al. [77] shows that promoting brand engagement through relationship marketing plays a crucial role in increasing customer loyalty, illustrating the importance of fostering emotional bonds and personalized interactions. Furthermore, research by Khamitov, et al. [19] shows that the nature of the consumer- brand relationship significantly influences brand loyalty, particularly in service-based industries, highlighting the long-term effects of brand trust and relationship quality [19]. A study by Zhang, et al. [78] explored the concept of expressive brand relationships and brand love, finding that emotional connections, such as brand love, are crucial for driving brand loyalty, especially in technology sectors. This suggests that fostering deep emotional bonds with customers can play a key role in long-term brand loyalty in various industries Zhang, et al. [78]. Based on a review of previous studies, Hypothesis H4 has been proposed. H4: Brand relationship (BR) has a positive and direct influence on brand loyalty (BL). 2.11. Brand Experience and Brand Loyalty A study conducted with a sample of smartphone users in Lebanon suggests that brand experience can foster brand loyalty through elements like brand connection and brand influence by Mostafa and Kasamani [79]. The research suggests that in order to establish a long-lasting customer-brand relationship, it is essential to foster strong emotional connections with customers [79]. The study reveals that brand experience shapes brand image, which, in turn, has a significant effect on brand loyalty. Therefore, brand experience affects brand loyalty through mediating variables. This explains why, when a brand establishes strong value and positioning in the minds of customers, it can achieve better profitability through its relationship with them. The level of customer loyalty to a brand is influenced by brand experience [80]. It is suggested that managers can build brand loyalty (including sustained purchase brand loyalty and attitudinal brand loyalty) by enhancing positive experiences for 3053 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate customers [80]. Another study indicates that brand experience has a stronger impact on behavioral loyalty than on attitudinal loyalty [81]. In this case, brand experience was studied independently of satisfaction, trust and commitment [81]. Customers can have various types of experiences with a brand through their senses, emotions, behaviors, and cognition by Ong, et al. [82]. These different brand experiences play a crucial role in building brand loyalty and impact each customer’s genuine loyalty in unique ways Ong, et al. [82]. All stages of the brand experience, from viewing ads, searching for products, purchasing products, receiving services, to consuming products, contribute to the impact on brand loyalty by Marliawati and Cahyaningdyah [83]. Brand experience is a key and essential factor in promoting brand loyalty. Based on a review of previous studies, Hypothesis H5 has been proposed. H5: Brand experience (BE) has a positive and direct influence on brand loyalty (BL). 2.12. Brand Trust and Brand Loyalty Numerous studies have been conducted to prove the direct impact of brand trust on brand loyalty. Research by Atulkar [84] argues that brand trust significantly influences brand loyalty and suggests various methods to foster brand trust, including content, videos, and contests. It also highlights that brand loyalty is shaped by factors like satisfaction, quality, and value, with brand trust playing a direct role [84]. Additionally, a study surveying 463 young customers of a leading sportswear brand in Turkey found that brand trust has a strong effect on brand loyalty, though it is developed through a gradual process by BOZBAY and BAŞLAR [85]. Brand trust reduces perceived risks for customers, helping businesses establish high-value relationships with their clients, and thus plays a significant role in fostering brand loyalty [86]. As the level of brand trust for a product grows, there is a corresponding increase in customer loyalty towards the brand, with higher trust fostering stronger and more lasting connections between consumers and the product. Marketing strategies through social media, such as making it easy for customers to access information related to the brand’s on these platforms, can enhance brand trust and brand loyalty by Shin, et al. [86]. When customers consistently trust the quality of a brand's products, even if the prices are higher than those of competing brands, they are more likely to remain loyal to that brand [86]. In addition to the strong influence of brand trust on brand loyalty, consumer engagement also plays a significant role, positively affecting both brand trust and brand loyalty by Kwon, et al. [87]. Based on a review of previous studies, Hypothesis H6 has been proposed. H6: Brand trust (BT) has a positive and direct influence on brand loyalty (BL). The research model is shown in Figure 1. Figure 1. Conceptual model. 3054 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate 3. Methodology 3.1. Research Stages This study follows a quantitative approach and is structured into five key stages. The process begins with the first stage, which involves developing a conceptual framework by formulating two research questions, reviewing relevant literature, establishing a theoretical framework and conceptual definitions, and formulating hypotheses. The second stage focuses on research design and planning, including the selection of research methods, identifying the population and sample, and determining measurement tools. The third stage involves data collection and validation of the variables under investigation. The fourth stage is the analysis phase, which includes data filtering, analysis, interpretation, and discussion of the results. Finally, the fifth stage involves dissemination, presenting the findings through theoretical and policy implications. 3.2. The Context of the Survey Artificial Intelligence (AI) is increasingly being utilized in the e-commerce sector in Vietnam. Notably, chatbot models are being adopted by brands to enhance communication with customers. Many brands are leveraging these tools to improve interactions with their target audiences, recommend products, and facilitate orders. Vietnam's e-commerce market, one of the fastest-growing in Southeast Asia, is poised for substantial expansion, fueled by a rising digital population and increasing internet penetration. With a favorable environment for e-commerce businesses, Vietnam offers significant growth opportunities. In the future, AI could be further applied through sentiment analysis across various aspects such as product descriptions, perceived value, size guides, and delivery practices, ultimately driving business performance for Vietnamese e-retailers. This could contribute to enhancing customer satisfaction and proposing improvement strategies for local sellers on popular e-marketplaces like Shopee and Lazada according to Nguyen and Nguyen [88]. Shopee, one of the most popular e- commerce platforms in Vietnam, saw high revenue from the beauty and women's fashion categories, which attracted many buyers and purchases in early 2022. AI livestreamers are also gaining popularity in China, and Vietnam is expected to adopt this model, particularly in the fashion and beauty sectors, which are prominent in livestream sales. Additionally, AI is being utilized in mobile applications for customer service. However, despite many brands claiming their mobile apps utilize the latest AI technologies, younger consumers are experiencing app fatigue and are increasingly skeptical of the authenticity of this touchpoint by Vo, et al. [89]. Many questions remain regarding the application of AI in the sales models of brands in Vietnam. While AI can create significant value, it may also lead to customer dissatisfaction. Unlike humans, AI lacks the ability to empathize, which can lead to customers feeling undervalued and disconnected due to impersonal interactions. 3.3. Measurements This study is quantitative research aimed at measuring and investigating the relationships between variables. Each variable is represented by a set of questions, all of which are inherited from previous studies. Interaction scale, information scale, accessibility scale, customization scale and brand experience scale are inherited from Ho and Chow [38]. Entertainment scale and brand loyalty scale are inherited from Cheng and Jiang [13]. Brand relationship scale and brand trust scale are inherited from Habibi, et al. [90]. The study then defines each construct using a five-point likert scale, ranging from 'strongly disagree' (1), 'disagree' (2), 'neutral' (3), 'agree' (4), to 'strongly agree' (5). 3.4. Sample and Data Collection The sample for this study was selected using a non-probability sampling method, specifically purposive sampling. Following the screening and profiling of the population, not all potential participants met the criteria for inclusion in the study. Purposive sampling is a method of sample selection based on specific criteria or considerations to ensure the collection of relevant and comprehensive data for the research. The inclusion criteria for this study were: (1) individuals who have 3055 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate engaged in shopping behavior on e-commerce platforms such as Shopee, Lazada, and TikTok Shop in Vietnam; (2) customers who purchase fashion products on these platforms; and (3) individuals who have utilized AI-driven services and features offered by these platforms. A total of 421 questionnaires were collected over a three-month period from October to December 2024. After screening for incomplete or inconsistent responses, 38 questionnaires were deemed invalid and removed. Consequently, 383 valid responses were retained for analysis. 3.5. Data Analysis The collected data is analyzed using the Partial Least Squares Structural Equation Modeling (PLS- SEM) method via SmartPLS4 software, focusing on both measurement model analysis and structural model analysis. PLS-SEM is chosen for this research due to the presence of multiple independent and dependent variables, complex relationships among them, and their interdependence. This approach enables the examination of the relationships between variables simultaneously. Table 1. Demographic profile of the respondents (n=383). Variable Range Frequency Percent Gender Male 133 34.73 Female 250 65.27 Age Under 18 0 0 18 - 30 272 71.02 31 - 40 68 17.75 40 - 50 38 9.92 Above 50 5 1.31 Occupation Professional/consultant 44 11.49 Academic 22 5.74 Technician/Operator 71 18.54 Manager/Executive 62 16.19 Retired 0 0 Housewife 0 0 Student 164 42.82 Unemployed 20 5.22 Other 0 0 Monthly Income Below 10 millions VND 189 49.35 10 - Below 20 millions VND 111 28.98 20 - Below 30 millions VND 64 16.71 30 millions VND or above 19 4.96 4. Results 4.1. Demographic Profiles Based on responses from 383 consumers in Vietnam (Table 1), the majority were female, with 250 individuals (65.27%), while males accounted for 133 individuals (34.73%). Regarding age, the group aged 18–30 made up the largest proportion with 272 individuals (71.02%), followed by the 31–40 age group with 68 individuals (17.75%), the 40–50 age group with 38 individuals (9.92%), and those above 50 years old with 5 individuals (1.31%). In terms of occupation, students made up the largest group with 164 individuals (42.82%), followed by technicians/operators with 71 individuals (18.54%), managers/executives with 62 individuals (16.19%), and professionals/consultants with 44 individuals (11.49%). Other categories, such as academics, unemployed individuals, homemakers, and retirees, had either minimal representation or none at all. Regarding monthly income, nearly half of the participants (189 individuals, 49.35%) had an income below 10 million VND. This was followed by 111 individuals (28.98%) earning between 10 and 20 million VND, 64 individuals (16.71%) earning between 20 and 30 million VND, and 19 individuals (4.96%) earning above 30 million VND. 3056 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate 4.2. Measurement Model To ensure the quality of the research model’s data, validity and reliability assessments were conducted. Convergent validity was evaluated using factor loadings and average variance extracted (AVE). As shown in Table 2, all factor loadings exceed the threshold of 0.7, and AVE values surpass 0.5, confirming that the research model meets the criteria for convergent validity. These findings align with previous studies emphasizing that AVE above 0.5 signifies that a construct explains more variance than measurement error, thereby ensuring robustness in construct measurement. Table 2. Convergent validity and reliability. Construct Item Convergent validity Reliability Loading Factor AVE Rho_A Rho_C Interaction (INT) INT1 0.848 0.750 0.833 0.833 0.900 INT2 0.855 INT3 0.895 Information (INF) INF1 0.861 0.709 0.801 0.841 0.880 INF2 0.874 INF3 0.789 Accessibility (ACC) ACC1 0.845 0.630 0.805 0.826 0.872 ACC2 0.746 ACC3 0.825 ACC4 0.756 Entertainment (ENT) ENT1 0.775 0.590 0.781 0.764 0.852 ENT2 0.808 ENT3 0.743 ENT4 0.744 Customization (CUS) CUS1 0.724 0.662 0.829 0.841 0.886 CUS2 0.834 CUS3 0.802 CUS4 0.887 Brand relationship (BR) BR1 0.775 0.564 0.743 0.743 0.838 BR2 0.724 BR3 0.752 BR4 0.753 Brand experience (BE) BE1 0.753 0.618 0.845 0.856 0.889 BE2 0.880 BE3 0.822 BE4 0.754 BE5 0.709 Brand trust (BT) BT1 0.848 0.641 0.719 0.720 0.843 BT2 0.774 BT3 0.778 Brand loyalty (BL) BL1 0.856 0.697 0.853 0.859 0.902 BL2 0.794 BL3 0.769 BL4 0.913 For discriminant validity, both the Heterotrait-Monotrait Ratio (HTMT) and Fornell-Larcker criteria were applied. As detailed in Table 3, the HTMT values remain below the threshold of 0.85, indicating that constructs are sufficiently distinct. Additionally, the Fornell-Larcker analysis (Table 4) confirms that the square root of AVE for each construct is higher than its correlations with other constructs, further validating discriminant validity. Lastly, reliability was assessed through Cronbach’s alpha and composite reliability (CR), including rho_c and rho_a. As shown in Table 2, all reliability 3057 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate coefficients exceed 0.7, indicating strong internal consistency. These results collectively support the robustness of the research model, ensuring the reliability and validity of the measurement constructs. Table 3. Discriminant validity - HTMT matrix. ACC BE BL BR BT CUS ENT INF INT ACC BE 0.250 BL 0.418 0.625 BR 0.221 0.250 0.731 BT 0.239 0.484 0.793 0.373 CUS 0.333 0.289 0.556 0.420 0.414 ENT 0.129 0.117 0.270 0.106 0.280 0.228 INF 0.207 0.199 0.357 0.243 0.254 0.269 0.090 INT 0.184 0.253 0.277 0.191 0.265 0.257 0.310 0.116 Table 4. Discriminant validity - Fornell-Larcker. ACC BE BL BR BT CUS ENT INF INT ACC 0.794 BE 0.219 0.786 BL 0.348 0.547 0.835 BR 0.173 0.202 0.584 0.751 BT 0.186 0.391 0.620 0.272 0.801 CUS 0.287 0.251 0.474 0.338 0.322 0.814 ENT 0.102 0.106 0.251 0.063 0.237 0.186 0.768 INF 0.161 0.174 0.308 0.195 0.200 0.222 0.072 0.842 INT 0.153 0.209 0.233 0.149 0.205 0.210 0.253 0.067 0.866 4.3. Structural Model As shown in Table 5, all hypotheses were tested, examining the relationships among accessibility, brand experience, brand relationship, brand trust, customization, entertainment, information, interaction, and brand loyalty. The results provide insights into how these constructs interact within the research model, reflecting their interconnected roles in shaping consumer perceptions and brand dynamics. Based on the results in Table 5, a positive and significant relationship is shown between interaction and brand experience (β=0.144, t=2.978, p<0.05), so H2a is supported. However, the relationship between interaction and brand relationship (β=0.078, t=1.581, p=0.114) is non-significant, so H1a is not supported. Table 5 shows a positive and significant relationship between interaction and brand trust (β=0.100, t=2.185, p<0.05), so H3a is supported. Table 5 shows a positive and significant relationship between information and brand relationship (β=0.119, t=2.081, p<0.05), so H1b is supported. Table 5 also shows a positive and significant relationship between information and brand experience (β=0.107, t=2.116, p<0.05), so H2b is supported. Table 5 also shows a positive and significant relationship between information and brand trust (β=0.121, t=2.582, p<0.05), so H3b is supported. Based on the results in Table 5, a positive and significant relationship could be seen between accessibility and brand experience (β=0.133, t=2.501, p<0.05), thus supporting H2c. However, there is a non-significant relationship between accessibility and brand relationship (β=0.063, t=1.148, p=0.251); H1c is, therefore, not supported. Likewise, the relationship between accessibility and brand trust is non- significant (β=0.071, t=1.380, p=0.168); thus, H3c is not supported. Table 5 also shows a positive and significant relationship between customization and brand relationship (β=0.281, t=5.541, p<0.05), so H1d is supported. Table 5 also shows a positive and 3058 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate significant relationship between customization and brand experience (β=0.155, t=2.805, p<0.05), so H2d is supported. Table 5 also shows a positive and significant relationship between customization and brand trust (β=0.225, t=4.371, p<0.05), so H3d is supported. Table 5, however, shows a non-significant relationship between entertainment and brand experience (β=0.019, t=0.369, p=0.712), so H2e is not supported. Table 5 also shows a non-significant relationship between entertainment and brand relationship (β=-0.024, t=0.375, p=0.708), so H1e is not supported. Nevertheless, there is a positive and significant relationship between entertainment and brand trust (β=0.154, t=3.259, p<0.05), so H3e is supported. On the other hand, a positive and significant relationship could be seen between brand relationship and brand loyalty (β=0.416, t=9.458, p<0.05), so H4 is supported. Table 5 also shows a positive and significant relationship between brand experience and brand loyalty (β=0.312, t=8.530, p<0.05); so H5 is supported. Table 5 also shows a positive and significant relationship between brand trust and brand loyalty (β=0.384, t=7.887, p<0.05), so H6 is supported. Table 5. The results of testing the significance level of the path coefficients. Hypothesis Path coefficients Std.B T-Statistics P-Values Result H1a INT → BR 0.078 0.079 1.581 0.114 Not Supported H2a INT → BE 0.144 0.145 2.978 0.003 Supported H3a INT → BT 0.100 0.099 2.185 0.029 Supported H1b INF → BR 0.119 0.120 2.081 0.037 Supported H2b INF → BE 0.107 0.110 2.116 0.034 Supported H3b INF → BT 0.121 0.122 2.582 0.010 Supported H1c ACC → BR 0.063 0.065 1.148 0.251 Not Supported H2c ACC → BE 0.133 0.136 2.501 0.012 Supported H3c ACC → BT 0.071 0.074 1.380 0.168 Not Supported H1d CUS → BR 0.281 0.283 5.541 0.000 Supported H2d CUS → BE 0.155 0.155 2.805 0.005 Supported H3d CUS → BT 0.225 0.225 4.371 0.000 Supported H1e ENT → BR -0.024 -0.022 0.375 0.708 Not Supported H2e ENT → BE 0.019 0.027 0.369 0.712 Not Supported H3e ENT → BT 0.154 0.161 3.259 0.001 Supported H4 BR → BL 0.416 0.416 9.458 0.000 Supported H5 BE → BL 0.312 0.311 8.530 0.000 Supported H6 BT → BL 0.384 0.385 7.887 0.000 Supported The variance inflation factor (VIF) was assessed to detect potential multicollinearity issues among the independent variables in the structural model. Multicollinearity occurs when predictor variables are highly correlated, which can inflate standard errors and compromise the reliability of regression estimates. Generally, a VIF value below 3 is considered acceptable, indicating no severe multicollinearity. All VIF values in this study fall within the acceptable range, with the highest value being 1.238 and the lowest at 1.064. These results confirm that collinearity does not pose a threat to the stability of the structural model. These findings strengthen the credibility of the model and reinforce the validity of the hypothesis testing results. The absence of multicollinearity enhances confidence in the causal inferences drawn from the data, ensuring that each independent variable contributes uniquely to explaining variations in brand loyalty. 3059 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate Table 6. R‑square, R‑square adjusted coefficient. Research variable R-square R-square adjusted BE 0.119 0.107 BL 0.652 0.649 BR 0.139 0.128 BT 0.167 0.156 The R² coefficient measures how well independent variables explain the variance of dependent variables. As shown in Table 6, brand loyalty (BL) has an R² of 0.652, meaning 65.2% of its variance is explained by brand trust (BT), brand relationship (BR), brand experience (BE) and artificial intelligence (AI). This highlights the strong impact of these factors in driving customer loyalty, emphasizing the need for brands to cultivate trust and meaningful relationships. 5. Discussion The research surveyed 383 representative customers in Vietnam who used any AI marketing activities from 10 brands leading fashion industries. The findings of the research underline the pivotal role of customization, and information in directly influencing brand experience, brand relationship, and brand trust within e-commerce platforms. These results reinforce the significance of these factors in enhancing customer satisfaction, engagement, and loyalty by Chung, et al. [31]. However, the study also reveals that not all elements, such as accessibility, entertainment and interaction, contribute equally to brand loyalty, with some outcomes diverging from prior research by Huang and Rust [33] and Cheng and Jiang [13]. The research concludes that entertainment has limited impact on brand experience and relationship aligns with findings from Huang and Rust [33] and Cheng and Jiang [13]. These studies suggest that customers on e-commerce platforms prioritize utilitarian benefits such as convenience, efficiency, and reliability over emotional engagement through entertainment. Therefore, features like gamification or interactive content often fail to influence customer satisfaction or loyalty meaningfully. This supports the notion that entertainment, while potentially engaging, does not meet the primary motivations of e- commerce users, who are more focused on functional advantages like seamless navigation and accurate product information. Recent studies underscore the significant impact of customization and information on brand experience. Customization, through personalized recommendations and tailored user experiences, strengthens the connection between customers and brands, fostering loyalty. Ho and Chow [38] demonstrates that personalized interactions lead to increased customer engagement and brand affinity. Clear, accurate, and timely information empowers customers to make informed decisions, building trust in the platform. In practice, businesses can leverage machine learning algorithms to analyze customer behavior and deliver personalized product suggestions, enhancing satisfaction. E-commerce platforms can further optimize the user experience by personalizing content, recommendations, and promotions based on individual preferences, thereby fostering deeper customer loyalty. The integration of customization and information is essential for building meaningful customer-brand relationships, ultimately increasing trust and long-term loyalty. The research reveals that accessibility significantly enhances brand experience but does not have a notable effect on brand relationship and brand trust. This suggests that while accessibility ensures a seamless and satisfying shopping experience, it is insufficient to establish deep emotional connections or trust with the brand. This emphasized that accessibility is often perceived as a fundamental expectation rather than a competitive advantage. To enhance the value of accessibility, fashion e-commerce platforms should incorporate advanced features such as personalized size-matching tools, localized language support, and AI-driven shopping assistants. These features align with customer preferences, creating a more engaging experience and differentiating the platform from competitors 3060 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate Interaction positively influences brand experience and brand trust but does not significantly affect brand relationship. This suggests that functional interactions, such as resolving queries or tracking orders, enhance trust but fail to establish emotional connections. Huang and Rust [33] similarly found that functional interactions are critical for trust-building but insufficient for relational bonding. Moreover, Chung, et al. [31] argued that richer engagement, such as personalized communication and community involvement, is essential for creating meaningful connections with customers. To maximize the impact of interaction, fashion e-commerce platforms should focus on emotional and personalized engagement strategies. For example, AI-powered tools like virtual try-ons or styling advice can provide customers with a more interactive and tailored experience. Additionally, incorporating social elements like livestream shopping events, influencer collaborations, or customer communities can encourage emotional bonds. The findings underscore the critical role of brand experience as a key driver of brand loyalty, aligning with the conclusions of Ebrahim, et al. [91] and Yasri, et al. [92]. A positive brand experience enhances customer preference for a platform and fosters repeated engagement, ultimately strengthening brand loyalty. Customers who consistently benefit from seamless navigation, personalized recommendations, and reliable service are more inclined to return, as these factors improve convenience and satisfaction. Moreover, satisfied customers tend to develop a deeper emotional connection with the brand, reinforcing their long-term commitment. Beyond brand experience, both brand trust and brand relationship play pivotal roles in shaping brand loyalty. Trust is established through consistent service quality, transparent communication, and data security, which in turn reduces perceived risks and strengthens customer confidence in purchasing decisions. A high level of trust fosters deeper brand loyalty, as customers feel assured in their interactions with the platform. Similarly, brand relationship, characterized by meaningful engagement and personalized interactions, enhances customers’ emotional attachment to the brand. When customers perceive a brand as responsive and aligned with their needs, they are more likely to remain committed over time. Together, these factors reinforce the long-term retention and advocacy essential for sustainable brand loyalty. This research underscores the pivotal role of customization and information in shaping brand experience, trust, and loyalty on e-commerce fashion platforms. Customization fosters stronger customer connections by offering personalized recommendations, tailored user experiences, and relevant product suggestions, creating a sense of value and engagement. Similarly, clear, accurate, and timely information empowers customers to make informed decisions, enhancing their confidence in the platform and building trust. On the other hand, accessibility and interaction, while improving functional satisfaction and trust through features like seamless navigation and efficient customer support, have limited impact on forming deep emotional bonds or brand relationships. This aligns with findings by Ho and Chow [38]and Huang and Rust [33] which highlight the prioritization of utilitarian benefits such as convenience and efficiency over emotional engagement in e-commerce. To translate these insights into actionable strategies, fashion e-commerce platforms should leverage machine learning to analyze customer behavior and preferences, enabling the delivery of highly personalized product recommendations and promotions. Providing detailed product descriptions, size guides, and user reviews can further enhance decision-making and build trust. Additionally, incorporating advanced tools like AI-powered virtual try-ons, dynamic styling advice, and curated fashion collections can create more immersive and engaging shopping experiences. By integrating these elements, platforms can deliver memorable brand experiences, foster stronger loyalty, and encourage advocacy, ensuring a competitive edge in the rapidly evolving e-commerce landscape. 6. Conclusion Based on the two research questions proposed, this study aims to understand the role of AI marketing efforts on brand relationship, brand experience, and brand trust. Meanwhile, this study aims to prove the impact and quantifying the extent of influence of the three factors—brand relationship, 3061 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate brand experience, and brand trust—on brand loyalty. Here are some findings from this study. First, the study successfully demonstrates that all three variables-brand relationship (BR), brand experience (BE), and brand trust (BT)-have a significant direct impact on brand loyalty (BL). Second, regarding the impact of the five AI marketing efforts variables on the three variables, brand xxperience (BE) and brand trust (BT) are significantly influenced by four out of the five AI efforts variables. Meanwhile, brand relationship (BR) is only affected by customization and information. Third, the study concludes that customization and information are two key AI marketing efforts that businesses should prioritize in order to strengthen customer relationships, enhance the customer experience during their interaction with the brand's services, and thereby foster customer loyalty. 7. Implications, Limitations and Suggestions for Future Research 7.1. Theoretical implications The findings of this study provide several significant theoretical contributions. Firstly, the study identifies five key AI-driven marketing efforts, including interaction, information, accessibility, entertainment, and customization, which are essential for understanding the business models of e- commerce platforms. In addition, by integrating and expanding upon previous research, this study offers a comprehensive perspective on AI marketing efforts by systematically examining all five dimensions. Furthermore, the results highlight the substantial positive impact of these AI-driven marketing efforts on brand experience and brand trust, thereby reinforcing their crucial role in shaping consumer perceptions. This research also introduces the factor of brand relationship, which can be influenced by certain AI marketing efforts, a topic that has not been extensively explored in prior studies. Moreover, the study enriches the existing literature by examining the relationships between brand relationship and brand loyalty, brand experience and brand loyalty, as well as brand trust and brand loyalty. The research focuses specifically on the e-commerce sector within the fashion industry, where the demand for utilizing AI to enhance marketing and sales efforts is increasingly growing. Based on the research findings, the study encourages future research endeavors to delve deeper into and further expand knowledge regarding the relationship between brands and customers. This call for further exploration emphasizes the importance of understanding how AI marketing efforts can shape and influence customer perceptions, experiences, and long-term loyalty toward brands. 7.2. Practical implications The research findings also propose several practical implications that could be applied within the business market or the business models of companies. At the macro level, the government could implement trade policies that encourage the adoption of scientific and technological advancements, including the AI developments that have been achieved globally, into online business models, especially on e-commerce platforms. This is particularly relevant in rapidly developing major cities in Vietnam, such as Ho Chi Minh City, Hanoi, and Da Nang, where the government could promote cooperation between industries such as advertising services, logistics delivery services, and manufacturing sectors, in order to enhance the capacity to serve a large customer base. Furthermore, the government should also consider introducing laws to protect the interests and intellectual property rights of brands, thereby enabling businesses to compete more effectively and fairly in the marketplace. At the enterprise level, the research findings encourage managers to enhance their marketing effectiveness by integrating AI marketing efforts into their information exchange processes and customer service operations. In addition to prioritizing product quality, managers are advised to also focus on improving service quality to increase their brand’s competitiveness amidst the numerous rivals in today’s market. This approach is crucial as service quality plays a key role in shaping consumer perceptions and ultimately influences their purchasing decisions. Consumers, by reading this research, can gain a deeper understanding of their own shopping behavior. The insights provided in the study can help them become more aware of the factors 3062 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate influencing their purchasing decisions, particularly in relation to AI-driven marketing strategies. This greater awareness could contribute to more informed choices, as consumers will be better equipped to recognize the marketing efforts that brands employ to meet their needs. 7.3. Limitations and Suggestions for Future Research The research, like any study, is not without its limitations. Firstly, there is the issue of sample selection. The sample used in the study consists of individual consumers and is primarily focused on a few major cities. As a result, the sample may not adequately represent the broader, more diverse consumer behavior of the general population or community. Moreover, there are several other AI marketing efforts that were not discussed in the study but may still be effective in real-world marketing strategies. Furthermore, the mediating roles of three variables-brand relationship, brand experience, and brand trust - within the relationship between the five AI marketing efforts and brand loyalty have not been substantiated in this study. The potential interplay and mutual influence between these variables could serve as a valuable area for future research. Investigating how brand relationship, brand experience, and brand trust interact with one another and affect brand loyalty could provide deeper insights and contribute significantly to the understanding of consumer behavior in the context of AI-driven marketing strategies. These factors warrant further exploration in subsequent studies to gain a more comprehensive understanding of the dynamics at play. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgements: The authors greatly appreciate the reviewers and editors whose comments are very helpful in improving the study’s quality Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] D. C. Chow, "Alibaba, Amazon, and Counterfeiting in the Age of the Internet," Nw. J. Int'l L. & Bus., vol. 40, p. 157, 2019. [2] A. Landim et al., "Chatbot design approaches for fashion E-commerce: an interdisciplinary review," International Journal of Fashion Design, Technology and Education, vol. 15, no. 2, pp. 200-210, 2022. https://doi.org/10.1080/17543266.2021.1990417 [3] L. Sanny, T. D. Julianto, S. Savionus, and B. W. bin Yus Kelena, "Purchase intention in the fashion industry on local and international E-commerce in Indonesia," International Journal of Asian Business and Information Management, vol. 13, no. 2, pp. 1-12, 2022. [4] M. Madanchian, "The impact of artificial intelligence marketing on e-commerce sales," Systems, vol. 12, no. 10, p. 429, 2024. https://doi.org/10.3390/systems12100429 [5] I. Boukrouh and A. Azmani, "Artificial intelligence applications in e-commerce: A bibliometric study from 1995 to 2023 using merged data sources," International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., vol. 9, no. 3, p. 9, 2024. [6] S. G. Thandekkattu and M. Kalaiarasi, "Customer-centric e-commerce implementing artificial intelligence for better sales and service," in In Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems: ICACECS 2021 (pp. 141-152). Singapore: Springer Nature Singapore, 2022. https://doi.org/10.1007/978-981-16-7389-4_14 2022. [7] X. Song, S. Yang, Z. Huang, and T. Huang, "The application of artificial intelligence in electronic commerce," in Journal of Physics: Conference Series, 2019, vol. 1302, no. 3, p. 032030. https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1080/17543266.2021.1990417 https://doi.org/10.3390/systems12100429 https://doi.org/10.1007/978-981-16-7389-4_14 3063 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate [8] J. X. Wu and L. Li, "AI-driven computational creativity in fashion design: a review," Textile Research Journal, vol. 95, no. 5-6, pp. 658-675, 2025. [9] Y. Zhou, S. S. Liu, X. Xu, and T. C. E. Cheng, "Enhancing competitive advantage through AI-driven live streaming sales," IEEE Transactions on Engineering Management, vol. 72, pp. 1348-1360, 2025. https://doi.org/10.1109/TEM.2025.3550400 [10] W. Ozuem, S. Ranfagni, M. Willis, G. Salvietti, and K. Howell, "Exploring the relationship between chatbots, service failure recovery and customer loyalty: A frustration–aggression perspective," Psychology & Marketing, vol. 41, no. 10, pp. 2253-2273, 2024. https://doi.org/10.1002/mar.22051 [11] L. Ramos, F. Rivas-Echeverría, A. G. Pérez, and E. Casas, "Artificial intelligence and sustainability in the fashion industry: A review from 2010 to 2022," SN Applied Sciences, vol. 5, no. 12, p. 387, 2023. https://doi.org/10.1007/s42452-023-05587-2 [12] M. M. Shafiee and N. A. Bazargan, "Behavioral customer loyalty in online shopping: The role of e-service quality and e-recovery," Journal of theoretical and applied electronic commerce research, vol. 13, no. 1, pp. 26-38, 2018. http://dx.doi.org/10.4067/S0718-18762018000100103 [13] Y. Cheng and H. Jiang, "Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts," Journal of Product & Brand Management, vol. 31, no. 2, pp. 252-264, 2022. http://dx.doi.org/10.1108/JPBM-05-2020-2907 [14] S. Sahai, R. Goel, A. Venaik, and V. Garg, "Impact of digital commerce on fashion industry to gain customer loyalty," International Journal of Engineering and Advanced Technology, vol. 8, no. 5, pp. 730-740, 2019. [15] D. T. Nguyen, D. H. A. Le, L. G. Truong, N. G. Truong, and V. V. Vu, "The effect of Generation Z's perceptions of brand activism on brand loyalty: evidence from Vietnam," Asia Pacific journal of marketing and logistics, vol. 35, no. 6, pp. 1494-1512, 2023. https://doi.org/10.1108/APJML-02-2022-0165 [16] P. D. Cahyani, H. Welsa, and G. K. Aji, "The effect of social media marketing, perceived quality on brand loyalty, with brand trust as intervening variables (study on tokopedia e-commerce in Yogayakarta)," International Journal of Economics, Business and Accounting Research, vol. 6, no. 2, pp. 1742-1751, 2022. https://doi.org/10.29040/ijebar.v6i2.4555 [17] N. T. T. Huyen, "The effect of brand image, perceived quality and brand experience on customer loyalty: an empirical investigation in the telecommunication industry in Vietnam," Journal of International Economics and Management, vol. 20, no. 3, pp. 60-74, 2020. https://doi.org/10.38203/jiem.020.3.0016 [18] H. Q. Nguyen, N. Chi, D. Nhung, N. Ngan, and L. Phong, "The influence of website brand equity, e-brand experience on e-loyalty: The mediating role of e-satisfaction," Management Science Letters, vol. 10, no. 1, pp. 63-76, 2020. https://doi.org/10.5267/j.msl.2019.8.015 [19] M. Khamitov, X. Wang, and M. Thomson, "How well do consumer-brand relationships drive customer brand loyalty? Generalizations from a meta-analysis of brand relationship elasticities," Journal of consumer research, vol. 46, no. 3, pp. 435-459, 2019. https://doi.org/10.1093/jcr/ucz006 [20] M. Adam, M. Wessel, and A. Benlian, "AI-based chatbots in customer service and their effects on user compliance," Electronic Markets, vol. 31, no. 2, pp. 427-445, 2021. https://doi.org/10.1007/s12525-020-00414-7 [21] J. Trivedi, "Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk," Journal of internet Commerce, vol. 18, no. 1, pp. 91-111, 2019. https://doi.org/10.1080/15332861.2019.1567188 [22] V. Kumar, B. Rajan, R. Venkatesan, and J. Lecinski, "Understanding the role of artificial intelligence in personalized engagement marketing," California management review, vol. 61, no. 4, pp. 135-155, 2019. https://doi.org/10.1177/0008125619859317 [23] N. H. Tien, R. J. S. Jose, N. P. Mai, H. T. Dung, N. T. H. Oanh, and N. H. Phuoc, "Digital marketing strategy of GUMAC and HNOSS in Vietnam fashion market," International journal of multidisciplinary education and research, vol. 5, no. 4, pp. 1-5, 2020. [24] J. Kim and H. Lee, "AI-driven personalization in fashion e-commerce: Examining customer engagement and loyalty," Journal of Retailing and Consumer Services, vol. 67, p. 102975, 2022. https://doi.org/10.1016/j.jretconser.2022.102975 [25] M. J. Kim, C.-K. Lee, and T. Jung, "Exploring consumer behavior in virtual reality tourism using an extended stimulus-organism-response model," Journal of travel research, vol. 59, no. 1, pp. 69-89, 2020. https://doi.org/10.1177/0047287518818915 [26] J. U. Islam, S. Shahid, A. Rasool, Z. Rahman, I. Khan, and R. A. Rather, "Impact of website attributes on customer engagement in banking: a solicitation of stimulus-organism-response theory," International Journal of Bank Marketing, vol. 38, no. 6, pp. 1279-1303, 2020. https://doi.org/10.1108/ijbm-12-2019-0460 [27] B. Zhu, S. Kowatthanakul, and P. Satanasavapak, "Generation Y consumer online repurchase intention in Bangkok: Based on Stimulus-Organism-Response (SOR) model," International Journal of Retail & Distribution Management, vol. 48, no. 1, pp. 53-69, 2020. https://doi.org/10.1108/ijrdm-04-2018-0071 [28] S. Wu, I. A. Wong, and Z. C. Lin, "Understanding the role of atmospheric cues of travel apps: A synthesis between media richness and stimulus–organism–response theory," Journal of Hospitality and Tourism Management, vol. 49, pp. 226-234, 2021. https://doi.org/10.1016/j.jhtm.2021.09.014 https://doi.org/10.1109/TEM.2025.3550400 https://doi.org/10.1002/mar.22051 https://doi.org/10.1007/s42452-023-05587-2 http://dx.doi.org/10.4067/S0718-18762018000100103 http://dx.doi.org/10.1108/JPBM-05-2020-2907 https://doi.org/10.1108/APJML-02-2022-0165 https://doi.org/10.29040/ijebar.v6i2.4555 https://doi.org/10.38203/jiem.020.3.0016 https://doi.org/10.5267/j.msl.2019.8.015 https://doi.org/10.1093/jcr/ucz006 https://doi.org/10.1007/s12525-020-00414-7 https://doi.org/10.1080/15332861.2019.1567188 https://doi.org/10.1177/0008125619859317 https://doi.org/10.1016/j.jretconser.2022.102975 https://doi.org/10.1177/0047287518818915 https://doi.org/10.1108/ijbm-12-2019-0460 https://doi.org/10.1108/ijrdm-04-2018-0071 https://doi.org/10.1016/j.jhtm.2021.09.014 3064 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate [29] H. Chen, S. Chan-Olmsted, J. Kim, and I. Mayor Sanabria, "Consumers’ perception on artificial intelligence applications in marketing communication," Qualitative Market Research: An International Journal, vol. 25, no. 1, pp. 125- 142, 2022. https://doi.org/10.1108/qmr-03-2021-0040 [30] B. Libai et al., "Brave new world? On AI and the management of customer relationships," Journal of Interactive Marketing, vol. 51, no. 1, pp. 44-56, 2020. https://doi.org/10.1016/j.intmar.2020.04.002 [31] M. Chung, E. Ko, H. Joung, and S. J. Kim, "Chatbot e-service and customer satisfaction regarding luxury brands," Journal of business research, vol. 117, pp. 587-595, 2020. https://doi.org/10.1016/j.jbusres.2018.10.004 [32] A. Maedche et al., "AI-based digital assistants: Opportunities, threats, and research perspectives," Business & Information Systems Engineering, vol. 61, pp. 535-544, 2019. https://doi.org/10.1007/s12599-019-00600-8 [33] M.-H. Huang and R. T. Rust, "The caring machine: Feeling AI for customer care," Journal of Marketing, vol. 88, no. 5, pp. 1-23, 2024. https://doi.org/10.1177/00222429231224748 [34] E. Stoilova, "AI chatbots as a customer service and support tool," ROBONOMICS: The Journal of the Automated Economy, vol. 2, pp. 21-21, 2021. [35] X. C. Le, "Inducing AI-powered chatbot use for customer purchase: the role of information value and innovative technology," Journal of Systems and Information Technology, vol. 25, no. 2, pp. 219-241, 2023. https://doi.org/10.1108/JSIT-09-2021-0206 [36] P. Sultan and H. Y. Wong, "How service quality affects university brand performance, university brand image and behavioural intention: The mediating effects of satisfaction and trust and moderating roles of gender and study mode," Journal of Brand Management, vol. 26, pp. 332-347, 2019. https://doi.org/10.1057/s41262-018-0131-3 [37] B. Zarouali, E. Van den Broeck, M. Walrave, and K. Poels, "Predicting consumer responses to a chatbot on Facebook," Cyberpsychology, Behavior, and Social Networking, vol. 21, no. 8, pp. 491-497, 2018. https://doi.org/10.1089/cyber.2017.0518 [38] S. P. S. Ho and M. Y. C. Chow, "The role of artificial intelligence in consumers’ brand preference for retail banks in Hong Kong," Journal of Financial Services Marketing, vol. 29, no. 23, pp. 292-305, 2023. https://doi.org/10.1057/s41264-022-00207-3 [39] P. B. Brandtzaeg and A. Følstad, "Why people use chatbots," presented at the In Internet Science: 4th International Conference, INSCI 2017, Thessaloniki, Greece, November 22-24, 2017, Proceedings 4 (pp. 377-392). Springer International Publishing. https://doi.org/10.1007/978-3-319-70284-1_30 2017. [40] A. J. Kim and E. Ko, "Impacts of luxury fashion brand’s social media marketing on customer relationship and purchase intention," Journal of Global fashion marketing, vol. 1, no. 3, pp. 164-171, 2010. https://doi.org/10.1080/20932685.2010.10593068 [41] B. Godey et al., "Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior," Journal of business research, vol. 69, no. 12, pp. 5833-5841, 2016. https://doi.org/10.1016/j.jbusres.2016.04.181 [42] M. A. Raji, H. B. Olodo, T. T. Oke, W. A. Addy, O. C. Ofodile, and A. T. Oyewole, "E-commerce and consumer behavior: A review of AI-powered personalization and market trends," GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 066-077, 2024. https://doi.org/10.30574/gscarr.2024.18.3.0090 [43] S. Fournier, "Consumers and their brands: Developing relationship theory in consumer research," Journal of consumer research, vol. 24, no. 4, pp. 343-373, 1998. https://doi.org/10.1086/209515 [44] S. R. Kumar, Marketing and branding: The Indian scenario. Pearson Education India. https://books.google.com.vn/books?id=WTdGMhl7N1cC, 2007. [45] N. H. B. Ghani and M. K. W. Tuhin, "Consumer brand relationships," International Review of Management and Marketing, vol. 6, no. 4, pp. 950-957, 2016. [46] P. Schreuder, M. Zeelenberg, and T. M. Pronk, "How consumers define brand relationships: a prototype analysis," Journal of Product & Brand Management, vol. 33, no. 1, pp. 43-56, 2024. https://doi.org/10.1108/jpbm-12-2022-4263 [47] B. Schmitt, "The concept of brand experience," Journal of brand management, vol. 16, no. 7, pp. 417-419, 2009. https://doi.org/10.1057/bm.2009.5 [48] I. Khan and Z. Rahman, "A review and future directions of brand experience research," International Strategic Management Review, vol. 3, no. 1-2, pp. 1-14, 2015. https://doi.org/10.1016/j.ism.2015.09.003 [49] F. A. Beig and F. A. Nika, "Brand experience and brand equity," Vision, vol. 23, no. 4, pp. 410-417, 2019. https://doi.org/10.1177/0972262919860963 [50] G. T. Lau and S. H. Lee, "Consumers' trust in a brand and the link to brand loyalty," Journal of market-focused management, vol. 4, pp. 341-370, 1999. https://doi.org/10.1023/A:1009886520142 [51] A. Alhaddad, "Perceived quality, brand image and brand trust as determinants of brand loyalty," Journal of Research in Business and Management, vol. 3, no. 4, pp. 01-08, 2015. [52] D. Surapto, "The impact of brand trust, brand loyalty, brand image on service quality," presented at the In 2nd International Seminar on Business, Economics, Social Science and Technology (ISBEST 2019) (pp. 162-167), Atlantis Press, 2020. https://doi.org/10.2991/aebmr.k.200522.033, 2020. [53] J. Mao, "Customer brand loyalty," International journal of business and management, vol. 5, no. 7, p. 213, 2010. https://doi.org/10.5539/ijbm.v5n7p213 https://doi.org/10.1108/qmr-03-2021-0040 https://doi.org/10.1016/j.intmar.2020.04.002 https://doi.org/10.1016/j.jbusres.2018.10.004 https://doi.org/10.1007/s12599-019-00600-8 https://doi.org/10.1177/00222429231224748 https://doi.org/10.1108/JSIT-09-2021-0206 https://doi.org/10.1057/s41262-018-0131-3 https://doi.org/10.1089/cyber.2017.0518 https://doi.org/10.1057/s41264-022-00207-3 https://doi.org/10.1007/978-3-319-70284-1_30 https://doi.org/10.1080/20932685.2010.10593068 https://doi.org/10.1016/j.jbusres.2016.04.181 https://doi.org/10.30574/gscarr.2024.18.3.0090 https://doi.org/10.1086/209515 https://books.google.com.vn/books?id=WTdGMhl7N1cC https://doi.org/10.1108/jpbm-12-2022-4263 https://doi.org/10.1057/bm.2009.5 https://doi.org/10.1016/j.ism.2015.09.003 https://doi.org/10.1177/0972262919860963 https://doi.org/10.1023/A:1009886520142 https://doi.org/10.2991/aebmr.k.200522.033 https://doi.org/10.5539/ijbm.v5n7p213 3065 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate [54] J. Rowley, "The four Cs of customer loyalty," Marketing intelligence & planning, vol. 23, no. 6, pp. 574-581, 2005. https://doi.org/10.1108/02634500510624138 [55] S. Ekakitie-Emonena, "Brand service quality as predictor of brand loyalty: A study of the Nigerian telecommunication industry," Edelweiss Applied Science and Technology, vol. 9, no. 1, pp. 1289-1301, 2025. https://doi.org/10.55214/25768484.v9i1.4403 [56] M. Laroche, M. R. Habibi, M.-O. Richard, and R. Sankaranarayanan, "The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty," Computers in human behavior, vol. 28, no. 5, pp. 1755-1767, 2012. https://doi.org/10.1016/j.chb.2012.04.016 [57] W. Handayani et al., "The effect of social media marketing on brand trust, brand equity, and brand loyalty," International Journal of Data and Network Science, vol. 6, no. 4, pp. 961–972 2022. https://doi.org/10.5267/j.ijdns.2022.1.015 [58] M. Fetscherin and D. Heinrich, "Consumer brand relationships research: A bibliometric citation meta-analysis," Journal of Business Research, vol. 68, no. 2, pp. 380-390, 2015. https://doi.org/10.1016/j.jbusres.2014.06.010 [59] J. O. Jeon and S. Baeck, "What drives consumer’s responses to brand crisis? The moderating roles of brand associations and brand-customer relationship strength," Journal of Product & Brand Management, vol. 25, no. 6, pp. 550-567, 2016. https://doi.org/10.1108/JPBM-10-2014-0725 [60] L. Matosas-López, "The influence of brand credibility and brand loyalty on customer satisfaction and continued use intention in new voice assistance services based on AI," Journal of Marketing Analytics, vol. 13, no. 1, pp. 180-201, 2025. https://doi.org/10.1057/s41270-023-00278-8 [61] P. Klaus and J. Zaichkowsky, "AI voice bots: a services marketing research agenda," Journal of Services Marketing, vol. 34, no. 3, pp. 389-398, 2020. https://doi.org/10.1108/JSM-01-2019-0043 [62] V. Gautam and V. Sharma, "The mediating role of customer relationship on the social media marketing and purchase intention relationship with special reference to luxury fashion brands," Journal of Promotion Management, vol. 23, no. 6, pp. 872-888, 2017. https://doi.org/10.1080/10496491.2017.1323262 [63] A. S. Bergner, C. Hildebrand, and G. Häubl, "Machine talk: How verbal embodiment in conversational AI shapes consumer–brand relationships," Journal of Consumer Research, vol. 50, no. 4, pp. 742-764, 2023. https://doi.org/10.1093/jcr/ucad014 [64] J. Hwang, J. Y. J. Choe, H. M. Kim, and J. J. Kim, "The antecedents and consequences of memorable brand experience: Human baristas versus robot baristas," Journal of Hospitality and Tourism Management, vol. 48, pp. 561-571, 2021. https://doi.org/10.1016/j.jhtm.2021.08.013 [65] J. Spajić, K. Mitrović, D. Lalić, B. Milić, and D. Bošković, "Personalized brand experience in metaverse," in Proceedings of the 10th International Conference on Mass Customization and Personalization – Community of Europe (MCP- CE 2022), pp. 158–166, 2022. https://mcp-ce.org/wp-content/uploads/2022/10/25.pdf 2022. [66] R. VONGURAI, "Factors influencing experiential value toward using cosmetic AR try-on feature in Thailand," Journal of Distribution Science, vol. 19, no. 1, pp. 75-87, 2021. https://doi.org/10.15722/JDS.19.1.202101.75 [67] S. R. Chandukala, K. S. REDDY, and Y.-C. Tan, "How augmented reality can-and can't-help your brand," 2022. [68] M. F. Shahzad, S. Xu, X. An, and I. Javed, "Assessing the impact of AI-chatbot service quality on user e-brand loyalty through chatbot user trust, experience and electronic word of mouth," Journal of Retailing and Consumer Services, vol. 79, p. 103867, 2024. https://doi.org/10.1016/j.jretconser.2024.103867 [69] R. Hasan, R. Shams, and M. Rahman, "Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri," Journal of Business Research, vol. 131, pp. 591-597, 2021. https://doi.org/10.1016/j.jbusres.2020.12.012 [70] V. Jham, G. Malhotra, and N. Sehgal, "Consumer-brand relationships with AI anthropomorphic assistant: role of product usage barrier, psychological distance and trust," The International Review of Retail, Distribution and Consumer Research, vol. 33, no. 2, pp. 117-133, 2023. https://doi.org/10.1080/09593969.2023.2178023 [71] N. Ameen, A. Tarhini, A. Reppel, and A. Anand, "Customer experiences in the age of artificial intelligence," Computers in human behavior, vol. 114, p. 106548, 2021. https://doi.org/10.1016/j.chb.2020.106548 [72] R. M. Morgan and S. D. Hunt, "The commitment-trust theory of relationship marketing," Journal of marketing, vol. 58, no. 3, pp. 20-38, 1994. https://doi.org/10.1177/002224299405800302 [73] J. Nalivaikė and G. Miliukaitė, "Influence of AI-generated avatars on consumer trust in the brand," in new trends in contemporary economics, business and management," in Selected Proceedings of the 14th International Scientific Conference “Business and Management 2024”, pp. 601–610, 2024. https://doi.org/10.3846/bm.2024.1191, 2024. [74] A. N. Giovanis and P. Athanasopoulou, "Consumer-brand relationships and brand loyalty in technology-mediated services," Journal of Retailing and Consumer Services, vol. 40, pp. 287-294, 2018. https://doi.org/10.1016/j.jretconser.2017.03.003 [75] E. S. Quaye, C. Taoana, R. Abratt, and P. Anabila, "Customer advocacy and brand loyalty: the mediating roles of brand relationship quality and trust," Journal of Brand Management, vol. 29, no. 4, pp. 363-382, 2022. https://doi.org/10.1057/s41262-022-00276-8 https://doi.org/10.1108/02634500510624138 https://doi.org/10.55214/25768484.v9i1.4403 https://doi.org/10.1016/j.chb.2012.04.016 https://doi.org/10.5267/j.ijdns.2022.1.015 https://doi.org/10.1016/j.jbusres.2014.06.010 https://doi.org/10.1108/JPBM-10-2014-0725 https://doi.org/10.1057/s41270-023-00278-8 https://doi.org/10.1108/JSM-01-2019-0043 https://doi.org/10.1080/10496491.2017.1323262 https://doi.org/10.1093/jcr/ucad014 https://doi.org/10.1016/j.jhtm.2021.08.013 https://mcp-ce.org/wp-content/uploads/2022/10/25.pdf https://doi.org/10.15722/JDS.19.1.202101.75 https://doi.org/10.1016/j.jretconser.2024.103867 https://doi.org/10.1016/j.jbusres.2020.12.012 https://doi.org/10.1080/09593969.2023.2178023 https://doi.org/10.1016/j.chb.2020.106548 https://doi.org/10.1177/002224299405800302 https://doi.org/10.3846/bm.2024.1191 https://doi.org/10.1016/j.jretconser.2017.03.003 https://doi.org/10.1057/s41262-022-00276-8 3066 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3046-3066, 2025 DOI: 10.55214/25768484.v9i5.7645 © 2025 by the authors; licensee Learning Gate [76] R. Anantharaman, S. Prashar, and S. V. Tata, "Examining the influence of customer-brand relationship constructs and bandwagon effect on brand loyalty," Benchmarking: An International Journal, vol. 30, no. 2, pp. 361–381, 2023. https://doi.org/10.1108/BIJ-06-2021-0365 [77] S. Chatzi, E. Peitzika, and G. Konsolaki, "Enhancing brand loyalty: A relationship marketing perspective within the context of contractual services," Journal of Relationship Marketing, vol. 23, no. 4, pp. 272-304, 2024. https://doi.org/10.1080/15332667.2024.2306019 [78] S. Zhang, M. Y.-P. Peng, Y. Peng, Y. Zhang, G. Ren, and C.-C. Chen, "Expressive brand relationship, brand love, and brand loyalty for tablet pcs: Building a sustainable brand," Frontiers in psychology, vol. 11, p. 231, 2020. https://doi.org/10.3389/fpsyg.2020.00231 [79] R. B. Mostafa and T. Kasamani, "Brand experience and brand loyalty: is it a matter of emotions?," Asia Pacific Journal of Marketing and Logistics, vol. 33, no. 4, pp. 1033-1051, 2021. https://doi.org/10.1108/APJML-11-2019-0669 [80] B. Ramaseshan and A. Stein, "Connecting the dots between brand experience and brand loyalty: The mediating role of brand personality and brand relationships," Journal of Brand Management, vol. 21, pp. 664-683, 2014. https://doi.org/10.1057/bm.2014.23 [81] B. Liang, "How brand experience, satisfaction, trust, and commitment affect loyalty: A reexamination and reconciliation," Italian Journal of Marketing, vol. 2022, no. 2, pp. 203-231, 2022. https://doi.org/10.1007/s43039-021- 00042-9 [82] C. H. Ong, H. W. Lee, and T. Ramayah, "Impact of brand experience on loyalty," Journal of Hospitality Marketing & Management, vol. 27, no. 7, pp. 755-774, 2018. https://doi.org/10.1080/19368623.2018.1445055 [83] A. Marliawati and D. Cahyaningdyah, "Impacts the brand of experience and brand image on brand loyalty: Mediators brand of trust," Management Analysis Journal, vol. 9, no. 2, pp. 140-151, 2020. https://doi.org/10.15294/maj.v9i2.36945 [84] S. Atulkar, "Brand trust and brand loyalty in mall shoppers," Marketing Intelligence & Planning, vol. 38, no. 5, pp. 559- 572, 2020. https://doi.org/10.1108/MIP-02-2019-0095 [85] Z. BOZBAY and E. K. BAŞLAR, "impact of brand trust on brand loyalty: Mediating role of brand affect," Journal of Academic Research in Economics, vol. 12, no. 2, 2020. [86] S. K. S. Shin, F. E. Amenuvor, R. Basilisco, and K. Owusu-Antwi, "Brand trust and brand loyalty: A moderation and mediation perspective," Current Journal of Applied Science and Technology, vol. 38, no. 4, pp. 1-17, 2019. https://doi.org/10.9734/CJAST/2019/v38i430376 [87] J.-H. Kwon, S.-H. Jung, H.-J. Choi, and J. Kim, "Antecedent factors that affect restaurant brand trust and brand loyalty: focusing on US and Korean consumers," Journal of Product & Brand Management, vol. 30, no. 7, pp. 990-1015, 2021. https://doi.org/10.1108/JPBM-02-2020-2763 [88] T. T. B. Nguyen and K. D. Nguyen, "Elevating customer satisfaction: AI sentiment analysis in vietnamese e-retail landscape," Springer, 2024, pp. 347-369. [89] D.-T. Vo, L. T. V. Nguyen, D. Dang-Pham, and A.-P. Hoang, "When young customers co-create value of AI-powered branded app: the mediating role of perceived authenticity," Young Consumers, vol. 25, no. 5, pp. 557-578, 2024. https://doi.org/10.1108/YC-06-2023-1759 [90] M. R. Habibi, M. Laroche, and M.-O. Richard, "The roles of brand community and community engagement in building brand trust on social media," Computers in human behavior, vol. 37, pp. 152-161, 2014. https://doi.org/10.1016/j.chb.2014.04.016 [91] R. Ebrahim, A. Ghoneim, Z. Irani, and Y. Fan, "A brand preference and repurchase intention model: the role of consumer experience," Journal of Marketing management, vol. 32, no. 13-14, pp. 1230-1259, 2016. https://doi.org/10.1080/0267257X.2016.1150322 [92] Y. Yasri, P. Susanto, M. E. Hoque, and M. A. Gusti, "Price perception and price appearance on repurchase intention of Gen Y: do brand experience and brand preference mediate?," Heliyon, vol. 6, no. 11, 2020. https://doi.org/10.1016/j.heliyon.2020.e05532 https://doi.org/10.1108/BIJ-06-2021-0365 https://doi.org/10.1080/15332667.2024.2306019 https://doi.org/10.3389/fpsyg.2020.00231 https://doi.org/10.1108/APJML-11-2019-0669 https://doi.org/10.1057/bm.2014.23 https://doi.org/10.1007/s43039-021-00042-9 https://doi.org/10.1007/s43039-021-00042-9 https://doi.org/10.1080/19368623.2018.1445055 https://doi.org/10.15294/maj.v9i2.36945 https://doi.org/10.1108/MIP-02-2019-0095 https://doi.org/10.9734/CJAST/2019/v38i430376 https://doi.org/10.1108/JPBM-02-2020-2763 https://doi.org/10.1108/YC-06-2023-1759 https://doi.org/10.1016/j.chb.2014.04.016 https://doi.org/10.1080/0267257X.2016.1150322 https://doi.org/10.1016/j.heliyon.2020.e05532