Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10, 721-745 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 14 July 2025; Revised: 25 August 2025; Accepted: 28 August 2025; Published: 13 October 2025 * Correspondence: tuongtg@uel.edu.vn Enhancing E-commerce purchase intentions through AI-personalized short videos: An SOR and flow theory perspective Gia Tuong Tong1*, Phuc Vi Uyen Ho1, Ngoc Lan Huong Vuong1, Lu Dinh Khoa Nguyen1, Tran Nhu Nguyet To1 1Faculty of Business Administration, University of Economics and Law, Vietnam; tuongtg@uel.edu.vn (G.T.T.). Abstract: This study examines the determinants of e-commerce purchase intentions in the context of AI- personalized short video recommendations, highlighting the mediating role of immersive experience and the moderating effects of privacy concerns and information quality. A quantitative survey was conducted with 317 experienced online shoppers in Ho Chi Minh City, and data were analyzed using SmartPLS 4 and SPSS 22 to test the proposed relationships. Findings indicate that relevant, inspirational, and emotional experiences significantly enhance immersive experience, with emotional experience exerting the strongest effect. Immersion, in turn, positively influences purchase intentions, serving as a crucial link between experiential stimuli and behavioral response. While privacy concerns weaken and information quality strengthens the relationship between immersion and purchase intention, their moderating effects are relatively minor compared to the direct impact of immersion. The study suggests that e-commerce platforms should optimize AI-driven short video content to emphasize relevance and emotional resonance, while providing accurate, transparent, and appealing product information to enhance conversion and sustain consumer trust. By integrating immersive experience as a central mediator and empirically testing the dual moderating roles of privacy concerns and information quality, this research extends the Stimulus–Organism–Response framework and Flow Theory, offering both theoretical advancement and practical insights for improving the effectiveness of AI personalization in digital commerce. Keywords: AI-personalized, E-Commerce, Immersive experience, Short video recommendation. 1. Introduction The growth of artificial intelligence (AI) is transforming the way businesses worldwide interact with customers, especially with the advancement of personalized recommendation systems [1]. For example, thanks to the ability to analyze human speech and gestures from available data, AI can easily support sales staff in solving customer problems [2], and in some simpler cases, AI can even replace customer service staff with conversational agents (CA) [3]. The significant changes brought by AI in the way businesses interact with customers are not only improving the customer experience but also enhancing the ability to recommend personalized products to each customer [4]. Therefore, it can be seen that AI in the digital age is gradually becoming a powerful tool to help businesses not only interact with consumers more easily but also maintain customer loyalty more sustainably [5] A clear example of the widespread application of AI personalization is the popularity of personalized recommendation systems on e-commerce platforms such as Amazon, eBay, and Shopee. These systems serve as strategic sales tools, encouraging exploration and shopping behaviors [6-8]. This system optimizes the user experience, helping to identify suitable products and even stimulating impulse buying [9, 10], while also acting as a "friend" that understands needs and preferences [11]. Beyond just product recommendations, AI personalization also demonstrates outstanding effectiveness when applied to short videos, optimizing the ability to convey dynamic content and helping consumers quickly visualize product https://orcid.org/0009-0007-6444-6641 722 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate features and functions, thereby enhancing awareness and interest [12, 13]. AI-personalized suggested video content can significantly shorten the customer journey, from awareness to action [14], opening a practical e-commerce pathway by meeting individual needs while promoting quicker and more accurate purchase decisions [15]. During the process of consuming content from short videos suggested by an AI-personalized system, consumers can experience positive emotions and excitement due to the dynamic rhythm, lively sounds, and storytelling elements that stimulate immediate emotional experiences and enhance engagement [16, 17]. The appeal of short videos lies not only in their entertainment value but also in their ability to personalize content based on behavioral data, thereby suggesting relevant experiences and boosting interest and purchase intentions [18, 19]. Additionally, inspirational content featuring creative themes, captivating styles, and visuals can evoke new consumer needs and motivations [16, 20]. When users encounter a personalized and engaging video, they can easily enter a state of immersion, nurturing their interest and enhancing their readiness to receive product information and creating a sense of real-life experience [21, 22]. Furthermore, short videos incorporating accurate and reliable product information help consumers solidify their purchase decisions without seeking additional sources [23, 24]. However, a high level of personalization also raises privacy concerns, especially when there is a lack of transparency regarding how personal data is collected and processed [25, 26]. Thus, it can be seen that these factors simultaneously influence online purchasing behavior and intention, contributing to the natural and effective transformation of interest into consumer action within the e-commerce environment [27, 28]. The topic of AI personalization in e-commerce has garnered significant attention within the research community in recent years [7]. Numerous studies have approached this topic from various perspectives, such as examining the effectiveness of product recommendation systems in enhancing engagement and retaining consumers [9], as well as the impact of AI on satisfaction, loyalty, and user experience on digital platforms [29]. These scholarly efforts indicate that AI personalization is a technological trend and a strategic solution for improving customer experience and driving consumer behavior in the e-commerce environment [30, 31]. To address the existing research gaps and limited empirical evidence within the context of cultural and technological characteristics in emerging markets, this study also aims to clarify the mediating role of immersive experience in the relationship between AI-personalized short video content and consumer behavior. At the same time, it expands the research scope to short video platforms beyond traditional e- commerce, investigating the transmission of commercial information through short videos. Accordingly, this study will design a theoretical framework, collect survey data, test hypotheses, and analyze results. The research objectives are as follows: (i) to explore and describe the role of AI-personalized short video recommendation systems in enhancing consumers’ experiences and perceptions; (ii) to examine the multidimensional relationships between consumer experiences (relevant, emotional, and inspirational), immersive experience, information-related factors (privacy concern, information quality), and e-commerce purchase intentions; and (iii) to propose strategic recommendations for e-commerce businesses in Vietnam to improve the effectiveness of AI-personalized applications in short video content. 2. Literature Review 2.1. SOR Theory (Stimulus-Organism-Responses) The Stimulus–Organism–Response (SOR) theory, developed from the cognitive learning framework [32], posits that consumer behavior is not formed directly from external stimuli but through an internal cognitive processing mechanism. The SOR theory has been widely used in e-commerce to examine factors that affect consumers' repurchase intentions [33] and the mechanisms behind impulsive buying behavior in livestreaming [34, 35]. In this study, factors such as relevant experience, inspirational experience, emotional experience, privacy concern, and information quality serve as stimuli (S). These stimuli affect the internal cognitive state (O), represented by immersive experience and consumers' sense of connection with the content, which leads to the response of e-commerce purchase intentions. 723 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate 2.2. Flow Theory The Flow theory, proposed by Csikszentmihalyi and Csikszentmihaly [36], describes an optimal psychological state in which individuals become fully immersed in an activity. In this state, they lose track of time and filter out irrelevant information. Flow has been identified as a mediating factor that fosters impulsive buying behavior when users engage with AI-personalized short videos on e-commerce platforms [13]; as a means to measure consumers’ emotional states in online shopping experiences that positively influence satisfaction and e-commerce purchase intentions [37, 38]. In this study, Flow Theory is utilized to clarify the mediating role of immersive experience when consumers are exposed to AI- personalized short video recommendations. Highly appealing and preference-congruent content increases the likelihood of entering the flow state, which not only enhances repeated interaction with the platform but also motivates e-commerce purchase intentions. 2.3. AI-Personalized Short video Recommendation Optimizing the customer experience by customizing content, products, and display interfaces to individual user preferences, behaviors, and data is at the heart of personalization in e-commerce [39, 40]. When powered by artificial intelligence (AI), personalization becomes automated, accurate, and adaptive in real time by analyzing large amounts of behavioral data and making highly relevant recommendations [41, 42]. In the past, research on personalization in e-commerce mainly focused on traditional recommendation forms, exploiting text and static image data such as product descriptions or reviews [43, 44]. Nowadays, the development of technology has opened a new avenue: personalized recommendations through short videos. In the context of digital consumption, short videos are not only an entertainment tool but are gradually becoming an important part of promoting shopping behavior through AI-based personalized content recommendation strategies [13], especially in e-commerce platforms in Southeast Asia such as TikTok Shop [45] or Shopee [46]. Unlike static images or text, short videos create a multi-sensory experience through moving images, sounds, background music, and dialogue [47], making users more easily emotionally engaged and maintaining attention longer [17]. Through the AI algorithm mechanism that tracks and analyzes real-time user behavior data [48] to automatically suggest videos with content, format, and topics that are close to users' interests, consumption habits, and current context. When suggested to the right needs and at the right time, short videos can stimulate emotions and convert into purchase intentions more effectively [49]. Therefore, AI-personalized short video recommendation in e- commerce is a combination of technology and a new customer experience strategy with deep personalization and high conversion potential. 2.4. Customer Experience on E-Commerce In e-commerce, customer experience is not only the result of user interactions but also plays a pivotal role in shaping online shopping behavior and brand engagement. Customer experience is the sum of emotions, perceptions, and impressions that consumers form during their interactions with content, products, or services [50] at various touchpoints in the consumer journey. The AI-Personalized Short Video Recommendation touchpoint, in particular, provides a more immersive and receptive experience through combining multimedia content relevant to the needs and internal context [51]. This form acts as a marketing tool and a cognitive and emotional trigger, initiating consumers' psychological processing during the shopping experience journey [52]. One of the core factors that shape the user experience of AI-personalized short video recommendations is the relevant experience of the content. According to Yin et al. [53], when consumers perceive the recommended short videos as highly relevant to their interests, needs, or context, the experience becomes more compelling, and the perception of the content increases. Through the AI system that tracks signals such as video viewing duration, skip rate, search history, etc., in real time [48], the 724 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate algorithm will automatically recommend short videos about products that are close to the user's current interests, needs, and context [54]. According to research by Bao et al. [55] the PR² (Personalized Retrieval & Ranking) system in the short-form video search environment increased CTR@10 by 10.2%, video viewing time increased by 20%, and daily active users increased by 1.6% by improving the level of content personalization to match user interests. This data highlights that in the fast-paced ecosystem of short-form videos in e-commerce, relevant experience plays a key role in retaining users. When video content matches current perception, viewers are more likely to enter a state of immersive experience, allowing them to focus more deeply, process emotions, and perceive product value more effectively, thereby increasing consumption intention [21]. The ability to inspire through AI-personalized short video recommendations activates deeper user interaction with the e-commerce platform. In marketing, an inspirational experience is a specific psychological state that occurs when consumers feel inspired by ideas, positive emotions, or the desire to act through receiving targeted content [56, 57], considered an emotional and cognitive factor between "knowing" and "wanting to act." Unlike relevant experiences directly associated with rational benefits, inspirational experiences can shift attitudes and awaken latent needs [58]. They help consumers envision what they would "look like" if they used the product. The study on tourism by Khoi et al. [59] is one of the most unmistakable pieces of evidence, which shows the ability of short video content to inspire not only initial attention but also to trigger a psychological state of immersion. When users feel present in the content environment, they are more likely to engage deeply with the video and the platform, thereby enhancing the ability to form consumer motivation [49]. According to research by Cachero-Martínez and Vázquez-Casielles [60], positive emotional experiences directly impact consumer engagement on e-commerce platforms, even without a specific call to purchase. In the online consumer environment, the central role in creating a connection between consumers and brands is the emotional experience [61] referring to the emotions evoked in consumers during the interaction with the platform, product, or content, including satisfaction, enjoyment, interest, curiosity, or emotion, and proven by [62] to have an important influence on customer experience, leading to online purchase behavior, short videos are interactive elements that can evoke viewers' emotions quickly and intensely thanks to the harmonious combination of visual and audio elements [17]. The higher level of personalization in AI-based short video recommendations increases the likelihood that consumers will experience positive emotions and immersion, which in turn drives impulsive or intentional consumption behavior [13]. Therefore, emotional experience is not just a mere emotional response but also a behavioral trigger mechanism in the consumer experience model within the e-commerce environment, particularly concerning AI-personalized short video content. 2.5. Customer Intrinsic Perception In marketing and consumer behavior, customer intrinsic perception refers to deep, subjective, and unobservable psychological states such as feelings of connection, focus, and presence [53, 63, 64]. These states are triggered when consumers are exposed to personalized digital content [65]. According to the S-O-R theoretical framework, this is the internal response (Organism), which acts as an intermediary between external stimuli (Stimulus) and behavior (Response). Research shows that intrinsic perception greatly influences purchase intentions in short-form video marketing, especially when platforms use AI to customize content for users [21, 66]. Immersive experience is a measurable and tangible manifestation of intrinsic perception, characterized by a state of immersion in which consumers are entirely absorbed in the content, to the point of losing track of time, space, and their behavior [67]. This state often occurs when the content is personalized in real time, highly emotional, and contextually relevant, especially short videos personalized by AI that are highly relevant to consumers [13, 53]. Unlike behaviors such as liking or commenting, immersion is an emotional, cognitive response deeply embedded in the viewer and needs to be captured by observing the viewer’s internalization process [68, 69]. This study inherits that approach, viewing immersive 725 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate experience as a core part of internal cognition, contributing to explaining the relationship between personalized video content and consumer behavior [70, 71]. 2.6. Information Factors Customers interact with the content provided by AI-personalized short video recommendations on e- commerce platforms and evaluate the security of their personal data and the accuracy of the information presented in the videos [51]. Privacy concerns, which are defined as the level of anxiety a person has about their personal information being gathered and used without their express consent, arise when they are aware that they are being tracked and that their online activity is being examined [72, 73]. Trust can be severely damaged and the immersive experience disrupted when AI-personalized recommendation systems present content that closely matches users' online activities [74, 75]. This study builds on earlier research by clarifying the negative impact of privacy concerns on consumer purchase intention, even in situations where users have initially responded emotionally favorably to the content. In addition to privacy issues, the high information quality provided by AI-personalized short video recommendations is essential for preserving customer confidence and assisting with e-commerce purchase decision-making [15]. Short videos require information that is accurate, succinct, and pertinent to the context, whereas traditional advertising formats allow for more elaboration. Completeness, clarity, timeliness, and relevance are essential for high information quality to convert immersive experiences into tangible consumer actions [76]. The immersive state may be disturbed if customers believe that product information is unclear or opaque; on the other hand, when information is reliable and unambiguous, it effectively increases customers' purchase intentions and reinforces positive feelings [15, 77]. 2.7. E-commerce Purchase Intentions In online consumer behavior research, purchase intention reflects the degree of readiness to buy and represents the final outcome of the customer’s reception and response process following the consumption experience [78]. It is a subjective psychological state that can be measured through surveys and has strong predictive validity for actual consumer behavior [66, 79, 80]. Building on prior studies, purchase intention is defined as the level of willingness or intention of a consumer to purchase a specific product [81, 82]. Additionally, purchase intention is a crucial metric for evaluating the efficacy of personalized content, a factor that is essential in determining the modern digital user experience, particularly in the context of AI-personalized short video recommendations on e-commerce platforms [13]. However, recent studies have shown that purchase intention in the digital environment is strongly influenced by emotional experiences and the degree of content personalization [65]. Platforms such as TikTok Shop and Shopee Video enable purchase intentions to emerge during interaction, even when consumers have not had a clearly defined need beforehand [15]. When users perceive the content as contextually relevant and emotionally engaging, they are more easily persuaded and willing to act [83, 84]. Therefore, purchase intention is a theoretically important dependent variable and a significant indicator for evaluating the effectiveness of AI-personalized short video recommendation systems on e- commerce platforms. 3. Hypothesis Development Mehrabian and Russell [32] proposed the S–O–R theoretical model, which is widely used in consumer behavior studies and in the analysis of the impact of short videos on users' shopping experiences and behaviors (e.g. Zhang and Tian [85]). This research paper focuses on e-commerce, particularly examining AI-recommended personalized video content and evaluating the role of three main experiential factors considered as stimuli. When consumers perceive a strong relevance between the video content and their personal needs, interests (relevant), experience creative inspiration (inspirational), or positive emotions (emotional), they tend to be naturally and deeply attracted to that content, manifested as a state of immersion (immersion). This state not only increases the level of involvement in the shopping journey but also contributes to the formation of higher purchase intention (Purchase Intention – Response) within 726 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate the e-commerce environment [27]. With its appeal, concise format, and easy-to-personalize nature according to the needs and interests of viewers, short videos help stimulate positive emotions and maintain consumers' attention throughout the interaction process [86]. When users feel that the content is relevant, engaging, and highly personalized, they tend to immerse themselves in the short video experience, thereby strengthening their trust in the product and increasing their ability to make quick purchase decisions [15, 66]. Therefore, the study suggests that experiential elements derived from AI- personalized short video recommendation content play an important role in driving immersive experiences, ultimately influencing the consumer decision-making process. 3.1. Customer experience with AI-Personalized Short Video Recommendation According to the Stimulus-Organism-Response (SOR) model [32], AI-recommended videos that align with an individual’s needs, interests, or goals serve as an essential psychological stimulus. When consumers are exposed to personalized content (e.g., short videos), the feeling of being “understood” or “personalized” increases their level of attention and concentration [87], leading to an immersive experience, in which they are entirely focused and have a deeply engaged experience with the content provided [53]. Numerous previous studies also indicate that the value of the marketing message or content, equivalent to delivering “relevant experiences”, is an important factor in driving focus and immersive experience with the platform (e.g., Joo and Yang [88] and Yan et al. [69]). Therefore, this study proposes the following hypothesis: H1: Relevant experience has a positive effect on immersive experience. Unlike informational or functional stimuli, inspirational experiences tend to elicit deeper emotional responses and have a greater ability to direct behavior. According to Böttger et al. [56], inspirational experiences strongly impact consumers’ internal states, leading them into a positive receptive state and encouraging deeper engagement with the message, which is consistent with the SOR theory [32]. In their study of travel and lifestyle, Khoi et al. [59] indicate that inspirational content can make viewers feel like they want to act; from simply viewing, they begin to have a genuine desire to do something. This motivation is often fueled by empathy with the content and the ability to imagine themselves in that environment. When users experience high inspiration, they usually become absorbed entirely by the content, reaching a state of immersion similar to Csikszentmihalyi and Csikszentmihalyi's [36] Flow theory. Therefore, the study proposes the following hypothesis: H2: Inspiration experience has a positive effect on immersive experience. In e-commerce, “emotional experience” refers to positive emotions such as enjoyment, excitement, satisfaction, and favorable responses when users interact with AI-Personalized Short Video Recommendations [62]. Emotional experiences influence the internal psychological state (Organism), particularly the immersive experience, thereby affecting consumer behavior, including both intentional and impulsive purchases [32]. Moreover, Flow theory emphasizes that positive emotional experiences can lead users to heightened concentration and deeper engagement with the content [36, 89]. The higher the level of emotional experience, the greater the immersive experience users tend to have, which strengthens their connection with digital content [90]. Therefore, this study proposes the following hypothesis: H3: Emotional experience has positive effects on the Immersive experience 3.2. Customer Intrinsic Engagement Immersive experience is considered a critical mediating factor between external stimuli and consumers' behavioral responses (Response), specifically regarding e-commerce purchase intentions within the SOR model [32]. The immersive state not only plays a role in maintaining users' continuous attention but also strengthens their belief in the effectiveness and quality of personalized content, thereby creating a foundation to promote the transformation from emotional response to actual consumption behavior [15, 77]. As a result, consumers tend to increase their desire to own the product at that time, 727 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate thereby positively affecting the purchase decision-making process [81, 82]. Therefore, this study proposes the following hypothesis: H4: Immersive experience has positive effects on the E-commerce purchase intentions 3.3. Mediator Role of Information Factor According to the Privacy Calculus, consumers often weigh the benefits of their experiences against the potential risks of sharing personal data [91]. In particular, in an AI environment that personalizes content in real time, users can easily fall into a state of immersion [91, 92]. However, when content is too personalized, consumers may feel they are being monitored or lose control of the information [93]. This perceived risk can diminish initial positive emotions and lead to more conservative purchasing behavior [93, 94]. Empirical studies on platforms such as TikTok Shop or Shopee also show that a segment of sophisticated consumers is increasingly sensitive to the level of AI intervention [53]. Recent studies also show that as the level of privacy concern increases, users will gradually lose trust, causing the connection between immersion and purchase intention to weaken or be interrupted [53]. From there, this study proposes the hypothesis: H5: Privacy concern has a negative impact on the relationship between Immersive experience and E-commerce purchase intentions According to the Information Acceptance Model (IAM), consumers respond positively when information is clear, accurate, and relevant to their needs [95, 96]. In the context of AI-personalized short video recommendation, immersion can create emotional connections. However, when the accompanying information is of high quality, the experience transforms into purchase intention [21, 97]. If the video content is unclear or misleading, users will likely lose trust and exit the state of immersion [98]. Therefore, information quality positively moderates the relationship between immersive experience and e-commerce purchase intentions. From there, this study proposes the hypothesis: H6: Information quality has a positive impact on the relationship between Immersive experience and E-commerce purchase intentions. 3.4. Research Model Based on the hypothesis, the research model used in this research is shown in Figure 1 Figure 1. Research Model. 4. Research Methodology 4.1. Content Validity Assessment for Adopted Scales (Pre-PLS-SEM Phase) 4.1.1. Participants This study used a sample of knowledgeable individuals with experience close to the research phenomenon. The sample of 15 interviewees had to meet the following criteria: (1) participants had to have at least 3 years of experience shopping online on e-commerce platforms (Shopee, TikTok Shop, 728 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate Lazada); (2) participants had a frequency of using e-commerce platforms >3 times/week in the past 6 months; (3) participants had to understand the concept and have interacted with AI personalized recommendation videos and recognized their impact on the shopping experience. Table 1. General Information of Interviewees (n=15). User Gender Age Year of e- commerce usage (years) Frequency of using the e- commerce platform (times/week) Does AI-personalized short video recommendation have an impact on e- commerce purchase intention Have already purchased through an AI- personalized short video recommendation on an e-commerce platform N1 female 21 3 4 TRUE TRUE N2 female 20 5 4 TRUE TRUE N3 female 25 4 5 TRUE TRUE N4 female 19 4 6 TRUE TRUE N5 male 22 3 4 TRUE TRUE N6 female 25 5 4 TRUE TRUE N7 female 23 6 6 TRUE TRUE N8 male 23 5 7 TRUE TRUE N9 female 21 4 4 TRUE TRUE N10 male 21 3 3 TRUE TRUE N11 female 24 4 4 TRUE TRUE N12 female 25 5 5 TRUE TRUE N13 female 21 6 4 TRUE TRUE N14 male 21 6 6 TRUE TRUE N15 male 22 4 3 TRUE TRUE 4.1.2. Research Design The study applied the content validity method [99] to evaluate the content relevance of the scale inherited from previous studies in the current context. Instead of using a panel of academic experts, the study selected a group of real users with long-term experience with e-commerce platforms (Shopee, TikTok Shop) and who had experienced the AI-personalized video recommendation feature. These individuals were deemed “context experts” because they had in-depth experience with the research phenomenon. This method was proposed in the study of Hardesty and Bearden [100], in which content validity assessment should include feedback from the target audience to improve practical applicability. Participants were asked to read and respond to each measurement item, evaluating the content's relevance and comprehensibility, and utilizing the relevance rating scale established by Davis [101] to assess content validity in this research. Context experts utilized the relevance rating scale established by Davis [101] to assess content validity in this research. A panel of context experts assessed the content validity of each measurement item by rating its relevance on a four-point ordinal scale (1 = not relevant, 2 = somewhat relevant, 3 = quite relevant, 4 = highly relevant). Based on the criteria of Davis [101], if the result of the relevance scale is 1 or 2, it can be recorded as 0, or if the result is 3 or 4, it can be considered as 1. 729 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate Table 2. The relevance rating on the scale of the items. Scale Item code N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 Context experts in Agreement I-CVI Relevant Experience RE1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 14 0.93 RE2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 RE3 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 14 0.93 RE4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 RE5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 S-CVI 0.97 Inspirational Experience IE1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 13 0.87 IE2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 IE3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 IE4 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 14 0.93 IE5 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 14 0.93 S-CVI 0.95 Emotional Experience EE1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 EE2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 EE3 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 12 0.80 EE4 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 13 0.87 EE5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 S-CVI 0.93 Immersive Experience IE1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 IE2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 IE3 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 12 0.80 IE4 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 13 0.87 S-CVI 0.92 Privacy Concern PC1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 14 0.93 PC2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 PC3 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 14 0.93 PC4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 S-CVI 0.97 Information Quality IQ1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 13 0.87 IQ2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 IQ3 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 14 0.93 IQ4 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 13 0.87 IQ5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 S-CVI 0.93 730 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate E-commerce Purchase Intention EPI1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 14 0.93 EPI2 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 14 0.93 EPI3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 14 0.93 EPI4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1.00 EPI5 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 13 0.87 S-CVI 0.93 731 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate The study used two CVI indices [102] to evaluate the scale's content validity, the first index, I-CVI (Item-level Content Validity Index), is the percentage of raters who rated each item as 3 or 4, divided by the total number of raters [99]. The second index, S-CVI (Scale-level Content Validity Index), reflects the percentage of items in the entire scale rated 3 or 4 by all raters [103]. The evaluation showed that the I-CVI above 0.78 and S-CVI/Ave above 0.90 met the criterion requirements [102]; thus, the scale reached the desired level of content validity. This result confirmed that the inherited measurement items were appropriate and ready for subsequent quantitative analysis using PLS-SEM. 4.2. Quantitative Research (SEM) The study employed a quantitative approach to examine the relationship model between different types of consumer experiences and purchase intentions in the context of AI-personalized short video recommendations on e-commerce platforms. Data were collected through a questionnaire based on specific conceptual definitions and scales, inherited and calibrated from previous studies. The observed variables included: emotional experience, inspirational experience, relevant experience, immersive experience, information quality, privacy concerns, and e-commerce purchase intentions; all measured using a 5-point Likert scale. The survey subjects were experienced online shoppers in Ho Chi Minh City, representing young users frequently exposed to technology. The study distributed 330 survey forms and collected 317 valid forms (response rate 96.06%). After processing and cleaning the data, the research team analyzed it using SmartPLS 4 and SPSS 22 software, tested reliability (Cronbach's Alpha), and employed the SEM structural model to evaluate the relationships between independent, mediating, and moderating variables in the model. Table 3. Sample descriptive statistics. Factors Detail Number Proportion (%) Male 80 25.2 Gender Female 226 71.3 Others 11 3.5 Age < 25 159 50.1 25 - 40 114 36.0 > 40 44 13.9 Educational Upper College/University 281 88.6 Under College/University 36 11.4 Income per month < 3 million 142 44.8 3 - under 5 million 105 33.1 5 - under 10 million 39 12.3 10 - under 20 million 22 6.9 > 20 million 9 2.9 4.3. Measures 4.3.1. Relevant Experience: In the study conducted by Yin et al. [53], consumers' experience with AI marketing technology was measured through three main dimensions: relevance, inspiration, and insight, in which relevance reflects the degree of match between the recommended content and the actual needs of users. Furthermore, research by De Groot [104] and Shen and Wang [66] highlighted the impact of relevance in AI- personalized content on consumer attitudes and behaviors. It emphasizes that clarity and completeness of information are crucial in maintaining attention and creating a positive experience on short-form video platforms. Building on these foundational studies, this research adapts and develops the Relevant Experience measurements to assess how AI-personalized short video recommendation content aligns with consumers' needs, habits, and personal characteristics, thereby supporting their purchase decision-making process. 732 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate 4.3.2. Inspirational Experience Drawing on the theoretical foundation of consumer inspiration proposed by Thrash and Elliot [105], inspiration is conceptualized as a psychological process involving the reception of stimuli and the motivation to act. Gao et al. [49] examined the mediating role of the inspiration experience in e-commerce contexts, demonstrating that conveyed content can stimulate exploration motivation and influence consumers’ impulsive purchase decisions. Similarly, Böttger et al. [56] emphasized inspiration as a temporary motivational state influencing customers’ exploratory intentions and behavioral conversion. Building on these studies, the present research adapts and develops the inspirational experience measurements to reflect how consumers perceive inspiration, motivation, and aroused interest when exposed to AI-Personalized Short-video Recommendation content during online shopping. 4.3.3. Emotional Experience The study by Hosany et al. [106] demonstrates that emotional states significantly influence tourists’ satisfaction, intentions to revisit, and purchasing decisions. Also, Meng et al. [107] emphasized the critical role of generating positive emotions in promoting consumer behavior through experiences derived from digital content. Cheng et al. [108] focused on the relationship between consumer emotions and purchasing behavior under environmental influences in physical stores and websites. Building on these studies, the emotional experience measurements in the present research assess the extent to which consumers feel positive emotions such as liking, excitement, joy, or enthusiasm when experiencing AI- personalized short-video recommendation content, which in turn may lead to a desire to own or further explore products in e-commerce environments utilizing short videos. 4.3.4. Immersive Experience According to the research of Novak et al. [109], the concepts of flow and immersion in digital interactive environments describe the state of immersion that occurs when users feel a loss of the idea of time and space while focusing intensely on digital content. When users perceive that the content has a strong connection to their personal needs and emotions, they may temporarily forget the real world to fully immerse themselves in the digital content experience. Following this direction, Cuny et al. [110] developed specific measurement categories for the immersion variable in a music-driven website environment, leading to revisit intention, and pointed out that immersion is not only a state of concentration but also includes forgetting surrounding factors, such as physical space or actual time. Recently, Yan et al. [69] adapted the scale to a new context with AI-personalized short video recommendations. They found that consumers are attracted to content tailored to their individual needs and interests. Furthermore, the appeal of a short video lies not only in the novelty of its format and context; emotional engagement is a powerful catalyst for immersive experiences. 4.3.5. Privacy Concern In the foundational study by Malhotra et al. [111], the privacy concern variable was constructed based on the Internet Users’ Information Privacy Concerns (IUIPC) theory, with three main components: collection, control, and awareness of secondary use. This scale has been widely applied in many later studies to assess the level of consumer concerns about the collection and use of personal information on the Internet. Based on this foundation, Wang et al. [112] developed and re-tested the scale in the context of short-video platforms, highlighting users’ perceived risks when the content recommendation system is too close to personal behavior. Therefore, in this study, the Privacy Concern measurement was constructed by inheriting the original structure of IUIPC and adjusting it to suit the characteristics of content AI-personalized short video recommendation. 4.3.6. Information Quality Liu et al. [113] developed a scale based on the grounded theory of Wang and Strong [114] on the aspects of information, including content validity, relevancy, quality, presentation quality, and hedonic 733 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate quality in the context of e-commerce platforms. Inheriting the above two studies, this study expanded the scope of information quality assessment by adding factors related to information presentation, such as images, video descriptions, comprehensibility, and attractiveness, especially in the context of AI- personalized short video recommendation. The measurement of information quality of short videos proposed in this study reflects both the objective characteristics of information and the level of reception and perception from users. 4.3.7. E-Commerce Purchase Intention Yin and Qiu [115] measured consumers’ experiences with AI marketing technology on online shopping platforms across three dimensions: accuracy, insight, and interactivity. Lorenzo-Romero et al. [116] adapted the consumer purchase intention construct to assess consumers' decision-making readiness in online shopping environments supported by AI marketing technology. Likewise, Dixit et al. [117] developed a distinct scale to evaluate consumers’ willingness to act upon recommendations from an AI- Personalized system. Drawing on these sources, this study adapts measurement items to capture multiple facets of e-commerce purchase intention, including product search tendency, actual purchasing behavior, reliance on AI recommendations, and impulse buying in the context of positive experiences, tailored to e- commerce settings where AI plays the supportive role of product discovery and final purchase decisions. 5. Data Analysis and Results 5.1. Measurement Model Assessment The study used standard evaluation criteria in PLS-SEM analysis to test the measurement model. Specifically, when the composite reliability coefficient (CR) and Cronbach's alpha coefficient are both higher than 0.7, the scale is deemed reliable [118]. Convergent validity is assessed through the external loading factor (> 0.7) and the average variance extracted (AVE > 0.5). In addition, the Fornell–Larcker method tests discriminant validity, requiring each concept's square root of AVE to be greater than its correlation coefficient with other concepts [119]. The results presented in Table 4 show that all scales meet the criteria of reliability, convergent validity, and discriminant validity, ensuring the suitability of the measurement model for subsequent analyses. 734 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate Table 4. Results of measurement model assessment. Scale Reliability Convergent validity Discriminant validity Cronbach’s alpha Composite reliability rho_a (CR) Outer loading Average variance extracted (AVE) Fornell– Larcker 0.7 – 0.99 0.7 – 0.99 > 0.7 > 0.5 (Pass/Fail) Relevant Experience RE1 0.955 0.959 0.906 0.848 Pass RE2 0.930 RE3 0.919 RE4 0.927 RE5 0.920 Inspirational Experience IE1 0.962 0.967 0.924 0.869 Pass IE2 0.943 IE3 0.941 IE4 0.931 IE5 0.922 Emotional Experience EE1 0.955 0.958 0.919 0.848 Pass EE2 0.930 EE3 0.920 EE4 0.923 EE5 0.912 Immersive experience IME1 0.858 0.858 0.835 0.701 Pass IME2 0.834 IME3 0.838 IME4 0.843 Privacy Concern PC1 0.981 0.984 0.967 0.945 Pass PC2 0.977 PC3 0.972 PC4 0.972 Information Quality IQ1 0.985 0.985 0.973 0.944 Pass IQ2 0.973 IQ3 0.970 IQ4 0.974 IQ5 0.968 E-commerce Purchase Intentions EPI1 0.956 0.956 0.934 0.850 Pass EPI2 0.908 EPI3 0.925 EPI4 0.915 EPI5 0.928 5.2. Structural Model Testing 5.2.1. Multicollinearity test for Independent Variables The study examined the VIF (Variance Inflation Factor) index between independent variables to ensure that the model is not affected by collinearity. The model is considered collinear when VIF < 3 [120]. The results in Table 5 show that all VIF indexes are very low, ranging from 1.004 to 1.012, indicating that the model is entirely free of collinearity problems and can continue to be used to analyze causal relationships in the study. 735 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate Table 5. Results of the multicollinearity test for independent variables. VIF Conclusion RE → IME 1.012 Accepted IE → IME 1.008 Accepted EE → IME 1.006 Accepted IME → EPI 1.004 Accepted PC x IME → EPI 1.010 Accepted IQ x IME → EPI 1.008 Accepted 5.2.2. Coefficient of Determination and Predictive Relevance Testing The model's explanatory and predictive abilities are evaluated using two indices: R² (coefficient of determination) and Q² (predictive ability). An R² value of 0.50 or higher is considered average, while Q² > 0 indicates that the model has predictive value [121]. Table 6 shows that the R² of IME and EPI are 0.618 and 0.545, respectively, indicating good explanatory ability. At the same time, Q² reaches 0.534 and 0.786, indicating that the model has strong predictive ability for both dependent variables. Table 6. Results of the coefficient of determination and predictive relevance. R2 Q2 IME 0.618 0.534 EPI 0.545 0.786 5.2.3. Effect Size (f²) Testing To assess the influence of factors in the model, the study used the f-squared impact coefficient (f²) to measure the importance of each structural path. According to Joseph et al. [121], f² < 0.02 indicates a very small or insignificant impact; from 0.02 to < 0.15 is a small impact; from 0.15 to < 0.35 is a medium impact; and f² ≥ 0.35 is a significant impact. Table 7. Results of effect size (f²). f2 RE → IME 0.488 IE → IME 0.352 EE → IME 0.502 IME → EPI 0.917 PC x IME → EPI 0.038 IQ x IME → EPI 0.046 The results in Table 7 indicate that emotional factors and independent variables such as EE, IE, and RE all significantly influence IME, with f² values ranging from 0.352 to 0.502. Additionally, the path from IME to EPI demonstrates a strong influence (f² = 0.917), confirming the central role of immersive experience in enhancing purchase intention. Conversely, the moderating effects of PC × IME and QI × IME on EPI are limited, with f² values of 0.038 and 0.046, respectively, corresponding to a small impact level, which suggests that their moderating role is minimal. 5.2.4. Structural Equation Modeling Hypothesis Testing The results of the structural model testing presented in Table 8 show that all relationships are statistically significant at p < 0.05. Specifically, the paths from RE, IE, and EE to IME all have positive impact coefficients (β = 0.474; 0.402; 0.480) and p-values = 0.000, confirming the positive role of these three factors in shaping immersive experience. Similarly, the path from IME to EPI also has a strong positive coefficient (β = 0.593; p = 0.000), indicating that immersive experience is an important mediator that promotes purchase intention. 736 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate Table 8. SEM hypothesis testing. Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values RE → IME 0.474 0.475 0.038 12.591 0.000 IE → IME 0.402 0.402 0.038 10.520 0.000 EE → IME 0.480 0.481 0.037 13.114 0.000 IME → EPI 0.593 0.594 0.030 19.572 0.000 PC x IME → EPI -0.120 -0.118 0.037 3.269 0.001 IQ x IME → EPI 0.137 0.136 0.036 3.821 0.000 Notably, the PC × IME → EPI path has a negative coefficient (β = –0.120; p = 0.001), suggesting that privacy concerns moderate the effect of immersive experiences on purchase intentions. In contrast, the QI × IME → EPI path has a positive coefficient (β = 0.137; p = 0.000), indicating that information quality strengthens this relationship. Both moderating effects are minor in magnitude but statistically significant, implying that information security and information quality continue to influence consumer behavior, especially in the digital environment. 6. Conclusion Short videos have become an essential tool in today's e-commerce environment, where AI-powered personalization is increasingly used for entertainment and influencing consumer behavior. Customers now demand captivating and highly customized content that aligns with their tastes, and the intense emotional connection generated by personalized videos can significantly influence purchasing decisions. However, cognitive aspects such as information quality, privacy concerns, and emotional appeal determine the effectiveness of AI personalization. In light of this, the study's main conclusions are: First, the findings indicate that immersion is positively (+) impacted by relevant experience. Users are more likely to view video content as "tailored for them" and pay closer attention when tailored to their past interests and behaviors. Rather than depending exclusively on popular engagement metrics, recommendation algorithms should prioritize content relevance, as this finding suggests that relevance is a key driver of interaction depth. Second, immersion is positively (+) impacted by inspirational experiences as well. Customers can express interest and feel a deep personal connection to content that inspires, motivates, or offers fresh viewpoints. This element is crucial for creating and maintaining long-term client engagement, even though it has less impact than emotional experience. Third, immersion is most strongly influenced by emotional experience. Customers are more likely to enter deep concentration when they experience emotional responses such as empathy, joy, or surprise. This emphasizes how important emotions are in drawing in viewers and motivating them to interact with content actively. The results also show that one of the best strategies to improve engagement and influence consumer behavior is to evoke the right feelings. Fourth, the results of the study demonstrate that purchase intention is significantly influenced by immersive experience. Customers who are truly engrossed in the content are more likely to actively respond by exploring the product or making a purchase decision and remembering the information more thoroughly. This acts as a vital link between feelings, thoughts, and behavior. Therefore, companies should invest in producing content with depth and the capacity to hold attention to increase conversion rates, rather than concentrating only on view counts or fleeting interactions. Fifth, it has been demonstrated that information quality, in its moderating role, strengthens the impact of immersion on purchase intention. Users are more likely to move from an immersive emotional state to tangible purchasing actions when they believe product information, such as thorough descriptions, origin, and return policies, is precise, reliable, and comprehensive. On the other hand, ambiguous or opaque content may disrupt the experience, rendering the initial feelings of satisfaction 737 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate insufficient to motivate buying. This emphasizes how crucial it is to ensure every video contains high- quality content to optimize the conversion effect from content to action. Sixth, the findings show that the relationship between immersion and purchase intention is negatively moderated by privacy concern. No matter how engaging the content is, customers will likely be hesitant to buy if they believe their privacy has been infringed upon or their personal information is being overused. The fact that users are becoming more wary of data collection in the digital age, especially when AI- driven personalization becomes unduly intrusive, is reflected in this finding. Therefore, platforms need to set up clear procedures and give users control over their data to maintain positive behavioral outcomes and build trust. This study concludes by showing that modern consumers evaluate the reliability of the information and the platforms' handling of their data critically, in addition to reacting to engaging content. Although AI-personalized recommendations have much promise, information management must also meet strict requirements for accountability and transparency. These results further elucidate the role of technological factors and user experience in the current context of digital consumption, and they are consistent with previous studies like Cheng et al. [12], Hu et al. [77] and Liu et al. [113]. Additionally, recent studies highlight the need to balance trustworthy content, customer experience, and an ethical environment for data collection and use to maximize the efficacy of AI recommendations. 7. Implication 7.1. Theoretical Implication First, the study clarifies the crucial mediating role of immersive experience variables in the relationship between perceived user experience factors, including relevant experience, inspirational experience, and emotional experience, and e-commerce purchase intentions in the context of AI- personalized short video recommendation. In addition, the study also confirms that immersive experience states are not only the result of immediate reactions to engaging content but also act as a mediating mechanism in the transformation process from perception to specific consumer purchase intention. Second, the inclusion of two moderator variables, Privacy Concern and Information Quality, has expanded the current theoretical framework on the scope of content personalization and consumer behavior, showing that the effectiveness of AI-personalization systems is not only based on the level of emotional appeal but also depends on consumers' cognitive and defensive psychological factors. These findings contribute to strengthening and expanding the role of underlying theories such as SOR Theory and Flow Theory, helping to provide an integrated research model that can be widely applied in the current digital consumption context. Lastly, the study enriches the theoretical foundation for utilizing AI-personalized short video recommendation, and thus emphasizes the importance of balancing perceived experiences, information quality, and consumer trust. This represents a significant advancement in the study of consumer behavior in the modern e-commerce environment, where technology and user psychology must be understood as complementary to optimize marketing effectiveness. 7.2. Managerial Implication First, the study suggests that e-commerce platforms should prioritize optimizing content relevant for users through AI personalized systems in the direction of enhancing emotional experiences and inspirational experiences. Instead of relying solely on standard engagement metrics, algorithms can analyze individual behaviors and preferences through their actions on the platform to provide more deeply personalized content. This helps businesses improve their ability to connect emotionally and convert behaviors, thereby helping consumers proactively receive information and increase their engagement with the brand. Second, to enhance the effectiveness of immersive experiences, businesses should focus on investing in content design and platform interfaces in a synchronized manner to bring consistency. This helps maintain focus and facilitates consumers' perception of content more deeply. Likewise, the continuous 738 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate collection and analysis of real-time interaction behavior allow the system to adjust the recommended content flexibly and promptly. As a result, the rate of users receiving appropriate content is increasing, helping to increase click-through rates and satisfaction with the experience and promoting purchasing behavior. Third, discovering the role of information quality and privacy concerns variables in regulating consumer behavior shows the importance of ensuring information quality and protecting consumer privacy while providing personalized content. Platforms must invest more in delivering transparent, trustworthy, and structured information in video content. Simultaneously, strictly comply with legal regulations related to data collection, processing, and use, commit to transparency in collecting information, and provide explicit consent instructions. Ensuring the above factors helps increase consumer trust and acceptance of technology, thereby driving click-through intent and online shopping behavior. 8. Limitation There are still some restrictions on this study. First of all, it ignores long-term elements like customer retention or brand loyalty in favor of concentrating solely on assessing the immediate impact, namely the customer's purchase intention. Second, the quantitative data were mainly gathered via self-reported surveys, which could be biased because of participants' propensity to give socially acceptable answers or inability to recall their actual actions precisely. The classification of particular AI-personalized algorithms or variations in short video formats across various product categories could subtly influence consumer perception and behavior. Future studies should broaden the survey sample to include a wider range of consumer groups for a more comprehensive understanding of the effects of AI-personalized short video content in e-commerce. The effectiveness of AI-personalized short video content within specific domains may also be determined by grouping participants by product category, such as fashion, cosmetics, or household goods. To further enhance the objectivity of the results, future research should utilize behavioral tracking tools on e- commerce platforms, such as conversion rates, scrolling behavior, and video view duration, instead of relying solely on self-reported survey data. Finally, by employing longitudinal studies or in-depth interviews to monitor changes in consumer perceptions and behaviors following repeated exposure to personalized content, future research could expand the model to investigate the relationship between personalized experiences and long-term factors. This approach would better evaluate AI's long-term efficacy in communication tactics on e-commerce platforms. Funding: This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under a project within the framework of the program titled “Strengthening the capacity for education and basic scientific research integrated with strategic technologies at VNU-HCM, aiming to achieve advanced standards comparable to regional and global levels during the 2025-2030 period, with a vision toward 2045”. 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. Copyright: © 2025 by the authors. 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RE3 I feel that AI-personalized short video recommendations are personalized as if they were designed specifically for me. RE4 AI video recommendations can flexibly adjust to changes in my needs and preferences. RE5 I find these videos useful for my purchase decisions. Inspirational Experience IE1 AI-personalized short video recommendations inspire me to explore new products. Böttger et al. [56] Gao et al. [49] Thrash and Elliot [105] IE2 AI-personalized short video recommendations have inspired me to purchase a product. IE3 AI-personalized short video recommendations have urged me to purchase a product. IE4 New product information from AI-personalized short video recommendations has motivated me to shop. IE5 AI-personalized short video recommendations spark my interest by suggesting products featured in the video. Emotional Experience EE1 AI-personalized short video recommendations make me feel happy, motivating me to purchase the product. Meng et al. [107] Hosany et al. [106] Cheng et al. [108] EE2 I feel affection for the product and want to buy it after watching an AI- personalized short video recommendation. EE3 AI-personalized short video recommendations make me feel excited and want to own the product immediately. EE4 AI-personalized short video recommendations make me feel curious, leading me to search for related products. EE5 AI-personalized short video recommendations make me feel attracted to and eager to own the product. Immersive Experience IME1 I feel immersed in short product videos recommended by AI. Novak et al. [109] Cuny et al. [110] Yan et al. [69] IME2 I become completely absorbed in watching AI-personalized short video recommendations and forget the outside world. IME3 I feel as if I am inside the world of the AI-personalized short video recommendation. IME4 I am immersed in the video and feel as though I am part of that experience. Privacy Concern PC1 I am concerned that platforms are collecting too much of my personal information. Malhotra et al. [111] Wang et al. [112] PC2 I am concerned that the information I provide to the platform will be used without my permission. PC3 I am concerned that my personal information provided to the platform could be accessed by unknown individuals, negatively affecting me. PC4 I am concerned that the personal information I provide to the platform could be sold or shared without my knowledge. Information Quality IQ1 The information I receive from recommended videos is accurate and reliable. Wang and Strong [114] Liu et al. [113] IQ2 The information provided is sufficient for me to make purchase decisions. IQ3 The information is always up-to-date and not outdated. IQ4 The information in the video matches my needs and preferences. 745 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 721-745, 2025 DOI: 10.55214/2576-8484.v9i10.10526 © 2025 by the authors; licensee Learning Gate IQ5 The presentation of information (images, descriptions, videos) is clear and appealing. E-commerce Purchase Intention EPI1 I am willing to browse the products/services recommended by the platform multiple times when shopping on an online platform personalized by AI technology. Lorenzo- Romero et al. [116] Yin and Qiu [115] Dixit et al. [117] EPI2 I am likely to purchase goods/services recommended by the platform when shopping on an AI-personalized online platform. EPI3 Using personalized recommendation systems has become an essential part of my online shopping decision-making process. EPI4 I am willing to purchase goods/services recommended by the platform when shopping on an AI-personalized online platform. EPI5 I may make impulsive purchases when using AI-personalized online shopping platforms. Descriptive analysis summary IE RE EE IME IQ PC IPE N Valid 317 317 317 317 317 317 317 Missing 0 0 0 0 0 0 0 Mean 3.519 3.502 3.842 3.6222 3.587 2.373 3.0229 Std. Deviation 0.6175 0.6458 0.5944 0.42125 0.9661 0.7526 0.97540 Kurtosis 0.044 -0.228 -0.156 -1.349 -0.623 0.777 -0.412 St. Error of Kurtosis 0.273 0.273 0.273 0.273 0.273 0.273 0.273 Minimum 1.6 2.0 2.0 2.75 1.0 1.0 1.0 Maximum 5.0 5.0 5.0 4.50 5.0 5.0 5.0 Figure 2. Structural model with path coefficients and R² values.