https://orcid.org/0000-0001-6124-5165
Figures
Abstract
Against the backdrop of the digital economy and the increasing integration of virtual and physical consumption scenarios, augmented reality (AR) e-commerce reshapes the shopping experience by overlaying virtual information onto real-world contexts, thereby accelerating the industry’s shift from traffic-driven competition to experience-based competition. However, existing studies remain insufficient in explaining the underlying mechanism through which AR e-commerce influences consumers’ purchase intention, and the applicability of the stimulus–organism–response (S–O–R) framework in virtual–real integrated environments has not been adequately extended. Drawing on the S–O–R paradigm, this study conceptualizes four key AR-enabled characteristics—immersive virtual try-on, interactive product display, real-time fusion shopping, and personalized recommendation—as stimulus variables (S). Perceived value and brand attitude are modeled as organism variables (O), and purchase intention is treated as the response variable (R), forming an integrated conceptual model. Using survey data collected from 482 valid respondents, structural equation modeling (SEM) is employed to test the proposed relationships. The results indicate that all four AR stimuli indirectly enhance purchase intention by positively affecting perceived value and brand attitude. Among the stimulus dimensions, real-time fusion shopping exerts the strongest positive effect on perceived value (β = 0.290), whereas personalized recommendation shows the most pronounced effect on brand attitude (β = 0.295). In addition, perceived value and brand attitude play partial mediating roles, and brand attitude demonstrates a stronger direct effect on purchase intention (β = 0.449) than perceived value (β = 0.347). Theoretically, the present study systematically translates AR e-commerce technological attributes into stimulus dimensions within the S–O–R framework, thereby extending its explanatory boundary in virtual–real integrated settings and offering an integrated “technology–scenario–behavior” analytical lens. Practically, the findings provide actionable insights for e-commerce platforms and brand managers seeking to optimize AR-driven experience design and improve conversion outcomes.
Citation: Zhang X, Yang P, Guo C, He P (2026) Research on the influencing factors and mechanism of AR e-commerce consumers’ purchase intention. PLoS One 21(6): e0351158. https://doi.org/10.1371/journal.pone.0351158
Editor: Sudarsan Jayasingh, MCC Boyd Tandon School of Business, INDIA
Received: October 7, 2025; Accepted: May 22, 2026; Published: June 16, 2026
Copyright: © 2026 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data underlying the results presented in this study are provided as Supporting information files.
Funding: This work was supported by the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (Grant No. NJYT24014); the “14th Five-Year Plan” Research Project of the Inner Mongolia Autonomous Region Educational Science Research Program (Grant No. NGJGH2025170); the General Research Project of China Society of Logistics and China Federation of Logistics and Purchasing (Grant No. 2026CSLKT3-549); the Inner Mongolia Natural Science Foundation (Grant No. 2024MS07009); the Interdisciplinary Research Fund of Inner Mongolia Agricultural University (Grant No. BR231518); and the National Social Science Fund of China Post-funded Project (Grant No. 20FGLB033).
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
With breakthroughs in enabling technologies such as 5G, cloud computing, and artificial intelligence, the deep integration of augmented reality (AR) with e-commerce has increasingly addressed key limitations of traditional online shopping, particularly the lack of vivid sensory experience and the inability to conduct direct product trials [1]. By enhancing product visualization and interaction, AR-enabled e-commerce has the potential to alleviate persistent industry challenges, including high return rates and low conversion efficiency. In parallel, consumers’ demand for richer and less homogenized offline shopping experiences continues to grow. In the context of experience-oriented retail transformation, AR e-commerce overlays virtual product information onto real-world environments, providing immersive try-on and interactive product presentation, and has gradually emerged as a critical driver of experience-oriented transformation in the e-commerce industry [2].
Although prior research has recognized the influence of AR technologies on consumer behavior [3–5], several important gaps remain. First, the internal mechanism through which AR e-commerce shapes purchase intention is still insufficiently explained; in particular, existing studies have not clearly articulated how specific technological attributes are translated into purchase intention through consumers’ psychological perceptions. Second, while the stimulus–organism–response (S–O–R) framework has been widely adopted to examine the relationship between environmental stimuli and behavioral responses, its application to virtual–real integrated consumption contexts lacks systematic extension. Specifically, the distinctive technological characteristics of AR e-commerce have not been adequately theorized and operationalized as context-appropriate “stimulus dimensions” within the S–O–R paradigm. Third, extant studies tend to focus on the effects of single AR features in isolation, leaving the synergistic influence of multiple AR-enabled attributes underexplored.
To address these gaps, this study pursues three main objectives: (1) to develop a conceptual model linking AR e-commerce technological attributes (stimuli) to purchase intention (response) through consumers’ psychological perceptions (organism); (2) to empirically test the mediating roles of perceived value and brand attitude; and (3) to identify the differentiated effects of distinct AR-enabled technological attributes on consumers’ purchase intention. Using survey data collected via a structured questionnaire, we employ structural equation modeling (SEM) to validate the proposed relationships. By doing so, the present study extends the applicability of the S–O–R framework to emerging digital consumption scenarios and provides both theoretical insights and practical guidance for experience optimization and sustainable development in AR-enabled e-commerce.
2. Definition, characteristics, and models of AR E-commerce
2.1 Definition of AR e-commerce
AR e-commerce refers to an online shopping method that integrates Augmented Reality (AR) technology into e-commerce platforms. Using devices such as computers, smartphones, and large displays, AR-enabled platforms overlay virtual product information onto the physical world. AR technology allows consumers to intuitively evaluate a product’s appearance, size, and other attributes in relation to themselves or their environment. By enabling access to rich product information and real-time usage simulations, AR can substantially enhance the overall shopping experience.
2.2 Characteristics of AR e-commerce
- (1) Immersive Virtual Try-On
A defining feature of AR technology is its ability to deliver immersive experiences. With AR-enabled functions, consumers can project virtual products—such as shoes, cosmetics, eyewear, and jewelry—onto corresponding parts of their bodies during online shopping. AR-enabled functions enable consumers to virtually try on products and intuitively perceive how the items would look in real life.
In 2020, Dior collaborated with Snapchat to launch a “virtual shoe try-on” feature, allowing consumers to experience new sneaker models without leaving home. Similarly, in 2022, Amazon’s fashion retail division introduced a “virtual try-on” service covering more than 1,500 products. Such virtual try-on experiences help establish a stronger connection between consumers and products by enabling immersive interactions. They also allow consumers to more accurately assess the compatibility of products with their personal use contexts [6,7].
- (2) Interactive Product Display
Gabriel et al. (2023) [8] argue that the interactivity of AR e-commerce lies in its ability to integrate virtual products with real environments. Based on camera-captured scenes, AR overlays virtual product information to support interactive product visualization. Consumers can freely rotate, zoom in, and zoom out on 3D models, examine product details and functions, and engage in interactive operations with virtual items, thereby gaining a comprehensive understanding of the products. Yang and Lin (2024) [9] further emphasize that AR enables real-time interaction with virtual models, allowing consumers to perceive product characteristics more fully and conveniently. These interactive display features can significantly enrich consumers’ experiences and strengthen their purchase intention in AR-enabled e-commerce settings.
- (3) Real-Time Fusion Shopping
AR e-commerce enables consumers to preview and select products such as home décor and furniture within their actual environments. Virtual items can be placed directly into real-world spaces, enabling consumers to evaluate product dimensions and spatial fit in real time. Consumers can also assess how the product’s appearance complements existing surroundings. Compared with traditional e-commerce, AR shopping offers stronger interactivity and real-time feedback [10], which can improve the accuracy of consumers’ online purchase decisions.
- (4) Personalization
AR e-commerce provides consumers with highly personalized experiences [11]. Through AR shopping interfaces, consumers can customize their experiences by selecting different virtual products [12]. In addition, AR systems can generate personalized product recommendations based on consumer preferences, browsing habits, style-matching data, and purchase histories. Compared with traditional e-commerce, such personalized services can reduce consumers’ search effort, help them quickly find suitable products, help them identify suitable products more efficiently, and ultimately improve the effectiveness of online shopping.
2.3 Models of AR e-commerce
AR-based online shopping has introduced new dynamics into e-commerce. It reduces barriers between consumers and platforms, reshapes the channels and modes of information acquisition, and enables the integration of virtual and real environments. By creating new forms of interaction between consumers and products, AR e-commerce has given rise to several representative application models, as summarized in Table 1.
3. Model construction of factors influencing consumer purchase intention in AR e-commerce
3.1 Theoretical background and literature review
3.1.1 Theoretical foundation of the S–O–R framework.
The stimulus–organism–response (S–O–R) framework was originally proposed by Mehrabian and Russell (1974) [13]. The central logic of this theory posits that external environmental stimuli (Stimulus) influence individuals’ internal psychological states (Organism), which in turn trigger specific behavioral responses (Response). The S–O–R framework has been widely adopted in e-commerce and consumer behavior research. In this stream of literature, “stimuli” typically refer to external factors such as platform functionalities and product presentation cues; “organism” focuses on consumers’ psychological evaluations (e.g., perceived value and attitudinal responses); and “response” is commonly operationalized as purchase intention or actual purchase behavior.
As a novel consumption context characterized by virtual–real integration, AR-enabled e-commerce introduces distinctive technological features that serve as external stimuli fundamentally different from those in conventional online shopping environments. During AR-based shopping experiences, consumers’ perceived value and brand attitude emerge as critical psychological mechanisms linking these technological attributes to purchase intention. Compared with traditional e-commerce settings, the “stimulus” component in AR e-commerce is inherently immersive, interactive, and real-time in nature, which calls for a context-specific extension of the S–O–R framework. This constitutes the primary rationale for adopting the S–O–R paradigm as the core theoretical lens in the present study.
3.1.2 Review of related studies and identification of research gaps.
Technological attributes of AR e-commerce: Existing research has examined certain technological characteristics of AR-enabled e-commerce, such as virtual try-on [14] and interactive product display [15], highlighting their positive effects on consumer experience. However, prior studies have largely investigated individual AR features in isolation. A systematic integration of four key AR e-commerce attributes—immersive virtual try-on, interactive product display, real-time fusion shopping, and personalized recommendation—remains limited. Moreover, the differentiated pathways through which these technological attributes affect consumer purchase intention have not been sufficiently clarified.
Mediating roles of perceived value and brand attitude: Perceived value has been consistently identified as a pivotal psychological driver of purchase intention in e-commerce contexts. AR technologies may enhance perceived value by increasing consumers’ sense of presence and improving the completeness and vividness of product information [16]. Brand attitude, in turn, can shape consumer decision-making by strengthening brand cognition, conveying brand meanings, and reinforcing brand-related evaluations [17]. Nevertheless, existing studies have not adequately elucidated how perceived value and brand attitude jointly mediate the relationship between AR technological attributes and purchase intention in AR-enabled shopping environments. In addition, the relative importance of these two mediators has rarely been empirically compared within a unified analytical framework.
Application of the S–O–R framework in AR e-commerce: Although some studies have attempted to apply the S–O–R framework to AR-related consumption scenarios, several limitations persist. First, AR e-commerce technological attributes have not been systematically theorized and operationalized as “stimulus” variables, resulting in insufficient theoretical alignment. Second, the transmission mechanism underlying the “stimulus–organism–response” process remains underexplored, and mediation effects are often not rigorously tested. Third, the empirical findings across studies are not fully consistent, which constrains the development of actionable and generalizable insights for practice.
In summary, prior research is characterized by limitations in theoretical adaptation, insufficient integration of multidimensional AR technological attributes, and inadequate examination of mediating mechanisms. To address these gaps, the present study develops and tests a multidimensional S–O–R model, aiming to provide a more systematic explanation of how AR e-commerce technological characteristics shape consumer purchase intention through key psychological pathways.
3.2 Research hypotheses
3.2.1 AR e-commerce characteristics and perceived value.
Shopping experience, product information, personalized recommendations, and services are critical determinants of consumers’ perceived value. AR e-commerce allows consumers to interact with products, access detailed product information, and experience more immersive shopping. Personalized recommendations in AR e-commerce platforms cater to individual consumer needs.
According to Dacko (2017) [18], the effective application of AR technology enhances consumers’ sense of presence in the shopping process, improving their overall evaluation of products and services. Hilken et al. (2022) [19] found that virtual try-on experiences and interactive visual experiences in AR e-commerce improve the accuracy of product displays, enhancing consumers’ excitement and perceived value of AR platforms. Qin (2025) [20] examined the effects of AR experience on online purchase intention in terms of controllability, entertainment, responsiveness, and presentation quality.In retail, many AR e-commerce platforms provide personalized fashion experiences. For instance, IKEA allows users to virtually personalize interior design elements using AR, which enhances their perceived value and engagement in the shopping process [21].
Based on the above research, the following hypotheses are proposed:
H1: Immersive virtual try-on affects consumers’ perceived value.
H2: Interactive product presentations affects consumers’ perceived value.
H3: Real-time fusion shopping affects consumers’ perceived value.
H4: Personalized recommendations affects consumers’ perceived value.
3.2.2 AR E-commerce characteristics and brand attitude.
AR technology enables consumers to engage in virtual try-on experiences, interactive presentations, and real-time fusion shopping, fostering direct interaction and connection with the brand. Scenario-based experiences on AR e-commerce platforms create consumer-brand connections by subtly conveying brand culture, building brand awareness, and enhancing brand image. These experiences can significantly influence consumers’ brand attitudes.
AR e-commerce platforms leverage AR technology to provide comprehensive product information, increasing brand awareness and improving brand attitudes [7]. Rauschnabel et al. (2019) [17] found that mobile AR apps can enhance brand attitudes by inspiring consumers, thereby strengthening the brand–consumer relationship. Lavoye et al. (2023) [22] found that virtual try-on services based on AR technology foster self-explorative engagement, which in turn enhances consumers’ brand recognition and positive brand-related outcomes. Smink et al. (2020) [23] demonstrated that augmented reality shopping environments enhance consumers’ perceived personalization, which in turn fosters more favorable brand and app evaluations compared to non-AR settings.
Based on these findings, the following hypotheses are proposed:
H5: Immersive virtual try-on affects consumers’ brand attitudes.
H6: Interactive product presentations affects consumers’ brand attitudes.
H7: Real-time fusion shopping affects consumers’ brand attitudes.
H8: Personalized recommendations affects consumers’ brand attitudes.
3.2.3 Perceived value, brand attitude, and consumer purchase intention.
Perceived value serves as a key antecedent of consumer purchase intention, as it reflects consumers’ experiences and behaviors in specific contexts. Liu et al. (2021) [24] found that in social e-commerce environments, technological features indirectly influence consumers’ purchase intentions through a chain mediation of customer interaction and perceived value. Miao et al. (2022) [25] found that perceived value mediates the effect of e-customer satisfaction and e-trust on consumers’ repurchase intention in B2C e-commerce. Their findings suggest that when consumers feel satisfied and trust an e-commerce platform, they perceive more value, which in turn increases their likelihood of repurchasing. Similarly, Dong (2023) [26] demonstrated that brand attitude mediates the relationship between consumer identity and purchase intention, highlighting its crucial role in shaping consumers’ willingness to buy a brand. Guo and Zhang (2024) [27] conducted an empirical study examining how sensory, affective, cognitive, behavioral, and relational experiences in AR online shopping influence consumers’ purchase intentions, with perceived ease of use and perceived usefulness serving as mediating variables.
Based on the above discussion, the following research hypotheses are proposed:
H9: Perceived value affects purchase intention.
H10: Brand attitude affects purchase intention.
3.2.4 The mediating role of perceived value and brand attitude.
Compared to traditional e-commerce, AR e-commerce enables consumers to intuitively understand a product’s appearance, size, texture, and other features through virtual try-on experiences. It also enhances interaction between consumers and products, providing deeper insights into product functionality and usage. Moreover, AR integrates virtual products into real-world environments, enabling consumers to observe real-time matching effects [28].
In terms of personalized consumption, AR technology enhances the shopping experience by offering customized product experiences and personalized recommendations through product interactions. The four characteristics of AR e-commerce act as external stimuli that influence purchase intentions via perceived value.
Based on the above discussion, the following research hypotheses are proposed:
H11: Perceived value mediates the relationship between immersive virtual try-on and consumer purchase intention.
H12: Perceived value mediates the relationship between interactive product presentation and consumer purchase intention.
H13: Perceived value mediates the relationship between real-time fusion shopping and consumer purchase intention.
H14: Perceived value mediates the relationship between personalized recommendations and consumer purchase intention.
AR technology enables e-commerce companies to enhance consumer-product interactions, fostering closer connections that shape brand image, improve brand attitude, and influence consumer behavior. AR online shopping features, such as virtual try-on experiences, product presentations, fusion shopping, and personalized recommendations, offer consumers greater product information value and an enhanced shopping experience. Higher perceived information value may strengthen consumers’ brand identity, thereby increasing purchase intention. A seamless interactive experience enhances brand favorability, further influencing consumers’ purchase decisions.
Based on the above discussion, the following research hypotheses are proposed:
H15: Brand attitude mediates the relationship between immersive virtual trials and consumer purchase intention.
H16: Brand attitude mediates the relationship between interactive product presentations and consumer purchase intention.
H17: Brand attitude mediates the relationship between real-time fusion shopping and consumer purchase intention.
H18: Brand attitude mediates the relationship between personalized recommendations and consumer purchase intention.
3.3 Model construction
Based on the research context and relevant research on AR e-commerce shopping, perceived value, brand attitude, and consumer purchase intention, this model integrates the S-O-R (Stimulus-Organism-Response) theory. It examines how consumers, after receiving external stimuli, form an overall evaluation of the product or service. Additionally, the emotional enjoyment or service experience provided by the product significantly influences perceived value.
AR e-commerce shopping enhances consumer-brand interaction and connection, which significantly influences consumer attitudes. Consequently, perceived value and brand attitude are considered internal organism variables responding to external stimuli, while purchase intention is treated as the behavioral response variable. The theoretical model of factors influencing consumer purchase intention in AR e-commerce is illustrated in Fig 1.
3.4 Variable measurement
Based on a comprehensive review of relevant literature and in consideration of the research context and practical conditions, 24 observed variables were constructed to measure seven latent variables in the AR e-commerce consumer purchase intention model: Immersive Virtual Try-On, Interactive Product Display, Real-Time Fusion Shopping, Perceived Value, Brand Attitude, and Purchase Intention (see Table 2). A five-point Likert scale was employed to evaluate the measurement items, where scores of 1, 2, 3, 4, and 5 correspond to “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree,” respectively.
4. Empirical analysis of factors influencing consumer purchase intention in AR e-commerce
4.1 Descriptive statistics of the sample
The survey targeted users who had experience with or were familiar with AR e-commerce shopping. Before data collection, a questionnaire link was generated using the Wenjuanxing platform. To ensure that respondents completed the survey conscientiously, screening items with predetermined correct options were included. In addition, restrictions were applied to allow only one submission per IP address, thereby ensuring the quality of the collected data. Before completing the questionnaire, all participants read and signed an electronic informed consent form. Participants were fully informed of the research purpose, data usage, and anonymity principles, and participation in the survey was voluntary. The present study strictly adhered to academic ethical standards. Before completing the questionnaire, all participants read and signed an electronic informed consent form and were fully informed of the research purpose, data usage, and anonymity principles before voluntarily participating in the survey.
A total of 527 questionnaires were collected through multiple channels, including WeChat, QQ, and the sample service function provided by Wenjuanxing. After excluding four categories of invalid questionnaires—those from respondents without AR shopping experience or familiarity, those completed in less than one minute, those with identical answers across all items, and those with incorrect responses to the screening items—482 valid questionnaires were retained. The effective response rate was 91.46%. The basic characteristics of the sample are presented in Table 3.
4.2 Reliability and validity analysis
Reliability refers to the assessment of a questionnaire’s consistency, reflecting the degree to which repeated measurements of the same object with the same method yield consistent results. In this study, Cronbach’s α coefficient was employed to assess reliability, with higher values indicating greater reliability. SPSS 26.0 was used to analyze both the overall reliability of the questionnaire and the reliability of each variable.
The questionnaire consisted of 24 items, and the overall Cronbach’s α coefficient was 0.946, indicating strong reliability of the scale. For the seven variables—Immersive Virtual Try-On, Interactive Product Display, Real-Time Fusion Shopping, Personalization, Perceived Value, Brand Attitude, and Consumer Purchase Intention—the Cronbach’s α coefficients ranged from 0.856 to 0.915, all exceeding the 0.8 threshold. These results demonstrate that the reliability of each variable meets the required standard (see Table 4).
Convergent validity refers to the degree of correlation among measurement items within the same factor. It is assessed using standardized factor loadings (Std. Estimate), composite reliability (CR), and average variance extracted (AVE). As shown in Table 5, the standardized factor loadings of the measurement items for Immersive Virtual Try-On, Interactive Product Display, Real-Time Fusion Shopping, Personalization, Perceived Value, Brand Attitude, and Consumer Purchase Intention were all greater than 0.7, indicating that the items adequately represent their respective latent variables. The AVE values of all latent variables exceeded 0.6, and the CR values were greater than 0.8. Therefore, the questionnaire demonstrates satisfactory convergent validity and is suitable for subsequent analysis.
Discriminant validity refers to the degree of distinction among measurement items of different factors. In this study, it was assessed by comparing the square root of the average variance extracted (AVE) with the correlation coefficients among the variables. As shown in Table 6, the square roots of the AVE values on the diagonal were all greater than the correlation coefficients between the variables, indicating that the questionnaire possesses good discriminant validity.
4.3 Correlation analysis
Correlation analysis examines the relationships and degrees of association between variables. This study employs the Pearson method to assess correlations between variables. Correlations were observed between the four characteristics of AR e-commerce and perceived value, brand attitude, and consumer purchase intention, with all correlation coefficients below 0.75. These results indicate that multicollinearity is not present (see Table 7).
4.4 Structural equation model analysis
AMOS 26.0 was used to conduct structural equation modeling with four exogenous latent variables—Immersive Virtual Try-On (IV), Interactive Product Display (IP), Real-Time Fusion Shopping (RF), and Personalization (VP)—and three endogenous latent variables—Perceived Value (PV), Brand Attitude (BA), and Consumer Purchase Intention (PI). The results are presented in Fig 2.
The overall fit indices obtained from AMOS 26.0 analysis are presented in Table 8. The CMIN/DF value of 2.260 satisfies the requirement of being less than 3. The GFI, NFI, CFI, and IFI indices all exceed the threshold of 0.9, while the RMSEA value of 0.051 is near the ideal 0.05. All fit indices fall within acceptable ranges, indicating a good model fit.
The significance of the standardized path coefficients is assessed using the C.R. value and the P-value. A C.R. value greater than 1.96 and a P-value less than 0.05 indicate a significant path effect between variables, supporting the corresponding hypothesis. Based on the C.R. values and P-values, all standardized path coefficients were statistically significant. The standardized path coefficients of the structural equation model and the hypothesis tests are presented in Table 9.
Empirical results indicate that the immersive virtual try-on feature of AR e-commerce has a significant positive effect on consumers’ perceived value (β = 0.187, p < 0.001) and brand attitude (β = 0.149, p < 0.01). Thus, H1 and H4 are supported, suggesting that immersive virtual try-on experiences positively influence perceived value and brand attitude. The interactive product display feature of AR e-commerce has a significant positive effect on perceived value (β = 0.196, p < 0.001) and brand attitude (β = 0.186, p < 0.001). Accordingly, H2 and H5 are supported, indicating that effective interactive product displays enhance both perceived value and brand attitude.
The real-time fusion shopping feature of AR e-commerce has a significant positive effect on perceived value (β = 0.290, p < 0.001) and brand attitude (β = 0.257, p < 0.001). Thus, H3 and H6 are supported, showing that real-time visualization experiences positively shape consumers’ perceived value and brand attitude. Furthermore, the personalization feature of AR e-commerce demonstrates a significant positive effect on perceived value (β = 0.193, p < 0.001) and brand attitude (β = 0.295, p < 0.001). Hence, H7 and H8 are supported, indicating that personalized recommendations enhance perceived value and brand attitude.
Finally, consumers’ perceived value (β = 0.347, p < 0.001) and brand attitude (β = 0.449, p < 0.001) exert significant positive effects on purchase intention. Therefore, H9 and H10 are supported, confirming that perceived value and brand attitude play critical roles in promoting consumers’ purchase intentions.
4.5 Mediation effect test
If a direct effect exists between the independent and dependent variables, it is further necessary to examine whether the independent variable significantly influences the mediator and whether the mediator significantly affects the dependent variable. If both paths are significant, the significance of the indirect regression coefficient is tested. A significant coefficient indicates that the mediator plays a partial mediating effect, whereas a non-significant coefficient suggests a full mediating effect.
To investigate the relationships among immersive virtual try-on, interactive product display, real-time fusion shopping, personalization, and consumers’ purchase intention in AR online shopping, this study applied the Bootstrap method to test the mediating roles of perceived value and brand attitude. The PROCESS macro (Hayes, 2013) was employed with 5,000 bootstrap resamples. At the 95% confidence level, if the confidence interval does not include zero, the mediating effect is considered significant.
- (1) Mediating Effect of Perceived Value
The test of perceived value as a mediator between immersive virtual try-on and purchase intention produced a 95% confidence interval of [0.067, 0.197], which did not include zero, confirming a significant mediating effect (effect size = 0.127). After controlling for perceived value, the direct effect of immersive virtual try-on on purchase intention remained significant, indicating that perceived value plays a partial mediating role in this relationship. Thus, H11 is supported.
Applying the same procedure, perceived value was also found to partially mediate the relationships between interactive product display and purchase intention (95% CI [0.087, 0.233], effect size = 0.159), real-time fusion shopping and purchase intention (95% CI [0.085, 0.237], effect size = 0.156), and personalization and purchase intention (95% CI [0.074, 0.211], effect size = 0.137). Accordingly, H12, H13, and H14 are supported.
- (2) Mediating Effect of Brand Attitude
Using the same method, brand attitude was tested as a mediator between each independent variable and purchase intention. The results indicate that brand attitude partially mediates the associations between immersive virtual try-on (95% CI [0.102, 0.251], effect size = 0.169), interactive product display (95% CI [0.125, 0.284], effect size = 0.194), real-time fusion shopping (95% CI [0.127, 0.283], effect size = 0.200), and personalization (95% CI [0.110, 0.266], effect size = 0.182) and purchase intention. These findings confirm H15, H16, H17, and H18. Detailed results are reported in Table 10.
The overall hypothesis testing results for both direct effects and mediation effects are shown in Table 11.
5. Influencing factors and mechanisms
5.1 Influencing factors
- (1) Effects of AR E-Commerce Features on Perceived Value and Brand Attitude
Compared with offline shopping, traditional online shopping offers limited cues for evaluating whether products meet consumers’ needs. Reliance on images, short videos, or live streams often leads to discrepancies between expectations and actual product experiences, thereby diminishing satisfaction. In contrast, AR-enabled shopping provides immersive virtual try-on and product trial experiences, allowing consumers to directly visualize how products look and perform in real-life contexts.. These immersive features substantially increase perceived value and foster more favorable brand attitudes.
Interactive product display is another critical mechanism. Unlike static two-dimensional presentations, AR’s advanced rendering and visualization capabilities create realistic 3D product models that vividly present appearance, texture, and internal structure. Consumers can freely rotate, zoom in on, and manipulate these models to examine details from multiple perspectives. This enhanced interactivity deepens product understanding, improves decision-making confidence, and strengthens consumers’ perceived connection with the product, thereby enhancing both perceived value and brand attitudes.
Furthermore, AR empowers real-time fusion shopping, enabling consumers to place virtual products such as furniture, appliances, or decorative items into their physical environments. This integration allows them to evaluate compatibility in terms of size, style, and color in real time, significantly enhancing experiential value. Personalized AR functions—such as customizing product colors, materials, or configurations—further enrich the experience. Tailored recommendations and product-matching services aligned with consumer preferences increase satisfaction and loyalty, thereby reinforcing positive brand attitudes [29,31].
- (2) Mediating Roles of Perceived Value and Brand Attitude in Purchase Intention
In AR e-commerce, perceived value and brand attitude serve as key mediating mechanisms linking AR features to purchase intention. When consumers perceive high value—through enriched product information, immersive experiences, personalization, and reliable service—their expectations are more fully met, which strengthens willingness to purchase [38].
Brand attitude also functions as a pivotal antecedent of purchase intention. Stronger recognition, trust, and emotional attachment toward a brand translate into greater willingness to engage in repeated transactions. When AR-enabled shopping is perceived as engaging, informative, and satisfying, consumers’ perceived value and brand attitudes are elevated. These enhanced psychological states, in turn, mediate the relationship between AR features and purchase intention, confirming that both constructs are critical drivers of consumer decision-making [39].
- (3) Direct Effects of AR Features on Purchase Intention
Beyond mediating pathways, AR e-commerce features directly influence consumer purchase intentions. As online shoppers increasingly prioritize experiential quality, AR creates a new “human–product–environment” interaction scenario that delivers richer product information and offline-like experiences. The more immersive the shopping experience, the stronger consumers’ visual and tactile engagement, which stimulates purchase interest and motivates buying behavior.
Greater interactivity fosters a stronger sense of involvement and recognition, reinforcing purchase intentions. Real-time fusion shopping provides vivid, context-specific product visualizations that increase the accuracy of product information, thereby enhancing trust, emotional reliance, and brand loyalty. Similarly, personalized recommendations that align with consumer preferences intensify brand affinity and strengthen purchase intentions. Collectively, these mechanisms confirm AR’s role as a transformative driver of consumer behavior in digital commerce [12].
5.2 Mechanism of action
- (1) The Impact of AR E-Commerce Features on Perceived Value and Brand Attitude
Compared to offline shopping, traditional online shopping do not allow consumers to fully assess whether a product meets their needs. Consumers often rely on images, short videos, or live streams to infer a product’s functionality. However, discrepancies between expectations and reality upon receiving the product can negatively impact the shopping experience [31].
In contrast, AR e-commerce provides virtual try-on and product trial experiences, allowing consumers to experience how the product looks and feels. This enhances perceived value and fosters a more favorable brand attitude [40].
Interactive product displays significantly influence consumers’ perceived value and brand attitude. Unlike traditional 2D images, AR technology offers advanced visualization capabilities. High-level rendering functions produce realistic visual effects, allowing 3D models to vividly showcase a product’s appearance, texture, and internal structure [7]. Consumers can freely rotate, zoom in, and zoom out on virtual products, examining details and features from every angle. This interactive process enhances consumer understanding of product characteristics and benefits, boosting confidence in purchase decisions.
The interactive product display feature of AR online shopping overcomes the limitations of traditional methods, fostering a dynamic connection between consumers and products. This enhances the overall shopping experience and perceived engagement.
AR technology integrates online and offline environments, creating a seamless shopping experience. It generates 3D effects for items like furniture, appliances, or decorative artworks, allowing consumers to visualize how the product would look in use on their screens. This capability accurately reproduces product dimensions and proportions, enabling consumers to freely place items in their homes and observe their compatibility with real-world environments. This interaction significantly boosts perceived value throughout the shopping experience.
Integrating online and offline services also strengthens brand recognition and affinity, leading to a more positive brand attitude. AR e-commerce enables highly personalized experiences, allowing consumers to design layouts, match products, adjust colors, and modify materials or shapes to meet their preferences [38]. Personalized recommendations, product pairings, and usage recommendations tailored to consumer needs enhance satisfaction and enrich the shopping experience. These factors foster greater brand loyalty and attachment, contributing to a more favorable attitude toward the brand.
- (2) The Role of Perceived Value and Brand Attitude in Consumer Purchase Intentions
In AR e-commerce shopping, when consumers perceive high value in a product or service, their needs and expectations are better satisfied, thereby strengthening their purchase intentions. Perceived value includes key factors such as shopping experience, product information, personalization, and service quality. The application of AR technology in online shopping enhances the shopping experience and increases consumers’ perceived value of products and services. When consumers exhibit strong recognition and affinity for a brand, they are more likely to trust and prefer that brand, further boosting their purchase intentions [41].
Perceived value and brand attitude serve as mediators between AR online shopping features and consumer purchase intentions. Consumers are more likely to recognize and accept a brand’s products and services when they find them interesting, satisfying, engaging, and informative, or when the provided information is perceived as accurate, reliable, and useful [29]. This recognition and acceptance, reflected in perceived value and brand attitude, ultimately influence their purchase intentions.
- (3) The Impact of AR E-Commerce Features on Consumer Purchase Intentions
Consumers are increasingly focused on the experiential aspects of online shopping and seek comprehensive, detailed product information when selecting products. AR technology in online shopping creates a new “human-product” interaction scenario. It allows consumers to experience a near-offline shopping environment and access detailed product information. The more immersive the shopping experience, the more it enhances consumers’ visual and tactile perceptions, sparking their interest and motivating them to make a purchase [2].
Greater product interactivity increases consumers’ involvement and perceived value during the online shopping process, leading to stronger purchase intentions. The real-time fusion shopping feature presents products in real-world settings with vivid visualizations. High-quality visualizations provide accurate product information, boosting consumer trust in the brand. This trust fosters emotional attachment and brand loyalty, ultimately enhancing purchase intentions.
Additionally, when a brand or retailer better meets consumers’ needs and preferences, the perceived quality of personalized service improves. This improvement leads to higher brand affinity and stronger purchase intentions [7].
5.3 Countermeasures and recommendations
- (1) Highlighting Key Performance Factors of AR E-Commerce
Although AR e-commerce is gaining visibility and acceptance, particularly among younger consumers, the rapid iteration of digital technologies shortens the lifecycle of applications and leaves consumers less time to adapt. To maintain competitiveness, businesses must keep pace with technological development, enhance consumer satisfaction, and mitigate uncertainties associated with technological change. The core appeal of AR shopping lies in its ability to provide experiences unavailable in traditional formats. Merchants should therefore prioritize optimizing performance factors that directly shape consumer perceptions, such as the completeness and accuracy of product information, as well as the usability and smoothness of AR systems. By improving these dimensions, firms can reinforce consumers’ perceived value and brand attitude—both of which are decisive drivers of purchase intention.
In immersive virtual try-on, firms should invest in advanced modeling and rendering technologies to ensure precise alignment between virtual products and consumers’ physical features, delivering a more realistic and immersive try-on experience. For interactive product displays, merchants should improve texture and material rendering to enhance detail and realism, while ensuring information accuracy and comprehensiveness. Interface design should also emphasize usability and convenience, accommodating consumer preferences and behaviors. Real-time feedback loops should be established to continuously refine user interaction and optimize satisfaction [21].
In real-time fusion shopping, greater investment in image recognition and tracking algorithms is necessary to enhance accuracy and responsiveness under complex conditions such as low light. Improved integration between virtual products and real environments can generate more authentic visualizations, strengthening consumers’ perceived value. Achieving precise alignment of “scenario–experience–consumption” fosters a closed-loop transaction system, enhancing satisfaction and building stronger brand goodwill.
- (2) Strengthening Content Quality in AR E-Commerce
Despite the value of technological innovation, product quality and utility remain the primary determinants of consumer trust. AR e-commerce firms must therefore not only enrich consumer experiences and provide detailed product information but also ensure that product and service quality consistently meets expectations. A positive impression of product quality and service reliability enhances both consumer satisfaction and platform credibility.
Equally important are timely and responsive pre-sales, in-sales, and after-sales services. Businesses should address consumer inquiries promptly and provide professional responses to negative reviews. Proactive reputation management not only demonstrates accountability to individual customers but also signals sincerity and reliability to broader audiences, thereby reinforcing trust and loyalty.
- (3) Enhancing User Sharing and Community Mechanisms
Although AR shopping primarily involves individual experiences such as virtual try-on and fusion shopping, social influence plays a critical role in adoption and diffusion. Firms should promote social synergy by integrating AR shopping platforms with social media channels (e.g., Xiaohongshu, TikTok/Douyin), encouraging users to share experiences and amplifying the reach and influence of AR shopping.
Intelligent algorithms can identify influential consumers or key opinion leaders (KOLs) to leverage network effects. Reward-based mechanisms may incentivize referrals and sharing behaviors, strengthening user acquisition through peer influence. Beyond individual sharing, firms should establish brand-centered virtual communities to foster interaction, emotional connection, and knowledge exchange among consumers [42]. Organizing exclusive brand activities within these communities enhances participation and nurtures belonging, which strengthens brand loyalty and long-term engagement.
- (4) Upgrading Privacy Protection in AR E-Commerce
AR e-commerce requires extensive consumer data, raising privacy and security concerns that may undermine trust. For example, facial data collected during virtual makeup trials could cause serious consumer distress if misused. The massive, heterogeneous, and easily accessible nature of digital data creates vulnerabilities, while regulatory frameworks and anonymization practices remain insufficient.
To address these challenges, AR e-commerce firms must adopt multi-dimensional strategies to strengthen privacy governance. This includes robust data protection systems, stricter monitoring against leakage and misuse, and safeguards ensuring lawful data use. By guaranteeing security and reliability, firms can provide personalized services without compromising user privacy [15]. Such measures alleviate consumer concerns, increase trust, and enhance willingness to engage in AR-enabled shopping.
6. Conclusions and implications
6.1 Conclusions
Based on 482 valid questionnaire responses, this study develops and tests an AR-enabled e-commerce purchase intention model grounded in the stimulus–organism–response (S–O–R) framework. The findings yield three key conclusions. First, the four technological attributes of AR e-commerce—immersive virtual try-on, interactive product display, real-time fusion shopping, and personalized recommendation—exert significant and positive effects on both perceived value and brand attitude. Second, perceived value and brand attitude play partial mediating roles in the relationships between AR technological attributes and consumers’ purchase intention, indicating that technological features shape purchase intention partly through consumers’ psychological evaluations. Third, the effect of real-time fusion shopping on perceived value is the strongest among the four AR attributes, whereas personalized recommendation demonstrates the most salient impact on brand attitude. Moreover, brand attitude exhibits a stronger direct influence on purchase intention than perceived value, highlighting the pivotal role of attitudinal responses in AR-based purchase decision-making.
6.2 Theoretical and practical implications
6.2.1 Theoretical implications.
This study offers several theoretical implications. First, it extends the applicability of the S–O–R framework to virtual–real integrated consumption settings, thereby contributing to a more context-sensitive understanding of consumer behavior in the digital economy. Second, by clarifying the psychological pathways through which AR technological attributes translate into purchase intention, this research enriches the technology marketing literature and provides a more mechanism-based explanation of consumer responses to AR-enabled shopping experiences. Third, the multidimensional operationalization of AR e-commerce attributes offers a standardized set of “stimulus” dimensions for future empirical research, facilitating more systematic and comparable investigations into AR-based consumption scenarios.
6.2.2 Practical implications.
The findings also provide actionable implications for practitioners.
Implications for e-commerce platforms. Platforms should prioritize technological investment to enhance the contextual adaptability of real-time fusion shopping, particularly by improving the accuracy of image recognition and tracking systems to ensure a seamless alignment between virtual products and real-world environments. In addition, platforms are encouraged to refine personalized recommendation algorithms by leveraging consumers’ browsing patterns, purchase histories, and preference signals to improve recommendation relevance and effectiveness. Finally, user interface design should be optimized to simplify AR interaction processes and reduce consumers’ perceived effort and technical barriers, thereby improving the overall usability of AR shopping functions [16].
Implications for brands. Brands should focus on strengthening immersive virtual try-on and interactive product display functionalities by adopting high-fidelity 3D modeling and visualization techniques that improve the consistency between virtual experiences and offline product realities. Furthermore, AR-based experiences can be strategically used to convey brand values and cultural meanings, enabling consumers to develop stronger brand identification through personalized engagement. Brands may also establish an integrated service mechanism linking AR experience and after-sales support, allowing timely responses to consumer concerns during the AR experience and ultimately reinforcing brand reputation.
Implications for the industry. At the industry level, stakeholders should consider establishing technical standards and privacy protection guidelines for AR e-commerce, clarifying the boundaries of data collection, storage, and usage to safeguard consumer privacy. Moreover, integrating AR-enabled shopping with social media platforms can encourage consumers to share AR experiences, thereby amplifying social diffusion effects and enhancing the market influence of AR e-commerce. Finally, cross-industry collaborations should be promoted to expand AR applications into additional product categories (e.g., fresh food and automobiles), which would broaden the boundaries and scalability of AR-enabled commerce.
6.3 Theoretical contributions and limitations
6.3.1 Theoretical contributions.
The present study contributes to the literature in three primary ways. First, it systematically conceptualizes and operationalizes four core technological attributes of AR-enabled e-commerce—immersive virtual try-on, interactive product display, real-time fusion shopping, and personalized recommendation—as “stimulus” dimensions within the S–O–R framework. By doing so, it addresses the theoretical adaptation gap of the S–O–R paradigm in virtual–real integrated consumption contexts and expands its boundary conditions. Second, the present study proposes an integrated analytical framework linking technological attributes, psychological perceptions, and purchase intention, and empirically validates the dual mediating roles of perceived value and brand attitude. This contributes to a more refined understanding of the “technology–scenario–behavior” transmission mechanism in AR e-commerce and offers a theoretically grounded perspective for future research on digital consumption contexts. Third, by comparing the differentiated effects of multiple AR technological attributes, the study identifies the particularly influential roles of real-time fusion shopping and personalized recommendation, thereby addressing the limited evidence in prior studies regarding the synergistic and heterogeneous impacts of multidimensional AR features.
6.3.2 Limitations and future research directions.
Despite its contributions, the present study has several limitations. First, the sample was collected primarily through online channels, which may introduce sampling bias in terms of demographic distribution (e.g., age structure) and consumption patterns. Future research may combine online and offline data collection approaches to enhance sample representativeness and external validity. Second, the present study does not incorporate potential moderating factors related to consumer heterogeneity, such as technology readiness, risk preference, or privacy concerns. Future studies are encouraged to include moderating variables to further enrich the explanatory power and boundary conditions of the proposed model.
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