Virtual fitting room technology: unleashing the transformative potential for fashion brands’ retail operational performance

The apparel industry, a critical economic sector, is marked by fierce competition among fashion brands, primarily driven by rapid technological advancements (Akhtar et al., 2022). Virtual fitting room, a type of XR technology in Retail 4.0 (Har et al., 2022; Nantel, 2004), has emerged as a solution that bridges the gap between online and offline shopping experiences. It offers new possibilities and revolutionizes how consumers interact with fashion brands (Yang et al., 2023). By employing multi-sensor body scanners, advanced algorithms, and social media technologies, VFR enables intuitive and “natural interaction” (Pachoulakis and Kapetanakis, 2012), moving away from the traditional desktop paradigm. The COVID-19 pandemic has compounded the challenges faced by offline fashion retail, restricting consumers from trying on clothing in physical stores and interacting directly with retailers online (Rhee and Lee, 2021). In this context, VFR technology has reemerged as a powerful tool that can accelerate the digital transformation of the apparel industry and enhance the overall shopping experience (Porterfield and Lamar, 2017; Wu et al., 2022). In traditional online shopping, consumers are often limited in evaluating clothing based solely on model displays, lacking the ability to visualize how the clothing will look on themselves (Blázquez, 2014; Yang and Xiong, 2019). The advent of VFR in online channels has revolutionized this experience, allowing consumers to virtually try on clothing and envision how it will look on their bodies (Nantel, 2004). This innovation has resulted in more accurate evaluations of clothing suitability (Lee et al., 2022). For instance, British fashion brand ASOS recently introduced the “See My Fit” service, leveraging augmented reality (AR) models of various body types to assist customers in checking the fit of clothes, particularly during the COVID-19 pandemic (Rhee and Lee, 2021). VFR technology offers consumers a more personalized, convenient, and effective shopping experience than traditional offline shopping. It eliminates the need for physical fitting rooms in offline channels, enabling consumers to bypass the limitations of space and time (Javornik, 2016).

However, despite the potential and advantages of VFR, the adoption of Virtual fitting room (VFR) technology is still relatively low; many retailers have been hesitant to fully embrace VFR technology due to concerns about its accuracy and functionality, which can result from consumer dissatisfaction with the fitting results (Gao et al., 2014; Kim and Labat, 2013).skeptics have raised questions about their effectiveness for online shopping (Kang et al., 2020). We speculate that the contribution of VFR technology application to retail operation performance potential may not be as aspiration as expected. Consequently, our research aims to address three important questions (RQs):

Previous studies have primarily examined how technology affects online purchases (Mclean and Wilson, 2019; Park and Yoo, 2020; Plotkina and Saurel, 2019; Yim et al., 2017) of older adults (Hwang et al., 2024) or Southeast Asian (Yang and Kim, 2024) consumers, our research takes a comprehensive approach by exploring the role of VFR technology in both online and offline purchases, within the context of omnichannel integration. We also consider post-purchase metrics (Yang and Xiong, 2019), including WOM (Yoo and Kim, 2014) and return intention (Yang and Xiong, 2019), which can significantly impact fashion brand retail operational performance (Beck and Crié, 2018). Returning purchased items affects a company’s revenue and profit flow and imposes a significant financial burden on them (Lee and Hwan, 2015). Nevertheless, VFR technology can help to reduce returns by ensuring an expected fit (Beck and Crié, 2018). Prior studies (Yoo and Kim, 2014) have primarily focused on the functional mechanisms of virtual technology, such as interactivity, novelty, and vividness (Mclean and Wilson, 2019; Park and Yoo, 2020; Yim et al., 2017), in contrast to previous research on technological boundary conditions, this study investigates the technology capabilities of VFR which affect consumers and examines the moderating impact of consumers’ perceived body image.

VFR technology offers a remarkable virtual product trial experience through simulated virtual models that mirror the consumers’ body measurements (Blázquez, 2014). VFR is an application of AR technology that combines the real physical environment with the virtual effects of the product to understand better the product (Xue et al., 2024). This is one form of image interactivity technology (IIT) that uses 3D virtual simulation techniques to offer rotational properties, mix-and-match functionality, and 3D product display (Lee et al., 2020) and is now called try-on technology (Batool and Mou, 2023). This technology enables shoppers to create customized virtual models that reflect their unique features, including facial characteristics, hair color, and body shape (Merle et al., 2012; Rese et al., 2017). Few parts of VFR make virtual fitting more realistic, and tracking the body’s movement is only one of them (Boonbrahm et al., 2015). By doing so, they can view products in various colors on a virtual model that closely resembles their actual appearance (Kim and Forsythe, 2010). Depending on the accuracy of the inputted data and the simulation technology, VFR can potentially provide online shopping consumers with a fitting experience that is very similar to that of conventional in-store fitting (Blázquez, 2014). This technique can also help customers shop online to select the correct size or type of garment (Kaewrat and Boonbrahm, 2017). Seven distinct VFR technologies differ based on how they measure body data (body scanning, photos, manual input), as well as their visualization and interactive capabilities (Lee and Xu, 2020; Merle et al., 2012). These technologies include full 3D body scanners, 3D avatars, 3D customer models, photo-accurate 3D customer models, robotic mannequins, and fitting rooms in augmented reality (AR) and virtual reality (VR) (Lee and Xu, 2019).

The available research on the effects of VFR technology on consumer attitudes and behaviors, both at home and abroad, is generally divided into two categories. The first category predominantly delves into the value of the technology and assesses consumer attitudes towards it. Past studies have focused on the functional aspects of VFR, including image quality, ease of use, and usefulness while overlooking their potential to provide emotional experiences (Cho and Schwarz, 2012; Huang and Qin, 2011). However, some researchers have recognized the technology’s capacity to serve as an experiential tool by providing entertaining and hedonic shopping experiences that immerse consumers in the technology (Merle et al., 2012).

Interactivity and vividness are important factors in technology adoption (Yim et al., 2017). Previous studies have examined the impact of media usefulness and enjoyment on consumers’ attitudes toward technology (Hwang et al., 2024; Yim et al., 2017). Another line of research focuses on the functional mechanisms of technology and their influence on consumers’ purchasing behavior. For example, the effects of AR technology on consumers’ intentions to use a brand, considering interactivity, vividness, and novelty (Mclean and Wilson, 2019). However, limited attention has been given to how technology affects consumers’ post-purchase behavior, such as word-of-mouth communication and return rates (Park and Yoo, 2020). Additionally, researchers have examined how offline virtual fitting rooms impact fitting-room evaluations and the overall shopping experience (Seo and Fiore, 2016). The relationship between consumers’ perceived media characteristics, telepresence, attitudes, and adoption intention towards augmented reality (AR)-based VFR has been investigated (Lee et al., 2021).

Although forefathers confirmed that technology capabilities are one of the important facilitators of task performance (Serrano and Karahanna, 2016), there is little research to study the impact of VFR technology capabilities. The technology capabilities of VFR can be categorized into three aspects: visual vividness, interactive control, and personalized provision (Jiang and Benbasat, 2004; Suh and Lee, 2005). Visual vividness encompasses the accuracy and quality of the simulation effects produced by the technology. Accuracy refers to the precision of the translated figure compared to the actual consumer figure and the representation of the visualized product on the model (Gao et al., 2014; Kim and Labat, 2013). Studies have shown that VFR’s visual accuracy can significantly help consumers evaluate the fitness and size of products, even when shopping online (Gao et al., 2014). The vividness of the technology is linked to the quality of product presentation and is considered clear, detailed, sharp, and well-defined (Flavian et al., 2017; Griffith et al., 2002; Yim et al., 2017). Machine learning empowers VFR technology with interactive features, such as clothing recommendations, mix-and-match options, and social sharing, to improve the user experience (Lee and Xu, 2019). Personalized provision is a feature of VFR technology that enables users to tailor content to their interests and preferences, resulting in a unique and personalized experience (Javornik, 2016). Users can modify body features, freely combine items, and receive personalized recommendations. Personalized VFR leads to a more positive consumer perception than non-personalized VFR, as the virtual model provides greater self-consistency (Merle et al., 2012).

Researchers often evaluate organizational performance by considering efficiency and effectiveness (Dhoopar et al., 2023). To achieve a sustainable competitive advantage, organizations must balance doing things right and doing the right things (Ho and Huang, 2020). Organizational results drive competitive priorities enhance firms’ profit by increasing consumption. Organizational effectiveness is represented most appropriately by a set of performance constructs encompassing corporate social, operational, and organizational performance (Åkestam et al., 2021). More specifically, OP is a dimension of organizational effectiveness. In comparison, previous studies established the multidimensionality of (Hamann and Schiemann, 2021). Operational performance is defined as the accomplishment of non-economic goals within the value chain activities of an organization, such as marketing effectiveness, customer or employee satisfaction, product quality, and value-added in manufacturing operational performance (Combs et al., 2005; Venkatraman and Ramanujam, 1986). In the context of retail operational performance, demand estimation is fundamental for retail store operations (Mou et al., 2018), which uses intention to patronize (online and offline) and intention to purchase (online and offline).

However, many experts believe that virtual fitting rooms (VFR) technology can enhance the shopping experience, leading to more purchases (Gao et al., 2014; Kim and Labat, 2013). By providing customers with interactive and personalized options, VFR technology satisfies their curiosity and turns them into active participants (Cho and Schwarz, 2012), ultimately resulting in a more satisfying and informed purchase decision (Bellezza et al., 2014). Virtual fitting rooms provide consumers with convenience, personalization, and an enhanced shopping experience. They also allow fashion brands to leverage data from virtual fitting rooms to gain insights into customer preferences, fit issues, and which sizes sell out fastest. The visual appeal of fitting technology has a significant impact on positive WOM. Compared to traditional purchasing methods, VFR technology is more engaging and practical, meeting consumers’ personalized needs, such as modifying body features, free collocation, and personalized recommendations (Goebert and Greenhalgh, 2019). Based on these observations, we hypothesize that VFR technology positively impacts consumer behavior.

The physical attributes of clothing, such as its fit and matching, are crucial factors that determine customers’ satisfaction and return rates. For online shoppers, the inability to try on clothes in person can be a significant pain point. Fortunately, VFR technology offers a solution by allowing customers to create avatars that accurately reflect their body shapes and try on clothes virtually. This technology creates a more lifelike and immersive shopping experience that closely mimics the in-person experience (Merle et al., 2012). For fashion companies, VFR technology can reduce returns and increase sales by ensuring customers receive clothing that fits as expected and generates interest in products (Huang and Qin, 2011).

A technology attitude is an individual’s accumulated negative or positive perception of a specific technology. In the Technology Acceptance Model (TAM), the importance of technology acceptance as a precursor to the use of technology has attracted much attention from researchers and practitioners (Davis, 1989; Kim et al., 2009; Oyman et al., 2022). Cognitive attitude is an important variable in explaining IS usage behaviors (Petty and Briñol, 2015; Yang and Yoo, 2004). Consumer cognition and emotion are crucial in shaping attitudes toward Virtual Reality (VR) technology. The analysis of technology attitude reveals that media function attributes significantly influence technology attitude, which, in turn, impacts purchase intention (Plotkina and Saurel, 2019). High levels of interactivity and vividness in media characteristics, without relying solely on novelty effects, contribute to the successful persuasion of consumers using media as an information source (Yim et al., 2017). Consequently, scholars generally agree that the media characteristics of VR technology, such as vividness and interactivity, positively affect technology attitude, thereby influencing consumers’ purchase behavior (Lee et al., 2021).

Research shows that when individuals experience the attitude involved, the relationship between attitude and behavior will be closer (Pantano et al., 2017). Some scholars have proved that online consumers’ attitudes toward shopping technology positively impact purchase intention (Kim and Forsythe, 2010). Enhancing technology affects consumers’ self-awareness and how consumers view themselves. Consumers gain various benefits from VFR, such as a better understanding of themselves and the possibility of risk-taking and discovering their inner selves when trying products they would not usually try (Lee et al., 2022). An attitude affects an individual’s behaviors by filtering information and shaping the individual’s perception of the world (Kim et al., 2009). In contrast, strength in the attitude amplifies or neutralizes the effect of the attitude on behaviors (Krosnick and Petty, 1995). Thus,

From a behavioral perspective, perceived value is defined as an “interactive relativistic preference experience” (Boksberger and Melsen, 2011). Perceived value implies an interaction between a consumer and a product (Sánchez-Fernández and Iniesta-Bonillo, 2007) as one of the most important measures for gaining a competitive edge (Morar, 2013). As for the dimension of perceived value, many scholars generally accept its exploration from two aspects of physics and psychology: utilitarian and hedonic (Yoo et al., 2010). Studies have shown that both hedonic value and utilitarian value are very important (Hilken et al., 2017; Merle et al., 2012). Still, some scholars have pointed out that one aspect may be more important than the other (Kim and Forsythe, 2010), with different importance (Rese et al., 2017). Hedonic value in the context of VR technology refers to the entertainment benefits experienced by users (Rintamäki et al., 2006; Yang and Han, 2021). It relates to consumers’ pleasure and stimulation during their shopping experience (Park and Yoo, 2020). Previous research suggests that consumers can enjoy hedonic benefits through increased curiosity and entertainment, deriving fun and pleasure from using technology (Beck and Crié, 2018; Kim and Forsythe, 2010). Respondents generally considered VFR technology more entertaining than purely functional in their focus group interviews (Kim and Forsythe, 2010). Additionally, communication and interaction with other consumers through VFR technology can fulfill social and emotional needs, further enhancing the enjoyment of the overall experience (Javornik, 2016).In terms of interactivity, the ability to interact with complex systems allows consumers to make more informed and comprehensive evaluations by accessing specific, targeted, and relevant information (Kim and Labat, 2013; Lee and Xu, 2019). Utilitarian value refers to the functional benefits and associated costs and income perceived by users (Rintamäki et al., 2006). VFR technology enhances consumers’ understanding of products, provides the pleasure of trying them on virtually, and saves time by eliminating the need for transportation and physical shopping (Baek et al., 2016; Pantano and Servidio, 2012). When studying the role of VFR in clothing mobile retail, the hedonic and utilitarian values of VFR technology positively impact shopping technology attitudes (Plotkina and Saurel, 2019).

Perceived body image has an important impact on consumer behavior, a literature synthesis establishes that the concepts of body image and clothing consumption are highly interrelated (Shetty and Kotian, 2023). Consumers’ body image satisfaction and the factors driving them to purchase apparel (Chattaraman et al., 2013). Body image had a negative linear relationship with aesthetic preference in styling, implying that lower body image and body cathexis correlate with a preference for greater body coverage through clothing and vice versa (Chattaraman and Rudd, 2006). Consumers’ perceived body image significantly affects consumers’ evaluation (Yim and Park, 2019). Female body ideals are strongly associated with thinness, toned, and moderately muscled (Betz et al., 2019). Male ideals are somewhat associated with leanness but more clearly with muscularity (Cash and Brown, 1989; Murnen, 2011; Tiggemann and Anderberg, 2020). Correlational studies consistently show that social media usage (particularly Facebook) is associated with body image concerns among young women and men (Fardouly and Vartanian, 2016; Vandenbosch et al., 2022). The stronger the men’s psychological drive to have a lean body and the more they focused on their appearance and invested in their looks, the more likely they were to view apparel as important to them (Strubel and Petrie, 2018).

High body-involving products are operationally defined as product categories that consumers highly rely on for body-related information in the purchase process (Rosa et al., 2006). Fashion clothing, shoes, and wearable items all belong to this product category. Therefore, we have reason to believe that consumers’ cognition of their bodies will affect their ability to process information about tall products (Yim and Park, 2019). Consumers with poor body image often have concerns about fitting in public places, making online fitting technology a suitable solution for their shopping needs. In contrast, consumers with confident body image may not be as interested in fitting technology. Therefore, we expect consumers’ perception of their bodies will significantly interact with technical factors to affect their response to VFR (Baek et al., 2016; Yim and Park, 2019). Compared with using standard models to present an ideal image, some believe that the technology may reduce customers’ expectations for product appearance and potentially reduce the impulse to purchase by truly showing the self-image of customers wearing products (Yang and Xiong, 2019). Therefore, we assume:

This study analyzes the influence of VFR technology on the retail operational performance of fashion brands, and the overarching conceptual framework is visually represented in Figure 1. As a result of the literature review, this study investigates the influence of technology on purchase intention (both online and offline), word-of-mouth communication, and return intention from three perspectives: visual vividness, interactive control, and personalized provision. We also examine the mediating role of consumer-perceived value in these relationships and explore the role of consumers’ perceived body image as a moderator.

All measures for dependent variables in the current study were modified from previous research studies of related topics and included five measurements: technical characteristics of VFR, modified from Yim et al. (2017), Mclean and Wilson (2019), and Park and Yoo (2020), consumer perceived value from Kosiba et al. (2018), consumer technology attitude, modified from Plotkina and Saurel (2019) and Yim et al. (2017), retail operational performance from Jena and Sarmah (2015) and Lee and Hwan (2015), perceived body image from Gleeson and Frith (2006) and Giovannelli et al. (2008) . The original scales were translated into Chinese, and a bilingual researcher conducted back-translation to ensure appropriateness. Subsequently, the questionnaire underwent pretesting with 79 respondents from various companies. Final adjustments to the questionnaire were made based on the pre-test results, as detailed in the  Appendix Table A1. Each item was measured on a seven-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”).

We arranged mainly the situational recall to scenario stimuli, including consumption habits of buying clothes at ordinary times and whether to use VFR Technology. In this way, we guarantee that the interviewees have real experience in using VFR technology.

Data was collected from consumers with online shopping experiences via an online survey. The survey focused on these consumers, as they are more likely to encounter problems such as being unable to try on the product, product fitness, and matching when purchasing clothing products online. Based on whether respondents used VFR technology in their online consumption history or not, they are divided into two groups, namely, the traditional shopping group and the shopping group with VFR experience.

A total of 845 subjects participated in the survey. Still, only 783 responses were retained for data analysis after 62 invalid surveys featuring abnormally consistent answers, extremely short answer times, or obvious contradictions in logical relationships were excluded. The survey finally turned out to have 327 valid questionnaires from the traditional shopping group and 394 valid questions from the shopping group with VFR experience. The respondents’ demographic profiles of the survey samples are shown in Table 1. Since previous studies have investigated the acceptance of VFR technology by older adults (Hwang et al., 2024; Yang and Xiong, 2019), we have focused on the attitude of young people toward VFR. Therefore, 76% of the samples are under 40 years old.

To test whether there is a common method deviation in this study, the Harman single-factor test method is used to test the homologous variance of the data. The variance interpretation rate of the first factor is 32.63%, less than 40%. This indicates no serious standard method deviation in this study, and the following data analysis can be carried out. Table 2 shows that the correlations among the eleven latent variables were less than 0.90, indicating a non-existent multi-collinearity problem.

The software of SPSS and Amos tested the reliability and validity of the measurement model. The standard load of each factor, Cronbach’s alpha value, combined reliability (CR), and mean-variance extraction (AVE) are shown in Table 3. It can be seen that the Cronbach’s alpha value and CR of the scale exceeded 0.7, and the MML exceeded 0.5. The results indicated that the measurement model was internally consistent, implying convergent validity.

Table 4 shows significant differences in “WOM,” “return intention,” and “offline purchase intention” in the factors of “whether VFR technology has been used or not.” However, there are no significant differences in online purchase intention. In addition, according to the mean value, the return intention was lower than that of the unused. Compared with traditional purchases, VFR technology had a positive impact on offline purchase intention and WOM but a negative impact on return intention. H1 was partly supported.

We use AMOS to analyze and verify our H1a, H1b, and H1c, and the results are shown in Table 5. For purchase Intention (PI) first. Three dimensions, Visual vividness (VV), Personalized provision (PP), and Interactive control (IC), have no significant influence on online purchase Intention. Visual vividness (VV) and Interactive control (IC) have a positive influence on offline purchase Intention (PI). Personalized provision (PP) does not significantly influence purchase intention (PI). H1a was partly supported. Secondly, for WOM. Visual vividness (VV) does not have a significant impact on word-of-mouth. Personalized Provision (PP) and Interactive Control (IC) have a positive influence on Word-of-Mouth (WOM). H1b was partly supported. Finally, for Return Intention (RI). Three dimensions, Visual vividness (VV), Personalized provision (PP), and Interactive control (IC), have a negative influence on Return Intention (RI). H1c was supported.

To determine whether these alternative mediators were still significant when modeled as parallel mediators along with our proposed serial mediators, we ran a structural equation model (SEM) analysis. The results of the whole model show that the direct effects of visual vividness (vv), personalized provision (PP), and Interactive Control (IC) directly affect the path, which is not significant. Utilitarian and hedonic values are also partially different from the path of the result variable. This means that the direct impacts of visual vividness (VV) and personalized provision (PP) on the outcome variables are replaced by intermediary variables. This suggests that the influence of Visual Vividness and Personalized Provision on Purchase Intention (PI), Word-of-Mouth (WOM), and Return Intention (RI) is mediated by Perceived Value and Technology Attitude. The direct effect of Interactive Control (IC) on Word-of-Mouth (WOM) and Return Intention (RI) remains significantly positive. This implies that Interactive Control has a direct positive impact on WOM and RI. Furthermore, we removed these insignificant paths and rerun AMOS for analysis, the fit of this model was significant (see Figure 2, CMIN/DF = 1.529; CFI = 0.88, TLI = 0.95, RMSEA = 0.04, CI90 = [0.035, 0.045]).

Results in Figure 2 showed that Technology Attitude mediates the relationship between the three independent variables (VV = visual vividness, PP = personalized provision) and the three outcome variables (PI = Purchase intention, WOM = Word-of-Mouth, RI = Return intention), but not between IC. H2 was partly supported. The two variables of Perceived Value (Utilitarian Value and Hedonic Value) and Technology Attitude partially mediate the relationship between the independent variables (VV, PP, IC) and the outcome variables (PI, WOM, RI). This means that these intermediary variables play a significant role in explaining the relationship between the independent and outcome variables, supporting H3a and H3b. However, for the outcome variables (PI, WOM, RI), the influence of Perceived Value (Utilitarian Value) is insignificant, leading to the rejection of H4a. The effect of Hedonic Value on RI is significant, and H4b is supported.

The moderating effect was examined using a model8 in the SPSS macro by Hayes (2018) and calculated by the process program. We tested the moderating effect of perceived body image by controlling the relevant statistical variables (Table 6). The significance test significance test results showed that the interaction between perceived body image and technology availability could significantly affect the perceived value (B = −0.17, t = −6.74, p < 0.001), and the moderating effect was negative. H5 (a) was supported. Similarly, the results showed that the interaction between perceived body image and technology availability could significantly affect the attitude towards technology (B = −0.14, t = −3.91, p < 0.001), and there was a negative moderating effect. H5 (b) was supported. Figure 3 shows that perceived value and technology attitude were significantly lower in the high-body-image condition than in the low-body-image condition.

This paper aimed to study VFR technology’s impact on the retail operational performance of fashion brands and the value realization mechanism.

Unlike the previous optimistic conclusion about VFR technology, our comprehensive evaluation of the potential transformation of VFR technology on retail performance is more objective and pertinent and can give constructive suggestions. By comparing with consumers who don’t use VFR technology, it is found that VFR technology promotes offline purchases instead of online. The explanation for this exciting discovery might be concluded with two aspects. The explanation for this exciting discovery might be concluded with two aspects. Firstly, technology-enabled consumers can search, browse, and study the suitability of a garment on the Internet and then purchase it in physical stores efficiently. This was in line with the anti-exhibition hall behavior of retail. Secondly, the technology may also reduce consumers’ impulsive online purchases, making purchasing more rational. Another reason might be the lack of marketing efforts to educate consumers about the functional benefits of VFR and the enjoyment they can provide consumers (Lee et al., 2020). The transformative potential of the VFR technology, based on the three technical attributes of visual vividness, interactive control, and personalized provision, positively impacts the retail operational performance of fashion brands, promoting the offline purchase intention, WOM, and reducing the return rate. This proves that VFR technology can create more value for fashion brand retailers in the background of a high return rate.

In addition, this study proved that perceived value mediated VFR technology’s effect on technology attitudes and determined the relative importance of hedonic and utilitarian value. According to the mediating effect analysis, both perceived hedonic and utilitarian values had partial mediating effects. This study also proved that consumers’ perceived body image moderated technology availability’s impact on perceived value and technology attitudes. Compared with consumers with high perceived body image, those with low perceived body image had greater interest and willingness to experience VFR technology. The reason was that the technology realized free fitting in a private place, weakening consumers’ concerns with low perceived body image and meeting the practical needs of consumers. Meanwhile, consumers with high perceived body image weren’t concerned about fitting, often showed great confidence, and enjoyed the feeling that all kinds of clothes were worn on themselves for real.

Theoretically, based on the perspective of Omni channel integration, this study explores the impact of VFR technology on online and offline purchases, enriches the relevant research on the impact of VFR technology on post-purchase indicators, and defines the theoretical basis for the impact of VFR on the retail operational performance of fashion brands. Secondly, based on consumer psychology, this study confirmed that consumers with low body image have a higher perception and better technology attitudes towards VFR technology, which positively impacts the retail operational performance of fashion brands. This research result provides a good boundary condition for the practical process of the technology. Furthermore, this research contributes to analyzing the technology’s hedonic and utilitarian benefits and the debate on their relative importance (Rese et al., 2017). Thirdly, based on the background of COVID-19, this study examined the retail operational performance of VFR technology in the post-epidemic era, enriching the research background’s diversity.

The study emphasized to managers that VFR technology can affect consumers’ perceived value, technical attitude, and consumption behavior. Software developers and managers must recognize the overall importance of VFR attributes. The first is visual vividness, which provides consumers with an objective and vivid visual perception of products; the second is interactive control, which meets the needs of human-computer interaction and human-computer interaction of consumers; the third is a personalized provision, which provides personalized solutions for consumers. Software developers should constantly optimize these three attributes to provide consumers with a better experience. Artificial intelligence can also support and improve the augmented reality experience for customers in the fashion retail industry by implementing an AI-powered virtual assistant. Furthermore, this study found that consumers with low body image have a higher perceived value and evaluation of this technology, so managers should lock in the target population and improve precision marketing. Retail brands should focus on channel integration strengthening and the seamless consumer experience to promote customer retention.

This study also has some limitations that need to be improved in future research. Firstly, this paper only studies the impact mechanism of VFR technology on retail operational performance from the perspective of technology and consumer characteristics. In the future, product type and brand awareness could be introduced as moderating variables to conduct joint research from multiple perspectives, such as consumers, technology, products, and brands, to explain the relationship of impact more scientifically and reasonably. Secondly, the respondents participating in this survey mainly come from mobile users, and most of them are young. The reliability of filling in the answers is imperfect. The respondents and research methods can be further improved in the future. In the future, researchers can continue to explore the moderating effect of other dimensions of consumer characteristics, such as consumer gender characteristics and consumer psychological characteristics.

This work was supported by the Fundamental Research Funds for the Central Universities (#2232018H-09).