Unveiling the Factors Influencing U.S. Consumer Adoption of the Apparel Digital Retail Theater

Abstract

In recent years, the fashion industry has undergone a significant transformation driven by digital innovations, particularly with the emergence of the digital retail theater (DRT). A DRT integrates augmented reality (AR), virtual reality (VR), and 3D modeling to create immersive shopping experiences that bridge the physical and digital worlds. This study specifically focuses on apparel DRTs and investigates the key factors influencing U.S. consumers’ intention to adopt this technology. Drawing on the unified theory of acceptance and use of technology (UTAUT) and perceived risk theory, we developed and tested an integrative research model. Primary data were collected through a structured online survey administered via Amazon Mechanical Turk (MTurk). A total of 400 valid responses were obtained from U.S. consumers. Data were analyzed using multiple regression analysis to examine the hypothesized relationships. The results indicate that effort expectancy (ease of use), facilitating conditions (technical infrastructure), physical risk (concerns about potential harm), and time/convenience loss risk significantly influence consumers’ intention to adopt apparel DRTs. Surprisingly, performance expectancy and social influence were not significant predictors of DRT adoption. These findings provide valuable insights for apparel retailers, emphasizing the importance of user-friendly designs, robust technical infrastructure, and minimizing perceived risks to foster DRT adoption.

1. Introduction

In the last decade, the fashion industry has experienced a dramatic shift towards digital marketing, a trend that continues to gain momentum as technological innovations evolve and consumer expectations for enhanced e-shopping experiences rise [1,2]. This acceleration is fueled by the growing demand for immersive and personalized shopping experiences that seamlessly integrate the physical and digital worlds, catering to consumers in their various micro-moments [3,4].

One significant development in this digital transformation is the emergence of the digital retail theater (DRT) model, which has rapidly gained traction among consumers, particularly in the United States. DRT combines augmented reality (AR), virtual reality (VR), and 3D modeling to create a unique and engaging virtual shopping experience [5]. This model integrates the traditional elements of retail theater: entertainment, novelty, and engagement with advanced digital technologies, offering an unparalleled in-store experience that can be accessed remotely [6]. Looking ahead, the widespread adoption of DRT technology suggests that anyone with a mobile device will soon be able to engage in lifelike shopping experiences through their personalized 3D avatars on virtual platforms. This technological shift not only reduces the operational costs associated with physical stores but also addresses sustainability concerns by curbing the overproduction of clothing [7].

The fashion industry is increasingly adopting technology-driven retail solutions to overcome the limitations of traditional shopping experiences. Conventional retail often requires consumers to spend significant time and effort searching for, evaluating, and comparing products, which can lead to frustration and disengagement [2]. To address these challenges, retailers are integrating innovative technologies that create more efficient, personalized, and engaging shopping environments, aligning with the evolving expectations of modern consumers [8]. Notably, advanced data analytics and data mining techniques are enabling deeper insights into consumer preferences, allowing for more precise targeting and customized offerings [9]. Additionally, virtual reality (VR) technologies have emerged as powerful tools for simulating immersive shopping experiences. These VR environments not only engage consumers but also provide retailers with valuable data to predict and analyze consumer behaviors in real time, ultimately optimizing retail strategies and enhancing customer satisfaction [10]. This technological transformation has paved the way for digital retail theaters (DRT), an emerging concept that combines immersive technologies, data analytics, and interactive experiences to bridge the gap between physical and digital shopping realms.

The COVID-19 pandemic has further accelerated this shift toward online shopping. In the United States, e-commerce’s share of total retail sales increased from 11% in 2019 to 16% in 2023, with projections estimating it could reach 21% by 2027 [11]. In response to this rapid growth, retailers have been compelled to develop innovative strategies to capture and retain consumer attention in an increasingly competitive landscape. While some have relied on aggressive discounting, others have embraced technology to enhance the shopping experience, focusing on personalization, engagement, and convenience to differentiate themselves in the digital marketplace.

Recent studies indicate that 63% of consumers expect their shopping habits to continue evolving, highlighting a growing demand for more immersive and personalized retail experiences that leverage cutting-edge technologies [12]. Prior research has explored the integration of augmented reality (AR), virtual reality (VR), and data-driven insights in various retail contexts, demonstrating their potential to enhance customer satisfaction and engagement [13,14]. For example, Hilken et al. [14] examined how AR can augment online service experiences, while Lombart et al. [15] analyzed consumer perceptions in immersive virtual store environments. Despite these advances, existing studies often focus on individual technologies or isolated aspects of the digital shopping experience, such as virtual fitting rooms or AR product previews. There remains a limited understanding of how these technologies can be integrated holistically to create comprehensive, engaging retail experiences like DRT.

While DRTs can include online virtual tutorials within physical stores, this study focuses on the virtual shopping experience through personal devices and the use of customized avatars. The primary goal of this research was to gain a deeper understanding of the DRT and the factors driving U.S. consumers’ intention to use it. Specifically, the study aimed to: (1) identify and examine the key technological and psychological factors that influence U.S. consumers’ intention to adopt apparel DRTs, by integrating the unified theory of acceptance and use of technology (UTAUT) with perceived risk theory; (2) assess the relative impact of effort expectancy, facilitating conditions, and perceived risks (financial, psychological, physical, and time/convenience loss) on consumers’ behavioral intention to use DRT technologies; (3) address the research gap in understanding consumer concerns about immersive virtual shopping experiences by providing empirical evidence on how perceived risks affect consumer acceptance of DRTs in the U.S. apparel retail context; and (4) offer practical recommendations for fashion retailers and technology developers on how to enhance the user experience and mitigate perceived risks, thereby promoting broader adoption of DRT technologies.

2. Literature Review and Hypothesis Development

2.1. Retailing Trend in the U.S.

Traditionally, retail operations were clearly divided between online and offline channels, with each operating independently. However, the modern retail landscape is increasingly characterized by the integration of these channels, a trend that has proven to enhance customer value propositions and reduce shopping complexity [16]. There are two primary types of channel integration: offline-to-online and online-to-offline [17]. The former involves integrating online experiences into physical stores, such as using mobile devices to browse and purchase products in-store, exemplified by Nike’s use of QR codes to drive customers to their app for additional benefits. The latter brings elements of the physical shopping experience to the online environment, where consumers can interact with 3D products on websites, a trend predicted to become increasingly popular in v-commerce [18].

In today’s retail market, the interaction between online and offline shopping channels is crucial, as they can influence each other in significant ways. Positive experiences in physical stores can enhance the brand image of the online store, and vice versa [2]. For instance, Burberry leverages its physical stores as showrooms to drive online sales and manage returns more efficiently. Furthermore, online promotions through social media can significantly boost sales in physical stores [19]. The interdependence between these channels is often attributed to the halo effect, where consumers’ perceptions of one channel influence their perceptions of the other [20]. For online retailing, factors such as system quality and service quality play a crucial role in customer satisfaction, while for offline retailing, emotional and social value are key determinants of consumer loyalty [21].

VR has redefined the apparel shopping experience by creating immersive, interactive 3D environments that enhance product evaluation [22,23,24]. VR technology addresses the sensory limitations of traditional online shopping by offering rich visual and auditory stimuli, though the development of tactile feedback remains ongoing [15]. VR also provides dynamic and cost-effective solutions for offline store displays. The realistic environments and high-quality graphics in VR retailing can significantly enhance shopping enjoyment and influence purchase intentions [25].

More than just a novelty, VR retailing is a strategic tool for making shopping both enjoyable and efficient [26]. Creating immersive virtual environments that evoke a strong sense of presence where users feel as though they are physically in the store is crucial for eliciting consumer behaviors similar to those in real-life shopping scenarios [27]. Enhanced immersion and presence in VR settings have been shown to positively impact consumer attitudes, providing a sense of escapism that is highly valued in shopping experiences [26].

Despite VR’s potential to reduce the perceived risks associated with online shopping, such as concerns about product quality [28], challenges remain, including issues related to simulator sickness and the high costs of investment [29]. However, as VR technology continues to evolve, it promises to deliver increasingly interactive and informative shopping environments, which could eventually include 3D avatars acting as sales assistants, thereby enhancing consumer satisfaction and the overall shopping experience [30].

Virtual fitting rooms and avatars virtual fitting rooms (VFRs) are a critical component of digital retail theater, offering a solution to the common issue of apparel misfit, which accounts for a significant portion of returns in both online and offline stores [31]. VFRs allow consumers to virtually try on clothing without the need for physical fitting rooms, saving time and reducing return rates. Current VFR technology uses augmented reality (AR) to superimpose virtual clothing onto the user’s 3D avatar, generated based on body measurements taken via sensors or manually entered data [32]. Despite being in the developmental stage, VFRs have the potential to revolutionize the shopping experience by providing accurate sizing and a more immersive shopping environment [33].

Similarly, the use of avatars in online shopping can significantly enhance the consumer experience by allowing users to visualize how clothing fits without physically trying it on. Advanced avatar systems, such as the avatar manager system, can generate 2D and 3D images based on detailed body measurements, enabling a more personalized shopping experience [34]. Although challenges remain, such as accurately simulating the fine details of garments [35], the integration of avatars in VR shopping environments holds great promise for improving customer satisfaction and reducing return rates.

2.2. Unified Theory of Acceptance and Use of Technology

As DRTs are an emerging form of retailing that leverages advanced technologies and is still in its developmental stages, it is crucial to understand consumers’ acceptance and perceived usefulness of this new retail format. Gaining insights into these factors will help in effectively applying DRT to the future market. To explore U.S. consumers’ acceptance of DRTs, this research utilizes the unified theory of acceptance and use of technology (UTAUT) developed by Venkatesh et al. [36]. The UTAUT model is a widely recognized framework for assessing the acceptance and usage of information technology (IT) and information systems (IS).

The UTAUT model identifies four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions that significantly impact behavioral intention and usage behavior. Venkatesh et al. [36] developed UTAUT by integrating eight previous behavioral intention models, including the theory of reasoned action (TRA) [37], the technology acceptance model (TAM) [38], the theory of planned behavior (TPB) [39], the combined TAM and TPB model [40], the model of PC utilization (MPCU) [41], the motivation model (MM) [42], social cognitive theory (SCT) [43], and innovation diffusion theory (IDT) [44]. This comprehensive model has since been validated across various contexts and remains one of the most effective frameworks for studying IT acceptance.

UTAUT has been extensively applied in commercial research, including studies on e-commerce service quality [45], online shopping anxiety [46], and omnichannel customer behavior [2]. It has also been widely utilized in the fashion and textiles industry, addressing topics such as fashion e-commerce [47], fashion mobile shopping applications [8], wearable technology [48], and virtual fitting rooms [49].

Given the relevance and applicability of the UTAUT model in examining technology acceptance in various sectors, this study aimed to delve deeper into the four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions, to develop hypotheses that investigate U.S. consumers’ intent to use DRTs. By understanding these determinants, retailers can better design and implement DRT experiences that align with consumer expectations and enhance the overall shopping experience.

2.2.1. Performance Expectancy

Venkatesh et al. [36] define performance expectancy as the degree to which an individual believes that using the system will help them to attain gains in job performance. In the context of consumer behavior, this concept extends to the expectation that a product or system will enhance task efficiency and effectiveness. Essentially, consumers anticipate that the technology will offer significant functional benefits, thereby improving their overall experience and productivity.

Performance expectancy is widely regarded as one of the most critical factors in predicting behavioral intention and has a direct influence on actual usage behavior [50]. In the realm of virtual shopping technologies, this construct plays a pivotal role in shaping consumer intentions. For instance, Sun and Chi [51] found that consumers’ positive perceptions of e-commerce were largely driven by the high performance and efficiency offered by online platforms. Similarly, Beck and Crié [49] demonstrated a strong relationship between performance expectancy and the intention to use a virtual fitting room, highlighting that consumers are more likely to adopt technologies they perceive as beneficial in enhancing their shopping experience.

As new technologies continue to emerge, their perceived usefulness rooted in their ability to meet or exceed consumer expectations remains a key determinant of consumer acceptance. Performance expectancy, therefore, is a crucial factor in understanding how consumers evaluate and decide to adopt innovative retail technologies such as DRT in the apparel industry. Therefore, the following hypothesis is proposed.

Hypothesis 1: 

Performance expectancy positively affects U.S. consumers’ intention to use apparel digital retail theaters (DRT).

2.2.2. Effort Expectancy

Effort expectancy remains a pivotal factor influencing behavioral intention toward adopting new technologies. Defined by Venkatesh et al. [36] as the degree of ease associated with the use of the system, effort expectancy closely parallels the concept of perceived ease of use. The core idea is that consumers are less likely to adopt technologies, regardless of their advanced features, if they perceive them as difficult to use. This principle has been supported by extensive research across various domains.

For instance, ease of use significantly impacts purchasing decisions for smartphones, as highlighted by Sun and Chi [51], who noted a strong consumer preference for intuitive and user-friendly devices. Similarly, Chao [50] found that effort expectancy is a critical determinant in adopting e-commerce platforms, with simplified interfaces enhancing consumer engagement. In the context of virtual shopping technologies, Beck and Crié [49] demonstrated a robust relationship between effort expectancy and the adoption of online virtual fitting rooms, underscoring the importance of seamless user experiences.

Recent studies further validate the critical role of effort expectancy in technology adoption. Alalwan et al. [52] highlighted its significance in mobile banking acceptance, showcasing its relevance across diverse consumer technology platforms. Likewise, a study by Dwivedi et al. [53] emphasized the importance of effort expectancy in adopting AI-driven customer service tools, showing that ease of interaction can significantly influence user intention. Additionally, research by Mkedder et al. [54] found that streamlined interfaces in augmented and virtual reality (AR/VR) applications for retail dramatically improve adoption rates by minimizing user effort.

In the specific domain of DRTs for apparel shopping, effort expectancy is paramount. Complex technologies, if perceived as difficult to use, can deter potential users. However, advances in AI-based personalization and voice-guided virtual assistance have significantly reduced perceived effort, enabling consumers to engage with virtual shopping environments more intuitively [55,56]. These developments highlight the necessity of user-friendly designs to encourage widespread adoption. Thus, the following hypothesis is proposed.

Hypothesis 2: 

Effort expectancy positively affects U.S. consumers’ intention to use apparel digital retail theaters (DRT).

2.2.3. Social Influence

Consumer acceptance of new technologies extends beyond the technical features or functionality of a system to encompass social dynamics that influence thoughts and decisions. Social influence refers to the degree to which an individual perceives that important others believe he or she should use the new system [57]. While earlier studies, such as Venkatesh et al. [36], suggested that social influence might be secondary to performance and effort expectancy in predicting purchase intentions, its impact is becoming increasingly significant in today’s hyper-connected digital landscape [58].

Recent studies offer diverse perspectives on the role of social influence in technology adoption. While Juaneda-Ayensa et al. [57] reported that social influence had minimal impact on omnichannel purchase intentions, placing greater emphasis on performance and effort expectancy, other research underscores its nuanced significance. Tandon, Kiran, and Sah [58] highlighted that social influence remains a crucial factor in online shopping behavior, where endorsements by peers and social networks significantly increase the likelihood of technology adoption. Similarly, Zhang and Xiong [59] illustrated that social influence plays a pivotal role in shaping consumers’ behavioral intentions toward using AI-driven recommender systems in e-commerce, particularly in scenarios where personalization and social proof converge.

In the era of social media, the influence of social networks on consumer behavior is even more pronounced. Platforms such as Instagram, TikTok, and Facebook have transformed how consumers discover, share, and endorse fashion products. Wang et al. [60] found that peer recommendations on social media platforms are increasingly pivotal in shaping consumer preferences, especially for emerging retail technologies. Kumar et al. [61] highlighted that user-generated content and influencer marketing amplify the impact of social influence on technology adoption, particularly in DRT experiences.

Moreover, the interconnectedness of today’s digital ecosystem enables rapid dissemination of social cues. LaRose et al. [62] underscored that interactions on social platforms significantly shape consumer behavior, particularly in fashion retailing, where trends and endorsements by peers or influencers carry considerable weight. This observation is corroborated by Kang and Choi [3], who noted that consumers are more likely to engage with virtual shopping experiences, such as DRT, when positively influenced by their social networks. Given the increasing integration of social networks into consumer decision-making and the critical role of digital communication in retail, we propose the following hypothesis.

Hypothesis 3: 

Social influence significantly affects U.S. consumers’ intention to use apparel digital retail theaters (DRT).

2.2.4. Facilitating Conditions

New technology adoption requires robust infrastructure to ensure its effective use. Facilitating conditions are defined as the degree to which an individual perceives that organizational and technical infrastructure exists to support system use [36]. This construct highlights the critical role of supportive resources and infrastructure, such as reliable technology platforms, internet connectivity, and user assistance systems, in fostering the adoption and sustained use of innovative technologies.

Recent research reveals a positive relationship between facilitating conditions and behavioral intention. Tandon [63] found that strong facilitating conditions, such as well-structured user interfaces and customer support, are critical for driving consumer engagement with online shopping platforms, particularly in emerging markets. Kim and Park [19] demonstrated that comprehensive support systems, including technical guidance and reliable internet access, significantly enhance consumers’ willingness to adopt digital retail technologies. Kang and Choi [3] further emphasized the importance of stable infrastructure in the adoption of augmented reality-based retail systems, noting that seamless technology integration can mitigate consumer frustrations and improve trust.

However, some studies suggest that when factors like performance expectancy and effort expectancy are prominent, the effect of facilitating conditions may diminish. Sukendro et al. [64] noted that in e-learning platforms, the influence of facilitating conditions was often overridden by system usability and relevance to user needs. Wulandari et al. [65] found that in recommender systems, facilitating conditions played a secondary role compared to perceived ease of use and expected benefits.

Despite these mixed findings, facilitating conditions remain essential for technologies heavily reliant on technical infrastructure, such as apparel DRT. DRT adoption depends not only on the inherent technology but also on the presence of reliable infrastructure, including high-speed internet, compatible devices, and user-friendly systems. These elements significantly enhance the user experience and reduce barriers to adoption. Thaichon et al. [66] stress the critical role of infrastructure investments in the success of immersive retail environments, highlighting the need for retailers to prioritize back-end and consumer-facing support. In this context, ensuring robust facilitating conditions is likely to be a critical factor for the success of DRT. Based on these considerations, the following hypothesis is proposed.

Hypothesis 4: 

Facilitating conditions significantly affect U.S. consumers’ intention to use apparel digital retail theaters (DRT).

2.3. Perceived Risk Theory

Perceived risk is a critical factor influencing consumer acceptance of innovative technologies. Featherman and Pavlou [67] define perceived risk as the potential for loss in the pursuit of a desired outcome of using an e-service. In the context of DRT, perceived risk is defined as the likelihood of consumers encountering financial, emotional, or other forms of loss when engaging with DRTs. This is especially relevant given the novelty of DRTs, which may amplify consumer apprehensions about unfamiliar technologies. Hanafizadeh et al. [68] assert that perceived risk can significantly deter consumers from adopting new technologies, even when potential benefits are evident. Common concerns often revolve around financial losses, privacy breaches, or fears of inadequate performance [52,63]. For instance, while consumers may appreciate the potential of DRTs to enhance their shopping experience, apprehensions about data security or technological reliability can inhibit its adoption.

Perceived risk is a multidimensional construct encompassing various factors, including financial, product performance, social, psychological, physical, and time/convenience risks [69]. Among these, financial and performance risks often hold the greatest influence on consumer decision-making. Ariffin et al. [70] demonstrated that financial risk, which involves concerns about monetary loss, and performance risk, related to fears of the technology not meeting expectations, are the strongest barriers to adoption. Conversely, physical risk (potential physical harm) and social risk (concerns about judgment or embarrassment) tend to exert a comparatively lesser influence. More recent studies reinforce the significance of perceived risk in technology adoption. Thakur and Srivastava [71] found that perceived financial and privacy risks substantially hinder the adoption of mobile payment systems, highlighting parallels with emerging technologies like DRT. Similarly, Slade et al. [72] argued that mitigating perceived risks during the early stages of technology deployment is critical to fostering consumer acceptance. Additionally, Gupta and Arora [73] emphasized the importance of transparent communication and robust security measures in alleviating consumer concerns and building trust in digital platforms. Thaichon et al. [66] extended this argument to immersive retail technologies, demonstrating that enhanced usability and secure data protocols play a pivotal role in mitigating perceived risks.

For DRTs, which depends heavily on consumer trust and confidence, addressing perceived risks is essential. Strategies such as providing clear, transparent communication about security measures, showcasing the tangible benefits of the technology, and offering comprehensive customer support can significantly reduce apprehensions. For example, Wu and Kim [74] highlighted the role of clear privacy policies and user-friendly interfaces in enhancing consumer trust in AR-enabled retail systems. By proactively addressing these concerns, retailers can foster greater consumer confidence and drive the widespread adoption of DRT.

2.3.1. Financial Risk

Kang and Kim [75] define financial risk as concerns about potential monetary and economic loss, which hinges upon the price of the focal product. In various contexts, including fashion renting, financial risk has been shown to negatively impact consumer attitudes and perceived enjoyment. Lee and Moon [76] stressed that financial risk is a significant factor influencing consumer purchase intentions, particularly for customized online apparel products. When consumers perceive a high likelihood of economic loss, they are less likely to commit to purchasing, even when the product offers customization or other unique benefits. Lang [77] found that concerns about potential financial loss can deter consumers from fully engaging with or enjoying rental services in the fashion industry.

In the context of DRTs, financial risk is a crucial consideration. Consumers may need to pay a premium for products or services that incorporate advanced virtual technologies, as the substantial investment required for these innovations often results in higher price tags. This potential increase in cost can amplify consumers’ concerns about financial risk, making them hesitant to adopt DRTs, despite its potential to enhance the shopping experience. Moreover, the reliance on online payment systems in virtual shopping environments introduces additional financial risks, particularly regarding the security of personal and financial information. As Cardoso and Martinez [78] noted, consumers often feel apprehensive about providing payment information online due to the risk of data breaches and fraud. The increased usage of online payments in DRTs heightens these concerns, potentially deterring consumers from fully embracing the technology.

Recent studies continue to underscore the importance of financial risk in consumer decision-making, particularly in the adoption of new digital technologies. Gupta and Arora [73] found that perceived financial risk significantly reduces consumers’ willingness to adopt mobile banking services, a finding that parallels the risks associated with DRT. Similarly, Tandon [63] emphasized that addressing financial risk is essential for encouraging the adoption of online services, including digital retail platforms. Given these considerations, financial risk is likely to play a significant role in shaping U.S. consumers’ intentions to use apparel DRTs. Therefore, the following hypothesis is proposed.

Hypothesis 5: 

Financial risk reduces U.S. consumers’ intention to use apparel DRTs.

2.3.2. Psychological Risk

Psychological risk refers to possible damage to one’s self-image [75]. This concept is particularly relevant in consumer behavior, as individuals tend to prefer shopping environments—whether online or offline—that align with their self-image [79]. While psychological risk may not exert as strong an influence as financial risk, it nonetheless plays a significant role in shaping consumer trust in online shopping behavior [80].

In the context of DRTs, psychological risk takes on new dimensions. The immersive and interactive nature of DRT, which utilizes advanced technologies such as AR and VR technologies, can have a profound impact on consumers’ self-concept. The key question is whether these technologies empower consumers by enhancing their shopping experience, thereby boosting their self-image, or whether they trigger heightened self-consciousness and insecurity.

Recent studies have explored the role of psychological risk in technology adoption, particularly in relation to emerging digital platforms. Wu et al. [81] emphasize that the perceived congruence between a consumer’s self-image and the brand’s image is crucial in determining the likelihood of adoption of new retail technologies. von der Au et al. [82] found that psychological risk is a significant factor in the adoption of AR applications, as consumers may feel vulnerable or exposed when using such highly immersive technologies. This finding is particularly relevant for DRTs, where the potential for psychological risk could influence consumer intentions to adopt the technology.

Moreover, psychological risk can influence not only the consumer’s decision to use a new technology but also their overall satisfaction and continued use. When consumers perceive a technology as threatening to their self-image, they are less likely to engage with it, even if the technology offers functional benefits [80,81]. This suggests that for DRTs to be successful, it must be designed and marketed in ways that reinforce positive self-concepts rather than triggering self-doubt or anxiety. Therefor, the following hypothesis is proposed.

Hypothesis 6: 

Psychological risk significantly affects U.S. consumers’ intention to use apparel DRTs.

2.3.3. Physical Risk

Physical risk in the context of consumer behavior refers to concerns related to the potential harm a product may cause to the user’s health or the fear that the product may not meet the consumer’s expectations in terms of appearance and functionality. As Kang and Kim [75] stated that consumers often worry about whether a product might be harmful or fail to deliver the desired aesthetic appeal. This concern is particularly relevant when considering emerging technologies like VR, where physical discomfort or health risks may deter adoption.

Wu and Kim [74] highlighted that simulator sickness, a form of motion sickness induced by immersive technologies such as Virtual Reality (VR), is a primary reason users may be reluctant to engage with these devices. Simulator sickness is characterized by symptoms such as nausea, dizziness, and disorientation, which can significantly disrupt the user’s experience and reduce their willingness to continue using the technology. This concern is especially important in the context of technologies that rely heavily on VR, as user discomfort can directly impact adoption rates. Zhang and Xiong [59] examined the effects of VR on users and confirmed that motion sickness is a prevalent side effect, posing a challenge to the widespread adoption of VR devices. They noted that while VR offers immersive and engaging experiences, the physical discomfort it can cause remains a critical barrier to its broader use, particularly in consumer-facing environments.

Kourtesis et al. [83] further explored the physiological impacts of VR on users and reaffirmed that motion sickness remains a persistent issue, even as VR technology advances. They pointed out that reducing these physical risks is essential for improving user engagement with VR systems across various applications. Likewise, von der Au et al. [82] conducted a systematic review of VR-related motion sickness and found that physical discomfort continues to be a significant obstacle to user satisfaction, particularly when VR is used in non-gaming contexts such as retail and education.

In the context of DRTs, which often incorporate VR and other immersive technologies, users may experience similar forms of motion sickness. This physical discomfort can negatively influence their intention to use such devices, as the risks associated with motion sickness may outweigh the perceived benefits of the immersive experience. Research into consumer behavior consistently shows that when physical risks are perceived as high, consumers are less likely to adopt new technologies, even if they are curious or excited about the potential benefits [82]. Recent studies also echo these concerns, showing that physical discomfort remains a central consideration for consumers interacting with advanced retail technologies [84]. Therefore, the following hypothesis was proposed.

Hypothesis 7: 

Physical risk significantly affects U.S. consumers’ intention to use apparel DRTs.

2.3.4. Time and Convenience Loss Risk

Time and convenience loss risk is a critical factor in shaping consumer behavior, particularly in online shopping environments. Forsythe and Shi [85] defined time and convenience loss risk as the loss of time and inconvenience incurred due to difficulty in navigation and/or submitting an order, finding appropriate websites, or delays in receiving products. This definition underscores how poorly designed digital platforms and delayed product deliveries can lead to customer dissatisfaction. As online retail expands, minimizing time and convenience loss has become a vital consideration for businesses seeking to enhance user experience.

Kang and Kim [75] broadened this understanding by suggesting that time risk is present not only at the consumer level but also across product categories, influencing how customers interact with various types of goods. Time risk can manifest in digital environments where users experience challenges related to slow website loading times, complicated interfaces, or lengthy processes for completing transactions. Khedmatgozar and Shahnazi [86] explored time risk in the context of internet banking, finding that it significantly impacted consumers’ intention to use online banking services. Their findings revealed that when customers perceive online services as time-consuming or inefficient, their likelihood of adopting such technologies decreases. This concept can also be applied to DRT, where devices play a growing role in providing immersive online shopping experiences. The more streamlined and efficient these platforms are, the more likely consumers are to use them. Similarly, Chi [8] studied the relationship between convenience and the adoption of mobile shopping applications, concluding that when apps are easy to navigate and provide a hassle-free experience, consumers demonstrate a higher intention to use them. The findings emphasize the importance of convenience in driving customer engagement, which is increasingly relevant as retailers integrate advanced technologies. The ability of v-commerce platforms to provide a seamless user experience, along with fast and efficient product delivery, could significantly enhance customer satisfaction and increase the adoption of apparel DRTs.

In the context of DRTs, understanding time and convenience loss risk is essential to improve consumer adoption rates. As v-commerce grows, ensuring that these platforms are efficient, easy to navigate, and offer timely delivery will play a critical role in shaping consumer satisfaction and engagement. Retailers that can minimize time and convenience loss risk are likely to see greater success in their efforts to integrate v-commerce into the broader retail experience. Therefore, the following hypothesis is proposed.

Hypothesis 8: 

Time/convenience loss risk significantly affects U.S. consumers’ intention to use apparel DRTs.

3. Methodology

3.1. Proposed Research Model and Developed Survey Instrument

Based on the detailed literature review of the UTAUT model and the perceived risk theory above, a research model including all the proposed relationships (eight hypotheses) is presented in Figure 1. U.S. consumers’ intention to use apparel DRTs may be significantly affected by performance expectancy, effort expectancy, social influence, facilitating conditions, financial risk, psychological risk, physical risk, and time/convenience loss risk. The demographic variables, including age, gender, income level, education level, and user experience, are included as control factors.

The scales for performance expectancy, effort expectancy, social influence, and facilitating conditions were adapted from Cimperman et al. [87]. The scales for four types of risk—financial, psychological, physical, and time/convenience loss risks were adapted from Hwang and Choe [88]. Consumer intention to use apparel DRTs was adapted from Hwang and Choe [88].

Table 1. Constructs and corresponding measurement items.

Construct	Measurement Items	Source
Performance Expectancy (PE)	PE1: I find that using apparel DRTs would be helpful in purchasing apparel products. PE2: Apparel DRTs could enhance the level of convenience in purchasing apparel products. PE3: Apparel DRTs could enhance the quality of my life. PE4: Overall, I find apparel DRTs would be highly useful.	Cimperman et al. [87]
Effort Expectancy (EE)	EE1: I find that using apparel DRTs would be simple. EE2: I find that using apparel DRTs would be easy to learn. EE3: I find that apparel DRTs would be easily understandable and clear for me. EE4: Overall, I find that using apparel DRTs would be convenient.	Cimperman et al. [87]
Social Influence (SI)	SI1: Peers and colleagues would support me in using apparel DRTs. SI2: People who influence my behavior would support me using apparel DRTs. SI3: People who are important to me would support my use of apparel DRTs.	Cimperman et al. [87]
Facilitating Conditions (FC)	FC1: I believe guidance will be available to me when deciding whether to use the system. FC2: I believe specialized instructions concerning the use of the system will be available to me. FC3: I believe specific persons (or a group) will be available for assistance with system difficulties (a call center).	Cimperman et al. [87]
Financial Risk (FR)	FR1: I worry that the conception of apparel DRTs would be more expensive than I expected. FR2: I worry that an additional fee must be paid for apparel DRTs. FR3: I worry that apparel DRTs would involve unexpected extra expenses.	Hwang & Choe [88]
Psychological Risk (PR)	PR1: The thought of apparel DRTs make me feel psychologically uncomfortable. PR2: The thought of apparel DRTs cause me to experience unnecessary tension. PR3: The thought of apparel DRTs give me a feeling of unwanted anxiety.	Hwang & Choe [88]
Physical Risk (PR)	PR1: I worry that apparel DRTs are harmful to me. PR2: I worry about my health because of using Apparel DRTs. PR3: I worry that using apparel DRTs is unhealthy.	Hwang & Choe [88]
Time and Convenience Loss Risk (TR)	TR1: I am worried that planning to use apparel DRTs would take too much time. TR2: I am worried that using apparel DRTs would be time-consuming. TR3: I am worried that using apparel DRTs would be a waste of time.	Hwang & Choe [88]
Apparel DRT Use Intention (UI)	UI1: I will use apparel DRTs for shopping. UI2: I am willing to use apparel DRTs for shopping. UI3: I am likely to use apparel DRTs for shopping.	Hwang & Choe [88]

Note: Five-point Likert scale from “strongly disagree = 1” to “strongly agree = 5”.

lists all the constructs and their corresponding measurement items.

3.2. Data Collection and Sampling Procedure

Primary data for this study was collected through an online survey administered via Qualtrics. The survey targeted U.S. consumers and was distributed using Amazon Mechanical Turk (MTurk), an established crowdsourcing platform frequently used in academic research for data collection. MTurk offers several advantages, including cost-effectiveness, rapid response rates, ease of administration, access to a diverse pool of participants, and reduced bias in experimental research settings [89,90]. A convenience sampling method was employed, as participants were self-selected from those registered on the MTurk platform. To ensure the relevance and appropriateness of the sample, screening criteria were applied to recruit participants who were U.S. residents, at least 18 years of age, and had prior experience with apparel DRTs. Participation was voluntary, and respondents received a small monetary incentive for completing the survey.

The survey instrument was reviewed by three faculty members with expertise in consumer behavior and retail technology to ensure clarity, relevance, and accuracy. Following their feedback, minor adjustments were made to improve question wording and survey flow. A pre-test was subsequently conducted with a sample of 20 graduate students to evaluate the questionnaire’s clarity, structure, and completion time. Feedback from the pre-test participants led to further refinements, including rewording of ambiguous items and improving the user experience of the survey interface. The finalized survey was launched on MTurk during the week of 14 October 2020, and data collection was completed within three days. A total of 400 eligible responses were collected. The profile of survey respondents is presented in

Table 2. Profile of the survey participants.

	Percent (%)		Percent (%)
Age		Income
18–24	5.75	Less than USD 25,000	22.00
25–34	42.75	USD 25,000 to USD 34,999	16.25
35–44	25.50	USD 35,000 to USD 49,999	18.25
45–54	12.00	USD 50,000 to USD 74,999	25.00
55–64	9.00	USD 75,000 to USD 99,999	9.50
65 or older	5.00	USD 100,000 to USD 149,999	6.75
Gender		USD 150,000 or more	2.25
Male	55.00	Annual apparel expenditure
Female	45.00	USD 0–99	8.25
Ethnicity		USD 100–299	26.75
White/Caucasian	74.00	USD 300–499	23.75
Black/African American	9.25	USD 500–699	16.00
Asian American/Pacific Islander	9.50	USD 700–899	5.25
Latino/Hispanic	4.25	USD 900–1099	6.75
Native American	1.75	USD 1100–1499	4.50
Others	1.25	USD 1500–1999	4.75
Education		USD 2000 and more	4.00
Less than high school	0.50	Region of the country
High school graduate	12.00	Midwest	20.50
Some college, no degree	15.00	Northeast	24.25
Associate degree	12.00	Southeast	22.75
Bachelor’s degree	46.75	Southwest	10.50
Graduate/Professional degree	12.25	West	22.00
Ph.D. degree	1.50

Note: total eligible responses were 400.

.

In terms of age distribution, the majority fell within the 25–34 age group (42.75%), followed by those aged 35–44 (25.50%), while only 5.00% were 65 or older. The sample was 55.00% male and 45.00% female. Regarding ethnicity, most respondents identified as White/Caucasian (74.00%), with smaller representations from Asian American/Pacific Islanders (9.50%), Black/African Americans (9.25%), Latino/Hispanics (4.25%), and Native Americans (1.75%).

The educational background of participants varied, with 46.75% holding a Bachelor’s degree, 12.25% possessing a Graduate/Professional degree, and 1.50% having a Ph.D. Others had an Associate degree (12.00%), some college experience (15.00%), or were high school graduates (12.00%), while only 0.50% had less than a high school education. Income distribution ranged widely, with 22.00% earning less than USD 25,000 annually and 25.00% in the USD 50,000–USD 74,999 income bracket. Higher-income earners (USD 100,000 or more) accounted for 9.00% of respondents.

Annual apparel expenditures also varied, with 26.75% spending between USD 100–USD 299, followed by 23.75% spending USD 300–USD 499, while 8.25% spent less than USD 100, and 4.00% spent USD 2000 or more. Geographically, respondents were fairly distributed across the U.S., with the Northeast (24.25%) having the highest representation, followed by the Southeast (22.75%), West (22.00%), and Midwest (20.50%), while the Southwest (10.50%) had the smallest share. This demographic profile provides valuable insights into the respondents’ backgrounds, spending behaviors, and regional representation.

3.3. Data Analysis

Various statistical assumptions, including normality, multicollinearity, and correlations, were rigorously examined to ensure the validity and reliability of the data analysis. Multivariate normality was assessed by analyzing skewness and kurtosis values for each variable, following established guidelines. According to Hair et al. [91], data is considered normal if skewness and kurtosis are between −2 and +2.

To evaluate multicollinearity, variance inflation factors (VIFs) were calculated, with values below 5.0 considered acceptable for ensuring that multicollinearity was not a concern. Ott and Longnecker [92] suggest that VIF values below 5 indicate no multicollinearity issues.

For constructs measured by multiple items, average scores were computed for subsequent analyses, which included Pearson correlation and multiple regression techniques. Chi et al. [93] and Morgan et al. [94] recommend using these methods to explore relationships between variables. Ping [95] also supports the use of multiple regression in such contexts.

To further assess the reliability, unidimensionality, and validity of the constructs, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were employed. Mariadoss et al. [96] emphasize the importance of these analyses in validating measurement models. Unidimensionality, indicating that a single underlying factor accounts for the variation in responses, was a key criterion in assessing the measurement model, as discussed by Byrne [97].

Reliability was measured using Cronbach’s alpha, which assesses internal consistency across the items. Convergent validity was established when the average variance extracted (AVE) exceeded the recommended threshold of 0.50 for all constructs, indicating that the items adequately captured the underlying construct. Jöreskog and Sörbom [98] provide guidelines for these thresholds. Discriminant validity was confirmed when the AVE values for each construct were greater than the squared correlations between constructs, following the criteria outlined by Fornell and Larcker [99].

To test the study hypotheses and predict the relationships between independent and dependent variables, multiple regression analysis was employed, a widely used statistical technique in behavioral and social sciences for examining the influence of several predictors on a single outcome. Cohen et al. [100] and Hair et al. [91] discuss the application of multiple regression in such research contexts. Multiple regression allows for the simultaneous consideration of multiple independent variables, providing insights into both the direct and partial effects of these variables on the dependent variable while controlling for other factors in the model, as noted by Tabachnick and Fidell [101]. This method is particularly effective for identifying significant predictors and understanding the magnitude and direction of their relationships with the outcome of interest, as described by Field [102].

3.4. Results and Discussions

3.4.1. Psychometric Properties of Investigated Constructs

As shown in

Table 3. Psychometric properties of investigated constructs.

	Factor Loading	Cronbach’s Alpha	Construct Reliability	AVE	χ2 Test p-Value
Performance Expectancy (PE)		0.880	0.920	0.743	1.895
PE1	0.914
PE2	0.808
PE3	0.816
PE4	0.904
Effort Expectancy (EE)		0.868	0.718	0.910	1.971
EE1	0.872
EE2	0.883
EE3	0.858
EE4	0.772
Social Influence (SI)		0.889	0.931	0.819	1.339
SI1	0.898
SI2	0.891
SI3	0.926
Facilitating Conditions (FC)		0.727	0.848	0.651	1.994
FC1	0.844
FC2	0.833
FC3	0.740
Financial Risk (FR)		0.899	0.938	0.834	1.284
FR1	0.879
FR2	0.926
FR3	0.933
Psychological Risk (PSR)		0.935	0.958	0.884	1.105
PSR1	0.936
PSR2	0.945
PSR3	0.940
Physical Risk (PHR)		0.925	0.953	0.870	1.177
PHR1	0.932
PHR2	0.932
PHR3	0.935
Time/Convenience Loss Risk (TR)		0.874	0.924	0.801	1.642
TR1	0.906
TR2	0.905
TR3	0.874
Use Intention (UI)		0.900	0.938	0.835	1.011
UI1	0.928
UI2	0.884
UI3	0.928

, all the factor loadings of the measurement items to their respective constructs are high (0.7 or higher) and met the criterion. This also shows unidimensionality for the constructs. In addition, the Chi-square tests of all constructs were insignificant, which established the evidence of unidimensionality. Both Cronbach’s alphas and construct reliability of all constructs are greater than 0.70, indicating reliability is rigorously met [103]. The AVE scores for all constructs are above the desired threshold of 0.50, suggesting convergent validity. All AVE scores are greater than the squared corresponding correlations, which demonstrate satisfactory discriminant validity (See Table 3 and

Table 4. Correlations and descriptive statistics of all constructs.

	PE	EE	SI	FC	FR	PSR	PHR	TR	UI
PE	1	0.395 **	0.581 **	0.469 **	0.722 **	0.421 **	0.005	−0.093	−0.154 **
EE	0.156	1	0.676 **	0.530 **	0.550 **	0.169 **	−0.202 **	−0.242 **	−0.303 **
SI	0.338	0.457	1	0.595 **	0.632 **	0.264 **	−0.138 **	−0.156 **	−0.254 **
FC	0.220	0.281	0.354	1	0.629 **	0.193 **	−0.139 **	−0.164 **	−0.177 **
FR	0.521	0.303	0.399	0.396	1	0.411 **	−0.079	−0.162 **	−0.235 **
PSR	0.177	0.029	0.070	0.037	0.169	1	0.516 **	0.312 **	0.197 **
PHR	0.000	0.041	0.019	0.019	0.006	0.266	1	0.734 **	0.634 **
TR	0.008	0.059	0.024	0.027	0.026	0.097	0.539	1	0.764 **
UI	0.024	0.092	0.065	0.031	0.055	0.039	0.402	0.584	1
Mean	4	4	4	4	4	3	3	2	2
S.D.	0.9	0.8	0.8	0.7	0.8	0.7	0.9	1.0	1.0
VIF	2.375	2.107	2.608	1.923	3.161	1.995	2.878	2.332	-
Skewness	−0.944	−0.805	−0.466	−0.520	−0.519	−0.054	0.328	0.872	0.681
Kurtosis	0.901	1.010	0.331	1.593	0.497	0.081	−0.180	0.106	0.185

Note: the italic numbers are the squared corresponding correlations. **: correlation is significant at the 0.01 level (two-tailed). UI = use intention, PE = performance expectancy, EE = effort expectancy, SI = social influence, FC = facilitating conditions, FR = financial risk, PSR = psychological risk, PHR = physical risk, TR = time/convenience loss risk.

).

Table 4 presents correlations and properties of all constructs. All skewness and kurtosis scores are between +2.0 and −2.0, which suggests there are no violations of the normality assumption [91]. All VIF values are below 5.0, suggesting there are no multicollinearity issues among constructs.

3.4.2. Hypothesis Testing Results and Discussion

Table 5. Results of hypothesis testing.

Hyp.		DV	IDV	Std. Coef. (β)	t-Value	Sig. at p < 0.05	Total R2	Sig. at p < 0.05
		UI	Constant	-	4.313	<0.001	0.629	<0.001 F = 50.389 (13/386)
H1	N		PE	0.015	0.311	0.756
H2	Y		EE	0.178	2.671	0.005
H3	N		SI	−0.081	−1.623	0.105
H4	Y		FC	0.083	2.106	0.045
H5	N		FR	−0.056	−1.019	0.309
H6	N		PSR	−0.057	−1.304	0.193
H7	Y		PHR	0.199	3.777	<0.001
H8	Y		TR	0.583	11.729	<0.001

Note: Hyp.= hypothesis; Y: hypothesis supported; N: hypothesis not supported; Std. coef. = standardized coefficients, DV: dependent variable. IDV: independent variable; UI = use intention, PE = performance expectancy, EE = effort expectancy, SI = social influence, FC = facilitating conditions, FR = financial risk, PSR = psychological risk, PHR = physical risk, TR = time/convenience loss risk.

presents the results of hypothesis testing, examining the factors influencing U.S. consumers’ intention to use apparel digital retail technology (DRT). Out of the eight hypotheses tested, four (H2, H4, H7, and H8) were found to be statistically significant at the p < 0.05 level, while the remaining four (H1, H3, H5, and H6) were not significant. Additionally, the effects of demographic variables—age, gender, education level, income level, and prior use experience—on consumers’ intention to use apparel DRTs were also found to be statistically insignificant at the p < 0.05 threshold.

Specifically, effort expectancy (EE) was shown to have a positive impact on U.S. consumers’ intention to use apparel DRT (β = 0.178, t = 2.671), thereby supporting H2. This finding suggests that the easier the technology is to use, the more likely consumers are to adopt it. This aligns with previous studies by Chidambaram et al. [104] and Zhang et al. [105], which emphasize that ease of use is a critical determinant in consumers’ willingness to adopt new technologies. These studies suggest that technologies perceived as simple and user-friendly tend to see higher adoption rates, particularly in consumer-facing industries like apparel.

Facilitating conditions (FC) also demonstrated a positive influence on U.S. consumers’ intention to use apparel DRT (β = 0.083, t = 2.106), supporting H4. This indicates that consumers recognize the importance of technical infrastructure in their ability to use apparel DRT effectively. Studies by Escobar-Rodríguez and Bonsón-Fernández [106] and Tandon et al. [58] similarly highlight that the availability of adequate facilitating conditions, such as supportive technical systems and resources, is vital for encouraging the adoption of new technologies. Without these, consumers may face challenges in integrating such technologies into their daily lives, hindering widespread acceptance.

Physical risk was found to significantly deter U.S. consumers from using apparel DRT (β = 0.199, t = 3.777), supporting H7. This finding suggests that concerns about potential physical harm—such as simulator sickness, a known side effect of virtual reality technologies—may discourage consumers from trying the technology. Similar concerns have been noted in previous studies by Alalwan et al. [52] and Menshikova et al. [106], which found that users often avoid virtual reality technologies due to fears of experiencing discomfort or negative physical reactions. Therefore, mitigating these risks could be crucial in promoting consumer confidence and encouraging the adoption of apparel DRTs.

Finally, time/convenience loss risk showed a significant effect on U.S. consumers’ intention to use apparel DRT (β = 0.583, t = 11.729), supporting H8. This finding highlights that consumers value time-saving and convenience features when considering new technologies. Any perception of inefficiency or inconvenience can serve as a barrier to adoption. This result is consistent with the findings of Chopdar et al. [107], who reported that the perceived efficiency of virtual reality technology in shopping environments plays a significant role in influencing consumer adoption. Consumers are more likely to engage with technologies that streamline their shopping experience and reduce time investment.

In summary, this study provides evidence that effort expectancy, facilitating conditions, physical risk, and time/convenience loss risk are critical factors influencing U.S. consumers’ intentions to use apparel DRT. These results underscore the importance of ensuring ease of use, robust technical infrastructure, minimized physical risks, and enhanced convenience to foster consumer adoption of new retail technologies.

Figure 2 illustrates the identified relationship in the proposed model. Effort expectancy, facilitating conditions, physical risk, and time/convenience loss risk significantly affect the U.S. consumers’ intention to use apparel DRT. There are no significant differences between ages, genders, education levels, income levels, and previous use experience in regards to their effects on the U.S. consumers’ intention to use apparel DRT. However, the directions of demographic variables’ effects are revealed. Younger, female, higher education level U.S. consumers are more likely to use apparel DRTs. A higher-income is unnecessary for motivating U.S. consumers to use apparel DRTs. Prior use experience contributes to the U.S. consumers’ willingness to use apparel DRTs. The proposed research model exhibits a good explanatory power, accounting for 62.9% of the variance in the U.S. consumers’ intention to use apparel DRTs.

4. Conclusions

This study provides valuable insights into the critical factors influencing U.S. consumers’ intention to adopt apparel DRT. As the fashion industry increasingly incorporates innovative digital tools such as VR and AR, understanding what drives or hinders consumer adoption of these technologies becomes imperative for both researchers and practitioners.

The findings reveal that effort expectancy and facilitating conditions are two primary factors significantly influencing consumer adoption of apparel DRT. Effort expectancy-defined as the perceived ease of use of the technology-emerged as a key driver of consumers’ willingness to engage with these systems. This finding underscores the vital importance of user-centered design in the development and implementation of DRT solutions. As VR and AR technologies continue to advance, their success in the retail sector hinges on simplicity and intuitiveness. Even the most technologically sophisticated systems are unlikely to achieve widespread adoption if consumers perceive them as complex or difficult to navigate. Retailers and developers must therefore prioritize creating seamless, intuitive user interfaces and minimizing any potential learning curve to encourage broader consumer engagement.

Equally important are facilitating conditions, which refer to the availability of technical and organizational infrastructure that enables consumers to effectively use the technology. This includes reliable internet connectivity, device compatibility, technical support services, and an overall infrastructure that ensures a smooth and consistent user experience. The significant influence of facilitating conditions emphasizes the need for retailers to look beyond simply offering innovative technology; they must also invest in the back-end support and infrastructure necessary to sustain these technologies. For example, ensuring that platforms run smoothly across various devices, providing responsive customer support, and offering guidance on how to use the technology are all critical components of successful DRT adoption. These efforts help mitigate barriers to entry and foster consumer confidence in using new digital tools.

Surprisingly, the study found that performance expectancy and social influence were not significant predictors of consumers’ intention to adopt apparel DRTs. This indicates that while consumers may recognize the potential benefits or efficiencies provided by these technologies, and while peer recommendations or societal trends may hold some sway, these factors alone are insufficient motivators for adoption in the context of apparel DRTs. Instead, consumers appear to place a greater emphasis on the ease of interacting with the technology and the supporting infrastructure that ensures a hassle-free experience. This finding suggests a shift from traditional technology acceptance factors toward more pragmatic considerations, where consumers prioritize convenience and usability over perceived performance or social validation.

In addition to these factors, the study highlights the deterrent effects of perceived risks, specifically physical risk and time/convenience loss risk, on consumer adoption. Concerns about physical risks such as simulator sickness, eye strain, or general discomfort associated with prolonged VR or AR use remain significant barriers. These concerns can be particularly pronounced in technologies requiring wearable hardware, such as headsets or glasses. Furthermore, consumers expressed apprehension about the possibility of time and convenience loss. Many worry that DRT solutions might complicate or prolong the shopping process rather than streamline it, undermining the convenience they expect from digital retail channels. This finding underscores the need for retailers and developers to focus not only on creating immersive experiences but also on ensuring these solutions enhance, rather than hinder, shopping efficiency and convenience.

While demographic factors such as age, education level, and income did not show statistically significant effects on consumers’ intention to use apparel DRTs, some notable trends were observed. The data suggests that younger consumers, particularly generation Z women with higher education levels, are more receptive to adopting these technologies. This demographic is typically more comfortable navigating digital environments and seeks innovative, immersive experiences that align with their tech-savvy lifestyles. Interestingly, income was not a significant determinant of adoption intent, which may reflect the democratization of DRT technologies. Many retailers have made these services more affordable or have adopted shared-cost models, reducing financial barriers and making the technology accessible to a broader range of consumers.

5. Implications

This study makes several distinct theoretical and practical contributions to the emerging field of virtual retail research, particularly in the context of DRTs. While previous research has predominantly focused on isolated components of DRTs, such as virtual reality applications, virtual fitting rooms, or specific aspects of immersive technologies, this study is among the first to provide a comprehensive and holistic examination of DRTs. Specifically, it investigates both online and offline implementations, offering an integrated conceptualization of DRTs that advances current theoretical understanding. This broader perspective allows for a deeper exploration of DRT’s evolving role within the retail landscape, moving beyond fragmented studies to present a unified view of how these technologies reshape consumer experiences and retail strategies.

Second, this study introduces and empirically validates a novel theoretical model that combines the unified theory of acceptance and use of technology (UTAUT) with perceived risk theory. While prior research has frequently applied UTAUT or perceived risk in isolation, the integrative approach in this study offers a more comprehensive framework for understanding U.S. consumers’ intentions to adopt apparel DRTs. By capturing both the drivers (e.g., performance expectancy, effort expectancy) and barriers (e.g., perceived risks) to technology adoption, the model delivers a more nuanced explanation of consumer behavior. This dual-theory integration is a unique contribution, offering theoretical clarity and depth that extends beyond prior models used in retail technology adoption studies.

Third, on a practical level, this study provides actionable insights specifically tailored to the fashion industry as it transitions from traditional e-commerce and mobile commerce toward virtual commerce (v-commerce). Unlike earlier studies that primarily examined consumer acceptance of virtual technologies in general retail or technology contexts, this research focuses explicitly on the apparel sector. The findings identify key factors—such as ease of use, perceived value, and technological infrastructure—that influence consumer adoption of DRTs, addressing practical challenges unique to fashion retailers. By offering clear guidance on reducing perceived risks and enhancing user experience design, the study equips fashion retailers with strategic insights to foster higher consumer engagement.

Furthermore, the study emphasizes the strategic advantage for early adopters of DRTs technologies in fashion retail. As consumer expectations increasingly shift toward more immersive, personalized, and interactive shopping experiences, the findings underscore the importance of proactive investment in DRTs. This research distinguishes itself by providing empirically grounded recommendations on how fashion brands can leverage DRTs to stay competitive, build consumer trust, and accelerate adoption.

The unique contribution of this study lies in its comprehensive conceptualization of DRTs, its integration of dual theoretical frameworks to better explain consumer adoption behavior, and its tailored practical insights for the fashion industry navigating the shift to virtual retail experiences.

6. Limitations and Future Studies

Despite the valuable insights provided by this study, several limitations should be acknowledged, offering opportunities for future research. First, this study focused on U.S. consumers, with a sample that was predominantly younger, well-educated, and skewed toward middle-income groups. As such, the findings may not be fully generalizable to other demographic segments or international markets, where consumer attitudes toward DRT may differ due to cultural, economic, and technological factors. Future research could explore cross-cultural comparisons and investigate how DRT adoption varies in different global retail environments.

Second, while this study examined key determinants of DRT adoption, such as effort expectancy, facilitating conditions, and perceived risks, it did not account for other emerging technological factors that may influence consumer behavior, such as artificial intelligence-driven personalization, biometric security, or haptic feedback. Future studies could integrate additional technological dimensions to provide a more comprehensive understanding of how cutting-edge innovations impact consumer acceptance of immersive retail technologies.

Finally, this study measured consumers’ intentions to use DRTs rather than their actual long-term adoption and usage behavior. Intentions may not always translate into sustained engagement, as real-world constraints such as evolving technological advancements, changes in consumer trust, and industry-wide shifts could influence adoption rates over time. Future research should conduct longitudinal studies to track actual usage behaviors and examine how sustained exposure to DRT affects consumer satisfaction, purchase decisions, and loyalty.

By addressing these limitations, future studies can further advance our understanding of DRT adoption and its evolving role in shaping the digital retail landscape.