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Article

Exploring User Engagement and Purchase Intentions in T-Shirt Retail Through Augmented Reality and Instagram Filters

by
Christopher Girsang
and
Chin-Hung Teng
*
Department of Information Communication, Yuan Ze University, Taoyuan 320315, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(18), 10161; https://doi.org/10.3390/app151810161
Submission received: 12 August 2025 / Revised: 30 August 2025 / Accepted: 8 September 2025 / Published: 18 September 2025
(This article belongs to the Special Issue Advances in Human–Machine Interaction)
Browse Figures
Versions Notes

Abstract

Augmented reality (AR) technologies—such as Instagram filters—bridge the digital and physical worlds by allowing users to virtually try on clothing, thereby reducing the risk of virus transmission. In the T-shirt retail industry, AR enables product personalization, decreases the need for physical production, minimizes textile waste, and lowers carbon emissions. It also benefits individuals with limited mobility or those who prefer shopping online. This study tested several hypotheses on 105 active Instagram filter users using filters from the ’Apprecio’ account on mobile devices. Data analyzed using the partial least squares method revealed that interactivity significantly influences both purchase intention and continued use of digital platforms. While hedonic and vivid features enhance the user experience, they have a limited impact on driving purchases or long-term engagement. Customers’ engagement and buying intent are more strongly shaped by practical and interactive elements. The study recommends that companies invest in developing interactive AR features to boost customer satisfaction and foster trust. Future research should involve larger participant samples and investigate specific interactive elements—such as virtual try-on tools—to better understand their impact on consumer behavior. This study highlights the critical role of interactivity in AR for delivering meaningful and engaging shopping experiences.

1. Introduction

After the COVID-19 pandemic, many people became hesitant to try items in person, such as clothes, pants, and other products. During this period, mobile shopping grew rapidly due to the widespread use of smartphones for browsing and purchasing, making shopping safer and more convenient, especially after health crises like pandemics [1]. This shift highlights how technology is reshaping the way that consumers interact with brands and products while reducing the need for physical contact. However, a new challenge has emerged: the content presented in online shopping apps—such as photos and videos—often does not accurately reflect the actual products received [2]. This discrepancy leads to customer disappointment and hesitation toward future online purchases. To address this issue, augmented reality (AR) has become a valuable innovation. It provides realistic, interactive product experiences that enhance the overall shopping journey, reduce the need for physical interaction, and lower the risk of virus transmission during outbreaks. AR try-on features, in particular, allow customers to fulfill their clothing needs without ever stepping into a physical store [3].
Augmented reality bridges the digital and physical worlds by overlaying virtual elements onto a person’s real environment [3]. Its use has rapidly expanded across healthcare, fashion, and e-commerce industries. AR offers practical solutions like virtual try-ons, which assist with assessing fit and style, reduce product returns, and help to prevent overproduction—thereby minimizing textile waste and lowering carbon emissions [4]. In addition to making shopping more convenient, AR enhances customer confidence and fosters trust in the buying process [5]. During pandemics, AR has proven especially valuable by providing safe, contactless experiences along with additional product information, further driving its adoption across sectors [3].
According to Forbes [6], companies lose billions in revenue because 91% of users are reluctant to download dedicated business apps. Solutions like Meta Spark AR have played an essential role in addressing this challenge by enabling users to interact directly with augmented content without requiring additional installations. This convenience has accelerated the growth of AR advertising on social media platforms like Instagram. Unlike specialized AR applications that typically provide immersive, precise, and customized shopping experiences [7], Instagram’s AR shopping tools stand out for their social-media-centric approach. Features like filters and lenses are designed to promote playful interaction, social sharing, and user-generated content that can quickly go viral. Integrated seamlessly into the Instagram platform, these tools allow users to try products and amplify brand visibility through influencers and peer-to-peer sharing, creating a socially driven and engaging shopping experience. However, despite their popularity, Instagram’s AR features remain limited in scope and depth compared to specialized AR tools, and there is still little understanding of how they influence marketing strategies, particularly in the clothing industry. Gaining deeper insights into how Instagram-based AR experiences shape consumer behavior can provide valuable guidance for businesses seeking to strengthen their marketing strategies [8].
The theory of interactive media effects (TIME), introduced by Sundar [9], explains how the features of interactive media influence users’ psychological responses and behaviors. In simple terms, TIME highlights that how media technologies are designed and presented can directly shape how people perceive, interact, and respond to them [9]. This perspective is particularly relevant to Instagram filters, one of the most widely used augmented reality forms in everyday life. Unlike more specialized AR applications that emphasize product accuracy, Instagram filters combine technological features with strong social sharing and entertainment elements, making them a unique case for applying the TIME framework. While TIME has often been used in studies of AR and other interactive media [10,11,12], its application to Instagram filters in retail contexts is still very limited. Key aspects emphasized in TIME, such as augmentation and interactivity, are clearly reflected in filters. For example, filters that allow users to try on clothing virtually demonstrate augmentation, while real-time customization illustrates interactivity. Thus, analyzing Instagram filters through the lens of TIME helps to explain how their technical features affect consumer perceptions and engagement and how they may influence purchase intentions in digital retail contexts.
Based on the previous discussions, this research aims to carry out the following:
  • Analyze consumer perceptions of augmented reality elements—such as Instagram filters on social media—using the TIME model framework.
  • Examine how Instagram’s augmented reality features enhance the shopping experience.
  • Investigate the impact of Instagram AR filters on purchasing decisions through the lens of the TIME model.
This study makes a valuable contribution to existing research on technology adoption by applying the TIME model specifically to augmented reality features on Instagram. Furthermore, it highlights the importance of characteristics such as perceived augmentation and flow within the research framework. Including these elements is crucial for evaluating the effectiveness of Instagram AR filters and enhancing the overall applicability of the model. This study provides significant insights into consumer satisfaction and confidence in adopting technology, particularly in the context of AR features on social media platforms.

2. Literature Review

2.1. Integration of AR in Social Commerce

Social commerce, as one of the rapidly growing subsets of e-commerce, integrates the dynamics of social networking with online trading platforms, creating a more interactive and engaging shopping experience [13,14]. Social commerce can be described as a shopping ecosystem within social media that leverages online social capital, enabling consumers to stay actively engaged before, during, and after transactions when platforms provide interactive and supportive features [15]. The unique characteristics of social commerce span social, commercial, technological, behavioral, and sharing dimensions [16], with its main distinction from traditional e-commerce lying in the deeper level of social engagement. Unlike standard e-commerce, where reviews are typically directed at anonymous buyers, social commerce grows through user-to-user interactions, such as sharing product information, experiences, and recommendations within their networks [14,17,18]. The presence of forums, communities, ratings, and user-generated content strengthens the relationship between sellers and buyers, building a trust foundation that influences decision-making and repeat purchasing behaviors [19].
Instagram has emerged as one of the leading platforms in social commerce by combining its visual-centric design with advanced interactive features. Since its launch in 2010, Instagram has evolved from merely a content-sharing platform into a powerful retail and marketing tool, with augmented reality filters as one of its most significant innovations. These AR filters, powered by Meta Spark Studio, allow users to add digital effects to their faces, bodies, or environments, directly creating immersive and interactive experiences through their smartphones. Beyond entertainment [20], education [21], and cultural heritage preservation [22], AR filters are increasingly being utilized in retail to capture consumer preferences, enable virtual product trials, and create more personalized shopping experiences [23].
The integration of AR in digital retail has been widely recognized for its positive impact on consumers’ decision-making processes. Previous studies have shown that AR not only enhances enjoyment but also increases the perceived usefulness of the shopping experience [24,25] by strengthening spatial presence, vividness, and value perception [26,27,28]. These immersive qualities form positive attitudes and stronger behavioral responses by balancing the perceived benefits and costs [29,30]. Within the Instagram ecosystem, these AR-driven experiences present innovative opportunities for fashion retailers (such as T-shirt promotions) to attract consumers by combining interactive visualization and social interaction, influencing attitudes, trust, and purchase decisions.
Shuhaiber [31] further reinforces this perspective by identifying trust, ease of use, and personalization as key factors driving consumer engagement and purchasing behavior in Instagram-based shopping contexts. These factors become even more significant when applied to the use of AR filters for fashion products such as T-shirts: AR filters enhance vividness and interactivity, allowing consumers to virtually try on products and visualize fit and style before making purchase decisions. AR filters deepen user engagement and boost consumer confidence in their purchase choices. However, the study also highlights existing challenges, such as unclear product information or fake reviews, which can reduce trust and emphasize the importance of transparent communication and reliable social proof when integrating AR technologies into fashion marketing strategies.
Building on this perspective, Leong [32] introduces the social commerce framework, which holistically integrates the social, technological, commercial, and behavioral dimensions within the social commerce ecosystem. This framework offers a comprehensive lens for analyzing the role of Instagram filters in retail, particularly in the fashion segment. From the social dimension, AR filters encourage user-generated content and peer-to-peer interactions, reinforcing trust and social proof. The technological dimension highlights the role of advanced AR technology in enhancing vividness and personalization, creating smoother and more intuitive shopping experiences. The commercial dimension emphasizes how filters function as strategic tools to support promotions, increase traffic, and accelerate purchase conversions. Finally, the behavioral dimension captures how interactive AR experiences influence user engagement and purchase intentions.
Overall, these studies provide a coherent understanding of how AR technology reshapes the digital shopping journey when integrated into Instagram as a social commerce platform. The combination of immersive visualization, social interaction, and advanced personalization drives consumers.

2.2. The Theory of Interactive Media Effects (TIME)

TIME explains how media affordances shape user perceptions, attitudes, and behaviors [9]. Affordances are system features that enable interaction. TIME proceeds through predictors, mediating variables, and outcomes. Key affordances include modality (format of information), agency (user control), interactivity (manipulation of media), and navigability (ease of exploration). In AR, perceived augmentation or digital overlay onto physical environments emerges as a distinct fifth affordance beyond traditional categories [33]. Mediating variables explain how media features are translated into real experiences for users. Heuristics, such as realism, control, and quick responsiveness, help users to process information more easily. Perceptual bandwidth, which includes ease of use, intuitiveness, and vivid presentation, makes interactions feel comfortable and natural. In addition, reciprocity or dialogue with the system (contingency) enhances engagement, while intrinsic motivation (self-determination), such as competence, autonomy, and social connection, strengthens long-term experiences. All these aspects ultimately shape positive perceptions of the source, interface, and content, encouraging users to gain knowledge, develop better attitudes, and foster sustainable behaviors such as continued system use or recommending it to others [34].
In social commerce, Instagram filters enhance decision-making by fostering control, immersion, and enjoyment. Unlike static product images, filters support continuous interactive visualization, making them effective for virtual try-on and product engagement [20]. This study focuses on how users customize and manipulate product appearances through filters. By directly interacting with augmented elements, users gain a sense of control, deeper immersion, and greater decision-making confidence, which are key factors in creating meaningful, user-centered shopping experiences.

3. Conceptual Framework

The TIME model will be employed to examine the factors influencing user engagement and purchasing decisions related to augmented reality on social media, with a focus on Instagram filters. The core components that we discussed in this study include predictors such as perceived augmentation (AUG) and interactivity (INT); mediators such as the hedonic component (HE), utilitarian component (UT), and vividness (VI); and outcome variables such as purchase intention (PI) and repeat usage (RU). These components serve as key indicators of user interaction with AR features on platforms like Instagram. Table 1 provides a detailed overview of these variables along with their respective measurement questionnaires.

3.1. The Effects of Perceived Augmentation

According to Javornik [10], perceived augmentation evaluates the effectiveness of user interaction in virtual environments that aim to mirror reality realistically and seamlessly. In the context of augmented reality try-ons, this concept is reflected in the digital embedding of 3D objects into the physical environment, which enhances users’ comprehension of the product and its attributes. Previous studies, such as those by Lee et al. [11], have demonstrated that perceived augmentation significantly influences both hedonic and utilitarian components of user experience in augmented reality garment try-ons. The hedonic component refers to the enjoyment, entertainment, and emotional gratification that users derive during virtual try-on interactions, such as excitement or engagement when exploring the technology. In contrast, the utilitarian component emphasizes functional and practical value, including ease of evaluating size, fit, and time efficiency during decision-making [37]. These findings suggest that perceived augmentation enhances users’ experiential satisfaction and supports more rational and informed purchase decisions. Therefore, drawing from these insights, it is hypothesized that higher levels of perceived augmentation will positively influence users’ experiential and behavioral outcomes in AR-based interactions.
We intend to evaluate the same concept with Instagram filters, and our hypotheses for this issue are as follows:
H1. 
The perceived augmentation in Instagram filters influences the utilitarian component.
H2. 
The perceived augmentation in Instagram filters influences the hedonic component.
Flow is a well-established concept in psychology, referring to a state of deep focus and immersion in an activity, typically triggered by a challenging task, as explained by Javornik [10]. In marketing research, flow has been widely applied to examine consumer engagement in digital environments, revealing its significant influence on user interaction and behavioral outcomes. In the context of Instagram AR filters, particularly T-shirt try-ons developed with Spark AR, flow is manifested when users experience a seamless and immersive interaction, such as virtually testing different designs in real time and sharing their experiences socially on the platform.
Vividness, on the other hand, refers to a technology’s capacity to create a rich sensory environment that encompasses both breadth (variety) and depth (intensity) of sensory stimulation [27,38]. It emphasizes the clarity, detail, and engagement that a medium provides. While vividness in earlier digital media was primarily achieved through static high-resolution images or basic 3D modeling, the evolution of AR technologies like Spark AR has elevated this experience. These filters now allow for real-time manipulation of garment styles, colors, and sizes, combined with spatially accurate overlays and dynamic visual effects. This advancement creates a more immersive experience that helps users to better visualize products in their real-world context.
Prior studies have demonstrated that higher levels of vividness enhance immersion and flow in interactive digital environments [10]. In the case of Instagram T-shirt try-on filters, vividness enables users to perceive the product more realistically, increasing enjoyment, emotional engagement, and practical utility, such as evaluating fit or style before purchase. Moreover, the degree of perceived augmentation—the extent to which digital elements are seamlessly integrated with the physical environment—can significantly shape perceptions of vividness. When augmentation is perceived as realistic and seamless, users are more likely to experience a heightened sense of clarity, richness, and interactivity in the AR environment.
Based on the discussion, our hypothesis is as follows:
H3. 
The perceived augmentation in Instagram filters influences vividness.

3.2. The Effects of Interactivity

Interactivity encompasses two core components: the technology itself and users’ perceptions of that technology. Technological features such as rapid responsiveness, precise controls, and content customization enable user engagement [39]. However, user motivation plays a critical role in how interactivity is perceived. Even with advanced technology, low motivation can lead to minimal interaction. Therefore, fostering user motivation is essential for promoting deeper and more effective engagement [40].
In augmented reality, interactivity is associated with both hedonic and utilitarian values. AR creates engaging and immersive experiences by modifying visual and sensory elements through various media and virtual content [33,41]. These enjoyable experiences boost user engagement and satisfaction, demonstrating how interactive features enhance the appeal of AR technology. At the same time, AR delivers practical benefits such as improving product visibility and supporting more informed decision-making [40,42].
The vividness of augmented reality can generate immersive sensory experiences through high-quality graphics, aiding users in visualizing product usage and enhancing their confidence in purchase decisions. The combination of vividness and interactivity enhances the whole experience, particularly with the addition of sound. Vividness enhances user comprehension of products and delivers an immersive experience that increases engagement and promotes higher purchase intentions [33].
Based on this information, the following hypotheses are proposed:
H4. 
The interactivity in Instagram filters influences the utilitarian component.
H5. 
The interactivity in Instagram filters influences the hedonic component.
H6. 
The interactivity in Instagram filters influences vividness.

3.3. Mediators to Purchase Intention and Repeat Usage

Hedonic value arises from augmented reality’s enjoyable and engaging experiences, fostering emotional involvement that makes interactions more satisfying and memorable [33]. This emotional engagement has been shown to enhance both purchase intention and repeat usage, as users who find the experience enjoyable are more likely to purchase and engage with the technology repeatedly over time. Thus, it is reasonable to expect the hedonic experience to positively influence users’ purchase intention and repeat usage [33].
Similarly, the utilitarian value of AR applications, which reflects their functional benefits, such as improving decision accuracy, convenience, and efficiency, is also expected to shape behavioral outcomes [33]. When users find the AR experience practically helpful in evaluating products, they are more likely to translate this perceived utility into purchase intention and repeat usage, as supported by prior research on technology acceptance and continued use [43,44].
In addition, vividness—the degree to which AR delivers a rich and immersive sensory experience—enhances users’ ability to visualize products in context. Vividness builds confidence in product evaluation and deepens trust and engagement with the technology [33]. Consequently, higher levels of vividness are expected to increase purchase intention and encourage repeat usage by fostering a stronger connection between the user and the interactive AR environment. However, insufficient evidence clearly demonstrates how AR directly influences these variables. Consequently, the following hypotheses are proposed:
H7. 
The utilitarian component influences repeat usage.
H8. 
The utilitarian component influences purchase intention.
H9. 
The hedonic component influences repeat usage.
H10. 
The hedonic component influences purchase intention.
H11. 
The vividness influences repeat usage.
H12. 
The vividness influences purchase intention.

3.4. Relationship Between Purchase Intention and Repeat Usage

Considering that AR applications are designed to encourage continuous engagement and repeated interactions, it is critical to understand how such repeated usage shapes user experiences and ultimately drives purchase decisions [45]. Drawing on the stimulus–organism–response (S-O-R) model [46], repeated interactions with AR environments serve as stimuli that enhance user familiarity, comfort, and perceived value over time. This, in turn, strengthens cognitive and affective responses, which are likely to translate into stronger behavioral intentions, such as the intention to purchase [45].
Existing research has predominantly focused on technology adoption or first-time usage [33], often overlooking how continued interaction with AR technologies influences deeper stages of the consumer decision process. In the context of AR-driven product try-ons, habitual engagement allows users to refine their preferences, reduce uncertainty, and build trust in the accuracy and reliability of the technology, thereby making purchase intentions more robust.
Accordingly, we propose the following hypothesis:
H13. 
Repeat usage influences purchase intention.
We summarize all the constructs, their relationships, and the associated hypotheses in Figure 1.

4. Methodology

4.1. Sample and Data Collection

According to the “10-times rule,” a commonly used guideline in partial least squares structural equation modeling (PLS-SEM), the minimum recommended sample size should be at least 10 times the maximum number of indicators associated with any latent variable [47]. This study also suggests that a minimum of 50 participants is sufficient to ensure reliable results. Our experiment included 105 people who participated voluntarily. Thus, the sample size of this study meets and exceeds both recommended thresholds, ensuring adequacy and robustness for PLS-SEM analysis. After agreeing to participate in the study, all participants were granted access to the designed Apprecio’s Instagram filters. Subsequently, the research team distributed the questionnaire online via Google Forms to collect data related to their user experience. Data for this study were collected between October and December 2023. Table 2 presents the age distribution of participants: 42.8% were between 18 and 25 years old, 40% were between 26 and 35, and 7.6% were between 36 and 45. Regarding gender, 43% of the participants were female, while 57% were male.
Participants were recruited using convenience sampling: a non-probability method where subjects are selected based on accessibility, availability, or willingness to participate [48,49]. This approach is affordable and practical but may introduce bias and limit the generalizability of results, as the sample may not fully represent the target population [50,51]. Researchers should also acknowledge any potential over- or under-representation when using this sampling method.

4.2. Questionnaire

Participants first participated in an experiment and then completed an online questionnaire. Figure 2 shows the procedures for operating our Instagram AR filter for T-shirt try-on. In the experiment, participants followed a sequence of interactions: (a) they began by trying the Instagram T-shirt filter on their mobile phones and testing all the available features within the filter; (b) they proceeded to scan a flat plane; (c) the T-shirt was then displayed on the screen, where participants could press the button to rotate it; (d) the pinch gesture was used to zoom in and zoom out, enabling closer inspection of garment details. The filter provided four color variations—(e) white, (f) pink, (g) black, and (h) beige—allowing participants to explore different style options. (i) Finally, participants could take photos, record videos, and save these outputs directly from the filter, completing the (j) AR T-shirt try-on demonstration.
The study utilized a survey created with Google Forms to examine the relevant variables. Based on previous research, the questionnaire included 27 items, as shown in Table 1. The survey was divided into two sections. The first section included two simple questions to gather demographic information: gender and age. The second section focused on seven variables and used a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), to measure each variable in the model.

4.3. Structural Equation Modeling

The study employed structural equation modeling (SEM) and smart partial least squares (SmartPLS) to analyze the relationships among variables, as depicted in Figure 1. SEM is a powerful statistical tool widely used for evaluating complex relationships among multiple variables while effectively accounting for measurement errors. It assesses causal relationships between external (exogenous) and internal (endogenous) variables through standardized coefficients and significance levels. As illustrated in Figure 1, the model includes seven variables: two external variables (perceived augmentation and interactivity), three mediating variables (hedonic component, utilitarian component, and vividness), and two outcome variables (purchase intention and repeat usage).

5. Result, Discussion, and Implication

5.1. Result

Table 3 presents the reliability and validity assessment of the constructs used in this research. The Cronbach’s alpha and composite reliability values exceed the recommended threshold of 0.7, and the average variance extracted (AVE) is also above the acceptable level of 0.5. These results indicate that the model meets the required standards, demonstrating strong internal consistency and reliability, as well as satisfactory convergent validity.
The model fit results presented in Table 4 indicate that the model used in this study is acceptable, even demonstrating relatively good performance. The SRMR (standardized root mean square residual) value of 0.069 falls below the recommended threshold of 0.08 [52], indicating that the model has a good fit between the theoretical structure and the observed data. This value suggests that the residual differences or discrepancies between the theoretical model and the actual data are relatively small and insignificant, meaning that the structural model can represent the relationships among the examined variables. In addition, the GoF (goodness of fit) index of 0.709 further strengthens the evidence that the model has good overall quality. This value is well above the commonly accepted minimum of 0.36 [53], which is generally used as an indicator of a model with adequate quality. The closer the value is to 1, the better the model fits the analyzed data. With such a high value, we can conclude that the model not only fits the existing data but also explains the variability among variables with a strong level of reliability, supporting the validity of the findings in this study.
Furthermore, the chi-square/df ratio, which falls below the recommended threshold of 5 [54], suggests that the model effectively balances complexity and data fit. This ratio reflects that the model is not overly complex or overfitted, allowing the analytical results to be generalized more confidently. The average R2 value of 0.615 and AVE of 0.817 shown in Table 5 also reflect good explanatory power and convergent validity across constructs. These indicators collectively support the robustness and validity of the proposed structural model.
Table 6 presents the test results of all our hypotheses. As shown in the table, our study supports most of the hypotheses, except that the hedonic component and vividness do not significantly affect repeat usage and purchase intention. Figure 3 presents the analysis results of all our hypotheses, including the path coefficients, statistical significance levels, and the scores for each questionnaire item. This figure shows that the interactivity variable has the most potent effect on the utilitarian variable, with a coefficient of 0.722 (p ≤ 0.001). This indicates that higher interactivity (such as real-time customization of T-shirt colors, sizes, and styles within the Instagram AR filter) significantly enhances the perceived practical value of the technology. Users are more likely to recognize its usefulness in making informed choices, such as evaluating fit or style, when the interactive elements are seamless and responsive.
Following this, the utilitarian variable significantly influences repeat usage, with a coefficient of 0.626 (p ≤ 0.001). This suggests that when users perceive clear practical benefits, they are more motivated to repeatedly engage with the AR filter, reinforcing the role of the utilitarian component as a key driver for sustained interaction.
The most significant impact on purchase intention comes from repeat usage, with a coefficient of 0.470 (p ≤ 0.001), followed by the utilitarian variable, with a coefficient of 0.381 (p ≤ 0.001). This highlights that repeated interactions increase familiarity and trust and build confidence in the product, making users more likely to commit to a purchase. Similarly, the utilitarian value supports this intention by demonstrating the filter’s functional benefits, such as realistic visualization, which aids decision-making.
In contrast, while conceptually important, the hedonic and vividness variables function more as mediating factors and do not show significant direct effects on purchase intention and repeat usage. This suggests that enjoyment and sensory richness alone are insufficient to drive behavioral outcomes unless they are complemented by practical and utilitarian benefits. In other words, users may find the AR experience fun and visually appealing. Still, their decision to reuse the feature or purchase is primarily influenced by its functional value and how effectively it integrates into their decision-making process.

5.2. Discussion

Interactivity is a critical factor shaping consumer decision-making, especially within the context of social media shopping. It acts as a primary motivator, encouraging individuals to make purchases and continue using a platform over time. A more engaging and interactive shopping environment improves the overall user experience and enhances the perceived value of the platform. When users find a functional and enjoyable platform, they are more likely to develop a sense of loyalty, leading to sustained engagement and increased sales. Therefore, businesses should acknowledge interactivity as a critical factor in shaping consumer behavior and supporting long-term success.
To maximize the benefits of interactivity, organizations should focus on expanding and refining the interactive features of their platforms. Enhancing UI/UX design can lead to smoother navigation and greater user satisfaction, encouraging more frequent engagement.
In addition, adopting innovative marketing approaches (such as interactive Instagram filters) can create a more immersive and personalized shopping experience. These filters enable consumers to visualize products in a more engaging way, strengthening their connection with the brand. By implementing such interactive strategies, businesses can improve user retention and increase the overall attractiveness of their platforms.
On the other hand, not all digital engagement factors significantly influence purchase intentions or repeat usage. Similarly, hedonic features (entertainment) and vividness (clarity) do not significantly drive these outcomes, which is contrary to the findings of [33], which argue that hedonic and vividness, referring to the extent to which AR delivers enjoyment or fun and a rich sensory experience, can enhance users’ ability to visualize products in a more realistic context. This capability increases confidence in evaluating products and deepens users’ trust and engagement with the technology. However, the absence of support for the effects of the hedonic component and vividness on repeat usage and purchase intention in this study may be explained by an emerging consumer trend in which functional value is prioritized over visual appeal. Moreover, based on the habituation–tedium theory [55], the novelty effect of immersive AR experiences quickly diminishes after repeated exposure, making vividness less effective in capturing attention or encouraging repeated interactions without continuous innovation or refreshed content.
As a result, organizations may need to reconsider prioritizing these aspects and instead focus on more effective strategies to encourage purchases and repeat usage. However, exploring how augmented reality features in Instagram filters can improve consumer understanding of T-shirt products could still offer some value. Ultimately, focusing more on strategies that have been shown to enhance interactivity and user engagement will likely be more effective in promoting business growth and improving the overall shopping experience.

5.3. Implication

5.3.1. Theoretical Implication

This study contributes to understanding how augmented reality in Instagram filters can help businesses to increase purchase intention for products like T-shirts. As mentioned earlier, there is still limited research exploring the key factors influencing purchase intention when using AR technology on social media platforms. Understanding these factors is crucial for gaining deeper insights into consumer behavior within this context.
The findings extend the TIME model by demonstrating how the model functions within the unique environment of social-media-based shopping. The strong effects of interactivity and utilitarian value on repeat usage and purchase intention highlight the importance of practical and functional engagement when consumers interact with AR filters. These results indicate that the TIME framework, which emphasizes technological affordances such as augmentation and interactivity, should be adapted to accommodate goal-oriented behaviors common in social commerce, where consumers prioritize functional benefits alongside entertainment.
Additionally, the results show that the direct effects of the hedonic component and vividness on user behavior are relatively limited. In the context of social media shopping, AR technology’s functional benefits play a more critical role than enjoyment or visually rich experiences. This finding refines the TIME model by emphasizing that, while immersive and enjoyable experiences can enhance user engagement, they are not sufficient to drive purchasing behavior without clear and practical value.
Finally, this study highlights the importance of integrating habitual usage into the TIME framework, as repeat usage strongly predicts purchase intention. This suggests that future applications of the TIME model in AR commerce should consider the long-term behavioral effects of repeated interactions, helping to bridge the gap between initial engagement and sustained consumer loyalty in social media shopping environments.

5.3.2. Practical Implication

This study contributes to existing research by demonstrating that interactivity, particularly through the utilitarian value, is a key driver in shaping purchase intention and repeat usage on digital platforms, especially when consumers engage with Instagram filters for T-shirt shopping experiences. The findings reinforce that interactive features such as the ability to virtually “try on” T-shirts, adjust colors, or explore different designs in real time significantly enhance users’ sense of engagement and confidence in their purchase decisions. For instance, when users interact with Instagram filters that allow them to switch between various T-shirt styles, fit options, or colors, they gain a more accurate impression of the product and form a stronger emotional connection to the brand. This interactive engagement creates a playful and immersive experience beyond static images, making shopping more dynamic and enjoyable.
Moreover, the study emphasizes that while vividness and hedonic elements, such as high-quality visuals or aesthetic effects, can make the filter experience more appealing, they do not necessarily drive purchase intention or repeat use. Instead, the ability to explore and interact with T-shirt designs in a personalized way serves as the primary motivator for consumers to proceed with purchases or revisit the platform. From a practical perspective, brands should prioritize interactive features. These include real-time customization that lets users switch between different T-shirt colors, patterns, and sizes; fit and style previews supported by body-tracking technology; interactive calls-to-action with direct links to product pages or “add to cart” buttons; and social sharing functions that allow users to post customized looks directly to Instagram Stories, thereby encouraging organic promotion. By emphasizing interactivity over purely aesthetic effects, brands can deliver a more engaging, confidence-building shopping experience that leads to higher conversion rates and stronger customer loyalty.

6. Limitations and Future Research

This study has limitations, which should be considered when interpreting the findings. First, the small sample size (n = 105) may limit the statistical power of the analysis and the ability to generalize the results to a broader population. Second, the participants in this study were limited to young and active AR filter users, which could introduce sampling bias and reduce the applicability of the findings to consumers who are less familiar with or engaged in AR-based shopping experiences. Third, there is the possibility of a novelty effect associated with AR features, where participants’ positive responses might be influenced by the excitement of experiencing innovative technology rather than by stable behavioral patterns. Fourth, this study focused solely on a single product category (T-shirts), which may limit the generalizability of the findings. Future research should address these limitations by employing larger and more diverse samples, exploring nonactive AR users, and incorporating longitudinal designs to better account for the diminishing novelty of AR experiences over time. Another promising direction is cross-cultural validation to gain deeper insights into how cultural contexts shape the impact of AR interactivity on consumer behavior. Furthermore, exploring AR filters across different product categories, such as shoes, bags, or accessories, could provide broader insights into the versatility of AR technology in influencing purchase decisions. Investigating specific types of interactivity, such as product customization, gesture-based interactions, or interactive tutorials, would also help to identify the most effective features for promoting sustained consumer engagement and purchase intention across industries.
In addition, this study is limited by the absence of demographic subgroup analyses, such as examining potential differences based on gender, age, or other relevant demo-graphic variables. Without these analyses, whether the observed relationships between AR features, user engagement, and purchase intention hold consistently across diverse user segments remains unclear. To mitigate this limitation, future studies should aim for larger and more balanced samples that enable comparative subgroup analyses. For example, researchers could investigate whether younger users respond more positively to AR interactivity or whether gender influences preferences for vividness, personalization, or trust-related factors. Incorporating these subgroup insights would offer a more nuanced understanding of how demographic characteristics shape AR shopping behaviors and enhance the overall generalizability of the findings.

7. Conclusions

This study offers valuable insights into how augmented reality features, such as Instagram filters, influence consumer behavior in the T-shirt industry. The findings reveal that perceived interactivity is pivotal in promoting repeat usage and purchase intention. Increased interactivity drives deeper engagement with the media, enhancing user involvement and ultimately raising the likelihood of purchase. These results underscore the importance of brands prioritizing the development of highly interactive AR experiences, enabling consumers to engage with products actively and fostering stronger brand connections.
However, the research also indicates that, while enhancing the overall user experience, vividness and hedonic factors do not significantly influence purchase intention and repeat usage within this context. This point challenges previous assumptions regarding the role of these elements in driving consumer behavior. It suggests that brands should emphasize utilitarian features and functional interactivity to achieve more effective AR marketing outcomes on social media platforms.

Author Contributions

Conceptualization, C.G.; methodology, C.G.; validation, C.G. and C.-H.T.; formal analysis, C.G.; investigation, C.G. and C.-H.T.; writing—original draft preparation, C.G.; writing—review and editing, C.-H.T.; supervision, C.-H.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science and Technology Council, Taiwan, under Grant Nos. NSTC 112-2221-E-155-024 and NSTC 114-2221-E-155-017.

Institutional Review Board Statement

Ethical review and approval were waived for this study because our system allowed participants to use their own mobile phones to operate the system through an Instagram filter, which posed no harm to the participants.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Applsci 15 10161 g001
Figure 1. The hypotheses and constructs used in this study.
Figure 1. The hypotheses and constructs used in this study.
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Figure 2. Procedures for using our Instagram AR filter for T-shirt try-on. Please refer to the text for the detailed explanation of the procedures (a–j).
Figure 2. Procedures for using our Instagram AR filter for T-shirt try-on. Please refer to the text for the detailed explanation of the procedures (a–j).
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Figure 3. Final SEM model for determining factors understanding user engagement and purchase intention through Instagram filter.
Figure 3. Final SEM model for determining factors understanding user engagement and purchase intention through Instagram filter.
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Table 1. Model construct and the corresponding measuring questionnaire.
Table 1. Model construct and the corresponding measuring questionnaire.
ConstructItemMeasures
A. Affordance/Predictor
Perceived Augmentation (AUG)
Javornik (2016) [10]		AUG1After using the Instagram filters, I could still imagine the T-shirt.
AUG2The shirt in the Instagram filters seemed to exist in real time.
AUG3The level of reality seemed high on the Instagram filters.
Interactivity (INT)
Lee (2020) [11]		INT1I was in control over the content of the Instagram filters that I wanted to see.
INT2When I interacted with the Instagram filters, the information shown was relevant.
INT3When I interacted with the Instagram filters, the information shown met my expectations.
INT4When I interacted with the Instagram filters, the information shown was suitable.
INT5When I interacted with the Instagram filters, the information shown was useful.
INT6This filter gave me valuable information.
B. Mediating Variables
Vividness (VI)
Yim (2017) [33]		VI1The visual display through the Instagram filters was clear.
VI2The visual display through the Instagram filters was detailed.
VI3The visual display through the Instagram filters was sharp.
Utilitarian Component (UT)
Lee (2020) [11]		UT1Using the Instagram filters for apparel shopping would be helpful.
UT2Using the Instagram filters for apparel shopping would be functional.
UT3Using the Instagram filters for apparel shopping would be necessary.
UT4The information that the filter showed me was what I expected it to be.
UT5The information shown to me when I used the filter was accurate.
Hedonic Component (HE)
Lee (2020) [11]		HE1Using the Instagram filters for apparel shopping would be fun.
HE2Using the Instagram filters for apparel shopping would be exciting.
HE3Using the Instagram filters for apparel shopping would be enjoyable.
C. Outcomes
Repeat Usage (RU)
Li and Fang (2019) [35]		RU1I am willing to actively participate in the activities on the Instagram filter.
RU2I will frequently use the Instagram filters in the future.
RU3I strongly recommend that others use the Instagram filter.
Purchase Intention (PI)
Li and Peng (2021) [36]		PI1More likely to purchase this product.
PI2More likely to recommend this product.
PI3More likely to try this product.
PI4More willing to purchase the item.
Table 2. Information of the respondents.
Table 2. Information of the respondents.
MeasuresItemCountPercentage (%)
Age18–254542.8
26–354240.0
36–4587.6
GenderMale6057.0
Female4543.0
Table 3. Reliability and validity.
Table 3. Reliability and validity.
ConstructsCronbach’s AlphaComposite ReliabilityAverage Variance Extracted (AVE)
Perceived Augmentation0.790.870.70
Interactivity0.940.950.79
Vividness0.870.920.79
Hedonic Component0.920.950.87
Repeat Usage0.920.950.86
Utilitarian Component0.910.930.74
Purchase Intention0.920.940.81
Table 4. Model fit indices.
Table 4. Model fit indices.
ResultValueCriterion
SRMR0.069<0.08 [52]
GoF index0.709>0.36 [53]
Chi square/df2.72<5 [54]
Table 5. Measurement properties: R2 and AVE of latent variables.
Table 5. Measurement properties: R2 and AVE of latent variables.
Latent VariableR2AVE
Hedonic Component0.6430.870
Purchase Intention0.5850.814
Repeat Usage0.5430.862
Utilitarian Component0.7790.741
Vividness0.5250.798
Average0.6150.817
Table 6. Test results of the proposed hypotheses.
Table 6. Test results of the proposed hypotheses.
NoPathDescriptionSupported?
1AUG → UTPerceived augmentation positively influences utilitarian component.Yes
2AUG → HEPerceived augmentation positively influences hedonic component.Yes
3AUG → VIPerceived augmentation positively influences vividness.Yes
4INT → UTInteractivity positively influences utilitarian componentYes
5INT → HEInteractivity positively influences hedonic component.Yes
6INT → VIInteractivity positively influences vividness.Yes
7UT → RUUtilitarian component positively influences repeat usage.Yes
8UT → PIUtilitarian component positively influences purchase intention.Yes
9HE → RUHedonic component positively influences repeat usage.No
10HE → PIHedonic component positively influences purchase intention.No
11VI → RUVividness positively influences repeat usage.No
12VI → PIVividness positively influences purchase intention.No
13RU → PIRepeat usage positively influences purchase intention.Yes
AUG: perceived augmentation, INT: interactivity, HE: hedonic component, UT: utilitarian component, VI: vividness, PI: purchase intention, RU: repeat usage.
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Girsang, C.; Teng, C.-H. Exploring User Engagement and Purchase Intentions in T-Shirt Retail Through Augmented Reality and Instagram Filters. Appl. Sci. 2025, 15, 10161. https://doi.org/10.3390/app151810161

AMA Style

Girsang C, Teng C-H. Exploring User Engagement and Purchase Intentions in T-Shirt Retail Through Augmented Reality and Instagram Filters. Applied Sciences. 2025; 15(18):10161. https://doi.org/10.3390/app151810161

Chicago/Turabian Style

Girsang, Christopher, and Chin-Hung Teng. 2025. "Exploring User Engagement and Purchase Intentions in T-Shirt Retail Through Augmented Reality and Instagram Filters" Applied Sciences 15, no. 18: 10161. https://doi.org/10.3390/app151810161

APA Style

Girsang, C., & Teng, C.-H. (2025). Exploring User Engagement and Purchase Intentions in T-Shirt Retail Through Augmented Reality and Instagram Filters. Applied Sciences, 15(18), 10161. https://doi.org/10.3390/app151810161

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