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Article

What Drives Consumer Engagement and Purchase Intentions in Fashion Live Commerce?

by
Kihyang Han
1 and
Hyeon Jo
2,*
1
Department of Fashion Design, College of Design, Konkuk University, Chungju-si 27478, Chungcheongbuk-do, Republic of Korea
2
Headquarters, HJ Institute of Technology and Management, 31 Gangnam-daero 92-gil, Gangnam-gu, Seoul 06134, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(13), 5734; https://doi.org/10.3390/su17135734 (registering DOI)
Submission received: 27 May 2025 / Revised: 18 June 2025 / Accepted: 19 June 2025 / Published: 22 June 2025
(This article belongs to the Special Issue Eco-Friendly E-commerce: Empowering Purchase Intentions for Sustainable Growth)
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Abstract

:
Fashion live commerce has rapidly emerged as a compelling format that blends entertainment, real-time interaction, and product promotion. However, limited research has examined how specific experiential and perceptual factors influence consumer behavior in this context. This study aims to identify the key psychological and environmental drivers of satisfaction, continued platform use, and purchase intention among viewers of fashion live commerce. Using the stimulus–organism–response framework, this research focuses on the effects of perceived credibility, social media influencer characteristics, informativeness, internal shop environment, and monetary savings. Data were collected from 300 users of fashion live commerce platforms and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that all predictor variables significantly influence either satisfaction or current use, and both satisfaction and current use significantly predict purchase intention. Among the factors, satisfaction plays a central role, acting as a strong predictor for both current engagement and future buying decisions. These findings offer theoretical insights into consumer engagement in live commerce and provide practical guidance for streamers, marketers, and platform designers aiming to improve user experience and conversion rates. This study contributes to understanding the evolving dynamics of digital shopping environments shaped by social and emotional interactions.

1. Introduction

The emergence of live streaming commerce has fundamentally reshaped the digital retail landscape. This form of online shopping, often referred to as live commerce or live shopping, combines real-time video broadcasting with e-commerce functionalities, enabling consumers to interact with sellers, watch product demonstrations, and make purchases—all within a single platform [1]. Originally popularized in China through platforms like Taobao Live, live commerce has rapidly gained momentum across global markets, including South Korea and the United States, marking a new era of immersive and interactive shopping experiences [2]. Unlike traditional e-commerce, which primarily emphasizes convenience and catalog-based product access, live commerce integrates entertainment, social presence, and real-time engagement to create a more dynamic and emotionally resonant consumer journey [3,4].
Scholars have increasingly focused on the effectiveness of live streaming commerce due to its distinctive features. These include synchronous interaction between consumers and streamers, persuasive storytelling, real-time Q&A, and visually rich product presentations [5,6]. The interactive environment facilitates trust-building, encourages impulsive behavior, and reduces the psychological distance between buyer and seller [7,8]. Moreover, the role of streamers as influencers or opinion leaders is particularly critical, as their perceived credibility and relatability significantly shape consumer attitudes and intentions [9,10,11,12]. While previous studies have explored constructs such as trust, parasocial interaction, and interactivity, the understanding of how these and other factors jointly influence satisfaction and behavioral intentions remains fragmented.
Despite the growing attention to live commerce, several limitations in the current body of literature hinder a holistic understanding of its effectiveness. Most studies tend to isolate individual variables—such as trust, product quality, or entertainment—without examining how multiple psychological, informational, and contextual elements interact within the live shopping environment [13,14]. Furthermore, few empirical studies provide a theoretical framework capable of explaining the cognitive and emotional mechanisms that translate real-time stimuli into consumer satisfaction and behavior. While constructs such as informativeness and credibility are known to influence purchasing decisions, their specific roles in the context of live fashion commerce, where immediacy and immersion dominate the consumer experience, remain underexplored [15,16].
Additionally, studies often overlook the influence of the internal shop environment recreated in digital form—such as the stream’s visual appeal, tone, layout, or comment system—on consumer behavior [17,18]. These factors, analogous to ambient cues in physical retail environments, may evoke emotional responses that influence platform loyalty and purchase intention. Similarly, while monetary savings have long been recognized as a driver of consumer behavior, their function in the context of real-time discount promotions in live commerce has not been adequately theorized or tested.
Given the accelerated adoption of live commerce across industries, understanding the drivers of user satisfaction and purchase behavior is no longer just of academic interest but a strategic imperative. Among various sectors utilizing live commerce, the fashion industry stands out for its effective use of streamers’ visual appeal, real-time product showcasing, and interpersonal influence, which align well with the visual and experiential nature of fashion consumption [19,20]. Yet, without a comprehensive understanding of what makes these live shopping experiences effective, brands risk misallocating resources or failing to meet evolving consumer expectations.
Moreover, as consumers navigate increasingly saturated digital environments, their loyalty hinges not only on product utility but also on emotional and experiential factors. Satisfaction, current usage behavior, and purchase intention are all shaped by both rational appraisals (e.g., information quality, perceived savings) and affective responses (e.g., enjoyment, trust, presence) [21,22,23]. Therefore, it is essential to examine how these diverse factors interrelate within a structured theoretical model, especially one that reflects the sensory, social, and real-time nature of live commerce.
To address these gaps, this study adopts the stimulus–organism–response (S-O-R) framework to systematically investigate how external stimuli—namely, perceived credibility, social media influencer attributes, informativeness, internal shop environment, and monetary savings—shape internal organismic states (satisfaction and current use), which, in turn, affect behavioral responses (purchase intention). The S-O-R model, originally developed by Mehrabian and Russell [24], has been widely used to explain how environmental cues elicit emotional and cognitive responses, leading to specific behaviors. This study extends its application to the digital retail context, particularly in fashion live commerce, by incorporating constructs specific to this format such as live streamer credibility and digitally simulated shop environments.
The research model also integrates insights from uses and gratifications theory, which emphasizes that media users actively seek content that fulfills their informational, emotional, and social needs [25]. This dual-theoretical grounding allows for a deeper understanding of both the stimuli that attract users and the psychological mechanisms that convert engagement into behavioral outcomes.
This study offers several contributions. First, it extends the S-O-R framework to the live commerce context by integrating emotional, social, and financial stimuli, offering a comprehensive model of consumer decision-making in real-time digital shopping environments. Second, it empirically validates the role of satisfaction and current use as key mediators between environmental stimuli and purchase intention—constructs often overlooked in favor of direct predictors. Third, by examining fashion live commerce specifically, this research sheds light on a highly visual and influencer-driven domain, where traditional retail theories may fall short. For scholars, the findings provide a validated model that captures the psychological and behavioral dynamics of live commerce. For practitioners, the study highlights key levers—such as streamer trust, informational quality, and atmosphere design—that can be optimized to drive satisfaction, continued use, and conversion.

2. Literature Review

2.1. Live Streaming Commerce

Live streaming commerce, also known as live commerce or live shopping, refers to the integration of real-time video broadcasting with e-commerce functionalities, enabling consumers to view, interact with, and purchase products simultaneously [1]. Originating in China through platforms like Taobao Live, the format has rapidly expanded across global markets, including Korea and the United States, revolutionizing how consumers engage with brands online [2,26]. Unlike traditional e-commerce, live commerce merges entertainment, social interaction, and real-time decision-making, which collectively reshape the customer journey [3,4,27].
Research has emphasized the unique value proposition of live streaming commerce, which combines synchronous communication with real-time product visualization [5]. Interactivity is a core feature of live commerce platforms in general [7,28]. However, in the fashion domain, it plays an even more pivotal role by enabling detailed inquiries about sizing, styling, or material, thereby enhancing trust and mitigating uncertainty in highly tactile product categories [29,30]. This immediacy replicates key aspects of in-person retail experiences, such as direct consultation and social presence, that are absent in conventional e-commerce settings [14].
Another significant stream of research explores the role of influencers or streamers, who serve as key agents in delivering both entertainment and product information [31,32]. Influencers not only promote products but also build parasocial relationships with viewers, creating a sense of closeness and trust that enhances consumer engagement and conversion rates [2,8,9]. Studies show that streamer credibility—measured through attributes such as trustworthiness, expertise, and attractiveness—strongly influences viewers’ attitudes toward the brand and their intention to purchase [10]. This highlights the importance of streamer selection in shaping the effectiveness of live commerce strategies.
Informational quality also plays a critical role in consumer decision-making. Accurate, clear, and detailed product demonstrations provided during live streams help consumers reduce uncertainty and make more confident decisions [15,33]. Research has shown that high informativeness, when combined with visual appeal and interactive Q&A sessions, contributes significantly to consumer satisfaction and perceived value [20,34,35].
The emotional environment within the live stream—such as the atmosphere created by background music, visuals, and presenter tone—has also been identified as a driver of consumer engagement. According to environmental psychology theories, sensory cues in shopping environments influence emotional states and behavioral outcomes. In live commerce, these cues are digitally recreated to enhance user experience and stimulate purchasing behavior [36,37].
Despite the growing body of work on live streaming commerce, existing research still lacks a comprehensive understanding of how various psychological, informational, and environmental factors jointly shape satisfaction and behavioral intentions. Most studies focus on isolated variables without fully integrating them into a cohesive model. Addressing this gap, the current study adopts the S-O-R framework to systematically examine how perceived credibility, informativeness, and shop atmosphere influence satisfaction, current use, and purchase intention in the context of fashion live commerce.

2.2. Stimulus–Organism–Response Framework

The S-O-R framework serves as a foundational model for explaining how environmental stimuli influence human emotions and behaviors [24]. In this model, “stimulus” refers to external environmental cues, “organism” captures the internal cognitive or emotional state of the individual, and “response” represents the resulting behavioral outcome. The S-O-R model has been widely applied in consumer behavior research, particularly in retail and digital contexts, to understand how environmental factors shape user engagement and purchase decisions [38,39,40].
In the context of online shopping, researchers have used the S-O-R framework to explain how website design, product information, and interactivity affect user satisfaction and loyalty [41]. More recently, the model has been extended to live streaming commerce, where dynamic and interactive stimuli—such as cross-border e-commerce live streaming features, social presence, telepresence, attractiveness, trustworthiness, and expertise—trigger internal responses like trust, enjoyment, or cognitive involvement [14,42,43,44]. These organismic responses subsequently influence behavioral outcomes such as repeated platform use or purchase intention.
What distinguishes the S-O-R model from other behavior theories is its ability to integrate both emotional and rational responses within a single explanatory structure. This makes it especially relevant to immersive shopping formats like live commerce, where users are simultaneously influenced by entertainment, information, and social presence. Thus, the S-O-R framework offers a robust theoretical lens for understanding how digital shopping environments drive psychological engagement and behavioral outcomes in fashion live commerce.

3. Theoretical Foundation and Research Hypotheses

This study is grounded in the S-O-R framework, which provides a robust lens for explaining how environmental and social cues influence consumer psychology and behavior in fashion live commerce. The S-O-R model posits that external stimuli (S)—such as environmental signals or social cues—induce internal emotional or cognitive states (O), which subsequently drive behavioral responses (R) [24]. In this study, perceived credibility, social media influencer characteristics, informativeness, internal shop environment, and monetary savings function as key stimuli that shape consumer perceptions during live commerce experiences. As noted by several works [2,15,45,46,47], such stimuli play a crucial role in shaping affective and evaluative responses. In this context, satisfaction and current use represent organismic states that reflect users’ experiential and cognitive engagement. These, in turn, lead to the behavioral outcome of purchase intention. This study extends the traditional S-O-R model by incorporating constructs relevant to digital and interactive commerce environments, emphasizing the emotional and experiential pathways that convert engagement into transactional behavior.
Complementing the S-O-R framework, this study also draws on uses and gratifications theory (UGT), which explains how and why individuals actively seek out media to fulfill various psychological needs [25], media users are not passive recipients but active participants who choose platforms that satisfy informational, social, and hedonic gratifications. In the context of fashion live commerce, consumers engage with live broadcasts to acquire detailed product information, experience social connection through real-time interaction, and enjoy entertainment value. These motivations help explain the appeal of live commerce beyond its functional utility. The integration of UGT enriches the S-O-R framework by highlighting the role of user agency and gratification-seeking in shaping organismic responses and subsequent behaviors. Figure 1 presents the overall research model.

3.1. Perceived Credibility

Perceived credibility refers to the extent to which consumers consider a communicator or source to be believable, trustworthy, and competent in the context of product recommendations [48]. In fashion live commerce, this perception becomes more salient due to the real-time and interactive nature of the medium, where trust in the streamer plays a pivotal role in shaping consumer attitudes [49]. When audiences believe that a fashion streamer genuinely endorses a product, they are more likely to feel secure and positive about their shopping experiences [11]. Credibility is a crucial antecedent of psychological comfort and confidence during decision-making, particularly in technology-mediated shopping platforms [12,50]. As emotional reassurance builds, consumers may internalize the value of the experience and feel fulfilled with their choices. Thus, this study suggests the following hypothesis.
H1. 
Perceived credibility is positively related to satisfaction.

3.2. Social Media Influencer

Social media influencer refers to an individual who builds credibility within a specific niche and leverages their reach on platforms to affect followers’ attitudes and behaviors [51]. In the context of live commerce, influencers serve as real-time mediators of both information and emotion, offering a blend of entertainment, expertise, and interpersonal connection [52]. While prior studies primarily emphasized influencers’ effects on trust or purchase intention [4,13], recent findings highlight their role in enhancing emotional satisfaction during live interactions [53,54]. These emotionally resonant experiences can increase user satisfaction with the platform and shopping process. Thus, this study suggests the following hypothesis.
H2. 
Social media influencer characteristics are positively related to satisfaction.

3.3. Informativeness

Informativeness refers to the degree to which communication provides helpful, accurate, and comprehensive product-related information that aids in consumer decision-making [51]. In live commerce, informativeness is especially crucial as consumers often rely on real-time demonstrations and explanations to reduce uncertainty before making purchases [15,47]. Well-structured and visually rich information shared by streamers can improve the shopping experience by increasing consumer knowledge and confidence [33]. This informational clarity contributes to satisfaction by enhancing perceived control and reducing the cognitive effort involved in evaluating alternatives [15]. Moreover, when live commerce platforms allow viewers to ask questions and receive immediate answers, the transparency and relevance of information further strengthen their emotional fulfillment [35,55]. Thus, this study suggests the following hypothesis.
H3. 
Informativeness is positively related to satisfaction.

3.4. Internal Shop Environment and In-Store Emotions

Internal shop environment and in-store emotions refer to the physical and sensory cues in a shopping context that influence how consumers feel and behave during their experience [56]. In the live commerce setting, although the interaction occurs online, visual design, product displays, ambient music, streamer tone, and interface usability replicate the in-store atmosphere digitally [57]. These environmental elements stimulate emotional engagement, such as excitement, comfort, or curiosity, which in turn motivate continued participation and use [58]. When viewers perceive the live stream as aesthetically pleasing and emotionally fulfilling, they are more likely to revisit and actively use the platform for future purchases [59,60,61]. This immersive atmosphere mimics offline shopping satisfaction and strengthens habitual use over time. Thus, this study suggests the following hypothesis.
H4. 
Internal shop environment and in-store emotions are positively related to current use.

3.5. Monetary Savings

Monetary savings refers to the perceived economic benefit a consumer gains by paying less or receiving more value for the same cost when purchasing products or services [62]. In the context of fashion live commerce, viewers are often drawn to real-time promotions, limited-time discounts, and exclusive bundle offers that create a sense of financial advantage [1,3]. When users consistently associate the platform with cost savings, it reinforces practical value, encouraging repeated use over time [7]. Additionally, price-conscious consumers are more likely to become habitual users when their expectations for deals and affordability are reliably met during live streams [63,64]. These positive financial experiences help form behavioral patterns rooted in trust and efficiency, further solidifying routine engagement with the platform [65]. Thus, this study suggests the following hypothesis.
H5. 
Monetary savings is positively related to current use.

3.6. Satisfaction

Satisfaction refers to a consumer’s overall contentment with a shopping experience, reflecting how well the service or platform meets or exceeds expectations [66]. In the context of fashion live commerce, satisfaction arises from various factors such as trust in the streamer, informational value, entertainment, and perceived savings. When users feel emotionally and functionally fulfilled during their shopping interaction, they are more likely to form favorable attitudes toward future purchases [67]. This emotional gratification serves as a psychological anchor that shapes consumer loyalty and future behavioral intentions [68,69]. Satisfied consumers tend to return to platforms that deliver enjoyable experiences and are more inclined to act on their positive evaluations by completing purchases [70]. Therefore, the hypothesis below is logically supported by the literature.
H6a. 
Satisfaction is positively related to current use.
H6b. 
Satisfaction is positively related to purchase intention.

3.7. Current Use

Current use refers to the frequency and regularity with which users engage with a platform or service for browsing or making purchases [71]. In fashion live commerce, frequent use indicates familiarity with platform functions, trust in streamers, and a habit of participating in live shopping sessions. Such habitual engagement strengthens platform attachment and builds cognitive fluency, which lowers psychological resistance to making purchases [72,73]. As consumers accumulate positive experiences through repeated use, their likelihood of forming strong purchase intentions increases due to enhanced confidence and satisfaction [74]. Additionally, higher usage often leads to increased exposure to promotional offers and persuasive messages, which further reinforce the desire to purchase. Based on these insights, the following hypothesis is proposed.
H7. 
Current use is positively related to purchase intention.

4. Methodology

4.1. Instrument

All measurement items used in this study were adopted from previously validated scales presented in Table A1. Table A1 outlines the constructs and measurement items used in this study, each adapted from validated sources. Perceived credibility reflects trust in fashion streamers, such as the belief that “fashion streamers are trustworthy” [48]. Social media influencer captures the perceived originality and expertise of streamers, including items like “fashion live streamers have novel perspectives” [51]. Informativeness assesses the usefulness and clarity of information, for example, “fashion live commerce provides the best information about fashion products” [51]. Internal shop environment and in-store emotions refer to the sensory and emotional atmosphere, as seen in the item “fashion live commerce offers attractive products and promotions” [56]. Monetary savings measures perceived economic benefits, such as “using fashion live commerce helps me save money” [62]. Satisfaction indicates overall enjoyment and fulfillment with the shopping experience, exemplified by “I am satisfied with my shopping experience using fashion live commerce” [66]. Current use refers to habitual engagement, including “I use live commerce frequently” [71]. Purchase intention captures the likelihood of future transactions, such as “I plan to purchase products through fashion live commerce” [75]. Each construct was measured using multiple items, adapted from existing literature to ensure conceptual alignment with the context of fashion live commerce. A 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) was used for all items to capture the degree of agreement with each statement.
The questionnaire was composed of three sections. The first section measured general usage patterns of fashion live commerce platforms. The second section evaluated participants’ perceptions regarding the key constructs of the study. The third section collected demographic information such as gender, age, and income level. This structure allowed for a logical flow from behavioral experience to psychological response and background profiling.
To ensure the accuracy and contextual relevance of the survey, the instrument was originally developed in English by the author and subsequently translated into Korean by a bilingual language expert. A back-translation procedure was implemented, whereby a separate expert translated the Korean version back into English. The back-translated version was then compared to the original to confirm consistency in meaning and clarity of the items. This rigorous translation protocol ensured linguistic equivalence and conceptual validity across both languages.
Content validity was assessed through a pre-test with two experts—three scholars in the fields of consumer behavior and marketing and two professionals with direct experience in fashion e-commerce and live commerce platform operations. Based on their feedback, minor refinements were made to item wording and contextual examples. Following this, a pilot test was conducted with some voluntary participants who had prior exposure to fashion live commerce. These participants confirmed the readability and relevance of the items, further reinforcing the instrument’s validity for full-scale data collection.

4.2. Sample

To empirically examine the proposed model, an online survey was administered to Korean adults who had experience using fashion live commerce platforms. Given the increasing popularity of mobile shopping and live-stream-based commerce in South Korea, the sample population was well-suited to explore psychological and behavioral mechanisms in this context [76,77].
This study adopted a non-probability purposive sampling method, targeting individuals with prior experience in using fashion live commerce platforms. Purposive sampling is appropriate when the research objective requires selecting participants with specific knowledge or experiences relevant to the study context [78]. Given that the aim of this study was to analyze psychological and behavioral mechanisms within fashion live commerce, only respondents who had engaged with such platforms were recruited. Furthermore, the panel recruitment was balanced by gender and age to enhance representativeness within the South Korean online shopping population, which is known for rapid adoption of mobile and interactive commerce technologies [69,76].
The data were collected through Embrain, a third-party professional survey agency with a large national consumer panel [79]. Embrain was chosen for its reputation and capacity to implement rigorous sampling procedures. The survey targeted users with actual experience in watching and interacting with fashion live commerce streams. Participation was entirely voluntary, and anonymity was assured. Respondents were informed of the study’s academic purpose, their right to withdraw at any time, and that all data would be analyzed anonymously for research use only. Informed consent was obtained at the beginning of the survey.
The online questionnaire was open for 3 days, from 20 May to 22 May 2025. Respondents completed the survey using either a desktop or mobile device. To ensure data integrity, a preprocessing stage was conducted. Responses were screened based on completion time, consistency across reverse-coded items, and response patterns. Cases with missing values or straight-line answers were excluded. Additionally, respondents who failed attention-check questions were removed to enhance data quality. After filtering, the final dataset of 300 participants comprised individuals with confirmed prior exposure to fashion live commerce platforms. This approach ensured that all participants were sufficiently familiar with the subject matter, allowing for more accurate assessments of their psychological and behavioral responses.
To ensure adequate statistical power, this study employed the A-priori Sample Size Calculator for Structural Equation Models [80]. The input parameters were set as follows: an anticipated effect size of 0.1, a desired statistical power level of 0.80, a probability level of 0.05, and a model with 8 latent variables and 24 observed variables. Based on these specifications, the minimum required sample size was calculated to be 200. This study used a sample of 300 participants, which exceeds the recommended threshold and ensures sufficient power to detect structural relationships and produce reliable parameter estimates.
Table 1 presents the demographic characteristics of the respondents (N = 300). The sample was evenly split by gender, with 50% male and 50% female participants. Each age group was equally represented, with 25% of respondents in each category from their 20s to those aged 50 and above. The most commonly used platform was Naver Shopping Live (64.3%), followed by Coupang Live (10.7%) and TikTok Live Shopping (6.3%). In terms of education, the majority held a bachelor’s degree (64.0%). Regarding monthly income, 36.0% earned between 3 and 5 million KRW, followed by 24.0% earning between 1 and 3 million KRW.

5. Results

This study employed PLS-SEM to analyze the relationships among latent variables due to its suitability for prediction-oriented research and small sample sizes. PLS-SEM is particularly effective when the research model is complex and the objective is theory development rather than theory testing [81]. It also accommodates non-normal data and formative constructs, making it ideal for examining user behavior in fashion live commerce [82]. The structural model was assessed to determine the path relationships.

5.1. Common Method Bias

To address concerns regarding common method bias (CMB), Harman’s single-factor test was conducted. The result showed that the first unrotated factor accounted for 51.058% of the total variance, which slightly exceeds the recommended threshold of 50%, suggesting a potential indication of CMB. However, to complement this result, the variance inflation factor (VIF) values for all inner model constructs were assessed to further diagnose CMB. As shown in Table 2, All VIF values ranged between 1.674 and 2.314, remaining well below the recommended threshold of 3.3, indicating that multicollinearity and CMB are not a major concern in this model [83]. This two-pronged approach offers a more robust and nuanced assessment, justifying the absence of serious method bias issues.

5.2. Measurement Model

The measurement model was assessed to examine the reliability and validity of the latent constructs. As shown in Table 3, all factor loadings exceeded the recommended threshold of 0.70, indicating adequate item reliability [81]. Cronbach’s alpha and composite reliability (rho_c) values for all constructs were above 0.70, confirming internal consistency. The average variance extracted (AVE) values also surpassed the 0.50 benchmark, demonstrating convergent validity across constructs.
Discriminant validity was evaluated using the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). Table 4 shows that the square root of each construct’s AVE (diagonal elements) was greater than the correlations with other constructs, supporting discriminant validity [84]. Furthermore, all HTMT values in Table 5 were below the conservative threshold of 0.85, providing additional evidence for discriminant validity [85].
Together, these results confirm that the measurement model meets the necessary psychometric standards. Each construct in the model—including perceived credibility, social media influencer, informativeness, internal shop environment and in-store emotions, monetary savings, satisfaction, current use, and purchase intention—was found to be both reliable and valid for further structural analysis.
To evaluate the overall model fit, the standardized root mean square residual (SRMR) index was examined. The SRMR value for the saturated model was 0.047 and 0.075 for the estimated model, both of which fall below the acceptable threshold of 0.08, indicating a good fit between the observed data and the proposed model structure [86].

5.3. Structural Model

The structural model was assessed using 5000 bootstrapping resamples. This study includes gender and age as control variables to account for potential demographic influences on consumer behavior in fashion live commerce. All hypothesized paths were statistically significant, supporting the proposed relationships. Notably, satisfaction and current use explained 62.0% of the variance in purchase intention. These results confirm the model’s predictive relevance and structural integrity, as shown in Table 6 [81].

6. Discussion

This study explored how various consumer perceptions and experiential factors influence satisfaction, current use, and purchase intention in the context of fashion live commerce. The findings offer significant insights that extend existing literature and provide practical implications for platform designers and streamers alike. Each causal relationship is interpreted below based on its observed impact, aligned with theoretical expectations and prior empirical findings.
First, the significant influence of perceived credibility on satisfaction confirms the importance of trust and authenticity in shaping users’ emotional evaluations of fashion live commerce. When viewers believe that a streamer is trustworthy and genuinely endorses a product, they experience greater psychological security and confidence in their shopping decisions. This outcome aligns with earlier research showing that credibility functions as a foundation for satisfaction in online retail settings, particularly where live interaction magnifies interpersonal cues [10,11,12,48]. Unlike traditional e-commerce platforms, where reviews and ratings substitute for human trust, live commerce leverages real-time presence and voice to establish this credibility. This study highlights that viewers may not only evaluate product value but also form emotional attachments rooted in their perception of the streamer’s character. Thus, credibility emerges as a key relational cue driving user contentment in dynamic digital commerce.
Second, social media influencer characteristics significantly predicted satisfaction, reinforcing the social and symbolic role of streamers in live commerce. Streamers who demonstrate expertise, charisma, or relatability act as both product endorsers and community builders. This result is consistent with prior studies emphasizing the parasocial dynamics between influencers and their followers, where perceived similarity and authenticity foster emotional gratification [2,8,52,87]. What distinguishes this finding is the emphasis on satisfaction, not just intention or trust. It implies that followers do not merely respond to content quality but internalize a sense of relational value during the shopping experience. The emotional resonance built through casual conversation, humor, or shared lifestyle cues seems to strengthen the psychological payoff for consumers. For marketers, this suggests that choosing the right influencers is not only a matter of brand alignment but also of emotional engagement strategy.
Third, informativeness was found to significantly enhance satisfaction, suggesting that detailed, relevant, and timely product information remains a core value in live commerce. Consumers seek clarity and control in decision-making, and live demonstrations or real-time Q&A sessions help reduce uncertainty. This aligns with previous findings that informative content strengthens perceived usefulness and reduces cognitive load during e-commerce transactions [15,33]. The live setting adds another layer of immediacy, where spontaneous responses to user queries can generate trust and delight. Notably, this study confirms that informativeness is not merely a rational determinant of perceived value but also a contributor to affective satisfaction. Consumers appear to derive emotional fulfillment from the sense of empowerment and confidence that quality information provides.
The influence of internal shop environment and in-store emotions on current use affirms the relevance of atmospherics in the virtual shopping space. Although digital, live commerce replicates sensory elements such as visual aesthetics, background music, interface design, and the tone of the streamer to simulate the traditional retail environment. The finding mirrors earlier research on how atmospheric cues shape shopper mood and behavioral outcomes in physical retail settings [57,58]. This study extends that understanding into a digital context, showing that even screen-based settings can evoke emotions strong enough to predict future usage. The emotional ambiance created by a well-structured stream can motivate viewers not only to stay longer but to return, reinforcing platform habit formation. Emotional design, therefore, emerges as a strategic dimension for live commerce growth.
Monetary savings was also positively related to current use, reflecting the enduring role of price consciousness in consumer decision-making. While the emotional and social aspects of live commerce are prominent, financial incentives continue to play a meaningful role. The finding resonates with studies that identify perceived economic value as a recurring driver of loyalty and engagement, especially in promotional contexts [62,88]. What this study adds is a live, interactive layer to those benefits—users are not just reacting to static coupons but actively participating in time-limited deals or bundle offers in real-time. This sense of urgency and exclusivity may amplify the perceived value of savings and reinforce repeated platform engagement.
Satisfaction emerged as a central driver of both current use and purchase intention, confirming its pivotal role in consumer behavior. Its relationship with current use suggests that emotional fulfillment leads to habitual behavior, a notion consistent with findings in mobile shopping and app-based commerce [66]. Consumers tend to revisit platforms that deliver reliable satisfaction because the psychological comfort reduces search anxiety and increases convenience. Meanwhile, its strong influence on purchase intention underscores that satisfaction not only closes the loop on one shopping journey but also opens the door to the next. This behavioral reinforcement cycle implies that emotional design, trust-building, and meaningful interaction must converge to sustain consumer loyalty in competitive live commerce environments.
Lastly, the effect of current use on purchase intention supports the theory that usage behavior strengthens commitment. Habitual use builds familiarity, and familiarity reduces risk perception and cognitive effort, thereby facilitating future purchases. This intimates that usage frequency acts as a bridge between satisfaction and long-term loyalty. More importantly, it shows that live commerce platforms function as behavioral ecosystems: once a viewer regularly participates in live events, their purchase intention becomes less about persuasion and more about routine. For practitioners, this suggests that fostering initial usage through onboarding incentives and interactive content can indirectly boost sales conversion.
Control variables such as gender and age were not significantly associated with purchase intention. This may suggest that fashion live commerce appeals across demographic segments or that behavioral factors outweigh static demographic ones in predicting outcomes. These results reflect a broader shift in consumer research where psychographic and experiential variables often provide more actionable insights than age or gender categories alone.

7. Conclusions

7.1. Theoretical Contribution

This study contributes to the theoretical advancement of fashion live commerce by applying the S-O-R framework and UGT to explain how environmental and psychological factors jointly shape consumer engagement and purchase intention. The S-O-R model provides a structured lens through which external stimuli—such as perceived credibility, informativeness, and shop atmosphere—elicit internal emotional or cognitive reactions, leading to behavioral responses like platform use and purchasing behavior [24,38]. Simultaneously, UGT complements this perspective by highlighting the active role of consumers in seeking media that fulfills informational, social, and hedonic needs [25]. Unlike previous research that isolated these theories or emphasized only one pathway to behavior [14,27,38,89,90], this study integrates both frameworks to offer a holistic understanding of how users navigate the live commerce environment. This dual-theory approach expands the conceptual boundaries of fashion e-commerce by situating user satisfaction and repeated use within a broader media gratification and stimulus-response paradigm.
Unlike earlier research that primarily focused on static e-commerce settings or traditional influencer marketing [52,91,92,93], this paper situates its findings in the dynamic and real-time context of fashion live commerce. This distinction is important because the temporal immediacy and social interaction in live commerce create unique psychological mechanisms. For instance, while previous works acknowledged the role of credibility in purchase decisions, this study links perceived credibility to satisfaction—an emotional state that mediates behavior—thereby demonstrating the emotional depth of trust in live-stream contexts [48].
Another novel contribution lies in isolating the emotional and atmospheric dimensions of the online environment. Prior research has treated informativeness or shop design as peripheral influences, but this study finds them central to the formation of satisfaction and current use. By showing how digital cues like design, streamer tone, and platform interactivity replicate physical store environments, this paper extends environmental psychology into the virtual shopping space [56,57].
Moreover, while previous studies often equated user satisfaction directly with purchase intention, this study introduces current use as a distinct and meaningful construct. By separating habitual platform engagement from one-time satisfaction, it sheds light on how emotional fulfillment can lead to repeated behavior, which, in turn, fosters transactional intent [74].
For scholars, these insights suggest the need to rethink established models of online behavior by incorporating constructs unique to live streaming formats. Researchers are encouraged to further examine how live commerce transforms passive consumption into participatory experiences, potentially blending theories from media studies, social presence, and affective computing.

7.2. Practical Implications

This study provides several actionable insights for practitioners operating in the fashion live commerce space. By identifying what drives satisfaction, continued platform use, and eventual purchase intention, the findings offer a practical roadmap for enhancing both viewer engagement and sales performance. For live commerce streamers, the results highlight the importance of being perceived as credible, relatable, and well-informed. Streamers who articulate product details clearly, respond to viewer questions in real-time, and share personal usage stories are more likely to foster trust and satisfaction. For example, a fashion streamer who explains the texture, fit, and styling tips of a garment while trying it on and answering live questions may increase viewers’ confidence and intention to purchase.
Platform providers should pay close attention to the digital shop environment. Features such as seamless chat systems, clear product visuals, background music, and clean interface design mimic in-store ambiance and create a pleasant emotional experience for users. These elements contribute to emotional immersion, which not only enhances satisfaction but also encourages return visits and habitual use. Features like countdown-based discounts or “only 5 left” tags can also activate a sense of urgency that reinforces monetary value.
Marketers should consider the dual appeal of entertainment and utility. Beyond promoting product features, they can design influencer partnerships that blend fun, fashion advice, and social validation. For example, short “style challenge” segments hosted by influencers can generate virality while showcasing multiple SKUs. These interactive promotions can make the shopping journey more engaging and less transactional.
Consumers can benefit from these improved experiences by feeling more informed, emotionally satisfied, and financially rewarded. This study indicates that those who participate frequently in live commerce feel a greater sense of control and enjoyment in their purchases, suggesting that the model can empower consumers when designed with care and interactivity.
Finally, fashion live commerce offers a powerful platform for promoting sustainable consumption. Practitioners can leverage live streaming to highlight eco-friendly products, explain sustainable materials, and emphasize ethical production processes. For example, streamers might demonstrate how garments are made from recycled fabrics or produced under fair labor conditions, helping to shape environmentally conscious purchase behavior. Partnering with sustainability-focused influencers can further legitimize brand efforts and appeal to ethically minded consumers. Live chats can be used to answer questions about environmental impact, enabling transparency and trust. Moreover, limited-time offers on sustainable collections or campaigns like “green shopping days” can be designed to nudge viewers toward eco-responsible choices. These initiatives not only support brand differentiation in a crowded marketplace but also align business objectives with broader social and environmental goals, thereby reinforcing the value proposition for both companies and consumers.

7.3. Limitations and Future Research Directions

While this study offers robust insights, one limitation is its focus on platform behavior without accounting for the algorithmic design that often shapes user experiences. Personalized content delivery, recommender systems, and notification timing may subconsciously influence both satisfaction and purchase intention. Future research could investigate how algorithmic transparency and platform-driven nudges interact with consumer psychology in live commerce. Moreover, exploring cross-cultural differences in emotional response and stream format preferences may enrich the global understanding of live commerce engagement.

Author Contributions

Conceptualization, K.H. and H.J.; methodology, K.H. and H.J.; validation, H.J.; formal analysis, H.J.; writing—original draft preparation, K.H. and H.J.; writing—review and editing, K.H. and H.J. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was supported by Konkuk University in 2025.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Konkuk University (KKUIRB-202505-HR-071) on 20 May 2025.

Informed Consent Statement

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

Data Availability Statement

The data used in this study are available from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Construct and item.
Table A1. Construct and item.
ConstructItemDescriptionSource
Perceived credibilityCRD1Fashion streamers are trustworthy.[48]
CRD2Products advertised by fashion streamers are reliable.
CRD3Purchasing products advertised by fashion streamers is worthwhile.
Social media InfluencerSMF1Fashion live streamers have novel perspectives.[51]
SMF2Fashion live streamers understand ongoing trends.
SMF3Fashion live streamers have experience with original products.
InformativenessIFM1Fashion live commerce provides the best information about fashion products.[51]
IFM2Through fashion live commerce, I can quickly obtain a variety of information.
IFM3Fashion live commerce allows me to broaden my perspective through various opinions.
Internal shop environment and in-store emotionsISE1Fashion live commerce offers attractive products and promotions.[56]
ISE2The broadcast design and environment of fashion live commerce are very appealing.
ISE3The physical environment of fashion live commerce platforms (e.g., payment system, use of comments) is very convenient.
Monetary savingsMSV1Using fashion live commerce helps me save money.[62]
MSV2Fashion live commerce allows me to purchase products at lower prices.
MSV3Fashion live commerce enables more financially economical shopping.
SatisfactionSTF1I am satisfied with my shopping experience using fashion live commerce.[66]
STF2Using fashion live commerce is an enjoyable experience.
STF3Overall, I am satisfied with fashion live commerce.
Current useCRU1I use live commerce frequently.[71,94]
CRU2I use live commerce regularly.
CRU3I frequently make purchases through live commerce.
Purchase intentionPCH1I plan to purchase products through fashion live commerce.[75]
PCH2If I repurchase similar products, I will use fashion live commerce.
PCH3I will purchase products through fashion live commerce in the future.

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Figure 1. Research framework.
Figure 1. Research framework.
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Table 1. Demographic characteristics of the samples.
Table 1. Demographic characteristics of the samples.
CategorySubcategoryFrequencyPercentage
GenderMale15050.0%
Female15050.0%
Age Group20–29 years old7525.0%
30–39 years old7525.0%
40–49 years old7525.0%
50 years and older7525.0%
Platform UsedNaver Shopping Live19364.3%
Coupang Live3210.7%
11st Live11103.3%
Musinsa Live144.7%
Grip93.0%
TikTok Live Shopping196.3%
Instagram Live Shopping165.3%
Others72.3%
Education LevelHigh school graduate or below3712.3%
Associate degree graduate3612.0%
Bachelor’s degree graduate19264.0%
Master’s degree graduate299.7%
Doctoral degree graduate62.0%
Monthly Income (KRW)Less than 1 million258.3%
1–3 million7224.0%
3–5 million10836.0%
5–7 million4515.0%
7–10 million3411.3%
More than 10 million165.3%
Table 2. Collinearity statistics (VIF).
Table 2. Collinearity statistics (VIF).
PathVIF
Perceived Credibility → Satisfaction1.674
Social Media Influencer → Satisfaction1.762
Informativeness → Satisfaction1.780
Internal Shop Environment and In-store Emotions → Current Use2.314
Monetary Savings → Current Use1.684
Satisfaction → Current Use2.298
Satisfaction → Purchase Intention1.909
Current Use → Purchase Intention1.900
Table 3. Factor analysis and reliability.
Table 3. Factor analysis and reliability.
ConstructItemMeanSt. Dev.Factor LoadingCronbach’s AlphaCR (rho_a)CR (rho_c)AVE
Perceived credibilityCRD14.3801.1640.9000.9040.9090.9400.838
CRD24.3471.2140.936
CRD34.2971.2060.910
Social media influencerSMF14.3001.3201.0000.8170.8230.8920.733
SMF24.8631.2021.000
SMF34.7831.2630.807
InformativenessIFM14.9571.0270.8680.8280.8290.8970.744
IFM25.2771.0460.881
IFM34.9701.0500.839
Internal shop environment and in-store emotionsISE15.2701.0440.8300.7780.7780.8710.693
ISE24.9531.0730.833
ISE35.0631.1400.834
Monetary savingsMSV14.9331.0660.8970.8770.8810.9240.803
MSV25.1070.9940.910
MSV35.0071.0770.881
SatisfactionSTF14.9830.9910.8960.9120.9120.9440.850
STF24.9471.0820.862
STF34.9301.0700.920
Current useCRU14.7201.2810.9360.9130.9140.9450.852
CRU24.6431.3500.928
CRU34.4031.3440.905
Purchase intentionPCH14.8700.9860.9180.9120.9160.9450.850
PCH24.6871.0870.905
PCH34.7731.0590.943
Table 4. Correlation of the research variables.
Table 4. Correlation of the research variables.
Construct12345678
1. Perceived credibility0.916
2. Social media influencer0.5640.856
3. Informativeness0.5700.5990.863
4. Internal shop environment and in-store emotions0.5890.5830.7710.832
5. Monetary savings0.4480.4050.4740.5940.896
6. Satisfaction0.6190.5890.5920.7250.5900.922
7. Current use0.5560.4900.5400.6470.5500.6880.923
8. Purchase intention0.6070.5470.5140.6100.5440.7460.6950.922
Note: Diagonal elements are the square root of AVE.
Table 5. HTMT matrix.
Table 5. HTMT matrix.
Construct12345678
1. Perceived credibility
2. Social media influencer0.661
3. Informativeness0.6600.730
4. Internal shop environment and in-store emotions0.7020.7310.961
5. Monetary savings0.5010.4780.5530.717
6. Satisfaction0.6790.6810.6810.8610.656
7. Current use0.6110.5680.6200.7670.6120.753
8. Purchase Intention0.6660.6310.5890.7220.6050.8150.761
Table 6. Results of structural model.
Table 6. Results of structural model.
HPredictorOutcomeβtpResult
H1Perceived credibilitySatisfaction0.3355.2660.000Supported
H2Social media influencerSatisfaction0.2494.1130.000Supported
H3InformativenessSatisfaction0.2513.0060.003Supported
H4Internal shop environment and in-store emotionsCurrent Use0.2584.1280.000Supported
H5Monetary savingsCurrent Use0.1552.8080.005Supported
H6aSatisfactionCurrent Use0.4096.8730.000Supported
H6bSatisfactionPurchase Intention0.5129.6550.000Supported
H7Current usePurchase Intention0.3435.7220.000Supported
CVGenderPurchase Intention−0.0410.5740.566Not Significant
CVAgePurchase Intention0.0300.8760.381Not Significant
Note: CV stands for control variable.
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Han, K.; Jo, H. What Drives Consumer Engagement and Purchase Intentions in Fashion Live Commerce? Sustainability 2025, 17, 5734. https://doi.org/10.3390/su17135734

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Han K, Jo H. What Drives Consumer Engagement and Purchase Intentions in Fashion Live Commerce? Sustainability. 2025; 17(13):5734. https://doi.org/10.3390/su17135734

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Han, Kihyang, and Hyeon Jo. 2025. "What Drives Consumer Engagement and Purchase Intentions in Fashion Live Commerce?" Sustainability 17, no. 13: 5734. https://doi.org/10.3390/su17135734

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Han, K., & Jo, H. (2025). What Drives Consumer Engagement and Purchase Intentions in Fashion Live Commerce? Sustainability, 17(13), 5734. https://doi.org/10.3390/su17135734

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