The rapid advancement of digital technology has significantly reshaped consumer behavior in the contemporary era, particularly in how individuals conduct shopping activities. In Indonesia, this transformation is characterized by a shift from traditional shopping methods to digital platforms, driven by the growing internet penetration and increasing smartphone usage. The convenience afforded by technology has accelerated the adoption of e-commerce services, making online shopping a widespread and normalized practice. According to Taiwan Business TOPICS in 2020, Indonesia experienced a 37% year-on-year growth in the online retail sector. Complementary to this, (Statista, 2024) Reported that the number of e-commerce users in Indonesia increased by nearly 20 million between 2020 and 2023. This growth reflects not only technological accessibility but also changing consumer preferences in acquiring goods and services.

Among the diverse forms of e-commerce, marketplaces have become particularly prominent. These platforms provide an integrated space where consumers and sellers can interact and transact in real-time. One of the most innovative features emerging within this domain is live streaming commerce, which enables sellers to present products interactively through digital video broadcasts. The live format enables direct demonstrations, real-time communication, and immediate feedback, collectively reducing information asymmetry and increasing consumer confidence. (Mou & Benyoucef, 2021) Describe live streaming as a Web 3.0 technology capable of facilitating multidimensional interactions, thereby enhancing consumer engagement and perceived value.

Theoretically, this phenomenon can be analyzed using the Stimulus Organism Response (SOR) model developed by (Mehrabian & Russell, 1974). This framework conceptualizes consumer behavior as the outcome of external stimuli (e.g., perceived value), internal evaluations (e.g., trust and engagement), and resultant behavioral responses (e.g., impulse buying). In the context of live streaming commerce, utilitarian, hedonic, and symbolic values function as stimuli that influence consumers’ cognitive and affective states, such as trust in the product, trust in the seller, and customer engagement, which ultimately shape purchasing decisions (Ma et al., 2022; Wu & Huang, 2023). The SOR model offers a comprehensive framework for understanding the psychological mechanisms underlying digital consumer behavior.

This trend is particularly salient among Generation Z, who constitute a significant proportion of Indonesia’s population and are distinguished by their status as digital natives. According to the 2020 Indonesian Population Census, Generation Z comprises approximately 27.94% of the national population, totaling nearly 75 million individuals. Their high degree of digital literacy, coupled with unique consumption preferences, positions this demographic as a critical focus for brands engaging in digital marketing. A survey by (Jakpat, 2023) Illustrates that 75% of Gen Z respondents prefer TikTok Shop for live shopping activities, indicating their preference for platforms that integrate entertainment and commerce.

The relevance of Generation Z is further underscored by their tendency to engage in impulse buying, particularly in product categories such as beauty and skincare. Research conducted by (ZAP & MarkPlus.Inc, 2024) Revealed that 30.4% of Gen Z consumers in Indonesia reported making skincare purchases through live streaming platforms, a figure that surpasses corresponding rates among Millennials (27.9%) and Generation X (17.2%). This suggests that interactive digital features, such as live demonstrations, influencer endorsements, and time-limited offers, effectively stimulate unplanned purchasing behavior within this cohort.

An illustrative case of the successful application of live streaming commerce in Indonesia is Somethinc, a local beauty brand established in 2019. Despite its relatively recent market entry, Somethinc has rapidly emerged as one of the top-selling skincare brands on Indonesian e-commerce. According to (Compas, 2022)The brand achieved sales of IDR 53.2 billion in the second quarter of 2022 alone, making it the best-selling brand in its category across several major platforms. Something’s marketing strategy involves leveraging TikTok’s live streaming features to present product information, respond to consumer queries, and build emotional connections with viewers. Such practices are consistent with findings by (Sun et al., 2019) and (Wongkitrungrueng & Assarut, 2020), who emphasize the role of interactivity, trust, and engagement in shaping online purchasing behavior.

The existing literature further supports the argument that trust, both in products and in sellers, is a critical determinant of online purchasing intentions. Trust in the product is shaped by perceived quality and usefulness, while trust in the seller is informed by reputation, transparency, and fulfillment reliability. (Senali et al., 2024). When consumers perceive a high degree of trustworthiness, they are more likely to engage with the brand, which in turn strengthens emotional and behavioral loyalty. (Sashi, 2012; Vivek et al., 2014). This is particularly important in live commerce contexts, where the immediacy and visibility of interactions can enhance the brand's credibility and the perceived authenticity of the shopping experience.

Despite the growing prevalence of live commerce, particularly in Southeast Asia, empirical research on this phenomenon remains relatively limited, especially within the specific context of e-commerce platforms, as opposed to the more widely studied domain of social commerce. Prior studies have primarily explored consumer motivations for live streaming engagement, focusing on entertainment and informational gratifications (Hilvert-Bruce et al., 2018). Meanwhile, more recent studies have begun to examine the role of perceived value, trust, and customer engagement in influencing impulse buying and purchase intentions (Li et al., 2023; Xin et al., 2024). Nevertheless, further investigation is needed to explore how these variables interact within a structured theoretical framework and how they manifest across different generational cohorts in emerging markets.

This study aims to address this gap by examining the impact of live streaming commerce on impulse buying behavior among Generation Z consumers in Indonesia. By employing the SOR framework, the study explores how perceived utilitarian, hedonic, and symbolic value influence trust and customer engagement, and how these mediating variables contribute to consumers' impulsive buying decisions. Through the case of Somethinc and its strategic use of TikTok live streaming, the research aims to offer insights into how digital marketing practices can effectively engage younger consumers and shape online consumption behavior in the context of an evolving digital marketplace.

SOR THEORY

The Stimulus–Organism–Response (SOR) framework provides a widely adopted lens for explaining consumer behavior in various contexts, particularly in digital and interactive environments. First introduced by Mehrabian and Russell (1974), the model emphasizes that individual behavioral outcomes are shaped by the quality of external stimuli, which in turn influence internal states and subsequent responses. In essence, the degree to which a stimulus is perceived as meaningful or engaging determines the likelihood of a specific behavioral reaction. The SOR framework conceptualizes human behavior as a process involving three interrelated components. The first component, stimulus, refers to external environmental factors that trigger internal processes within the individual (Song et al., 2021). In the context of live streaming commerce, real-time interactions between viewers and streamers serve as powerful stimuli. Prior studies have shown that these interactions can generate a strong sense of presence, satisfy viewers’ psychological needs, and subsequently shape their attitudes and purchase intentions (Gao et al., 2018). Within this research, live streaming commerce is therefore positioned as a channel through which stimuli influence consumer responses.

The second component, organism, denotes the intermediary cognitive and affective states that mediate the relationship between external stimuli and behavioral responses (Y. L. Wu & Li, 2018). Affective states are understood as emotional reactions evoked by environmental stimuli (H. Sun & Zhang, 2015), while cognitive states involve the mental processes engaged when interpreting and responding to such stimuli (S. Fu et al., 2018). These internal conditions act as critical determinants of how stimuli are processed and translated into behavioral outcomes. Finally, the response refers to the ultimate actions or decisions undertaken by individuals as a result of their cognitive and affective evaluations (Sherman et al., 1997). In the context of online consumer behavior, responses may manifest in the form of purchase intentions, repeat buying, or continued engagement with live streaming platforms. By integrating these three dimensions, the SOR framework provides a robust theoretical foundation for examining consumer decision-making in live streaming commerce. It highlights the importance of understanding not only external marketing stimuli but also the internal psychological processes that ultimately drive consumer responses.

PERCEIVED VALUE

Utilitarian Value

Consumers engage in purchasing activities, either offline or through digital platforms, with specific goals in mind. In online settings, concerns about seller credibility and product authenticity often arise (S. C. Chen & Dhillon, 2003). Utilitarian value emphasizes efficiency and task completion, where live streaming facilitates product evaluation by allowing sellers to provide real-time demonstrations and direct responses to consumer inquiries (Gilmore & Pine, 2007). This reduces the need for additional information searches and enhances decision-making. Utilitarian value therefore refers to the functional and practical benefits consumers derive when products fulfill their needs effectively and economically (Wongkitrungrueng & Assarut, 2020; L. C. Wang et al., 2007).

Hedonic Value

While utilitarian value focuses on rationality and efficiency, hedonic value highlights multisensory, emotional, and experiential aspects of shopping (Hirschman & Holbrook, 1982). It is associated with enjoyment, entertainment, and escapism (Özen & Kodaz, 2016; Arnold & Reynolds, 2003). Online features such as interactive tools and gamified promotions provide pleasurable experiences, fostering positive emotions that encourage repeat purchases (Gulfraz et al., 2022). In live streaming commerce, hedonic value is created through interactive sessions, entertainment elements, and social engagement, which enhance consumer satisfaction beyond functional outcomes (Fiore et al., 2005; Sobari, 2022).

Symbolic Value

Shopping also carries symbolic meaning, reflecting social identity and group integration (Firat & Venkatesh, 1993; Sirgy et al., 2000). Symbolic value arises when purchases contribute to self-expression and strengthen social bonds (Hewer & Campbell, 1997; Wongkitrungrueng & Assarut, 2020). In e-commerce, live streaming facilitates community building and interaction, reinforcing trust and symbolic associations between consumers and sellers (Hamilton et al., 2014). Thus, symbolic value reflects the extent to which shopping experiences contribute to consumers’ self-identity and social recognition (De Vries & Carlson, 2014).

Customer Trust

Trust represents consumers’ belief in sellers’ reliability and products’ authenticity, serving as a foundation for long-term relationships (Sumer & Parilti, 2023; Cheng et al, 2017). In digital contexts, the absence of physical interaction often generates uncertainty, making trust a critical determinant of transaction success (Komiak & Benbasat, 2003). Live streaming mitigates this issue by enabling real-time demonstrations and direct communication, thereby strengthening trust in both sellers and products (Y. Lu et al., 2010; Pappas, 2016). This study therefore distinguishes between trust in sellers, referring to confidence in the seller’s integrity, and trust in products, reflecting the expectation that product performance aligns with seller claims.

Customer Engagement

Customer engagement refers to the degree of consumers’ active participation in brand- or seller-related interactions, aimed at building and reinforcing relationships (Brodie et al., 2011; Vivek et al., 2014). Social media platforms have expanded opportunities for engagement by enabling two-way communication through likes, comments, and shares (Agrawal, 2021; Khan & Vong, 2014). In live streaming commerce, engagement is facilitated by interactive features such as comment sections, likes, and real-time chats, which enhance consumer involvement and emotional connection with sellers. Such engagement has been shown to influence satisfaction, loyalty, and overall relationship quality (Gummerus et al., 2012).

Impulse Buying

Impulse buying refers to unplanned purchasing decisions driven by spontaneous urges rather than rational deliberation (Lim et al., 2017; Sohn & Ko, 2021). Online shopping environments, characterized by convenience and immediacy, are more conducive to impulsive behavior compared to traditional retail (Yang Wu et al., 2022). Live streaming, in particular, intensifies impulse buying by combining product demonstrations, persuasive communication, and real-time interaction (X. Xu et al., 2020; Akram et al., 2021). Consequently, engagement and trust developed during live streams can stimulate consumers to make impulsive purchases without prior intention.

Figure 1. Research Model

Based on prior studies, the following hypotheses were proposed: H1a: Utilitarian value positively influences trust in products; H1b: Utilitarian value positively influences trust in sellers; H2a: Hedonic value positively influences trust in products; H2b: Hedonic value positively influences trust in sellers; H3a: Symbolic value positively influences trust in products; H3b: Symbolic value positively influences trust in sellers; H4: Trust in products positively influences trust in sellers; H5: Utilitarian value positively influences customer engagement; H6: Hedonic value positively influences customer engagement; H7: Symbolic value positively influences customer engagement; H8a: Trust in products positively influences customer engagement; H8b: Trust in sellers positively influences customer engagement; H9: Customer engagement positively influences impulse buying; H10: Utilitarian value positively influences impulse buying; H11: Hedonic value positively influences impulse buying; H12: Symbolic value positively influences impulse buying.

Sampling

The present study employed a purposive sampling method to select respondents in the Jabodetabek area. This was since most dominated TikTok users in Indonesia is located at Jakarta (22%) and West Java (13%) (Ginee, 2021). Data was gathered using an online questionnaire distributed via Google Forms. Respondents were selected through purposive sampling, based on several inclusion criteria: they must be daily TikTok users aged 17 or older and reside in the Jabodetabek area. Furthermore, participants were required to have viewed, interacted with, and purchased products during at least two separate TikTok live shopping sessions for each activity. Based on the described respondent criteria, we will select consumers on the TikTok platform who meet the requirements outlined in this study. Due to the unknown numbers, the sample will be selected to minimum of 200 respondents, which represents the entire population to be studied. However, the researchers managed to obtain data from 229 respondents, exceeding the minimum number set.

Questionnaire and data analysis

Primary data for this study were collected through a self-administered questionnaire, a method involving written questions to be answered directly by respondents. (Sugiyono, 2010). The questionnaire was distributed online via Google Forms, allowing participants to complete it independently. The instrument was structured into three sections: the first part gathered respondent demographic data (gender, age, occupation, and education); the second part included items to measure behavioral indicators related to live streaming; and the final section contained the measurement items for the primary variables of the study.

The collected data were analyzed statistically using SmartPLS 3 software to test the proposed hypotheses. Before hypothesis testing, the measurement instrument underwent a rigorous evaluation of its psychometric properties, specifically its validity and reliability. The validity analysis was conducted to confirm that the instrument accurately measures its intended theoretical constructs. (Sugiyono, 2010). Subsequently, reliability analysis was performed to assess the internal consistency and stability of the measurement scales. This was evaluated using two standard metrics: Composite Reliability and Cronbach’s Alpha. Establishing the instrument's validity and reliability was a crucial prerequisite for proceeding with the primary data analysis and hypothesis testing, from which the study's conclusions were drawn.

The data was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 3 software. This variance-based approach was selected for its predictive power and its suitability for research that may not meet the strict assumptions of large sample sizes or normal data distribution required by other methods. (Ghozali, 2014). The analytical procedure involved a comprehensive evaluation of both the measurement and structural models. The measurement model (outer model) was first assessed for its psychometric properties, confirming its internal consistency through Composite Reliability (CR) of ≥ 0.70 and establishing both convergent and discriminant validity. Subsequently, the structural model (inner model) was evaluated to determine its explanatory power and predictive relevance by examining the coefficient of determination (R²), predictive relevance (Q²), and the effect size (f²) of the path relationships. Hypothesis testing was then conducted using a non-parametric bootstrapping procedure (5,000 resamples) to determine the significance of path coefficients based on their T-statistics. This enabled the examination of both direct and indirect effects, with the strength of any mediating relationships quantified using the Variance Accounted For (VAF) metric.

Table 1. Profile of Respondents

The hypothesis analysis was conducted to determine the correlation between the research variable, including the effects of Utilitarian Value, Hedonic Value, and Symbolic Value on Impulse Buying, and the role of Trust in Products and Trust in Sellers in increasing Customer Engagement. Refers to (Hair et al., 2013), the effect is significant if the p-value is less than 0.05, where the effect is not significant if the p-value is higher than 0.05. Based on our path coefficient analysis, the variables that have p-value lower than 0.05 and give substantial effect are Utilitarian Value to Trust in Product, Utilitarian Value to Trust in Seller, Hedonic Value to Trust in Product, Hedonic Value to Trust in Seller, Symbolic Value to Trust in Product, Symbolic Value to Trust in Seller, Utilitarian Value to Customer Engagement, Hedonic Value to Customer Engagement, Symbolic Value to Customer Engagement, Trust in Product to Customer Engagement, Customer Engagement to Impulse Buying, Hedonic Value to Impulse Buying, and Symbolic Value to Impulse Buying. The remaining variables with higher p-values (>0.05) are Utilitarian Value to Trust in Seller, Trust in Seller to Customer Engagement, and Utilitarian Value to Impulse Buying. The discussion of the results will be presented in the following subsection.

Following the direct effect analysis, an indirect effect was also examined to understand the underlying mechanism by which an exogenous variable influences an endogenous variable through the mediation of an intermediate variable. The analysis revealed that only Customer Engagement had a significant effect on the relationship between Trust in Product and Impulse Buying, as indicated by a p-value of less than 0.05. In contrast, the other variables presented no mediation effect due to a higher p-value obtained, such as Trust in Seller to Customer Engagement to Impulse Buying, Hedonic Value to Trust in Product to Trust in Seller, Symbolic Value to Trust in Product to Trust in Seller, and Utilitarian Value to Trust in Product to Trust in Seller, respectively.

In the present study, the Variance Accounted For (VAF) analysis was used to assess the extent to which independent variables influence dependent variables through mediators. The VAF analysis will be used to explore the relationship between Trust in Product and Customer Engagement, with Impulse Buying as the outcome, where Customer Engagement serves as a mediator. The calculation revealed that the VAF value was 100%, indicating a complete mediation in the relationship between Trust in Product and Impulse Buying through Customer Engagement. This also means that the entire influence of Trust in Products on Impulse Buying can occur through Customer Engagement as a mediator variable.

Table 2. Analysis of Validity and Reliability

For structural validity, this study employed two indicators: convergent validity and discriminant validity. Convergent validity was assessed using the Average Variance Extracted (AVE) and Composite Reliability (CR) values, as presented in Table 2. The analysis covered six factors and thirty measurement items. The results indicate that all AVE values exceeded the threshold of 0.50 and CR values were above 0.70, confirming satisfactory convergent validity.

Discriminant validity was examined using the Fornell–Larcker criterion, with results shown in Table 3. The square root of the AVE for each construct was greater than its correlations with other constructs, thereby confirming that the measurement model achieved adequate discriminant validity.

Table 3. Discriminant Validity (Fornell–Larcker Criterion)

Structural Equation Modelling (Direct Effects)

Structural Equation Modelling (Direct Effects)

The structural model was assessed using SmartPLS 3 to examine the hypothesized relationships among constructs. Path coefficients, t-statistics, and p-values were evaluated to determine the significance of each relationship. The results of the hypothesis testing are presented in Table 4. The findings indicate that utilitarian value significantly influences trust in product (β = 0.240, t = 2.438, p < 0.01) and trust in seller (β = 0.233, t = 2.284, p < 0.01). This suggests that consumers’ perceptions of functional benefits contribute positively to their trust both in the product and in the seller. Similarly, hedonic value shows a significant positive effect on trust in product (β = 0.186, t = 1.703, p < 0.05) and a strong positive effect on trust in seller (β = 0.368, t = 4.267, p < 0.001). These results highlight that enjoyment and emotional experiences during shopping enhance consumer trust, particularly toward the seller.

Symbolic value also demonstrates a significant impact on trust, with positive effects on trust in product (β = 0.141, t = 2.150, p < 0.05) and trust in seller (β = 0.117, t = 1.928, p < 0.05). This indicates that symbolic attributes associated with products and sellers, such as identity and status representation, reinforce consumer trust. However, the relationship between trust in product and trust in seller was not supported (β = −0.008, t = 0.116, p > 0.05), suggesting that trust in the product does not necessarily translate into trust in the seller. With respect to consumer engagement, utilitarian value (β = 0.331, t = 2.120, p < 0.05), hedonic value (β = 0.178, t = 2.464, p < 0.01), and symbolic value (β = 0.237, t = 2.758, p < 0.01) all positively influence engagement, indicating that functional, experiential, and symbolic benefits collectively drive consumers to interact and engage more actively. Trust in product also shows a significant effect on engagement (β = 0.226, t = 1.854, p < 0.05), while trust in seller does not exert a significant impact (β = 0.026, t = 0.378, p > 0.05). These results suggest that consumers’ involvement is more strongly shaped by the perceived value dimensions and trust in products rather than in sellers. Furthermore, customer engagement has a strong positive effect on impulse buying (β = 0.259, t = 2.553, p < 0.01), supporting the notion that active consumer participation increases the likelihood of unplanned purchases. Interestingly, utilitarian value does not show a significant influence on impulse buying (β = 0.027, t = 0.267, p > 0.05), suggesting that rational evaluations of functional benefits may not directly trigger impulsive behavior. In contrast, hedonic value (β = 0.355, t = 4.011, p < 0.001) and symbolic value (β = 0.300, t = 3.848, p < 0.001) both exert strong positive effects on impulse buying. These findings emphasize that emotional enjoyment and symbolic meanings play a central role in driving spontaneous purchase decisions, as consumers are motivated by pleasure-seeking and identity expression.

Table 4. Path Coefficient and Hypotheses Testing

Overall, the results reveal that perceived value dimensions (utilitarian, hedonic, symbolic) play a critical role in building trust, enhancing engagement, and encouraging impulse buying. While utilitarian aspects contribute mainly to trust and engagement, hedonic and symbolic values exert a stronger influence on impulsive purchasing behavior.

Structural Equation Modelling (Indirect Effects)

The mediation effects were tested using SmartPLS 3 with bootstrapping procedures to examine the indirect relationships among constructs. The results are presented in Table 5. The findings reveal that customer engagement significantly mediates the relationship between trust in product and impulse buying (p = 0.048, < 0.05). This indicates that consumer trust in product can indirectly foster impulse buying behavior through the enhancement of their engagement. In other words, when consumers trust the quality and reliability of a product, they are more likely to engage actively, which in turn increases the likelihood of unplanned purchases. In contrast, the mediating role of customer engagement in the relationship between trust in seller and impulse buying was not supported (p = 0.367, > 0.05). This suggests that consumer trust in sellers alone does not significantly strengthen engagement to the extent of driving impulsive buying behavior. Similarly, the mediating role of trust in product in the relationship between hedonic value and trust in seller was not statistically significant (p = 0.463, > 0.05). This implies that although hedonic value enhances trust in product, such trust does not function as a pathway that meaningfully translates into increased trust in the seller.

A comparable result was observed for symbolic value, where trust in product did not mediate the relationship with trust in seller (p = 0.456, > 0.05). This indicates that symbolic perceptions associated with products do not significantly spill over into greater trust in sellers through product trust. Finally, the mediation effect of trust in product between utilitarian value and trust in seller was also not supported (p = 0.462, > 0.05). Thus, while utilitarian value contributes positively to trust in product, this trust does not extend to strengthening trust in sellers through an indirect mechanism.

Table 5. Mediating Effects Testing

Taken together, the mediation analysis underscores that customer engagement plays a crucial role in linking trust in product to impulse buying, whereas other proposed mediation pathways involving trust in product as an intervening variable were not supported. This finding highlights the distinct role of product trust in shaping consumer engagement and impulsive decision-making, as opposed to its limited mediating influence in fostering trust toward sellers.

Implications

This study offers both theoretical and practical contributions to the field of digital marketing, particularly in the context of Generation Z’s live streaming commerce. From a theoretical perspective, the findings reinforce and extend the Stimulus–Organism–Response (SOR) framework by showing how utilitarian, hedonic, and symbolic function as stimuli that influence consumer trust and engagement, which ultimately drive impulse buying behavior. These results confirm the continued relevance of the SOR model for explaining consumer behavior in digital environments, especially in live streaming commerce that integrates social interaction with commercial activities. In addition, this research advances the understanding of customer engagement in live streaming commerce. The non-significant relationship between trust in seller and customer engagement provides a novel insight: in interactive digital shopping settings, engagement is shaped more strongly by perceived value and product trust than by seller trust. This contributes to the literature by highlighting the unique behavioral dynamics of Generation Z consumers in live streaming commerce, which differ from patterns typically observed in conventional e-commerce.

From a practical standpoint, the results suggest that businesses and digital marketers should design strategies that emphasize both the hedonic and symbolic dimensions of consumer value. The significant effects of these values on impulse buying underscore the importance of creating enjoyable, interactive, and identity-enhancing shopping experiences. Marketers should therefore focus live streaming content not only on functional product information (utilitarian value) but also on entertainment and interactive features that stimulate hedonic value, while highlighting the symbolic meanings of products that resonate with consumers’ identity and social status. Moreover, the finding that trust in product significantly influences customer engagement, whereas trust in seller does not, implies that sellers should prioritize product credibility as a driver of engagement. This can be achieved through transparent information, clear product demonstrations, and authentic user testimonials during live streaming sessions. While building trust in sellers remains relevant, strategies to enhance consumer engagement and stimulate impulse buying should focus more heavily on strengthening product value and creating positive, interactive shopping experiences. Taken together, these implications underscore that the success of live streaming commerce with Generation Z consumers depends on aligning marketing strategies with the psychological drivers of value, trust, and engagement, thereby translating digital interactions into impulsive purchasing behaviors.

Limitations and Future Research

This study provides valuable insights into the dynamics of perceived value, trust, customer engagement, and impulse buying within the context of Generation Z’s live streaming commerce. However, several limitations should be acknowledged. First, the research focuses on a specific product category and platform, which may limit the generalizability of the findings across other product types or live streaming environments. Consumer behavior may differ depending on product characteristics, industry contexts, or the technological features of various platforms. Second, while the study highlights the importance of perceived utilitarian, hedonic, and symbolic values, it does not incorporate additional factors that may influence consumer decision-making, such as social presence, interactivity, or parasocial relationships with streamers.

Future research can address these limitations in several ways. Expanding the scope of inquiry to include multiple product categories and diverse live streaming platforms would allow for testing the robustness and consistency of the findings across contexts. Further studies could also investigate moderating variables such as demographic characteristics, prior online shopping experience, or perceived risk, which may shape the relationship between perceived value, trust, engagement, and impulse buying. Incorporating variables such as social presence, interactivity, and parasocial interaction would enrich the SOR framework and provide a more comprehensive understanding of consumer behavior in live streaming commerce. By integrating these additional perspectives, future research could generate deeper insights into how digital marketing strategies can be optimized for different consumer segments and contexts.

This study reveals that perceived value, which includes its utilitarian, hedonic, and symbolic value, is a foundational driver in the e-commerce live streaming context. This value significantly fosters both trust in products and trust in sellers. For Generation Z consumers, practical product information, engaging emotional experiences, and aspirational social identity all work together to build trust. However, a critical distinction emerged between the two forms of trust. The findings indicate that trust in a product does not significantly translate into trust in the seller. Instead, trust in sellers appears to be uniquely forged through their direct performance, responsiveness, and the atmosphere they create during the live broadcast, independent of the product's credibility.

Furthermore, the path to consumer action is primarily paved by engagement. Both perceived value and trust in the product were found to be significant drivers of customer engagement. Interestingly, trust in the seller did not significantly impact customer engagement, suggesting that consumers engage more with the product and the overall experience than with the broadcaster alone. This engagement is a powerful catalyst, as it was the most direct and significant predictor of impulse buying. While perceived value also influences impulse purchases, the effect is nuanced. The emotional and identity-driven aspects directly encourage unplanned purchases, whereas practical value does not. This underscores that in the fast-paced, interactive environment of live streaming commerce, impulse decisions are driven less by practical need and more by the fusion of enjoyment and self-expression.

Appendix A. Measurement Items

1. Agrawal, S. R. (2021). Adoption of WhatsApp for Strengthening Internal CRM through Social Network Analysis. Journal of Relationship Marketing, 20(4), 261–281. https://doi.org/10.1080/15332667.2020.1802643
2. Akram, U., Junaid, M., Zafar, A. U., Li, Z., & Fan, M. (2021). Online purchase intention in Chinese social commerce platforms: Being emotional or rational? Journal of Retailing and Consumer Services, 63(June), 102669. https://doi.org/10.1016/j.jretconser.2021.102669
3. Arnold, M. J., & Reynolds, K. E. (2003). Hedonic shopping motivations. Journal of Retailing, 79(2), 77–95. https://doi.org/10.1016/S0022-4359(03)00007-1
4. Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703
5. Chen, S. C., & Dhillon, G. S. (2003). Interpreting Dimensions of Consumer Trust in E-Commerce. Information Technology and Management, 4(2), 303–318.
6. Cheng, J.-C., Chen, C.-Y., Yen, C.-H., & Teng, H.-Y. (2017). Building customer satisfaction with tour leaders: the roles of customer trust, justice perception, and cooperation in group package tours. Asia Pacific Journal of Tourism Research, 22(4), 395–407. https://doi.org/10.1080/10941665.2016.1271816
7. (2022). Top 5 Brand Perawatan Wajah di E-commerce Indonesia dengan Penjualan di atas Rp 10 Miliar. https://compas.co.id/article/top-5-brand-perawatan-wajah/
8. De Vries, N. J., & Carlson, J. (2014). Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. Journal of Brand Management, 21(6), 495–515. https://doi.org/10.1057/bm.2014.18
9. Fiore, A. M., Kim, J., & Lee, H. H. (2005). Effect of image interactivity technology on consumer responses toward the online retailer. Journal of Interactive Marketing, 19(3), 38–53. https://doi.org/10.1002/dir.20042
10. Firat, A. F., & Venkatesh, A. (1993). Postmodernity: The age of marketing. International Journal of Research in Marketing, 10(3), 227–249. https://doi.org/10.1016/0167-8116(93)90009-N
11. Gao, W., Liu, Y., Liu, Z., & Li, J. (2018). How does presence influence purchase intention in online shopping markets? An explanation based on self-determination theory. Behaviour & Information Technology, 37(8), 786–799. https://doi.org/10.1080/0144929X.2018.1484514
12. Ghozali, I. (2014). Structural equation modeling metode alternatif dengan partial least squares (PLS). Badan Penerbit Universitas Diponegoro.
13. Gilmore, J. H., & Pine, B. J. (2007). Authenticity: What consumers really want. Harvard Business School Press.
14. (2021). Pengguna TikTok Indonesia. https://ginee.com/id/insights/pengguna-tiktok/
15. Gulfraz, M. B., Sufyan, M., Mustak, M., Salminen, J., & Srivastava, D. K. (2022). Understanding the impact of online customers’ shopping experience on online impulsive buying: A study on two leading E-commerce platforms. Journal of Retailing and Consumer Services, 68(September), 103000. https://doi.org/10.1016/j.jretconser.2022.103000
16. Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customer engagement in a Facebook brand community. Management Research Review, 35(9), 857–877. https://doi.org/10.1108/01409171211256578
17. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
18. Hamilton, W. A., Garretson, O., & Kerne, A. (2014). Streaming on twitch: fostering participatory communities of play within live mixed media. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1315–1324. https://doi.org/10.1145/2556288.2557048
19. Hewer, P., & Campbell, C. (1997). Research on shopping: a brief history and selected literature (The shoppi). Sage Publications.
20. Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018). Social motivations of live-streaming viewer engagement on Twitch. Computers in Human Behavior, 84, 58–67. https://doi.org/10.1016/j.chb.2018.02.013
21. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic Consumption: Emerging Concepts, Methods and
22. Journal of Marketing, 46(3), 92. https://doi.org/10.2307/1251707
23. (2023). Indonesia E-Commerce Trends-1st Semester 2023.
24. Khan, G. F., & Vong, S. (2014). Virality over youtube: An empirical analysis. Internet Research, 24(5), 629–
25. https://doi.org/10.1108/IntR-05-2013-0085
26. Komiak, S. X., & Benbasat, I. (2003). Understanding Customer Trust in Agent-Mediated Electronic Commerce, Web-Mediated Electronic Commerce, and Traditional Commerce. Information Technology and Management, 5(1/2), 181–207. https://doi.org/10.1023/b:item.0000008081.55563.d4
27. Lim, S. H., Lee, S., & Kim, D. J. (2017). Is Online Consumers’ Impulsive Buying Beneficial for E-Commerce Companies? An Empirical Investigation of Online Consumers’ Past Impulsive Buying Behaviors. Information Systems Management, 34(1), 85–100. https://doi.org/10.1080/10580530.2017.1254458
28. Luo, X., Cheah, J. H., Hollebeek, L. D., & Lim, X. J. (2024). Boosting customers’ impulsive buying tendency in live-streaming commerce: The role of customer engagement and deal proneness. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2023.103644
29. Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. The MIT Press.
30. Mou, J., & Benyoucef, M. (2021). Consumer behavior in social commerce: Results from a meta-analysis. Technological Forecasting and Social Change, https://doi.org/10.1016/j.techfore.2021.120734
31. Özen, H., & Kodaz, N. (2016). Utilitarian or Hedonic? A Cross Cultural Study in Online Shopping. In Developments in Marketing Science: Proceedings of the Academy of Marketing Science (Vol. 3, Issue 2, pp. 234–239). https://doi.org/10.1007/978-3-319-24148-7_71
32. Pappas, N. (2016). Marketing strategies, perceived risks, and consumer trust in online buying behaviour. Journal of Retailing and Consumer Services, 92–103. https://doi.org/10.1016/j.jretconser.2015.11.007
33. Sashi, C. M. (2012). Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253–272. https://doi.org/10.1108/00251741211203551
34. Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64(March 2023), 101370. https://doi.org/10.1016/j.elerap.2024.101370
35. Sherman, E., Mathur, A., & Smith, R. B. (1997). Store environment and consumer purchase behavior: Mediating role of consumer emotions. Psychology and Marketing, 14(4), 361–378. https://doi.org/10.1002/(SICI)1520-6793(199707)14:4<361::AID-MAR4>3.0.CO;2-7
36. Sirgy, M. J., Grewal, D., & Mangleburg, T. (2000). Retail environment, self-congruity, and retail patronage: An integrative model and a research agenda. Journal of Business Research, 49(2), 127–138. https://doi.org/10.1016/S0148-2963(99)00009-0
37. Sobari, N. (2022). the Effect of Live Streaming on Purchase Intention of E-Commerce Customers. International Conference on Family Business and Entrepreneurship, 282–290.
38. Sohn, Y. S., & Ko, M. T. (2021). The impact of planned vs. unplanned purchases on subsequent purchase decision making in sequential buying situations. Journal of Retailing and Consumer Services, 59, 102419. https://doi.org/https://doi.org/10.1016/j.jretconser.2020.102419
39. (2025). Somethinc.
40. Song, S., Yao, X., & Wen, N. (2021). What motivates Chinese consumers to avoid information about the COVID-19 pandemic?: The perspective of the stimulus-organism-response model. Information Processing and Management, 58(1), 102407. https://doi.org/10.1016/j.ipm.2020.102407
41. (2024). Number of users of e-commerce in Indonesia from 2020 to 2029. https://www.statista.com/forecasts/251635/e-commerce-users-in-indonesia
42. (2010). Metode Penelitian Pendidikan Pendekatan Kuantitatif, kualitatif, dan R&D. Alfabeta.
43. Sumer, S. I., & Parilti, N. (2023). Social Media Analytics in Predicting Consumer Behavior (1st Editio). CRC Press. https://doi.org/10.1201/9781003200154
44. Sun, H., & Zhang, P. (2015). The Role of Affect in Information System Research: A Critical Survey and a Research Model. Taylor and Francis.
45. Vivek, S. D., Beatty, S. E., Dalela, V., & Morgan, R. M. (2014). A Generalized Multidimensional Scale for Measuring Customer Engagement. Journal of Marketing Theory and Practice, 22(4), 401–420. https://doi.org/10.2753/MTP1069-6679220404
46. Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007). Can A Retail Web Site be Social? Journal of Marketing, 71(3), 143–157. https://doi.org/10.1509/jmkg.71.3.143
47. Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117(August 2018), 543–556. https://doi.org/10.1016/j.jbusres.2018.08.032
48. Wu, Yanyan, & Huang, H. (2023). Influence of Perceived Value on Consumers’ Continuous Purchase Intention in Live-Streaming E-Commerce—Mediated by Consumer Trust. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15054432
49. Xin, M., Liu, W., & Jian, L. (2024). Live streaming product display or social interaction: How do they influence consumer intention and behavior? A heuristic-systematic perspective. Electronic Commerce Research and Applications, 67, 101437. https://doi.org/https://doi.org/10.1016/j.elerap.2024.101437
50. Xu, X., Wu, J. H., & Li, Q. (2020). What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 21(3), 144–167.
51. ZAP & MarkPlus.Inc. (2024). ZAP Beauty Index 2024 (pp. 1–40). ZAP, MarkPlus.Inc.