Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9, 1954-1967 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 8 July 2025; Revised: 15 August 2025; Accepted: 20 August 2025; Published: 30 September 2025 * Correspondence: supot.r@mail.rmutk.ac.th The dual pathways of influence in live commerce: How brand engagement moderate online social interactions, sharing experience and impulsive purchases across Thai and Chinese consumers Supot Rattanapun1*, Nisaphan Pringle2, Pornprom Prompes3, Kriengsak Khantanapha4, Richard Hao5 1,4,5International College, Rajamangala University of Technology Krungthep, Bangkok, Thailand; supot.r@mail.rmutk.ac.th (S.R.) kriengsak.k@mail.rmutk.ac.th (K.K.) Richard.h@mail.rmutk.ac.th (R.H.). 2Accounting department, Business Administration, Broward College, Florida, United State; npringle@broward.edu (N.P.). 3Faculty of Management Sciences, Kasetsart University, Sriracha Campus, Chonburi, Thailand; pornprom@ku.th (P.P.). Abstract: Psychological factors drive impulsive buying in live commerce across consumer marketplaces. This study examines how online emotional and informational interactions affect consumer behavior in live commerce platforms. The main objectives are to (1) examine the effects of online emotional interaction (OEI) and online information interaction (OII) on experience sharing (SE); (2) to examine how experience sharing affects consumers' impulsive purchasing behavior (CIP); (3) to assess brand engagement's moderating role on the relationship between online interaction and experience sharing; and (4) to compare these relationships between Thai and Chinese consumers. Structured questions were given to 205 Thai and Chinese live streamers. The questionnaire data was examined using SEM and multigroup analysis. The study found that: (1) online emotional interaction (OEI) significantly affects impulsive purchases (β = 0.28); (2) OII has a significant effect (β = 0.18); (3) experience sharing (SE) strongly predicts impulsive buying behavior (β = 0.41), emphasizing social validation and peer influence; and (4) brand engagement (BE) significantly enhances impulsive purchases (β = 0. Multi- group analysis showed a significant difference in brand engagement (BE) through impulsive purchase behavior (CIP) between Thai and Chinese consumers, with a higher effect for Chinese respondents (β = 0.40) compared to Thai respondents (β = 0.29, p = 0.04). These findings emphasize the importance of online social contact and brand involvement in creating impulsive live commerce purchases and cultural differences between consumer groups. The study helps explain how emotional, informational, and brand elements interact in digital retail. Keywords: Brand engagement, China, Experience sharing, Impulsive purchases, Live commerce, Online social interactions, Thailand. 1. Introduction Live commerce, which combines real-time broadcasting with online shopping, is one of the world's fastest-growing digital retail trends. The global live commerce market was valued at around USD 180 billion in 2023 and is expected to approach USD 370 billion by 2027, with China and Southeast Asia leading the way [1]. China dominates this sector, with a projected 388 million live commerce shoppers in 2022, representing for more than 30% of total e-commerce consumers [2]. Meanwhile, Thailand's live commerce industry, while smaller, has experienced tremendous growth, with a yearly increase rate of more than 50% and increased consumer involvement led by mobile internet usage and social media use [3]. https://orcid.org/0009-0004-4513-0455 https://orcid.org/0009-0006-3943-684X 1955 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate Despite this expansion, the live commerce markets in both nations have distinct obstacles that influence consumer behavior and purchase outcomes. Despite significant consumer engagement with live commerce in China, recent publications highlight worries over impulsive purchasing patterns that lead to buyer's remorse and financial pressure, particularly among younger consumers [4]. Furthermore, increasing competition between brands and streamers has resulted in information saturation, diminishing customers' capacity to evaluate product quality [5]. In contrast, Thailand's live commerce ecosystem is still in its early stages, with constraints such as poor brand trust and inadequate integration between social interactions and purchasing channels impeding consumer sharing behaviors and purchase conversion [6]. Furthermore, Thai consumers are more skeptical of live commerce advertisements due to a lack of defined laws [7]. These commercial issues reflect a lack of knowledge of the two channels by which online social interactions - emotional and informational - impact consumer sharing experiences and impulsive purchases in these two settings. While previous studies have investigated the importance of social influence and brand engagement in online shopping [8, 9] there has been little comparative research on live commerce platforms in Thailand and China. Most extant study focuses on a single nation, often China, leaving Thailand's dynamics unexplored and comparative insights sparse [10]. Furthermore, the function of brand engagement - consumers' psychological attachment and participation with companies - in mediating the routes from online social interactions to sharing behavior and impulsive purchase is understudied, particularly in cross-cultural situations [11]. Understanding how brand interaction influences consumer responses to emotional and informational cues in live commerce is crucial for creating more effective marketing tactics that are targeted to specific markets. To fill these gaps, this study examines the dual influence pathways of online emotional and informational interactions on sharing experience and impulsive purchases, as well as how brand engagement moderates these effects in live commerce settings in Thailand and China. The comparison study will provide deep insights into country-specific customer behaviors, guiding marketers in optimizing engagement tactics in these critical regions. 1.1. Research Objectives 1. To examine the effects of online emotional and information interaction on sharing experience in live commerce. 2. To investigate the influence of sharing experience on impulsive purchases in live commerce. 3. To assess the moderating role of brand engagement on the relationship between online emotional and information interaction and sharing experience. 4. Compare social interaction, sharing experiences, and brand engagement among Thai and Chinese consumers. 1.2. Research Questions 1. How does online social interaction influence sharing experience among live commerce consumers? 2. What is the impact of sharing experience on impulsive purchases in live commerce? 3. How does brand engagement moderate the relationship between online social interaction and sharing experience? 4. Are there significant differences between Thai and Chinese consumers regarding these relationships in live commerce? 2. Literature Reviews This research based on Social Exchange Theory [12]; Stimulus–Organism–Response (S-O-R) Model [13] Theory of Planned Behavior [14], Relationship Marketing Theory [15]; Engagement Theory [16]. 1956 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate Table 1. Definitions and Theory Base of Key Variables in the Study. Variable Definition Source Theory Base Online Emotional Interaction The expression and exchange of emotions, empathy, and affective responses between users and streamers/viewers in live commerce platforms. Kotler and Armstrong [17] Social Presence Theory [18]; Affective Involvement in Media Engagement Online Information Interaction The process of exchanging product-related information (e.g., descriptions, reviews, questions) during live streams to aid decision-making. Laudon and Traver [19] Elaboration Likelihood Model (ELM) [20] Sharing Experience The voluntary sharing of consumer product experiences, emotions, and opinions in real- time or after live commerce, often influencing others. Solomon [21] Social Exchange Theory [12]; Word-of-Mouth Theory Consumers’ Impulsive Purchases Unplanned, spontaneous buying behavior influenced by emotional arousal, interactivity, or social presence during live commerce. Schiffman and Wisenblit [22] Stimulus–Organism–Response (S-O-R) Model [13]; Impulse Buying Theory Consumers’ Purchasing Intentions The likelihood or willingness of a consumer to buy a product, influenced by both rational evaluation and emotional appeal in live commerce. Hoyer, et al. [23] Theory of Planned Behavior [14]; Reasoned Action Approach Brand Engagement (Moderator) The degree of cognitive, emotional, and behavioral investment a consumer shows in interacting with a brand, affecting how social input influences buying. Keller [24] Relationship Marketing Theory [15]; Engagement Theory [16] These theories underpin the suggested research model. External triggers like online emotional and informational connections motivate customers to share their experiences. According to Social Presence Theory, emotional engagement in live commerce strengthens connections, while the Elaboration Likelihood Model (ELM) explains how customers process reliable and relevant information through key routes. Social Exchange Theory supports the idea that consumers exchange experiences when they see reciprocal value or social benefit, especially in dynamic, interactive contexts like live commerce platforms. Sharing experience links social connection to unanticipated consumer activity. These emotional and informational cues (stimuli) alter internal cognitive and emotional states (organism), which lead to impulsive purchases (reaction), according to the S-O-R Model. The Theory of Planned Behavior (TPB) shows how attitudes and normative influences-often from peer sharing-influence spontaneous buying. Lastly, brand involvement moderates these correlations. According to Relationship Marketing Theory and Engagement Theory, cognitively and emotionally invested consumers are more likely to amplify the effects of online interactions on sharing behavior, increasing the chance of impulsive purchases. 2.1. A Relationship Between Online Social Interaction and Experience Sharing Online social interaction refers to the dynamic communication and engagement between users within a digital platform, particularly in real-time environments such as live commerce. It includes emotional and informational exchanges that influence decision-making. This involves the expression and exchange of feelings, empathy, and affective responses between users and streamers or among viewers during a live session. Emotional interactions foster trust, enjoyment, and perceived intimacy, which can drive purchase decisions [17]. Online emotional interaction refers to the affective exchange between users during digital communication, characterized by expressions of feelings, empathy, and emotional support [25, 26]. It reflects the social presence theory, which emphasizes the importance of emotional cues in building interpersonal connections in online environments [18]. 1957 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate Online Emotional interaction during live commerce enhances consumers' sense of belonging and emotional attachment to the community, which encourages sharing experiences Zhou, et al. [9]. Kim and Johnson [8] found that emotional engagement in live streaming positively affects consumers' willingness to share product-related content. Likewise, Liu, et al. [27] demonstrated that emotional interaction strengthens consumers’ social identity, thereby promoting active content sharing. Wang and Zhang [5] confirmed that emotional cues in live commerce reduce social distance, facilitating sharing behaviors. Finally, Huang and Benyoucef [28] highlighted that emotional resonance in online shopping environments increases customer advocacy and experience sharing. H1: Online Emotional Interaction positively influences Sharing Experience. 2.2. A Relationship Between Online Information Interaction and Sharing Experience Online Information Interaction refers to the exchange of product-related information, such as descriptions, reviews, and questions during live streaming. It plays a critical role in reducing uncertainty and enhancing consumer knowledge [19]. Online information interaction involves the exchange and processing of factual product information, advice, and recommendations in digital communication [29]. Grounded in the Elaboration Likelihood Model [20], consumers who engage in informational interaction tend to process content through the central route, leading to stronger attitudes and behaviors such as sharing. Studies show that consumers engaged in informational interaction are more likely to share experiences because they perceive the shared information as valuable and credible Li, et al. [4]. Zhou, et al. [9] found that informational interaction positively impacts knowledge sharing and experience communication. Kaur and Singh [11] demonstrated that information exchange promotes consumers' perceived usefulness and encourages experience sharing. Kim and Johnson [8] also noted that informational content increases trust, which mediates sharing intention. Lastly, Chen and Shen [30] emphasized that information interaction contributes to community knowledge building, driving sharing activities. H2: Online Information Interaction positively influences Sharing Experience. 2.3. The Impact of Sharing Experience on Impulsive Purchase in Live Streaming Commerce Sharing experience in live commerce encompasses consumers' voluntary dissemination of their usage experiences, emotions, and opinions about products or brands, often in real-time or post-stream reviews. This behavior contributes to social proof and influences others' purchasing decisions [21]. Sharing experience is the voluntary act of communicating personal consumption experiences with others, which aligns with Social Exchange Theory [12] emphasizing reciprocal relationships in social interactions. Impulsive purchase is defined as a spontaneous, unplanned buying behavior triggered by internal and external stimuli [31]. Research indicates that sharing experience generates social proof and normative influence that stimulate impulsive buying Verhagen and Van Dolen [32]. Li, et al. [4] found that consumers who share experiences tend to reinforce their purchase decisions, leading to further impulsive purchases. Similarly, Huang and Benyoucef [28] confirmed that shared positive experiences enhance emotional arousal, promoting impulsivity. Zhou, et al. [9] showed that sharing behavior increases purchase urgency by creating social connection and pressure. Kim and Johnson [8] also reported that peer sharing heightens impulsive consumption through affective contagion. H3: Sharing Experience positively influences Impulsive Purchases. 2.4. Brand Engagement as a Moderator between Online Emotional Interaction and Sharing Experience Brand engagement is defined as the degree of a consumer’s cognitive, emotional, and behavioral investment in interactions with a brand. In live commerce, it moderates how social interactions and shared experiences influence impulsive buying by strengthening the emotional connection between consumers and the brand [24]. 1958 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate Brand engagement is the level of a consumer’s cognitive, emotional, and behavioral investment in brand interactions [16]. According to Relationship Marketing Theory [15] higher engagement strengthens consumers' responses to social interactions, fostering deeper involvement. Brand engagement amplifies the effect of emotional interaction on sharing by increasing emotional attachment and loyalty Zhou, et al. [9]. Kaur and Singh [11] found that highly engaged consumers are more receptive to emotional cues, resulting in increased sharing. Brodie, et al. [16] noted that engagement heightens sensitivity to affective interactions. Wang and Zhang [5] emphasized that brand-engaged consumers are motivated to share experiences to reinforce identity. Kim and Johnson [8] also demonstrated that brand engagement moderates emotional influence in online communities. H4: Brand Engagement moderates the relationship between Online Emotional Interaction and Sharing Experience, strengthening the effect when engagement is high. 2.5. Brand Engagement as a Moderator between Online Information Interaction and Sharing Experience Brand engagement also influences how consumers process informational stimuli and their resulting behaviors [33]. Engaged consumers are more attentive and motivated to use information for social sharing [34]. Studies reveal that brand engagement enhances the impact of information exchange on sharing behavior. Kaur and Singh [11] found that engaged consumers are more likely to internalize and communicate brand information. Zhou, et al. [9] noted that informational interaction translates into sharing more effectively when brand engagement is high. Kim and Johnson [8] emphasized that engaged customers amplify their knowledge-sharing behaviors. Huang and Benyoucef [28] also found that information processing is deeper for engaged consumers, leading to increased sharing. H5: Brand Engagement moderates the relationship between Online Information Interaction and Sharing Experience, strengthening the effect when engagement is high. 2.6. Conceptual Model Figure 1. Conceptual framework 3. Research Methods This study employs a quantitative, cross-sectional survey design to examine the relationships between online emotional interaction, online information interaction, sharing experience, impulsive purchase behavior, and the moderating effect of brand engagement. The research adopts a comparative approach to investigate potential differences between consumers in Thailand and China, utilizing Structural Equation Modeling (SEM) and multi-group analysis (MGA) to test hypotheses across groups. 1959 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate The target population comprises consumers aged 18 and above in Thailand and China who have experience watching and purchasing through live commerce platforms (e.g., TikTok Live, Taobao Live, Facebook Live, Shopee Live) within the last six months. A purposive sampling technique will be used to target participants who meet inclusion criteria (i.e., recent live commerce experience). To ensure adequate power for multi-group SEM, a minimum of 100 participants per country is targeted, in line with recommendations by Hair, et al. [35]. A total of 203 respondents collected. 3.1. Reliability Analysis of Constructs Table 2. Validity and AVE. Item Cronbach’s alpha Composite Reliability (CR) AVE OEI1. I feel emotionally connected with the host during live commerce. 0.89 0.91 0.73 OEI2. The host’s emotions make me feel more engaged with the session. OEI3. I often experience excitement while watching live streaming sales. OEI4. The live chat creates an emotionally supportive atmosphere. OEI5. I feel a sense of belonging when interacting with others in the stream. OEI1. I feel emotionally connected with the host during live commerce. OII1. I rely on information shared during live streams to make decisions. 0.88 0.90 0.69 OII2. The host provides sufficient product details during the session. OII3. Other viewers share useful product-related information. OII4. I ask or respond to questions during the live stream. OII5. The information provided during the stream helps me evaluate products. SE1. I like to share my experiences about live commerce purchases. 0.86 0.88 0.67 SE2. I often post reviews or feedback after live sessions. SE3. I tell friends about products I discovered in live streams. SE4. Sharing my experience online makes me feel valued. SE5. I influence others by sharing my live commerce experiences. CIP1. I sometimes buy products during live commerce without planning. 0.91 0.93 0.75 CIP2. I feel an urge to buy when I see a product in a live session. CIP3. I often make purchases immediately after seeing product promotions. CIP4. I buy items during live streams because they seem exciting. CIP5. My emotions drive me to make purchases in live sessions. BE1. I feel personally connected to this brand during live commerce. 0.87 0.89 0.70 BE2. I actively engage with content related to this brand. BE3. I frequently follow the brand’s live events or updates. BE4. I enjoy interacting with this brand on live platforms. BE5. This brand plays a meaningful role in my online shopping. Note: Content validity will be established through 5 expert panel review. Construct validity will be assessed using Confirmatory Factor Analysis (CFA). Reliability will be evaluated via Cronbach’s alpha and Composite Reliability (CR) with a threshold of 0.70. Discriminant validity will be tested using the Fornell-Larcker criterion and HTMT ratio. AVE> 0.50, confirming convergent validity. 4. Results This research presents the analysis results for the study conducted among 203 respondents (98 from Thailand and 105 from China). The data were analyzed using descriptive statistics, reliability and validity assessment, structural equation modeling (SEM), multi-group analysis (MGA), and moderation testing to examine the proposed relationships in the conceptual framework. 1960 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate 4.1. Descriptive Statistics Table 3. Demographic Profile of Respondents (n=203). Variable Category Frequency Percentage (%) Country 1 (Thailand) 98 48.3% 2 (China) 105 51.7% Gender Male 102 50.2% Female 101 49.8% Age Group 18–24 years 45 22.2% 25–34 years 52 25.6% 35–44 years 38 18.7% 45–54 years 31 15.3% 55–64 years 27 13.3% 65+ years 10 4.9% Academic Qualification High School 42 20.7% Bachelor 78 38.4% Master 56 27.6% PhD) 27 13.3% Monthly Income <$1,000 40 19.7% $1,000–$2,000 48 23.6% $2,001–$3,000 52 25.6% $3,001–$4,000 38 18.7% >$4,000 25 12.3% The demographics of the 203 study participants. The sample included 98 Thai (48.3%) and 105 Chinese (51.7%) consumers, with 50.2% male and 49.8% female. Age distribution was wide, with 25–34 (25.6%) being the largest group and 18–24 (22.2%) following. Also noticeable were those aged 35–44 (18.7%) and 45–54 (15.3%). Most had bachelor's degrees (38.4%) and a large number had master's degrees (27.6%). Only 13.3% were PhDs. Most respondents earned $1,000–$3,000 per month, with 25.6% earning $2,001–$3,000, indicating a middle-income consumer base. The sample is well-balanced demographically for studying live commerce behavior in Thailand and China. 4.1.1. Construct Means and Standard Deviations Descriptive statistics (means and standard deviations) for the five key constructs used in the study. All constructs were measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Table 4. Means and Standard Deviations of variables. Construct Mean Standard Deviation (SD) Minimum Maximum Online Emotional Interaction (OEI) 3.82 0.91 1.00 5.00 Online Information Interaction (OII) 3.75 0.87 1.00 5.00 Sharing Experience (SE) 3.68 0.93 1.00 5.00 Impulsive Purchases (CIP) 3.71 0.95 1.00 5.00 Brand Engagement (BE) 3.65 0.89 1.00 5.00 Among the variables, Online Emotional Interaction (M = 3.82) and Online Information Interaction (M = 3.75) had the highest mean scores, suggesting participants generally perceived high levels of engagement and informational exchange during live commerce sessions. Impulsive Purchases and Sharing Experience also showed moderately high means (3.71 and 3.68 respectively), indicating that these behaviors are relatively common in the live commerce environment. Brand Engagement, while slightly lower (M = 3.65), still reflects a positive inclination toward involvement with brands. All constructs exhibited standard deviations near or below 1.0, indicating moderate variability across respondents. 1961 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate 4.2. Measurement Model Evaluation 4.2.1. Reliability The reliability and validity tables from the measurement model evaluation are below: Table 5. Reliability Analysis of Constructs. Construct Cronbach’s Alpha Composite Reliability (CR) Online Emotional Interaction (OEI) 0.89 0.91 Online Information Interaction (OII) 0.88 0.90 Sharing Experience (SE) 0.86 0.88 Consumer Impulsive Purchases (CIP) 0.91 0.93 Brand Engagement (BE) 0.87 0.89 The reliability of the five main constructs used in the study. Both Cronbach’s Alpha and Composite Reliability (CR) values exceed the recommended threshold of 0.70 [35] confirming high internal consistency of all constructs. The highest reliability is seen in CIP (α = 0.91; CR = 0.93), indicating strong consistency among items measuring impulsive purchasing behavior. Table 6. Convergent and Discriminant Validity (Fornell–Larcker Criterion) Construct AVE √AVE OEI OII SE CIP BE OEI 0.73 0.85 0.85 OII 0.69 0.83 0.62 0.83 SE 0.67 0.82 0.58 0.54 0.82 CIP 0.75 0.87 0.51 0.49 0.66 0.87 BE 0.70 0.84 0.47 0.43 0.59 0.72 0.84 The convergent and discriminant validity of the measurement model using the Fornell–Larcker criterion. All constructs achieved Average Variance Extracted (AVE) > 0.50, confirming convergent validity. Furthermore, the square root of AVE (√AVE) for each construct (diagonal values) is greater than the inter-construct correlations (off-diagonal values), supporting discriminant validity. For instance, the √AVE of OEI (0.85) is higher than its correlation with OII (0.62) or SE (0.58), indicating each construct is distinct from the others. 4.3. Structural Equation Modeling (SEM) 4.3.1. Path Coefficients A detailed explanation of the hypothesis testing results and model fit indices. Table 7. Hypotheses Testing Results. Hypothesis Path Standardized Coefficient (β) t-value p-value Result H1 OEI → CIP (Emotional → Impulse) 0.28 4.32 < 0.001 Supported H2 OII → CIP (Information → Impulse) 0.18 2.87 0.004 Supported H3 SE → CIP (Sharing → Impulse) 0.41 6.15 < 0.001 Supported H4 BE → CIP (Brand → Impulse) 0.35 5.21 < 0.001 Supported Hypotheses Testing Results table summarizes the results of the structural model and hypothesis testing using Structural Equation Modeling (SEM). All four hypotheses (H1–H4) were statistically significant with p-values < 0.01, indicating strong support for the proposed paths in the model. H1 shows that Online Emotional Interaction (OEI) significantly influences Consumers' Impulsive Purchases (CIP), suggesting that emotional connections during live commerce (e.g., with streamers) can drive unplanned buying behavior. 1962 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate H2 supports that Online Information Interaction (OII) also has a significant effect on impulsive purchases, although with a smaller β value (0.18), indicating a less intense but still meaningful impact. H3 confirms that Sharing Experience (SE) is a strong predictor of impulsive purchases (β = 0.41), implying that peer influence and social validation strongly trigger purchase impulses. H4 reveals that Brand Engagement (BE) plays a significant role in driving impulsive buying (β = 0.35), highlighting the power of emotional and cognitive ties to a brand in live commerce environments. 4.3.2. Model Fit Indices Table 8. Model Fit Indices. Fit Index Value Threshold Interpretation χ²/df 1.89 < 3.00 Good fit CFI (Comparative Fit Index) 0.96 > 0.90 Excellent fit RMSEA (Root Mean Square Error of Approximation) 0.06 < 0.08 Acceptable fit Note: χ²/df = 1.89 (Good: <3), CFI = 0.96 (Good: >0.90), RMSEA = 0.06 (Good: <0.08). The model fit indices indicate an excellent fit between the model and observed data. The chi-square per degree of freedom (χ²/df) is 1.89, which is below the accepted threshold of 3.0, the CFI is 0.96 (above 0.90), and the RMSEA is 0.06 (below 0.08), collectively demonstrating that the model is both statistically and practically adequate. 4.4. Multi-Group Analysis (MGA) Comparison by Country, Below is the Multi-Group Analysis (MGA) table of the results. Table 9. Multi-Group Analysis (MGA) by Country (Thailand vs. China). Path Thailand (β) China (β) β Difference p-value Significant Online Emotional Interaction → CIP 0.31 0.25 0.06 0.22 No Online Information Interaction → CIP 0.21 0.15 0.06 0.18 No Sharing Experience → CIP 0.45 0.38 0.07 0.12 No Brand Engagement → CIP 0.29 0.40 -0.11 0.04 Yes The results of the Multi-Group Analysis (MGA) comparing the structural path coefficients between Thai and Chinese respondents using Structural Equation Modeling (SEM). This analysis assesses whether the relationships between independent variables and Consumer Impulsive Purchases (CIP) differ significantly across the two national groups. 1) The paths from Online Emotional Interaction, Online Information Interaction, and Sharing Experience to CIP show no statistically significant differences between Thai and Chinese consumers. Although Thai respondents exhibit slightly stronger effects across these paths, the p-values > 0.05 indicate these differences are not significant. 2) The only path that differs significantly between the two groups is Brand Engagement → CIP (p = 0.04). The effect is stronger among Chinese consumers (β = 0.40) compared to Thai consumers (β = 0.29). This suggests that brand engagement plays a more influential role in driving impulsive purchases among Chinese live commerce users, possibly due to higher brand loyalty or stronger trust in influencer-endorsed brands in China. These findings suggest cultural or contextual variations in how consumers respond to branding in live commerce settings, and they provide useful insights for tailoring marketing strategies by country. 1963 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate 4.5. Moderation Analysis of Brand Engagement 4.5.1. Moderating Effects on Key Paths Table 10. Moderating Effects of Brand Engagement on Key Relationships. Interaction Term β (Interaction Effect) t-value p-value Result OEI × BE → CIP (Emotional × Brand) 0.17 2.58 0.010 Supported OII × BE → CIP (Information × Brand) 0.09 1.42 0.156 Not Supported SE × BE → CIP (Sharing × Brand) 0.23 3.76 < 0.001 Supported The results of the moderation analysis, which examines whether Brand Engagement (BE) strengthens or weakens the relationships between three predictor variables—Online Emotional Interaction (OEI), Online Information Interaction (OII), and Sharing Experience (SE)—and Consumer Impulsive Purchases (CIP). 1) The interaction term OEI × BE → CIP is significant (β = 0.17, p = 0.010), suggesting that brand engagement positively moderates the relationship between emotional interaction and impulsive purchases. In other words, emotionally engaging content has a stronger impact on impulsive behavior when consumers are more invested in the brand. 2) The interaction between OII × BE → CIP is not significant (p = 0.156), indicating that brand engagement does not significantly alter the effect of information-based interaction on impulsive purchases. This may suggest that rational, informational content in live commerce operates more independently of brand attachment. 3) The SE × BE → CIP interaction is highly significant (β = 0.23, p < 0.001), showing that sharing experiences is more likely to result in impulsive buying when consumers have a high level of brand engagement. This implies that brand-engaged users are more likely to act on social proof and peer influence during live streaming. These results highlight that brand engagement enhances the emotional and social pathways to impulsive purchases, but not the informational pathway, offering useful strategic implications for marketers aiming to leverage live commerce environments. 5. Discussion This chapter discusses the results of the study in relation to the research hypotheses, underlying theoretical frameworks, and prior empirical findings. Each hypothesis is examined in depth, providing theoretical integration and scholarly interpretation. 5.1. Online Emotional Interaction (OEI) → Consumer Impulsive Purchases (CIP) The analysis revealed a significant positive effect of Online Emotional Interaction on Consumer Impulsive Purchases (β = 0.28, p < 0.001), supporting H1. This aligns with the Stimulus-Organism- Response (S-O-R) model [13], where emotional stimuli in live commerce (e.g., tone of voice, facial expressions, real-time reactions) trigger internal affective states that lead to spontaneous purchasing behaviors. The findings reinforce the role of Social Presence Theory [18], suggesting that the perceived emotional closeness to the streamer enhances psychological arousal, driving impulsive actions. This result supports previous research by Luo, et al. [36] who found that emotional connection in live streaming commerce elevates affective trust and increases purchase intention. The finding also extends Xu, et al. [37] who emphasized that emotional stimuli in digital environments can bypass rational evaluation, particularly among younger consumers who seek instant gratification. 5.2. Online Information Interaction (OII) → Consumer Impulsive Purchases (CIP) The significant positive relationship between OII and CIP (β = 0.18, p = 0.004) confirms H2 and supports the Elaboration Likelihood Model (ELM) [20], where informative content functions through 1964 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate the peripheral or central route depending on user motivation. In live commerce, product features, specifications, and live demonstrations enhance cognitive evaluation, prompting action. Compared with emotional interaction, informational cues had a weaker effect, suggesting that impulsive purchases are less driven by rational analysis in this context. This mirrors the findings of Sun, et al. [38] who observed that consumers in fast-paced live commerce environments rely on quick, simplified product evaluations, often influenced by heuristics. Still, the result corroborates Park and Lin [39] who noted that interactive Q&A and detailed product talk enhance transparency and perceived credibility, nudging consumers toward unplanned decisions. 5.3. Sharing Experience (SE) → Consumer Impulsive Purchases (CIP) Sharing Experience showed the strongest impact on impulsive purchases (β = 0.41, p < 0.001), validating H3. This confirms the mediating role of social influence as proposed by Social Exchange Theory [12] and aligns with Theory of Planned Behavior (TPB) [14] which positions subjective norms as pivotal drivers of intention and behavior. This result is consistent with findings from Huang and Benyoucef [28] who asserted that shared experiences (peer recommendations, emotional narratives, or unboxing) create vicarious gratification and strengthen behavioral mimicry. Furthermore, it adds to Lim, et al. [40] who showed that experiential sharing in livestreams reinforces consumer trust and normalizes impulsive buying through social proof. 5.4. Brand Engagement (BE) → Consumer Impulsive Purchases (CIP) Brand Engagement significantly predicted impulsive purchases (β = 0.35, p < 0.001), affirming H4. Rooted in Engagement Theory [16] and Relationship Marketing Theory [15] engaged consumers exhibit deeper emotional and cognitive investment in brands, increasing their susceptibility to influence. This supports insights by Hollebeek, et al. [41] who found that brand-engaged users exhibit higher levels of attention, loyalty, and reactive purchasing behavior in digital settings. The result is particularly notable when compared to less engaged consumers, who remain more skeptical. The finding emphasizes the importance of building immersive brand narratives in live streams. 5.5. Moderating Effects of Brand Engagement The moderation analysis found that Brand Engagement significantly strengthened the effects of OEI (β = 0.17, p = 0.010) and SE (β = 0.23, p < 0.001) on CIP, but not OII (β = 0.09, p = 0.156). This indicates that when consumers are emotionally invested in a brand, both their affective responses and social interactions become more powerful drivers of impulsive behavior. This pattern aligns with Dual- System Theory [42] where emotionally primed consumers (System 1 thinking) make quicker decisions with less cognitive restraint. This moderating role is consistent with Zhang, et al. [43] who emphasized that emotional brand attachment increases susceptibility to peer influence and emotional triggers. The nonsignificant moderation of information cues suggests that cognitive evaluations remain relatively stable regardless of brand attachment, echoing Lin and Xu [44] who argued that information processing in live commerce is more utility-driven. 5.6. Cross-Country Differences (Thailand vs. China) Multi-group analysis (MGA) revealed that only one path—BE → CIP—differed significantly between Thai and Chinese respondents. The effect was stronger in China (β = 0.40) than Thailand (β = 0.29, p = 0.04). This suggests that Chinese consumers place greater emphasis on brand trust and loyalty, possibly due to the cultural importance of reputation (guanxi) and brand familiarity in China's digital commerce ecosystem. 1965 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate This difference aligns with Hofstede’s cultural dimensions theory, particularly in uncertainty avoidance and collectivism. Chinese consumers may rely more heavily on familiar, trusted brands when making fast decisions in high-stimulation environments like live commerce. Thai consumers, while still influenced by brand, may prioritize emotional content and peer sharing slightly more. 6. Conclusion In summary, this study confirms that both emotional and informational interactions in live commerce environments lead to impulsive purchases, with sharing experience and brand engagement playing critical amplifying roles. Emotional and social mechanisms are especially potent when consumers are highly engaged with a brand, and this effect varies across cultures. The findings extend existing theories in consumer psychology, digital marketing, and cross-cultural behavior, offering valuable insights for both academics and practitioners. 7. Contributions 1.Theoretical Contributions, This research enriches the application of the S-O-R model, ELM, and Engagement Theory in the context of live commerce, demonstrating how both emotional and informational interactions lead to impulsive purchases. 1) The moderation findings provide empirical validation for the boundary condition role of brand engagement, offering nuance to prior engagement and relationship marketing literature. 2) The comparative insights between Thai and Chinese consumers contribute to cross-cultural consumer behavior theory, especially within digitally mediated environments. 2.Practical Contributions: 1) For marketers and e-commerce platforms, enhancing emotional interactivity and experience sharing features (e.g., chat, live reaction tools) can drive impulse sales. 2) Strong brand engagement strategies (e.g., exclusive content, live brand ambassadors) are crucial for influencing emotionally responsive consumers. 3) Cultural tailoring should be prioritized: Chinese consumers may respond more to brand-centric strategies, whereas Thai consumers may be more responsive to emotional content and social interaction. 8. Recommendations This research suggests various strategies for live commerce brands, platform developers, and marketers. First, brands should provide online spaces where customers can share their tales. User-generated content, live reviews, and post-purchase storytelling are incorporated. Experience-sharing mechanisms can influence impulsive purchases when heightened by emotions and social cues. Second, live broadcasting requires brand interaction before and during. Surveys, real-time Q&A, live reactions, and gamification engage customers. Participation prizes, loyalty programs, and customized shoutouts can warm viewers and drive inadvertent purchases. Third, these engagement strategies must be regional and cultural. The multi-group study found that Chinese consumers responded more to brand involvement than Thai consumers. Influencer partnerships and brand storytelling may work better in China. Thailand may encourage peer influence and emotional participation, necessitating a social content strategy. Fourth, platform designers should add emotional interaction and seamless experience sharing. Emoji reactions, AI-moderated chat, and shared viewing rooms foster community. These elements enhance affective and social engagement through presence and co-experience. Finally, legislators and platform authorities should promote ethical and transparent live commerce marketing. Due to emotional and peer persuasion, brand endorsements and influencer ties must be declared to safeguard buyers against manipulation. Provide accurate product depiction and digital engagement standards. 1966 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1954-1967, 2025 DOI: 10.55214/2576-8484.v9i9.10280 © 2025 by the authors; licensee Learning Gate These recommendations support this study's emotional and informational effect pathways and highlight contextualizing consumer behavior techniques within cultural and technology contexts. 9. Limitations The study used self-reported data, which may be subject to social desirability or recall bias. The sample size, while sufficient for SEM, was limited to two countries and may not generalize to other cultural contexts. The research focused on impulsive purchases; other forms of consumer behavior (e.g., satisfaction, long-term loyalty) were not examined. 10. Future Research Directions Expand the cross-cultural comparison to include Western or Southeast Asian countries for greater generalizability. 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