Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out

Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out YADONG LI, MOHAMAD ZUBER ABD MAJID, Ahmad Firdhaus Arham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7039008/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the psychological mechanisms underlying consumers’ impulse buying behavior in social commerce (S-commerce) by adopting the stimulus-organism-response (SOR) framework. Specifically, it examines how platform interactivity (INT) and social cue intensity (SCI), as environmental stimuli, influence the urge to buy impulsively (UBI) through the mediating roles of perceived utilitarian value (PUV) and perceived hedonic value (PHV). Furthermore, it explores the moderating role of Fear of Missing Out (FoMO) in the perceived value-behavior link. Data were collected from 398 social commerce users in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that both INT and SCI significantly enhance PUV and PHV, which in turn have a positive effect on UBI. FoMO is found to strengthen the effect of perceived value (especially PHV) on UBI. These findings enrich the application of the SOR model in digital consumption contexts and offer new insights into the dual value pathways and emotional moderators that drive impulsive behavior in social commerce environments. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 1. Introduction Over the past two decades, the convergence of social media and e-commerce has driven the rapid growth of s-commerce. S-commerce refers to online commercial activities conducted through social media platforms, allowing consumers to discover, evaluate, and purchase products in everyday social interactions (Dhaigude & Mohan, 2023; Liang & Turban, 2011). Unlike traditional e-commerce, social commerce leverages user-generated content, peer influence, and real-time interactions to make shopping more interactive and impulsive (Bernstein & Guo, 2022). In China, taking the s-commerce platform Douyin as an example, it has more than 600 million daily active users, and its total transaction volume (GMV) is expected to reach approximately US $ 490 billion by 2024, representing a year-on-year increase of 30% (S. Hu et al., 2023). Research indicates that approximately 71% of Douyin users make purchases without planning (Wang et al., 2023). Despite the significant commercial value of impulse purchases, understanding the underlying psychological mechanisms remains limited (Lo et al., 2022). As online impulse purchasing behavior becomes increasingly prevalent, consumer online impulse purchasing has emerged as a key focus for scholars and practitioners. Extensive research has confirmed that technological and platform characteristics significantly influence consumers’ cognitive and emotional responses and behavior (Baines, 2017; Liu et al., 2020; Zhu et al., 2023). In s-commerce, interactivity and cue intensity are key platform characteristics that drive consumer engagement, value perception, and behavioral responses (Dhaigude & Mohan, 2023). Specifically, interactivity refers to the ability of users to exchange information with the platform or others, such as comments, replies, votes, and private messages (Wang, 2021). Social cue intensity emphasizes consumers’ subjective perceptions of the visibility, frequency, and influence of social cues within the platform during use, such as the number of likes, comment heat, and product reviews. These cues can stimulate users’ social identity and conformity, influencing their decision-making (Cheng et al., 2023). Although these platform features have been proven to influence consumers’ perceptions and behaviors, existing studies mostly describe them as contextual backgrounds and rarely systematically examine how they drive impulsive purchasing behavior through consumers’ perceived value. Naseem and Yaprak (2023) note that consumers purchase products because they perceive value, which influences their decision-making preferences. Consumer perceived value is typically categorized into two types: perceived utilitarian value, which includes efficiency, convenience and task completion, and perceived hedonic value, which encompasses entertainment, pleasure, and social experiences (Vieira et al., 2018). In s-commerce, consumers gradually perceive the value of products and platforms through continuous interaction with the platform system and social content (Chen et al., 2020; Wu & Huang, 2023). X. Hu et al. (2023) further pointed out that utilitarian and hedonic values jointly drive consumers’ purchase intentions and behavioral choices. Therefore, perceived value is a key psychological antecedent variable driving buying behavior. With the widespread adoption of social media and increased user interaction, FoMO has emerged as a prominent psychological phenomenon. FoMO refers to the anxiety individuals experience from the fear of missing out on valuable experiences or information (Przybylski et al., 2013). In a s-commerce environment, consumers are often exposed to social shopping information that is immediate, interactive, and scarce (e.g., limited-time offers, friend recommendations, user reviews). This environment can significantly increase consumers’ FoMO levels, further amplifying their impulse buying tendencies (Das et al., 2022). However, there has been limited discussion on how FoMO regulates the relationship between consumer perceived value and impulse buying behavior in s-commerce environments. This study draws on the SOR theoretical framework and takes the environmental stimuli of social commerce platforms as a starting point to examine how consumers’ perceived value (utilitarian value and hedonic value) affects their impulse buying behavior, and further explores the moderating role of FoMO in this process. Specifically, this study aims to answer the following questions: (1) Which environmental factors of social commerce platforms induce consumers’ PUV and PHV? (2) How do consumers’ perceived values (PUV and PHV) influence their UBI on social commerce platforms? (3) How does FoMO moderate the relationship between consumers’ perceived values and UBI? 2. Theoretical Background This study adopts the SOR model as its theoretical framework. First proposed by Mehrabian and Russell (1974), this model emphasizes that environmental stimuli (S) trigger changes in an individual's internal psychological state (O), which in turn induce observable behavioral responses (R). This framework is widely used in consumer behavior research to explain how individuals generate emotional and cognitive responses when faced with different platforms or media environments, ultimately forming purchasing decisions (Liu et al., 2023; Peng & Kim, 2014). This study considers the INT and SCI of social commerce platforms as key environmental stimuli (S). The PUV and PHV represent their internal cognitive and emotional reactions (O). The UBI is the final behavioral reaction (R). The SOR model provides a structured analytical path for this study to systematically explore how multiple stimuli in s-commerce platforms influence consumers’ impulsive buying behavior through their perceived value. 2.1 Stimulus In a S-commerce environment, consumer interaction with platforms is not limited to receiving product information but is deeply embedded in social mechanisms and content participation processes. Therefore, this study regards INT and SCI, two key technical features of social commerce platforms, as environmental stimuli. It explores how they induce consumer value perception and subsequently influence their UBI. Interactivity refers to the ability of users to engage in two-way communication and feedback with platform content, systems, or other users, manifested in features such as comments, likes, private messages, real-time chat, and live streaming comments (Janlert & Stolterman, 2017). Social platforms with high interactivity can enhance consumers’ information engagement and sense of control, thereby stimulating their cognitive trust and emotional investment in the platform (Wang et al., 2024). Social cue intensity refers to the frequency, visibility, and social influence that consumers perceive from the behavioral traces of others (e.g., number of likes, number of comments) when using social commerce platforms (Cheng et al., 2023; Jia et al., 2024). Social cue strength influences consumers’ decision-making process, as they rely on their preferences and are driven by the psychological influence of others’ participation, amplifying their perceived value of platform content. Therefore, INT and SCI have been incorporated into current research as environmental stimuli influencing consumers’ UBI. 2.2 Organism In s-commerce, consumers’ decision-making process depends on the product’s attributes and is more influenced by their subjective perception of the platform’s interactive environment (Hussain et al., 2021). Chang and Chen (2008) pointed out that consumers typically undergo a series of cognitive and emotional mediating reactions after exposure to external stimuli. These psychological mechanisms play a crucial role in driving individual behavioral transformation. This study regards consumers’ PUV as a cognitive reaction and PHV as an emotional reaction. Consumer perceived value is defined as consumers’ overall evaluation of a product or service, representing their subjective trade-off between perceived gains and costs (Blut et al., 2024). It is an important prerequisite for consumer purchasing behavior (X. Hu et al., 2023). In previous studies, perceived value has been constructed as a multidimensional concept. PUV and PHV are the most commonly used dimensions in marketing and consumer behavior research (Yang et al., 2021). PUV reflects cognitive evaluations of the instrumental or task-oriented benefits of interacting with a product or service, such as cost-saving opportunities, product information, and convenience(Kang et al., 2020). PHV emphasizes the emotional arousal and pleasurable experiences consumers experience during shopping, which are more reflected in psychological satisfaction brought about by entertainment, enjoyment, or social engagement (Sharma et al., 2023). Numerous empirical studies support the significant impact of PUV and PHV on consumer decision-making processes in s-commerce (Laran & Tsiros, 2013). Therefore, this study uses PUV and PHV to explain consumers’ UBI in s-commerce. 2.3 Response Within the SOR theoretical framework, consumers’ responses to external stimuli manifest as specific behavioral tendencies or outcomes (Narayanan & Singh, 2025). Impulse buying is a typical consumer behavior driven by immediate cognitive and emotional reactions. This impulsivity typically occurs without prior planning, accompanied by significant emotional arousal and pleasure, with extremely fast decision-making processes involving little deliberation (Beatty & Ferrell, 1998). In other words, impulsive purchasing is characterized by immediacy and spontaneity, with purchasing behavior primarily driven by current stimuli and emotions rather than prior planning or rational consideration. Research on online impulsive shopping can be primarily categorized into the following three types: (1) marketing stimuli represented by price and promotions (Anoop & Rahman, 2025; Luo et al., 2021), (2) website stimuli represented by visual appeal and navigability(Liu et al., 2013; M Jois et al., 2024), and (3) social factors such as social norms and social proof (Wang, 2015; Zhang et al., 2014) and social psychology represented by FOMO (Montag et al., 2023). In traditional e-commerce platforms, which are product-centric, research has mainly focused on how marketing stimuli and website characteristics stimulate consumers’ impulsive reactions (Liu & Xiao, 2018). In contrast, social commerce is consumer-centric and integrates social interaction, user content co-creation, and instant feedback mechanisms to build a highly contextualized and interactive consumption environment (Gülbaşı & Taşkın, 2024). In this environment, consumers make decisions based on product information and form purchase motivations through continuous social cues and emotional interactions. At the same time, social commerce provides an opportunity to clarify further how the third type of factor drives consumers’ impulse shopping decisions. Although previous studies have introduced perceived value into the analysis of consumer behavior in the context of social commerce and explored its role in predicting purchase intention (Al-Omoush & Shuhaiber, 2024; Gan & Wang, 2017), they have not fully revealed its mediating role as an ‘organic response’ under the stimulation of the social environment. Therefore, this study examines consumers’ UBI from the perspective of consumer perceived value. The urge to buy impulsively is a sudden and intense desire to buy experienced in a given environment. This state is usually described as complex, sudden, sometimes irresistible, and persistent (Pacheco et al., 2021). It is only in this state that consumers engage in actual impulse buying behavior. In other words, actual impulse buying behavior occurs only after consumers experience the urge to buy impulsively. Therefore, this study uses UBI as a response factor. 3. Research model and assumptions This study adopts the SOR model as the theoretical framework. Within this framework, social cue strength and interactivity are conceptualized as environmental stimuli in the s-commerce context. These stimuli influence consumers’ PUV and PHV, which in turn directly stimulate their urge to buy impulsively. Furthermore, this relationship is moderated by consumers’ level of FoMO. Figure 1 illustrates the conceptual model of this study. 3.1 Environmental stimuli and perceived value Interactivity refers to the ability of social commerce platforms to facilitate effective, immediate, two-way communication and information exchange between users and sellers (Alsoud et al., 2022). Previous studies have shown that interactivity can effectively improve consumers’ efficiency in obtaining product information, reduce uncertainty in online purchasing, and enhance consumers’ task orientation and decision-making efficiency during shopping (Chen, 2024; Xq et al., 2021). Guo and Li (2022) further noted that in social commerce, consumers can quickly obtain product information, ask questions, and receive timely responses, thereby completing the information search and judgment process more effectively throughout the shopping experience. Therefore, the following hypothesis is proposed: H1a: INT positively affects PUV. Interactivity on social commerce platforms also affects consumers’ PHV. Specifically, highly interactive platforms offer various forms of social interaction, including comment exchanges, private message feedback, and live Q&A, enabling consumers to have a more immersive and engaging shopping experience, thereby stimulating their sense of pleasure and participation (Ye & Liu, 2021). Such social and real-time feedback mechanisms enhance consumers’ emotional investment in shopping activities and strengthen their hedonic evaluations of the platform (Xq et al., 2021; Zhang & Leı, 2012). Therefore, the following hypothesis is proposed: H1b: INT positively affects PHV. Social cues are among the most significant perceptions of consumers on social commerce platforms (Bryant & Basu, 2023). Consumers can usually quickly perceive the strength of social cues formed by the behavioral traces of others on the platform, and this perception may further influence their evaluation of other attributes of social commerce activities (Li et al., 2023; Rieh et al., 2014). For example, high-intensity social cues can enhance consumers’ trust, helping them judge product information’s practicality more quickly and improve shopping efficiency (Cheng et al., 2023). Specifically, wealthy and prominent social cues can help consumers search for, evaluate, and select products or services that suit them on the platform, enhancing their task-oriented evaluations of the shopping experience. Therefore, the following hypothesis is proposed: H2a: SCI positively affects PUV. The strength of social cues on social commerce platforms also significantly affects consumers’ PHV. High-intensity social cues, such as a large number of likes, comments, and interactions, as well as KOLs posting their purchases on the platform, contribute to creating a warm social atmosphere and positive interactive experiences (Mere et al., 2024; Voramontri & Klieb, 2019). These strong social signals satisfy consumers’ social needs and enhance emotional pleasure by creating fun and immersive experiences, making shopping activities more entertaining and interactive (Guiry, 2012). Therefore, the following hypothesis is proposed: H2b: SCI positively affects PHV. 3.2 Perceived value and urge to buy impulsively Unlike shopping in physical stores (where consumers can directly touch products), consumers’ shopping decisions on social commerce platforms mainly depend on their online experience rather than a single feature of the platform (Chen & Shen, 2015; Hussain et al., 2021). In other words, improving online shopping efficiency and the smoothness of platform interaction are important factors determining consumer purchasing behavior. Previous studies have shown that higher shopping efficiency and better platform interaction experiences encourage consumers to browse more products (Liu, 2024; Razaq & Kristin, 2024), increasing the possibility of consumers being exposed to shopping stimuli and further inducing their impulse to buy. Othman et al. (2019) noted that when consumers perceive the practicality and convenience (i.e., PUV) offered by social commerce platforms, they are more likely to desire immediate purchase. Therefore, the following hypothesis is proposed: H3: PUV positively affects UBI. PHV refers to the pleasure and entertainment that consumers experience when shopping on social commerce platforms. It is an important driver of consumer behavior in the online environment (Çelik, 2011). Previous studies have indicated that consumers who experience pleasant emotions during online shopping are more likely to engage in impulse purchasing (Arruda Filho & Oliveira, 2023; Batara et al., 2024). Arruda Filho and Oliveira (2023) also argue that positive emotional perceptions are a crucial prerequisite for consumers to develop an impulsive desire to buy. Therefore, the following hypothesis is proposed: H4: PHV positively affects UBI. 3.3 The moderating effects of fear of missing out FoMO is defined as an anxious emotional state that individuals experience due to concerns about missing out on potential social rewards or experiences (Przybylski et al., 2013). Its core characteristics are intense emotional urgency and motivational drive. This highly emotional nature aligns FoMO with hedonistic values rooted in emotional satisfaction and immediate gratification. According to the dual-system theory (Metcalfe & Mischel, 1999), during impulsive decision-making, the ‘hot system’ (emotionally driven) typically dominates, while the ‘cold system’ (cognitively driven) emphasizes rational analysis and goal-oriented decision-making processes. Although FoMO itself does not directly enhance cognitive analytical abilities, the intense urgency it induces may weaken an individual's inhibitory control over the gap between rational assessment and actual behavior, thereby prompting the results of rational assessment to be more quickly and directly translated into immediate purchasing behavior. Caruana et al. (2016) noted that consumers’ positive attitudes are more likely to translate into actual behavior only when social contexts or important others reinforce them. As a typical social-emotional factor, FoMO not only amplifies consumers’ responses to emotional stimuli but also enhances the effectiveness of the transition from cognitive value assessment to behavioral responses (Flecha Ortiz et al., 2024). Therefore, the following hypotheses are proposed: H5a: FoMO positively moderates the relationship between PUV and UBI. H5b: FoMO positively moderates the relationship between PHV and UBI. 4. Methodology 4.1 Measurements The questionnaire is divided into two parts. The first part includes demographic information about the respondents, such as gender, age, and monthly income. The second part constitutes the main body of the questionnaire, covering six key variables: INT, SCI, PUV, PHV, FoMO, and UBI. All measurement items are derived from previous studies to ensure the validity and reliability of the data. All concepts are measured using a five-point Likert scale (‘1 = strongly disagree’ to ‘5 = strongly agree’). INT was measured with three items from Liu (2003), SCI was adapted from Lou et al. (2022), PUV and PHV followed by Sweeney and Soutar (2001), FoMO was based on Przybylski et al. (2013), and UBI was adapted from Rook and Fisher (1995). Since the measurement items were sourced from English-language literature and the target respondents were Chinese consumers, this study employed a back-translation procedure to ensure semantic and conceptual equivalence between the original and translated versions. It translated texts (Brislin, 1970). 4.1. Data collection This study employed a questionnaire survey method to collect data, with the questionnaire designed and distributed via the wenjuanxing platform (www.wjx.cn). A pilot test was conducted prior to the formal survey, yielding 45 valid questionnaires. Cronbach's α coefficient was used to assess internal consistency, with results indicating good reliability (α = 0.900), exceeding the recommended threshold of 0.7 (Sarstedt et al., 2021), thereby proceeding with the formal survey. The formal sample targeted users of social commerce platforms, including Douyin, Xiaohongshu, and Taobao. Respondents were required to be at least 18 years old and have experience with social commerce shopping. The questionnaire was distributed online via instant messaging tools such as WeChat, with a total of 431 questionnaires returned. After data cleaning, 33 questionnaires with missing values or identical scores were excluded, leaving 398 valid questionnaires for SmartPLS analysis. Partial least squares structural equation modeling (PLS-SEM) is suitable for analyzing non-normally distributed data and moderate sample sizes under 500 (Leguina, 2015). Therefore, SmartPLS 4.0 was used for data analysis in this study. 5. Results 5.1 Descriptive statistics Among the 398 respondents, 57.5% were female, and more than 97% were under the age of 41. Most respondents had a monthly income between RMB 3,001 and RMB 9,000. The demographic characteristics of the respondents are shown in Table 1 . Table 1 Demographic characteristics Characteristic Category Frequency Percentage Gender Male 169 42.5% Female 229 57.5% Age Group 18–25 87 21.9% 26–30 194 48.7% 31–40 105 26.4% ≥ 41 12 3% Income (RMB/Month) ≤ 3000 33 8.2% 3001–6000 120 30.2% 6001–9000 175 44.0% ≥ 9001 70 17.6% 5.2 Common method bias This study employed a cross-sectional survey method targeting Chinese respondents. Data analysis was conducted using SPSS 29.0 software, and the Harman single-factor test was applied to assess potential common method bias (CMB). The first factor explained 39.4% of the total variance, which was below the recommended 50% threshold (Podsakoff et al., 2003), indicating that there was no significant CMB. Additionally, the variance inflation factor (VIF) values ranged from 1.762 to 2.592, far below the cutoff value of 3.3 (Kock, 2015), further confirming that CMB was not a significant issue in this study. 5.3 Measurement model This study assessed the reliability and validity of the measurement model, with results shown in Table 2 . Following the recommendations of Hair et al. (1998), Cronbach’s alpha (α) and composite reliability (CR) for constructs should be greater than 0.70, and the Average Variance Extracted (AVE) should exceed 0.50. The results indicate that all constructs’ α, CR, and AVE values exceed the recommended standards, suggesting that all constructs exhibit good reliability (Fornell & Larcker, 1981). All constructs in this study are reflective constructs, so convergent validity was assessed by examining each construct’s standardized loadings of the measurement items. The standardized loadings of the measurement items exceeded 0.70, indicating that the model has good internal consistency and convergent validity (Hair et al., 2019). Table 2 Loadings, α, CR, and AVE Constructs Items Loading α CR AVE Interactivity (INT) INT1 0.837 0.855 0.912 0.775 INT2 0.888 INT3 0.914 Social Cues Intensity (SCI) SCI1 0.836 0.827 0.897 0.743 SCI2 0.855 SCI3 0.894 Perceived Utlitarian Value (PUV) PUV1 0.838 0.830 0.898 0.747 PUV2 0.861 PUV3 0.892 Perceived Hedonic Value (PHV) PHV1 0.857 0.860 0.915 0.781 PHV2 0.886 PHV3 0.907 Fear of Missing Out (FoMO) FoMO1 0.890 0.863 0.914 0.781 FoMO2 0.849 FoMO3 0.911 Urge to Buy Impulsively (UBI) UBI1 0.838 0.837 0.902 0.754 UBI2 0.873 UBI3 0.892 Note: Cronbach’s Alpha (α), Average variance extracted (AVE), Composite reliability (CR) This study used the heterogeneity-monotonicity trait correlation (HTMT) to assess discriminant validity. According to the threshold proposed by Henseler et al. (2015), an HTMT value below 0.90 indicates acceptable discriminant validity. All constructs in this study had HTMT values below this threshold, confirming their sufficient discriminant validity, as shown in Table 3 . Table 3 Discriminant validity (HTMT ratios) FoMO UBI INT PHV PUV SCI FoMO UBI 0.084 INT 0.044 0.562 PHV 0.034 0.685 0.645 PUV 0.026 0.707 0.764 0.871 SCI 0.047 0.449 0.101 0.694 0.599 Note: Fear of Missing Out (FoMO), Urge to Buy Impulsively (UBI), Interactivity (INT), Perceived Hedonic Value (PHV), Perceived Utlitarian Value (PUV), Social Cues Intensity (SCI) 5.2 Structural model Before evaluating the structural model, the explanatory power and predictive ability of each endogenous variable were assessed using R² and Q² values. As suggested by previous studies, acceptable thresholds are R² ≥ 0.10 and Q² >0 (Hair Jr et al., 2021). The results showed that the R² values ranged from 0.605 to 0.618, and the Q² values ranged from 0.506 to 0.611, indicating that the model has strong explanatory power and satisfactory predictive relevance. Subsequently, the structural model was validated using SmartPLS 4.0 software through a bootstrapping procedure with 5,000 subsamples (two-tailed test, significance level = 0.05), with results presented in Table 4 . INT has a significant positive effect on both PUV and PHV (β = 0.608, p < 0.001; β = 0.510, p < 0.001), supporting H1a and H1b. SCI also has a significant positive effect on both PUV and PHV (β = 0.449, p < 0.001; β = 0.546, p < 0.001), supporting H2a and H2b. PUV and PHV had significant positive predictive effects on UBI (β = 0.298, p < 0.001; β = 0.373, p < 0.001), validating H3 and H4. Moderation effect analysis revealed that FoMO significantly enhanced the relationship between PUV and UBI (β = 0.184, p = 0.002) and between PHV and UBI (β = 0.317, p < 0.001), thus supporting H5a and H5b. Table 4 Hypothesis results Hypothesis Relationships β T-value P-value Bootstrapping 95% CI Result Lower limit Upper limit H1a INT → PUV 0.608 20.983 0.000 0.548 0.662 Supported H1b INT → PHV 0.510 16.666 0.000 0.449 0.568 Supported H2a SCI → PUV 0.449 13.674 0.000 0.385 0.512 Supported H2b SCI → PHV 0.546 16.713 0.000 0.478 0.607 Supported H3 PUV → UBI 0.298 6.040 0.000 0.201 0.393 Supported H4 PHV → UBI 0.373 7.097 0.000 0.268 0.473 Supported H5a FoMO x PUV → UBI 0.184 3.172 0.002 0.069 0.284 Supported H5b FoMO x PHV → UBI 0.317 4.082 0.000 0.192 0.435 Supported Note: Fear of Missing Out (FoMO), Urge to Buy Impulsively (UBI), Interactivity (INT), Perceived Hedonic Value (PHV), Perceived Utlitarian Value (PUV), Social Cues Intensity (SCI) In addition, Fig. 2 illustrates that the positive correlation between PUV and UBI is stronger at higher levels of FoMO (+ 1 SD), suggesting that FoMO amplifies the effect of utilitarian value on impulse buying intention. Figure 3 shows a similar moderating effect of FoMO on the relationship between PHV and UBI. When FoMO is high (+ 1 SD), the positive effect of PHV on UBI is significantly enhanced. In contrast, this effect becomes much weaker when FoMO is low (–1 SD). These findings suggest that FoMO intensifies impulse buying intention driven by both utilitarian and hedonic consumption values. To further investigate the effects of INT and SCI on UBI, this study employed the product-of-coefficients method and estimated 95% confidence intervals (CIs) using bias-corrected bootstrapping. An indirect effect was considered statistically significant if the corresponding confidence interval did not include zero (Preacher & Hayes, 2008). As shown in Table 5 , both PUV and PHV significantly mediated the relationship between platform characteristics and UBI. Specifically, the indirect effect of INT on UBI through PUV was significant (β = 0.181, T = 5.603, p < 0.001, 95% CI [0.201, 0.393]), as was the pathway through PHV (β = 0.190, T = 6.305, p < 0.001, 95% CI [0.268, 0.473]). Likewise, SCI exhibited a significant indirect effect on UBI via both PUV (β = 0.134, T = 5.507, p < 0.001, 95% CI [0.069, 0.284]) and PHV (β = 0.204, T = 6.507, p < 0.001, 95% CI [0.192, 0.435]). Table 5 Mediating effect results Path β Stand deviation T-value P-value Bootstrapping 95% CI Lower limit Upper limit INT → PUV → UBI 0.181 0.032 5.603 0.000 0.120 0.245 INT → PHV → UBI 0.190 0.030 6.305 0.000 0.134 0.252 SCI → PUV → UBI 0.134 0.024 5.507 0.000 0.088 0.182 SCI → PHV → UBI 0.204 0.031 6.507 0.000 0.145 0.265 6. Discussions This study, based on the SOR model, examines how INT and SCI impact consumers’ UBI through perceived value (PUV and PHV) on social commerce platforms, particularly the moderating role of FoMO. First, the results validate that INT and SCI have a significant and positive influence on consumers' perceived PUV and PHV. This suggests that the highly interactive information feedback mechanism and social cues in social commerce platforms can effectively enhance consumers' cognitive evaluation and emotional experience of platform content, thereby strengthening their motivation to consume. This finding aligns with the results of Huang and Benyoucef (2017), highlighting the role of platform characteristics in shaping consumer value perception. Second, PUV and PHV both exert significant positive predictive effects on UBI, validating the critical role of perceived value in impulsive decision-making. This result builds upon the research framework proposed by Arruda Filho and Oliveira (2023), which suggests that the practical benefits and emotional satisfaction consumers derive from social contexts can respectively trigger their rational and emotional pathways toward purchase impulses. More importantly, FoMO has a significant positive moderating effect on the relationship between PUV, PHV, and UBI, indicating that high levels of fear of missing out amplify the impact of perceived value on UBI. Among these, FoMO has a more moderating effect on the PHV pathway, consistent with the findings of Przybylski et al. (2013), who argue that FoMO, as a psychological state characterized by emotional urgency, tends to intensify consumers’ responses to emotional cues, thereby driving them to make quick purchasing decisions under high perceived hedonic value. 6.1 Theoretical contributions This study makes several theoretical contributions to the existing literature. First, although previous social commerce research has extensively explored the impact of platform characteristics on consumer behavior, the underlying psychological mechanisms through which platform interactivity and social cue intensity influence consumers’ perceived value (PUV and PHV) have not been thoroughly revealed. Therefore, based on the SOR theoretical framework, this study clearly defines and empirically verifies the dual-path mechanism through which INT and SCI, as stimuli, influence UBI via PUV and PHV, thereby providing a more detailed explanation of the mechanism through which platform characteristics affect consumer psychological responses. Thus, this study not only extends the theoretical applicability of the SOR model in the field of s-commerce but also responds to the theoretical call from other scholars to further clarify the relationship between platform characteristics and psychological mechanisms in digital environments. Second, previous studies on impulsive buying behavior often treat perceived value as a single-dimensional construct, failing to adequately consider the differential roles of PUV and PHV in cognitive and emotional pathways. This oversight has limited the theoretical explanation of consumer decision-making processes. This study refines and validates the dual mediating role of perceived value in UBI, revealing the different roles of PUV and PHV in consumers’ internal psychological mechanisms. This effectively addresses the theoretical shortcomings of previous studies, which were too general in their treatment of perceived value, and promotes the further development of perceived value theory in the field of consumer behavior. Third, while previous studies have noted the direct driving role of FoMO in impulsive buying behavior, they have rarely explored how FoMO, as a moderating variable, interacts with consumers’ perceived value to influence UBI. This study empirically found that FoMO exhibits a significant positive moderating effect in both the PUV and PHV pathways influencing UBI, with a more pronounced effect in the PHV pathway. This finding further validates the theoretical perspective that FoMO, as a situational emotional variable, can enhance consumers’ sensitivity to immediate emotional stimuli and behavioral response speed. It also expands the theoretical perspective on the mechanism through which the emotional system influences impulsive behavior within the dual-system theory and addresses the existing research gap in the exploration of FoMO’s moderating role. 6.2 Practicality contributions This study provides important practical recommendations for social commerce platforms, brand marketers, and consumer behavior guidance. First, the research results show that INT and SCI significantly enhance consumers’ PUV and PHV, thereby promoting their UBI. Therefore, we recommend that social commerce platform operators focus on improving the interactivity of their platforms, such as optimizing real-time communication functions, enhancing live streaming interaction experiences, and increasing the display intensity of social signals (e.g., number of likes and comments), to effectively stimulate users’ sense of participation and shopping interest, thereby improving conversion rates. Second, given that PHV has a more significant effect on UBI, we recommend that brand marketers pay more attention to consumers’ emotional experiences when formulating social e-commerce marketing strategies. They should appropriately utilize entertaining and fun content, such as short videos, interactive games, and real-time reward mechanisms, to enhance consumers’ shopping pleasure and more effectively stimulate their consumption impulses. Additionally, given the significant moderating role of FoMO in this process, marketers can also moderately utilize scarcity strategies such as limited-time flash sales, limited-quantity releases, and social recommendations to guide consumers toward stronger shopping urgency and buying behavior. Third, from a consumer protection perspective, this study found that high levels of FoMO significantly amplify impulsive consumption tendencies, potentially leading to irrational overconsumption issues. Therefore, we recommend that government regulatory agencies and social organizations establish corresponding consumer risk education and guidance measures, such as promoting platforms to clarify information transparency principles and advocating rational consumption concepts among consumers. 6.3 Limitations and future research recommendation This study has several limitations. First, this study only collected data from users of Chinese social commerce platforms, which may limit the generalizability of the results. Future studies could further validate the theoretical model proposed in this paper in other countries or cultural contexts (e.g., Western countries or Southeast Asia) to examine the model's robustness in diverse cultural and regional contexts. Second, this study selected INT and SCI as key stimulus factors of s-commerce platforms. Although these two factors have been proven to have a significant impact on consumer perceptions, other factors of social commerce platforms (such as platform trust, user stickiness, and content quality) may also significantly influence consumers’ perceived value and buying behavior. Therefore, future research should consider incorporating more platform feature variables to explore the relationship between platform characteristics and consumer behavior more comprehensively. Third, this study used a cross-sectional survey method, which may limit the rigor of causal inference to some extent. Future research can adopt experimental designs or longitudinal data methods to dynamically explore the causal relationship between INT, SCI, and perceived value. In addition, combining advanced methods such as big data analysis and consumer behavior tracking can more accurately capture consumers’ real-time behavioral responses and psychological changes. Fourth, this study only examined the moderating role of the psychological variable of FoMO. Although significant findings were obtained, other individual differences among consumers (such as self-control, risk preference, and personality traits) may also play a key moderating role between perceived value and impulse buying. Future research can further explore these potential individual difference moderating factors to reveal the individual heterogeneity of consumers’ impulsive buying behavior. 7. Conclusions This study, based on the SOR theoretical framework, systematically examined how platform interactivity and social cue intensity influence consumers’ impulsive buying behavior in social commerce contexts through two dimensions: perceived utilitarian value and perceived hedonic value. The findings revealed that these two platform functions significantly enhanced consumers’ perceived value, thereby strengthening their urge to buy impulsively. Additionally, the study highlights the significant moderating role of fear of missing out, indicating that individuals with FoMO are more susceptible to the influence of perceived value, especially hedonic value, and are thus more likely to exhibit impulsive tendencies. These findings not only enrich the theoretical understanding of emotional and cognitive mechanisms underlying digital impulsive purchasing but also provide practical implications for platform design and consumer engagement strategies targeting specific consumer groups. Overall, this study deepens our understanding of how technological and social availability in social commerce environments stimulate consumer psychological responses and impulsive purchasing. It also underscores the importance of focusing on FOMO as a contextual amplifier of impulsive buying behavior. Future research could further explore various psychological moderating factors and longitudinal effects to develop more comprehensive interventions that balance commercial objectives with consumer well-being. Declarations Competing interests: The authors declare no competing interests. Ethics statements Ethical approval was not required for this study, as it involved an anonymous, minimal-risk online survey of adult participants and did not collect any identifiable personal information. The research was conducted in accordance with the Declaration of Helsinki and the ethical guidelines established by the Malaysian Ministry of Health’s Medical Review & Ethics Committee (MREC). This study qualified for exemption from ethical review, as outlined in the Guidelines for Ethical Review of Clinical Research or Research Involving Human Subjects (2006). According to these guidelines, research that involves surveys, questionnaires, and interviews, which does not collect identifiable private information, is exempt from MREC review. Informed consent: All participants provided their informed consent prior to their inclusion in the study. The consent form, presented on the first page of the online questionnaire administered via the Wenjuanxing platform in June 2025, outlined the study's purpose, the voluntary and anonymous nature of participation, withdrawal rights, and the academic use of data. The study was open only to adults aged 18 and above. No personally identifiable information was collected, and the study involved no foreseeable physical or psychological risks. Funding: This research received no external funding. Author Contribution LYD designed the study and wrote the manuscript. MZAM and AFA provided comments and supervision. All authors reviewed and approved the final version of the manuscript. Data availability statement The datasets generated and analysed during the current study are not publicly available due to privacy considerations, but are available from the corresponding author on reasonable request. References Al-Omoush, K. S., & Shuhaiber, A. Predicting user behavior on s-commerce platforms: a novel model. Kybernetes . 2024. Alsoud, M., Al-Muani, L., & Alkhazali, Z. Digital platform interactivity and Jordanian social commerce purchase intention. International Journal of Data & Network Science . 2022, 6 (2). Anoop, T., & Rahman, Z. Online impulse buying: a systematic review of 25 years of research using meta regression. Journal of Consumer Behaviour . 2025, 24 (1), 363-391. Arruda Filho, E. J. M., & Oliveira, R. L. S. The mood effect in relation to impulsive online buying behavior. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7039008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513233109,"identity":"18105bf5-843a-4b4d-8026-ae35df75a143","order_by":0,"name":"YADONG LI","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"YADONG","middleName":"","lastName":"LI","suffix":""},{"id":513233110,"identity":"20fbd34a-0956-483b-9fdb-7be8b599b096","order_by":1,"name":"MOHAMAD ZUBER ABD MAJID","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACNiC2YZAAMXlI0JJGupbDJGjR7T9j9uDjnvOJM6cdYHzwto0h2uAAAS1mB86YG854djtxtnQCs+HcNobcDQS1HOwxk+Y5cDtxnnQCmzQvUVoO85hJ/zlwDqSF/TdxWo4BtTAcOAByGBszcVrOsJUb9hxINp45O7FZcs45idyZBLWcP7ztwY8DdrIzbicf/PCmzCa3j5AWJMDYACQkGBRI0AIF8g0kaxkFo2AUjIJhDgBxckSqjiqWAgAAAABJRU5ErkJggg==","orcid":"","institution":"National University of Malaysia","correspondingAuthor":true,"prefix":"","firstName":"MOHAMAD","middleName":"ZUBER ABD","lastName":"MAJID","suffix":""},{"id":513233111,"identity":"fd52410e-a58d-4f71-8feb-6ef2a333b009","order_by":2,"name":"Ahmad Firdhaus Arham","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Firdhaus","lastName":"Arham","suffix":""}],"badges":[],"createdAt":"2025-07-03 14:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7039008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7039008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91120593,"identity":"8f0362e9-bd0a-4104-ae8b-550de7516758","added_by":"auto","created_at":"2025-09-11 19:01:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140625,"visible":true,"origin":"","legend":"\u003cp\u003eResearch model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7039008/v1/d64f87423d9abb20c87591d4.png"},{"id":91120594,"identity":"19b85b92-d843-4aa2-aab0-331fe29222fc","added_by":"auto","created_at":"2025-09-11 19:01:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169427,"visible":true,"origin":"","legend":"\u003cp\u003eFoMO x PUV - UBI\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7039008/v1/18f9ef5dbc5772459b0705f3.png"},{"id":91120602,"identity":"381aa2ff-8402-442a-a2d5-fcc829f6b32d","added_by":"auto","created_at":"2025-09-11 19:01:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175537,"visible":true,"origin":"","legend":"\u003cp\u003eFoMO x PHV – UBI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7039008/v1/6fe4c8dc8dcf829c5f9b6fdc.png"},{"id":102241066,"identity":"5c2effc6-510e-457a-adb1-706e349efecf","added_by":"auto","created_at":"2026-02-09 16:57:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1354059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7039008/v1/43ad66f5-663b-4d05-870a-804839ea7db5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past two decades, the convergence of social media and e-commerce has driven the rapid growth of s-commerce. S-commerce refers to online commercial activities conducted through social media platforms, allowing consumers to discover, evaluate, and purchase products in everyday social interactions (Dhaigude \u0026amp; Mohan, 2023; Liang \u0026amp; Turban, 2011). Unlike traditional e-commerce, social commerce leverages user-generated content, peer influence, and real-time interactions to make shopping more interactive and impulsive (Bernstein \u0026amp; Guo, 2022). In China, taking the s-commerce platform Douyin as an example, it has more than 600\u0026nbsp;million daily active users, and its total transaction volume (GMV) is expected to reach approximately US\u003cspan\u003e$\u003c/span\u003e490\u0026nbsp;billion by 2024, representing a year-on-year increase of 30% (S. Hu et al., 2023). Research indicates that approximately 71% of Douyin users make purchases without planning (Wang et al., 2023). Despite the significant commercial value of impulse purchases, understanding the underlying psychological mechanisms remains limited (Lo et al., 2022). As online impulse purchasing behavior becomes increasingly prevalent, consumer online impulse purchasing has emerged as a key focus for scholars and practitioners.\u003c/p\u003e\u003cp\u003eExtensive research has confirmed that technological and platform characteristics significantly influence consumers\u0026rsquo; cognitive and emotional responses and behavior (Baines, 2017; Liu et al., 2020; Zhu et al., 2023). In s-commerce, interactivity and cue intensity are key platform characteristics that drive consumer engagement, value perception, and behavioral responses (Dhaigude \u0026amp; Mohan, 2023). Specifically, interactivity refers to the ability of users to exchange information with the platform or others, such as comments, replies, votes, and private messages (Wang, 2021). Social cue intensity emphasizes consumers\u0026rsquo; subjective perceptions of the visibility, frequency, and influence of social cues within the platform during use, such as the number of likes, comment heat, and product reviews. These cues can stimulate users\u0026rsquo; social identity and conformity, influencing their decision-making (Cheng et al., 2023). Although these platform features have been proven to influence consumers\u0026rsquo; perceptions and behaviors, existing studies mostly describe them as contextual backgrounds and rarely systematically examine how they drive impulsive purchasing behavior through consumers\u0026rsquo; perceived value.\u003c/p\u003e\u003cp\u003eNaseem and Yaprak (2023) note that consumers purchase products because they perceive value, which influences their decision-making preferences. Consumer perceived value is typically categorized into two types: perceived utilitarian value, which includes efficiency, convenience and task completion, and perceived hedonic value, which encompasses entertainment, pleasure, and social experiences (Vieira et al., 2018). In s-commerce, consumers gradually perceive the value of products and platforms through continuous interaction with the platform system and social content (Chen et al., 2020; Wu \u0026amp; Huang, 2023). X. Hu et al. (2023) further pointed out that utilitarian and hedonic values jointly drive consumers\u0026rsquo; purchase intentions and behavioral choices. Therefore, perceived value is a key psychological antecedent variable driving buying behavior.\u003c/p\u003e\u003cp\u003eWith the widespread adoption of social media and increased user interaction, FoMO has emerged as a prominent psychological phenomenon. FoMO refers to the anxiety individuals experience from the fear of missing out on valuable experiences or information (Przybylski et al., 2013). In a s-commerce environment, consumers are often exposed to social shopping information that is immediate, interactive, and scarce (e.g., limited-time offers, friend recommendations, user reviews). This environment can significantly increase consumers\u0026rsquo; FoMO levels, further amplifying their impulse buying tendencies (Das et al., 2022). However, there has been limited discussion on how FoMO regulates the relationship between consumer perceived value and impulse buying behavior in s-commerce environments.\u003c/p\u003e\u003cp\u003eThis study draws on the SOR theoretical framework and takes the environmental stimuli of social commerce platforms as a starting point to examine how consumers\u0026rsquo; perceived value (utilitarian value and hedonic value) affects their impulse buying behavior, and further explores the moderating role of FoMO in this process. Specifically, this study aims to answer the following questions:\u003c/p\u003e\u003cp\u003e(1) Which environmental factors of social commerce platforms induce consumers\u0026rsquo; PUV and PHV?\u003c/p\u003e\u003cp\u003e(2) How do consumers\u0026rsquo; perceived values (PUV and PHV) influence their UBI on social commerce platforms?\u003c/p\u003e\u003cp\u003e(3) How does FoMO moderate the relationship between consumers\u0026rsquo; perceived values and UBI?\u003c/p\u003e"},{"header":"2. Theoretical Background","content":"\u003cp\u003eThis study adopts the SOR model as its theoretical framework. First proposed by Mehrabian and Russell (1974), this model emphasizes that environmental stimuli (S) trigger changes in an individual's internal psychological state (O), which in turn induce observable behavioral responses (R). This framework is widely used in consumer behavior research to explain how individuals generate emotional and cognitive responses when faced with different platforms or media environments, ultimately forming purchasing decisions (Liu et al., 2023; Peng \u0026amp; Kim, 2014).\u003c/p\u003e\u003cp\u003eThis study considers the INT and SCI of social commerce platforms as key environmental stimuli (S). The PUV and PHV represent their internal cognitive and emotional reactions (O). The UBI is the final behavioral reaction (R). The SOR model provides a structured analytical path for this study to systematically explore how multiple stimuli in s-commerce platforms influence consumers\u0026rsquo; impulsive buying behavior through their perceived value.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Stimulus\u003c/h2\u003e\u003cp\u003eIn a S-commerce environment, consumer interaction with platforms is not limited to receiving product information but is deeply embedded in social mechanisms and content participation processes. Therefore, this study regards INT and SCI, two key technical features of social commerce platforms, as environmental stimuli. It explores how they induce consumer value perception and subsequently influence their UBI.\u003c/p\u003e\u003cp\u003eInteractivity refers to the ability of users to engage in two-way communication and feedback with platform content, systems, or other users, manifested in features such as comments, likes, private messages, real-time chat, and live streaming comments (Janlert \u0026amp; Stolterman, 2017). Social platforms with high interactivity can enhance consumers\u0026rsquo; information engagement and sense of control, thereby stimulating their cognitive trust and emotional investment in the platform (Wang et al., 2024).\u003c/p\u003e\u003cp\u003eSocial cue intensity refers to the frequency, visibility, and social influence that consumers perceive from the behavioral traces of others (e.g., number of likes, number of comments) when using social commerce platforms (Cheng et al., 2023; Jia et al., 2024). Social cue strength influences consumers\u0026rsquo; decision-making process, as they rely on their preferences and are driven by the psychological influence of others\u0026rsquo; participation, amplifying their perceived value of platform content.\u003c/p\u003e\u003cp\u003eTherefore, INT and SCI have been incorporated into current research as environmental stimuli influencing consumers\u0026rsquo; UBI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Organism\u003c/h2\u003e\u003cp\u003eIn s-commerce, consumers\u0026rsquo; decision-making process depends on the product\u0026rsquo;s attributes and is more influenced by their subjective perception of the platform\u0026rsquo;s interactive environment (Hussain et al., 2021). Chang and Chen (2008) pointed out that consumers typically undergo a series of cognitive and emotional mediating reactions after exposure to external stimuli. These psychological mechanisms play a crucial role in driving individual behavioral transformation. This study regards consumers\u0026rsquo; PUV as a cognitive reaction and PHV as an emotional reaction.\u003c/p\u003e\u003cp\u003eConsumer perceived value is defined as consumers\u0026rsquo; overall evaluation of a product or service, representing their subjective trade-off between perceived gains and costs (Blut et al., 2024). It is an important prerequisite for consumer purchasing behavior (X. Hu et al., 2023). In previous studies, perceived value has been constructed as a multidimensional concept. PUV and PHV are the most commonly used dimensions in marketing and consumer behavior research (Yang et al., 2021). PUV reflects cognitive evaluations of the instrumental or task-oriented benefits of interacting with a product or service, such as cost-saving opportunities, product information, and convenience(Kang et al., 2020). PHV emphasizes the emotional arousal and pleasurable experiences consumers experience during shopping, which are more reflected in psychological satisfaction brought about by entertainment, enjoyment, or social engagement (Sharma et al., 2023). Numerous empirical studies support the significant impact of PUV and PHV on consumer decision-making processes in s-commerce (Laran \u0026amp; Tsiros, 2013). Therefore, this study uses PUV and PHV to explain consumers\u0026rsquo; UBI in s-commerce.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Response\u003c/h2\u003e\u003cp\u003eWithin the SOR theoretical framework, consumers\u0026rsquo; responses to external stimuli manifest as specific behavioral tendencies or outcomes (Narayanan \u0026amp; Singh, 2025). Impulse buying is a typical consumer behavior driven by immediate cognitive and emotional reactions. This impulsivity typically occurs without prior planning, accompanied by significant emotional arousal and pleasure, with extremely fast decision-making processes involving little deliberation (Beatty \u0026amp; Ferrell, 1998). In other words, impulsive purchasing is characterized by immediacy and spontaneity, with purchasing behavior primarily driven by current stimuli and emotions rather than prior planning or rational consideration.\u003c/p\u003e\u003cp\u003eResearch on online impulsive shopping can be primarily categorized into the following three types: (1) marketing stimuli represented by price and promotions (Anoop \u0026amp; Rahman, 2025; Luo et al., 2021), (2) website stimuli represented by visual appeal and navigability(Liu et al., 2013; M Jois et al., 2024), and (3) social factors such as social norms and social proof (Wang, 2015; Zhang et al., 2014) and social psychology represented by FOMO (Montag et al., 2023).\u003c/p\u003e\u003cp\u003eIn traditional e-commerce platforms, which are product-centric, research has mainly focused on how marketing stimuli and website characteristics stimulate consumers\u0026rsquo; impulsive reactions (Liu \u0026amp; Xiao, 2018). In contrast, social commerce is consumer-centric and integrates social interaction, user content co-creation, and instant feedback mechanisms to build a highly contextualized and interactive consumption environment (G\u0026uuml;lbaşı \u0026amp; Taşkın, 2024). In this environment, consumers make decisions based on product information and form purchase motivations through continuous social cues and emotional interactions. At the same time, social commerce provides an opportunity to clarify further how the third type of factor drives consumers\u0026rsquo; impulse shopping decisions.\u003c/p\u003e\u003cp\u003eAlthough previous studies have introduced perceived value into the analysis of consumer behavior in the context of social commerce and explored its role in predicting purchase intention (Al-Omoush \u0026amp; Shuhaiber, 2024; Gan \u0026amp; Wang, 2017), they have not fully revealed its mediating role as an \u0026lsquo;organic response\u0026rsquo; under the stimulation of the social environment. Therefore, this study examines consumers\u0026rsquo; UBI from the perspective of consumer perceived value.\u003c/p\u003e\u003cp\u003eThe urge to buy impulsively is a sudden and intense desire to buy experienced in a given environment. This state is usually described as complex, sudden, sometimes irresistible, and persistent (Pacheco et al., 2021). It is only in this state that consumers engage in actual impulse buying behavior. In other words, actual impulse buying behavior occurs only after consumers experience the urge to buy impulsively. Therefore, this study uses UBI as a response factor.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research model and assumptions","content":"\u003cp\u003eThis study adopts the SOR model as the theoretical framework. Within this framework, social cue strength and interactivity are conceptualized as environmental stimuli in the s-commerce context. These stimuli influence consumers’ PUV and PHV, which in turn directly stimulate their urge to buy impulsively. Furthermore, this relationship is moderated by consumers’ level of FoMO. Figure 1 illustrates the conceptual model of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Environmental stimuli and perceived value\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInteractivity refers to the ability of social commerce platforms to facilitate effective, immediate, two-way communication and information exchange between users and sellers (Alsoud et al., 2022). Previous studies have shown that interactivity can effectively improve consumers’ efficiency in obtaining product information, reduce uncertainty in online purchasing, and enhance consumers’ task orientation and decision-making efficiency during shopping \u0026nbsp;(Chen, 2024; Xq et al., 2021). Guo and Li (2022) further noted that in social commerce, consumers can quickly obtain product information, ask questions, and receive timely responses, thereby completing the information search and judgment process more effectively throughout the shopping experience.\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1a:\u0026nbsp;\u003c/strong\u003eINT positively affects PUV.\u003c/p\u003e\n\u003cp\u003eInteractivity on social commerce platforms also affects consumers’ PHV. Specifically, highly interactive platforms offer various forms of social interaction, including comment exchanges, private message feedback, and live Q\u0026amp;A, enabling consumers to have a more immersive and engaging shopping experience, thereby stimulating their sense of pleasure and participation (Ye \u0026amp; Liu, 2021). Such social and real-time feedback mechanisms enhance consumers’ emotional investment in shopping activities and strengthen their hedonic evaluations of the platform (Xq et al., 2021; Zhang \u0026amp; Leı, 2012).\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1b:\u0026nbsp;\u003c/strong\u003eINT positively affects PHV.\u003c/p\u003e\n\u003cp\u003eSocial cues are among the most significant perceptions of consumers on social commerce platforms (Bryant \u0026amp; Basu, 2023). Consumers can usually quickly perceive the strength of social cues formed by the behavioral traces of others on the platform, and this perception may further influence their evaluation of other attributes of social commerce activities (Li et al., 2023; Rieh et al., 2014). For example, high-intensity social cues can enhance consumers’ trust, helping them judge product information’s practicality more quickly and improve shopping efficiency (Cheng et al., 2023). Specifically, wealthy and prominent social cues can help consumers search for, evaluate, and select products or services that suit them on the platform, enhancing their task-oriented evaluations of the shopping experience.\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2a:\u003c/strong\u003e SCI positively affects PUV.\u003c/p\u003e\n\u003cp\u003eThe strength of social cues on social commerce platforms also significantly affects consumers’ PHV. High-intensity social cues, such as a large number of likes, comments, and interactions, as well as KOLs posting their purchases on the platform, contribute to creating a warm social atmosphere and positive interactive experiences (Mere et al., 2024; Voramontri \u0026amp; Klieb, 2019). These strong social signals satisfy consumers’ social needs and enhance emotional pleasure by creating fun and immersive experiences, making shopping activities more entertaining and interactive (Guiry, 2012).\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2b:\u003c/strong\u003e SCI positively affects PHV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Perceived value and urge to buy impulsively\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnlike shopping in physical stores (where consumers can directly touch products), consumers’ shopping decisions on social commerce platforms mainly depend on their online experience rather than a single feature of the platform (Chen \u0026amp; Shen, 2015; Hussain et al., 2021). In other words, improving online shopping efficiency and the smoothness of platform interaction are important factors determining consumer purchasing behavior. Previous studies have shown that higher shopping efficiency and better platform interaction experiences encourage consumers to browse more products (Liu, 2024; Razaq \u0026amp; Kristin, 2024), increasing the possibility of consumers being exposed to shopping stimuli and further inducing their impulse to buy. Othman et al. (2019) noted that when consumers perceive the practicality and convenience (i.e., PUV) offered by social commerce platforms, they are more likely to desire immediate purchase.\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e PUV positively affects UBI.\u003c/p\u003e\n\u003cp\u003ePHV refers to the pleasure and entertainment that consumers experience when shopping on social commerce platforms. It is an important driver of consumer behavior in the online environment (Çelik, 2011). Previous studies have indicated that consumers who experience pleasant emotions during online shopping are more likely to engage in impulse purchasing (Arruda Filho \u0026amp; Oliveira, 2023; Batara et al., 2024). Arruda Filho and Oliveira (2023) also argue that positive emotional perceptions are a crucial prerequisite for consumers to develop an impulsive desire to buy.\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e PHV positively affects UBI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 The moderating effects of fear of missing out\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFoMO is defined as an anxious emotional state that individuals experience due to concerns about missing out on potential social rewards or experiences (Przybylski et al., 2013). Its core characteristics are intense emotional urgency and motivational drive. This highly emotional nature aligns FoMO with hedonistic values rooted in emotional satisfaction and immediate gratification. According to the dual-system theory (Metcalfe \u0026amp; Mischel, 1999), during impulsive decision-making, the ‘hot system’ (emotionally driven) typically dominates, while the ‘cold system’ (cognitively driven) emphasizes rational analysis and goal-oriented decision-making processes. Although FoMO itself does not directly enhance cognitive analytical abilities, the intense urgency it induces may weaken an individual's inhibitory control over the gap between rational assessment and actual behavior, thereby prompting the results of rational assessment to be more quickly and directly translated into immediate purchasing behavior.\u003c/p\u003e\n\u003cp\u003eCaruana et al. (2016) noted that consumers’ positive attitudes are more likely to translate into actual behavior only when social contexts or important others reinforce them. As a typical social-emotional factor, FoMO not only amplifies consumers’ responses to emotional stimuli but also enhances the effectiveness of the transition from cognitive value assessment to behavioral responses \u0026nbsp;(Flecha Ortiz et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5a:\u0026nbsp;\u003c/strong\u003eFoMO positively moderates the relationship between PUV and UBI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5b:\u0026nbsp;\u003c/strong\u003eFoMO positively moderates the relationship between PHV and UBI.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003e\u003cstrong\u003e4.1 Measurements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire is divided into two parts. The first part includes demographic information about the respondents, such as gender, age, and monthly income. The second part constitutes the main body of the questionnaire, covering six key variables: INT, SCI, PUV, PHV, FoMO, and UBI. All measurement items are derived from previous studies to ensure the validity and reliability of the data. \u0026nbsp;All concepts are measured using a five-point Likert scale (‘1 = strongly disagree’ to ‘5 = strongly agree’). \u0026nbsp;INT was measured with three items from Liu (2003), SCI was adapted from Lou et al. (2022), PUV and PHV followed by Sweeney and Soutar (2001), FoMO was based on Przybylski et al. (2013), and UBI was adapted from Rook and Fisher (1995).\u003c/p\u003e\n\u003cp\u003eSince the measurement items were sourced from English-language literature and the target respondents were Chinese consumers, this study employed a back-translation procedure to ensure semantic and conceptual equivalence between the original and translated versions. It translated texts (Brislin, 1970).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a questionnaire survey method to collect data, with the questionnaire designed and distributed via the wenjuanxing platform (www.wjx.cn). A pilot test was conducted prior to the formal survey, yielding 45 valid questionnaires. Cronbach's α coefficient was used to assess internal consistency, with results indicating good reliability (α = 0.900), exceeding the recommended threshold of 0.7 (Sarstedt et al., 2021), thereby proceeding with the formal survey.\u003c/p\u003e\n\u003cp\u003eThe formal sample targeted users of social commerce platforms, including Douyin, Xiaohongshu, and Taobao. Respondents were required to be at least 18 years old and have experience with social commerce shopping. The questionnaire was distributed online via instant messaging tools such as WeChat, with a total of 431 questionnaires returned. After data cleaning, 33 questionnaires with missing values or identical scores were excluded, leaving 398 valid questionnaires for SmartPLS analysis.\u003c/p\u003e\n\u003cp\u003ePartial least squares structural equation modeling (PLS-SEM) is suitable for analyzing non-normally distributed data and moderate sample sizes under 500 (Leguina, 2015). Therefore, SmartPLS 4.0 was used for data analysis in this study.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Descriptive statistics\u003c/h2\u003e\u003cp\u003eAmong the 398 respondents, 57.5% were female, and more than 97% were under the age of 41. Most respondents had a monthly income between RMB 3,001 and RMB 9,000. The demographic characteristics of the respondents are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eIncome (RMB/Month)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3001\u0026ndash;6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6001\u0026ndash;9000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;9001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Common method bias\u003c/h2\u003e\u003cp\u003eThis study employed a cross-sectional survey method targeting Chinese respondents. Data analysis was conducted using SPSS 29.0 software, and the Harman single-factor test was applied to assess potential common method bias (CMB). The first factor explained 39.4% of the total variance, which was below the recommended 50% threshold (Podsakoff et al., 2003), indicating that there was no significant CMB. Additionally, the variance inflation factor (VIF) values ranged from 1.762 to 2.592, far below the cutoff value of 3.3 (Kock, 2015), further confirming that CMB was not a significant issue in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Measurement model\u003c/h2\u003e\u003cp\u003eThis study assessed the reliability and validity of the measurement model, with results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Following the recommendations of Hair et al. (1998), Cronbach\u0026rsquo;s alpha (α) and composite reliability (CR) for constructs should be greater than 0.70, and the Average Variance Extracted (AVE) should exceed 0.50. The results indicate that all constructs\u0026rsquo; α, CR, and AVE values exceed the recommended standards, suggesting that all constructs exhibit good reliability (Fornell \u0026amp; Larcker, 1981).\u003c/p\u003e\u003cp\u003eAll constructs in this study are reflective constructs, so convergent validity was assessed by examining each construct\u0026rsquo;s standardized loadings of the measurement items. The standardized loadings of the measurement items exceeded 0.70, indicating that the model has good internal consistency and convergent validity (Hair et al., 2019).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLoadings, α, CR, and AVE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstructs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLoading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eInteractivity (INT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSocial Cues Intensity\u003c/p\u003e\u003cp\u003e(SCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePerceived Utlitarian Value\u003c/p\u003e\u003cp\u003e(PUV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePUV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePUV2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePUV3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePerceived Hedonic Value\u003c/p\u003e\u003cp\u003e(PHV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHV2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHV3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFear of Missing Out\u003c/p\u003e\u003cp\u003e(FoMO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUrge to Buy Impulsively\u003c/p\u003e\u003cp\u003e(UBI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUBI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUBI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUBI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Cronbach\u0026rsquo;s Alpha (α), Average variance extracted (AVE), Composite reliability (CR)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study used the heterogeneity-monotonicity trait correlation (HTMT) to assess discriminant validity. According to the threshold proposed by Henseler et al. (2015), an HTMT value below 0.90 indicates acceptable discriminant validity. All constructs in this study had HTMT values below this threshold, confirming their sufficient discriminant validity, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant validity (HTMT ratios)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUBI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePHV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePUV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoMO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePUV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Fear of Missing Out (FoMO), Urge to Buy Impulsively (UBI), Interactivity (INT), Perceived Hedonic Value (PHV), Perceived Utlitarian Value (PUV), Social Cues Intensity (SCI)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Structural model\u003c/h2\u003e\u003cp\u003eBefore evaluating the structural model, the explanatory power and predictive ability of each endogenous variable were assessed using R\u0026sup2; and Q\u0026sup2; values. As suggested by previous studies, acceptable thresholds are R\u0026sup2; \u0026ge; 0.10 and Q\u0026sup2; \u0026gt;0 (Hair Jr et al., 2021). The results showed that the R\u0026sup2; values ranged from 0.605 to 0.618, and the Q\u0026sup2; values ranged from 0.506 to 0.611, indicating that the model has strong explanatory power and satisfactory predictive relevance. Subsequently, the structural model was validated using SmartPLS 4.0 software through a bootstrapping procedure with 5,000 subsamples (two-tailed test, significance level\u0026thinsp;=\u0026thinsp;0.05), with results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. INT has a significant positive effect on both PUV and PHV (β\u0026thinsp;=\u0026thinsp;0.608, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; β\u0026thinsp;=\u0026thinsp;0.510, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H1a and H1b. SCI also has a significant positive effect on both PUV and PHV (β\u0026thinsp;=\u0026thinsp;0.449, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; β\u0026thinsp;=\u0026thinsp;0.546, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H2a and H2b. PUV and PHV had significant positive predictive effects on UBI (β\u0026thinsp;=\u0026thinsp;0.298, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; β\u0026thinsp;=\u0026thinsp;0.373, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), validating H3 and H4. Moderation effect analysis revealed that FoMO significantly enhanced the relationship between PUV and UBI (β\u0026thinsp;=\u0026thinsp;0.184, p\u0026thinsp;=\u0026thinsp;0.002) and between PHV and UBI (β\u0026thinsp;=\u0026thinsp;0.317, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), thus supporting H5a and H5b.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelationships\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBootstrapping 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003cp\u003elimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003elimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT \u0026rarr; PUV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT \u0026rarr; PHV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCI \u0026rarr; PUV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCI \u0026rarr; PHV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePUV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO x PUV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO x PHV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Fear of Missing Out (FoMO), Urge to Buy Impulsively (UBI), Interactivity (INT), Perceived Hedonic Value (PHV), Perceived Utlitarian Value (PUV), Social Cues Intensity (SCI)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that the positive correlation between PUV and UBI is stronger at higher levels of FoMO (+\u0026thinsp;1 SD), suggesting that FoMO amplifies the effect of utilitarian value on impulse buying intention. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a similar moderating effect of FoMO on the relationship between PHV and UBI. When FoMO is high (+\u0026thinsp;1 SD), the positive effect of PHV on UBI is significantly enhanced. In contrast, this effect becomes much weaker when FoMO is low (\u0026ndash;1 SD). These findings suggest that FoMO intensifies impulse buying intention driven by both utilitarian and hedonic consumption values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the effects of INT and SCI on UBI, this study employed the product-of-coefficients method and estimated 95% confidence intervals (CIs) using bias-corrected bootstrapping. An indirect effect was considered statistically significant if the corresponding confidence interval did not include zero (Preacher \u0026amp; Hayes, 2008). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, both PUV and PHV significantly mediated the relationship between platform characteristics and UBI. Specifically, the indirect effect of INT on UBI through PUV was significant (β\u0026thinsp;=\u0026thinsp;0.181, T\u0026thinsp;=\u0026thinsp;5.603, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.201, 0.393]), as was the pathway through PHV (β\u0026thinsp;=\u0026thinsp;0.190, T\u0026thinsp;=\u0026thinsp;6.305, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.268, 0.473]). Likewise, SCI exhibited a significant indirect effect on UBI via both PUV (β\u0026thinsp;=\u0026thinsp;0.134, T\u0026thinsp;=\u0026thinsp;5.507, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.069, 0.284]) and PHV (β\u0026thinsp;=\u0026thinsp;0.204, T\u0026thinsp;=\u0026thinsp;6.507, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.192, 0.435]).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediating effect results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStand deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBootstrapping 95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003cp\u003elimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003elimit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u0026rarr; PUV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u0026rarr; PHV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCI \u0026rarr; PUV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCI \u0026rarr; PHV \u0026rarr; UBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Discussions","content":"\u003cp\u003eThis study, based on the SOR model, examines how INT and SCI impact consumers\u0026rsquo; UBI through perceived value (PUV and PHV) on social commerce platforms, particularly the moderating role of FoMO.\u003c/p\u003e\u003cp\u003eFirst, the results validate that INT and SCI have a significant and positive influence on consumers' perceived PUV and PHV. This suggests that the highly interactive information feedback mechanism and social cues in social commerce platforms can effectively enhance consumers' cognitive evaluation and emotional experience of platform content, thereby strengthening their motivation to consume. This finding aligns with the results of Huang and Benyoucef (2017), highlighting the role of platform characteristics in shaping consumer value perception.\u003c/p\u003e\u003cp\u003eSecond, PUV and PHV both exert significant positive predictive effects on UBI, validating the critical role of perceived value in impulsive decision-making. This result builds upon the research framework proposed by Arruda Filho and Oliveira (2023), which suggests that the practical benefits and emotional satisfaction consumers derive from social contexts can respectively trigger their rational and emotional pathways toward purchase impulses.\u003c/p\u003e\u003cp\u003eMore importantly, FoMO has a significant positive moderating effect on the relationship between PUV, PHV, and UBI, indicating that high levels of fear of missing out amplify the impact of perceived value on UBI. Among these, FoMO has a more moderating effect on the PHV pathway, consistent with the findings of Przybylski et al. (2013), who argue that FoMO, as a psychological state characterized by emotional urgency, tends to intensify consumers\u0026rsquo; responses to emotional cues, thereby driving them to make quick purchasing decisions under high perceived hedonic value.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Theoretical contributions\u003c/h2\u003e\u003cp\u003eThis study makes several theoretical contributions to the existing literature. First, although previous social commerce research has extensively explored the impact of platform characteristics on consumer behavior, the underlying psychological mechanisms through which platform interactivity and social cue intensity influence consumers\u0026rsquo; perceived value (PUV and PHV) have not been thoroughly revealed. Therefore, based on the SOR theoretical framework, this study clearly defines and empirically verifies the dual-path mechanism through which INT and SCI, as stimuli, influence UBI via PUV and PHV, thereby providing a more detailed explanation of the mechanism through which platform characteristics affect consumer psychological responses. Thus, this study not only extends the theoretical applicability of the SOR model in the field of s-commerce but also responds to the theoretical call from other scholars to further clarify the relationship between platform characteristics and psychological mechanisms in digital environments.\u003c/p\u003e\u003cp\u003eSecond, previous studies on impulsive buying behavior often treat perceived value as a single-dimensional construct, failing to adequately consider the differential roles of PUV and PHV in cognitive and emotional pathways. This oversight has limited the theoretical explanation of consumer decision-making processes. This study refines and validates the dual mediating role of perceived value in UBI, revealing the different roles of PUV and PHV in consumers\u0026rsquo; internal psychological mechanisms. This effectively addresses the theoretical shortcomings of previous studies, which were too general in their treatment of perceived value, and promotes the further development of perceived value theory in the field of consumer behavior.\u003c/p\u003e\u003cp\u003eThird, while previous studies have noted the direct driving role of FoMO in impulsive buying behavior, they have rarely explored how FoMO, as a moderating variable, interacts with consumers\u0026rsquo; perceived value to influence UBI. This study empirically found that FoMO exhibits a significant positive moderating effect in both the PUV and PHV pathways influencing UBI, with a more pronounced effect in the PHV pathway. This finding further validates the theoretical perspective that FoMO, as a situational emotional variable, can enhance consumers\u0026rsquo; sensitivity to immediate emotional stimuli and behavioral response speed. It also expands the theoretical perspective on the mechanism through which the emotional system influences impulsive behavior within the dual-system theory and addresses the existing research gap in the exploration of FoMO\u0026rsquo;s moderating role.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Practicality contributions\u003c/h2\u003e\u003cp\u003eThis study provides important practical recommendations for social commerce platforms, brand marketers, and consumer behavior guidance. First, the research results show that INT and SCI significantly enhance consumers\u0026rsquo; PUV and PHV, thereby promoting their UBI. Therefore, we recommend that social commerce platform operators focus on improving the interactivity of their platforms, such as optimizing real-time communication functions, enhancing live streaming interaction experiences, and increasing the display intensity of social signals (e.g., number of likes and comments), to effectively stimulate users\u0026rsquo; sense of participation and shopping interest, thereby improving conversion rates.\u003c/p\u003e\u003cp\u003eSecond, given that PHV has a more significant effect on UBI, we recommend that brand marketers pay more attention to consumers\u0026rsquo; emotional experiences when formulating social e-commerce marketing strategies. They should appropriately utilize entertaining and fun content, such as short videos, interactive games, and real-time reward mechanisms, to enhance consumers\u0026rsquo; shopping pleasure and more effectively stimulate their consumption impulses. Additionally, given the significant moderating role of FoMO in this process, marketers can also moderately utilize scarcity strategies such as limited-time flash sales, limited-quantity releases, and social recommendations to guide consumers toward stronger shopping urgency and buying behavior.\u003c/p\u003e\u003cp\u003eThird, from a consumer protection perspective, this study found that high levels of FoMO significantly amplify impulsive consumption tendencies, potentially leading to irrational overconsumption issues. Therefore, we recommend that government regulatory agencies and social organizations establish corresponding consumer risk education and guidance measures, such as promoting platforms to clarify information transparency principles and advocating rational consumption concepts among consumers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Limitations and future research recommendation\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, this study only collected data from users of Chinese social commerce platforms, which may limit the generalizability of the results. Future studies could further validate the theoretical model proposed in this paper in other countries or cultural contexts (e.g., Western countries or Southeast Asia) to examine the model's robustness in diverse cultural and regional contexts.\u003c/p\u003e\u003cp\u003eSecond, this study selected INT and SCI as key stimulus factors of s-commerce platforms. Although these two factors have been proven to have a significant impact on consumer perceptions, other factors of social commerce platforms (such as platform trust, user stickiness, and content quality) may also significantly influence consumers\u0026rsquo; perceived value and buying behavior. Therefore, future research should consider incorporating more platform feature variables to explore the relationship between platform characteristics and consumer behavior more comprehensively.\u003c/p\u003e\u003cp\u003eThird, this study used a cross-sectional survey method, which may limit the rigor of causal inference to some extent. Future research can adopt experimental designs or longitudinal data methods to dynamically explore the causal relationship between INT, SCI, and perceived value. In addition, combining advanced methods such as big data analysis and consumer behavior tracking can more accurately capture consumers\u0026rsquo; real-time behavioral responses and psychological changes.\u003c/p\u003e\u003cp\u003eFourth, this study only examined the moderating role of the psychological variable of FoMO. Although significant findings were obtained, other individual differences among consumers (such as self-control, risk preference, and personality traits) may also play a key moderating role between perceived value and impulse buying. Future research can further explore these potential individual difference moderating factors to reveal the individual heterogeneity of consumers\u0026rsquo; impulsive buying behavior.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eThis study, based on the SOR theoretical framework, systematically examined how platform interactivity and social cue intensity influence consumers\u0026rsquo; impulsive buying behavior in social commerce contexts through two dimensions: perceived utilitarian value and perceived hedonic value. The findings revealed that these two platform functions significantly enhanced consumers\u0026rsquo; perceived value, thereby strengthening their urge to buy impulsively.\u003c/p\u003e\u003cp\u003eAdditionally, the study highlights the significant moderating role of fear of missing out, indicating that individuals with FoMO are more susceptible to the influence of perceived value, especially hedonic value, and are thus more likely to exhibit impulsive tendencies. These findings not only enrich the theoretical understanding of emotional and cognitive mechanisms underlying digital impulsive purchasing but also provide practical implications for platform design and consumer engagement strategies targeting specific consumer groups.\u003c/p\u003e\u003cp\u003eOverall, this study deepens our understanding of how technological and social availability in social commerce environments stimulate consumer psychological responses and impulsive purchasing. It also underscores the importance of focusing on FOMO as a contextual amplifier of impulsive buying behavior. Future research could further explore various psychological moderating factors and longitudinal effects to develop more comprehensive interventions that balance commercial objectives with consumer well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics statements\u003c/h2\u003e\n\u003cp\u003eEthical approval was not required for this study, as it involved an anonymous, minimal-risk online survey of adult participants and did not collect any identifiable personal information. The research was conducted in accordance with the Declaration of Helsinki and the ethical guidelines established by the Malaysian Ministry of Health’s Medical Review \u0026amp; Ethics Committee (MREC). This study qualified for exemption from ethical review, as outlined in the Guidelines for Ethical Review of Clinical Research or Research Involving Human Subjects (2006). According to these guidelines, research that involves surveys, questionnaires, and interviews, which does not collect identifiable private information, is exempt from MREC review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e All participants provided their informed consent prior to their inclusion in the study. The consent form, presented on the first page of the online questionnaire administered via the Wenjuanxing platform in June 2025, outlined the study's purpose, the voluntary and anonymous nature of participation, withdrawal rights, and the academic use of data. The study was open only to adults aged 18 and above. No personally identifiable information was collected, and the study involved no foreseeable physical or psychological risks.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eLYD designed the study and wrote the manuscript. MZAM and AFA provided comments and supervision. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to privacy considerations, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Omoush, K. S., \u0026amp; Shuhaiber, A. 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The influences of livestreaming on online purchase intention: examining platform characteristics and consumer psychology. \u003cem\u003eIndustrial Management \u0026amp; Data Systems\u003c/em\u003e. 2023,\u003cem\u003e 123\u003c/em\u003e(3), 862-885. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7039008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7039008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the psychological mechanisms underlying consumers\u0026rsquo; impulse buying behavior in social commerce (S-commerce) by adopting the stimulus-organism-response (SOR) framework. Specifically, it examines how platform interactivity (INT) and social cue intensity (SCI), as environmental stimuli, influence the urge to buy impulsively (UBI) through the mediating roles of perceived utilitarian value (PUV) and perceived hedonic value (PHV). Furthermore, it explores the moderating role of Fear of Missing Out (FoMO) in the perceived value-behavior link. Data were collected from 398 social commerce users in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that both INT and SCI significantly enhance PUV and PHV, which in turn have a positive effect on UBI. FoMO is found to strengthen the effect of perceived value (especially PHV) on UBI. These findings enrich the application of the SOR model in digital consumption contexts and offer new insights into the dual value pathways and emotional moderators that drive impulsive behavior in social commerce environments.\u003c/p\u003e","manuscriptTitle":"Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 19:01:26","doi":"10.21203/rs.3.rs-7039008/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35914f21-a484-4f64-a609-e87ce8c704bf","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54524319,"name":"Business and commerce/Business and management"},{"id":54524320,"name":"Social science/Business and management"},{"id":54524321,"name":"Business and commerce/Information systems and information technology"},{"id":54524322,"name":"Biological sciences/Psychology"},{"id":54524323,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-02-09T16:55:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 19:01:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7039008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7039008","identity":"rs-7039008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}