Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11, 1076-1086 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 4 September 2025; Revised: 14 October 2025; Accepted: 17 October 2025; Published: 17 November 2025 * Correspondence: shu-ya.cha@dpu.ac.th The influence of online social interaction propensity on purchase intention: The moderating role of neuroticism Cheng-Liang Yang1,2, Shu-Ya Chang1* 1Dhurakij Pundit University, Chinese International College, Bangkok, Thailand; 2006002@zzuli.edu.cn (C.L.Y.) shu- ya.cha@dpu.ac.th (S.Y.C.). 2Zhengzhou University of Light Industry, College of Economics and Management, Zhengzhou, China. Abstract: This study examines how online social interaction propensity influences consumers’ purchase intentions in E-commerce live-streaming contexts. The research draws on the Social Identity Model of Deindividuation Effects (SIDE) to clarify the psychological mechanisms linking individuals’ online social interaction propensity to their purchasing behavior, with consumer escapism as a mediator and neuroticism as a moderator. A quantitative research design was adopted using survey data from 469 live-streaming commerce users, and structural equation modeling (SEM) was applied to test the proposed relationships. The findings show that online social interaction propensity significantly enhances purchase intention, consumer escapism partially mediates this effect, and neuroticism amplifies the positive association between social interaction and escapism. These results demonstrate that psychological and personality factors jointly shape consumer decisions in interactive digital environments. The study provides both theoretical and practical contributions by enriching the conceptualization of escapism as a consumption motive and offering actionable insights for brands and live-streaming platforms to develop personalized engagement strategies aligned with users’ psychological traits. Keywords: Consumer escapism, Neuroticism, Online social interaction propensity, Purchase intention. 1. Introduction Social media, live streaming platforms, and short video applications have become deeply embedded in everyday life, reshaping how individuals access information, engage in social interactions, and make consumption decisions. In particular, the emergence of e-commerce live streaming has introduced mechanisms such as real-time comments, fan rewards, and interactive features that transform consumers from passive information recipients into active participants in the consumption process [1-4]. Such social participation has been shown to strengthen consumer engagement, foster brand trust, and enhance emotional connections, thereby increasing purchase intention [3, 5]. Nevertheless, online social interaction is not purely rational; it often reflects underlying and complex psychological motivations. According to the Social Identity Model of Deindividuation Effects (SIDE), anonymous interactions in online environments encourage consumers to adopt group identities and exhibit online disinhibition, thereby increasing their susceptibility to group influence and predisposing them to irrational consumption decisions [6]. Furthermore, prior research identifies escapism as a central psychological driver of digital media use, particularly in gaming and online streaming [7] and as a compensatory consumption strategy through which consumers attempt to escape the monotony of everyday life via online shopping [8]. However, different individuals do not respond consistently to the psychological and behavioral reactions triggered by online social interactions. Personality traits, particularly neuroticism, may function as critical moderating factors. Neuroticism is typically characterized by emotional instability 1077 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate and a heightened tendency toward anxiety, worry, sadness, and anger, making individuals more sensitive to stress and more likely to exhibit disproportionate reactions to everyday events [9]. Highly neurotic individuals are also more prone to excessive social media use and to negatively interpret emotional cues, which can intensify the adverse impact of real-world stress [10, 11]. Such tendencies may further amplify the role of online social interaction propensity in driving consumer escapism. Therefore, clarifying the relationships among Online Social Interaction Propensity, consumer escapism, purchase intention, and neuroticism is essential for advancing understanding of how online social interaction shapes consumer behavior. Accordingly, this study investigates how Online Social Interaction Propensity affects purchase intention through escapism in the context of e-commerce live streaming and further examines the moderating role of neuroticism. 2. Literature Review 2.1. The Social Identity Model of Deindividuation Effects The Social Identity Model of Deindividuation Effects (SIDE) is a key theoretical framework in social psychology that explains changes in individual behavior and group dynamics within computer- mediated environments [6]. The model posits that deindividuation reflects a shift in self-awareness from the individual to the group level, rendering perceptions and behaviors more strongly influenced by group identity and norms. Group immersion and anonymity constitute the core mechanisms of this process [6]. In anonymous group settings, individual differences are diminished, group identity is accentuated, and members are more likely to conform to group norms and act as part of the “ingroup” [12]. Moreover, anonymity offers strategic benefits for disadvantaged groups, allowing them to conceal personal identities and engage in behaviors that might otherwise be sanctioned under non-anonymous conditions [13]. Grounded in the SIDE model, this study examines how online social interaction propensity influences consumers’ purchase intentions through escapism. In anonymous or visually uninformed interaction contexts, individuals are more likely to strengthen their group identity and rely on group- provided information to guide judgments, perceiving collective behavior as normative and actionable [14]. Within consumption-oriented settings, individuals tend to align with group purchasing behaviors, thereby reinforcing purchase intentions. Moreover, consumers with high social interaction tendencies are more likely to immerse themselves in group communication, gaining a sense of belonging and identity that temporarily relieves real-world pressures and fosters escapist motivations. Under such conditions, they are more prone to impulsive, emotion-driven purchase decisions influenced by group demonstrative cues and marketing stimuli. 2.2. The Influence of Online Social Interaction Propensity on Purchase Intention Online social interaction propensity is defined as a trait-based individual difference reflecting the extent to which consumers are inclined to engage with others in online environments [15]. Consumers with higher levels of this propensity are more likely to actively participate in platform interactions. Prior studies demonstrate that online social interaction enhances consumers’ sense of participation and belonging, strengthens perceptions of brand usefulness and trustworthiness, and thereby positively shapes purchase intention [2, 16]. In e-commerce live streaming contexts, individuals with a stronger propensity for online social interaction are more responsive to social recognition and are more inclined to adopt group-endorsed opinions, attitudes, and behaviors, including purchasing products recommended by live streamers or other consumers. Accordingly, the following hypothesis is proposed: H1: Online social interaction positively influences purchase intention. 2.3. The Influence of Online Social Interaction Propensity on Consumer Escapism Online social interaction propensity enhances immersive experiences, thereby increasing consumers’ tendency to escape from reality. Continuous information flow, interactive design, and personalized recommendations on digital platforms intensify emotional engagement and cognitive involvement, 1078 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate diverting attention from real-world challenges [17]. In addition, immediate feedback mechanisms on social media provide positive reinforcement, enabling individuals with a high propensity for social interaction to shift their focus to virtual spaces more readily [18, 19]. Moreover, online social interaction not only facilitates information exchange but also fosters a sense of belonging and emotional support, offering psychological comfort for individuals experiencing real-world stress [20]. Taken together, these mechanisms suggest that Online Social Interaction Propensity promotes consumers’ escapism through three primary pathways: reality detachment, cognitive distraction, and anticipated relief. Accordingly, the following hypothesis is proposed: H2: Online social interaction propensity positively influences consumers' escapism. 2.4. The Impact of Consumer Escapism on Purchase Intention Consumer escapism is defined as the motivational mechanism through which individuals seek psychological relief and detachment from reality under conditions of stress, self-discrepancy, or negative emotions [21]. At its core, escapism functions as a compensatory consumption strategy that redirects attention and alleviates psychological distress [19]. According to the SIDE model, anonymous and highly immersive digital environments amplify deindividuation, whereby escapism strengthens consumers’ receptiveness to social identities provided by platforms or communities [6]. Empirical evidence further demonstrates that escapism reinforces the “player” identity through behavioral immersion, thereby driving in-game purchases [22] and fostering identity construction among participants in social e-commerce contexts, which in turn enhances purchase intention [23]. Accordingly, the following hypothesis is proposed: H3: Consumer escapism positively influences purchase intention. 2.5. The Mediating Role of Consumer Escapism Online social interaction propensity can foster escapism through mechanisms such as reality detachment, cognitive distraction, and anticipated relief [18, 19, 24]. More importantly, escapism has been shown to enhance consumers’ purchase intentions. Prior research demonstrates that escapism exerts a significant positive influence on purchase intention in social e-commerce settings [23], and this effect is particularly salient in immersive shopping environments [25]. Accordingly, we propose the following hypothesis: H4: Consumer escapism mediates the relationship between online social interaction propensity and purchase intention. 2.6. The Moderating Role of Neuroticism Neuroticism is a core dimension of the Big Five personality traits and is defined as an individual’s tendency to experience negative emotional responses and related cognitive patterns under stressful conditions [26]. Highly neurotic individuals, due to their emotional instability and heightened sensitivity to negative evaluations, are more likely to rely on online social interactions to alleviate anxiety and compensate for diminished self-worth. They also engage in excessive self-monitoring during interactions, which reinforces their tendency toward escapism [27]. By contrast, individuals low in neuroticism exhibit greater emotional stability and prefer problem-focused coping strategies [28]. Rather than depending on virtual communities to regulate negative emotions, thereby weakening the influence of Online Social Interaction Propensity on escapism. Accordingly, we propose the following hypothesis: H5: Neuroticism positively moderates the relationship between online social interaction propensity and consumer escapism. 2.7. Research Framework Building on the theoretical foundation and proposed hypotheses, the research framework of this study is presented in Figure 1. 1079 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate Online Social Interaction Propensity Consumer Escapism Purchase Intention Neuroticism H4 Figure 1. Research Framework Diagram. 3. Research Methods and Design This study targeted consumers aged 18 and above in mainland China who had used mainstream e- commerce live streaming platforms and made at least one purchase within the past month. Data collection adopted purposive sampling, and questionnaires were designed and distributed via the SurveyPlus platform (www.surveyplus.cn). To reduce common method bias, data were collected at multiple time points, and all procedures were strictly followed by established research ethics guidelines. The questionnaire employed established scales to measure all variables, with items appropriately adapted to the context of e-commerce live streaming. Responses were assessed on a five-point Likert scale (1=strongly disagree, 5=strongly agree), with higher scores indicating stronger tendencies toward the respective construct. Online social interaction propensity was measured using an 8-item scale adapted from Blazevic et al. [15], which captures individuals’ tendency to interact with others in online environments. Example item: “In general, I am the type of person who interacts with others online whenever I have the opportunity.” Consumer escapism was measured using a 9-item scale developed by Orazi et al. [21], measuring the extent to which individuals experience reality detachment, cognitive distraction, and anticipated relief in e-commerce live streaming contexts. Example item: “When watching a live stream, I temporarily forget about the real world.” Purchase intention was measured with a 3-item scale adapted from Zhang et al. [29] in the live streaming context, evaluating consumers’ purchase tendencies and behavioral intentions after watching live content. Example item: “I am willing to purchase the products recommended by the live streamer.” Neuroticism was measured using the 12- item Neuroticism subscale of the Big Five Personality Inventory (NEO-FFI-3) [30], which captures emotional sensitivity across dimensions such as anxiety, anger, hostility, depression, self-consciousness, and vulnerability. Example item: “I often feel tense and jittery.” 4. Analysis of Research Results 4.1. Sample Description This study collected 469 valid responses, yielding a response rate of 76.6%. Regarding gender distribution, 269 participants were female (57.4%), slightly exceeding the proportion of males. The sample was concentrated among younger and middle-aged respondents, with 191 participants aged 26– 29 (40.7%), representing the largest age group. In terms of marital status, 268 participants were married (57.1%), slightly higher than the unmarried group. Educational attainment was predominantly at the bachelor’s level, with 354 participants (75.5%), indicating a relatively high overall education level. Regarding income, both the 6,001–9,000 CNY group and the over 9,001 CNY group included 149 participants each (31.8%), forming the primary income tiers. With respect to occupation, 316 respondents were employees of enterprises (67.4%), constituting the largest occupational group. Overall, the demographic distribution of the sample appears balanced and provides a representative basis for subsequent analyses. Specific details are presented in Table 1. http://www.surveyplus.cn/ 1080 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate Table 1. Frequency distribution table of the sample. Item Group Frequency Percentage Gender Male 200 42.6 Female 269 57.4 Age 18–25 61 13.0 26-29 191 40.7 30-39 169 36.0 40–49 32 6.8 50 years old and above 16 3.4 Marital status Unmarried 201 42.9 Married 268 57.1 Education High school or below 17 3.6 Associate degree 61 13.0 Bachelor's degree 354 75.5 Master's degree or above 37 7.9 Income Below 3,000 CNY 66 14.1 3,001–6,000 CNY 105 22.4 6,001–9,000 CNY 149 31.8 Over 9,001 CNY 149 31.8 Occupation Corporate employee 316 67.4 Civil servant/public institution staff 45 9.6 Self-employed/Freelancers 31 6.6 Student 68 14.5 Other 9 1.9 4.2. Common Method Bias As the questionnaire was self-reported by respondents based on their live streaming platform experiences, potential concerns regarding common method bias may arise. To assess this issue, Harman’s single-factor test was conducted. The results indicated that the first factor accounted for 38.64% of the variance, well below the 50% threshold, suggesting that no single factor dominated the variance [31, 32]. Thus, common method bias was not a serious concern in this study. 4.3. Reliability and Validity Analysis To assess the internal consistency of the scales, this study employed Cronbach’s α coefficient. In general, an α value above 0.70 indicates good reliability, while values between 0.60 and 0.70 are considered acceptable [33]. The results showed that the α coefficients for all variables exceeded 0.60 (see Table 2), suggesting that the scales demonstrated satisfactory reliability. 1081 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate Table 2. Reliability and validity analysis. Construct Item Std. Factor Loadings Cronbach’s α AVE CR Online social interaction propensity (OI) OI1 0.847 0.956 0.736 0.957 OI2 0.838 OI3 0.865 OI4 0.908 OI5 0.862 OI6 0.898 OI7 0.751 OI8 0.884 consumer escapism (CE) CE1 0.757 0.927 0.588 0.928 CE2 0.719 CE3 0.750 CE4 0.824 CE5 0.781 CE6 0.792 CE7 0.781 CE8 0.757 CE9 0.734 Neuroticism (NT) NT1 0.547 0.948 0.615 0.950 NT2 0.815 NT3 0.810 NT4 0.767 NT5 0.773 NT6 0.879 NT7 0.789 NT8 0.879 NT9 0.773 NT10 0.773 NT11 0.770 NT12 0.784 Purchase Intention (PI) PI1 0.846 0.880 0.717 0.884 PI2 0.880 PI3 0.813 This study employed AMOS 28 to conduct confirmatory factor analysis (CFA) on the variables to evaluate the reliability and validity of the measurement model. As shown in Table 2, the standardized factor loadings of the 32 items across the six variables ranged from 0.547 to 0.908, all exceeding the recommended threshold of 0.50 [34]. The average variance extracted (AVE) for each construct ranged from 0.588 to 0.736, surpassing the minimum criterion of 0.50, thereby supporting convergent validity. Composite reliability (CR) values ranged from 0.884 to 0.957, all above 0.70 and greater than the corresponding AVE values [35]. As shown in Table 3, the square roots of the AVEs for all constructs exceeded their inter-construct correlations, providing evidence of discriminant validity. In addition, model fit indices indicated a satisfactory overall model fit (χ²/df=1.859, GFI=0.893, CFI=0.967, TLI=0.964, RMSEA=0.043, SRMR=0.033), meeting the commonly accepted thresholds [36-38]. Taken together, these results confirm that the measurement model demonstrates strong reliability, convergent validity, and discriminant validity, thereby providing a sound basis for subsequent structural modeling analyses. 1082 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate Table 3. Discriminant Validity and Correlations of Variables. OI CE PI NT OI 0.858 CE 0.586*** 0.767 PI 0.588*** 0.555*** 0.847 NT 0.172*** 0.480*** 0.257*** 0.784 Note: ***p<0.001. Bold values on the diagonal represent the square roots of the corresponding AVEs. OI=Online Social Interaction Propensity; CE=Consumer Escapism; PI=Purchase Intention; NT=Neuroticism. 4.4. Structural Equation Modeling Analysis AMOS 28 was employed to test the hypothesized structural paths. The structural model demonstrated a good overall fit (χ²/df=1.646, GFI=0.944, CFI=0.985, TLI=0.983, RMSEA=0.037, SRMR=0.028), exceeding commonly accepted thresholds [36-38]. These results support the adequacy of the model for subsequent hypothesis testing. As shown in Table 4, the standardized path coefficients and direct effect tests indicate that Online Social Interaction Propensity exerts a significant positive effect on purchase intention (β=0.399, p<0.001), thus supporting H1. Online Social Interaction Propensity also has a significant positive effect on consumer escapism (β= 0.586, p<0.001), supporting H2. In addition, consumer escapism positively influences purchase intention (β= 0.322, p<0.001), supporting H3. Table 4. Structural model path coefficients. Hypothesis Path Standardized Path Coefficient (β) p-value Result H1 OI→PI 0.39 *** Support H2 OI→CE 0.586 *** Support H3 CE→PI 0.322 *** Support Note: ***p<0.001. OI=Online Social Interaction Propensity; CE=Consumer Escapism; PI=Purchase Intention; NT=Neuroticism. 4.5. Mediating Effect Analysis of Consumer Escapism Building on the path analysis results, this study further tested the mediating role of consumer escapism in the relationship between online social interaction propensity and purchase intention. A bootstrap resampling procedure (ML estimation) was performed with 2,000 resamples, and the results are presented in Table 5. The indirect effect of online social interaction propensity on purchase intention through consumer escapism was 0.188, with the 95% confidence interval excluding zero, indicating a significant mediation effect and supporting H4. Moreover, the total effect of online social interaction propensity on purchase intention was 0.588, with the indirect effect accounting for 32.0% of the total, suggesting that consumer escapism serves as a partial mediator in this relationship. Table 5. Standardized bootstrap mediation effect test. Hypothesis Path Indirect Effect SE Bias-corrected 95% CI P value Test Result Lower Upper H4 OI→CE→PI 0.188 0.037 0.123 0.27 0.001 Support Note: OI=Online Social Interaction Propensity; CE=Consumer Escapism; PI=Purchase Intention. 4.6. Analysis of the Moderating Effect of Neuroticism This study employed multiple regression analysis to test the moderating role of neuroticism in the relationship between online social interaction propensity and consumer escapism. As shown in Table 6, the interaction term was significant (β=0.145, p<0.001), indicating that neuroticism strengthens the 1083 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate positive effect of online social interaction propensity on consumer escapism. The model explained 47.1% of the variance (R²=0.471, F=138.134). Conditional effect analysis further revealed that when neuroticism was low (M – 1SD, –0.827), the effect of online social interaction propensity on consumer escapism was significant but weaker (β=0.259, p<0.001); at the mean level, the effect became stronger (β=0.378, p<0.001); and at high levels (M + 1SD, 0.827), the effect was further amplified (β=0.498, p<0.001). Collectively, these findings support H5. Table 6. Moderating effect of NT between OI and CE. Item β SE t p Constant 3.677 0.023 160.504 0.000 OI 0.378 0.026 14.370 0.000 NT 0.285 0.028 10.149 0.000 OI × NT 0.145 0.030 4.896 0.000 R-sq 0.471 F 138.134 *** Note: ***p<0.001. OI=Online Social Interaction Propensity; NT=Neuroticism; CE=Consumer Escapism. 5. Discussion This study examined the positive influence of online social interaction propensity on consumer purchase intention from an individual trait perspective, identified the mediating mechanism of escapism, and explored the moderating role of neuroticism. The results show that online social interaction propensity significantly enhances consumers’ purchase intention, consistent with prior findings on social interaction and consumer behavior [5, 39]. This suggests that individuals with high levels of social interaction propensity are more responsive to group dynamics and social conformity, thereby demonstrating stronger purchase intentions. The study further revealed that consumer escapism partially mediates the relationship between online social interaction propensity and purchase intention. Immersive experiences are recognized as a key driver of purchase motivation [40, 41] while escapism from reality represents the underlying psychological motive, namely alleviating real-world stress and self-discrepancy through virtual interactions. This mechanism aligns with the compensatory consumption model [19] and has also been validated in studies of immersive gaming and virtual experiences [22, 25]. Additionally, this study confirmed the moderating role of neuroticism within the proposed mechanism. The results indicate that individuals high in neuroticism are more prone to escapist tendencies in social interactions, consistent with their heightened susceptibility to anxiety and self- doubt [42, 43]. Therefore, individual traits should be integrated into consumer behavior models in e- commerce live streaming contexts to provide a more nuanced understanding of consumer decision- making processes. Overall, this study establishes a coherent framework that links individual traits, psychological motives, and behavioral outcomes, thereby revealing the mechanisms through which consumers form purchase decisions in e-commerce live streaming. 6. Conclusions and Recommendations 6.1. Conclusions This study examined the mechanisms through which Online Social Interaction Propensity influences consumer purchase intentions. The findings indicate that Online Social Interaction Propensity not only directly enhances purchase intentions but also exerts an indirect effect through consumer escapism, with neuroticism further strengthening this mediating pathway. Specifically, individuals with higher Online Social Interaction Propensity are more likely to immerse themselves in 1084 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate online social contexts to alleviate real-world pressures through escapism, while those with higher levels of neuroticism are more prone to develop escapist motives during this process, thereby reinforcing their purchase intentions. Overall, this study extends the explanatory framework of escapism as a purchase motive. It also highlights the interactive effects of individual traits and psychological states in shaping consumer behavior within live-streaming e-commerce contexts. 6.2. Theoretical and Practical Implications This study adopted an individual trait perspective, extending prior research that has predominantly examined contextual factors or overt interactive behaviors. It underscores the role of Online Social Interaction Propensity as an individual characteristic shaping consumer behavior. By introducing escapism as a mediating variable and integrating SIDE theory, the study advances understanding of the psychological motivations underlying immersive experiences. Moreover, the moderating role of neuroticism was confirmed, emphasizing the significance of individual differences in the formation of purchase intentions within social interaction contexts. Collectively, these findings enrich the literature on the intersection of personality traits and consumer psychology. From a practical standpoint, this study suggests that live-streaming platforms can leverage consumers’ escapism to enhance purchase conversion by creating immersive, community-oriented interaction environments. For consumers with high neuroticism, platforms may provide emotional support and empathetic content to foster emotional resonance and strengthen purchase intention. When designing interactive scenarios, brand merchants should carefully consider consumers’ psychological states and personality traits to implement more personalized and targeted marketing strategies. 6.3. Limitations and Future Prospects The sample of this study primarily consisted of consumers from Chinese e-commerce live- streaming platforms, which may limit the generalizability of the findings. Future research should validate these results across cross-cultural settings and diverse consumption contexts. In addition, while the measurement of consumer escapism was adapted from prior studies, future research could further differentiate between positive and negative forms of escapism as distinct motivational mechanisms [7] to enhance both the explanatory power and practical value of the research model. Funding: This work was financially supported by the Annual Project of the Philosophy and Social Sciences Planning Foundation of Henan Province, China (Grant Number: 2024BSH017). Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] H. Shen, C. Zhao, D. X. Fan, and D. Buhalis, "The effect of hotel livestreaming on viewers’ purchase intention: Exploring the role of parasocial interaction and emotional engagement," International Journal of Hospitality Management, vol. 107, p. 103348, 2022. https://doi.org/10.1016/j.ijhm.2022.103348 [2] M. Wang, L.-L. Sun, and J.-D. Hou, "How emotional interaction affects purchase intention in social commerce: The role of perceived usefulness and product type," Psychology Research and Behavior Management, vol. 14, pp. 467-481, 2021. https://doi.org/10.2147/PRBM.S301286 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1016/j.ijhm.2022.103348 https://doi.org/10.2147/PRBM.S301286 1085 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate [3] S. Zhang and L. Zhang, "The influence of brand social interaction on purchase intention: A perspective of social capital," Sage Open, vol. 13, no. 2, p. 21582440231169933, 2023. https://doi.org/10.1177/21582440231169933 [4] H. Sima, S. Wu, Z. J. Yan, N. Luo, and Y. Chen, "The impact of livestreaming e-commerce on consumer purchase intention in China," Journal of Consumer Marketing, vol. 42, no. 6, pp. 717–730, 2025. https://doi.org/10.1108/JCM- 02-2024-6564 [5] W. Zhou, J. Dong, and W. Zhang, "The impact of interpersonal interaction factors on consumers’ purchase intention in social commerce: a relationship quality perspective," Industrial Management & Data Systems, vol. 123, no. 3, pp. 697- 721, 2023. https://doi.org/10.1108/IMDS-06-2022-0392 [6] R. Spears, "Social identity model of deindividuation effects," The International Encyclopedia of Media Effects, pp. 1-9, 2017. https://doi.org/10.1002/9781118783764.wbieme0091 [7] F. Stenseng, J. Falch-Madsen, and B. W. Hygen, "Are there two types of escapism? Exploring a dualistic model of escapism in digital gaming and online streaming," Psychology of Popular Media, vol. 10, no. 3, pp. 319-329, 2021. https://doi.org/10.1037/ppm0000339 [8] M. Dharmesti, T. R. S. Dharmesti, S. Kuhne, and P. Thaichon, "Understanding online shopping behaviours and purchase intentions amongst millennials," Young Consumers, vol. 22, no. 1, pp. 152-167, 2021. https://doi.org/10.1108/YC-12-2018-0922 [9] M. Butler, N. Turiano, L. Buckley, M. McGeehan, and P. S. O'Súilleabháin, "Neuroticism facets and mortality risk in adulthood: A systematic review and narrative synthesis," Journal of Psychosomatic Research, vol. 175, p. 111500, 2023. https://doi.org/10.1016/j.jpsychores.2023.111500 [10] T. Bowden-Green, J. Hinds, and A. Joinson, "Understanding neuroticism and social media: A systematic review," Personality and Individual Differences, vol. 168, p. 110344, 2021. https://doi.org/10.1016/j.paid.2020.110344 [11] X. Chen, J. Pu, W. Shi, and Y. Zhou, "The impact of neuroticism on symptoms of anxiety and depression in elderly adults: The mediating role of rumination," Current Psychology, vol. 39, pp. 42-50, 2020. https://doi.org/10.1007/s12144-017-9740-3 [12] R. Spears and T. Postmes, "Group identity, social influence, and collective action online: Extensions and applications of the SIDE model," The Handbook of the Psychology of Communication Technology, pp. 23-46, 2015. https://doi.org/10.1002/9781118426456.ch2 [13] G. Y. Gao, C. Cheung, T. K. H. Chan, and Z. W. Y. Lee, "Zoombombing: Understanding we‑intention to engage in collective trolling among online community members through the social identity model of deindividuation effects," in Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS‑57) (pp. 6066–6075). University of Hawaiʻi at Mānoa, 2024. [14] R. B. Cialdini and N. J. Goldstein, "Social influence: Compliance and conformity," Annual Review of Psychology, vol. 55, pp. 591-621, 2004. https://doi.org/10.1146/annurev.psych.55.090902.142015 [15] V. Blazevic, C. Wiertz, J. Cotte, K. De Ruyter, and D. I. Keeling, "GOSIP in cyberspace: Conceptualization and scale development for general online social interaction propensity," Journal of Interactive Marketing, vol. 28, no. 2, pp. 87- 100, 2014. https://doi.org/10.1016/j.intmar.2013.09.003 [16] M. Harrigan, K. Feddema, S. Wang, P. Harrigan, and E. Diot, "How trust leads to online purchase intention founded in perceived usefulness and peer communication," Journal of Consumer Behaviour, vol. 20, no. 5, pp. 1297-1312, 2021. https://doi.org/10.1002/cb.1936 [17] C. Preece and A. Skandalis, "Time to imagine an escape: investigating the consumer timework at play in augmented reality," European Journal of Marketing, vol. 58, no. 1, pp. 92-118, 2024. https://doi.org/10.1108/EJM-09-2021-0695 [18] S. Jones, J. Cronin, and M. G. Piacentini, "Mapping the extended frontiers of escapism: binge-watching and hyperdiegetic exploration," Journal of Marketing Management, vol. 34, no. 5-6, pp. 497-508, 2018. https://doi.org/10.1080/0267257X.2018.1477818 [19] N. Mandel, D. D. Rucker, J. Levav, and A. D. Galinsky, "The compensatory consumer behavior model: How self- discrepancies drive consumer behavior," Journal of Consumer Psychology, vol. 27, no. 1, pp. 133-146, 2017. https://doi.org/10.1016/j.jcps.2016.05.003 [20] A. Chen and K. Lemmer, "Seeking social support on social media: A coping perspective," Internet Research, vol. 35, no. 2, pp. 664-690, 2025. https://doi.org/10.1108/INTR-05-2022-0346 [21] D. C. Orazi, K. Y. Mah, T. Derksen, and K. B. Murray, "Consumer escapism: Scale development, validation, and physiological associations," Journal of Business Research, vol. 160, p. 113805, 2023. https://doi.org/10.1016/j.jbusres.2023.113805 [22] I. N. Huda and I. Salehudin, "Escapism, family and friends, and social interactions: How watching esports increases gaming and in-game purchase intentions," Jurnal Manajemen dan Pemasaran Jasa, vol. 17, no. 1, pp. 1-22, 2024. https://doi.org/10.25105/jmpj.v17i1.18463 [23] D. Hidayat, E. Fernando, and C. H. Pangaribuan, "Understanding the factors influencing consumer purchase intentions via Instagram shopping feature," in 2022 International Conference on Information Management and Technology (ICIMTech) (pp. 246-250). IEEE, 2022. https://doi.org/10.1177/21582440231169933 https://doi.org/10.1108/JCM-02-2024-6564 https://doi.org/10.1108/JCM-02-2024-6564 https://doi.org/10.1108/IMDS-06-2022-0392 https://doi.org/10.1002/9781118783764.wbieme0091 https://doi.org/10.1037/ppm0000339 https://doi.org/10.1108/YC-12-2018-0922 https://doi.org/10.1016/j.jpsychores.2023.111500 https://doi.org/10.1016/j.paid.2020.110344 https://doi.org/10.1007/s12144-017-9740-3 https://doi.org/10.1002/9781118426456.ch2 https://doi.org/10.1146/annurev.psych.55.090902.142015 https://doi.org/10.1016/j.intmar.2013.09.003 https://doi.org/10.1002/cb.1936 https://doi.org/10.1108/EJM-09-2021-0695 https://doi.org/10.1080/0267257X.2018.1477818 https://doi.org/10.1016/j.jcps.2016.05.003 https://doi.org/10.1108/INTR-05-2022-0346 https://doi.org/10.1016/j.jbusres.2023.113805 https://doi.org/10.25105/jmpj.v17i1.18463 1086 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1076-1086, 2025 DOI: 10.55214/2576-8484.v9i11.11055 © 2025 by the authors; licensee Learning Gate [24] J. Hwang and S. O. Lyu, "The antecedents and consequences of well-being perception: An application of the experience economy to golf tournament tourists," Journal of Destination Marketing & Management, vol. 4, no. 4, pp. 248-257, 2015. https://doi.org/10.1016/j.jdmm.2015.09.002 [25] T. Saleem, M. I. Ishaq, A. Raza, and M. Junaid, "Exploring the effect of telepresence and escapism on consumer post- purchase intention in an immersive virtual reality environment," Journal of Retailing and Consumer Services, vol. 81, p. 104014, 2024. https://doi.org/10.1016/j.jretconser.2024.104014 [26] R. R. McCrae and O. P. John, "An introduction to the five-factor model and its applications," Journal of Personality, vol. 60, no. 2, pp. 175-215, 1992. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x [27] C. J. Larche, P. Tran, T. B. Kruger, N. Dhaliwal, and M. J. Dixon, "Escaping the woes through flow? Examining the relationship between escapism, depression, and flow experience in role‑playing and platform games," Journal of Gambling Issues, vol. 46, 2021. https://doi.org/10.4309/jgi.2021.46.9 [28] R. S. Lazarus, Stress, appraisal, and coping. New York: Springer, 1984. [29] Y. Zhang, X. Li, R. Liu, Q. Shuai, and C. Huang, "How facial cues of streamers drive purchase intention in live streaming commerce: Based on a lens model," Journal of Business Research, vol. 190, p. 115193, 2025. https://doi.org/10.1016/j.jbusres.2025.115193 [30] R. R. McCrae and J. Costa, Paul T, "Brief versions of the NEO-PI-3," Journal of Individual Differences, vol. 28, no. 3, pp. 116-128, 2007. https://doi.org/10.1027/1614-0001.28.3.116 [31] H. H. Harman, Modern factor analysis, 3rd ed. Chicago: University of Chicago Press, 1976. [32] P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, and N. P. Podsakoff, "Common method biases in behavioral research: A critical review of the literature and recommended remedies," Journal of Applied Psychology, vol. 88, no. 5, pp. 879-903, 2003. https://doi.org/10.1037/0021-9010.88.5.879 [33] D. G. Bonett and T. A. Wright, "Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning," Journal of Organizational Behavior, vol. 36, no. 1, pp. 3-15, 2015. https://doi.org/10.1002/job.1960 [34] J. F. Hair Jr, W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham, Multivariate data analysis, 8th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2019. [35] C. Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error," Journal of Marketing Research, vol. 18, no. 1, pp. 39-50, 1981. https://doi.org/10.1177/002224378101800104 [36] L. t. Hu and P. M. Bentler, "Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives," Structural Equation Modeling: A Multidisciplinary Journal, vol. 6, no. 1, pp. 1-55, 1999. https://doi.org/10.1080/10705519909540118 [37] R. B. Kline, Principles and practice of structural equation modeling, 4th ed. New York: Guilford Press, 2015. [38] R. P. McDonald and M.-H. R. Ho, "Principles and practice in reporting structural equation analyses," Psychological Methods, vol. 7, no. 1, pp. 64-82, 2002. https://doi.org/10.1037/1082-989X.7.1.64 [39] R. Geng and J. Chen, "The influencing mechanism of interaction quality of UGC on consumers’ purchase intention– an empirical analysis," Frontiers in Psychology, vol. 12, p. 697382, 2021. https://doi.org/10.3389/fpsyg.2021.697382 [40] S. A. Ettis, "Examining the relationships between online store atmospheric color, flow experience and consumer behavior," Journal of Retailing and Consumer Services, vol. 37, pp. 43-55, 2017. https://doi.org/10.1016/j.jretconser.2017.03.007 [41] C.-P. Hsu and C.-W. Chang, "Does the social platform established by MMORPGs build social and psychological capital?," Computers in Human Behavior, vol. 129, p. 107139, 2022. https://doi.org/10.1016/j.chb.2021.107139 [42] D. Dong et al., "Neuroticism modulates neural activities of posterior cingulate cortex and thalamus during psychosocial stress processing," Journal of Affective Disorders, vol. 262, pp. 223-228, 2020. https://doi.org/10.1016/j.jad.2019.11.003 [43] J. G. Hutchinson and P. G. Williams, "Neuroticism, daily hassles, and depressive symptoms: An examination of moderating and mediating effects," Personality and Individual Differences, vol. 42, no. 7, pp. 1367-1378, 2007. https://doi.org/10.1016/j.paid.2006.10.014 https://doi.org/10.1016/j.jdmm.2015.09.002 https://doi.org/10.1016/j.jretconser.2024.104014 https://doi.org/10.1111/j.1467-6494.1992.tb00970.x https://doi.org/10.4309/jgi.2021.46.9 https://doi.org/10.1016/j.jbusres.2025.115193 https://doi.org/10.1027/1614-0001.28.3.116 https://doi.org/10.1037/0021-9010.88.5.879 https://doi.org/10.1002/job.1960 https://doi.org/10.1177/002224378101800104 https://doi.org/10.1080/10705519909540118 https://doi.org/10.1037/1082-989X.7.1.64 https://doi.org/10.3389/fpsyg.2021.697382 https://doi.org/10.1016/j.jretconser.2017.03.007 https://doi.org/10.1016/j.chb.2021.107139 https://doi.org/10.1016/j.jad.2019.11.003 https://doi.org/10.1016/j.paid.2006.10.014