Multiple pathways to inhibition in face-to-face socializing among active social media users: an fsQCA analysis

Multiple pathways to inhibition in face-to-face socializing among active social media users: an fsQCA analysis | 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 Multiple pathways to inhibition in face-to-face socializing among active social media users: an fsQCA analysis Xiaohua Lei, Xiuhong Tan², Jia Wang², Jing Liu², Yu Zhenlei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8772917/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Inhibition in face-to-face socializing (IFFS) undermines offline interaction quality and relationship development among active social media users. Prior research has largely relied on net-effect approaches and has not sufficiently captured how multiple online–offline factors combine to produce high versus low IFFS. To address this gap, this study adopts a configurational perspective and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how social media usage intensity, offline interaction unease, sensitivity to others’ evaluation, concern about tension exposure, and perceived superiority of online communication control jointly shape IFFS. Based on survey data from 503 social media users, the results show that no single antecedent constitutes a necessary condition for high IFFS; instead, high social media activity exhibits a pervasive presence across high-IFFS solutions. Four configurational patterns are identified as sufficient for high IFFS: (1) a tension-externalization–driven pattern, (2) an offline anxiety–dominant pattern, (3) an evaluation–media overlay pattern, and (4) an online preference lock-in pattern, indicating equifinal mechanisms through which active users develop offline inhibition. In contrast, low IFFS emerges through three distinct pathways characterized by the joint absence of key inhibitors, including reduced evaluation sensitivity and online preference, reduced fear of tension exposure and online preference, and reduced usage intensity and offline unease. Notably, the configuration combining high online activity with offline interaction unease is the most prevalent, highlighting the interactive coupling between intensive online engagement and offline discomfort. This study enriches understanding of offline interaction barriers in social media contexts and offers actionable insights for designing targeted interventions to foster more natural, low-defensive face-to-face socializing. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Active social media users Inhibition in Face-to-Face Socializing Configuration perspective fsQCA Offline awkwardness Online preferences Figures Figure 1 1 Introduction Inhibition in face-to-face socializing is a crucial indicator of individuals’ limited participation and avoidance tendencies in offline interactions, and it has long been a focus of research in social psychology and media use [ 1 ] . Against the backdrop of social media being deeply integrated into daily life and significantly increasing the frequency of online interactions, the differences between online and offline interaction modes create a complex social interaction context [ 2 ] . As a result, the degree of social inhibition among different users in face-to-face interactions exhibits significant differences. Why do active users still exhibit varying degrees of inhibition in face-to-face socializing interactions under the same context of high social media use? How do the relationships among various psychological and situational factors emerge and interact in different combinations to systematically shape the formation mechanism of face-to-face inhibition? These questions not only concern the practical issues of active users’ offline social adaptation and relationship development but also point to the key scientific questions regarding the formation mechanism of inhibition in face-to-face socializing and the generation of variable relationships. Active users in social media contexts refer to individuals who, beyond merely browsing information on social platforms, actively engage in multiple interactive behaviors such as liking, commenting, reposting, private messaging, and group chats [ 3 ] . Their participation in offline face-to-face interactions depends on their psychological capacity and the tension and synergy between key psychological and situational factors, which in turn influence their level of Inhibition in Face-to-Face Socializing [ 4 ] . Existing research on key factors influencing Inhibition in Face-to-Face Socializing among active users can be categorized into three types: platform engagement characteristics [ 5 ] , offline interaction pressure experiences [ 6 ] , and online communication controllability resources [ 7 ] . Against the backdrop of co-occurring psychological capacity and offline contexts, the complex coupling of these factors may generate distinct operational patterns—such as supportive facilitation, substitute inhibition, coexisting tension, or conditional transformation [ 8 ] —thereby determining the differentiated manifestations of Inhibition in Face-to-Face Socializing among active users. Existing research predominantly focuses on typical explanatory pathways like social anxiety [ 9 ] , self-presentation pressure [ 10 ] , or media dependency [ 11 ] , providing crucial references for researchers exploring the phenomenon of online activity coupled with inhibition in face-to-face socializing. However, most related studies adopt a single-factor perspective, examining the net effects of individual factors on offline interactions, with limited research revealing the combined relationships and concurrent mechanisms of multiple factors within the same interactive system [ 12 ] . Individual psychological traits and contextual factors interact through varying combinations of strength, resulting in significant causal complexity and diverse pathways for Inhibition in Face-to-Face Socializing [ 13 ] . Within this framework, whether a single factor constitutes a key condition for inhibition in face-to-face socializing, and how multiple behavioral, psychological, and contextual elements co-generate high or low levels of inhibition through different configurational patterns, remains to be systematically examined and clarified. Therefore, this study introduces a configurational perspective and employs the fsQCA method. Starting from necessary and sufficient causal relationships, it identifies the multiple condition configurations leading to high or low levels of inhibition in face-to-face socializing and their asymmetric formation mechanisms. It aims to answer the following questions: In social media contexts, how do multiple behavioral and psychological attributes and their interactive environments interact to generate different condition configurations? Do these conditions constitute necessary prerequisites for inhibition in face-to-face socializing, and to what extent? Which condition configurations produce high or low levels of inhibition in face-to-face socializing? This study's potential contributions include: Systematically integrating multidimensional factors influencing Inhibition in face-to-face socializing in social media contexts from a configurational perspective, revealing multiple pathways underlying this phenomenon; Within a necessary-and-sufficient causal framework, it identifies whether key conditions form core constraints for inhibition in face-to-face socializing, clarifying their mechanisms and boundaries; it distinguishes the asymmetric formation mechanisms of high versus low levels of inhibition in face-to-face socializing, deepening our understanding of why active users experience limited offline interaction. This provides actionable theoretical foundations for future platform governance and the promotion of users' offline social engagement. 2 Research Methods 2.1 Fuzzy-set Qualitative Comparative Analysis (fsQCA) Method Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a method for exploring the complexity of causal relationships, particularly suited for analyzing combinations of conditions and their effects on outcomes. Unlike traditional regression analysis, which focuses on the net effect of individual variables, fsQCA emphasizes the interdependence and configuration of conditions, revealing how different pathways can lead to the same outcome [ 14 ] . In social media contexts, the formation mechanism of inhibition in face-to-face socializing among active users may exhibit characteristics such as multi-factor concurrence, equivalent multiple pathways, and causal asymmetry [ 15 ] . Necessary conditions indicate that the absence of a specific condition prevents the outcome from occurring, while sufficient conditions signify that a particular condition or combination of conditions is adequate to generate the outcome [ 16 ] . Compared to traditional methods, fsQCA focuses more on the role of condition combinations, effectively explaining the complex causal pathways underlying this phenomenon [ 17 ] . Given that this study examines social inhibition in social media contexts characterized by multiple causal pathways, the fsQCA approach facilitates a deeper understanding of this complex mechanism from a configurational perspective. Figure 1 presents a configurational conceptual model illustrating how multiple individual-level conditions jointly contribute to inhibition in face-to-face socializing among active social media users. The model emphasizes causal complexity and equifinality rather than net effects. 2.2 Data Sources The data for this study were derived from a questionnaire survey. Questionnaire items were developed by systematically reviewing relevant domestic and international research, prioritizing established scales, and contextualizing them for social media interactions and face-to-face communication settings to ensure measurements align with participants' actual behaviors and psychological experiences. The formal questionnaire comprises two sections: Part One collects demographic information including gender, age, and educational background; Part Two measures core variables through items designed around five antecedent conditions and one outcome variable, outlining behavioral patterns and social interaction characteristics exhibited by active users across different social platforms. To ensure the content validity of the questionnaire and minimize item comprehension bias, this study conducted a pretest with a focus group comprising five doctoral candidates after developing the initial measurement items. The pretest focused on evaluating items with ambiguities or comprehension difficulties, which were further revised based on expert interview feedback. Given that all questionnaire items were grounded in relevant literature and established scales, and underwent pretesting and multiple rounds of refinement, the questionnaire demonstrated strong overall content validity. After data collection, the research team conducted quality control and sample cleaning according to established protocols. This involved excluding samples with significantly short response times, identifying potentially invalid responses using reverse-scored items, and removing questionnaires with multiple identical selections or obvious logical inconsistencies across items to enhance data validity and reliability. Ultimately, 566 questionnaires were collected, yielding 503 valid responses after cleaning, representing an 88.9% valid response rate. The sample structure showed a relatively balanced gender distribution, with male and female respondents accounting for 47.1% and 52.9%, respectively. The age distribution was predominantly concentrated among 18-to 40-year-olds, accounting for 76.9% of the sample. This aligns with the characteristic of social media being predominantly used by young people and is consistent with the study's focus on active users in social media contexts. Respondents exhibited a high overall educational attainment, with over 70% holding associate degrees or higher. This provides a suitable sample foundation for analyzing the formation mechanisms of inhibition in face-to-face socializing among active users in social media contexts. 2.3 Reliability and Validity Tests 2.3.1Reliability Test To ensure the reliability and validity of the measurement tools, this study conducted reliability and validity tests on the collected data, with results presented in Table 1 . Cronbach's Alpha values greater than or equal to 0.70 are considered acceptable levels of internal consistency [ 18 ] . The Cronbach’s Alpha coefficients for each variable ranged from 0.759 to 0.844, all exceeding 0.70, indicating that the scales possess good reliability. Table 1 Results of Reliability and Validity Tests Antecedent variables Items Factor loadings Cronbach’s α CR AVE Usage intensity(UI) UI1 UI2 UI3 0.709 0.719 0.718 0.759 0.786 0.512 Offline unease(OU) OU1 OU2 OU3 OU4 0.790 0.759 0.767 0.722 0.844 0.845 0.577 Sensitivity to Others’ Evaluation(SOE) SOE1 SOE2 SOE3 SOE4 0.757 0.757 0.768 0.734 0.840 0.841 0.569 Fear of Tension Exposure(FTE) FTE1 FTE2 FTE3 FTE4 0.731 0.710 0.771 0.737 0.826 0.827 0.544 Online Preference(OP) OP1 OP2 OP3 0.694 0.728 0.806 0.789 0.788 0.554 Inhibition in Face-to-Face Socializing(IFFS) IFFS1 IFFS2 IFFS3 IFFS4 0.745 0.722 0.727 0.731 0.821 0.821 0.535 2.3.2 Validity Test Validity test encompasses content validity and construct validity. Items were derived from established scales and reviewed by experts, ensuring strong content validity. Construct validity is further divided into convergent validity and discriminant validity. Convergent validity is typically assessed through standardized factor loadings, CR, and average variance extracted (AVE); AVE ≥ 0.50 indicates an acceptable level of variance explained by the construct [ 19 ] . CR is commonly used to assess internal consistency in latent variable measurement, with composite reliability CR ≥ 0.6 generally recommended [ 20 ] . All variables exhibited CR values ranging from 0.786 to 0.845, all exceeding 0.6, indicating good internal consistency of the questionnaire. The results of discriminant validity testing are shown in Table 2 . The square root of AVE (main diagonal section) for each variable is greater than the correlation coefficient between that variable and other variables [ 21 ] , indicating that the questionnaire possesses good discriminant validity. Table 2 Discriminant Validity Test Usage intensity (UI) Offline unease (OU) Sensitivity to others’ evaluation (SOE) Manifestation of nervousness (FTE) Online preference (OP) Inhibition in Face-to-Face Socializing (IFFS) Usage intensity (UI) 0.715 Offline unease (OU) 0.654 0.760 Sensitivity to others’ evaluation (SOE) 0.631 0.562 0.754 Fear of Tension Exposure(FTE) 0.583 0.555 0.601 0.738 Online preference (OP) 0.541 0.596 0.595 0.579 0.744 Inhibition in Face-to-Face Socializing (IFFS) 0.599 0.608 0.589 0.643 0.648 0.731 2.4 Variable Measurement and Calibration All variables in this study were measured using established scales from both domestic and international sources, with necessary contextual adaptations made to align with social media interaction scenarios and face-to-face communication settings. While preserving the core measurement dimensions and theoretical underpinnings of the original scales, adjustments were made to the referents and contextual focus of certain items based on the research context. Variable names were also redefined to more accurately reflect the actual behaviors and psychological experiences of active users within social media environments [ 22 ] .Antecedent variables, measurement items and sources in the social media context are shown in Table 3 . 2.4.1 Variable Measurement (1) Outcome Variables Inhibition in face-to-face socializing is measured using relevant items from the Social Inhibition Questionnaire 15-Word Version (SIQ15-W), specifically items 3, 6, 9, and 15. The sum of scores from these items describes an individual's tendency toward withdrawal and avoidance in face-to-face interactions [ 23 ] . This scale is widely used to measure the degree of interactive withdrawal and participation limitations in real-world social contexts. It effectively reflects inhibitory behaviors in face-to-face communication, aligning with the definition of the outcome variable in this study. (2) Antecedent Variables Usage intensity(UI). This metric describes the overall level of engagement among active users during social media usage, emphasizing the comprehensive participation in behaviors such as information browsing, content posting, and interactive engagement across multiple social media platforms. In terms of measurement, this paper synthesizes existing research approaches on dimensions such as usage frequency, usage duration, and interactive engagement. It develops corresponding measurement items tailored to the reality of concurrent multi-platform social media usage [ 24 ] , aiming to distinguish differences in social media engagement levels within the defined active user sample. Offline Unease(OU). Primarily measures the unease, tension, and discomfort experienced by individuals in face-to-face communication situations, reflecting their subjective levels of negative experience during real-world interactions. This variable draws on the Social Interaction Anxiety Scale (SIAS) for measurement [ 25 ] , which focuses on anxiety and discomfort experienced in real-world social interactions. It aligns closely with the face-to-face interaction discomfort examined in this study. Sensitivity to others' evaluation(SOE). This term describes an individual's vigilance and sensitivity to others' negative evaluative cues, reflecting their psychological preoccupation with the risk of rejection or judgment during social interactions. In terms of measurement, this study primarily references the Brief Fear of Negative Evaluation Scale [ 26 ] . This scale effectively reflects an individual's concern and sensitivity toward others' negative evaluations, providing a measurement foundation for analyzing evaluative pressure in face-to-face interactions. Fear of Tension Exposure (FTE).Primarily measures an individual's concern about others perceiving their internal states such as tension or anxiety, reflecting the self-monitoring pressure experienced during face-to-face interactions. This variable draws from the dimension related to fear of being watched in the Social Phobia Scale (SPS) [ 27 ] , focusing on individuals' subjective concerns about the external display of physiological or emotional tension. Online preference(OP).This measure assesses individuals' subjective perception of the controllability advantages inherent in online interactions compared to face-to-face communication, reflecting their cognitive judgments regarding self-presentation, response pacing, and risk management in online communication environments. In terms of measurement, this paper primarily references the Preference for Online Social Interaction (POSI) scale [ 28 ] . This scale emphasizes individuals' perceptions of communicative control within online environments, aligning closely with the controllability advantages of online communication examined herein. Table 3 Antecedent variables, measurement items and sources in the social media context Measurement variables Items Source Usage intensity(UI) UI1: Considering your overall usage habits, how often do you use your commonly used social platforms? UI2: How often do you usually initiate interactions on social platforms? UI3: In the past 30 days, approximately how many people have you maintained "frequent contact" with through social media? Li J et al. [24] Offline Unease(OU) OU1: When participating in face-to-face social interactions, I often feel embarrassed or uncomfortable and find it difficult to truly relax. OU2: During face-to-face conversations, I tend to experience obvious physical tension, which affects my comfort. OU3: During face-to-face conversations, I often suddenly can’t think of what to say and find it difficult to naturally continue the conversation or pick up the topic. OU4: During face-to-face conversations, I’m often distracted by my own tension or discomfort and find it difficult to focus on what the other person is saying or the content of the conversation itself. Peters L \(\:{\text{}}^{\left[\text{25}\right]}\) Sensitivity to others' evaluation(SOE) SOE1: Before engaging in face-to-face communication activities, I worry about what others think of me. SOE2: During face-to-face conversations, I pay special attention to the other person’s expression, tone, or pauses and judge whether I’m being recognized based on them. SOE3: When the other person’s reaction is cold or brief, I often think I’ve done something wrong. SOE4: After the conversation, I often repeatedly recall what I’ve said and worry that there are inappropriate parts. Carleton R N et al. \(\:{\text{}}^{\left[\text{26}\right]}\) Fear of Tension Exposure (FTE) FTE1: I worry that the other person will notice my subtle signs of tension. FTE2: I’m afraid that others will interpret my signs of tension as "lack of confidence, incompetence, or unnaturalness" rather than a normal reaction. FTE3: Once I notice that I’m starting to get tense, I worry that the tension will worsen and lead to a loss of control in my expression. FTE4: I’m worried that the signs of tension will reduce the effectiveness of communication. Ge Yingnan et al. \(\:{\text{}}^{\left[\text{27}\right]}\) Online Preference (OP) OP1: Compared with face-to-face communication, I prefer to interact on social platforms because I don’t have to respond immediately and can think before replying. OP2: Compared with face-to-face communication, when interacting on social platforms, I’m less worried that non-verbal expressions such as facial expressions, tones, and pauses will affect others’ judgments of me. OP3: In multi-person interaction scenarios, compared with face-to-face communication, I can more easily find opportunities to participate in the conversation on social platforms and am less likely to be interrupted. Caplan SE \(\:{\text{}}^{\left[\text{28}\right]}\) Inhibition in face-to-face socializing (IFFS) IFFS1: When communicating face-to-face, I often find it difficult to initiate a conversation or find a suitable topic to start. IFFS2: When communicating face-to-face, I also often feel constrained, express myself unnaturally, or have difficulty interacting smoothly. IFFS3: In a face-to-face group setting, I often deliberately reduce my speaking to avoid being the center of attention. IFFS4: After a face-to-face interaction, I usually don’t take the initiative to continue the contact or deepen the relationship. Cheek JM et al. [23] 2.5 Variable Calibration First, the questionnaire data must undergo calibration processing. Using fsQCA 4.1 software, the raw measurement values are normalized and converted into set membership degrees within the range [0, 1]. Based on calibration thresholds of 0.95 (complete membership threshold), 0.5 (intersection threshold), and 0.05 (complete non-membership threshold), along with data distribution characteristics [ 29 ] , calibration standards for primary variables are set as shown in Table 4 . In fuzzy set analysis, cases with a membership degree of 0.5 are regarded as a critical state of “neither belonging nor non-belonging.” A uniform fine-tuning adjustment of + 0.001 is applied to all membership degree values to ensure the operability and discrimination capability of cases in set membership determination [ 30 ] . The specific calibration anchor settings for each condition variable and result variable are shown in Table 4 . Table 4 Data Calibration Results Variable Calibration point Fully-membership point 95% Intersection point 50% Non-membership point 5% Usage intensity(UI) 4.67 3.67 1.33 Offline Unease(OU) 4.5 3.75 1.5 Sensitivity to others' evaluation(SOE) 4.5 3.75 1.5 Fear of Tension Exposure (FTE) 4.5 3.75 1.5 Online Preference (OP) 4.67 3.67 1.33 Inhibition in Face-to-Face Socializing (IFFS) 4.5 3.75 1.5 3 Discussion 3.1 Analysis of Necessary Conditions Within the fsQCA framework, necessary condition analysis is employed to examine whether a single condition and its negation constitute a necessary prerequisite for the occurrence of an outcome—specifically, whether the outcome set is a subset of the condition set. When consistency exceeds 0.90, the condition is deemed a necessary condition for the outcome [ 31 ] . This study conducted separate necessity tests for each antecedent condition and its negation, reporting consistency and coverage metrics. Results are presented in Table 5 . The analysis results indicate that the consistency of necessity for each individual antecedent condition did not reach 0.90, suggesting that no single condition can independently constitute the necessary condition for inhibition in face-to-face socializing(IFFS) or its negation. Therefore, it is necessary to further explore the formation pathways of IFFS and ~ IFFS under concurrent combinations of multiple conditions through configurational sufficiency analysis. Table 5 Necessity Test of Single Conditions Variable Inhibition in face-to-face Socializing (IFFS) ~Inhibition in face-to-face Socializing(~ IFFS) Consistency level Coverage Consistency level Coverage Usage Intensity (UI) 0.790 0.741 0.632 0.540 ~Usage Intensity (~ UI) 0.510 0.603 0.697 0.752 Offline Unease(OU) 0.756 0.743 0.593 0.532 ~Offline Unease(~ OU) 0.524 0.586 0.713 0.727 Sensitivity to Others’ Evaluation (SOE) 0.731 0.715 0.595 0.531 ~Sensitivity to Others’ Evaluation (~ SOE) 0.521 0.585 0.680 0.698 Fear of Tension Exposure (FTE) 0.754 0.754 0.586 0.535 ~Fear of Tension Exposure (~ FTE) 0.535 0.587 0.731 0.730 Online Preference (OP) 0.751 0.756 0.596 0.547 ~Online Preference (~ OP) 0.550 0.599 0.734 0.728 3.2 Configuration Analysis This paper employs fsQCA4.1 software to analyze the antecedent configurations leading to both IFFS and~IFFS. Different configurations reflect multiple equivalent pathways through which active users in social media contexts achieve the same outcome under the concurrent influence of varying conditions [ 30 ] . In constructing the truth table, the case frequency threshold was set to 2, the raw consistency threshold to 0.9, and the PRI consistency threshold to 0.75 [ 32 ] . Core and peripheral conditions were identified through nested comparisons of intermediate and simplified solutions: Conditions appearing in both intermediate and simplified solutions are core conditions, while those appearing only in intermediate solutions are peripheral conditions [ 32 ] . Standardized analysis results are shown in Tables 6 and 7 (where “●” indicates core condition presence, “●” indicates peripheral condition presence, “U” indicates core condition absence, “U” indicates peripheral condition absence, and blank cells indicate optional conditions). 3.2.1 Configuration Paths for Generating Inhibition in Face-to-Face Socializing(IFFS) Based on the analysis results, eight effective configurational pathways leading to inhibition in face-to-face Socializing (IFFS) were identified. The consistency of each pathway ranged between 0.835 and 0.929, with an overall configurational consistency of 0.767. with an overall coverage of 0.828. This indicates that these eight condition combinations can explain approximately 82.8% of high social inhibition cases, demonstrating strong explanatory power. These paths were categorized based on the dominant logic of core conditions and consolidated into four representative configuration types, which are shown in Table 6 . Different types reflect the differentiated causal mechanisms through which active users develop face-to-face social inhibition in social media contexts, embodying the configuration characteristics of multiple concurrent causality and equivalent multiple pathways. (1) Tension-Externalization Driven Type. This pathway centers on the psychological dimension of tension externalization, where the perceived risk of emotional externalization in face-to-face interactions remains continuously activated. This consistently leads to high levels of social inhibition across various condition combinations. In Pathway H1a, when usage intensity is high and tension externalization is pronounced, individuals are more likely to suppress expression and reduce participation in real-world interactions, exhibiting high social inhibition. Path H1b: When both offline awkwardness and tension externalization are central, the combined experience of awkwardness and externalization anxiety in face-to-face settings further promotes avoidance or inhibition. Path H1c: When tension externalization is pronounced and online preference is prominent, the preference for and reliance on online interaction accentuates the uncertainty and exposure risk of face-to-face interaction, leading individuals to favor avoiding real-world interactions. Overall, when individuals are highly concerned about the externalization of nervousness, they intensify self-monitoring and risk anticipation in face-to-face situations. This leads them to adopt strategies such as suppressing expression, reducing interaction, or avoiding participation to lessen the psychological burden of exposure. Different combination conditions (usage intensity, offline awkwardness, online preference) amplify or contextualize this process, but the externalization of nervousness serves as the common driving force for this type. (2) Offline Anxiety-Dominant Type. This pathway manifests as differentiated combinations of offline anxiety with other conditions, encompassing both structures characterized by “insufficient online regulatory resources” and those marked by “low media usage yet high inhibition.” In Path H2a, when usage intensity and offline discomfort coexist as core factors while online preference is absent, individuals endure the discomfort of face-to-face interactions without the compensatory preferences and resources provided by online alternatives. This accumulated pressure leads to heightened social inhibition. In Path H2b, even when usage intensity is absent as a core factor, significant offline discomfort alone can still trigger avoidance and inhibition in face-to-face situations, indicating that offline discomfort itself can be a major trigger for high inhibition. When individuals persistently experience discomfort in face-to-face communication, their behavioral adjustments increasingly lean toward reducing social exposure and interaction intensity. Particularly in Pathway H4, the absence of online preference further diminishes available situational adjustment resources, making it harder for individuals to obtain compensation or buffering through online interactions. This makes them more prone to becoming entrenched in high inhibition. (3) Others' Evaluation and Media Use Overlay Type. This pathway type is grounded in the reinforcing effect of usage intensity, overlaid with others' evaluation sensitivity (manifesting at core or peripheral levels), thereby making individuals more prone to concerns about self-presentation consequences and cautious behavior in face-to-face interactions. Path H3a indicates that under conditions of high usage intensity and absent online preference, individuals are more likely to transfer comparative pressures and feedback stress from online interactions to real-world settings, thereby elevating face-to-face inhibition levels. Path H3b, primarily emerging under peripheral condition combinations, demonstrates the combined effect of usage intensity, offline discomfort, and sensitivity to others' evaluations. This suggests that when multiple stress signals coexist, even without a single dominant core factor, they may collectively contribute to heightened social inhibition. High usage intensity exposes individuals more frequently to social comparison and feedback cues. When combined with sensitivity to others' evaluations, this increases the likelihood of adopting more conservative self-presentation strategies in face-to-face interactions—manifesting as reduced expression, decreased participation, or avoidance of interaction. The marginal overlap in Path H8 further demonstrates that high inhibition does not solely depend on a single core stressor; the accumulation of stress cues can equally produce the same outcome. (4) Online Preference Lock-in Type. This pathway is characterized by the simultaneous presence of usage intensity and online preference as core factors, while offline constraints and sensitivity to others' evaluations appear as core absences. That is, individuals do not experience overtly high real-world pressures yet still exhibit high levels of face-to-face social inhibition. Path H4 indicates that even when neither real-world constraints nor evaluative pressures are prominent, individuals may still develop stronger avoidance or inhibition tendencies in face-to-face interactions due to long-term high investment and stable preference formation for online interactions. This implies that when individuals gain more controllable, comfortable, or low-risk interaction experiences online and sustain high usage intensity over time, their social engagement strategies may gradually shift toward online platforms. Face-to-face interactions are perceived as higher-cost options with uncertain benefits, leading to persistent face-to-face inhibition even without significant real-world pressure. This also indicates that high social inhibition is not always directly driven by real-world pressures; the structure of online interactions and preference orientation can similarly shape behavioral choices in real-world contexts. Table 6 Configurations for achieving in Inhibition in Face-to-Face Socializing (IFFS) Antecedent variable Inhibition in Face-to-Face Socializing (IFFS) H1a H1b H1c H2a H2b H3a H3b H4 Usage Intensity (UI) ● ● ⊗ ● ● ● Offline Unease(OU) ● ● ● ● ⊗ Sensitivity to Others’ Evaluation (SOE) ● ● ⊗ Fear of Tension Exposure (FTE) ● ● ● Online Preference (OP) ● ⊗ ● ⊗ ● Consistency 0.835 0.84 0.846 0.889 0.911 0.881 0.86 0.929 Raw coverage 0.653 0.625 0.614 0.437 0.397 0.436 0.566 0.29 Unique coverage 0.018 0.016 0.023 0.006 0.011 0.006 0.017 0.006 Overall consistency 0.767 Overall coverage 0.828 3.2.2 Configuration paths leading to ~Inhibition in face-to-face socializing (~ IFFS) Table 7 shows that there are six configurations leading to ~IFFS. The consistency of each path ranges between 0.813% and 0.868%, with an overall configuration consistency of 0.805 and an overall coverage of 0.686. This indicates that the six combinations can explain approximately 68.6% of low social inhibition cases. Based on the dominant logic of missing core conditions, the aforementioned paths were consolidated into three representative configurational types. Dual-Deficit Type: Sensitivity to Others' Evaluation and Online Preference. The common feature of this pathway type is that both sensitivity to others' evaluation and online preference manifest as core condition deficits. That is, individuals neither excessively focus on others' evaluations in face-to-face interactions nor develop a significant preference or dependence on online interactions. Consequently, their offline social behaviors are less susceptible to interference from evaluation pressure or tendencies toward online substitution. Specifically, in Path L1, when individuals are insensitive to others' evaluations and do not favor online interactions as their primary choice, their face-to-face interactions are more likely to unfold in a natural, low-defensive manner, resulting in lower social inhibition. Path L2 further indicates that even when individuals face differing real-world contexts, maintaining low inhibition levels remains possible as long as their face-to-face interactions remain unhindered by evaluation pressure and online preference. Overall, when individuals neither excessively worry about others' evaluations nor prioritize online interactions as their primary space of reliance, face-to-face communication more readily reverts to its everyday and instrumental nature, naturally reducing social inhibition. Dual Absence Type of Fear of Tension Exposure and Online Preference. The defining characteristic of this pathway is the simultaneous absence of both FTE and online preference as core conditions. This implies that individuals neither worry about others perceiving their tension during face-to-face interactions nor prefer online interactions as their primary risk-avoidance or comfort substitute channel. Path L3 indicates that without concerns about tension display or self-monitoring pressure, individuals are more likely to engage in face-to-face interactions with a relaxed mindset, thereby reducing social inhibition. Path L4 further clarifies that even with low usage intensity, individuals' real-world social participation remains largely unimpeded as long as they neither worry about tension exposure nor develop pronounced online preferences. Thus, when individuals refrain from excessive self-scrutiny of their performance and do not rely on online environments for psychological buffering, their psychological barriers to face-to-face interaction are significantly lowered, resulting in lower levels of social inhibition. Dual-Absence Type of Usage Intensity and Offline Unease. This pathway is characterized by the simultaneous absence of both usage intensity and offline unease as core conditions—meaning individuals neither excessively engage in online social activities nor persistently experience significant discomfort during face-to-face interactions. In Path L5, when an individual exhibits low usage intensity and minimal offline awkwardness, their face-to-face social behavior is more likely to remain natural and fluid, with smoother initiation and maintenance of interactions. Path L6 further indicates that without excessive online engagement or offline social awkwardness pressure, individuals are less likely to develop avoidance or inhibition tendencies. Evidently, when neither online nor offline contexts impose significant pressure, individuals avoid frequent trade-offs between interaction modes, thereby more readily maintaining an open, low-inhibition state in face-to-face social settings. Table 7 Configurations for achieving ཞInhibition in Face-to-Face Socializing (ཞIFFS) Antecedent variables ཞInhibition in Face-to-Face Socializing (ཞIFFS) L1 L2 L3 L4 L5 L6 Usage Intensity (UI) ● ⊗ ⊗ ⊗ Offline Unease(OU) ⊗ ⊗ ● ⊗ ⊗ Sensitivity to Others’ Evaluation (SOE) ⊗ ⊗ Fear of Tension Exposure (FTE) ⊗ ⊗ ⊗ ● ⊗ Online Preference (OP) ⊗ ⊗ ⊗ ⊗ ⊗ ● Consistency 0.868 0.858 0.834 0.835 0.824 0.813 Raw coverage 0.495 0.486 0.257 0.25 0.251 0.248 Unique coverage 0.022 0.024 0.024 0.016 0.024 0.047 Overall consistency 0.805 Overall coverage 0.686 3.2.3 Robustness test This paper conducts robustness testing on the pre-configuration that generates high face-to-face social inhibition. As a set-theoretic method, QCA considers results robust when minor adjustments to key operations yield new outcomes that maintain a subset relationship with the original findings without altering their substantive interpretation [ 33 ] . First, raising the case frequency threshold from 2 to 3 still identified two configurations, which were largely consistent with the two solutions in the original configurations. Second, increasing the PRI consistency threshold from 0.80 to 0.85 yielded configurations consistent with the original results upon reanalysis. These robustness tests indicate that the configurations identified in this study are relatively stable. 4 Research implications (1) Theoretical Implications In social media contexts, active users' online engagement does not necessarily translate into offline social investment. Conversely, the coexistence of high online activity and offline inhibition has become a pervasive behavioral tension. This phenomenon suggests that explanations for face-to-face social inhibition should not be confined to single psychological factors or singular pathways. Instead, its underlying logic should be revealed through the lens of concurrent multiple conditions, differentiated combinations, and situational dependence. Based on this, this paper employs fsQCA to examine, from a configurational perspective, how different combinations of conditions—including social media usage intensity, offline awkwardness, sensitivity to others' evaluations, explicit anxiety about tension, and online preferences—lead to high or low levels of face-to-face social inhibition. This analysis yields the following implications for related research. First, this paper reveals that the suppression of high-level face-to-face social interaction does not stem from a single pathway or primary cause. Under different conditions, multiple structural combinations can lead to the same outcome, demonstrating the causal complexity of equivalent multiple pathways and diverse realizations. This conclusion indicates that face-to-face social inhibition is not a linear extrapolation of any single risk factor, but rather the result of multiple conditions interacting within specific structural contexts. Consequently, it provides a more explanatory causal framework for understanding the co-occurrence of active users' online and offline behaviors. Second, building upon the theoretical framework of group dynamics, this paper identifies and integrates four representative types of high-level social inhibition, further illustrating that the formation of high social inhibition exhibits distinct structural variations and mechanistic differentiation. The tension-driven overt expression type indicates that persistent perception of emotional disclosure risks intensifies self-monitoring and stabilizes inhibitory strategies. The offline discomfort-dominated type demonstrates that unpleasant experiences in real-world interactions can independently trigger avoidance and inhibition across different media contexts. The Overlap of Evaluation and Mediation reveals that the combination of high usage intensity and evaluation sensitivity amplifies anticipated consequences of self-presentation, leading individuals to adopt more conservative offline interaction strategies. The Online Preference Lock-in indicates that even when real-world pressures are not prominent, long-term high engagement and a preference for online interaction can still shape a stable orientation toward communication strategies, systematically viewing face-to-face interaction as a high-cost option. Thus, high social inhibition may stem both from offline pressures and from the solidification of online strategic structures and preferences, expanding the explanatory boundaries of existing research that primarily attributes inhibition to real-world pressures. Third, this study further reveals from the perspective of outcome asymmetry that low-level face-to-face social inhibition is not a simple reversal of high-level social inhibition. Instead, it is primarily driven by the simultaneous absence of several core stress conditions, reflecting the asymmetry in the causal structure between generation and alleviation. Specifically, when sensitivity to others' evaluations and online preference are both absent, or when explicit anxiety about tension and online preference are both absent, or when usage intensity and offline awkwardness are both absent, face-to-face interactions are more likely to unfold with low defensiveness and low self-monitoring, resulting in lower inhibition levels. This finding suggests that reducing social inhibition does not necessarily depend on increasing specific resource conditions. Rather, it critically involves uncoupling the structural linkage of several core stressors, thereby providing mechanistic evidence explaining why some highly active users maintain effective offline interactions. Fourth, at the methodological level, this paper simultaneously analyzes the sufficient-condition configurations of high-level and low-level face-to-face social inhibition, revealing the substitutive, additive, and contextual characteristics of social behavior. Different conditions may complement or substitute for one another across various pathways, with high- and low-level outcomes exhibiting structural asymmetry. This provides a more granular empirical characterization of the causal complexity underlying individual social choices in social media contexts, while also offering a reusable analytical framework for subsequent path-based explanations and typological accumulation. (2) Practical Significance The configuration results of this study indicate that interventions to curb face-to-face social inhibition among active users should not adopt a single-point strategy. Instead, they should implement tiered governance and combined optimization centered on different types of core structures. For tension-manifestation-driven pathways, priority should be given to reducing perceived risks of emotional display and excessive self-monitoring. By lowering the intensity of interaction exposure and the visibility of evaluations, this approach diminishes individuals' motivation to adopt avoidance and inhibition as risk management strategies. For offline awkwardness-dominated pathways, real-world interaction discomfort should be the core governance focus. By enhancing the accessibility and predictability of offline interactions, the awkwardness stemming from unfamiliarity and uncertainty can be mitigated, helping individuals regain their ability to initiate and sustain interactions. For the path characterized by overlapping peer evaluation and media use, interventions should focus on mitigating the combined effects of evaluative pressure and media intensity. This involves weakening competitive evaluation cues and social comparison triggers, reducing the spillover of online feedback pressure into offline interactions, and thereby decreasing defensive self-presentation. For the online preference lock-in pathway, interventions should reshape interaction choice structures by enhancing the certainty, controllability, and reward density of face-to-face interactions. This guides individuals to convert online interaction advantages into preparatory resources for offline interactions, preventing persistent offline avoidance caused by entrenched online strategies. Overall, this study demonstrates that both the formation and alleviation of face-to-face social inhibition exhibit characteristics of type-specific differences and multiple pathways. Implementing combination interventions based on type identification can more effectively reduce social inhibition levels, thereby promoting natural engagement and relationship development among active social media users in offline interactions. 5 Research Limitations and Future Prospects This study also has the following limitations that warrant further investigation in future research. First, although it provides theoretical explanations for the identified configuration paths, it faces the common challenge of insufficient qualitative analysis depth, similar to other large-sample fsQCA studies. Second, constrained by data acquisition methods and availability, the analysis primarily relies on online survey data. This approach introduces limitations in sample randomness and representativeness, potentially affecting the generalizability of findings. Third, due to limited prior research and long-term data accumulation, the study employs only a single cross-sectional dataset, failing to examine the dynamic evolution of different condition combinations over time. Finally, while this study employs fsQCA as the primary analytical method to reveal the relationship between multiple condition combinations and face-to-face social inhibition, it does not systematically examine linear effects among variables. Future research could complement this by introducing regression analysis, structural equation modeling, or other methods to supplementally test the relational structures among variables, thereby forming a complementary explanatory framework. Declarations Acknowledgments We thank the participants of the study. Author contributions Xiaohua Lei conceived and designed the study, performed the data analysis, and drafted the manuscript. Xiuhong Tan, Jia Wang,Jing Liu and Yu Zhenlei contributed to research design refinement, interpretation of results, and critical revision of the manuscript. Yu Zhenlei supervised the research process and served as the corresponding author, providing overall guidance and final approval of the manuscript. All authors reviewed and approved the final version of the manuscript. Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Additional information Funding This research received no external funding . Ethics declarations Ethical approval All methods were carried out in accordance with relevant ethical guidelines and regulations, including the principles of the Declaration of Helsinki and applicable national regulations on human-subject research. The study protocol was reviewed and approved by the Academic Ethics Committee of Qilu University of Technology (Shandong Academy of Sciences). Informed consent Informed consent was obtained from all participants prior to participation. At the beginning of the online questionnaire, participants were provided with a detailed informed consent statement describing the study purpose, voluntary nature of participation, anonymity, confidentiality, and their right to withdraw at any time without penalty. Participants indicated their informed consent by proceeding with and completing the questionnaire, which was explicitly stated as constituting agreement to participate in the study. Only adults (18 years or older) were eligible to participate. No personally identifiable information was collected, and all data were recorded anonymously and used solely for academic research purposes. Competing interests The author(s) declare no competing interests. References Wang Jun, L. & Hongde, W. The influence of social interaction in knowledge sharing communities on users’ privacy information disclosure[J]. J. Syst. Manage. 33 (5), 1284 (2024). Lieberman, A. & Schroeder, J. Two social lives: How differences between online and offline interaction influence social outcomes[J]. Curr. Opin. Psychol. 31 , 16–21 (2020). Wang Yifei, W. & Feng, W. Design and validation of the social behavior scale for entrepreneurs in the digital age[J]. J. Syst. Manage. 34 (5), 1433 (2025). Lu, X. et al. Synergistic impacts of online and offline social participation on older adults’ subjective well-being: evidence from the Canadian Longitudinal Study on Aging[J]. Eur. J. Inform. Syst. 33 (5), 699–716 (2024). Kunpeng, S. & Dan, W. 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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-8772917","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":637138924,"identity":"0a481e3e-0717-4606-a54e-6dea5be4785c","order_by":0,"name":"Xiaohua Lei","email":"","orcid":"","institution":"¹Information Research Institute of Shandong Academy of Sciences, Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Lei","suffix":""},{"id":637138925,"identity":"67b70e5d-5b5e-4b0f-be64-dbddaedadec2","order_by":1,"name":"Xiuhong Tan²","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)LIBRARY","correspondingAuthor":false,"prefix":"","firstName":"Xiuhong","middleName":"","lastName":"Tan²","suffix":""},{"id":637138926,"identity":"e3adc502-6baa-45fb-a7d3-06d49b47e734","order_by":2,"name":"Jia Wang²","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)LIBRARY","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Wang²","suffix":""},{"id":637138927,"identity":"e64d48b3-5818-4b37-b874-6343e0cc94a5","order_by":3,"name":"Jing Liu²","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)LIBRARY","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu²","suffix":""},{"id":637138928,"identity":"33508518-4ed9-4e93-9e52-f4ab76e82098","order_by":4,"name":"Yu Zhenlei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYDAC+YPtPz5USMjJE69FgrlBcsYZC2PDBuK1sDdI87ZVJDIcIFaH7uzGBgPeeRIJjA3MDx/dIEaL2Z2DDQmS2yTy2BnYjI1ziNJyILHhgOE2iWLGBh42aWK1NDYkzpEAaiRay43EZoaDDSRpOXOwjbHhmISxYTPRfjne/oz5T02dnDx788PHRGlBAGbSlI+CUTAKRsEowAcAU8QybIwYIwgAAAAASUVORK5CYII=","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)LIBRARY","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhenlei","suffix":""}],"badges":[],"createdAt":"2026-02-03 08:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8772917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8772917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109252135,"identity":"a2fd20d4-769f-44db-bae0-a8bf00484760","added_by":"auto","created_at":"2026-05-14 09:15:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138932,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of inhibition in face-to-face socializing\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8772917/v1/8f647b9f9c11aac3794ca45c.jpeg"},{"id":109252137,"identity":"2c498dd5-680e-4a77-bcf0-e91b508a75b3","added_by":"auto","created_at":"2026-05-14 09:15:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8772917/v1/bdd4e3b0-b1d3-4135-a0af-cdd7e83363ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiple pathways to inhibition in face-to-face socializing among active social media users: an fsQCA analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eInhibition in face-to-face socializing is a crucial indicator of individuals\u0026rsquo; limited participation and avoidance tendencies in offline interactions, and it has long been a focus of research in social psychology and media use\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Against the backdrop of social media being deeply integrated into daily life and significantly increasing the frequency of online interactions, the differences between online and offline interaction modes create a complex social interaction context\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As a result, the degree of social inhibition among different users in face-to-face interactions exhibits significant differences. Why do active users still exhibit varying degrees of inhibition in face-to-face socializing interactions under the same context of high social media use? How do the relationships among various psychological and situational factors emerge and interact in different combinations to systematically shape the formation mechanism of face-to-face inhibition? These questions not only concern the practical issues of active users\u0026rsquo; offline social adaptation and relationship development but also point to the key scientific questions regarding the formation mechanism of inhibition in face-to-face socializing and the generation of variable relationships.\u003c/p\u003e \u003cp\u003eActive users in social media contexts refer to individuals who, beyond merely browsing information on social platforms, actively engage in multiple interactive behaviors such as liking, commenting, reposting, private messaging, and group chats\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Their participation in offline face-to-face interactions depends on their psychological capacity and the tension and synergy between key psychological and situational factors, which in turn influence their level of Inhibition in Face-to-Face Socializing\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Existing research on key factors influencing Inhibition in Face-to-Face Socializing among active users can be categorized into three types: platform engagement characteristics\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, offline interaction pressure experiences \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and online communication controllability resources\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Against the backdrop of co-occurring psychological capacity and offline contexts, the complex coupling of these factors may generate distinct operational patterns\u0026mdash;such as supportive facilitation, substitute inhibition, coexisting tension, or conditional transformation\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;thereby determining the differentiated manifestations of Inhibition in Face-to-Face Socializing among active users. Existing research predominantly focuses on typical explanatory pathways like social anxiety\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, self-presentation pressure\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, or media dependency\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, providing crucial references for researchers exploring the phenomenon of online activity coupled with inhibition in face-to-face socializing. However, most related studies adopt a single-factor perspective, examining the net effects of individual factors on offline interactions, with limited research revealing the combined relationships and concurrent mechanisms of multiple factors within the same interactive system\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Individual psychological traits and contextual factors interact through varying combinations of strength, resulting in significant causal complexity and diverse pathways for Inhibition in Face-to-Face Socializing\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Within this framework, whether a single factor constitutes a key condition for inhibition in face-to-face socializing, and how multiple behavioral, psychological, and contextual elements co-generate high or low levels of inhibition through different configurational patterns, remains to be systematically examined and clarified. Therefore, this study introduces a configurational perspective and employs the fsQCA method. Starting from necessary and sufficient causal relationships, it identifies the multiple condition configurations leading to high or low levels of inhibition in face-to-face socializing and their asymmetric formation mechanisms. It aims to answer the following questions: In social media contexts, how do multiple behavioral and psychological attributes and their interactive environments interact to generate different condition configurations? Do these conditions constitute necessary prerequisites for inhibition in face-to-face socializing, and to what extent? Which condition configurations produce high or low levels of inhibition in face-to-face socializing? This study's potential contributions include: Systematically integrating multidimensional factors influencing Inhibition in face-to-face socializing in social media contexts from a configurational perspective, revealing multiple pathways underlying this phenomenon; Within a necessary-and-sufficient causal framework, it identifies whether key conditions form core constraints for inhibition in face-to-face socializing, clarifying their mechanisms and boundaries; it distinguishes the asymmetric formation mechanisms of high versus low levels of inhibition in face-to-face socializing, deepening our understanding of why active users experience limited offline interaction. This provides actionable theoretical foundations for future platform governance and the promotion of users' offline social engagement.\u003c/p\u003e"},{"header":"2 Research Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Fuzzy-set Qualitative Comparative Analysis (fsQCA) Method\u003c/h2\u003e \u003cp\u003eFuzzy Set Qualitative Comparative Analysis (fsQCA) is a method for exploring the complexity of causal relationships, particularly suited for analyzing combinations of conditions and their effects on outcomes. Unlike traditional regression analysis, which focuses on the net effect of individual variables, fsQCA emphasizes the interdependence and configuration of conditions, revealing how different pathways can lead to the same outcome\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In social media contexts, the formation mechanism of inhibition in face-to-face socializing among active users may exhibit characteristics such as multi-factor concurrence, equivalent multiple pathways, and causal asymmetry\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Necessary conditions indicate that the absence of a specific condition prevents the outcome from occurring, while sufficient conditions signify that a particular condition or combination of conditions is adequate to generate the outcome \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Compared to traditional methods, fsQCA focuses more on the role of condition combinations, effectively explaining the complex causal pathways underlying this phenomenon\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Given that this study examines social inhibition in social media contexts characterized by multiple causal pathways, the fsQCA approach facilitates a deeper understanding of this complex mechanism from a configurational perspective.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a configurational conceptual model illustrating how multiple individual-level conditions jointly contribute to inhibition in face-to-face socializing among active social media users. The model emphasizes causal complexity and equifinality rather than net effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eThe data for this study were derived from a questionnaire survey. Questionnaire items were developed by systematically reviewing relevant domestic and international research, prioritizing established scales, and contextualizing them for social media interactions and face-to-face communication settings to ensure measurements align with participants' actual behaviors and psychological experiences. The formal questionnaire comprises two sections: Part One collects demographic information including gender, age, and educational background; Part Two measures core variables through items designed around five antecedent conditions and one outcome variable, outlining behavioral patterns and social interaction characteristics exhibited by active users across different social platforms.\u003c/p\u003e \u003cp\u003eTo ensure the content validity of the questionnaire and minimize item comprehension bias, this study conducted a pretest with a focus group comprising five doctoral candidates after developing the initial measurement items. The pretest focused on evaluating items with ambiguities or comprehension difficulties, which were further revised based on expert interview feedback. Given that all questionnaire items were grounded in relevant literature and established scales, and underwent pretesting and multiple rounds of refinement, the questionnaire demonstrated strong overall content validity.\u003c/p\u003e \u003cp\u003eAfter data collection, the research team conducted quality control and sample cleaning according to established protocols. This involved excluding samples with significantly short response times, identifying potentially invalid responses using reverse-scored items, and removing questionnaires with multiple identical selections or obvious logical inconsistencies across items to enhance data validity and reliability. Ultimately, 566 questionnaires were collected, yielding 503 valid responses after cleaning, representing an 88.9% valid response rate. The sample structure showed a relatively balanced gender distribution, with male and female respondents accounting for 47.1% and 52.9%, respectively. The age distribution was predominantly concentrated among 18-to 40-year-olds, accounting for 76.9% of the sample. This aligns with the characteristic of social media being predominantly used by young people and is consistent with the study's focus on active users in social media contexts. Respondents exhibited a high overall educational attainment, with over 70% holding associate degrees or higher. This provides a suitable sample foundation for analyzing the formation mechanisms of inhibition in face-to-face socializing among active users in social media contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Reliability and Validity Tests\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1Reliability Test\u003c/h2\u003e \u003cp\u003eTo ensure the reliability and validity of the measurement tools, this study conducted reliability and validity tests on the collected data, with results presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Cronbach's Alpha values greater than or equal to 0.70 are considered acceptable levels of internal consistency\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The Cronbach\u0026rsquo;s Alpha coefficients for each variable ranged from 0.759 to 0.844, all exceeding 0.70, indicating that the scales possess good reliability.\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\u003eResults of Reliability and Validity Tests\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=\"left\" 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\u003eAntecedent variables\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\u003eFactor loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\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\"\u003e \u003cp\u003eUsage intensity(UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUI1\u003c/p\u003e \u003cp\u003eUI2\u003c/p\u003e \u003cp\u003eUI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003cp\u003e0.719\u003c/p\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOU1\u003c/p\u003e \u003cp\u003eOU2\u003c/p\u003e \u003cp\u003eOU3\u003c/p\u003e \u003cp\u003eOU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003cp\u003e0.759\u003c/p\u003e \u003cp\u003e0.767\u003c/p\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to Others\u0026rsquo; Evaluation(SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOE1\u003c/p\u003e \u003cp\u003eSOE2\u003c/p\u003e \u003cp\u003eSOE3\u003c/p\u003e \u003cp\u003eSOE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003cp\u003e0.757\u003c/p\u003e \u003cp\u003e0.768\u003c/p\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of Tension Exposure(FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTE1\u003c/p\u003e \u003cp\u003eFTE2\u003c/p\u003e \u003cp\u003eFTE3\u003c/p\u003e \u003cp\u003eFTE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003cp\u003e0.710\u003c/p\u003e \u003cp\u003e0.771\u003c/p\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference(OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP1\u003c/p\u003e \u003cp\u003eOP2\u003c/p\u003e \u003cp\u003eOP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003cp\u003e0.728\u003c/p\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition in Face-to-Face Socializing(IFFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFFS1\u003c/p\u003e \u003cp\u003eIFFS2\u003c/p\u003e \u003cp\u003eIFFS3\u003c/p\u003e \u003cp\u003eIFFS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003cp\u003e0.722\u003c/p\u003e \u003cp\u003e0.727\u003c/p\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.535\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=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Validity Test\u003c/h2\u003e \u003cp\u003eValidity test encompasses content validity and construct validity. Items were derived from established scales and reviewed by experts, ensuring strong content validity. Construct validity is further divided into convergent validity and discriminant validity. Convergent validity is typically assessed through standardized factor loadings, CR, and average variance extracted (AVE); AVE\u0026thinsp;\u0026ge;\u0026thinsp;0.50 indicates an acceptable level of variance explained by the construct\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. CR is commonly used to assess internal consistency in latent variable measurement, with composite reliability CR\u0026thinsp;\u0026ge;\u0026thinsp;0.6 generally recommended\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. All variables exhibited CR values ranging from 0.786 to 0.845, all exceeding 0.6, indicating good internal consistency of the questionnaire. The results of discriminant validity testing are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The square root of AVE (main diagonal section) for each variable is greater than the correlation coefficient between that variable and other variables\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, indicating that the questionnaire possesses good discriminant validity.\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\u003eDiscriminant Validity Test\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=\"char\" char=\".\" 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\u003eUsage intensity\u003c/p\u003e \u003cp\u003e(UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffline unease\u003c/p\u003e \u003cp\u003e(OU)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity to others\u0026rsquo; evaluation (SOE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eManifestation of nervousness\u003c/p\u003e \u003cp\u003e(FTE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOnline preference\u003c/p\u003e \u003cp\u003e(OP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInhibition in Face-to-Face Socializing\u003c/p\u003e \u003cp\u003e(IFFS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage intensity (UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.715\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\u003eOffline unease (OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\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\u003eSensitivity to others\u0026rsquo; evaluation (SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\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\u003eFear of Tension Exposure(FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\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\u003eOnline preference (OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \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\u003eInhibition in Face-to-Face Socializing (IFFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.599\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\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.731\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 \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variable Measurement and Calibration\u003c/h2\u003e \u003cp\u003eAll variables in this study were measured using established scales from both domestic and international sources, with necessary contextual adaptations made to align with social media interaction scenarios and face-to-face communication settings. While preserving the core measurement dimensions and theoretical underpinnings of the original scales, adjustments were made to the referents and contextual focus of certain items based on the research context. Variable names were also redefined to more accurately reflect the actual behaviors and psychological experiences of active users within social media environments\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Antecedent variables, measurement items and sources in the social media context are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Variable Measurement\u003c/h2\u003e \u003cp\u003e(1) Outcome Variables\u003c/p\u003e \u003cp\u003eInhibition in face-to-face socializing is measured using relevant items from the Social Inhibition Questionnaire 15-Word Version (SIQ15-W), specifically items 3, 6, 9, and 15. The sum of scores from these items describes an individual's tendency toward withdrawal and avoidance in face-to-face interactions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This scale is widely used to measure the degree of interactive withdrawal and participation limitations in real-world social contexts. It effectively reflects inhibitory behaviors in face-to-face communication, aligning with the definition of the outcome variable in this study.\u003c/p\u003e \u003cp\u003e(2) Antecedent Variables\u003c/p\u003e \u003cp\u003eUsage intensity(UI). This metric describes the overall level of engagement among active users during social media usage, emphasizing the comprehensive participation in behaviors such as information browsing, content posting, and interactive engagement across multiple social media platforms. In terms of measurement, this paper synthesizes existing research approaches on dimensions such as usage frequency, usage duration, and interactive engagement. It develops corresponding measurement items tailored to the reality of concurrent multi-platform social media usage\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, aiming to distinguish differences in social media engagement levels within the defined active user sample.\u003c/p\u003e \u003cp\u003eOffline Unease(OU). Primarily measures the unease, tension, and discomfort experienced by individuals in face-to-face communication situations, reflecting their subjective levels of negative experience during real-world interactions. This variable draws on the Social Interaction Anxiety Scale (SIAS) for measurement\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, which focuses on anxiety and discomfort experienced in real-world social interactions. It aligns closely with the face-to-face interaction discomfort examined in this study.\u003c/p\u003e \u003cp\u003eSensitivity to others' evaluation(SOE). This term describes an individual's vigilance and sensitivity to others' negative evaluative cues, reflecting their psychological preoccupation with the risk of rejection or judgment during social interactions. In terms of measurement, this study primarily references the Brief Fear of Negative Evaluation Scale\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This scale effectively reflects an individual's concern and sensitivity toward others' negative evaluations, providing a measurement foundation for analyzing evaluative pressure in face-to-face interactions.\u003c/p\u003e \u003cp\u003eFear of Tension Exposure (FTE).Primarily measures an individual's concern about others perceiving their internal states such as tension or anxiety, reflecting the self-monitoring pressure experienced during face-to-face interactions. This variable draws from the dimension related to fear of being watched in the Social Phobia Scale (SPS)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, focusing on individuals' subjective concerns about the external display of physiological or emotional tension.\u003c/p\u003e \u003cp\u003eOnline preference(OP).This measure assesses individuals' subjective perception of the controllability advantages inherent in online interactions compared to face-to-face communication, reflecting their cognitive judgments regarding self-presentation, response pacing, and risk management in online communication environments. In terms of measurement, this paper primarily references the Preference for Online Social Interaction (POSI) scale\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This scale emphasizes individuals' perceptions of communicative control within online environments, aligning closely with the controllability advantages of online communication examined herein.\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\u003eAntecedent variables, measurement items and sources in the social media context\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement variables\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\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage intensity(UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUI1: Considering your overall usage habits, how often do you use your commonly used social platforms? \u003c/p\u003e \u003cp\u003eUI2: How often do you usually initiate interactions on social platforms? \u003c/p\u003e \u003cp\u003eUI3: In the past 30 days, approximately how many people have you maintained \"frequent contact\" with through social media?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLi J et al. [24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline Unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOU1: When participating in face-to-face social interactions, I often feel embarrassed or uncomfortable and find it difficult to truly relax. \u003c/p\u003e \u003cp\u003eOU2: During face-to-face conversations, I tend to experience obvious physical tension, which affects my comfort. \u003c/p\u003e \u003cp\u003eOU3: During face-to-face conversations, I often suddenly can\u0026rsquo;t think of what to say and find it difficult to naturally continue the conversation or pick up the topic. \u003c/p\u003e \u003cp\u003eOU4: During face-to-face conversations, I\u0026rsquo;m often distracted by my own tension or discomfort and find it difficult to focus on what the other person is saying or the content of the conversation itself.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeters L \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{}}^{\\left[\\text{25}\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to others' evaluation(SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOE1: Before engaging in face-to-face communication activities, I worry about what others think of me. \u003c/p\u003e \u003cp\u003eSOE2: During face-to-face conversations, I pay special attention to the other person\u0026rsquo;s expression, tone, or pauses and judge whether I\u0026rsquo;m being recognized based on them. \u003c/p\u003e \u003cp\u003eSOE3: When the other person\u0026rsquo;s reaction is cold or brief, I often think I\u0026rsquo;ve done something wrong.\u003c/p\u003e \u003cp\u003eSOE4: After the conversation, I often repeatedly recall what I\u0026rsquo;ve said and worry that there are inappropriate parts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarleton R N et al. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{}}^{\\left[\\text{26}\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of Tension Exposure (FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTE1: I worry that the other person will notice my subtle signs of tension. \u003c/p\u003e \u003cp\u003eFTE2: I\u0026rsquo;m afraid that others will interpret my signs of tension as \"lack of confidence, incompetence, or unnaturalness\" rather than a normal reaction. \u003c/p\u003e \u003cp\u003eFTE3: Once I notice that I\u0026rsquo;m starting to get tense, I worry that the tension will worsen and lead to a loss of control in my expression. \u003c/p\u003e \u003cp\u003eFTE4: I\u0026rsquo;m worried that the signs of tension will reduce the effectiveness of communication.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGe Yingnan et al. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{}}^{\\left[\\text{27}\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference (OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP1: Compared with face-to-face communication, I prefer to interact on social platforms because I don\u0026rsquo;t have to respond immediately and can think before replying.\u003c/p\u003e \u003cp\u003eOP2: Compared with face-to-face communication, when interacting on social platforms, I\u0026rsquo;m less worried that non-verbal expressions such as facial expressions, tones, and pauses will affect others\u0026rsquo; judgments of me. \u003c/p\u003e \u003cp\u003eOP3: In multi-person interaction scenarios, compared with face-to-face communication, I can more easily find opportunities to participate in the conversation on social platforms and am less likely to be interrupted.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaplan SE \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{}}^{\\left[\\text{28}\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition in face-to-face socializing (IFFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFFS1: When communicating face-to-face, I often find it difficult to initiate a conversation or find a suitable topic to start. \u003c/p\u003e \u003cp\u003eIFFS2: When communicating face-to-face, I also often feel constrained, express myself unnaturally, or have difficulty interacting smoothly. \u003c/p\u003e \u003cp\u003eIFFS3: In a face-to-face group setting, I often deliberately reduce my speaking to avoid being the center of attention. IFFS4: After a face-to-face interaction, I usually don\u0026rsquo;t take the initiative to continue the contact or deepen the relationship.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheek JM et al. [23]\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 \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Variable Calibration\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFirst, the questionnaire data must undergo calibration processing. Using fsQCA 4.1 software, the raw measurement values are normalized and converted into set membership degrees within the range [0, 1]. Based on calibration thresholds of 0.95 (complete membership threshold), 0.5 (intersection threshold), and 0.05 (complete non-membership threshold), along with data distribution characteristics\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, calibration standards for primary variables are set as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In fuzzy set analysis, cases with a membership degree of 0.5 are regarded as a critical state of \u0026ldquo;neither belonging nor non-belonging.\u0026rdquo; A uniform fine-tuning adjustment of +\u0026thinsp;0.001 is applied to all membership degree values to ensure the operability and discrimination capability of cases in set membership determination\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The specific calibration anchor settings for each condition variable and result variable are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \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\u003eData Calibration Results\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCalibration point\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFully-membership point 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntersection point 50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-membership point 5%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage intensity(UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline Unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to others' evaluation(SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of Tension Exposure (FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference (OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition in Face-to-Face Socializing (IFFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\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":"3 Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of Necessary Conditions\u003c/h2\u003e \u003cp\u003eWithin the fsQCA framework, necessary condition analysis is employed to examine whether a single condition and its negation constitute a necessary prerequisite for the occurrence of an outcome\u0026mdash;specifically, whether the outcome set is a subset of the condition set. When consistency exceeds 0.90, the condition is deemed a necessary condition for the outcome\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. This study conducted separate necessity tests for each antecedent condition and its negation, reporting consistency and coverage metrics. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe analysis results indicate that the consistency of necessity for each individual antecedent condition did not reach 0.90, suggesting that no single condition can independently constitute the necessary condition for inhibition in face-to-face socializing(IFFS) or its negation. Therefore, it is necessary to further explore the formation pathways of IFFS and\u003cb\u003e~\u003c/b\u003eIFFS under concurrent combinations of multiple conditions through configurational sufficiency analysis.\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\u003eNecessity Test of Single Conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInhibition in face-to-face Socializing (IFFS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e~Inhibition in face-to-face Socializing(~\u0026thinsp;IFFS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistency level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Intensity (UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~Usage Intensity (~\u0026thinsp;UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline Unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~Offline Unease(~\u0026thinsp;OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to Others\u0026rsquo; Evaluation (SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~Sensitivity to Others\u0026rsquo; Evaluation (~\u0026thinsp;SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of Tension Exposure (FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~Fear of Tension Exposure (~\u0026thinsp;FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference (OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~Online Preference (~\u0026thinsp;OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Configuration Analysis\u003c/h2\u003e \u003cp\u003eThis paper employs fsQCA4.1 software to analyze the antecedent configurations leading to both IFFS and~IFFS. Different configurations reflect multiple equivalent pathways through which active users in social media contexts achieve the same outcome under the concurrent influence of varying conditions\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In constructing the truth table, the case frequency threshold was set to 2, the raw consistency threshold to 0.9, and the PRI consistency threshold to 0.75 \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Core and peripheral conditions were identified through nested comparisons of intermediate and simplified solutions: Conditions appearing in both intermediate and simplified solutions are core conditions, while those appearing only in intermediate solutions are peripheral conditions\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Standardized analysis results are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (where \u0026ldquo;●\u0026rdquo; indicates core condition presence, \u0026ldquo;●\u0026rdquo; indicates peripheral condition presence, \u0026ldquo;U\u0026rdquo; indicates core condition absence, \u0026ldquo;U\u0026rdquo; indicates peripheral condition absence, and blank cells indicate optional conditions).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Configuration Paths for Generating Inhibition in Face-to-Face Socializing(IFFS)\u003c/h2\u003e \u003cp\u003eBased on the analysis results, eight effective configurational pathways leading to inhibition in face-to-face Socializing (IFFS) were identified. The consistency of each pathway ranged between 0.835 and 0.929, with an overall configurational consistency of 0.767. with an overall coverage of 0.828. This indicates that these eight condition combinations can explain approximately 82.8% of high social inhibition cases, demonstrating strong explanatory power. These paths were categorized based on the dominant logic of core conditions and consolidated into four representative configuration types, which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Different types reflect the differentiated causal mechanisms through which active users develop face-to-face social inhibition in social media contexts, embodying the configuration characteristics of multiple concurrent causality and equivalent multiple pathways.\u003c/p\u003e \u003cp\u003e(1) Tension-Externalization Driven Type. This pathway centers on the psychological dimension of tension externalization, where the perceived risk of emotional externalization in face-to-face interactions remains continuously activated. This consistently leads to high levels of social inhibition across various condition combinations. In Pathway H1a, when usage intensity is high and tension externalization is pronounced, individuals are more likely to suppress expression and reduce participation in real-world interactions, exhibiting high social inhibition. Path H1b: When both offline awkwardness and tension externalization are central, the combined experience of awkwardness and externalization anxiety in face-to-face settings further promotes avoidance or inhibition. Path H1c: When tension externalization is pronounced and online preference is prominent, the preference for and reliance on online interaction accentuates the uncertainty and exposure risk of face-to-face interaction, leading individuals to favor avoiding real-world interactions. Overall, when individuals are highly concerned about the externalization of nervousness, they intensify self-monitoring and risk anticipation in face-to-face situations. This leads them to adopt strategies such as suppressing expression, reducing interaction, or avoiding participation to lessen the psychological burden of exposure. Different combination conditions (usage intensity, offline awkwardness, online preference) amplify or contextualize this process, but the externalization of nervousness serves as the common driving force for this type.\u003c/p\u003e \u003cp\u003e(2) Offline Anxiety-Dominant Type. This pathway manifests as differentiated combinations of offline anxiety with other conditions, encompassing both structures characterized by \u0026ldquo;insufficient online regulatory resources\u0026rdquo; and those marked by \u0026ldquo;low media usage yet high inhibition.\u0026rdquo; In Path H2a, when usage intensity and offline discomfort coexist as core factors while online preference is absent, individuals endure the discomfort of face-to-face interactions without the compensatory preferences and resources provided by online alternatives. This accumulated pressure leads to heightened social inhibition. In Path H2b, even when usage intensity is absent as a core factor, significant offline discomfort alone can still trigger avoidance and inhibition in face-to-face situations, indicating that offline discomfort itself can be a major trigger for high inhibition. When individuals persistently experience discomfort in face-to-face communication, their behavioral adjustments increasingly lean toward reducing social exposure and interaction intensity. Particularly in Pathway H4, the absence of online preference further diminishes available situational adjustment resources, making it harder for individuals to obtain compensation or buffering through online interactions. This makes them more prone to becoming entrenched in high inhibition.\u003c/p\u003e \u003cp\u003e(3) Others' Evaluation and Media Use Overlay Type. This pathway type is grounded in the reinforcing effect of usage intensity, overlaid with others' evaluation sensitivity (manifesting at core or peripheral levels), thereby making individuals more prone to concerns about self-presentation consequences and cautious behavior in face-to-face interactions. Path H3a indicates that under conditions of high usage intensity and absent online preference, individuals are more likely to transfer comparative pressures and feedback stress from online interactions to real-world settings, thereby elevating face-to-face inhibition levels. Path H3b, primarily emerging under peripheral condition combinations, demonstrates the combined effect of usage intensity, offline discomfort, and sensitivity to others' evaluations. This suggests that when multiple stress signals coexist, even without a single dominant core factor, they may collectively contribute to heightened social inhibition. High usage intensity exposes individuals more frequently to social comparison and feedback cues. When combined with sensitivity to others' evaluations, this increases the likelihood of adopting more conservative self-presentation strategies in face-to-face interactions\u0026mdash;manifesting as reduced expression, decreased participation, or avoidance of interaction. The marginal overlap in Path H8 further demonstrates that high inhibition does not solely depend on a single core stressor; the accumulation of stress cues can equally produce the same outcome.\u003c/p\u003e \u003cp\u003e(4) Online Preference Lock-in Type. This pathway is characterized by the simultaneous presence of usage intensity and online preference as core factors, while offline constraints and sensitivity to others' evaluations appear as core absences. That is, individuals do not experience overtly high real-world pressures yet still exhibit high levels of face-to-face social inhibition. Path H4 indicates that even when neither real-world constraints nor evaluative pressures are prominent, individuals may still develop stronger avoidance or inhibition tendencies in face-to-face interactions due to long-term high investment and stable preference formation for online interactions. This implies that when individuals gain more controllable, comfortable, or low-risk interaction experiences online and sustain high usage intensity over time, their social engagement strategies may gradually shift toward online platforms. Face-to-face interactions are perceived as higher-cost options with uncertain benefits, leading to persistent face-to-face inhibition even without significant real-world pressure. This also indicates that high social inhibition is not always directly driven by real-world pressures; the structure of online interactions and preference orientation can similarly shape behavioral choices in real-world contexts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfigurations for achieving in Inhibition in Face-to-Face Socializing (IFFS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntecedent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eInhibition in Face-to-Face Socializing (IFFS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH1b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH1c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH2a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH2b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH3a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH3b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Intensity (UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\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 \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline Unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to Others\u0026rsquo; Evaluation (SOE)\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 \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of Tension Exposure (FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference (OP)\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 \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\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=\"Section3\"\u003e \u003ch2\u003e3.2.2 Configuration paths leading to ~Inhibition in face-to-face socializing (~\u0026thinsp;IFFS)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that there are six configurations leading to ~IFFS. The consistency of each path ranges between 0.813% and 0.868%, with an overall configuration consistency of 0.805 and an overall coverage of 0.686. This indicates that the six combinations can explain approximately 68.6% of low social inhibition cases. Based on the dominant logic of missing core conditions, the aforementioned paths were consolidated into three representative configurational types.\u003c/p\u003e \u003cp\u003eDual-Deficit Type: Sensitivity to Others' Evaluation and Online Preference. The common feature of this pathway type is that both sensitivity to others' evaluation and online preference manifest as core condition deficits. That is, individuals neither excessively focus on others' evaluations in face-to-face interactions nor develop a significant preference or dependence on online interactions. Consequently, their offline social behaviors are less susceptible to interference from evaluation pressure or tendencies toward online substitution. Specifically, in Path L1, when individuals are insensitive to others' evaluations and do not favor online interactions as their primary choice, their face-to-face interactions are more likely to unfold in a natural, low-defensive manner, resulting in lower social inhibition. Path L2 further indicates that even when individuals face differing real-world contexts, maintaining low inhibition levels remains possible as long as their face-to-face interactions remain unhindered by evaluation pressure and online preference. Overall, when individuals neither excessively worry about others' evaluations nor prioritize online interactions as their primary space of reliance, face-to-face communication more readily reverts to its everyday and instrumental nature, naturally reducing social inhibition.\u003c/p\u003e \u003cp\u003eDual Absence Type of Fear of Tension Exposure and Online Preference. The defining characteristic of this pathway is the simultaneous absence of both FTE and online preference as core conditions. This implies that individuals neither worry about others perceiving their tension during face-to-face interactions nor prefer online interactions as their primary risk-avoidance or comfort substitute channel. Path L3 indicates that without concerns about tension display or self-monitoring pressure, individuals are more likely to engage in face-to-face interactions with a relaxed mindset, thereby reducing social inhibition. Path L4 further clarifies that even with low usage intensity, individuals' real-world social participation remains largely unimpeded as long as they neither worry about tension exposure nor develop pronounced online preferences. Thus, when individuals refrain from excessive self-scrutiny of their performance and do not rely on online environments for psychological buffering, their psychological barriers to face-to-face interaction are significantly lowered, resulting in lower levels of social inhibition.\u003c/p\u003e \u003cp\u003eDual-Absence Type of Usage Intensity and Offline Unease. This pathway is characterized by the simultaneous absence of both usage intensity and offline unease as core conditions\u0026mdash;meaning individuals neither excessively engage in online social activities nor persistently experience significant discomfort during face-to-face interactions. In Path L5, when an individual exhibits low usage intensity and minimal offline awkwardness, their face-to-face social behavior is more likely to remain natural and fluid, with smoother initiation and maintenance of interactions. Path L6 further indicates that without excessive online engagement or offline social awkwardness pressure, individuals are less likely to develop avoidance or inhibition tendencies. Evidently, when neither online nor offline contexts impose significant pressure, individuals avoid frequent trade-offs between interaction modes, thereby more readily maintaining an open, low-inhibition state in face-to-face social settings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfigurations for achieving ཞInhibition in Face-to-Face Socializing (ཞIFFS)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntecedent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eཞInhibition in Face-to-Face Socializing (ཞIFFS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Intensity (UI)\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 \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline Unease(OU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity to Others\u0026rsquo; Evaluation (SOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\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\u003eFear of Tension Exposure (FTE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Preference (OP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e0.686\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=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Robustness test\u003c/h2\u003e \u003cp\u003eThis paper conducts robustness testing on the pre-configuration that generates high face-to-face social inhibition. As a set-theoretic method, QCA considers results robust when minor adjustments to key operations yield new outcomes that maintain a subset relationship with the original findings without altering their substantive interpretation\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. First, raising the case frequency threshold from 2 to 3 still identified two configurations, which were largely consistent with the two solutions in the original configurations. Second, increasing the PRI consistency threshold from 0.80 to 0.85 yielded configurations consistent with the original results upon reanalysis. These robustness tests indicate that the configurations identified in this study are relatively stable.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Research implications","content":" \u003cp\u003e(1) Theoretical Implications\u003c/p\u003e \u003cp\u003eIn social media contexts, active users' online engagement does not necessarily translate into offline social investment. Conversely, the coexistence of high online activity and offline inhibition has become a pervasive behavioral tension. This phenomenon suggests that explanations for face-to-face social inhibition should not be confined to single psychological factors or singular pathways. Instead, its underlying logic should be revealed through the lens of concurrent multiple conditions, differentiated combinations, and situational dependence. Based on this, this paper employs fsQCA to examine, from a configurational perspective, how different combinations of conditions\u0026mdash;including social media usage intensity, offline awkwardness, sensitivity to others' evaluations, explicit anxiety about tension, and online preferences\u0026mdash;lead to high or low levels of face-to-face social inhibition. This analysis yields the following implications for related research.\u003c/p\u003e \u003cp\u003eFirst, this paper reveals that the suppression of high-level face-to-face social interaction does not stem from a single pathway or primary cause. Under different conditions, multiple structural combinations can lead to the same outcome, demonstrating the causal complexity of equivalent multiple pathways and diverse realizations. This conclusion indicates that face-to-face social inhibition is not a linear extrapolation of any single risk factor, but rather the result of multiple conditions interacting within specific structural contexts. Consequently, it provides a more explanatory causal framework for understanding the co-occurrence of active users' online and offline behaviors.\u003c/p\u003e \u003cp\u003eSecond, building upon the theoretical framework of group dynamics, this paper identifies and integrates four representative types of high-level social inhibition, further illustrating that the formation of high social inhibition exhibits distinct structural variations and mechanistic differentiation. The tension-driven overt expression type indicates that persistent perception of emotional disclosure risks intensifies self-monitoring and stabilizes inhibitory strategies. The offline discomfort-dominated type demonstrates that unpleasant experiences in real-world interactions can independently trigger avoidance and inhibition across different media contexts. The Overlap of Evaluation and Mediation reveals that the combination of high usage intensity and evaluation sensitivity amplifies anticipated consequences of self-presentation, leading individuals to adopt more conservative offline interaction strategies. The Online Preference Lock-in indicates that even when real-world pressures are not prominent, long-term high engagement and a preference for online interaction can still shape a stable orientation toward communication strategies, systematically viewing face-to-face interaction as a high-cost option. Thus, high social inhibition may stem both from offline pressures and from the solidification of online strategic structures and preferences, expanding the explanatory boundaries of existing research that primarily attributes inhibition to real-world pressures.\u003c/p\u003e \u003cp\u003eThird, this study further reveals from the perspective of outcome asymmetry that low-level face-to-face social inhibition is not a simple reversal of high-level social inhibition. Instead, it is primarily driven by the simultaneous absence of several core stress conditions, reflecting the asymmetry in the causal structure between generation and alleviation. Specifically, when sensitivity to others' evaluations and online preference are both absent, or when explicit anxiety about tension and online preference are both absent, or when usage intensity and offline awkwardness are both absent, face-to-face interactions are more likely to unfold with low defensiveness and low self-monitoring, resulting in lower inhibition levels. This finding suggests that reducing social inhibition does not necessarily depend on increasing specific resource conditions. Rather, it critically involves uncoupling the structural linkage of several core stressors, thereby providing mechanistic evidence explaining why some highly active users maintain effective offline interactions.\u003c/p\u003e \u003cp\u003eFourth, at the methodological level, this paper simultaneously analyzes the sufficient-condition configurations of high-level and low-level face-to-face social inhibition, revealing the substitutive, additive, and contextual characteristics of social behavior. Different conditions may complement or substitute for one another across various pathways, with high- and low-level outcomes exhibiting structural asymmetry. This provides a more granular empirical characterization of the causal complexity underlying individual social choices in social media contexts, while also offering a reusable analytical framework for subsequent path-based explanations and typological accumulation.\u003c/p\u003e \u003cp\u003e(2) Practical Significance\u003c/p\u003e \u003cp\u003eThe configuration results of this study indicate that interventions to curb face-to-face social inhibition among active users should not adopt a single-point strategy. Instead, they should implement tiered governance and combined optimization centered on different types of core structures. For tension-manifestation-driven pathways, priority should be given to reducing perceived risks of emotional display and excessive self-monitoring. By lowering the intensity of interaction exposure and the visibility of evaluations, this approach diminishes individuals' motivation to adopt avoidance and inhibition as risk management strategies. For offline awkwardness-dominated pathways, real-world interaction discomfort should be the core governance focus. By enhancing the accessibility and predictability of offline interactions, the awkwardness stemming from unfamiliarity and uncertainty can be mitigated, helping individuals regain their ability to initiate and sustain interactions. For the path characterized by overlapping peer evaluation and media use, interventions should focus on mitigating the combined effects of evaluative pressure and media intensity. This involves weakening competitive evaluation cues and social comparison triggers, reducing the spillover of online feedback pressure into offline interactions, and thereby decreasing defensive self-presentation. For the online preference lock-in pathway, interventions should reshape interaction choice structures by enhancing the certainty, controllability, and reward density of face-to-face interactions. This guides individuals to convert online interaction advantages into preparatory resources for offline interactions, preventing persistent offline avoidance caused by entrenched online strategies.\u003c/p\u003e \u003cp\u003eOverall, this study demonstrates that both the formation and alleviation of face-to-face social inhibition exhibit characteristics of type-specific differences and multiple pathways. Implementing combination interventions based on type identification can more effectively reduce social inhibition levels, thereby promoting natural engagement and relationship development among active social media users in offline interactions.\u003c/p\u003e "},{"header":"5 Research Limitations and Future Prospects","content":"\u003cp\u003eThis study also has the following limitations that warrant further investigation in future research. First, although it provides theoretical explanations for the identified configuration paths, it faces the common challenge of insufficient qualitative analysis depth, similar to other large-sample fsQCA studies. Second, constrained by data acquisition methods and availability, the analysis primarily relies on online survey data. This approach introduces limitations in sample randomness and representativeness, potentially affecting the generalizability of findings. Third, due to limited prior research and long-term data accumulation, the study employs only a single cross-sectional dataset, failing to examine the dynamic evolution of different condition combinations over time. Finally, while this study employs fsQCA as the primary analytical method to reveal the relationship between multiple condition combinations and face-to-face social inhibition, it does not systematically examine linear effects among variables. Future research could complement this by introducing regression analysis, structural equation modeling, or other methods to supplementally test the relational structures among variables, thereby forming a complementary explanatory framework.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;Acknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the participants of the study.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eXiaohua Lei conceived and designed the study, performed the data analysis, and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eXiuhong Tan, Jia Wang,Jing Liu and Yu Zhenlei contributed to research design refinement, interpretation of results, and critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eYu Zhenlei supervised the research process and served as the corresponding author, providing overall guidance and final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Data availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAdditional information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding .\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant ethical guidelines and regulations, including the principles of the Declaration of Helsinki and applicable national regulations on human-subject research.\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Academic Ethics Committee of Qilu University of Technology (Shandong Academy of Sciences).\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Informed consent\u003cbr\u003e\u003c/strong\u003eInformed consent was obtained from all participants prior to participation. At the beginning of the online questionnaire, participants were provided with a detailed informed consent statement describing the study purpose, voluntary nature of participation, anonymity, confidentiality, and their right to withdraw at any time without penalty. Participants indicated their informed consent by proceeding with and completing the questionnaire, which was explicitly stated as constituting agreement to participate in the study.\u003c/p\u003e\n\u003cp\u003eOnly adults (18 years or older) were eligible to participate. No personally identifiable information was collected, and all data were recorded anonymously and used solely for academic research purposes.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang Jun, L. \u0026amp; Hongde, W. 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Stud.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (09), 1312\u0026ndash;1323 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Active social media users, Inhibition in Face-to-Face Socializing, Configuration perspective, fsQCA, Offline awkwardness, Online preferences","lastPublishedDoi":"10.21203/rs.3.rs-8772917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8772917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInhibition in face-to-face socializing (IFFS) undermines offline interaction quality and relationship development among active social media users. Prior research has largely relied on net-effect approaches and has not sufficiently captured how multiple online\u0026ndash;offline factors combine to produce high versus low IFFS. To address this gap, this study adopts a configurational perspective and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how social media usage intensity, offline interaction unease, sensitivity to others\u0026rsquo; evaluation, concern about tension exposure, and perceived superiority of online communication control jointly shape IFFS. Based on survey data from 503 social media users, the results show that no single antecedent constitutes a necessary condition for high IFFS; instead, high social media activity exhibits a pervasive presence across high-IFFS solutions. Four configurational patterns are identified as sufficient for high IFFS: (1) a tension-externalization\u0026ndash;driven pattern, (2) an offline anxiety\u0026ndash;dominant pattern, (3) an evaluation\u0026ndash;media overlay pattern, and (4) an online preference lock-in pattern, indicating equifinal mechanisms through which active users develop offline inhibition. In contrast, low IFFS emerges through three distinct pathways characterized by the joint absence of key inhibitors, including reduced evaluation sensitivity and online preference, reduced fear of tension exposure and online preference, and reduced usage intensity and offline unease. Notably, the configuration combining high online activity with offline interaction unease is the most prevalent, highlighting the interactive coupling between intensive online engagement and offline discomfort. This study enriches understanding of offline interaction barriers in social media contexts and offers actionable insights for designing targeted interventions to foster more natural, low-defensive face-to-face socializing.\u003c/p\u003e","manuscriptTitle":"Multiple pathways to inhibition in face-to-face socializing among active social media users: an fsQCA analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 09:15:46","doi":"10.21203/rs.3.rs-8772917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-05T07:07:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T05:23:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T08:58:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-09T08:37:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21fd7113-5fbb-4097-bd33-bfb392af4e2f","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"16","date":"2026-05-05T07:07:23+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67819704,"name":"Physical sciences/Mathematics and computing"},{"id":67819705,"name":"Biological sciences/Psychology"},{"id":67819706,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-14T09:15:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 09:15:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8772917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8772917","identity":"rs-8772917","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}