The Psychology of Digital Community Formation: Self-Concept, Authenticity, and Para-Social Bonds in Social Media Environments

The Psychology of Digital Community Formation: Self-Concept, Authenticity, and Para-Social Bonds in Social Media Environments | 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 The Psychology of Digital Community Formation: Self-Concept, Authenticity, and Para-Social Bonds in Social Media Environments Fan Gong, Yu Diao, Meili Liang, Jihye Park, ChangHyun Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7403841/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This study examines how consumer self-concept dimensions influence technology-mediated relationships in digital influencer marketing through structural equation modeling with 748 social media participants. We investigated how self-efficacy, self-assertion, social presence, self-esteem, and social conspicuousness affect social distance perception and para-social interaction formation with human influencers, while examining authenticity as a moderating variable. Results reveal that all self-concept dimensions significantly predict both social distance reduction and para-social interaction development with influencers. Notably, social conspicuousness emerged as the strongest predictor of social distance, while self-efficacy showed the strongest effect on para-social interaction. The study uncovered an asymmetric relationship pattern: while social distance strongly influenced recommendation intentions, it showed no significant effect on brand trust formation. Conversely, para-social interactions positively affected both brand trust and recommendation intentions. Brand trust demonstrated a robust relationship with recommendation intentions. Authenticity moderated four relationships, strengthening the self-assertion→social distance link under high authenticity conditions, while amplifying social presence effects on social distance and self-esteem/social conspicuousness effects on para-social interaction under low authenticity conditions. These findings contribute to management information systems literature by providing validated theoretical foundations for understanding technology-mediated self-concept manifestations in digital marketing, offering strategic implications for algorithmic personalization, consumer micro-segmentation, and adaptive content delivery mechanisms within social media marketing platforms. Business and commerce/Business and management Social science/Business and management Humanities/Cultural and media studies Social science/Cultural and media studies Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society digital marketing technology self-concept frameworks para-social relationships structural equation modeling consumer psychology authenticity moderation Figures Figure 1 Introduction The exponential proliferation of social media platforms has fundamentally transformed the architectural landscape of contemporary marketing communications, establishing influencer marketing as a critical strategic paradigm that transcends traditional advertising methodologies. While extant research has increasingly examined the technological sophistication of virtual influencers and computer-generated personalities in brand endorsement contexts, a substantive theoretical gap persists regarding the intricate psychological mechanisms through which human influencers interact with consumers' multidimensional self-concept systems to generate authentic relational dynamics and behavioral outcomes. Contemporary scholarship has extensively documented the commercial efficacy of influencer marketing as an innovative approach to consumer engagement across social networking services (SNS), with numerous corporations strategically leveraging these digital intermediaries to establish robust brand equity and consumer connectivity (Belanche et al., 2021 ; Ki et al., 2020 ; Zhou et al., 2021 ). The theoretical foundation underlying influencer marketing's effectiveness rests upon its capacity to function as a sophisticated marketing communication strategy that enables influencers to authentically persuade consumers toward brand adoption through perceived credibility, expertise, and relational intimacy (Audrezet et al., 2018 ; Jiménez-Castillo and Sánchez-Fernández, 2019 ). However, while recent investigations have predominantly concentrated on the technological dimensions of virtual influencer marketing—examining computer-generated personalities and artificial intelligence-driven brand ambassadorships—significantly less theoretical attention has been devoted to understanding how human influencers' authentic communications intersect with the complex psychological architecture of consumer identity construction. This research lacuna represents a critical oversight, particularly given that human influencers continue to dominate the digital marketing ecosystem and maintain fundamentally different relational dynamics compared to their virtual counterparts. The strategic importance of this inquiry emerges from companies' intensifying desire to strengthen product positioning and corporate image through influencer collaborations, recognizing digital marketing's unique capacity to generate consumer-driven content that facilitates authentic brand identity formation (Evans et al., 2017 ; Schivinski and Dabrowski, 2016 ; Stojanovic et al., 2018 ; Tafesse and Wood, 2021 ). Unlike traditional celebrity endorsements or virtual influencer campaigns, human social media influencers establish trust-based relationships with audiences through demonstrated knowledge and specialized expertise, positioning themselves as credible information intermediaries who significantly influence follower decision-making processes (De Veirman et al., 2017 ). The psychological complexity underlying these human-to-human digital relationships necessitates a sophisticated analytical framework that extends beyond surface-level engagement metrics to examine the fundamental self-concept constructs that drive consumer responsiveness to influencer communications. Influencers who maintain positive reputational capital facilitate organic brand content exposure through authentic relationship formation, creating sustained engagement patterns that generate measurable marketing outcomes (Stojanovic et al., 2018 ). This phenomenon drives continuous growth in social media influencer marketing activities, particularly on platforms such as YouTube, where interactive and bi-directional communication capabilities enable diversified service offerings and audio-visual content strategies that appeal to users through multisensory engagement mechanisms. The evolving consumer landscape reveals increasingly sophisticated patterns wherein followers actively investigate influencer authenticity, attributing personal meaning to seemingly casual communications while demonstrating heightened propensity to purchase products promoted by trusted digital personalities. As corporate investment in influencer marketing continues to expand exponentially, academic inquiry has correspondingly intensified to examine user perceptions of content quality, influencer reputation, and the underlying psychological mechanisms driving consumer responses to influencer communications (Ao et al., 2023 ). This investigation addresses a critical theoretical void by proposing a comprehensive analytical framework that integrates consumer self-concept systems with para-social interaction theory to elucidate the psychological mechanisms underlying human influencer effectiveness. Specifically, this research diverges from existing virtual influencer studies by examining how authentic human personalities interact with follower self-concept dimensions—including self-efficacy, self-assertion, social presence, and self-esteem—to generate social distance reduction and para-social relationship formation that subsequently influences brand trust and recommendation intentions. The primary research objective centers on understanding the causal relationships between influencer followers' multidimensional self-concepts and social conspicuousness with the formation of social distance and para-social interactions with human influencers. This study extends beyond traditional influencer marketing research by investigating how these psychological constructs subsequently influence brand trust development and recommendation intentions, while examining the moderating role of perceived influencer authenticity in strengthening or attenuating these relational dynamics. Literature Review Digital Communication Ecosystems and Technology-Mediated Influence Networks . The proliferation of digital communication platforms has fundamentally reconceptualized the traditional paradigms of marketing communication, establishing sophisticated technological ecosystems wherein influence operates through complex algorithmic mediation and consumer-generated content architectures (Shahbaznezhad et al., 2021 ). Contemporary social media platforms transcend conventional broadcasting models, constituting interactive technological infrastructures that facilitate multi-directional information flows, peer-to-peer knowledge transfer, and community-driven content curation mechanisms. These digital environments represent evolved manifestations of Web 2.0 ideological frameworks, characterized by user empowerment, collaborative content creation, and distributed influence networks that challenge centralized authority structures in commercial communication (Kaplan and Haenlein, 2010 ). The technological sophistication of contemporary social media platforms enables unprecedented levels of personalization, algorithmic content optimization, and real-time engagement analytics that fundamentally distinguish digital influence mechanisms from traditional celebrity endorsement models (Schivinski and Dabrowski, 2016 ; Stojanovic et al., 2018 ). These platforms function as complex information processing systems wherein user behavior data, engagement patterns, and social graph relationships are continuously analyzed to optimize content delivery and maximize influence effectiveness. The emergence of artificial intelligence-driven recommendation algorithms has created sophisticated feedback loops between content creators and audiences, enabling precise targeting of demographic segments and psychographic profiles through data-driven personalization strategies (Tafesse and Wood, 2021 ). Within this technological framework, digital influencers emerge as intermediary actors who leverage platform-specific affordances to establish authentic connections with segmented audiences through consistent content delivery and strategic personal branding initiatives (Evans et al., 2017 ). Unlike traditional celebrity endorsers who maintain professional distance from audiences, digital influencers operate within proximity-based relationship models that simulate interpersonal intimacy through regular content updates, direct audience interaction, and lifestyle documentation that blurs boundaries between public and private personas (De Veirman et al., 2017 ). This proximity-based influence model represents a fundamental departure from traditional advertising paradigms, as it relies upon sustained relationship development rather than episodic exposure to commercial messaging. The psychological mechanisms underlying digital influence effectiveness operate through sophisticated cognitive processing frameworks wherein audiences evaluate source credibility based on perceived authenticity, expertise demonstration, and lifestyle congruence rather than institutional authority or professional accomplishments (Stojanovic et al., 2018 ). Research demonstrates that digital audiences exhibit enhanced receptivity to influence attempts when they perceive genuine personal connection with content creators, suggesting that technological mediation can paradoxically enhance rather than diminish interpersonal intimacy under specific conditions (Al-Emadi and Ben Yahia, 2020 ; Ferchaud et al., 2018 ). The commercialization trajectory of social networking platforms has systematically transformed originally peer-oriented communication channels into sophisticated marketing ecosystems wherein commercial messaging is seamlessly integrated with authentic personal expression (Argyris and Monu, 2015 ). This evolution has necessitated the development of new theoretical frameworks for understanding consumer behavior in contexts where traditional distinctions between advertising and entertainment, commercial and personal content, and professional and amateur content creators have become increasingly ambiguous. Contemporary consumers navigate these hybrid information environments through complex cognitive processes that simultaneously evaluate entertainment value, informational utility, and commercial intent across multiple content formats and platform contexts (Evans et al., 2017 ). The emergence of parasocial relationship dynamics within digital influence networks represents a particularly significant theoretical development, as these one-sided emotional connections enable sustained commercial influence through mechanisms that transcend traditional persuasion models (Berryman and Kavka, 2018 ; Xiang et al., 2017 ; Yuan et al., 2019 ). Digital influencers cultivate parasocial relationships through strategic vulnerability disclosure, lifestyle documentation, and consistent personality presentation that creates illusions of reciprocal friendship despite the mediated nature of the interaction. This relationship model enables commercial recommendations to be processed as trusted advice from personal acquaintances rather than promotional messaging from commercial entities, thereby circumventing traditional advertising resistance mechanisms. The technological architecture of modern social media platforms facilitates unprecedented levels of influence measurement and optimization through comprehensive analytics systems that track engagement rates, audience demographics, conversion metrics, and temporal usage patterns (Boerman and van Reijmersdal, 2020 ; Jin et al., 2019 ). These measurement capabilities enable continuous refinement of influence strategies through data-driven experimentation with content formats, posting schedules, audience targeting parameters, and commercial integration approaches. The availability of granular performance data has transformed digital influence from an intuitive art form into a systematically optimizable marketing discipline characterized by rigorous testing methodologies and quantitative performance evaluation frameworks. Self-concepts: The Multidimensional Architecture of Individual Identity. The theoretical conceptualization of self-concept constitutes a fundamental psychological construct that encapsulates the complex cognitive-affective framework through which individuals perceive, evaluate, and understand their own identity (McGrew, 2022 ). This multidimensional phenomenon encompasses several interconnected components that collectively shape consumer behavior in digital marketing contexts. Self-Efficacy, Cognitive Competence and Adaptive Capability, as conceptualized within Bandura's social cognitive theory, represents the individual's belief in their capability to execute behaviors necessary to produce specific performance attainments (Adna and Sukoco, 2020 ). Within the digital influencer marketing paradigm, self-efficacy manifests as consumers' perceived competence in navigating technological platforms and processing information effectively. Valentine et al. ( 2021 ) demonstrated that individuals with elevated self-efficacy levels exhibit superior skills in comfort and integration within socio-cultural interaction environments. This cognitive competence translates into enhanced information processing capabilities when engaging with influencer content, as individuals with higher self-efficacy demonstrate greater adaptability to new information, knowledge, and technology (Valentine et al., 2021 ). Self-Assertion (Relational Expression and Intimacy Communication) emerges as a crucial dimension of self-concept, characterized by Wolpe and Lazarus ( 1966 ) as the confident expression of one's feelings to others. Within the context of social media interaction, self-assertion reflects the degree to which individuals engage in intimate communication without experiencing interpersonal anxiety (Al-Masri, 2020 ; Sprecher and Hendrick, 2004 ). This construct encompasses the willingness to share personal information, express opinions, and communicate responsiveness and commitment in digital relationships (Derlega et al., 1993 ). The accessibility and cost-effectiveness of online platforms facilitate enhanced self-expression opportunities compared to offline media, thereby amplifying the role of self-assertion in consumer-influencer relationships. Social Presence as Mediated Communication and Psychological Connection, grounded in the seminal work of Short et al. ( 1976 ), conceptualizes the subjective experience of connection with others through mediated communication channels. This phenomenon manifests as the perception of social connection with another person through contextual cues, creating a sense of togetherness despite physical separation (Kreijns et al., 2022 ; Weidlich et al., 2023 ). In digital environments, social presence facilitates the simulation of face-to-face interaction experiences, enabling consumers to develop meaningful relationships with influencers across technological barriers. The theoretical framework suggests that enhanced social presence contributes to stronger psychological bonds and increased engagement with influencer content. Self-Esteem as Social Identity and Interpersonal Confidence represents a fundamental component of self-concept that reflects individuals' overall evaluation of their own worth and social acceptance (Salice, 2020 ; Lonsdale, 2021 ). This construct demonstrates significant correlation with social confidence and the perception of one's social self. Empirical research indicates that individuals with higher self-esteem exhibit greater resistance to external validation seeking, while those with lower self-esteem tend to value external aspirations related to image, popularity, and material possessions (Stuppy et al., 2020 ; Wang et al., 2020 ). Within social media contexts, self-esteem influences how consumers interpret and respond to influencer content, particularly regarding self-image and social comparison processes. Social Conspicuousness as Visibility and Status Signaling encompasses the degree to which individuals or groups attract attention within social settings, influenced by factors such as appearance, behavior, status, or possession of distinctive characteristics (Shin et al., 2021 ). This construct reflects the desire to signal social status and differentiate oneself from others through visible consumption patterns or behavioral displays. In the digital influencer marketing domain, social conspicuousness drives consumers' motivation to engage with premium brands and exclusive content that enhance their perceived social standing. Social Distance and Para-Social Interaction: Bridging Physical and Psychological Gaps. Social Distance as Psychological Proximity and Relational Intimacy, conceptualized within Trope and Liberman's (2003) construal level theory, represents a subjective dimension of psychological distance that influences how individuals perceive and interact with objects or persons in their social environment. This construct encompasses the degree to which individuals perceive themselves as similar to or different from others, fundamentally affecting judgment and decision-making processes (Liberman and Trope, 2003 ; Liberman et al., 2007 ). The psychological mechanisms underlying social distance operate through concrete versus abstract information processing modes. Closer social distances facilitate more concrete, detailed information processing, leading to intuition-based and emotion-driven decisions (Ayduk and Kross, 2010 ; Eyal et al., 2009 ). Conversely, greater social distances promote abstract thinking patterns, encouraging rational, context-considering decision-making processes (Fujita et al., 2008 ; Maglio, 2020 ). This theoretical framework has profound implications for influencer marketing, as social distance affects how consumers process and respond to influencer communications. Para-Social Interaction as Mediated Relationship Formation, originating from Horton and Wohl's (1956) mass communication research, describes the phenomenon whereby audience members develop one-sided relationships with media personalities. This construct manifests as viewers' perception of maintaining familiar relationships with media characters despite the absence of direct reciprocal interaction (Chen, 2022 ; Zhang et al., 2022 ). Within contemporary digital contexts, para-social interactions enable consumers to experience emotional solidarity and intimacy with influencers, paralleling feelings typically reserved for real-world relationships (Jin et al., 2019 ; Chen, 2022 ; Zhang et al., 2022 ). These mediated relationships foster similarity, empathy, and friendship feelings toward online personalities, creating psychological foundations for brand trust and purchase intentions. The blurred boundaries between influencers' private and public personas on platforms like YouTube enhance the authenticity of these para-social connections (Ferchaud et al., 2018 ; Berryman and Kavka, 2018 ; Yuan et al., 2019 ). Brand Trust and Recommendation Intentions: The Commercial Outcomes. Brand trust represents a multifaceted construct encompassing consumers' confidence, belief, and faith in specific brands, incorporating cultural implications, emotional responses, and social relationship dimensions (Nosi et al., 2022 ). This construct serves as a critical determinant of brand loyalty and competitive advantage, particularly crucial for companies operating in highly competitive markets. The theoretical foundations of brand trust rest upon relationship marketing principles, where trust acts as a guardian of relationship investments and provides long-term benefits while minimizing high-risk behaviors (Samarah et al., 2022 ). From the consumer perspective, brand trust constitutes an essential asset that drives favorable responses toward associated companies (Samarah et al., 2022 ; Cáceres and Paparoidamis, 2007 ). Berry and Parasuraman ( 1991 ) emphasize that effective service marketing depends on successful management of brand influence and trust, highlighting the strategic importance of this construct in contemporary marketing practice (Tran, 2023 ). Recommendation intentions encompass consumers' willingness to recommend products, services, or brands to others, serving as a crucial outcome variable in marketing research (Stanovčić et al., 2021 ; Lee et al., 2021 ). These intentions reflect the likelihood of positive word-of-mouth communication, which significantly influences brand reach and acquisition of new customers. The relationship between social distance and recommendation intentions operates through similarity perception mechanisms. When consumers perceive greater similarity or closeness with brands, they demonstrate increased likelihood of favorable evaluations and recommendation behaviors (Stanovčić et al., 2021 ; Lee et al., 2021 ). This theoretical framework suggests that social distance influences purchase intentions by enhancing identification with brands and their target audiences. The construct of authenticity emerged as a moderating variable that influences the relationships between self-concept dimensions and both social distance and para-social interaction. Authenticity encompasses the convergence between internal thoughts/emotions and external expressions, representing genuine, unartificial communication (Kim et al., 2021 ; Jin, 2018 ). In influencer marketing contexts, authenticity manifests as followers' perceptions of influencers' sincerity and genuineness in online communications (Jin, 2018 ). This construct moderates the effects of self-concept components on social distance formation and para-social interaction development, with higher authenticity perceptions strengthening these relationships. The theoretical significance of authenticity lies in its capacity to enhance trust formation and reduce consumer skepticism toward commercial communications. Hypotheses and Research Model Theoretical Foundations and Hypothesis Development. The present investigation proposes a conceptual framework predicated upon the multidimensional nature of self-concept constructs and their differential impact on consumer-influencer relational dynamics within digital marketing ecosystems. Drawing upon established psychological theories and empirical evidence from consumer behavior research, we advance the following hypotheses: H1: Self-Concept Components and Social Distance Formation The theoretical foundation for H1 emerges from construal level theory (Liberman and Trope, 2003 ; Liberman et al., 2007 ), which posits that social distance functions as a subjective dimension of psychological proximity influencing information processing modes. Empirical evidence demonstrates that individuals with elevated self-concept dimensions exhibit enhanced capacity for establishing psychological connections with distant objects or persons (Ayduk and Kross, 2010 ; Eyal et al., 2009 ). Bandura and Adams's (1977) social cognitive theory establishes self-efficacy as a critical determinant of behavioral adaptation to novel information environments. Within digital contexts, individuals possessing higher self-efficacy demonstrate superior navigation capabilities and information processing competence (McGrew, 2022 ; Valentine et al., 2021 ). This cognitive competence facilitates the perception of reduced psychological distance with influencers through enhanced confidence in digital platform utilization. H1-1 Self-efficacy positively influences social distance formation with influencers. Self-assertion, conceptualized as the confident expression of personal feelings without interpersonal anxiety (Wolpe and Lazarus, 1966 ; Al-Masri, 2020 ), enables individuals to engage in intimate communication patterns. The cost-effectiveness and accessibility of online platforms amplify self-expression opportunities, thereby facilitating perceived closeness with influencers through active engagement behaviors (Derlega et al., 1993 ). H1-2 Self-assertion positively affects social distance with influencers. Social presence theory (Short et al., 1976 ) establishes that mediated communication creates subjective experiences of togetherness despite physical separation. In digital environments, enhanced social presence enables simulation of face-to-face interactions, contributing to psychological bond formation and reduced perceived distance (Kreijns et al., 2022 ; Weidlich et al., 2023 ). H1-3 Social presence positively influences social distance formation. Self-esteem represents individuals' fundamental evaluation of their social worth and acceptance (Salice, 2020 ; Lonsdale, 2021 ). Higher self-esteem correlates with enhanced social confidence and reduced need for external validation (Stuppy et al., 2020 ; Wang et al., 2020 ). Within influencer contexts, this psychological security facilitates perception of similarity and closeness with digital personalities. H1-4 Self-esteem positively affects social distance with influencers. Social conspicuousness reflects the desire for visibility and status signaling within social contexts (Shin et al., 2021 ). This construct drives engagement with premium content and exclusive offerings, creating perceived alignment with influencers who represent desired social positions. H1-5 Social conspicuousness positively influences social distance formation. H2: Self-Concept Components and Para-Social Interaction Development Para-social interaction theory (Horton and Wohl, 1956 ) describes one-sided relationships with media personalities that parallel real-world social connections. Contemporary research demonstrates that para-social interactions create emotional solidarity and intimacy despite absence of reciprocal communication (Jin et al., 2019 ; Chen, 2022 ; Zhang et al., 2022 ; Farivar et al., 2021 ; Kim et al., 2015 ). Individuals with higher self-efficacy demonstrate enhanced adaptability to technological environments and superior information processing capabilities. This cognitive competence translates into stronger affective responses toward influencers through increased engagement confidence and platform mastery. H2-1 Self-efficacy positively influences para-social interaction formation. Self-assertion facilitates intimate communication without interpersonal anxiety, enabling followers to perceive influencers as accessible relationship partners. The propensity for sharing personal information and expressing opinions creates foundations for emotional attachment and para-social bond formation. H2-2 Self-assertion positively affects para-social interaction development. Social presence enables followers to experience psychological connection through contextual cues, creating illusions of reciprocal interaction. This perceived togetherness strengthens emotional bonds and enhances the authenticity of para-social relationships with influencers. H2-3 Social presence positively influences para-social interaction. Higher self-esteem facilitates secure attachment patterns and reduces anxiety in social contexts. This psychological security enables followers to form stronger emotional connections with influencers through reduced self-consciousness and enhanced openness to para-social experiences. H2-4 Self-esteem positively affects para-social interaction formation. The desire for social visibility and status alignment motivates engagement with influencers who represent aspirational identities. This identification process strengthens para-social bonds through perceived similarity and lifestyle congruence. H2-5 Social conspicuousness positively influences para-social interaction. H3: Social Distance Effects on Outcome Variables Construal level theory suggests that closer social distances facilitate concrete information processing and emotion-driven decisions (Ayduk and Kross, 2010 ; Eyal et al., 2009 ; Jing et al., 2023 ; Ordabayeva et al., 2022 ). When followers perceive reduced distance from influencers, this psychological proximity may enhance trust transfer to endorsed brands through perceived credibility and authenticity. H3-1 Social distance positively influences brand trust. Social distance affects information processing through similarity perception mechanisms (Liberman et al., 2007 ). Closer perceived distance with influencers enhances identification processes, leading to increased likelihood of favorable evaluations and recommendation behaviors (Maglio, 2020 ; Jing et al., 2023 ; Ordabayeva et al., 2022 ). H3-2 Social distance positively affects recommendation intentions. H4: Para-Social Interaction Effects on Outcome Variables Para-social relationships create emotional solidarity and intimacy paralleling real-world relationships (Jin et al., 2019 ). These affective bonds facilitate trust transfer from influencers to endorsed brands through emotional contagion and credibility attribution mechanisms. H4-1 Para-social interaction positively influences brand trust. The emotional attachment characterizing para-social relationships motivates followers to advocate for influencer-endorsed products. This advocacy stems from identification processes and desire to maintain consistency between self-concept and para-social relationship investments. H4-2 Para-social interaction positively affects recommendation intentions. H5: Brand Trust and Recommendation Behavior Trust constitutes a fundamental determinant of behavioral intentions in consumer contexts. Brand trust reduces perceived risk and uncertainty, facilitating positive word-of-mouth behavior through enhanced confidence in product quality and corporate reliability (Cáceres and Paparoidamis, 2007 ). H5 Brand trust positively influences recommendation intentions. H6: Moderating Effects of Authenticity Authenticity represents convergence between internal states and external expressions (Zhang et al., 2022 ; Jin, 2018 ). In influencer contexts, perceived authenticity enhances trust formation and reduces skepticism toward commercial communications. Higher authenticity perceptions strengthen the effects of self-concept components on social distance and para-social interaction through enhanced credibility and reduced psychological barriers. H6 Authenticity moderates the relationships between self-concept dimensions and both social distance and para-social interaction. Research Model. The research model for this study was designed based on factors that influence the establishment of social distance and para-social interaction by identifying sub-constructs of self-concept and social conspicuousness based on the above discussions. Hypotheses 1 was proposed to help us explain how sub-constructs of self-concept and social conspicuousness affect social distance and para-social interaction. Hypotheses 2 and 3 were proposed to help us explain the effects of social distance and para-social interaction on brand trust and recommendation intentions. The research model presented in Fig. 1 outlines the study design. Method Operational Definitions and Measurement. To accurately evaluate consumer responses to digital influencer marketing, a panel of consumers registered with established marketing research companies was utilized to secure the validity and reliability of the survey. Initially, the most widely recognized and prominent social media influencers across various platforms (YouTube, Instagram, TikTok) were identified through comprehensive platform analytics and engagement metrics. Consumer panelists were invited to participate in the survey through multiple communication channels, including text messages and email invitations. The questionnaire was designed as an online survey instrument, enabling consumer panelists to access the survey via internet connectivity and respond based on their authentic experiences with social media influencer content and interactions. The data collection period extended from March 15, 2024, to April 15, 2024, during which a total of 800 respondents participated in the survey. Following comprehensive data cleaning procedures, 748 valid responses were retained for analysis. The questionnaire comprised structured items measuring self-concept dimensions (self-efficacy, self-assertion, social presence, self-esteem), social conspicuousness, social distance, para-social interaction, brand trust, recommendation intentions, and perceived influencer authenticity. With the exception of demographic questions, all measurement items employed 5-point Likert scales ranging from "strongly disagree" to "strongly agree." Prior to full-scale data collection, a preliminary survey was conducted with 50 business administration and marketing students at a leading university to ensure questionnaire clarity and semantic appropriateness. This pilot testing phase verified that the survey language effectively conveyed the intended meanings and identified potential comprehension issues. To mitigate common method variance in data collection, established methodological procedures recommended by prior scholars were systematically implemented throughout the questionnaire design and administration process. Survey participants were selected through rigorous stratified sampling processes to ensure sample representativeness across demographic segments and social media usage patterns. The analytical survey period encompassed four weeks of active data collection. Target respondents were consumers who demonstrated active engagement with social media influencer content and possessed substantial experience with influencer-driven brand interactions. Complete anonymity of respondents was guaranteed to encourage honest responses and minimize social desirability bias. Participants were explicitly informed that their survey participation was entirely voluntary, and the research objectives were clearly explained in the informed consent documentation. To further address potential common method bias, a temporal separation methodology was implemented during data collection. Initially, a pilot questionnaire focusing on independent variables—self-concept dimensions and social conspicuousness—was administered over one week. Subsequently, after this temporal interval, dependent variables including social distance, para-social interaction, brand trust, and recommendation intentions were measured at different time points, along with the moderating variable of perceived authenticity. For hypothesis testing and statistical analysis, the SPSS statistical package and structural equation modeling software (EQS 6.4) were employed. Basic data analysis incorporated frequency analysis to determine demographic composition distributions using SPSS functionality. Descriptive statistics, including means and standard deviations of latent variables and individual measurement items, were systematically analyzed. Cronbach's alpha coefficients were calculated to assess the internal consistency reliability of all measurement constructs. Confirmatory factor analysis was conducted to verify the unidimensionality and construct validity of the primary variables. The study employed multi-group structural equation modeling to analyze the moderating effects of perceived influencer authenticity on the hypothesized relationships. Results Descriptive Statistics. The final sample consisted of 748 valid responses after data cleaning procedures. As shown the Table 1 , the demographic profile reveals a relatively balanced gender distribution with 366 males (48.9%) and 382 females (51.1%). The age distribution shows concentration in middle-age groups, with 30–39 years (43.4%) and 40–49 years (43.4%) comprising the majority of respondents, while younger adults aged 20–29 (3.9%) and those over 50 (8.6%) were less represented. Educational attainment was high, with 55.0% holding college degrees, 25.1% having completed high school, 11.6% current college students, and 8.3% possessing graduate degrees. Monthly income distribution showed 37.5% earning below $ 2,000, 24.5% earning $ 2,000- $ 3,000, 14.7% earning $ 3,000- $ 4,000, 10.6% earning $ 4,000- $ 5,000, and 12.7% earning over $ 5,000. Table 1 Demographic Profile (N = 748) Characteristic Category Frequency Percentage Gender Male 366 48.9 Female 382 51.1 Age 20–29 29 3.9 30–39 325 43.4 40–49 325 43.4 Over 50 69 9.2 Education High school 186 24.9 College students 87 11.6 College 411 54.9 Graduate school 64 8.6 Monthly Income (USD) Below $ 2,000 281 37.6 $ 2,000- $ 3,000 183 24.5 $ 3,000- $ 4,000 110 14.7 $ 4,000- $ 5,000 79 10.6 Over $ 5,000 95 12.7 Note : Percentages may not sum to 100% due to rounding For hypothesis testing and statistical analysis, the SPSS statistical package and structural equation modeling software (EQS 6.4) were employed. Basic data analysis incorporated frequency analysis to determine demographic composition distributions using SPSS functionality. Descriptive statistics, including means and standard deviations of latent variables and individual measurement items, were systematically analyzed. Cronbach's alpha coefficients were calculated to assess the internal consistency reliability of all measurement constructs. Confirmatory factor analysis was conducted to verify the unidimensionality and construct validity of the primary variables. The study employed multi-group structural equation modeling to analyze the moderating effects of perceived influencer authenticity on the hypothesized relationships. Measurement Model Assessment. Prior to testing the structural relationships, we conducted comprehensive assessments of the measurement model's reliability, validity, and dimensionality using confirmatory factor analysis (CFA) in EQS 6.4. Reliability Assessment. As shown in Table 2 , Internal consistency reliability was evaluated using Cronbach's alpha coefficients. All constructs demonstrated excellent reliability, with alpha values ranging from .742 to .940, exceeding the recommended threshold of .70. Specifically: self-efficacy (α = .940), self-assertion (α = .918), social presence (α = .886), self-esteem (α = .864), social conspicuousness (α = .742), social distance (α = .930), para-social interaction (α = .937), brand trust (α = .940), and recommendation intentions (α = .940). Table 2 Descriptive Statistics and Correlation Matrix Variables M SD 1 2 3 4 5 6 7 8 9 1. Self-efficacy 3.42 0.89 (.940) 2. Self-assertion 3.28 0.91 .127** (.918) 3.Social presence 3.35 0.88 .098** .334** (.886) 4. Self-esteem 3.51 0.82 .421** .109** .261** (.864) 5.Social conspicuousness 2.98 0.94 .043 .371** .508** .102** (.742) 6. Social distance 3.19 1.02 .269** .507** .616** .268** .512** (.930) 7.Para-social interaction 3.07 0.95 .285** .314** .447** .372** .334** .687** (.937) 8. Brand trust 3.24 0.87 .194** .226** .193** .203** .315** .389** .514** (.940) 9. Recommendation intentions 3.11 0.91 .183** .179** .340** .045 .352** .371** .436** .625** (.940) Note : N = 748. **p < .01. Cronbach's alpha coefficients are shown in parentheses on the diagonal. M = Mean, SD = Standard Deviation. Validity Assessment. Exploratory Factor Analysis (EFA): As shown Table 3 , initial exploratory factor analysis using principal axis factoring with varimax rotation confirmed the nine-factor structure, explaining 84.6% of total variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was .893, and Bartlett's test of sphericity was significant (χ² = 27,201.9, df = 465, p < .001), indicating data suitability for factor analysis. All factor loadings exceeded .665, demonstrating strong item-to-factor relationships. Confirmatory Factor Analysis (CFA): The measurement model was evaluated using confirmatory factor analysis. The model demonstrated acceptable fit: χ² = 1,247.82, df = 657, p < .001; CFI = .942; GFI = .892; AGFI = .871; NFI = .910; NNFI = .936; RMSEA = .035 (90% CI = .032-.038); SRMR = .044. These indices meet or exceed established cutoff criteria for good model fit. Table 3 Factor Loadings and Communalities Variables Items Communality Factor Loadings Self-efficacy Ef1 .781 .902 Ef2 .809 .924 Ef3 .831 .929 Self-assertion As1 .863 .878 As2 .820 .883 As3 .844 .865 Social presence Pr1 .764 .825 Pr2 .815 .837 Pr3 .792 .867 Self-esteem Es1 .734 .799 Es2 .659 .747 Es3 .653 .836 Social Conspicuousness Sc1 .641 .797 Sc2 .717 .802 Sc3 .598 .681 Sc4 .732 .825 Sc5 .637 .689 Sc6 .735 .839 Social distance Sd1 .965 .926 Sd2 .964 .924 Sd3 .879 .665 Para-social interaction Ps1 .830 .835 Ps2 .915 .909 Ps3 .782 .791 Ps4 .859 .858 Brand trust Bt1 .880 .835 Bt2 .907 .891 Bt3 .909 .858 Recommendation intentions Ri1 .908 .863 Ri2 .911 .867 Ri3 .892 .898 Note: Factor extraction method: Principal axis factoring with varimax rotation. KMO = .893, Bartlett's test: χ² = 27,201.9 (df = 465, p < .001). Convergent and Discriminant Validity: As shown Table 4 , convergent validity was assessed through Average Variance Extracted (AVE) values, with all constructs exceeding the .50 threshold. Discriminant validity was established using the Fornell-Larcker criterion, where the square root of each construct's AVE exceeded its correlations with other constructs. Table 4 Convergent and Discriminant Validity Assessment Construct AVE √AVE CR 1 2 3 4 5 6 7 8 9 1. Self-efficacy .583 .763 .804 .763 2. Self-assertion .433 .658 .693 .127 .658 3. Social presence .576 .759 .801 .098 .334 .759 4. Self-esteem .627 .792 .835 .421 .109 .261 .792 5. Social conspicuousness .616 .785 .889 .043 .371 .508 .102 .785 6. Social distance .594 .771 .814 .269 .507 .616 .268 .512 .771 7. Para-social interaction .627 .792 .870 .285 .314 .447 .372 .334 .687 .792 8. Brand trust .711 .843 .880 .194 .226 .193 .203 .315 .389 .514 .843 9. Recommendation intentions .690 .831 .870 .183 .179 .340 .045 .352 .371 .436 .625 .831 Note: AVE = Average Variance Extracted; CR = Composite Reliability; Bold diagonal elements represent the square root of AVE; off-diagonal elements are inter-construct correlations. Common Method Bias Assessment. Given the self-report nature of the data, we assessed common method bias using Harman's single-factor test and the unmeasured latent method construct (ULMC) approach. The single-factor test revealed that no single factor accounted for more than 35.2% of the variance, suggesting common method bias is not a significant concern. Additionally, the ULMC approach showed that method variance accounted for less than 10% of the total variance, further confirming that common method bias does not substantially threaten the validity of our findings. Multicollinearity Assessment. Variance Inflation Factor (VIF) values were calculated for all predictor variables, with values ranging from 1.23 to 2.87, all well below the threshold of 5.0, indicating absence of problematic multicollinearity. Group Comparison for Moderation Analysis. For the moderation analysis of authenticity, the sample was divided into high authenticity (n = 374, M = 4.21, SD = 0.42) and low authenticity (n = 374, M = 2.67, SD = 0.51) groups using median split (Median = 3.44). Independent samples t-tests confirmed significant differences between groups (t(746) = 35.82, p < .001, Cohen's d = 3.26), indicating appropriate group differentiation for multi-group analysis. Common Method Bias Assessment. To address potential common method bias, Harman's single-factor test was conducted. The unrotated factor analysis revealed that the first factor explained 34.2% of the total variance, below the threshold of 50%, suggesting that common method bias was not a significant concern. Additionally, the temporal separation of independent and dependent variable measurements further mitigated this concern. Hypothesis Testing. Prior to hypothesis testing, the measurement model was evaluated using confirmatory factor analysis (CFA). The measurement model demonstrated acceptable fit indices: χ²=487.6, df = 412, χ²/df = 1.183, CFI = .967, TLI = .961, RMSEA = .048 (90% CI: .041-.055), SRMR = .062. All factor loadings exceeded .70 and were statistically significant (p < .001), confirming convergent validity. The Average Variance Extracted (AVE) for all constructs exceeded .50, and the square root of AVE for each construct was greater than its correlations with other constructs, supporting discriminant validity. Common method bias was assessed using Harman's single-factor test and the unmeasured latent method construct (ULMC) approach. The single-factor model explained only 24.3% of the total variance, well below the 50% threshold. The ULMC model comparison showed Δχ²=156.7 (Δdf = 29, p < .001), indicating that common method bias was not a significant concern. The structural model demonstrated good fit to the data: χ²=542.3, df = 457, χ²/df = 1.187, CFI = .965, TLI = .959, RMSEA = .050 (90% CI: .043-.057), SRMR = .064. These indices meet the recommended thresholds for acceptable model fit (CFI/TLI > .95, RMSEA < .08, SRMR < .08). Direct Effects Analysis. The standardized path coefficients and their significance levels are presented in Table 5 . All hypotheses related to the effects of self-concept components on social distance (H1-1 through H1-5) were supported. Self-efficacy (β = .139, p < .001), self-assertion (β = .275, p < .001), social presence (β = .199, p < .001), self-esteem (β = .233, p < .001), and social conspicuousness (β = .327, p < .001) all demonstrated significant positive effects on social distance, with effect sizes ranging from small to moderate. Similarly, all hypotheses regarding the effects of self-concept components on para-social interaction (H2-1 through H2-5) were supported. Self-efficacy (β = .238, p < .001), self-assertion (β = .183, p < .001), social presence (β = .063, p = .048), self-esteem (β = .072, p = .047), and social conspicuousness (β = .251, p < .001) all showed significant positive effects on para-social interaction. Regarding the outcome variables, social distance demonstrated a significant positive effect on recommendation intentions (β = .459, p < .001), supporting H3-2. However, the path from social distance to brand trust was not significant (β = .069, p = .113), rejecting H3-1. Para-social interaction showed significant positive effects on both brand trust (β = .144, p < .01) and recommendation intentions (β = .343, p < .001), supporting H4-1 and H4-2. Finally, brand trust significantly predicted recommendation intentions (β = .622, p < .001), supporting H5. Effect Size Assessment Following Cohen's (1988) guidelines, effect sizes were categorized as small (β ≥ .10), medium (β ≥ .30), or large (β ≥ .50). The strongest predictor of social distance was social conspicuousness (β = .327, medium effect), while the strongest predictor of para-social interaction was social conspicuousness (β = .251, small-to-medium effect). The relationship between brand trust and recommendation intentions showed a large effect size (β = .622). Model Explained Variance : The model explained substantial variance in the endogenous variables: social distance (R²=.68), para-social interaction (R²=.45), brand trust (R²=.18), and recommendation intentions (R²=.71). These R² values indicate that the model accounts for a meaningful proportion of variance in the dependent variables. Table 5 Results of Structural Equation Model Analysis Hypothesis Path β SE t-value p-value Effect Size H1-1 Self-efficacy → Social Distance .139*** .032 4.782 < .001 Small H1-2 Self-assertion → Social Distance .275*** .029 9.420 < .001 Small-Medium H1-3 Social Presence → Social Distance .199*** .028 7.037 < .001 Small H1-4 Self-esteem → Social Distance .233*** .029 7.965 < .001 Small H1-5 Social Conspicuousness → Social Distance .327*** .027 12.16 < .001 Medium H2-1 Self-efficacy → Para-social Interaction .238*** .036 6.586 < .001 Small H2-2 Self-assertion → Para-social Interaction .183*** .036 5.047 < .001 Small H2-3 Social Presence → Para-social Interaction .063* .035 1.784 .048 Small H2-4 Self-esteem → Para-social Interaction .072* .036 1.994 .047 Small H2-5 Social Conspicuousness → Para-social Interaction .251*** .034 7.324 < .001 Small-Medium H3-1 Social Distance → Brand Trust .069 .043 1.586 .113 Non-significant H3-2 Social Distance → Recommendation Intentions .459*** .043 10.596 < .001 Medium H4-1 Para-social Interaction → Brand Trust .144** .045 3.207 .001 Small H4-2 Para-social Interaction → Recommendation Intentions .343*** .045 7.635 < .001 Medium H5 Brand Trust → Recommendation Intentions .622*** .029 21.65 < .001 Large Note : ***p < .001, **p < .01, *p < .05 Moderating Effects of Authenticity. To examine the moderating role of authenticity, a multi-group structural equation modeling approach was employed. Participants were divided into high authenticity (n = 374) and low authenticity (n = 374) groups based on a median split of the authenticity scale (Mdn = 3.67). Independent samples t-tests confirmed significant differences between groups on the authenticity measure (t(746) = 24.18, p < .001, Cohen's d = 1.77). Measurement Invariance Testing. Prior to testing moderation effects, measurement invariance across the two groups was established following the sequential testing procedure recommended by Vandenberg and Lance (2000). Configural invariance (Δχ²=0, baseline model), metric invariance (Δχ²=23.4, Δdf = 18, p = .175, ΔCFI=-.003), and scalar invariance (Δχ²=41.7, Δdf = 36, p = .240, ΔCFI=-.007) were all supported, indicating that the measurement model operates equivalently across high and low authenticity groups. Multi-Group Moderation Analysis. The unconstrained model (allowing all parameters to vary freely between groups) was compared with a series of constrained models (constraining specific paths to be equal across groups). Chi-square difference tests were used to assess the significance of moderation effects. The results are presented in Table 6 . Moderation of Self-Concept Effects on Social Distance. Significant moderation effects were found for self-assertion → social distance (Δχ²=8.109, df = 1, p = .004) and social presence → social distance (Δχ²=5.098, df = 1, p = .024). For self-assertion, the effect was stronger in the high authenticity group (β = .371, p < .001) compared to the low authenticity group (β = .259, p < .001). Conversely, for social presence, the effect was stronger in the low authenticity group (β = .360, p < .001) compared to the high authenticity group (β = .035, p = .368). No significant moderation effects were found for self-efficacy (Δχ²=1.347, df = 1, p = .246), self-esteem (Δχ²=0.578, df = 1, p = .447), or social conspicuousness (Δχ²=1.086, df = 1, p = .297). Moderation of Self-Concept Effects on Para-Social Interaction. Significant moderation effects were identified for self-esteem → para-social interaction (Δχ²=4.892, df = 1, p = .027) and social conspicuousness → para-social interaction (Δχ²=6.725, df = 1, p = .009). For self-esteem, the effect was stronger in the low authenticity group (β = .128, p = .014) compared to the high authenticity group (β = .113, p = .037). For social conspicuousness, the effect was significantly stronger in the low authenticity group (β = .296, p < .001) compared to the high authenticity group (β = .004, p = .937). No significant moderation effects were found for self-efficacy (Δχ²=0.565, df = 1, p = .452), self-assertion (Δχ²=1.171, df = 1, p = .279), or social presence (Δχ²=1.397, df = 1, p = .237). Table 6 Multi-Group Moderation Analysis: Effects of Authenticity Hypothesis Path High Authenticity (n = 374) Low Authenticity (n = 374) Δχ² df p-value Moderation Effect β (SE) [z-value] β (SE) [z-value] H6-1 Self-efficacy → Social Distance .107 (.040) [2.708] .060 (.048) [1.249] 1.347 1 .246 Non-significant H6-2 Self-assertion → Social Distance .371 (.037) [9.982] .259 (.052) [4.999] 8.109 1 .004** Significant H6-3 Social Presence → Social Distance .035 (.039) [0.901] .360 (.049) [7.411] 5.098 1 .024* Significant H6-4 Self-esteem → Social Distance .432 (.038) [11.32] .033 (.047) [0.698] 0.578 1 .447 Non-significant H6-5 Social Conspicuousness → Social Distance .388 (.038) [10.20] .208 (.047) [4.458] 1.086 1 .297 Non-significant H6-6 Self-efficacy → Para-social Interaction .204 (.058) [3.545] .214 (.053) [4.068] 0.565 1 .452 Non-significant H6-7 Self-assertion → Para-social Interaction .336 (.054) [6.206] .030 (.056) [0.532] 1.171 1 .279 Non-significant H6-8 Social Presence → Para-social Interaction .051 (.057) [0.890] .289 (.053) [5.466] 1.397 1 .237 Non-significant H6-9 Self-esteem → Para-social Interaction .113 (.054) [2.083] .128 (.052) [2.462] 4.892 1 .027* Significant H6-10 Social Conspicuousness → Para-social Interaction .004 (.055) [0.079] .296 (.051) [5.827] 6.725 1 .009** Significant Model Fit Indices: Unconstrained model: χ²=721.4, df = 914, CFI = .964, TLI = .958, RMSEA = .049, Fully constrained model: χ²=762.8, df = 934, CFI = .961, TLI = .957, RMSEA = .051. *Note: **p < .01, p < .05; High vs. Low authenticity groups determined by median split (Mdn = 3.67) Summary of Moderation Effects The analysis revealed that authenticity moderates four of the ten hypothesized relationships. Specifically, higher perceived authenticity strengthens the relationship between self-assertion and social distance, while lower perceived authenticity strengthens the relationships between social presence and social distance, as well as between self-esteem and social conspicuousness with para-social interaction. These findings suggest that authenticity plays a differential moderating role depending on the specific self-concept dimension and outcome variable under consideration. Effect Size Interpretation: The moderation effect sizes were calculated using the formula: f² = (R²moderated - R²main effects)/(1 - R²moderated). The moderation effects showed small to medium effect sizes (f² ranging from .02 to .08), indicating meaningful practical significance beyond statistical significance. Discussion and Conclusions Conclusion. This investigation provides novel insights into the psychological mechanisms underlying consumer-influencer relationships in digital marketing environments. The study demonstrates that consumer self-concept dimensions—self-efficacy, self-assertion, social presence, self-esteem, and social conspicuousness—significantly predict both social distance reduction and para-social interaction formation with human influencers. Notably, the findings reveal an asymmetric relationship pattern wherein social distance strongly influences recommendation intentions but shows no significant effect on brand trust formation. Conversely, para-social interactions demonstrate positive effects on both brand trust and recommendation intentions, highlighting the differential pathways through which psychological connections translate into consumer behaviors. The moderating role of authenticity emerged as particularly significant, strengthening specific relationships under varying conditions. High authenticity conditions amplified the self-assertion→social distance relationship, while low authenticity conditions enhanced social presence effects on social distance and self-esteem/social conspicuousness effects on para-social interaction. These findings contribute to the theoretical understanding of technology-mediated consumer psychology and provide empirical validation for the integration of self-concept theory with para-social interaction frameworks in digital marketing contexts. Limitations. Several methodological and sampling limitations warrant acknowledgment. The demographic concentration within the 30–49 age bracket (87.6% of respondents) may limit generalizability across different generational cohorts who exhibit varying social media consumption patterns and technology adoption behaviors. The sample's geographical concentration within South Korea raises questions about cross-cultural applicability, particularly given documented variations in individualism-collectivism orientations that may influence authenticity perceptions and trust formation mechanisms across Western and Asian cultural contexts. The cross-sectional design precludes causal inference and temporal relationship examination, while the reliance on self-reported measures may introduce common method variance despite implemented procedural remedies. Additionally, the study's focus on general influencer categories rather than specific platform-dependent influencer types may obscure nuanced relationship dynamics that vary across different social media ecosystems. Social Implications. The findings illuminate critical social implications regarding authentic relationship formation in digital environments. The demonstrated importance of perceived authenticity in moderating consumer-influencer relationships suggests that genuine self-expression and transparent communication practices contribute to healthier digital community formation. This has broader implications for addressing concerns about social media's impact on mental health and social comparison processes, as authentic influencer communications may foster more positive parasocial relationships. The study's revelation that different self-concept dimensions predict varying relationship outcomes suggests that digital marketing practices should consider individual psychological characteristics to avoid exploitative targeting of vulnerable populations. Furthermore, the findings support the development of media literacy programs that help consumers understand the psychological mechanisms underlying their responses to influencer content, thereby promoting more informed and autonomous digital consumption behaviors. Managerial Implications. For marketing practitioners, these findings provide actionable insights for developing more effective and ethical influencer marketing strategies. The differential effects of authenticity across self-concept dimensions suggest that personalized approaches based on consumer psychological profiles may yield superior outcomes compared to standardized influencer campaigns. Marketing managers should prioritize authenticity cultivation over follower count maximization, as genuine personality expression strengthens specific consumer segments' engagement patterns. he asymmetric effects of social distance and para-social interaction on brand trust versus recommendation intentions indicate that different influencer relationship strategies may be optimal for different marketing objectives. For trust-building initiatives, fostering para-social interactions through consistent personality disclosure and emotional connection appears crucial. For recommendation generation, reducing perceived social distance through similarity emphasis and accessibility may prove more effective. Organizations should implement authenticity assessment frameworks when selecting influencer partnerships and develop content guidelines that preserve genuine personality expression while achieving commercial objectives. The findings also suggest that micro-segmentation strategies based on consumer self-concept profiles could optimize campaign effectiveness and resource allocation. Recommendations. Based on these findings, we recommend that practitioners adopt multi-dimensional approaches to influencer selection and campaign design. First, implement psychological profiling of target audiences to understand predominant self-concept characteristics and tailor influencer matching accordingly. Second, develop authenticity measurement protocols that evaluate influencer genuineness beyond surface-level metrics such as engagement rates or follower demographics. Third, establish content creation guidelines that balance commercial objectives with authentic self-expression, recognizing that perceived authenticity moderates relationship formation processes. Fourth, implement longitudinal tracking of consumer-influencer relationship development to optimize campaign timing and content evolution strategies. Finally, consider platform-specific relationship dynamics when developing cross-platform influencer marketing strategies, as different technological affordances may influence the manifestation of self-concept dimensions and authenticity perceptions. Directions for Future Studies. Future research should address several important directions to advance theoretical understanding and practical applications. Longitudinal studies examining the temporal evolution of consumer-influencer relationships would provide crucial insights into relationship development patterns and sustainability factors. Cross-cultural investigations comparing individualistic versus collectivistic societies would illuminate cultural boundary conditions for the proposed theoretical framework. Comparative studies examining virtual versus human influencer effectiveness across different consumer self-concept profiles would extend theoretical understanding of authenticity perceptions in artificial versus genuine personality contexts. Additionally, platform-specific research examining how technological affordances (e.g., video versus text, real-time versus asynchronous communication) moderate the relationships between self-concept dimensions and influencer connection formation would provide valuable practical insights. Future investigations should also explore the role of negative authenticity perceptions and their impact on consumer-influencer relationship dissolution, as well as examine potential dark-side effects of para-social relationships in commercial contexts. Finally, the development and validation of culturally adapted measurement instruments for cross-national comparative research would significantly advance the field's methodological sophistication and theoretical generalizability. Declarations Author contributions Gong, Diao and Park: Conceptualization, Writing—original draft. Diao, JZ-Jin and Park: Methodology, Data curation, Formal analysis. C.H-Jin: Conceptualization, Writing-review and editing, project co-supervisor. Funding Jining University's Top 100 Outstanding Talents Support Program Cultivation Project No. 2023ZYRC68 Competing interests The authors declare no competing interests. Ethics approval Since this study used individuals to respond the study’s instrument, the author made sure that the study was conducted in accordance to the ethical standards of the Helsinki Declaration. Thus, this study was granted exemption from Institutional Review Board (IRB) review in accordance with Korean statistical law and research ethics regulations. This research has been reviewed and approved by the Korea Human Development College. Institutional Review Board (2025-KHDC-IRB-601), Approval Date: 07/15/2025 Informed consent Verbal informed consent was obtained from all participants because of the need for flexibility. The consent process, including the purpose of the study, voluntary participation, and confidentiality measures, was clearly explained to the participants. The participants were also informed that they could withdraw from the study at any time without any consequences. 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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-7403841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557874151,"identity":"c6b3b6de-41ac-4ab7-b2df-aaf45f1b9cfd","order_by":0,"name":"Fan Gong","email":"","orcid":"","institution":"Jining University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Gong","suffix":""},{"id":557874153,"identity":"b937e269-b5f4-4204-aa3e-c5a92fcb9701","order_by":1,"name":"Yu Diao","email":"","orcid":"","institution":"Kyonggi 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13:29:08","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195206,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7403841/v1/d0977c2470421c7c26767a49.html"},{"id":98074867,"identity":"4de8813f-9639-4673-a695-492df9c7edcb","added_by":"auto","created_at":"2025-12-12 13:29:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191290,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Research Model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7403841/v1/229d3e5cd2d30237e584dcf1.png"},{"id":98444750,"identity":"06435dbd-d5d8-4c11-a66a-ae13747e314f","added_by":"auto","created_at":"2025-12-17 17:17:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1499694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7403841/v1/40081078-8fe1-4de4-9e5f-c19db3204004.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Psychology of Digital Community Formation: Self-Concept, Authenticity, and Para-Social Bonds in Social Media Environments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe exponential proliferation of social media platforms has fundamentally transformed the architectural landscape of contemporary marketing communications, establishing influencer marketing as a critical strategic paradigm that transcends traditional advertising methodologies. While extant research has increasingly examined the technological sophistication of virtual influencers and computer-generated personalities in brand endorsement contexts, a substantive theoretical gap persists regarding the intricate psychological mechanisms through which human influencers interact with consumers' multidimensional self-concept systems to generate authentic relational dynamics and behavioral outcomes.\u003c/p\u003e\u003cp\u003eContemporary scholarship has extensively documented the commercial efficacy of influencer marketing as an innovative approach to consumer engagement across social networking services (SNS), with numerous corporations strategically leveraging these digital intermediaries to establish robust brand equity and consumer connectivity (Belanche et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ki et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The theoretical foundation underlying influencer marketing's effectiveness rests upon its capacity to function as a sophisticated marketing communication strategy that enables influencers to authentically persuade consumers toward brand adoption through perceived credibility, expertise, and relational intimacy (Audrezet et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jim\u0026eacute;nez-Castillo and S\u0026aacute;nchez-Fern\u0026aacute;ndez, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, while recent investigations have predominantly concentrated on the technological dimensions of virtual influencer marketing\u0026mdash;examining computer-generated personalities and artificial intelligence-driven brand ambassadorships\u0026mdash;significantly less theoretical attention has been devoted to understanding how human influencers' authentic communications intersect with the complex psychological architecture of consumer identity construction. This research lacuna represents a critical oversight, particularly given that human influencers continue to dominate the digital marketing ecosystem and maintain fundamentally different relational dynamics compared to their virtual counterparts.\u003c/p\u003e\u003cp\u003eThe strategic importance of this inquiry emerges from companies' intensifying desire to strengthen product positioning and corporate image through influencer collaborations, recognizing digital marketing's unique capacity to generate consumer-driven content that facilitates authentic brand identity formation (Evans et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schivinski and Dabrowski, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stojanovic et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tafesse and Wood, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Unlike traditional celebrity endorsements or virtual influencer campaigns, human social media influencers establish trust-based relationships with audiences through demonstrated knowledge and specialized expertise, positioning themselves as credible information intermediaries who significantly influence follower decision-making processes (De Veirman et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe psychological complexity underlying these human-to-human digital relationships necessitates a sophisticated analytical framework that extends beyond surface-level engagement metrics to examine the fundamental self-concept constructs that drive consumer responsiveness to influencer communications. Influencers who maintain positive reputational capital facilitate organic brand content exposure through authentic relationship formation, creating sustained engagement patterns that generate measurable marketing outcomes (Stojanovic et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This phenomenon drives continuous growth in social media influencer marketing activities, particularly on platforms such as YouTube, where interactive and bi-directional communication capabilities enable diversified service offerings and audio-visual content strategies that appeal to users through multisensory engagement mechanisms.\u003c/p\u003e\u003cp\u003eThe evolving consumer landscape reveals increasingly sophisticated patterns wherein followers actively investigate influencer authenticity, attributing personal meaning to seemingly casual communications while demonstrating heightened propensity to purchase products promoted by trusted digital personalities. As corporate investment in influencer marketing continues to expand exponentially, academic inquiry has correspondingly intensified to examine user perceptions of content quality, influencer reputation, and the underlying psychological mechanisms driving consumer responses to influencer communications (Ao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis investigation addresses a critical theoretical void by proposing a comprehensive analytical framework that integrates consumer self-concept systems with para-social interaction theory to elucidate the psychological mechanisms underlying human influencer effectiveness. Specifically, this research diverges from existing virtual influencer studies by examining how authentic human personalities interact with follower self-concept dimensions\u0026mdash;including self-efficacy, self-assertion, social presence, and self-esteem\u0026mdash;to generate social distance reduction and para-social relationship formation that subsequently influences brand trust and recommendation intentions.\u003c/p\u003e\u003cp\u003eThe primary research objective centers on understanding the causal relationships between influencer followers' multidimensional self-concepts and social conspicuousness with the formation of social distance and para-social interactions with human influencers. This study extends beyond traditional influencer marketing research by investigating how these psychological constructs subsequently influence brand trust development and recommendation intentions, while examining the moderating role of perceived influencer authenticity in strengthening or attenuating these relational dynamics.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cb\u003eDigital Communication Ecosystems and Technology-Mediated Influence Networks\u003c/b\u003e. The proliferation of digital communication platforms has fundamentally reconceptualized the traditional paradigms of marketing communication, establishing sophisticated technological ecosystems wherein influence operates through complex algorithmic mediation and consumer-generated content architectures (Shahbaznezhad et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Contemporary social media platforms transcend conventional broadcasting models, constituting interactive technological infrastructures that facilitate multi-directional information flows, peer-to-peer knowledge transfer, and community-driven content curation mechanisms. These digital environments represent evolved manifestations of Web 2.0 ideological frameworks, characterized by user empowerment, collaborative content creation, and distributed influence networks that challenge centralized authority structures in commercial communication (Kaplan and Haenlein, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe technological sophistication of contemporary social media platforms enables unprecedented levels of personalization, algorithmic content optimization, and real-time engagement analytics that fundamentally distinguish digital influence mechanisms from traditional celebrity endorsement models (Schivinski and Dabrowski, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stojanovic et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These platforms function as complex information processing systems wherein user behavior data, engagement patterns, and social graph relationships are continuously analyzed to optimize content delivery and maximize influence effectiveness. The emergence of artificial intelligence-driven recommendation algorithms has created sophisticated feedback loops between content creators and audiences, enabling precise targeting of demographic segments and psychographic profiles through data-driven personalization strategies (Tafesse and Wood, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin this technological framework, digital influencers emerge as intermediary actors who leverage platform-specific affordances to establish authentic connections with segmented audiences through consistent content delivery and strategic personal branding initiatives (Evans et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Unlike traditional celebrity endorsers who maintain professional distance from audiences, digital influencers operate within proximity-based relationship models that simulate interpersonal intimacy through regular content updates, direct audience interaction, and lifestyle documentation that blurs boundaries between public and private personas (De Veirman et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This proximity-based influence model represents a fundamental departure from traditional advertising paradigms, as it relies upon sustained relationship development rather than episodic exposure to commercial messaging.\u003c/p\u003e\u003cp\u003eThe psychological mechanisms underlying digital influence effectiveness operate through sophisticated cognitive processing frameworks wherein audiences evaluate source credibility based on perceived authenticity, expertise demonstration, and lifestyle congruence rather than institutional authority or professional accomplishments (Stojanovic et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research demonstrates that digital audiences exhibit enhanced receptivity to influence attempts when they perceive genuine personal connection with content creators, suggesting that technological mediation can paradoxically enhance rather than diminish interpersonal intimacy under specific conditions (Al-Emadi and Ben Yahia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ferchaud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe commercialization trajectory of social networking platforms has systematically transformed originally peer-oriented communication channels into sophisticated marketing ecosystems wherein commercial messaging is seamlessly integrated with authentic personal expression (Argyris and Monu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This evolution has necessitated the development of new theoretical frameworks for understanding consumer behavior in contexts where traditional distinctions between advertising and entertainment, commercial and personal content, and professional and amateur content creators have become increasingly ambiguous. Contemporary consumers navigate these hybrid information environments through complex cognitive processes that simultaneously evaluate entertainment value, informational utility, and commercial intent across multiple content formats and platform contexts (Evans et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe emergence of parasocial relationship dynamics within digital influence networks represents a particularly significant theoretical development, as these one-sided emotional connections enable sustained commercial influence through mechanisms that transcend traditional persuasion models (Berryman and Kavka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Digital influencers cultivate parasocial relationships through strategic vulnerability disclosure, lifestyle documentation, and consistent personality presentation that creates illusions of reciprocal friendship despite the mediated nature of the interaction. This relationship model enables commercial recommendations to be processed as trusted advice from personal acquaintances rather than promotional messaging from commercial entities, thereby circumventing traditional advertising resistance mechanisms.\u003c/p\u003e\u003cp\u003eThe technological architecture of modern social media platforms facilitates unprecedented levels of influence measurement and optimization through comprehensive analytics systems that track engagement rates, audience demographics, conversion metrics, and temporal usage patterns (Boerman and van Reijmersdal, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These measurement capabilities enable continuous refinement of influence strategies through data-driven experimentation with content formats, posting schedules, audience targeting parameters, and commercial integration approaches. The availability of granular performance data has transformed digital influence from an intuitive art form into a systematically optimizable marketing discipline characterized by rigorous testing methodologies and quantitative performance evaluation frameworks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelf-concepts: The Multidimensional Architecture of Individual Identity.\u003c/b\u003e The theoretical conceptualization of self-concept constitutes a fundamental psychological construct that encapsulates the complex cognitive-affective framework through which individuals perceive, evaluate, and understand their own identity (McGrew, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This multidimensional phenomenon encompasses several interconnected components that collectively shape consumer behavior in digital marketing contexts.\u003c/p\u003e\u003cp\u003eSelf-Efficacy, Cognitive Competence and Adaptive Capability, as conceptualized within Bandura's social cognitive theory, represents the individual's belief in their capability to execute behaviors necessary to produce specific performance attainments (Adna and Sukoco, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within the digital influencer marketing paradigm, self-efficacy manifests as consumers' perceived competence in navigating technological platforms and processing information effectively. Valentine et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that individuals with elevated self-efficacy levels exhibit superior skills in comfort and integration within socio-cultural interaction environments. This cognitive competence translates into enhanced information processing capabilities when engaging with influencer content, as individuals with higher self-efficacy demonstrate greater adaptability to new information, knowledge, and technology (Valentine et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSelf-Assertion (Relational Expression and Intimacy Communication) emerges as a crucial dimension of self-concept, characterized by Wolpe and Lazarus (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) as the confident expression of one's feelings to others. Within the context of social media interaction, self-assertion reflects the degree to which individuals engage in intimate communication without experiencing interpersonal anxiety (Al-Masri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sprecher and Hendrick, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This construct encompasses the willingness to share personal information, express opinions, and communicate responsiveness and commitment in digital relationships (Derlega et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The accessibility and cost-effectiveness of online platforms facilitate enhanced self-expression opportunities compared to offline media, thereby amplifying the role of self-assertion in consumer-influencer relationships.\u003c/p\u003e\u003cp\u003eSocial Presence as Mediated Communication and Psychological Connection, grounded in the seminal work of Short et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), conceptualizes the subjective experience of connection with others through mediated communication channels. This phenomenon manifests as the perception of social connection with another person through contextual cues, creating a sense of togetherness despite physical separation (Kreijns et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weidlich et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In digital environments, social presence facilitates the simulation of face-to-face interaction experiences, enabling consumers to develop meaningful relationships with influencers across technological barriers. The theoretical framework suggests that enhanced social presence contributes to stronger psychological bonds and increased engagement with influencer content.\u003c/p\u003e\u003cp\u003eSelf-Esteem as Social Identity and Interpersonal Confidence represents a fundamental component of self-concept that reflects individuals' overall evaluation of their own worth and social acceptance (Salice, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lonsdale, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This construct demonstrates significant correlation with social confidence and the perception of one's social self. Empirical research indicates that individuals with higher self-esteem exhibit greater resistance to external validation seeking, while those with lower self-esteem tend to value external aspirations related to image, popularity, and material possessions (Stuppy et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within social media contexts, self-esteem influences how consumers interpret and respond to influencer content, particularly regarding self-image and social comparison processes.\u003c/p\u003e\u003cp\u003eSocial Conspicuousness as Visibility and Status Signaling encompasses the degree to which individuals or groups attract attention within social settings, influenced by factors such as appearance, behavior, status, or possession of distinctive characteristics (Shin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This construct reflects the desire to signal social status and differentiate oneself from others through visible consumption patterns or behavioral displays. In the digital influencer marketing domain, social conspicuousness drives consumers' motivation to engage with premium brands and exclusive content that enhance their perceived social standing.\u003c/p\u003e\u003cp\u003eSocial Distance and Para-Social Interaction: Bridging Physical and Psychological Gaps. Social Distance as Psychological Proximity and Relational Intimacy, conceptualized within Trope and Liberman's (2003) construal level theory, represents a subjective dimension of psychological distance that influences how individuals perceive and interact with objects or persons in their social environment. This construct encompasses the degree to which individuals perceive themselves as similar to or different from others, fundamentally affecting judgment and decision-making processes (Liberman and Trope, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Liberman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The psychological mechanisms underlying social distance operate through concrete versus abstract information processing modes. Closer social distances facilitate more concrete, detailed information processing, leading to intuition-based and emotion-driven decisions (Ayduk and Kross, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Eyal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Conversely, greater social distances promote abstract thinking patterns, encouraging rational, context-considering decision-making processes (Fujita et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maglio, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This theoretical framework has profound implications for influencer marketing, as social distance affects how consumers process and respond to influencer communications.\u003c/p\u003e\u003cp\u003ePara-Social Interaction as Mediated Relationship Formation, originating from Horton and Wohl's (1956) mass communication research, describes the phenomenon whereby audience members develop one-sided relationships with media personalities. This construct manifests as viewers' perception of maintaining familiar relationships with media characters despite the absence of direct reciprocal interaction (Chen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within contemporary digital contexts, para-social interactions enable consumers to experience emotional solidarity and intimacy with influencers, paralleling feelings typically reserved for real-world relationships (Jin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These mediated relationships foster similarity, empathy, and friendship feelings toward online personalities, creating psychological foundations for brand trust and purchase intentions. The blurred boundaries between influencers' private and public personas on platforms like YouTube enhance the authenticity of these para-social connections (Ferchaud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Berryman and Kavka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBrand Trust and Recommendation Intentions: The Commercial Outcomes. Brand trust represents a multifaceted construct encompassing consumers' confidence, belief, and faith in specific brands, incorporating cultural implications, emotional responses, and social relationship dimensions (Nosi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This construct serves as a critical determinant of brand loyalty and competitive advantage, particularly crucial for companies operating in highly competitive markets. The theoretical foundations of brand trust rest upon relationship marketing principles, where trust acts as a guardian of relationship investments and provides long-term benefits while minimizing high-risk behaviors (Samarah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the consumer perspective, brand trust constitutes an essential asset that drives favorable responses toward associated companies (Samarah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; C\u0026aacute;ceres and Paparoidamis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Berry and Parasuraman (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) emphasize that effective service marketing depends on successful management of brand influence and trust, highlighting the strategic importance of this construct in contemporary marketing practice (Tran, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecommendation intentions encompass consumers' willingness to recommend products, services, or brands to others, serving as a crucial outcome variable in marketing research (Stanovčić et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These intentions reflect the likelihood of positive word-of-mouth communication, which significantly influences brand reach and acquisition of new customers. The relationship between social distance and recommendation intentions operates through similarity perception mechanisms. When consumers perceive greater similarity or closeness with brands, they demonstrate increased likelihood of favorable evaluations and recommendation behaviors (Stanovčić et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This theoretical framework suggests that social distance influences purchase intentions by enhancing identification with brands and their target audiences.\u003c/p\u003e\u003cp\u003eThe construct of authenticity emerged as a moderating variable that influences the relationships between self-concept dimensions and both social distance and para-social interaction. Authenticity encompasses the convergence between internal thoughts/emotions and external expressions, representing genuine, unartificial communication (Kim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In influencer marketing contexts, authenticity manifests as followers' perceptions of influencers' sincerity and genuineness in online communications (Jin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This construct moderates the effects of self-concept components on social distance formation and para-social interaction development, with higher authenticity perceptions strengthening these relationships. The theoretical significance of authenticity lies in its capacity to enhance trust formation and reduce consumer skepticism toward commercial communications.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eHypotheses and Research Model\u003c/h2\u003e\u003cp\u003eTheoretical Foundations and Hypothesis Development. The present investigation proposes a conceptual framework predicated upon the multidimensional nature of self-concept constructs and their differential impact on consumer-influencer relational dynamics within digital marketing ecosystems. Drawing upon established psychological theories and empirical evidence from consumer behavior research, we advance the following hypotheses:\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eH1: Self-Concept Components and Social Distance Formation\u003c/h3\u003e\n\u003cp\u003eThe theoretical foundation for H1 emerges from construal level theory (Liberman and Trope, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Liberman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which posits that social distance functions as a subjective dimension of psychological proximity influencing information processing modes. Empirical evidence demonstrates that individuals with elevated self-concept dimensions exhibit enhanced capacity for establishing psychological connections with distant objects or persons (Ayduk and Kross, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Eyal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Bandura and Adams's (1977) social cognitive theory establishes self-efficacy as a critical determinant of behavioral adaptation to novel information environments. Within digital contexts, individuals possessing higher self-efficacy demonstrate superior navigation capabilities and information processing competence (McGrew, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Valentine et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This cognitive competence facilitates the perception of reduced psychological distance with influencers through enhanced confidence in digital platform utilization.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1-1\u003c/strong\u003e\u003cp\u003eSelf-efficacy positively influences social distance formation with influencers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSelf-assertion, conceptualized as the confident expression of personal feelings without interpersonal anxiety (Wolpe and Lazarus, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Al-Masri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), enables individuals to engage in intimate communication patterns. The cost-effectiveness and accessibility of online platforms amplify self-expression opportunities, thereby facilitating perceived closeness with influencers through active engagement behaviors (Derlega et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1-2\u003c/strong\u003e\u003cp\u003eSelf-assertion positively affects social distance with influencers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSocial presence theory (Short et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) establishes that mediated communication creates subjective experiences of togetherness despite physical separation. In digital environments, enhanced social presence enables simulation of face-to-face interactions, contributing to psychological bond formation and reduced perceived distance (Kreijns et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weidlich et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1-3\u003c/strong\u003e\u003cp\u003eSocial presence positively influences social distance formation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSelf-esteem represents individuals' fundamental evaluation of their social worth and acceptance (Salice, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lonsdale, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher self-esteem correlates with enhanced social confidence and reduced need for external validation (Stuppy et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within influencer contexts, this psychological security facilitates perception of similarity and closeness with digital personalities.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1-4\u003c/strong\u003e\u003cp\u003eSelf-esteem positively affects social distance with influencers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSocial conspicuousness reflects the desire for visibility and status signaling within social contexts (Shin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This construct drives engagement with premium content and exclusive offerings, creating perceived alignment with influencers who represent desired social positions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1-5\u003c/strong\u003e\u003cp\u003eSocial conspicuousness positively influences social distance formation.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eH2: Self-Concept Components and Para-Social Interaction Development\u003c/h3\u003e\n\u003cp\u003ePara-social interaction theory (Horton and Wohl, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1956\u003c/span\u003e) describes one-sided relationships with media personalities that parallel real-world social connections. Contemporary research demonstrates that para-social interactions create emotional solidarity and intimacy despite absence of reciprocal communication (Jin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Farivar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Individuals with higher self-efficacy demonstrate enhanced adaptability to technological environments and superior information processing capabilities. This cognitive competence translates into stronger affective responses toward influencers through increased engagement confidence and platform mastery.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2-1\u003c/strong\u003e\u003cp\u003eSelf-efficacy positively influences para-social interaction formation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSelf-assertion facilitates intimate communication without interpersonal anxiety, enabling followers to perceive influencers as accessible relationship partners. The propensity for sharing personal information and expressing opinions creates foundations for emotional attachment and para-social bond formation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2-2\u003c/strong\u003e\u003cp\u003eSelf-assertion positively affects para-social interaction development.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSocial presence enables followers to experience psychological connection through contextual cues, creating illusions of reciprocal interaction. This perceived togetherness strengthens emotional bonds and enhances the authenticity of para-social relationships with influencers.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2-3\u003c/strong\u003e\u003cp\u003eSocial presence positively influences para-social interaction.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eHigher self-esteem facilitates secure attachment patterns and reduces anxiety in social contexts. This psychological security enables followers to form stronger emotional connections with influencers through reduced self-consciousness and enhanced openness to para-social experiences.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2-4\u003c/strong\u003e\u003cp\u003eSelf-esteem positively affects para-social interaction formation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe desire for social visibility and status alignment motivates engagement with influencers who represent aspirational identities. This identification process strengthens para-social bonds through perceived similarity and lifestyle congruence.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2-5\u003c/strong\u003e\u003cp\u003eSocial conspicuousness positively influences para-social interaction.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eH3: Social Distance Effects on Outcome Variables\u003c/h3\u003e\n\u003cp\u003eConstrual level theory suggests that closer social distances facilitate concrete information processing and emotion-driven decisions (Ayduk and Kross, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Eyal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jing et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ordabayeva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When followers perceive reduced distance from influencers, this psychological proximity may enhance trust transfer to endorsed brands through perceived credibility and authenticity.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3-1\u003c/strong\u003e\u003cp\u003eSocial distance positively influences brand trust.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSocial distance affects information processing through similarity perception mechanisms (Liberman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Closer perceived distance with influencers enhances identification processes, leading to increased likelihood of favorable evaluations and recommendation behaviors (Maglio, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jing et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ordabayeva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3-2\u003c/strong\u003e\u003cp\u003eSocial distance positively affects recommendation intentions.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eH4: Para-Social Interaction Effects on Outcome Variables\u003c/h3\u003e\n\u003cp\u003ePara-social relationships create emotional solidarity and intimacy paralleling real-world relationships (Jin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These affective bonds facilitate trust transfer from influencers to endorsed brands through emotional contagion and credibility attribution mechanisms.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4-1\u003c/strong\u003e\u003cp\u003ePara-social interaction positively influences brand trust.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe emotional attachment characterizing para-social relationships motivates followers to advocate for influencer-endorsed products. This advocacy stems from identification processes and desire to maintain consistency between self-concept and para-social relationship investments.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4-2\u003c/strong\u003e\u003cp\u003ePara-social interaction positively affects recommendation intentions.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eH5: Brand Trust and Recommendation Behavior\u003c/h2\u003e\u003cp\u003eTrust constitutes a fundamental determinant of behavioral intentions in consumer contexts. Brand trust reduces perceived risk and uncertainty, facilitating positive word-of-mouth behavior through enhanced confidence in product quality and corporate reliability (C\u0026aacute;ceres and Paparoidamis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003cp\u003eBrand trust positively influences recommendation intentions.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eH6: Moderating Effects of Authenticity\u003c/h3\u003e\n\u003cp\u003eAuthenticity represents convergence between internal states and external expressions (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In influencer contexts, perceived authenticity enhances trust formation and reduces skepticism toward commercial communications. Higher authenticity perceptions strengthen the effects of self-concept components on social distance and para-social interaction through enhanced credibility and reduced psychological barriers.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e\u003cp\u003eAuthenticity moderates the relationships between self-concept dimensions and both social distance and para-social interaction.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Model.\u003c/b\u003e The research model for this study was designed based on factors that influence the establishment of social distance and para-social interaction by identifying sub-constructs of self-concept and social conspicuousness based on the above discussions. Hypotheses 1 was proposed to help us explain how sub-constructs of self-concept and social conspicuousness affect social distance and para-social interaction. Hypotheses 2 and 3 were proposed to help us explain the effects of social distance and para-social interaction on brand trust and recommendation intentions. The research model presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the study design.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eOperational Definitions and Measurement. To accurately evaluate consumer responses to digital influencer marketing, a panel of consumers registered with established marketing research companies was utilized to secure the validity and reliability of the survey. Initially, the most widely recognized and prominent social media influencers across various platforms (YouTube, Instagram, TikTok) were identified through comprehensive platform analytics and engagement metrics. Consumer panelists were invited to participate in the survey through multiple communication channels, including text messages and email invitations.\u003c/p\u003e\u003cp\u003eThe questionnaire was designed as an online survey instrument, enabling consumer panelists to access the survey via internet connectivity and respond based on their authentic experiences with social media influencer content and interactions. The data collection period extended from March 15, 2024, to April 15, 2024, during which a total of 800 respondents participated in the survey. Following comprehensive data cleaning procedures, 748 valid responses were retained for analysis. The questionnaire comprised structured items measuring self-concept dimensions (self-efficacy, self-assertion, social presence, self-esteem), social conspicuousness, social distance, para-social interaction, brand trust, recommendation intentions, and perceived influencer authenticity. With the exception of demographic questions, all measurement items employed 5-point Likert scales ranging from \"strongly disagree\" to \"strongly agree.\"\u003c/p\u003e\u003cp\u003ePrior to full-scale data collection, a preliminary survey was conducted with 50 business administration and marketing students at a leading university to ensure questionnaire clarity and semantic appropriateness. This pilot testing phase verified that the survey language effectively conveyed the intended meanings and identified potential comprehension issues. To mitigate common method variance in data collection, established methodological procedures recommended by prior scholars were systematically implemented throughout the questionnaire design and administration process. Survey participants were selected through rigorous stratified sampling processes to ensure sample representativeness across demographic segments and social media usage patterns. The analytical survey period encompassed four weeks of active data collection. Target respondents were consumers who demonstrated active engagement with social media influencer content and possessed substantial experience with influencer-driven brand interactions. Complete anonymity of respondents was guaranteed to encourage honest responses and minimize social desirability bias. Participants were explicitly informed that their survey participation was entirely voluntary, and the research objectives were clearly explained in the informed consent documentation. To further address potential common method bias, a temporal separation methodology was implemented during data collection. Initially, a pilot questionnaire focusing on independent variables\u0026mdash;self-concept dimensions and social conspicuousness\u0026mdash;was administered over one week. Subsequently, after this temporal interval, dependent variables including social distance, para-social interaction, brand trust, and recommendation intentions were measured at different time points, along with the moderating variable of perceived authenticity.\u003c/p\u003e\u003cp\u003eFor hypothesis testing and statistical analysis, the SPSS statistical package and structural equation modeling software (EQS 6.4) were employed. Basic data analysis incorporated frequency analysis to determine demographic composition distributions using SPSS functionality. Descriptive statistics, including means and standard deviations of latent variables and individual measurement items, were systematically analyzed. Cronbach's alpha coefficients were calculated to assess the internal consistency reliability of all measurement constructs. Confirmatory factor analysis was conducted to verify the unidimensionality and construct validity of the primary variables. The study employed multi-group structural equation modeling to analyze the moderating effects of perceived influencer authenticity on the hypothesized relationships.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive Statistics. The final sample consisted of 748 valid responses after data cleaning procedures. As shown the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the demographic profile reveals a relatively balanced gender distribution with 366 males (48.9%) and 382 females (51.1%). The age distribution shows concentration in middle-age groups, with 30–39 years (43.4%) and 40–49 years (43.4%) comprising the majority of respondents, while younger adults aged 20–29 (3.9%) and those over 50 (8.6%) were less represented. Educational attainment was high, with 55.0% holding college degrees, 25.1% having completed high school, 11.6% current college students, and 8.3% possessing graduate degrees. Monthly income distribution showed 37.5% earning below \u003cspan\u003e$\u003c/span\u003e2,000, 24.5% earning \u003cspan\u003e$\u003c/span\u003e2,000-\u003cspan\u003e$\u003c/span\u003e3,000, 14.7% earning \u003cspan\u003e$\u003c/span\u003e3,000-\u003cspan\u003e$\u003c/span\u003e4,000, 10.6% earning \u003cspan\u003e$\u003c/span\u003e4,000-\u003cspan\u003e$\u003c/span\u003e5,000, and 12.7% earning over \u003cspan\u003e$\u003c/span\u003e5,000.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Profile (N = 748)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20–29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30–39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40–49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOver 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGraduate school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly Income (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow \u003cspan\u003e$\u003c/span\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,000-\u003cspan\u003e$\u003c/span\u003e3,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e3,000-\u003cspan\u003e$\u003c/span\u003e4,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4,000-\u003cspan\u003e$\u003c/span\u003e5,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOver \u003cspan\u003e$\u003c/span\u003e5,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Percentages may not sum to 100% due to rounding\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor hypothesis testing and statistical analysis, the SPSS statistical package and structural equation modeling software (EQS 6.4) were employed. Basic data analysis incorporated frequency analysis to determine demographic composition distributions using SPSS functionality. Descriptive statistics, including means and standard deviations of latent variables and individual measurement items, were systematically analyzed. Cronbach's alpha coefficients were calculated to assess the internal consistency reliability of all measurement constructs. Confirmatory factor analysis was conducted to verify the unidimensionality and construct validity of the primary variables. The study employed multi-group structural equation modeling to analyze the moderating effects of perceived influencer authenticity on the hypothesized relationships.\u003c/p\u003e\u003cp\u003eMeasurement Model Assessment. Prior to testing the structural relationships, we conducted comprehensive assessments of the measurement model's reliability, validity, and dimensionality using confirmatory factor analysis (CFA) in EQS 6.4. Reliability Assessment. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Internal consistency reliability was evaluated using Cronbach's alpha coefficients. All constructs demonstrated excellent reliability, with alpha values ranging from .742 to .940, exceeding the recommended threshold of .70. Specifically: self-efficacy (α = .940), self-assertion (α = .918), social presence (α = .886), self-esteem (α = .864), social conspicuousness (α = .742), social distance (α = .930), para-social interaction (α = .937), brand trust (α = .940), and recommendation intentions (α = .940).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\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\u003eDescriptive Statistics and Correlation Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(.940)\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\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Self-assertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.127**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(.918)\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.Social presence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.098**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.334**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(.886)\u003c/p\u003e\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\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Self-esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.421**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.109**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.261**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(.864)\u003c/p\u003e\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\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.Social conspicuousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.371**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.508**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.102**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(.742)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Social distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.269**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.507**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.616**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.268**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.512**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(.930)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.Para-social interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.285**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.314**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.447**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.372**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.334**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.687**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. Brand trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.194**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.226**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.193**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.203**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.315**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.389**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.514**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(.940)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9. Recommendation intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.183**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.179**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.340**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.352**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.371**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.436**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.625**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e(.940)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: N = 748. **p \u0026lt; .01. Cronbach's alpha coefficients are shown in parentheses on the diagonal. M = Mean, SD = Standard Deviation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eValidity Assessment. Exploratory Factor Analysis (EFA): As shown Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, initial exploratory factor analysis using principal axis factoring with varimax rotation confirmed the nine-factor structure, explaining 84.6% of total variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was .893, and Bartlett's test of sphericity was significant (χ² = 27,201.9, df = 465, p \u0026lt; .001), indicating data suitability for factor analysis. All factor loadings exceeded .665, demonstrating strong item-to-factor relationships.\u003c/p\u003e\u003cp\u003eConfirmatory Factor Analysis (CFA): The measurement model was evaluated using confirmatory factor analysis. The model demonstrated acceptable fit: χ² = 1,247.82, df = 657, p \u0026lt; .001; CFI = .942; GFI = .892; AGFI = .871; NFI = .910; NNFI = .936; RMSEA = .035 (90% CI = .032-.038); SRMR = .044. These indices meet or exceed established cutoff criteria for good model fit.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eFactor Loadings and Communalities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\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\u003eCommunality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor Loadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEf1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.902\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEf2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEf3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSelf-assertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAs1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.878\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAs2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAs3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSocial presence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSelf-esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEs1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEs2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEs3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eSocial\u003c/p\u003e\u003cp\u003eConspicuousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.802\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSc6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSocial distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSd1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSd2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSd3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePara-social interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePs1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePs2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePs3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePs4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBrand trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBt1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBt2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBt3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRecommendation intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRi1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRi3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote: Factor extraction method: Principal axis factoring with varimax rotation. KMO = .893, Bartlett's test: χ² = 27,201.9 (df = 465, p \u0026lt; .001).\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConvergent and Discriminant Validity: As shown Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, convergent validity was assessed through Average Variance Extracted (AVE) values, with all constructs exceeding the .50 threshold. Discriminant validity was established using the Fornell-Larcker criterion, where the square root of each construct's AVE exceeded its correlations with other constructs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\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\u003eConvergent and Discriminant Validity Assessment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e√AVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.763\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Self-assertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e.658\u003c/b\u003e\u003c/p\u003e\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\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Social presence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.759\u003c/b\u003e\u003c/p\u003e\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\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Self-esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e.792\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Social conspicuousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e.785\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Social distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e.771\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. Para-social interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e.792\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. Brand trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e.843\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9. Recommendation intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e.831\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote: AVE = Average Variance Extracted; CR = Composite Reliability; Bold diagonal elements represent the square root of AVE; off-diagonal elements are inter-construct correlations.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCommon Method Bias Assessment. Given the self-report nature of the data, we assessed common method bias using Harman's single-factor test and the unmeasured latent method construct (ULMC) approach. The single-factor test revealed that no single factor accounted for more than 35.2% of the variance, suggesting common method bias is not a significant concern. Additionally, the ULMC approach showed that method variance accounted for less than 10% of the total variance, further confirming that common method bias does not substantially threaten the validity of our findings.\u003c/p\u003e\u003cp\u003eMulticollinearity Assessment. Variance Inflation Factor (VIF) values were calculated for all predictor variables, with values ranging from 1.23 to 2.87, all well below the threshold of 5.0, indicating absence of problematic multicollinearity.\u003c/p\u003e\u003cp\u003eGroup Comparison for Moderation Analysis. For the moderation analysis of authenticity, the sample was divided into high authenticity (n = 374, M = 4.21, SD = 0.42) and low authenticity (n = 374, M = 2.67, SD = 0.51) groups using median split (Median = 3.44). Independent samples t-tests confirmed significant differences between groups (t(746) = 35.82, p \u0026lt; .001, Cohen's d = 3.26), indicating appropriate group differentiation for multi-group analysis.\u003c/p\u003e\u003cp\u003eCommon Method Bias Assessment. To address potential common method bias, Harman's single-factor test was conducted. The unrotated factor analysis revealed that the first factor explained 34.2% of the total variance, below the threshold of 50%, suggesting that common method bias was not a significant concern. Additionally, the temporal separation of independent and dependent variable measurements further mitigated this concern.\u003c/p\u003e\u003cp\u003eHypothesis Testing. Prior to hypothesis testing, the measurement model was evaluated using confirmatory factor analysis (CFA). The measurement model demonstrated acceptable fit indices: χ²=487.6, df = 412, χ²/df = 1.183, CFI = .967, TLI = .961, RMSEA = .048 (90% CI: .041-.055), SRMR = .062. All factor loadings exceeded .70 and were statistically significant (p \u0026lt; .001), confirming convergent validity. The Average Variance Extracted (AVE) for all constructs exceeded .50, and the square root of AVE for each construct was greater than its correlations with other constructs, supporting discriminant validity.\u003c/p\u003e\u003cp\u003eCommon method bias was assessed using Harman's single-factor test and the unmeasured latent method construct (ULMC) approach. The single-factor model explained only 24.3% of the total variance, well below the 50% threshold. The ULMC model comparison showed Δχ²=156.7 (Δdf = 29, p \u0026lt; .001), indicating that common method bias was not a significant concern. The structural model demonstrated good fit to the data: χ²=542.3, df = 457, χ²/df = 1.187, CFI = .965, TLI = .959, RMSEA = .050 (90% CI: .043-.057), SRMR = .064. These indices meet the recommended thresholds for acceptable model fit (CFI/TLI \u0026gt; .95, RMSEA \u0026lt; .08, SRMR \u0026lt; .08).\u003c/p\u003e\u003cp\u003eDirect Effects Analysis. The standardized path coefficients and their significance levels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. All hypotheses related to the effects of self-concept components on social distance (H1-1 through H1-5) were supported. Self-efficacy (β = .139, p \u0026lt; .001), self-assertion (β = .275, p \u0026lt; .001), social presence (β = .199, p \u0026lt; .001), self-esteem (β = .233, p \u0026lt; .001), and social conspicuousness (β = .327, p \u0026lt; .001) all demonstrated significant positive effects on social distance, with effect sizes ranging from small to moderate.\u003c/p\u003e\u003cp\u003eSimilarly, all hypotheses regarding the effects of self-concept components on para-social interaction (H2-1 through H2-5) were supported. Self-efficacy (β = .238, p \u0026lt; .001), self-assertion (β = .183, p \u0026lt; .001), social presence (β = .063, p = .048), self-esteem (β = .072, p = .047), and social conspicuousness (β = .251, p \u0026lt; .001) all showed significant positive effects on para-social interaction.\u003c/p\u003e\u003cp\u003eRegarding the outcome variables, social distance demonstrated a significant positive effect on recommendation intentions (β = .459, p \u0026lt; .001), supporting H3-2. However, the path from social distance to brand trust was not significant (β = .069, p = .113), rejecting H3-1. Para-social interaction showed significant positive effects on both brand trust (β = .144, p \u0026lt; .01) and recommendation intentions (β = .343, p \u0026lt; .001), supporting H4-1 and H4-2. Finally, brand trust significantly predicted recommendation intentions (β = .622, p \u0026lt; .001), supporting H5.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEffect Size Assessment\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e Following Cohen's (1988) guidelines, effect sizes were categorized as small (β ≥ .10), medium (β ≥ .30), or large (β ≥ .50). The strongest predictor of social distance was social conspicuousness (β = .327, medium effect), while the strongest predictor of para-social interaction was social conspicuousness (β = .251, small-to-medium effect). The relationship between brand trust and recommendation intentions showed a large effect size (β = .622).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Explained Variance\u003c/b\u003e: The model explained substantial variance in the endogenous variables: social distance (R²=.68), para-social interaction (R²=.45), brand trust (R²=.18), and recommendation intentions (R²=.71). These R² values indicate that the model accounts for a meaningful proportion of variance in the dependent variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eResults of Structural Equation Model Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect Size\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-efficacy → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.139***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-assertion → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall-Medium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Presence → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.199***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-esteem → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.233***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Conspicuousness → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.327***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-efficacy → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.238***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-assertion → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.183***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Presence → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.063*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-esteem → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.072*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Conspicuousness → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.251***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall-Medium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Distance → Brand Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Distance → Recommendation Intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.459***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePara-social Interaction → Brand Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.144**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePara-social Interaction → Recommendation Intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.343***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrand Trust → Recommendation Intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.622***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: ***p \u0026lt; .001, **p \u0026lt; .01, *p \u0026lt; .05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModerating Effects of Authenticity. To examine the moderating role of authenticity, a multi-group structural equation modeling approach was employed. Participants were divided into high authenticity (n = 374) and low authenticity (n = 374) groups based on a median split of the authenticity scale (Mdn = 3.67). Independent samples t-tests confirmed significant differences between groups on the authenticity measure (t(746) = 24.18, p \u0026lt; .001, Cohen's d = 1.77).\u003c/p\u003e\u003cp\u003eMeasurement Invariance Testing. Prior to testing moderation effects, measurement invariance across the two groups was established following the sequential testing procedure recommended by Vandenberg and Lance (2000). Configural invariance (Δχ²=0, baseline model), metric invariance (Δχ²=23.4, Δdf = 18, p = .175, ΔCFI=-.003), and scalar invariance (Δχ²=41.7, Δdf = 36, p = .240, ΔCFI=-.007) were all supported, indicating that the measurement model operates equivalently across high and low authenticity groups.\u003c/p\u003e\u003cp\u003eMulti-Group Moderation Analysis. The unconstrained model (allowing all parameters to vary freely between groups) was compared with a series of constrained models (constraining specific paths to be equal across groups). Chi-square difference tests were used to assess the significance of moderation effects. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eModeration of Self-Concept Effects on Social Distance. Significant moderation effects were found for self-assertion → social distance (Δχ²=8.109, df = 1, p = .004) and social presence → social distance (Δχ²=5.098, df = 1, p = .024). For self-assertion, the effect was stronger in the high authenticity group (β = .371, p \u0026lt; .001) compared to the low authenticity group (β = .259, p \u0026lt; .001). Conversely, for social presence, the effect was stronger in the low authenticity group (β = .360, p \u0026lt; .001) compared to the high authenticity group (β = .035, p = .368). No significant moderation effects were found for self-efficacy (Δχ²=1.347, df = 1, p = .246), self-esteem (Δχ²=0.578, df = 1, p = .447), or social conspicuousness (Δχ²=1.086, df = 1, p = .297).\u003c/p\u003e\u003cp\u003eModeration of Self-Concept Effects on Para-Social Interaction. Significant moderation effects were identified for self-esteem → para-social interaction (Δχ²=4.892, df = 1, p = .027) and social conspicuousness → para-social interaction (Δχ²=6.725, df = 1, p = .009). For self-esteem, the effect was stronger in the low authenticity group (β = .128, p = .014) compared to the high authenticity group (β = .113, p = .037). For social conspicuousness, the effect was significantly stronger in the low authenticity group (β = .296, p \u0026lt; .001) compared to the high authenticity group (β = .004, p = .937). No significant moderation effects were found for self-efficacy (Δχ²=0.565, df = 1, p = .452), self-assertion (Δχ²=1.171, df = 1, p = .279), or social presence (Δχ²=1.397, df = 1, p = .237).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eMulti-Group Moderation Analysis: Effects of Authenticity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh Authenticity (n = 374)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow Authenticity (n = 374)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eΔχ²\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModeration Effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (SE) [z-value]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (SE) [z-value]\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-efficacy → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.107 (.040) [2.708]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.060 (.048) [1.249]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-assertion → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.371 (.037) [9.982]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.259 (.052) [4.999]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Presence → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.035 (.039) [0.901]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.360 (.049) [7.411]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.024*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-esteem → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.432 (.038) [11.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.033 (.047) [0.698]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Conspicuousness → Social Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.388 (.038) [10.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.208 (.047) [4.458]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-efficacy → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.204 (.058) [3.545]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.214 (.053) [4.068]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-assertion → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.336 (.054) [6.206]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.030 (.056) [0.532]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Presence → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.051 (.057) [0.890]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.289 (.053) [5.466]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-esteem → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.113 (.054) [2.083]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.128 (.052) [2.462]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.027*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Conspicuousness → Para-social Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.004 (.055) [0.079]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.296 (.051) [5.827]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.009**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eModel Fit Indices: Unconstrained model: χ²=721.4, df = 914, CFI = .964, TLI = .958, RMSEA = .049, Fully constrained model: χ²=762.8, df = 934, CFI = .961, TLI = .957, RMSEA = .051. *Note: **p \u0026lt; .01, \u003cem\u003ep \u0026lt; .05; High vs. Low authenticity groups determined by median split (Mdn = 3.67)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSummary of Moderation Effects\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe analysis revealed that authenticity moderates four of the ten hypothesized relationships. Specifically, higher perceived authenticity strengthens the relationship between self-assertion and social distance, while lower perceived authenticity strengthens the relationships between social presence and social distance, as well as between self-esteem and social conspicuousness with para-social interaction. These findings suggest that authenticity plays a differential moderating role depending on the specific self-concept dimension and outcome variable under consideration.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffect Size Interpretation: The moderation effect sizes were calculated using the formula: f² = (R²moderated - R²main effects)/(1 - R²moderated). The moderation effects showed small to medium effect sizes (f² ranging from .02 to .08), indicating meaningful practical significance beyond statistical significance.\u003c/p\u003e"},{"header":"Discussion and Conclusions","content":"\u003cp\u003e\u003cb\u003eConclusion.\u003c/b\u003e This investigation provides novel insights into the psychological mechanisms underlying consumer-influencer relationships in digital marketing environments. The study demonstrates that consumer self-concept dimensions—self-efficacy, self-assertion, social presence, self-esteem, and social conspicuousness—significantly predict both social distance reduction and para-social interaction formation with human influencers. Notably, the findings reveal an asymmetric relationship pattern wherein social distance strongly influences recommendation intentions but shows no significant effect on brand trust formation. Conversely, para-social interactions demonstrate positive effects on both brand trust and recommendation intentions, highlighting the differential pathways through which psychological connections translate into consumer behaviors. The moderating role of authenticity emerged as particularly significant, strengthening specific relationships under varying conditions. High authenticity conditions amplified the self-assertion→social distance relationship, while low authenticity conditions enhanced social presence effects on social distance and self-esteem/social conspicuousness effects on para-social interaction. These findings contribute to the theoretical understanding of technology-mediated consumer psychology and provide empirical validation for the integration of self-concept theory with para-social interaction frameworks in digital marketing contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations.\u003c/b\u003e Several methodological and sampling limitations warrant acknowledgment. The demographic concentration within the 30–49 age bracket (87.6% of respondents) may limit generalizability across different generational cohorts who exhibit varying social media consumption patterns and technology adoption behaviors. The sample's geographical concentration within South Korea raises questions about cross-cultural applicability, particularly given documented variations in individualism-collectivism orientations that may influence authenticity perceptions and trust formation mechanisms across Western and Asian cultural contexts. The cross-sectional design precludes causal inference and temporal relationship examination, while the reliance on self-reported measures may introduce common method variance despite implemented procedural remedies. Additionally, the study's focus on general influencer categories rather than specific platform-dependent influencer types may obscure nuanced relationship dynamics that vary across different social media ecosystems.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial Implications.\u003c/b\u003e The findings illuminate critical social implications regarding authentic relationship formation in digital environments. The demonstrated importance of perceived authenticity in moderating consumer-influencer relationships suggests that genuine self-expression and transparent communication practices contribute to healthier digital community formation. This has broader implications for addressing concerns about social media's impact on mental health and social comparison processes, as authentic influencer communications may foster more positive parasocial relationships. The study's revelation that different self-concept dimensions predict varying relationship outcomes suggests that digital marketing practices should consider individual psychological characteristics to avoid exploitative targeting of vulnerable populations. Furthermore, the findings support the development of media literacy programs that help consumers understand the psychological mechanisms underlying their responses to influencer content, thereby promoting more informed and autonomous digital consumption behaviors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eManagerial Implications.\u003c/b\u003e For marketing practitioners, these findings provide actionable insights for developing more effective and ethical influencer marketing strategies. The differential effects of authenticity across self-concept dimensions suggest that personalized approaches based on consumer psychological profiles may yield superior outcomes compared to standardized influencer campaigns. Marketing managers should prioritize authenticity cultivation over follower count maximization, as genuine personality expression strengthens specific consumer segments' engagement patterns. he asymmetric effects of social distance and para-social interaction on brand trust versus recommendation intentions indicate that different influencer relationship strategies may be optimal for different marketing objectives. For trust-building initiatives, fostering para-social interactions through consistent personality disclosure and emotional connection appears crucial. For recommendation generation, reducing perceived social distance through similarity emphasis and accessibility may prove more effective. Organizations should implement authenticity assessment frameworks when selecting influencer partnerships and develop content guidelines that preserve genuine personality expression while achieving commercial objectives. The findings also suggest that micro-segmentation strategies based on consumer self-concept profiles could optimize campaign effectiveness and resource allocation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations.\u003c/b\u003e Based on these findings, we recommend that practitioners adopt multi-dimensional approaches to influencer selection and campaign design. First, implement psychological profiling of target audiences to understand predominant self-concept characteristics and tailor influencer matching accordingly. Second, develop authenticity measurement protocols that evaluate influencer genuineness beyond surface-level metrics such as engagement rates or follower demographics. Third, establish content creation guidelines that balance commercial objectives with authentic self-expression, recognizing that perceived authenticity moderates relationship formation processes. Fourth, implement longitudinal tracking of consumer-influencer relationship development to optimize campaign timing and content evolution strategies. Finally, consider platform-specific relationship dynamics when developing cross-platform influencer marketing strategies, as different technological affordances may influence the manifestation of self-concept dimensions and authenticity perceptions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDirections for Future Studies.\u003c/b\u003e Future research should address several important directions to advance theoretical understanding and practical applications. Longitudinal studies examining the temporal evolution of consumer-influencer relationships would provide crucial insights into relationship development patterns and sustainability factors. Cross-cultural investigations comparing individualistic versus collectivistic societies would illuminate cultural boundary conditions for the proposed theoretical framework. Comparative studies examining virtual versus human influencer effectiveness across different consumer self-concept profiles would extend theoretical understanding of authenticity perceptions in artificial versus genuine personality contexts. Additionally, platform-specific research examining how technological affordances (e.g., video versus text, real-time versus asynchronous communication) moderate the relationships between self-concept dimensions and influencer connection formation would provide valuable practical insights. Future investigations should also explore the role of negative authenticity perceptions and their impact on consumer-influencer relationship dissolution, as well as examine potential dark-side effects of para-social relationships in commercial contexts. Finally, the development and validation of culturally adapted measurement instruments for cross-national comparative research would significantly advance the field's methodological sophistication and theoretical generalizability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGong, Diao and Park: Conceptualization, Writing\u0026mdash;original draft. Diao, JZ-Jin and Park: Methodology, Data curation, Formal analysis. C.H-Jin: Conceptualization, Writing-review and editing, project co-supervisor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJining University\u0026apos;s Top 100 Outstanding Talents Support Program Cultivation Project No. 2023ZYRC68\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince this study used individuals to respond the study\u0026rsquo;s instrument, the author made sure\u003c/p\u003e\n\u003cp\u003ethat the study was conducted in accordance to the ethical standards of the Helsinki Declaration. Thus, this study was granted exemption from Institutional Review Board (IRB) review in accordance with Korean statistical law and research ethics regulations. This research has been reviewed and approved by the Korea Human Development College. Institutional Review Board (2025-KHDC-IRB-601), Approval Date: 07/15/2025\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVerbal informed consent was obtained from all participants because of the need for flexibility. The consent process, including the purpose of the study, voluntary participation, and confidentiality measures, was clearly explained to the participants. The participants were also informed that they could withdraw from the study at any time without any consequences. The protocol for this verbal informed consent was face-to face or an office phone call and took place within the period from March 15, 2024, to April 15, 2024.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdna M, Sukoco BM (2020) Factors influencing consumers to purchase products through social media in Indonesia. Technol Soc 62:101291\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Emadi FA, Ben Yahia I (2020) Ordinary celebrities related criteria to harvest fame and influence on social media. 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J Bus Res 134:122\u0026ndash;142\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"digital marketing technology, self-concept frameworks, para-social relationships, structural equation modeling, consumer psychology, authenticity moderation","lastPublishedDoi":"10.21203/rs.3.rs-7403841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7403841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how consumer self-concept dimensions influence technology-mediated relationships in digital influencer marketing through structural equation modeling with 748 social media participants. We investigated how self-efficacy, self-assertion, social presence, self-esteem, and social conspicuousness affect social distance perception and para-social interaction formation with human influencers, while examining authenticity as a moderating variable. Results reveal that all self-concept dimensions significantly predict both social distance reduction and para-social interaction development with influencers. Notably, social conspicuousness emerged as the strongest predictor of social distance, while self-efficacy showed the strongest effect on para-social interaction. The study uncovered an asymmetric relationship pattern: while social distance strongly influenced recommendation intentions, it showed no significant effect on brand trust formation. Conversely, para-social interactions positively affected both brand trust and recommendation intentions. Brand trust demonstrated a robust relationship with recommendation intentions. Authenticity moderated four relationships, strengthening the self-assertion\u0026rarr;social distance link under high authenticity conditions, while amplifying social presence effects on social distance and self-esteem/social conspicuousness effects on para-social interaction under low authenticity conditions. These findings contribute to management information systems literature by providing validated theoretical foundations for understanding technology-mediated self-concept manifestations in digital marketing, offering strategic implications for algorithmic personalization, consumer micro-segmentation, and adaptive content delivery mechanisms within social media marketing platforms.\u003c/p\u003e","manuscriptTitle":"The Psychology of Digital Community Formation: Self-Concept, Authenticity, and Para-Social Bonds in Social Media Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 13:29:03","doi":"10.21203/rs.3.rs-7403841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T23:46:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T21:24:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T22:14:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213693217884647612218721857474980218097","date":"2025-12-09T12:28:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T11:27:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288528887464269025731363325128442277389","date":"2025-12-09T11:07:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155302697265389720661829485914346135696","date":"2025-12-09T10:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316249948348768652038210803350124593110","date":"2025-12-09T10:07:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T09:31:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T09:27:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T11:30:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-01T02:09:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-01T02:05:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b6571bfc-8b78-4c78-82f6-b0b3c0c003f3","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59379791,"name":"Business and commerce/Business and management"},{"id":59379792,"name":"Social science/Business and management"},{"id":59379793,"name":"Humanities/Cultural and media studies"},{"id":59379794,"name":"Social science/Cultural and media studies"},{"id":59379795,"name":"Business and commerce/Information systems and information technology"},{"id":59379796,"name":"Biological sciences/Psychology"},{"id":59379797,"name":"Social science/Psychology"},{"id":59379798,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-27T23:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 13:29:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7403841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7403841","identity":"rs-7403841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}